Podcasts about network analysis

Dive into an engaging session where we unravel the complexities of Network Rules and PERT through thought-provoking MCQs, detailed explanations, and real-world insights. Whether you're preparing for competitive exams or want to sharpen your concepts, this video has everything you need!

In this episode, Mark gets to chat with his old pal Hilton Barbour about the power of organizational network analysis (ONA). Hilton works at the intersection of strategy, culture and change and has some helpful insights to share about how to use ONA in the ever-changing workplace.You can learn more about the work of Innovisor here:https://www.innovisor.comYou can download a copy of the book "Fix Your Culture!" co-authored by Hilton with the Innovisor CEO here.https://jeppehansgaard.gumroad.com/l/fixyourculture?And connect with Hilton on LinkedIn here:https://www.linkedin.com/in/hiltonbarbour/Quick reminderDon't forget to sign up for our weekly newsletter foHRsight at http://www.futurefohrward.com/subscribe.Follow us on LinkedIn:Mark - www.linkedin.com/in/markedgarhr/Naomi - www.linkedin.com/in/naomititlemancolla/future foHRward - www.linkedin.com/company/future-fohrward/And on Instagram - www.instagram.com/futurefohrward/Support the show

In this Episode, we focus on understanding packets and protocols, key components of network communication, and their crucial role in network analysis with Wireshark. Wireshark is a powerful tool that allows you to capture and inspect network traffic, helping you monitor data flow and troubleshoot issues effectively. To make the most of Wireshark, it's essential to understand what packets are and how they carry data across networks.

Our new season "Graphs and Networks" begins here!  We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf discuss ideas to cover in the season and explore Asaf's work in the field.

We're thrilled to have Rediet Alemu, Harvard psychology grad, on our latest episode.

'What the Tech?' is a podcast powered by the Computer Science Department of UCalgary. Here to deconstruct complex computer science concepts, and explain what the tech is going on. In this episode, we had the pleasure of chatting with Katie Ovens, Assistant Professor of Computer Science at UCalgary. We discuss Katie's bioinformatics research on gene co-expressions and biological network analysis, her love of teaching, and more! If you enjoyed today's episode, make sure to subscribe on whatever platform you're listening on. We encourage you to reach out to us, ask us questions about the show, or even suggest topics of interest to you! You can do so by following us on Instagram @uofc_cpsc. Music: Intro / Outro Nowhere Land by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4148-nowhere-land || License: CC BY http://creativecommons.org/licenses/by/4.0/ Background Loopster by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4991-loopster || License: CC BY http://creativecommons.org/licenses/by/4.0/  Funkorama by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3788-funkorama || License: CC BY http://creativecommons.org/licenses/by/4.0/ I Knew a Guy by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3895-i-knew-a-guy || License: CC BY (http://creativecommons.org/licenses/by/4.0/) Cool Vibes by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3553-cool-vibes || License: CC BY (http://creativecommons.org/licenses/by/4.0/)  Thinking Music by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4522-thinking-music || License: CC BY (http://creativecommons.org/licenses/by/4.0/) Funk Game Loop by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3787-funk-game-loop || License: CC BY http://creativecommons.org/licenses/by/4.0/ Umbrella Pants by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4559-umbrella-pants || License: CC BY (http://creativecommons.org/licenses/by/4.0/) ---Send in a voice message: https://podcasters.spotify.com/pod/show/whatthetech-ucalgary/message --- Send in a voice message: https://podcasters.spotify.com/pod/show/whatthetech-ucalgary/message

Are traditional performance reviews outdated? Join host Shari Simpson on the "HR Mixtape" podcast as she interviews Josh Merrill, CEO and founder of Confirm, discussing the innovative approach to performance reviews using Organizational Network Analysis (ONA). In this episode, Josh shares his journey from Carta to founding Confirm, highlighting the importance of understanding employee influence and impact. Guest(s): Josh Merrill, CEO & Founder, Confirm

Tune in to #aBitOfCCS Podcast as we explore cross-cultural communication in a pandemic with Ofer Shinar, a research student and teaching assistant at Tel-Aviv University, currently at LMU Munich. Ofer shares insights from his study, "Semantic Network Analysis of Students' Confessions During a Global Pandemic: A Cross-National Study," delving into intercultural media usage and Semantic Network Analysis. Hosted by Jana Bernhard, this episode offers a brief yet insightful journey into the method of semantic network anlaysis. For further discussion or inquiries, connect with Ofer at ofershinar@mail.tau.ac.il. Find the study slides here (https://www.slideshare.net/ofershinar/semantic-network-analysis-of-student-confessions-during-a-global-pndemicpptx) for a deeper dive into this intriguing research!

In this episode of Transform Your Workplace, host Brandon Laws talks with Josh Merrill, the CEO and Founder of Confirm, an innovative people platform utilizing Organizational Network Analysis (ONA) for performance reviews. Josh shares the transformative journey of integrating this new methodology into performance evaluations. Tune in to learn the truth about your distribution of talent, the importance of eliminating bias in performance reviews, and how to use ONA to get a pulse on your company's top talent.   TAKEAWAYS Talent distribution follows a power law rather than the commonly assumed bell curve which means that a few employees are creating a disproportionate impact.  Since 60% of manager ratings are influenced by biases, businesses should adopt ONA to provide a more objective evaluation that focuses on network dynamics. ONA, like the wisdom of the crowd, provides accurate insights by considering collective perspectives. Business leaders should implement periodic evaluations to identify impactful employees and address concerns promptly.   A QUICK GLIMPSE INTO OUR PODCAST 

Welcome back to another exciting episode of the Break The Wheel HR Podcast! In this episode, David Murray, our host, will be joined by Josh Merrill, Cofounder and CEO of Confirm. Together, they delve into the fascinating world of HR, exploring topics such as the performance reviews today how and when it started and the importance of recognizing talent beyond managerial perspectives.Josh shares thought-provoking stories and insights, ranging from personal experiences to industry trends. Tune in as they discuss the impact of storytelling for founders and CEOs, the potential pitfalls of AI in recruiting, and the ever-changing landscape of work measurement.Get ready to expand your HR knowledge and challenge traditional practices as we break down the wheel in this captivating episode. Topics Covered:-They introduce the concept of organizational network analysis (ONA) as a way to measure work based on how it is actually done.-The podcast explores the optimism of Generation Z workers compared to Millennials and Gen X.-They highlight the struggles and challenges faced by workers across all generations, including excessive work and lack of clarity in job roles-The conversation touches on the bias in manager ratings and the need for more objective evaluation methods.-They discuss the potential benefits and limitations of continuous feedback in performance management..-The episode ends with a segment on personal experiences and insights, including the importance of being mindful of one's actions and the challenges of being fired.The incentive structure of how we do work today is that your destiny, your future as an employee, your opportunities for advancement and for growth really depend on your manager's ability to advocate and to influence on your behalf." — Josh MerrillTimestamps:(00:23) - Introduction(01:16) - Uncork and Unwind: Discussing Severance Pay and Social Safety Nets(02:42) - The Cost of Health Insurance and Hiring Abroad(03:27) - The Challenge of Contractor vs. Employee Definitions(04:48) - HR News Flash: Generation Z's Optimism in the Workplace(07:26) - Reality Check: Reinventing Performance Reviews with ONA(09:49) - The Impact of Manager Advocacy on Career Progression(12:06) - Avoiding Popularity Contests in Performance Feedback(14:08) - People Misunderstand: Misconceptions About Being a Founder or CEO(20:00) - Break the Wheel or Break a Heel: Continuous Feedback(22:12) - Manager Ratings and Their Effectiveness(23:33) - Peer 360s vs. Organizational Network Analysis(28:02) - The Effectiveness of OKRs(29:23) - The Future of Performance Reviews with ONA(30:02) - Wheel Breaker of the Week: LinkedIn's AI-Assisted Recruiting Tools(37:39) - What Should I Have Done: Working with Pneumonia(37:57) - HR Horror Story: Mismanaged Performance Reviews(45:09) - Water Cooler Whispers: AI Bubble in Venture Capital(46:24) - HR Speaks Funny: The Peter Principle(48:16) - Decline to Comment: Josh's hardest work moment(49:43) - Least Enjoyable Co-worker Traits(50:39) - Eye-Rolling LinkedIn Posts Template (53:21) - Cheers to Change: Celebrating Thoropass Use of ONAYou can also watch Break the Wheel on YouTube:https://www.youtube.com/@ConfirmHR Connect with Josh Merrill:Linkedin:

Join host James Mackey and guests David Murray, Cofounder & President, and Joshua Merrill, Chief Executive Officer from Confirm as they challenge the status quo of performance evaluations. Gain insights into the limitations of outdated methods and explore the potential of Organizational Network Analysis to provide a holistic perspective. Discover the truth about quiet contributors and uncover the hidden dynamics that shape talent recognition. From biases to future data ownership, this episode explores a new era in assessing performance.   0:31 David Murray's background   1:08 Josh Merril's background   1:54  Rethinking performance reviews in the workplace   8:29  Reimagining performance management with network analysis20:36 Organizational network analysis32:19 Bias and performance reviews in organizations40:53 Improving the hiring process with performance data Thank you to our sponsor, SecureVision, for making this show possible! Our host James Mackey Follow us:https://www.linkedin.com/company/82436841/#1 Rated Embedded Recruitment Firm on G2!https://www.g2.com/products/securevision/reviewsThanks for listening!

Directionally Correct podcast is sponsored by Worklytics! https://www.worklytics.co/directionallycorrect/ Human pattern finding machines: https://twitter.com/emollick/status/1595291532621271041?s=20&t=C0zbEX8vPUV8fkdNAWgIZQ SHRM - People analytics still in early stages: https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/shrm-research-people-analytics-still-in-early-stages.aspx How opposition research can be used in people analytics: https://drjohnsullivan.com/articles/opposition-research-improves-employer-branding-recruiting-retention/

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.20.549825v1?rss=1 Authors: Harvey, J., Smith, A. R., Weymouth, L. S., Smith, R. G., Castanho, I., Hubbard, L., Creese, B., Bresner, C., Williams, N., Pishva, E., Lunnon, K. Abstract: Parkinsons disease is a highly heterogeneous disorder, encompassing a complex spectrum of clinical presentation including motor, sleep, cognitive and neuropsychiatric symptoms. We aimed to investigate genome-wide DNA methylation networks in post-mortem Parkinsons disease brain samples and test for region-specific association with common neuropsychiatric and cognitive symptoms. Of traits tested, we identify a co-methylation module in the substantia nigra with significant correlation to depressive symptoms and with ontological enrichment for terms relevant to neuronal and synaptic processes. Notably, expression of the genes annotated to the methylation loci present within this module are found to be significantly enriched in neuronal subtypes within the substantia nigra. These findings highlight the potential involvement of neuronal-specific changes within the substantia nigra with regard to depressive symptoms in Parkinsons disease. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.04.547622v1?rss=1 Authors: Hao, Y., Li, C., Ming, C. Abstract: Alzheimer's disease (AD), particularly late-onset Alzheimer's disease (LOAD), is a prevalent form of dementia that significantly affects patients' cognitive and behavioral capacities and longevity. Although approximately 70 genetic risk factors linked with AD have been identified, their influence on patient longevity remains unclear. Further, recent studies have associated copy number variations (CNVs) with the longevity of healthy individuals and immune-related pathways in AD patients. This study aims to investigate the role of CNVs on the longevity of AD patients by integrating multi-omics data from the Religious Orders Study/Memory and Aging Project (ROSMAP) cohort through causality network inference. Our comprehensive analysis led to the construction of a CNV-gene-age of death (AOD) causality network. We successfully identified three key CNVs (DEL5006, mCNV14192, and DUP42180) and seven AD-longevity causal genes (PLGRKT, TLR1, PLAU, CALB2, SYTL2, OTOF, and NT5DC1) impacting AD patient longevity, independent of disease severity. This outcome emphasizes the potential role of plasminogen activation and chemotaxis in longevity. We propose several hypotheses regarding the role of identified CNVs and the plasminogen system on patient longevity. However, experimental validation is required to further corroborate these findings and uncover precise mechanisms. Despite these limitations, our study offers promising insights into the genetic influence on AD patient longevity and contributes to paving the way for potential therapeutic interventions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

