Podcasts about pnma

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.09.527862v1?rss=1 Authors: Xu, J., Erlendsson, S., Singh, M., Regier, M., Ibiricu, I., Day, G. S., Piquet, A. L., Clardy, S. L., Feschotte, C., Briggs, J. A. G., Shepherd, J. D. Abstract: The paraneoplastic Ma antigen (PNMA) genes are associated with cancer-induced paraneoplastic syndromes that present with neurological symptoms and autoantibody production. How PNMA proteins trigger a severe autoimmune disease is unclear. PNMA genes are predominately expressed in the central nervous system with little known functions but are ectopically expressed in some tumors. Here, we show that PNMA2 is derived from a Ty3 retrotransposon that encodes a protein which forms virus-like capsids released from cells as non-enveloped particles. Recombinant PNMA2 capsids injected into mice induce a robust autoimmune reaction with significant generation of autoantibodies that preferentially bind external spike PNMA2 capsid epitopes, while capsid-assembly-defective PNMA2 protein is not immunogenic. PNMA2 autoantibodies present in cerebrospinal fluid of patients with anti-Ma2 paraneoplastic neurologic disease show similar preferential binding to PNMA2 spike capsid epitopes. These observations suggest that PNMA2 capsids released from tumors trigger an autoimmune response that underlies Ma2 paraneoplastic neurological syndrome. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Back Pack Matt and BeMo Brown begin the discussion of black gentrifiers in Washington, DC! BeMo Goes too deep on the definition of gentrifiers vs. expectations and Matt’s bag is filled with Biggie’s Dream. We’ve go musical selections from PNMA. As always, we will continue our conversation about the ups, downs, disruptions and inspirations that mold a creative! Make sure you follow us on all social media @OTSpod! Pick up these gems we’re dropping every week LIVE from the LINE Hotel on FULL Service Radio!

BeMo Brown is joined by singer/songwriter/producer PNMA! This week you can eavesdrop on a conversation about listening to women, respectful distance, remembrance of Nipsey Hussle and a special conversation with PNMA about the future of music distribution. As always, we will continue our cnversation about the ups, downs, distruptions and inspirations that mold a creative! Make sure you follow us on all social media @OTSpod! Pick up these gems we’re dropping every week LIVE from the LINE Hotel!

Rambo 2 vs. Commando 02-26-18That took longer than expected.  I blame Diego.  Anyway, I hope you enjoy our 10th podcast.  Which of these classic 80's movies holds up the best?  There is actually a clear winner this week and a general consensus for maybe the first time ever.  Enjoy!

theEVRYDYWKND presents #theBASSLINEpodcast discussing the hottest and most important topics in Hip Hop Culture from the #DMV perspective. This week c0pp0 x Young Mitchell x Mr. Anderson chop it up with Reggie Volume x PNMA aka The HVNS Sounds on a bunch of topics including: #MITMondays Anniversary: Grammy's 2018 Migos release Culture II and we're not impressed Superbowl Predictions and much more... theeverydayweekend.com

theEVRYDYWKND presents #theBASSLINEpodcast touching on the hottest and most important topics in Hip Hop Culture from the #DMV perspective. This week c0pp0 x Young Mitchell x Mr. Anderson chop it up with Reggie Volume x PNMA aka The HVNS Sounds on a bunch of topics including: -#MITMonday: Young Mitchell's 2018 Predictions -Wale continues work on go-go album with J. Cole -The battle for streaming supremacy -#NewMusic .....Omen- Phone Home ft. Ari Lennox .....Manny Wellz- Do Not Disturb .....OddMojo- 10ATD ft. Mudi x Kassim .....Kali Uchis- After the Storm ft. Tyler, the Creator x Bootsy Collins .....PNMA- California Kryptonite (TEWXclusive)

Dc's RnB and soul song writer PNMA joins the podcast for the first time to explain what its like working a 9 to 5 while still trying to achieve your goals in the music industry .

