Nikolay Dobrev

Discover and Dive into Innovative Philosophical Perspectives! 👍 Don't miss the opportunity to review the featured articles in Sophia 37. Explore new insights on physics, metaphysics, and education. We invite you to check out the following link, where you can find the complete issue in PDF format, free to access and download: https://lnkd.in/eKGJ7J53 #SoyUPS #PublicacionesUPS #ScientificJournal #Knowledge #Philosophy #Education #Science #Index #GoogleScholar #Ecuador #SophiaJournal #PhilosophyOfEducation

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Take a look at our recently published article! https://lnkd.in/dnTFzfAa Paper title: 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 𝐖𝐢𝐭𝐡 𝐂𝐅𝐒-𝐈𝐆𝐖𝐎–𝐁𝐚𝐬𝐞𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐂𝐚𝐧𝐜𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐌𝐢𝐜𝐫𝐨𝐚𝐫𝐫𝐚𝐲 𝐃𝐚𝐭𝐚 Journal name: 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐉𝐨𝐮𝐫𝐧𝐚𝐥 𝐨𝐟 𝐓𝐞𝐥𝐞𝐦𝐞𝐝𝐢𝐜𝐢𝐧𝐞 𝐚𝐧𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Impact Factor: 3.1 Cite Score: 6.9 Cancer is the top cause of death worldwide, and machine learning (ML) has made an indelible mark on the field of early cancer detection, thereby lowering the death toll. ML-based model for cancer diagnosis is done using two forms of data: gene expression data and microarray data. In this work, two ensemble techniques are proposed using majority voting technique and weighted average technique. Correlation feature selection (CFS) is used for feature selection, and improved grey wolf optimizer (IGWO) is used for feature optimization. #cancer #ArtificialIntelligence #Healthcare #Wiley #feature_optimization #Medical #Telemedicine

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Vertical stacking of two-dimensional materials with weak van der Waals (vdW) interactions has enabled breakthroughs in both physics and technology. In studying heterobilayers of transition-metal dichalcogenides, researchers found two mechanisms affecting the band gaps of vdW heterostructures beyond the Anderson rule. Interfacial hybridization decreases the band gap by shifting the valence band maxima, while the formation of an interfacial electric dipole increases the effective gap in type-II junctions. https://lnkd.in/dMGgZpWJ

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Researchers from the Max Planck Institute for the Science of Light, in collaboration with partners, have published a study on how bacteria use passive physical processes to form and maintain biofilms. Biofilms are complex communities of bacteria that are resistant to environmental stresses and difficult to treat with antibiotics. This research highlights the role of biophysical interactions in biofilm formation, comparing biofilms to human tissues due to their complexity and self-secreted extracellular matrix, which provides stability, nutrients, and protection. The study also explores how water, essential for biofilms, influences their structure and behavior.

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New YouTube video out: Providing freedom in research labs: why it's important, how to do it, and what are the risks Hope you enjoy it, and please let me know in the comments what your thoughts are. #academia #research https://lnkd.in/gfeswvGv

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DSML trend: AI-based scientists win 2024 Nobel Prize in Chemistry The 2024 Nobel Prize in Chemistry was awarded to three researchers for their groundbreaking work in protein structure prediction and design using artificial intelligence (AI): ·     Demis Hassabis (British): Co-founder and CEO of Google DeepMind ·     John M. Jumper (American): Director at Google DeepMind ·     David Baker (American): Professor of Biochemistry at the University of Washington Hassabis and Jumper, working together at Google DeepMind, developed AlphaFold, an AI tool that revolutionized protein structure prediction. In 2020, AlphaFold solved a decades-old problem: predicting the three-dimensional structure of proteins from their amino acid sequences. Since then, AlphaFold has been used to predict the shapes of all known proteins, and their latest version, AlphaFold 3, extends these predictions to DNA, RNA, and small molecules like ligands, which are vital for drug discovery. David Baker, meanwhile, pioneered AI tools for protein design and structure prediction, including the widely-used Rosetta family of tools. He also created ProteinMPNN, an open-source AI tool that aids researchers in discovering unknown proteins and designing new ones. Recently, Baker’s lab developed custom molecules capable of precisely targeting and eliminating disease-associated proteins in living cells. That’s my take on it: This award underscores the growing role of AI in scientific research, marking the second Nobel Prize awarded for AI-related work in 2024, following the Nobel Prize in Physics for neural network research. The recognition of AI in both chemistry and physics has ignited discussions about how computational methods are reshaping traditional scientific disciplines. In response to the Nobel Prize in Physics, psychologist Gary Marcus raised concerns, noting that while Geoffrey Hinton has significantly influenced machine learning, it remains unclear how his work advanced physics specifically. He referenced Steve Hanson, arguing that the Nobel committee might not fully grasp the history of neural networks. However, Marcus sees the Chemistry Prize for Hassabis and Jumper as a clear and well-deserved win. This complicated issue, which involves the tension between the connectionist and the symbolic approaches to AI, necessitates deeper discussions. Links: https://lnkd.in/gXk9tGjk https://lnkd.in/gC9sJ99y