The A Better HR Business podcast looks at how consultants and tech firms in the broad Human Resources field grow their businesses; and how they help employers get the best out of their people. Today I'm joined on the show by Jaakko Kaikuluoma from Teamspective to talk about growing an Organizational Network Analysis software & consulting company - with Jaakko Kaikuluoma from Teamspective. How  Teamspective helps employers. How Jaakko and the team started and grew the business. Jaakko's HR and marketing advice. And much more. Thanks,  Jaakko! For show notes and to see details of my previous guests, check out the podcast page here: www.GetMoreHRClients.com/Podcast WANT MORE CUSTOMERS OR CLIENTS? Want more clients for your HR-related consultancy or HR Tech business? Check out: www.GetMoreHRClients.com/Services. WANT TO START AN HR BUSINESS? Want to launch your own consulting business in the broad Human Resources sector? Check out: www.GetMoreHRClients.com/Start for resources.

In today's episode of Psych Talk, I discuss the symptom overlap in mental health diagnoses as classified by the Diagnostic and Statistical Manual of Mental Disorders. As many people are aware, there are various symptoms of mental health disorders that are found across multiple diagnoses, which can complicate getting the correct diagnosis or result in individuals receiving multiple diagnoses based on a few symptoms. A few weeks ago, there was a paper, Elemental Psychopathology: Distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5, going around Twitter that researched the overlap in symptoms across mental health diagnoses. In this episode, I break down the methods and findings of the research paper and discuss implications of such, including how this impacts diagnosing, both by a mental health professional and self-diagnosis, as well as research implications. Further, I discussed proposed alternative classification systems to the DSM including RDoc, Network Analysis, and HiTOP. Mentioned in this Episode: Elemental Psychopathology: Distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5 Connect with Me: Follow me on IG @jessicaleighphd Follow the podcast on IG @psych.talk.podcast Follow me on TikTok @jessicaleighphd Follow me on Youtube  Welcome to Group Therapy Podcast Join my Facebook community: Grow Through What You Go Through Ways to Work With Me: Mind Over Matter LGBTQ+ Affirming Masterclass Be a guest on my podcast Resources: Anti-Racism Resources LGBTQ+ Affirming Resources The Helping Professional's Guide to Boundary Setting Intro/Outro Music: Life of Riley by Kevin MacLeod Music License

Ecological concerns and climate change have risen on governmental policies around the globe, but regulatory differences between nations may leave gaps - or even work against each other - if not planned deliberately. Research led by Associate Professor Ishani Mukherjee at Singapore Management University focuses on the case of biodiesel policy in Indonesia, using policy network analysis to investigate types of relationships between policymakers and between the policies they enact. Read the original research: http://doi.org/10.1111/ropr.12479

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.21.529361v1?rss=1 Authors: Akbari, S., Deevband, M. R., Alvar, A. A., Zadeh, E. F., Tabar, H. R., Kelley, P., Tavakoli, M. Abstract: Development of Parkinon's disease causes functional impairment in the brain network of Parkinson's patients. The aim of this study is to analyze brain networks of people with Parkinson's disease based on higher resolution parcellations and newer graphical features. The topological features of brain networks were investigated in Parkinson's patients (19 individuals) compared to healthy individuals (17 individuals) using graph theory. In addition, four different methods were used in graph formation to detect linear and nonlinear relationships between functional magnetic resonance imaging (fMRI) signals. The functional connectivity between the left precuneus and the left amygdala, as well as between the vermis_1_2 and the left temporal lobe was evaluated for the healthy and the patient groups. The difference between the healthy and patient groups was evaluated by non-parametric t-test and U-test. Based on the results, Parkinson's patients showed a significant decrease in centrality criterion compared to healthy subjects. Furtheremore, changes in regional features of brain network were observed. There was also a significant difference between the two groups of healthy subjects and Parkinson's patients in different areas by applying centrality criterion and the correlation coefficients. The results obtained for topological features indicate changes in the functional brain network of Parkinson's patients. Finally, similar areas obtained by all three methods of graph formation in the evaluation of connectivity between paired regions in the brain network of Parkinson's patients increased the reliability of the results. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.15.528643v1?rss=1 Authors: Jauny, G., Mijalkov, M., Canal-Garcia, A., Volpe, G., Pereira, J. B., Eustache, F., Hinault, T. T. Abstract: Brain structure and function are intimately linked; however, this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterized by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we used the multilayer brain network analysis on structural (diffusion tensor imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) was associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

In this episode 16, Thibault Schrepel discusses Stanford Computational Antitrust's newest article, “Deploying Network Analysis in Antitrust Law,” with Yann Guthmann & Adrien Frumence (French Competition Agency) // Read the article at https://law.stanford.edu/computationalantitrust

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.28.526029v1?rss=1 Authors: Acri, D. J., You, Y., Tate, M. D., McCord, B., Sharify, A. D., John, S. K., Karahan, H., Kim, B., Dabin, L. C., Philtjens, S., Wijeratne, H. R. S., McCray, T. J., Smith, D. C., Bissel, S. J., Lamb, B. T., Lasagna-Reeves, C. A., Kim, J. Abstract: Mouse genetic backgrounds have been shown to modulate amyloid accumulation and propagation of tau aggregates. Previous research into these effects has highlighted the importance of studying the impact of genetic heterogeneity on modeling Alzheimer's disease. However, it is unknown what mechanisms underly these effects of genetic background on modeling Alzheimer's disease, specifically tau aggregate-driven pathogenicity. In this study, we induced tau aggregation in wild-derived mice by expressing MAPT (P301L). To investigate the effect of genetic background on the action of tau aggregates, we performed RNA sequencing with brains of 6-month-old C57BL/6J, CAST/EiJ, PWK/PhJ, and WSB/EiJ mice (n=64). We also measured tau seeding activity in the cortex of these mice. We identified three gene signatures: core transcriptional signature, unique signature for each wild-derived genetic background, and tau seeding-associated signature. Our data suggest that microglial response to tau seeds is elevated in CAST/EiJ and PWK/PhJ mice. Together, our study provides the first evidence that mouse genetic context influences the seeding of tau. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

It's a nostalgia episode for our two hosts, Patrick and Dan. They tackle Mustafa Emirbayer's 1997 article in the American Journal of Sociology, "Manifesto for a Relational Sociology." According to Emirbayer, "Sociologists today are faced with a fundamental dilemma: whether to conceive of the social world as consisting primarily in substances or processes, in static 'things' or in dynamic, unfolding relations." Was that also true of International Relations? PTJ and Dan certainly thought so back in 1999. Is it still true today? The two may or may not answer this question, but they do work through Emirbayer's article in no little detail.Additional works alluded to in this podcast include Bhaskar, A Realist Theory of Science (1975); Emirbayer and Goodwin, "Network Analysis, Culture, and the Problem of Agency" (1994); Emirbayer and Mische, "What is Agency" (1998); Mann, The Sources of Social Power, Volume II (1993); Pratt, "From Norms to Normative Configurations: A Pragmatist and Relational Approach to Theorizing Normativity in IR" (2020); Sommers, "The Narrative Constitution of Identity: A Relational and Network Approach" (1994); Tilly, Durable Inequality (1998); and Wiener, Contestation and Constitution of Norms in Global International Relations (2018). 

Directionally Correct podcast is now sponsored by Orgnostic! https://orgnostic.com/ Scott & Michael's NEW Published Article on ONA: https://www.sciencedirect.com/science/article/pii/S0090261623000025?dgcid=coauthor Dr. Who and Team Creativity ONA Article: https://twitter.com/emollick/status/1592970834704228352?s=20&t=C0zbEX8vPUV8fkdNAWgIZQ Hated Office Traditions Article: https://twitter.com/emollick/status/1594069479977271296?s=20&t=C0zbEX8vPUV8fkdNAWgIZQ

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.27.524249v1?rss=1 Authors: Kelly, K., Lewis, P., Plun-Favreau, H., Manzoni, C. Abstract: Whilst the majority of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. In the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring increased risk of developing PD across the population. A recent mitophagy screening assay of GWAS candidates has functionally implicated the non-specific lethal (NSL) complex in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the proteome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial NSL interactome has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. We built; i) the 'mitochondrial interactome', applying gene-set enrichment analysis (GSEA) to explore the relevance of the NSL mitochondrial interactome to PD and, ii) the 'PD-oriented interactome' to uncover biological pathways underpinning the NSL /PD association. In this study, we find the mitochondrial NSL interactome to be significantly enriched for the protein products of PD associated genes, including the Mendelian PD genes LRRK2 and VPS35. Additionally, the PD associated interactome is enriched for mitochondrial processes; "mitochondrial cell death", "mitochondrial protein localisation", "membrane protein localisation" and "mitochondrial transport". Our data points to NSL complex members OGT and WDR5 as key drivers of this increased PD association. These findings strengthen a role for mitochondrial quality control in both familial and sporadic disease. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

https://psychiatry.dev/wp-content/uploads/speaker/post-11326.mp3?cb=1672302131.mp3 Playback speed: 0.8x 1x 1.3x 1.6x 2x Download: Machine learning and network analysis of the gut microbiome from patients with schizophrenia and non-psychiatric subject controls reveal behavioral risk factorsFull EntryMachine learning and network analysis of the gut microbiome from patients with schizophrenia and non-psychiatric subject controls reveal behavioral risk factors and bacterial interactions –

In this episode, Ralf Buechsenschuss explains how he aligns HR Strategy, People Analytics and Digital Transformation to gain the greatest benefit from both formal and informal networks within an organisation.Ralf explains how these fields intersect to create a comprehensive picture to guide decision making to deliver strategic priorities.He also provides an example of how he partnered with an organisation to bring in a neuro diverse workforce, integrate them into his team to develop a complex algorithm.Truly inspiring stuff Ralf!The Reimagined Workforce podcast is brought to you by Workforce Transformations Australia Pty. Ltd.All opinions expressed are the speaker's and not the organisations they represent.If you have a story about a workforce transformation to share and would like to be a guest on this podcast, please contact us at kathhume@workforcetransformations.com.au.Connect with Kath Hume on LinkedIn