#Salute to the creatives geniuses that you are! Check out the latest episode of The 2 Bros In The Studio Show: Podcast brought to you by Navada & Tobari | Powered by Haven House Music Group. This week the BROS. spoke with singer & rapper, "NKO" and producers "The Light" (Reggie Volume & PNMA). They spoke about becoming a unit, how they work together, and how it is they are so able to mix and master various genres of music to create something brand new! So if you want to learn about this show and this awesome content... Check us out... the #2BrosShow now on #StitcherRadio, #GooglePlay, and #iTunes as well as #YouTube, #TuneIn, and here on #SoundCloud every Monday... THIS ONE IS FOR YOU! Co-Host: NKO - @nkowakeup 1/2 of The Light - @ReggieVolume | @ohmikkiusofine

Recent technological advances have made it possible to measure various parameters of biological processes in a genome-wide manner. While traditional molecular biology focusses on individual processes using targeted experiments (reductionistic approach), the field of systems biology utilizes high-throughput experiments to determine the state of a complete system such as a cell at once (holistic approach). Systems biology is not only carried out in wet-lab, but for the most part also requires tailored computational methods. High-throughput experiments are able to produce massive amounts of data, that are often too complex for a human to comprehend directly, that are affected by substantial noise, i.e. random measurement variation, and that are often subject to considerable bias, i.e. systematic deviations of the measurement from the truth. Thus, computer science and statistical methods are necessary for a proper analysis of raw data from such large-scale experiments. The goal of systems biology is to understand a whole system such as a cell in a quantitative manner. Thus, the computational part does not end with analyzing raw data but also involves visualization, statistical analyses, integration and interpretation. One example for these four computational tasks is as follows: Processes in biological systems are often modeled as networks, for instance, gene regulatory networks (GRNs) that represent the interactions of transcription factors (TFs) and their target genes. Experiments can provide both, the identity and wiring of all constituent parts of the network as well as parameters that allow to describe the processes in the system in a quantative manner. A network provides a straight-forward way to visualize the state and processes of a whole system, its statistical analysis can reveal interesting properties of biological systems, it is able to integrate several datasets from various experiments and simulations of the network can aid to interpret the data. In recent years, microRNAs emerged as important contributors to gene regulation in eukaryotes, breaking the traditional dogma of molecular biology, where DNA is transcribed to RNA which is subsequently translated into proteins. MicroRNAs are small RNAs that are not translated but functional as RNAs: They are able to target specific messenger RNAs (mRNA) and typically lead to their downregulation. Thus, in addition to TFs, microRNAs also play important roles in GRNs. Interestingly, not only animal genomes including the human genome encode microRNAs, but microRNAs are also encoded by several pathogens such as viruses. In this work I developed several computational systems biology methods and applied them to high-throughout experimental data in the context of a project about herpes viral microRNAs. Three methods, ALPS, PARma and REA, are designed for the analysis of certain types of raw data, namely short RNA-seq, PAR-CLIP and RIP-Chip data, respectively. All of theses experiments are widely used and my methods are publicly available on the internet and can be utilized by the research community to analyze new datasets. For these methods I developed non-trivial statistical methods (e.g. the EM algorithm kmerExplain in PARma) and implemented and adapted algorithms from traditional computer science and bioinformatics (e.g. alignment of pattern matrices in ALPS). I applied these novel methods to data measured by our cooperation partners in the herpes virus project. I.a., I discovered and investigated an important aspect of microRNA-mediated regulation: MicroRNAs recognize their targets in a context-dependent manner. The widespread impact of context on regulation is widely accepted for transcriptional regulation, and only few examples are known for microRNA-mediated regulation. By integrating various herpes-related datasets, I could show that context-dependency is not restricted to few examples but is a widespread feature in post-transcriptional regulation mediated by microRNAs. Importantly, this is true for both, for human host microRNAs as well as for viral microRNAs. Furthermore, I considered additional aspects in the data measured in the context of the herpes virus project: Alternative splicing has been shown to be a major contributor to protein diversity. Splicing is tightly regulated and possibly important in virus infection. Mass spectrometry is able to measure peptides quantitatively genome-wide in high-throughput. However, no method was available to detect splicing patterns in mass spectrometry data, which was one of the datasets that has been meausred in the project. Thus, I investigated whether mass spectrometry offers the opportunity to identify cases of differential splicing in large-scale. Finally, I also focussed on networks in systems biology, especially on their simulation. To be able to simulate networks for the prediction of the behavior of systems is one of the central goals in computational systems biology. In my diploma thesis, I developed a comprehensive modeling platform (PNMA, the Petri net modeling application), that is able to simulate biological systems in various ways. For highly detailed simulations, I further developed FERN, a framework for stochastic simulation that is not only integrated in PNMA, but also available stand-alone or as plugins for the widely used software tools Cytoscape or CellDesigner. In systems biology, the major bottleneck is computational analysis, not the generation of data. Experiments become cheaper every year and the throughput and diversity of data increases accordingly. Thus, developing new methods and usable software tools is essential for further progress. The methods I have developed in this work are a step into this direction but it is apparent, that more effort must be devoted to keep up with the massive amounts of data that is being produced and will be produced in the future.