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Another #AI related The Nobel Prize: the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis Hassabis and John Jumper “for protein structure prediction.” The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins. Google DeepMind 👏 In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.

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The most cited article in JBS address the issue of bias control in observational studies #JBS, #observationalstudies, #biascontrol, #frequentistmodelaveraging https://lnkd.in/ea92RRzJ

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A new AI foundation model in bio is claimed to set the new "gold standard" for microscopy data analysis, outperforming CellProfiler. Now, Utah-based Recursion launched a new model, OpenPhenom, which is a non-commercial, publicly available foundation model built on microscopy data that the company believes has set the new "gold standard" outperforming CellProfiler. Large-scale cell microscopy assays are crucial for discovering new biological processes, drug candidates, and potential targets. Traditional software for analyzing these assays, however, requires constant fine-tuning for each new experiment and can easily break down if assay conditions change. As massive datasets like RxRx3 and JUMP-CP grow, these outdated tools struggle to keep up with the data demands. OpenPhenom-S/16, designed by Recursion, offers a powerful alternative for non-commercial researchers. Company says it can replace old workflows with an adaptable, ready-to-use model that consistently beats traditional analysis methods, with no extra tuning or training needed. For example, OpenPhenom-S/16 significantly enhances recall of known biological relationships in the JUMP-CP cpg0016 dataset, as cross-verified with the StringDB database. Notably, Recursion also has models for commercial use for its own pipeline and partnership programs. Recursion has featured the performance of two generations of proprietary models trained on Recursion’s internal microscopy, Phenom-1 (introduced at CVPR 2024) and Phenom-2 (introduced at NeurIPS FM4S 2024) -- also see image below. Recursion proprietary models are stronger than OpenPhenom (surprise!) but that does not diminish the value the public model has for the drug discovery community. Interestingly, just yesterday we covered another foundation model by Recursion -- MolE, for molecular property prediction (link in comments). Undoubtedly, foundation models are on the rise in biotech! I previously compiled a list of 19 companies pioneering AI foundation models in pharma and biotech (link in comments), and two new arrivals by Recursion, and some other new foundation models on the market, prompted me to update the article (follow me to stay tuned!). Image credit: Recursion

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There is need for more transparent data analysis all over many STEM fields. Such “code as paper” solutions can have many benefits: - direct access to data and analysis - ability to do rapid tests, alternative analysis - educational value (STEM is inherently as self-taught field after all) and sharing knowledge-how, best practices

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xLSTM shines at biological sequences: https://lnkd.in/dasriyma. For DNA, protein, and chemical sequences, xLSTM performs better than transformer and state-space-models. xLSTM evolved into the prime candidate for foundational models in molecular biology. xLSTM excels at in context learning of proteins and molecules. For protein sequences, xLSTM is able to learn long-range dependencies, which allows for the generation of homologous proteins. The results of xLSTM are overwhelming. Big congratulation to team @Niklas Schmidinger @Lisa Schneckenreiter @Philipp Seidl @Johannes Schimunek, Pieter-Jan H. Johannes Brandstetter @Andreas Mayr, Sohvi Luukkonen Günter Klambauer

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How to set up a successful transdisciplinary real-world lab is described by Niko Schäpke, Oskar Marg, Matthias Bergmann, Franziska Stelzer and Daniel Lang in their revisited i2Insights contribution at https://lnkd.in/g_vA4CkR. Key issues are: 1) right balance between scientific & social goals, 2) needs, interests, restrictions of participants to be addressed, 3) active use of experiments, 4) proactive communication, 5) collaboration culture between science & society, 6) attachment to a locality eg neighbourhood, street, building, 7) creation of lasting impact & transferability, 8) sufficient time & money, 9) preparedness to adapt when circumstances change, 10) learning opportunities for everyone involves and 11) consideration of dependence on external factors eg local politics.  #I2Sresources  Marina Knickel Guido Caniglia Karoline Augenstein Matthias Wanner Dena F., Abby Mellick Lopes, Alexandra Crosby Dr. Katie Ross Antonietta Di Giulio Rico Defila Michael Stauffacher Katja Brundiers Arnim Wiek