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.19.512949v1?rss=1 Authors: Merchant, J. P., Zhu, K., Henrion, M. Y. R., Zaidi, S. S. A., Branden, L., Moein, S., Alamprese, M. L., Pearse, R. V., Bennett, D. A., Ertekin-Taner, N., Young-Pearse, T. L., Chang, R. Abstract: Despite decades of genetic studies on late onset Alzheimer disease (LOAD), the molecular mechanisms of Alzheimer disease (AD) remain unclear. Furthermore, different cell types in the central nervous system (CNS) play distinct roles in the onset and progression of AD pathology. To better comprehend the complex etiology of AD, we used an integrative approach to build robust predictive (causal) network models which were cross-validated over multiple large human multi-omics datasets in AD. We employed a published method to delineate bulk-tissue gene expression into single cell-type gene expression and integrated clinical and pathologic traits of AD, single nucleotide variation, and deconvoluted gene expression for the construction of predictive network models for each cell type in AD. With these predictive causal models, we are able to identify and prioritize robust key drivers of the AD-associated network state. In this study, we focused on neuron-specific network models and prioritized 19 predicted key drivers modulating AD pathology. These targets were validated via shRNA knockdown in human induced pluripotent stem cell (iPSC) derived neurons (iNs), in which 10 out of the 19 neuron-related targets (JMJD6, NSF, NUDT2, YWHAZ, RBM4, DCAF12, NDRG4, STXBP1, ATP1B1, and FIBP) significantly modulated levels of amyloid-beta and/or phosphorylated tau peptides in the postmitotic iNs. Most notably, knockdown of JMJD6 significantly altered the neurotoxic ratios of A{beta} to 40 and p231-tau to total tau, indicating its potential therapeutic relevance to both amyloid and tau pathology in AD. Molecular validation by RNA sequencing (RNAseq) in iNs further confirmed the network structure, showing significant enrichment in differentially expressed genes after knockdown of the validated targets. Interestingly, our network model predicts that these 10 key drivers are upstream regulators of REST and VGF, two recently identified key regulators of AD pathogenesis. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Handbook of Graphs and Network Analysis in PA: https://ona-book.org/ Handbook of Regression Modelling in PA: https://peopleanalytics-regression-book.org/ Keith McNulty's Medium Blog: https://keith-mcnulty.medium.com/ Adam Grant post on Open Offices: https://www.linkedin.com/posts/adammgrant_open-offices-are-high-on-my-list-of-things-activity-6977396920344322048-3GuJ/?utm_source=share&utm_medium=member_desktop Nature Article on Zoom killing creativity: https://www.nature.com/articles/s41586-022-04643-y 20% of Professors come from 8 colleges: https://www.insidehighered.com/news/2022/09/23/new-study-finds-80-faculty-trained-20-institutions Moving online decreased the advantage for attractive female students, but not attractive male students: https://www.sciencedirect.com/science/article/pii/S016517652200283X Impact of Open Offices on Communication: https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0239

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.05.506618v1?rss=1 Authors: Mittring, A., Moser, T., Huet, A. T. Abstract: Optogenetic control of neural activity enables causal investigation of neural circuits and new perspectives in sensory restoration. Optimal design of the experiment needs to match the optogenetic technology to the precise neural circuit and research question. This often precludes out-of-the-box solutions and require users to thorough characterize the neural response and to tailor the optogenetic tools. Here, we provide a framework to parametrize optogenetic control at the single and population levels and apply it, in mice, to the spiral ganglion neurons (SGN) that form the input stage of the auditory pathway. We employed the ultrafast targeting-optimized Chronos and characterized the light-evoked response by in vivo single-unit recordings in SGNs and neurons of the anteroventral cochlear nucleus (AVCN) that detect coincident SGN input. We demonstrate that spike probability can be gradually dialed by adjusting the width of light pulses like what can be achieved with acoustic clicks of different sound pressure levels. We identified an optimal pulse width of 1.6 ms for maximizing the amount of light intensity information in the spike trains. Further, we identified a spike failure induced during high rate optical stimulation that likely was due to depolarization block and required a few tens of milliseconds to recover. Finally, a semi-stochastic stimulus allowed us to rapidly (within minutes) establish the transfer function from light to SGNs firing and to generate firing of different statistics for approximating the time constant of neuronal integration in AVCN neurons. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Joost van Houten, Chief Executive Officer, SentinelsFenergo, which supplies digital KYC and CLM solutions recently acquired transaction monitoring platform Sentinels, which aims to streamline AML and transaction monitoring workflows for financial institutions, with the aim of increasing fraud protection and reducing false positives. Robin Amlôt of IBS intelligence speaks to Joost van Houten, CEO of Sentinels about how smart transaction monitoring can relieve the compliance burden and the need to aggregate historical transaction data across multiple financial institutions. 

Our guests in this episode are Ryan Gallagher, a PhD Candidate in Network Science at Northeastern University, and Brooke Foucault Welles, an Associate Professor in Communication Studies and the Network Science Institute at Northeastern University. We discuss their 2019 CSCW paper, "Reclaiming Stigmatized Narratives: The Networked Disclosure Landscape of #MeToo" with co-authors Elizabeth Stowell and Andrea G. Parker. We talk about their substantive motivation for focusing on #metoo, the networked counter public, and hashtags' influence on social change. Ryan and Brooke also walk us through the advantages of pairing qualitative and quantitative work, weaving ethics throughout every stage of the research process, dealing with missing Tweets, and taking seriously both the "computational" and "social science" sides of CSS.

Today I'm joined on the show by Jeppe Hansgaard, CEO & Founder of Innovisor. The team at Innovisor helps organizations connect to their people. Organizational network analysis is their 'shortcut' to building the connection.  Innovisor website: https://www.innovisor.com/ Innovisor on LinkedIn: https://www.linkedin.com/company/innovisor/ Jeppe on LinkedIn: https://www.linkedin.com/in/jeppehansgaard/ Jeppe on Twitter: https://twitter.com/JeppeHansgaard For show notes and to see details of my previous guests, check out the podcast page here: www.GetMoreHRClients.com/Podcast WANT MORE CLIENTS? Want more clients for your HR-related consultancy or HR Tech business? Check out: www.GetMoreHRClients.com/Services. WANT TO START AN HR BUSINESS? Want to launch your own consulting business in the broad Human Resources sector? Check out the guide and the new online course: www.GetMoreHRClients.com/blog/How-To-Start-An-HR-Consulting-Business.

27th March 2022 The Spokesmen Cycling Podcast EPISODE 294: Building a Better World — an Activist Planner's Network Analysis of Bike Lanes in Paris SPONSOR: Jenson USA HOST: Carlton Reid GUEST: Marcel Moran TOPICS: This is a show about network analysis, specifically of the coranapistes of Paris but also how the University of Californina Berkeley has a strong history of what's known as "activist planning" where there is an acknowledgement that scholars will want to build a better world. With Marcel Moran, a PhD Candidate at the Department of City & Regional Planning University of California, Berkeley LINKS: https://www.Jensonusa.com/thespokesmen https://www.the-spokesmen.com https://twitter.com/carltonreid https://twitter.com/marcelemoran https://sites.google.com/berkeley.edu/marcelmoran/home https://findingspress.org/article/33765-treating-covid-with-bike-lanes-design-spatial-and-network-analysis-of-pop-up-bike-lanes-in-paris https://scholar.google.com/citations?user=GvdJ5eEAAAAJ&hl=en&oi=ao

Sponsor by SEC Playground แบบสอบถามเพื่อปรับปรุง Chill Chill Security Channel: https://forms.gle/e5K396JAox2rZFp19 Music by https://www.bensound.com/ --- Support this podcast: https://anchor.fm/chillchillsecurity/support

In this episode, co-hosts Ron Landis and Jennifer Miller deconstruct organizational network analysis or sometimes referred to as ONA. Social interactions are becoming increasingly important to understand in the context of organizational success. While many have access to data related to interactions (i.e., communication patterns including email, chat) little has been done to analyze those patterns. Using ONA to understand and quantify such relational data provides organizations with a means for identifying whether individuals (or groups of individuals) have similar or different employee experiences.  In this episode, we had conversations around these questions:  What is organizational network analysis?  How can organizational network analysis be used?  What kind of data do I need for an organizational network analysis?  Key Takeaways:  Network analysis is a field that studies the relations among a set of actors. Everyday examples include social media platforms (i.e., connections between individuals) and the electric grid. The goal of network analysis is to examine the nature and patterns of those connections.  To establish a network, you need certain kinds of data. Networks have components such as actors, nodes, or vertices. The interactions between the components are sometimes referred to as links or edges. Organizational network analysis examines the patterns of interactions between the components.  At the end of the episode, Jennifer and Ron recommend steps for folks just starting out in this space all the way to the more advanced HR professional.  Related Links  Millan Chicago   

In this week's episode Greg and Patrick have a wonderfully engaging conversation with social network analysis expert Tracy Sweet who is an Associate Professor in the Department of Human Development and Quantitative Methodology at the University of Maryland. Tracy patiently helps us understand what social network analysis is, and how it can be used to better understand the complexities of human behavior. Along the way they also discuss sliding into DMs, fax machines, older millennials, baboons, too much math, inside voices, penguin data, swiping left, probation advice, unreciprocated social isolates, Wordle, the floss dance, power, and talking to your dog. 

Token Metrics Media LLC is a regular publication of information, analysis and commentary focused especially on blockchain technology and business, cryptocurrency, blockchain-based tokens, market trends, and trading strategies. Like the podcast to let us know you like the content!

Token Metrics Media LLC is a regular publication of information, analysis and commentary focused especially on blockchain technology and business, cryptocurrency, blockchain-based tokens, market trends, and trading strategies. Like the podcast to let us know you like the content!

To make Network Analysis accessible, we'll learn about a free and popular Network Analysis Software called Gephi. In this episode, Gephi's founder, Mathieu Jacomy, will reveal new features and make you an offer you can't refuse if you're a Java developer

If you're new, welcome! If you're not, welcome back! This week we spoke with Martin Bouchard who is a professor of Criminology and Criminal Justice at Simon Fraser University. We ask Martin about the social nature of gangs and gang members. He tells us why he thinks gangs are a very social and not necessarily anti-social phenomenon. Martin then discusses the network analysis method and why it may be useful for the study of crime. We then discuss a paper Martin wrote discussing the ways in which network analysis can aid in the study of organized crime.  Martin has published in journals such as Journal of Quantitative Criminology, Justice Quarterly, and Global Crime. You can find him on Twitter @MBouchardCrim. You can find us on Twitter, Facebook, and Instagram @TheCrimAcademy. Please visit our website www.thecriminologyacademy.com. Whether or not you have an iPhone or iTunes, please rate and review us there. These are the lifeblood of the podcast. Thanks for listening!

Analysing networks is a growing area of research in academia and industry. In order to be able to answer questions about large or complex relationships it is necessary to have fast and efficient algorithms that can process the data quickly. In this episode Eugenio Angriman discusses his contributions to the NetworKit library to provide an accessible interface for these algorithms. He shares how he is using NetworKit for his own research, the challenges of working with large and complex networks, and the kinds of questions that can be answered with data that fits on your laptop.

CPT Josh Bedingfield discusses Human Network Analysis with host Sean Acosta. This episode is sponsored by Tesla Government and the Civil Affairs Association. 

When Professor Kathleen Carley of Carnegie Mellon University agreed to talk with us about network analysis and its impact on insider risks, we scooched our chairs a little closer to our screens and leaned right in. In this episode of Uncovering Hidden Risks, Liz Willets and Christophe Fiessinger get schooled by Professor Carley about the history of Network Analysis and how social and dynamic networks affect the way that people interact with each other, exchange information and even manage social discord. 0:00 Welcome and recap of   1:30 Meet our guest: Kathleen Carley, Professor at Carnegie Mellon University; Director of Computational Analysis & Social and Organizational Systems; and Director of Ideas for Informed Democracy and Social Cybersecurity 3:00 Setting the story: Understanding Network Analysis and its impact on company silos, insider threats, counter terrorism and social media. 5:00 The science of social networks: how formal and informal relationships contribute to the spread of information and insider risks 7:00 The influence of dynamic networks: how locations, people and beliefs impact behavior and shape predictive analytics 13:30 Feelings vs Facts:  Using sentiment analysis to identify positive or negative sentiments via text 19:41 Calming the crowd: How social networks and secondary actors can stave off social unrest 22:00 Building a sentiment model from scratch: understanding the challenges and ethics of identifying offensive language and insider threats 26:00 Getting granular: how to differentiate between more subtle sentiments such as anger, disgust and disappointment 28:15 Staying Relevant: the challenge of building training sets and ML models that stay current with social and language trends.   Liz Willets: Well, hi, everyone. Uh, welcome back to our podcast series Uncovering Hidden Risks, um, our podcast where we uncover insights from the latest trends, um, in the news and in research through conversations with some of the experts in the insider risk space. Um, so, my name's Liz Willets, and I'm here with my cohost, Christophe Fiessinger, to dis- just discuss and deep dive on some interesting topics.             Um, so, Christophe, can you believe we're already on Episode 3? (laughs) Christophe Fiessinger: No, and so much to talk about, and I'm just super excited about this episode today and, and our guest. Liz Willets: Awesome. Yeah, no. I'm super excited. Um, quickly though, let's recap last week. Um, you know, we spoke with Christian Rudnick. He's from our Data Science, um, and Research team at Microsoft and really got his perspective, uh, a little bit more on the machining learning side of things. Um, so, you know, we talked about all the various signals, languages, um, content types, whether that's image, text that we're really using ML to intelligently detect inappropriate communications. You know, we talked about how the keyword and lexicon approach just won't cut it, um, and, and kind of the value of machine learning there. Um, and then, ultimately, you know, just how to get a signal out of all of the noise, um, so super interesting, um, topic.             And I think today, we're gonna kind of change gears a bit. I'm really excited to have Kathleen Carley here. Uh, she's a professor across many disciplines at Carnigen Melligan, Carnegie Mellon University, um, you know, focused with your research around network analysis and computational social theory. Um, so, so, welcome, uh, Kathleen. Uh, we're super excited to have you here and, and would love to just hear a little bit about your background and really how you got into this space. Professor Kathleen Carley: So, um, hello, Liz and Christophe, and I'm, I'm really thrilled to be here and excited to talk to you. So, I'm a professor at Carnegie Mellon, and I'm also the director there of two different, uh, centers. One is Computational Analysis of Social and Organizational Systems, which is, you know, it brings computer science and social science together to look at everything from terrorism to insider threat to how to design your next organization. And then, I'm also the director of a new center that we just set up called IDeaS for Informed Democracy and Social Cybersecurity, which is all about disinformation, uh, hate speech, and extremism online. Liz Willets: Wow. Professor Kathleen Carley: Awesome. Liz Willets: Sounds like you're (laughs) definitely gonna run the gamut over there (laughs) at, uh, CMU. Um, that's great to hear and definitely would love, um, especially for the listeners and even for my own edification to kinda double-click on that network analysis piece, um, and l- learn a little bit more about what that is and kind of how it's developed over the past, um, couple years. Professor Kathleen Carley: So, network analysis is the scientific field that actually started before World War II, and it's all about connecting things. And it's the idea that when you have a set of things, the way they're connected both constrains and enables them and makes different things possible.             The field first started it was called social networks. This is long before social media. And, um, people were doing things like watching kindergartners play with each other, and they realized that the way which kids played with which, which kids bashed each other over the head with the, their sand shovel was really informative at effect at telling how they would actually do in the various kind of studies they needed to do. The same kind of thing was applied to hermit crabs and to deers and other kinds of animals to identify pecking orders, and, from those groups, and identify which animals had the best survival rate.             Today, of course, the field's grown up a lot, and we now, uh, talk about kind of networks+. So, we apply network science to everything from, you know, how your company ... Where are the silos in your company? Who should be talking to 'em? We also apply to things like insider threat and look at it there to say, "Ah, well, maybe these two people should be talking, but they're not. That's a potential problem," a, and we apply to things like counterterrorism. We apply it to social media and so on. So, people now look at really large networks and very what are called high-dimensional or meta networks such as, who's talking to whom, who's talking about what, and how those ideas are connected to each other. Liz Willets: Awesome. Yeah, I think, I know Christophe and I, we're very interested around that space and thinking about who should be talking to one another, um, you know, as we think about communication risks in an organization, especially in the (laughs) financial services industry. You've got things, um, that, you know, you're mandated by law to, um, kind of detect for like collusion between two parties whether it's your sales and trading group who just should not be, um, communicating with one another. So, I think that certainly applies, um, to your point earlier around the insider threat space. Professor Kathleen Carley: Well, one of the great things in, in, uh, using social networks, especially depending what data you have access to, you may be able to find informal linkages. So, not just who's, uh, formally connected because they're like in an authority relationship, like you report to your boss, but, you know, who you're friends with or who you go to lunch with or, you know, and all these kind of informal relationships. And we often find that those are as or more important for affecting, you know, house, how information goes through a group, how information gets traded, and even for such things as promotion and your health. Christophe Fiessinger: And to not only to, to add to, uh, what you were saying, Kathleen, is like the context is usually important to make an informed decisions of what's going on in that network. Professor Kathleen Carley: And then, cer- Christophe Fiessinger: Isn't that what you think about it? Professor Kathleen Carley: Yeah, certainly. In fact, the context is very important, and it's also important to realize that one context doesn't, um, capture all of s- somebody's interactions, right? So, for example, when Twitter started, people were trying to predict elections from, uh, interactions on Twitter among people. Well, the problem was not only was not everybody on Twitter, so you didn't have a full social network, not all communication even with people who were on Twitter, that's not the only way they communiticated with each other. They might have also gone to the bars together or, or whatever. Liz Willets: Um, I was actually kinda reading through some of your research (laughs) as I was prepping for this interview and, um, read, um, some of your research around the difference between social network analysis and dynamic network analysis. And so, as you think about, kind of as we're talking, contexts and, you know, it's not just maybe the social connections, but it's adding in now the organization or the location or someone's beliefs. Um, I'd love if you could just kind of, you know, double-click there for us and tell us a little bit more about that. Professor Kathleen Carley: Yeah. So, when, um, when the field started, right, people were really dealing with fairly small groups. And so, it was not unusual to say go into a small, like, startup company, and you would have maybe 20, 25 people. Um, for each one of 'em, you would know who was friends with who and who went to 'em for advice, and that was your data set, right? It was all people, and it was all just one or two types of links. Technically, we call that one-mode data 'cause there's only one type of node, and there's two types of links. So, it's t- ... It's multiplex and one mode.             Um, but now what's happened, as the field has gotten grown up in some sense, uh, we're dealing with much larger data sets, and you happen to have multiple modes of data. So, you'll have things like people, organizations, locations, beliefs, resources, tasks, et cetera, and when you have all of that, you have multiple modes of data. And in fact, this is great because you need multiple modes of data to be able to do things like do predictive analytics, but in addition, you have way ... And you have lots of different kinds of ties. So, I not only have ties between people, I have ties of people to these things like what resources they have, what knowledge they have, and so on. So, it's called by bipartite data.             But then, I also have the connections among those things themselves, like words to words, and because you have all of that high-dimensional data and you have it through time, you now have a kind of a dynamic, high-dimensional networks. And so, the big difference here is that you've got more data, more kinds of data, and you've got it dynamically. And we even talk about it sometimes as geospatial because sometimes, you even have locations and you have to take into account, uh, both the distance physically as well as the distance socially. Christophe Fiessinger: Interesting. And Kathleen, I, I, I can't resist- Professor Kathleen Carley: Mm-hmm (affirmative). Christophe Fiessinger: I mean, I got kids and, and, uh, uh, I'm originally from Europe, and the way my k- kids interact with their family non-members, grandmothers in Europe is obviously very different than how I did it when I was growing up. So, to your point on all those dimensions is you also see a difference where a person might talk one way on a channel or, uh, an app and talk another way in another app, and then layer that, you know, I would talk differently on a PC where I get a full form. I can be very verbalist in my email or whatever versus my phone wherever I'm located. Are you seeing some of those patterns as well influence? Professor Kathleen Carley: Absolutely. Yeah, and then they're ... Yeah. And you, you've probably even seen these in your own work lives because, for example, you'll communicate one way on LinkedIn. You'll communicate a different way on Facebook, a different way on Twitter, and a different way in person. So, it also matters what media you're on, and it also matters whether or what kind of others you surround yourself with. I mean, I know people who use different variants of their names on- Christophe Fiessinger: Mm-hmm (affirmative). Professor Kathleen Carley: ... different platforms to signal to themselves, "Oh, when I'm on this one, I don't talk about money," or, "When I'm on this one, I don't talk politics," you know? And so, people not only change how they talk, they change what they talk about, and they change who they talk to. Christophe Fiessinger: Yeah. And I think the personas as well. I've seen my younger one who plays, uh, who does a lot of gaming. Professor Kathleen Carley: Yep. Christophe Fiessinger: Typically, they have their own persona, and, and then obviously, there's a different realm then of, of, of a different network, but they even put a different hat going into that mode of, of talking in the context of a game. Professor Kathleen Carley: Well, and for there, it's just doing a game, right? But what we're actually seeing on social media is, you know, you do see adversarial actors- Christophe Fiessinger: Uh-huh (affirmative). Professor Kathleen Carley: ... under fake personas doing things like trying to do fishing expeditions or trying, you know, trying to convince you that they're just one of the other people in the neighborhood- Christophe Fiessinger: Yeah. Professor Kathleen Carley: ... and they really aren't, you know, and try, and trying to suck you into things. Christophe Fiessinger: Yeah. Professor Kathleen Carley: So, we see a lot of that as well. Christophe Fiessinger: Yeah. Liz Willets: Grooming. Christophe Fiessinger: I guess grooming is also not a new problem but also something that, that's present in those communities or anywhere. Professor Kathleen Carley: Yeah. Liz Willets: Definitely, and I think what we've seen especially with the pandemic is, yes, you might have these different personas, um, but now, like your, your home is become your workplace. And so, how you might have typically behaved, um, you know, when you'd come home at the end of a long day versus now, you're in the context of work. Um, you know, I think we've seen a lot of organizations think about the risks that, that that could pose, um, in addition to all the other, um, you know, (laughs) stresses that people have on their day-to-day lives.             Um, but I think it's interesting, um, to your point earlier around, you know, having all the context. Um, you know, we're seeing signals come through from Teams, email, Zoom, uh, you know, social media, et cetera, and, uh, um, also detecting for things like repeated bullying, um, behavior. And so, it's not just, uh, a way f- to your point and around using the analytics to predict something, but it's also to say, "Hey, this is a pattern, and, uh, you know, we should probably step in and do something about it." Professor Kathleen Carley: Yeah, absolutely. And I think people are becoming more aware of these patterns themselves because they're actually not just seeing their own communication. They're actually seeing their kids' communication or their parents communication or whatever. And so, they're starting to realize that the people around them may be comm- communicating in ways that impacts them, and so there's a variety of now new technologies that people are talking about trying to develop to try to help people manage this more collectively. Liz Willets: Definitely. And I think, um, you know, another area that I'd love to explore with you is just around sentiment analysis. So, you know, you have all these signals, but, um, how do you know if someone's talking about something positively or negatively, um, and g- kind of would love to kind of hear if you've done any research in that spaces? Professor Kathleen Carley: Oh, yes, we ... Yeah. I and my group, of course, we do a lot of work on sentiment. So, um, so, sentiment is one of those really tricky things when you're, uh, when you're not there because it depends on how many different modalities you have. Like, if you only have text, it's harder to detect than if you have text plus images, which is still harder than if you also have sound. So, the ... So, it's kind of tricky, and there's new techniques for all of those.             But let's just think about text for the moment. The way people often de- try to detect sentiment and then where they started out was just by, um, counting the number of positive versus negative words. Okay? And that's kinda okay, but it more tells you about overall, was the message kind of written from an upbeat or a downbeat kind of way. That's really all it really tells you, but people thought that that meant that if there was a something they cared about, like let's say I wanna know if it's about vaccines and are they happy about the vaccines or upset. Well, they would just say, "Here's a message. It has the word vaccine in it. Oh, there's more happy words than sad words, so it must be positive toward vaccines." No. Not even close.             