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New Fluorescent Raman Technique Enhances Detection of Microplastics in Seawater A novel method using fluorescence labeling and differential Raman spectroscopy claims to offer a more efficient, accurate approach to detect microplastics in seawater. Developed by researchers at the Ocean University of China, this method improves both the speed and precision of microplastic identification, addressing a key environmental issue affecting marine ecosystems. https://lnkd.in/gsNxYi8v

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Explore the tutorial led by Dr. Alexander von Lühmann and Dr. Eike Middell from TU Berlin's IBS Lab on precise fNIRS optode placement with the Cedalion toolbox. This Python solution provides a structured approach for researchers, ensuring accurate positioning. Don't miss out on this insightful webinar opportunity! Reserve your spot today. #research #neuroscience

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Bookmark this single resource to know "everything" about LLMs! This collection of blogs is an essential resource for anyone looking to master Large Language Models (LLMs). It offers comprehensive coverage of the architectural and mathematical foundations of LLMs, including Transformers and GPTs. Whether you're interested in understanding the intricacies of LLMs, evaluating their performance, or enhancing their safety, this series provides in-depth analysis and insights across all dimensions. The topics range from deconstructing GPTs and assessing their generalization capabilities to exploring adversarial attacks and ethical frameworks. I would say, It's a must-read for gaining a thorough understanding of LLMs and their underlying mechanics. #LLM #ArtificialIntelligence #AI #DeepLearning #ChatGPT https://lnkd.in/grXkGrBj

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Try finding a novel telomere repeat motif and estimate telomere lengths with TeloSearchLR! All you need is a fairly standard genomic long-read library using Nanopore or PacBio. https://lnkd.in/eeg9keFK

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Could scientific peer reviewing (or quality checking of scientific work) one day evolve into a full-time profession? Share your insights and earn a reward for your contribution. https://lnkd.in/dx4ufUz2 To provide an analogy, in the software development industry, it is common for developers to either focus on writing code or reviewing/testing it. Both roles are equally important and often become full-time careers, each with a different focus. Interestingly, many reviewers or testers started as active coders in which they gain first-hand experience and gut feeling to sense bug or issue with the code, but as they gained experience, they transitioned into these specialized roles for ensuring quality. I’m opening this bounty to gather input from peer-reviewer community here on ResearchHub. So please kindly share your thought on making scientific peer reviewing or the quality assessment of others' work a full-time research/career profession? Here for the sake of the argument, let's assume the role could offer a comparable income stream equivalent to a careers dedicated to active research, and even more so if one does a great job in improving research quality of others. I will award the bounty to the best answer with the most votes and will also provide a 30-50 $RSC reward to the top 5-10 answers.

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Western scientists more likely to get rejected papers published – and do it faster   Teplitskiy and his colleagues worked with data provided by IOP Publishing (IOPP), a company based in Bristol, UK, that publishes more than 90 English-language journals and is owned by the Institute of Physics. They examined around 203,000 manuscripts that were submitted to 62 of IOPP’s physical-sciences journals between 2018 and 2022. Some 62% were rejected. The researchers scoured a bibliometric database to see whether the same (or similar) work was subsequently published elsewhere. They then sorted these publications by the geographical region of the corresponding author — the researcher who is usually in charge of a study’s publication process — and compared the outcomes for authors from the West (which they define as North America, Europe and Oceania) with those from the rest of the world. The analysis showed that corresponding authors from Western countries are 5.7% more likely to publish a manuscript after rejection than those from other regions. In a process that often takes up to 300 days, they did so 23 days faster, on average. These authors also revised the abstract of their manuscript — a proxy for the overall paper — 5.9% less often, as defined by a computational ‘edit distance’ metric. And, ultimately, they published in journals with 0.8% higher impact factors. #researcher #article #publication #journal #deviation #review #impact_factor #technologycenter   https://lnkd.in/dmkgw5x7

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