Because locally, it coulda been, "I'm so happy I don't have to take the vaccine." That woulda come out as overall positive, but it's really negative about the vaccine. So, then, the people came up with loads. So, then, we work on locals then, but how do I tell for a particular word?             But the thing is when I make a statement like that, that's out of context still because there could've been this whole dialogue discussion, right? And in the ... And when we actually then looked at, at, at these kind of sentences within the context of the discussion, over 50% of time, we had to change our mind about what the sentiment really was in that particular and what was really meant, you know?             And then, there's issue of sarcasm and humor, which we were terrible at detecting, right? Liz Willets: (laughs) Professor Kathleen Carley: And so, peep ... And one of the ways people start to detect that is by looking at what's written and then looking for an emoji or emoticon, and if it's at the opposite sentiment of the what's written, you go, "Ah, this must be a joke." Okay? Christophe Fiessinger: Or just sarcastic again. Professor Kathleen Carley: Yeah. So, it goes cra- ... It goes on and on from there, but there's a couple of a ... There's ... That's kind of the classic line. And now, of course, we do all that with machine learning as opposed to just based on just keywords.             But there's two other things that are in the sentiment field that people often forget about. One is, um, these subconscious almost supplemental cues that are in messages. So, when you write things and use images, your reader will pick up on things in it and it will cause them to respond and with particular emotional reactions.             So, for example, you've probably gotten an email or a text from someone where it was in all caps, and your, and your initial response is, "Oh, my gosh. They must be mad at me," right? Or, "What did I do wrong now?" It's like, "Oh, okay." But that's a subliminal cue, okay? It's like things like all caps, use of pronouns. There're special words that people use that will evoke emotions in others, so we look for these subliminal cues also.             And, uh, an emergent field is looking for these in images, like the use of light versus dark images, the use of cute little kitties, right? Christophe Fiessinger: Yeah. Professor Kathleen Carley: There's a whole bunch of things that people know now make them happy. And then, so, that's another aspect of it.             And then, the third aspect of it is that, um, sentiment is actually very tied to your social networks. Your emotional state is tied to your social networks. So, the more I can get you excited either really happy or really angry, the more I can change your social network patterns. So, we can actually look at for our detections in changes in social network patterns as a way of figuring out something about sentiment as well. Liz Willets: Interesting. So, are you saying essentially that through your social networks, it kind of like reinforces or, or strengthen, strengthens your connections with that group that you're identifying yourself with? Professor Kathleen Carley: So, I'm saying that, well, it does. It's kind of a cycle because your mind likes to, um, maintain balance, okay? It likes to be emotionally balanced. You don't ... You really don't like to be overly excited in any direction, right? Most people don't. And so, if something's making you very uncomfortable, you will either ... If it, like, your connection with someone's making you, uh, very uncomfortable, you will either change your opinion to be more like theirs so you're less more comfortable, or you will drop your connection with that person. So, your affect of your emotional state modulates your social networks, and your social networks that affect what information and emotions come to you and modulate what emotions you have. So, it's kind of this cycle. Christophe Fiessinger: Then- Professor Kathleen Carley: And so, we actually can watch this happening in groups where I can form them into ... I can prime groups to be ready to be emotionally triggered simply by building up social network connections among them. And then, I can emotionally trigger them, and the people in them will either get more involved in the group or they'll say, "I'm not really feeling comfortable anymore. I'm gonna leave." Christophe Fiessinger: Mm-hmm (affirmative). I'm sure you've got a trove of data to research with COVID or with recent election in the U.S. that would- Liz Willets: (laughs) Christophe Fiessinger: ... that would prove those theories of the relationship between your social network and h- your, your sentiment, right? Professor Kathleen Carley: Yes. Yeah. Yeah. Christophe Fiessinger: Well, actually, going back, tying this to, um, to what you were mentioning earlier, Kathleen, like, sometimes, we say that the conversation at the edges are, are the one, um, are the highest risk one, and the ones that are happening on the fringes and, you know ... A- And then, you add to that like something you mentioned earlier which is a, and also looking at how you, how you are potentially detecting like social unrest and things like that. And, and because those are like at the fringes, it might start very small in a network with very few people, but it could definitely have a network effect very quickly. How do you find those needles that that did, didn't exist before the, a theory, a pattern, an opinion? Professor Kathleen Carley: So, the short answer is it's really hard, and we're not good at it yet. (laughing) Christophe Fiessinger: Okay. Professor Kathleen Carley: Um, but there's a couple of techniques that first off, sometimes, you find 'em by luck. You just happened on 'em. Sometimes, you find 'em just through, um, good journalistic forensics, um, and sometime, but sometimes, we can aid and help that a bit by actually looking for, um, critical secondary actors. Christophe Fiessinger: Sure. Professor Kathleen Carley: And these are like there's these kinda network metrics for finding these kinda critical secondary actors, and we look for those because those are the kind of actors that could emerge into leaders of these kinds of things. So, they're kind of ... It's not quite anomaly detection, but it's kind of like anomaly detection for networks. Christophe Fiessinger: Oh. Is it kind of like that secondary actor is potentially a placebo that could flip and you're trying to either a, a change compared to that, that baseline? Professor Kathleen Carley: I think that's probably the wrong, the wrong model of it. Christophe Fiessinger: Okay. Professor Kathleen Carley: Like, uh, a s- secondary actor is often someone who does things like brokerage relationships between two big actors, okay? Christophe Fiessinger: Ah, okay. Professor Kathleen Carley: Yeah. Christophe Fiessinger: So, that person would be potentially more of a f- will ... Pride, whatever, would be a fire starter and will accelerate on that. Professor Kathleen Carley: Yeah. Exactly. Christophe Fiessinger: Two people having a point of view to suddenly a wildfire is spreading out across the entire network. Professor Kathleen Carley: Exactly. Yeah. Christophe Fiessinger: Okay, I get it. Thanks. Liz Willets: Yeah, but back to your point around some of the challenges with the, for example, detecting sarcasm, and is it an emoji? Um, would love to hear your thoughts on just some of the other challenges more generally if you're thinking about building, uh, a, a sentiment model from scratch, um, whether it's for, you know, threats or offensive language, um, or things like burnout and suicide. Um, how do you go about doing that, and how do you go to do about doing that in an ethical, um, manner? Professor Kathleen Carley: Okay. So, um, so, one of the challenges is culture and language because the way we express sentiment vary, differs, even though there's like basic emotions that are, that are built in cognitively in our brain. The way we express those is socially, culturally defined. Christophe Fiessinger: Mm-hmm (affirmative). Professor Kathleen Carley: So, one of the big issues is making sure you understand the culture and the language that's associated with it. So, that's part of it.             The second, a second, uh, critical thing is the fact that, um, when people express themselves, when you're using, and if you're mainly using online data, um, people can go silent, in which case, you don't have any data. Your data could just be a sample. They could choose to enact one of their personas and be lying. Christophe Fiessinger: Yeah. Professor Kathleen Carley: So, there's lots of ways in which your data- Christophe Fiessinger: Mm-hmm (affirmative). Professor Kathleen Carley: ... itself could be wrong, okay? And that's another big challenge in the area. So, those, I would say, are, uh, so those are examples of some of the challenges in addition to having to have the whole discussion and having to, you know, be careful what you're looking at sentiment around and so on.             So, from an ethical perspective, um, I would say that part of this is, is that when you're collecting data and trying to analyze it and create, like a model for one of these issues, one of the biggest chall- one of the biggest issues is making sure that you haven't over focused on a certain class of people, like only focused on young white guys or only focused on, you know, um, agent, uh, Hispanic women. You wanna make sure that you're as k- much as possible balanced across the different kinds of publics you want to serve. So, that's, that's part and the ... That's one of the challenge, or one of the kind of ethical guidelines and challenges at the same time.             Um, the other part is if you were actually going to, to intervene, then you'd need to think about intervention from a, you know, what does the community consider appropriate ethically within that community for the way you intervene? And the answer may be very different if you're talking about, you know, intervening with children versus intervening with, uh, young adults versus intervening with people with autism. So, so you need to look at it more from a community perspective. So, those are two I would raise. Liz Willets: That's fine. Yeah. I think, um, you know, especially at Microsoft, we are committed to having unbiased, um, training data so that we aren't, you know, discriminating by against someone because they have these, um, certain characteristics, um, and definitely keep that top of mind, um, as well as, you know, remediation and, and how do you go about now that you've identified that this person is at risk for whatever, uh, reason? Now, how do you reach out to them and give them the support they might need, or how do you alert, um, you know, someone who, who might need to step in? And so, I think that's been, um, a really interesting challenge that we're digging into on our end as well.             Um, and I think to the first piece you were talking about just more generally the challenges, um, I know you've done some research around control theory, um, and would love to get your perspective on, you know, especially, uh, in some of these more granular sentiments. Like, how do you differentiate between anger, disgust, disappointment, um, and, and really, um, kind of define exactly what you're looking for in the communications to pull that out? Professor Kathleen Carley: Yeah. So, um, basically, we, we start with what are thought to be the basic emotions, the ones that are built in cognitively? So, and we would take those ones, and those, you can distinguish fairly reasonably on the basis of the cues I was talking about, and they're kind of big swaths of things. Of course, most of the basic emotions are ones that are kind of more on the negative side, so it's really more on the positive side, discriminating, you know, happy from ecstatic from mildly amused. That, it's much harder there 'cause that's, none of those are ba- basic, just happiness is basic, right? All the others are variations of happiness.             So, we start with the basic of the emotion and try to discriminate into those categories, and to go further than that, we often find we don't need to. If we need to, um, then really, it's because the context demands that you have to pay attention to a parti- ... So, you're looking for something particular in a particular environment. And so, then, we let the context dictate what the difference is that it's interested in.             Um, so, for example, if I, if I was doing this for Disney for, you know, people's response to a new ride, for example, that context would dictate that what I really wanna focus on is not just happiness but their satisfaction and pull that out. And so, then, I would actually develop my technology around that, around the, the different people who fell into the different categories, and I might do it first by getting survey data or something like that. Christophe Fiessinger: Yeah. Professor Kathleen Carley: But, you know, you said something that made me realize that I hadn't mentioned one of the major challenges- Christophe Fiessinger: That was good. Professor Kathleen Carley: ... that, um, people often overlook because we're so in love with machine learning, right? And we so think, "Training sets," right? Well, the trouble is, in a social space, your training sets are yesterday's news. Christophe Fiessinger: Yeah. Professor Kathleen Carley: They're never up-to-date. They're always, they're always a mess, and a lot of things where you wanna use sentiment and wanna look at behavior of people, you don't have time to build a training set. So, this is an area where we really need new technologies like match functions and things like that, or where you can just get the bare minimum training set and then do some kind of leapfrogging on it. Christophe Fiessinger: Yeah. I think it- Liz Willets: Yeah. Christophe Fiessinger: I think this is to, to that point. I can relate to that. I think the ... And also, what you were s- saying early on the key part where you look at demographics or what is that target audience with that pattern you're trying to detect is even that let's say that sp- specific demographics, you did a good job on day zero. We know language is this constant evolving function, and just because, to your point, you know, it was yesterday's data set. Just because you would put ball on sweats to do a white paper to detect blah for those demographics. Professor Kathleen Carley: Yep. Christophe Fiessinger: That was great at that point in time, but I'm sure it already changed rapidly because of, of the today's availability of social network and things like that, you know. My, when I was visiting Europe, my, my nephew and niece speak English from what they've seen on YouTube and Netflix. Professor Kathleen Carley: Yeah. Christophe Fiessinger: So, just it'll almost feel like language is even moving faster with that, uh, availability of, of that, all those tools worldwide that's making researchers I'm sure his job even harder to stay up-to-date. Professor Kathleen Carley: Absolutely. (laughs) Yeah. The level of new jargon and new phrases out, it's crazy. Liz Willets: (laughs) Christophe Fiessinger: Yeah. Liz Willets: And that's not just in English, too, you know? Professor Kathleen Carley: That's right. That's right. Liz Willets: We were talking last week with Christian around languages (laughs) and, you know, how many languages there are in the world and how you have to kind of build your models to be trained to kind of reason over, uh, you know, diable, double-byte characters and, um, you know- Professor Kathleen Carley: Yep. Liz Willets: ... Japanese and, and Chinese characters. And so, it just (laughs) it's never ending. Christophe Fiessinger: Yeah. Professor Kathleen Carley: And sometimes, the fact that you have the multiple character sets and multiple languages can be diagnostic, right? So, for, like, when we look at, um, response to say natural disasters in various areas, typically, people, when they communicate online, will communicate in one language with others in the same language. And there'll be a few people who will communicate in multiple languages, but they'll have different groups like, "Here's my English group. Here's my Spanish group." Okay?             But during a disaster, you'll see, actually see more messages come out where you've got mixed part English, part Spanish in the same message- Christophe Fiessinger: Mm-hmm (affirmative). Professor Kathleen Carley: ... and, and so it can be diagnostic of, "Oh, this is a bilingual community," for example. Liz Willets: Interesting. Christophe Fiessinger: Interesting. Liz Willets: Well, great. I know, um, Kathleen, I have certainly learned a lot and wanna thank you again for, for joining us today. Um, Christophe, I thought that was a great conversation. Christophe Fiessinger: Yeah. I, after that, I wish I was a student, and I could join, uh, CMU and be one of your students and write a PhD. It sounds like a infinite number of fascinating topics so and, and research topics, so it sounds- Professor Kathleen Carley: Well- Christophe Fiessinger: ... very fascinating.

ESPN & SEC Network analysis Cole Cubelic joins the show and shares his thoughts on Josh Heupel and what the Vols will look like in 2021. We also discuss his thoughts about the rest of the SEC and the competition we are going to be facing. Kyler and Reed break down the Vols in the Draft and their chances on their new teams. Kyler also shares a story during "Big Orange Juice" of a former teammate stealing from him.RATE & SUBSCRIBE!!YouTube - https://youtu.be/JUOBj3MGghA

Matt Cauthorn is VP & Evangelist of Cybersecurity and Cloud at ExtraHop, makers of a cloud-native cybersecurity solution and a place he's worked for the last decade. Matt has more than two decades of experience in tech, having worked as a senior manager of servers, storage, and hosting at Manheim and an engineer and engineering manager at F5 Networks, among other positions. Join Corey and Matt as they talk about how ExtraHop provides sophisticated network security analytics for the enterprise in the cloud, how Corey discovered ExtraHop after seeing their name on the side of a bus in San Francisco, what Matt thinks is the beauty and the danger of the cloud, what the state-of-the-art network analysis experience feels like, who’s best positioned to benefit from ExtraHop, how beyond a certain point of scale companies need to fall back on broader coverage of security requirements instead of relying exclusively on cloud-native tools, and more.

Self control is thought to be closely associated with the ability to abstain or regulate substance use and is just one aspect of self-regulation, or the ability to organize behavior toward a goal. Many have suggested that self-regulation is damaged in those with chronic patterns of harmful substance use. However, it has also been noted that substance use itself is a highly goal directed behavior and requires self-regulation. Kyle Walters discusses his work on the interaction between self-regulation and the environment and suggests that this relationship may not be as simple as we once thought. Kyle also briefly discusses his forays into Network Analysis as an alternative approach to traditional conceptualizations of psychopathology. Kyle Walters is a doctoral candidate in the Department of Psychology at the University of South Dakota.

In today's world of disruption, organizations need to embrace the non-traditional. One essential element for success is to understand the informal structures within the organization using organizational network analysis. In this episode, we are sitting down with Mark Reid and Victoria Bovaird, national leaders of organizational design and transformational change, respectively to discuss organizational Network Analysis and how it relates to an organization's transformation. Listen now to hear Kate Morican and her guests discuss the importance of network analysis, balancing these invisible networks with the traditional model and how analysis can help drive transformation.

What do Facebook, Bitcoin and an ad campaign for designer glasses have in common? They all conform to Network Science laws. These laws are universal and are manifested in a n y network. Understanding these laws would change your perception of the world, and your data. I've warned you - Network Analysis is a powerful tool. Use it wisely - don't lose your head. PLS review here: http://bit.ly/NETfrix_Review Transcripts are available on SNApod.net See you on the other side of NETfrix.

Talk by Dr Sebastian Ahnert, Dept. of Chemical Engineering & Biotechnology (University of Cambridge) & Alan Turing Institute

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.15.383869v1?rss=1 Authors: Simpson-Kent, I. L., Fried, E. I., Akarca, D., Mareva, S., Bullmore, E. T., The CALM Team,, Kievit, R. A. Abstract: Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.14.382580v1?rss=1 Authors: Koutrouli, M., Theodosiou, T., Iliopoulos, I., Pavlopoulos, G. Abstract: In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare the topological features of multiple networks simultaneously. NAP is written in R and Shiny and currently offers both 2D and 3D network visualization as well as simultaneous visual comparisons of node- and edge-based topological features both as bar charts or as a scatterplot matrix. NAP is fully interactive and users can easily export and visualize the intersection between any pair of networks using Venn diagrams or a 2D and a 3D multi-layer graph-based visualization. NAP supports weighted, unweighted, directed, undirected and bipartite graphs and is available at: http://bib.fleming.gr:3838/NAP/. Its code can be found at: https://github.com/PavlopoulosLab/NAP Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.02.324319v1?rss=1 Authors: Pirayre, A., Duval, L., Blugeon, C., Firmo, C., Perrin, S., Jourdier, E., Margeot, A., Bidard-Michelot, F. Abstract: Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network. Results: Experimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network (GRN) built using the BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the {beta}-glucosidase regulation andiii)$ a negative regulation of the development process and growth. Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the {beta}-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.28.317206v1?rss=1 Authors: Ghorbani, M., Brooks, B. R., Klauda, J. B. Abstract: The ongoing pandemic caused by coronavirus SARS-COV-2 continues to rage with devastating consequences on human health and global economy. The spike glycoprotein on the surface of coronavirus mediates its entry into host cells and is the target of all current antibody design efforts to neutralize the virus. The glycan shield of the spike helps the virus to evade the human immune response by providing a thick sugar-coated barrier against any antibody. To study the dynamic motion of glycans in the spike protein, we performed microsecond-long MD simulation in two different states that correspond to the receptor binding domain in open or closed conformations. Analysis of this microsecond-long simulation revealed a scissoring motion on the N-terminal domain of neighboring monomers in the spike trimer. Role of multiple glycans in shielding of spike protein in different regions were uncovered by a network analysis, where the high betweenness centrality of glycans at the apex revealed their importance and function in the glycan shield. Microdomains of glycans were identified featuring a high degree of intra-communication in these microdomains. An antibody overlap analysis revealed the glycan microdomains as well as individual glycans that inhibit access to the antibody epitopes on the spike protein. Overall, the results of this study provide detailed understanding of the spike glycan shield, which may be utilized for therapeutic efforts against this crisis. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.09.243576v1?rss=1 Authors: Sun, Z., Sudhof, T. C. Abstract: Ca2+-imaging is a powerful tool to measure neuronal dynamics and network activity. To monitor network-level changes in cultured neurons, neuronal activity is often evoked by electrical or optogenetic stimulation and assessed using multi-electrode arrays or sophisticated imaging. Although such approaches allow detailed network analyses, multi-electrode arrays lack single-cell precision, whereas optical physiology requires advanced instrumentation. Here we developed a simple, stimulation-free protocol with associated Matlab algorithms that enables scalable analyses of spontaneous network activity in cultured human and mouse neurons. The approach allows analysis of overall networks and single-neuron dynamics, and is amenable to scale-up for screening purposes. We validated the new method by assessing human neurons with a heterozygous conditional deletion of Munc18-1, and mouse neurons with homozygous conditional deletions of neurexins. The approach described here enabled identification of differential changes in the amplitudes and synchronicity of neuronal spikes during network activity in these mutant neurons, demonstrating the utility of the approach. Compared with current imaging platforms, our method is simple, scalable, accessible, and easy to implement. It enables quantification of more detailed parameters than multi-electrode arrays, but does not have the resolution and depth of more sophisticated yet labour-intensive analysis methods, such as patch-clamp electrophysiology. The method reported here is scalable for a rapid direct assessment of neuronal function in culture, and can be applied to both human and mouse neurons. Thus, the method can serve as a basis for phenotypical analysis of mutations and for drug discovery efforts. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.24.215889v1?rss=1 Authors: Matlock, W., Chau, K. K., AbuOun, M., Stubberfield, E., Barker, L., Kavanagh, J., Pickford, H., Gilson, D., Smith, R. P., Gweon, H. S., Hoosdally, S. J., Swann, J., Sebra, R., Bailey, M. J., Peto, T. E. A., Crook, D. W., Anjum, M. F., Read, D. S., Walker, A. S., Stoesser, N., Shaw, L. P., REHAB Consortium Abstract: IncF plasmids are diverse and of great clinical significance, often carrying genes conferring antimicrobial resistance (AMR) such as extended-spectrum {beta}-lactamases, particularly in Enterobacteriaceae. Organising this plasmid diversity is challenging, and current knowledge is largely based on plasmids from clinical settings. Here, we present a network community analysis of a large survey of IncF plasmids from environmental (influent, effluent, and upstream/downstream waterways surrounding wastewater treatment works) and livestock settings. We use a tractable and scalable methodology to examine the relationship between plasmid metadata and network communities. This reveals how niche (sampling compartment and host genera) partition and shape plasmid diversity. We also perform pangenome-style analyses on network communities. We show that such communities define unique combinations of core genes, with limited overlap. Building plasmid phylogenies based on alignments of these core genes, we demonstrate that plasmid accessory function is closely linked to core gene content. Taken together, our results suggest that stable IncF plasmid backbone structures can persist in environmental settings while allowing dramatic variation in accessory gene content that may be linked to niche adaptation. The recent association of IncF plasmids with AMR likely reflects their suitability for rapid niche adaptation. Copy rights belong to original authors. Visit the link for more info

Searching For The Question Live Streaming onFacebook http://facebook.com/searchingforthequ...Twitter http://twitter.com/davidorbanYouTube http://youtube.com/davidorban Become a supporter of the show on Patreonhttp://patreon.com/davidorban

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.05.137166v1?rss=1 Authors: Teeple, E., Jindal, K., Kiragasi, B., Annaldasula, S., Byrne, A., Chai, L., Sadeghi, M., Kayatekin, C., Shankara, S., Klinger, K., Sardi, S. P., Madden, S. L., Kumar, D. Abstract: Alpha-synuclein(SNCA) aggregates are pathological hallmarks of synucleinopathies, neurodegenerative disorders including Parkinson's Disease (PD) and Lewy Body Dementia (LBD). Functional networks are not yet well-characterized for SNCA by CNS cell type. We investigated cell-specific differences in SNCA expression using Allen Brain Database single-nucleus RNA-seq data from human Middle Temporal Gyrus (MTG, 15,928 nuclei) and Anterior Cingulate Cortex (ACC, 7,258 nuclei). Weighted gene co-expression analysis (WGCNA) and hierarchical clustering identified a conserved SNCA co-expression module. Module genes were highly conserved (p

C4ADS analyst Sofia Vargas is mapping out and analyzing ships' behavior in and near the Venezuelan Exclusive Economic Zone following U.S. sanctions on the Venezuelan oil sector. By fusing AIS data, vessel identifier data, and corporate ownership information, C4ADS is harnessing publicly available information to monitor risks posed by sanctions in the maritime domain. To see some of C4ADS cited work, check out the following article by Armando.info: https://armando.info/Reportajes/Details/2557 Access the story mentioned in the webinar here: https://www.washingtonpost.com/world/the_americas/in-the-us-embargo-on-venezuelan-oil-russia-is-a-clear-winner/2020/02/06/c45ca39e-476e-11ea-91ab-ce439aa5c7c1_story.html

Conversations avec...un article. C'est 10-15 minutes où je rends compte d'un article scientifique récent paru dans une revue en sciences humaines et sociales. Épisode 5 : Le recours aux drones pour préserver les écosystèmes fragiles et tous les "pièges" générés par ce recours. L'article original : Adam Fish, "Crash Theory: Entrapments of Conservation Drones and Endangered Megafauna", Science, Technology, & Human Values, mars 2020. --------- Les autres références universitaires citées dans l'article et mobilisées dans le podcast : Karen Barad, Meeting the Universe Halfway : Quantum Physics and the Entanglement of Matter and Meaning, Durham, Duke University Press, 2007. Donna Haraway, "Staying with the Trouble for Multispecies Environmental Justice", Dialogues in Human Geography, 8 (1): 2018, p. 102-105. Ian Hodder et Angus Mol, "Network Analysis and Entanglement" Journal of Archaeological Method and Theory 23 (1), 2016, p. 1-29. Steven J. Jackson, "Rethinking Repair" dans Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot (des), Media Technologies: Essays on Communication, Cambridge, MIT Press, 2014, p. 221-40. --------- Pour aller plus loin : **Sur l'écologie** : H.-S. Afeissa, Ethique environnementale. Nature, valeur, respect, Librarie Philosophique J. Vrin, Paris, 1744. Roberto Barbanti et Lorraine Verner (des), Les Limites du vivant, Paris, Éditions Dehors, 2016. Deborah Bird Rose, Vers des humanités écologiques, Wilproject, 2010. Geremia Cometti et al., Au seuil de la forêt. Hommage à Philippe Descola. L'anthropologie de la nature, Mirebeau-sur-Bèze, Totem, 2020. Philippe Descola et Tim Ingold, Etre au monde : quelle expérience commune, Presses Universitaires de Lyon, 2014. Viviane Despret, Habiter en oiseau, Arles, Actes Sud, 2019. Emilie Hache (eds), Reclaim. Recueil de textes écoféministes, Éditions Cambourakis, Paris, 2016. Edouardo Kohn, comment pensent les forêts, Z/S, Paris, 2017. Alert Piette, Contre le relationnisme. Lettre aux anthropologues, Le Bor de l'eau, 2014. Marin Schaffner, Un sol commun. Lutter, habiter, penser, Wildproject Éditions, 2019. Charles Stéphanoff, Voyage dans l'invisible. Techniques chamaniques de l'imagination, Paris, 2019. **Sur les pannes, les erreurs, les infrastructures** : Jonathan Chibois, "Le vote électronique à l'Assemblée. Prévenir et contenir la panne en République", Techniques & Culture. Revue semestrielle d'anthropologie des techniques, 2019. Adresse : http://journals.openedition.org/tc/13224 [Consulté le : 20 avril 2020]. Christiane Chauviré, Albert Ogien et Louis Quéré (dir.), Dynamiques de l'erreur, Éditions de l'EHESS, 2009. Jérôme Denis, Le travail invisible des données. Eléments pour une sociologie des infrastructures scripturales, Paris, Presses des Mines, 2018. Jérôme Denis et David Pontille, "Travailleurs de l'écrit, matières de l'information", Revue d'anthropologie des connaissances, 61(1), 2012, p. 1‑20. **Sur l'ontologie, les théories du social et du réel** : Emmanuel Alloa et Elie During (dir.), Choses en soi. Métaphysique du réalisme, Paris, Métaphysiques, 2018. Iona Vultur, "Une ontologie distribuée des faits sociaux" dans Iona Vultur, Comprendre. L'herméneutique et les sciences humaines, p. 199-209. Pierre Liet et Ruwen Ogen, L'Enquête ontologique. Du monde d'existence des objets sociaux, Éditions de l'EHESS, coll. "Raisons pratiques", 2000. Pierre Livet et Frédéric Nef, Les êtres sociaux: Ontologie des processus et virtualité du social, Paris, Hermann, 2009.

Steven Alexander is a Science Advisor based at Fisheries and Oceans Canada and holds an appointment as Adjunct Assistant Professor in the Faculty of Environment, University of Waterloo. Steven began working at the science-policy interface as a Mitacs Canadian Science Policy Fellow. Prior to this he was a Postdoctoral Research Fellow affiliated with both the National Socio-Environmental Synthesis Center in the US and the Stockholm Resilience Centre in Sweden. He calls himself an environmental social scientist, and his research focuses on community-based conservation and natural resource management, environmental governance, and the human dimensions of environmental change. Steven’s Google Scholar https://scholar.google.ca/citations?user=naPWaUwAAAAJ&hl=en   Steven’s twitter https://twitter.com/salexander_11?lang=en   Steven’s ResearchGate profile https://www.researchgate.net/profile/Steven_Alexander2   Qualitative data sharing paper published in Nature Sustainability https://www.nature.com/articles/s41893-019-0434-8   Qualitative Data Sharing Resources: https://www.sesync.org/for-you/cyberinfrastructure/research-and-tools/qualitative-data-initiative   Link to white paper from the Qualitative Data Initiative that was established at SESYNC: https://www.sesync.org/qualitative-data-sharing-and-re-use-for-socio-environmental-systems-research-a-synthesis-of   Link to a webinar on qualitative data management as part of a broader Webinar series run by Data ONE  https://www.dataone.org/webinars/qualitative-data-management-interdisciplinary-research   Link to a webinar on social network analysis as part of a new series organized by Michael Schoon and the Programme on Ecosystem Change and Society’s (PECS) Collaborative Governance and Management Working Group https://essnetwork.net/pecs-webinar

James and Jane are joined by Alexander Schwall from Rhabit Analytics to discuss Organizational Network Analysis (ONA). The conversation explores what ONA is, what some benefits of it are and how organizations undertake it.

Have you considered that too much collaboration could be causing the high performers on your team to suffer? ‘Collaborative overload’ is a real problem in the workplace, and it tends to affect the best employees and leaders the most. On this episode of The TalentGrow Show, professor and consultant Rob Cross joins Halelly to discuss how to leverage network analysis to create collaboratively efficient networks and improve your collaborative process. You’ll learn the steps leaders can take to implement network analysis in our organizations, how to avoid overloading high-performers or ‘energizers’ by being more strategic about collaboration, and how to leverage what Rob calls ‘second tier connectors’ for maximum collaborative efficiency. Plus, find out how creating more collaboratively efficient networks can help you buy back one day a week of your time and be happier! Listen and be sure to share this episode with others. Shownotes: http://www.talentgrow.com/podcast/episode166 Apple Podcasts: http://apple.co/1NiWyZo Stitcher: http://www.stitcher.com/s?fid=62847&refid=stpr Google Play Music: https://play.google.com/music/listen#/ps/Ijwlgz7lklnxqnfzjna7gcr65be iHeartRadio Podcasts: http://www.iheart.com/show/263-The-TalentGrow-Show-Gr/ Soundcloud: https://soundcloud.com/talentgrowshow Spotify: http://spoti.fi/2CpgIk1 TuneIn: http://tun.in/pjuHL Download the free guide: 10 Mistakes Leaders Make and How to Avoid Them http://www.talentgrow.com/10mistakes Don't forget to LEAVE A RATING/REVIEW ON APPLE PODCASTS! http://apple.co/1NiWyZo  

Hidden influencers are those with personal power, but not necessarily positional power. Other employees are drawn to them due to their expertise, influence, and role modeling. These informal influencers are crucial for engagement, information flow, decision-making, best practice transfer, institutional knowledge, mentoring, and retention. Correctly identifying and engaging hidden influencers is one of the greatest levers leaders have to increase organization-wide engagement. Learn how to do it in this insightful conversation between Beth Wilkins, Ph. D., Christian Nielson, MBA, and host Justin Warner.

In this week’s episode, hosts Spriha Gupta and Vanessa Kolb speak with Dr. Kathleen Carley about bots and disinformation on social media. Dr. Carley is a professor in Carnegie Mellon University’s School of Computer Science and is the director of the Center for Computational Analysis of Social and Organizational Systems. She tells us about her pioneering work in dynamic network analysis, which has been applied to disaster and disease response, counter-terrorism, evaluation of cybersecurity threats, and now to social media. For more information about Heinz Radio, or to listen to past episodes, visit heinz-radio.com Incidental music provided by Audiobinger from Free Music Archives.

Show Notes: (2:16) Thomas talked about the study of Food Science and Technology in which he focused on microbiology. (3:15) Thomas stressed the importance of user empathy, something useful he gained from his degree. (4:39) Thomas discussed the reason to pursue a Ph.D. in Bioinformatics at the Technical University of Denmark. (6:10) Thomas talked in-depth about the tools he developed for his Ph.D. thesis, which are able to handle large-scale pangenome analyses using sequential data. (9:11) Thomas talked about using the ggplot2 package for his R package “Find My Friends.” (11:11) Thomas worked on the ggforce package, which aims at providing missing functionalities to ggplot2 during his internship at RStudio. (13:34) Thomas recalled the best learning he got from his internship with RStudio. (15:08) Thomas gave advice for people who want to contribute to open-source projects. (18:57) Thomas shared the experience working on the package ggraph, also known as the grammar of graphics for relational data. (22:02) Thomas discussed 2 other packages, tidygraph and particles, that he built to bring graph and network data into the tidyverse, the very popular collection of R packages designed for data science. (25:37) Thomas provided resources for R users who want to learn more about network analysis and network visualization. (27:05) Thomas went over his job as a data scientist at SKAT, where he handled all the advanced analytics going on in the Danish Tax Authorities. (32:15) Thomas talked about the intuition behind working on patchwork, a package that can combine multiple ggplots in the same graphics. (35:50) Thomas summarized his most recent projects, gganimate (a package that extends ggplot2 to include the description of animation) and tweenr (a package for interpolating data mainly for animations). (40:47) Thomas discussed his current job as a software engineer at RStudio. (43:04) Thomas gave his two cent on the Python and R comparison. (45:53) Thomas talked about using Twitter to share his work, where he has more than 10,000 followers. (47:07) Thomas went over something he works on during his spare time, generative art visualization (check his Instagram account!). (49:09) Thomas gave some thoughts on the tech community in Copenhagen. (51:09) Closing segments. His Contact Info: Website Twitter GitHub LinkedIn His Recommended Resources: Hilary Parker from Stitch Fix David Robinson from DataCamp Unflattening Comic Book DataCamp's Network Analysis in R Katya Ognyanova's Network Analysis and Visualization with R and igraph

Show Notes: (3:08) Leni explains how studying Theatre Critics in college help her develop analytical thinking. (4:00) Leni briefly her first job out of college in brand management at a lifestyle brand called Life is Porno. (5:54) Leni goes over the most interesting classes she took during her Master of Arts in New Media Studies at Charles University. (7:52) Leni talks about her 1-year residency in the social media team of New Media department at Czech TV. (12:02) Leni reflects on the career lesson she got out from her time at Czech TV. (13:47) Leni discusses her first professional role working with data at Socialbakers, a social media marketing company in Prague. (16:35) Leni talks about choosing R as her main programming language to learn data science. (19:43) Leni explains the study of social network analysis. (21:50) Leni discusses the technical aspects of her Master’s thesis about Czech journalists on Twitter. (25:00) Leni goes over her brief stint as a marketing specialist at Zeleznakoule, a gym in Prague and a community of health enthusiasts. (26:57) Leni talks about her next career move to Seznam, the most visited web portal and search engine in the Czech Republic. (29:11) Leni also volunteers as a mentor for Czechitas, a non-profit organization focused on educating girls about programming, data analysis and graphics. (30:17) Leni describes the data science community in Prague. (32:51) Leni talks about her passion for the tech scene in Stockholm. (35:22) Leni talks about her popular Facebook page Dataholka. (36:45) Leni shares some tips for aspiring data scientists who want to improve their social media presence. (39:00) Leni gives recommendations for social media users regarding concerns about data privacy. (41:30) Leni shares her thoughts on the current state of digital journalism. (43:48) Leni discusses her current plan to pursue a Ph.D. degree in the US. (47:52) Closing segment. Her Contact Info: Website Twitter Facebook Instagram Her recommended resources: Netflix Tech Blog Spotify Labs

Show Notes: (3:08) Leni explains how studying Theatre Critics in college help her develop analytical thinking. (4:00) Leni briefly her first job out of college in brand management at a lifestyle brand called Life is Porno. (5:54) Leni goes over the most interesting classes she took during her Master of Arts in New Media Studies at Charles University. (7:52) Leni talks about her 1-year residency in the social media team of New Media department at Czech TV. (12:02) Leni reflects on the career lesson she got out from her time at Czech TV. (13:47) Leni discusses her first professional role working with data at Socialbakers, a social media marketing company in Prague. (16:35) Leni talks about choosing R as her main programming language to learn data science. (19:43) Leni explains the study of social network analysis. (21:50) Leni discusses the technical aspects of her Master’s thesis about Czech journalists on Twitter. (25:00) Leni goes over her brief stint as a marketing specialist at Zeleznakoule, a gym in Prague and a community of health enthusiasts. (26:57) Leni talks about her next career move to Seznam, the most visited web portal and search engine in the Czech Republic. (29:11) Leni also volunteers as a mentor for Czechitas, a non-profit organization focused on educating girls about programming, data analysis and graphics. (30:17) Leni describes the data science community in Prague. (32:51) Leni talks about her passion for the tech scene in Stockholm. (35:22) Leni talks about her popular Facebook page Dataholka. (36:45) Leni shares some tips for aspiring data scientists who want to improve their social media presence. (39:00) Leni gives recommendations for social media users regarding concerns about data privacy. (41:30) Leni shares her thoughts on the current state of digital journalism. (43:48) Leni discusses her current plan to pursue a Ph.D. degree in the US. (47:52) Closing segment. Her Contact Info: Website Twitter Facebook Instagram Her recommended resources: Netflix Tech Blog Spotify Labs

Network Analysis

Lucio Rennó (University of Brasília) gives a talk at the workshop on Coalitional Presidentialism at the Federal Congress of Brazil, Brasília.

Achieving community-wide participation is an admirable but challenging goal. Identifying the multiple layers of community can be the difference between success or failure of a project. Orton’s Community Network Analysis (CNA) brings fresh new voices and solutions to the table and is a powerful way to understand who lives, works, and plays in your town and how best to reach them. Alece Montez-Greigo, Orton’s director of programs, explains the tool. Community Heart & Soul project coordinator Alexis Halbert of Paonia, Colorado, and Gabrielle Ratté Smith, senior associate for strategic partnerships at Orton and of Essex, Vermont, join her to share their on-the-ground experience with CNA. Follow along with our Google Doc: http://bit.ly/1w9PKBY

In this episode, we talk with David Knoke, Professor of Sociology at the University of Minnesota. We discuss the uses and benefits of network analysis, drawing upon his work on terrorist networks. Though podcasting is at the heart of our project, we also plan to publish our episodes in a book of edited transcripts, making […]

Complex network analysis is an area of network science and part of graph theory that can be used to rank things, one of the most famous examples of which is the Google PageRank algorithm. But it can also be applied to sport. Cricket is a sport in which it is difficult to rank teams (there are three forms of the game, the various countries do not play each other very often etc.), whilst it is notoriously difficult to rank individual players (for how the ICC do it, see Ep 107: Ranking Cricketers).Satyam Mukherjee at Northwestern University became a bit famous when The economist picked up his work (more famous than when we picked it up!) and he has published extensively on complex network analysis as applied to cricket rankings. I had a very interesting chat with Satyam about his various works concerning the evaluation of cricket strategy, leadership, team and individual performance, and the papers we discuss in the podcast are listed below. One of the more interesting findings was that left-handed captains and batsmen are generally ranked higher than their right-handed counterparts, whilst this is not true for left-handed bowlers.Tune in to this episode here: Songs in the podcast: Loveshadow / CC BY-NC 3.0 Speck / CC BY-NC 3.0 Zep Hurme / CC BY 2.5 Stefan Kartenberg / CC BY-NC 3.0References:  Satyam Mukherjee (2013). Ashes 2013 - A network theory analysis of Cricket strategies arXiv arXiv: 1308.5470v1  Satyam Mukherjee (2013). Left handedness and Leadership in Interactive Contests arXiv arXiv: 1303.6686v1  Satyam Mukherjee (2012). Quantifying individual performance in Cricket - A network analysis of Batsmen and Bowlers arXiv arXiv: 1208.5184v2  Satyam Mukherjee (2012). Complex Network Analysis in Cricket : Community structure, player's role and performance index arXiv arXiv: 1206.4835v4  Satyam Mukherjee (2012). Identifying the greatest team and captain - A complex network approach to cricket matches arXiv arXiv: 1201.1318v2

Institute of Historical Research From classification to network analysis: the Burlington Magazine Online Index Barbara Pezzini The Burlington magazine is a leading monthly publication devoted to the fine and decorative arts and first publishe...

The activity of a lipid kinase influences the production of invadopodia by cancer cells.

An increasing amount of social interaction is taking place online: analyzing this data computationally offers enormous potential to address long-standing scientific questions, and to harness and inform the design of future social computing applications. With an increasing amount of social interaction taking place online, we are accumulating large amounts of data about phenomena that were once essentially invisible to us: the collective behaviour and social interactions of hundreds of millions of people. Analyzing this data computationally offers enormous potential to address both long-standing scientific questions, and to harness and inform the design of future social computing applications. In this talk, Jure discusses how the computational perspective can be applied to questions involving the structure of online networks and the dynamics of information that flow through such networks.

An increasing amount of social interaction is taking place online: analyzing this data computationally offers enormous potential to address long-standing scientific questions, and to harness and inform the design of future social computing applications. With an increasing amount of social interaction taking place online, we are accumulating large amounts of data about phenomena that were once essentially invisible to us: the collective behaviour and social interactions of hundreds of millions of people. Analyzing this data computationally offers enormous potential to address both long-standing scientific questions, and to harness and inform the design of future social computing applications. In this talk, Jure discusses how the computational perspective can be applied to questions involving the structure of online networks and the dynamics of information that flow through such networks.

Wed, 16 May 2012 12:00:00 +0100 https://edoc.ub.uni-muenchen.de/14465/ https://edoc.ub.uni-muenchen.de/14465/1/Sachs_Angelika.pdf Sachs, Angelika ddc:330, ddc:300, Volkswirtschaftliche Fakultät

Lewis Lancaster, UC Berkeley

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

Targeted removal of individual enzymes elicits changes throughout the entire network of kinases and phosphatases in yeast.

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Votes are cast by the full membership in each house of Congress, but much of the important maneuvering occurs in committees. Graph theory and linear algebra are two mathematics subjects that have revealed a level of organization in Congress groups of committees above the known levels of subcommittees and committees. The result is based on strong connections between certain committees that can be detected by examining their memberships, but which were virtually unknown until uncovered by mathematical analysis. Mathematics has also been applied to individual congressional voting records. Each legislator.s record is represented in a matrix whose larger dimension is the number of votes cast (which in a House term is approximately 1000). Using eigenvalues and eigenvectors, researchers have shown that the entire collection of votes for a particular Congress can be approximated very well by a two-dimensional space. Thus, for example, in almost all cases the success or failure of a bill can be predicted from information derived from two coordinates. Consequently it turns out that some of the values important in Washington are, in fact, eigenvalues. For More Information: Porter, Mason A; Mucha, Peter J.; Newman, M. E. J.; and Warmbrand, Casey M., A Network Analysis of Committees in the United States House of Representatives, Proceedings of the National Academy of Sciences, Vol. 102 [2005], No. 20, pp. 7057-7062.

Enhanced Audio PodcastAired date: 6/12/2007 10:00:00 AM Eastern Time

Enhanced Video PodcastAired date: 6/12/2007 10:00:00 AM Eastern Time