Integrated Calendar

Myoblast cell fusion is essential for skeletal muscle development and regeneration. Yet, the molecular machinery that drives myoblast fusion remains incompletely understood. Myoblast cell fusion is an intricate multistep process, making it challenging to identify the specific proteins involved. Until now, no approach was available to capture fusing cells and dissect the dynamic changes in their cellular transitions. To fill this gap, we have developed a method using small-molecule inhibitors to synchronize muscle differentiation ex vivo and capture cells before, during, and after fusion. This allows us to identify and associate proteins with specific stages of muscle cell differentiation and fusion. Using this method, we have identified the Paralemmin A-kinase anchor protein (PALM2-AKAP2), a protein of unknown function, as a potential regulator of muscle regeneration. Hence, this work provides valuable data and will provide new insight into the mechanism of myoblast fusion and muscle regeneration.

As machine learning models get more complex, they can outperform traditional algorithms and tackle a broader range of problems, including challenging combinatorial optimization tasks. However, this increased complexity can make understanding how the model makes its decisions difficult. Explainable models can increase trust in the model’s decisions and may even lead to improvements in the algorithm itself. Algorithms like GradCAM or SHAP provide good explanations in terms of feature importance, typically for classification tasks. Still, they provide little insight when the ML pipeline is designed to work, for example, as an algorithm for solving optimization problems.                                                                      In this talk, we present a concept-learning framework for explaining a neural machine-learning model’s decision-making process from an algorithmic point of view. Using the NeuroSAT algorithm for SAT solving as a case study, we demonstrate how our framework finds the algorithmic concepts that drive the operation of NeuroSAT. Using the concepts that we discover, we can re-write the black box NeuroSAT net as a text-book algorithm that performs typical algorithmic moves like (a) compute confidence levels for every variable, (b) fix variables with the highest confidence and simplify the instance, (c) solve the residual formula using some simple technique. (Such a principle guides, for example, the well-known Belief-Propagation-Decimation algorithm).

Joint work with Elad Shoham (PhD student BGU), Kahalil Wattad (MSc student BGU), Hadar Cohen (MSc student BGU), and Havana Rika (Tel-Aviv-Yafo Academic College). 

Short bio:

Dan Vilenchik holds a PhD in computer science from Tel Aviv University. He did a postdoc at UC Berkeley and UCLA. He is currently a tenured member of the Electrical Engineering School at Ben-Gurion University. His research includes various aspects of machine learning, such as the challenges of high-dimensional data, explainable AI, NLP, and multidisciplinary projects.

 

The statistical study of human memory requires large-scale experiments, involving many stimuli conditions and test subjects. While this approach has proven to be quite fruitful for meaningless material such as random lists of words, naturalistic stimuli, like narratives, have until now resisted such a large-scale study, due to the quantity of manual labor required to design and analyze such experiments.
Large language models (LLMs) have provided the necessary technological breakthrough for this purpose, given their ability to generate human-like text and carry out novel tasks after being prompted by instructions in natural language, without additional training. In this work, we develop a pipeline that uses large language models (LLMs) both to design naturalistic narrative stimuli for large-scale recall and recognition memory experiments, as well as to analyze the results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different sizes. We found that both recall and recognition performance scale linearly with narrative length

The use of nanoparticles in diagnostics, therapeutics and imaging has revolutionized these fields with new properties not available with small molecules. Nanoparticle interface provide possibilities for polyvalent and independent attachment of different molecules serving as recognition/targeting structures, optical probes, spin probes or catalysts. However, nanoparticles operating in biological environments require precise control of multiple factors related to surface chemistry and their composition. To avoid for example aggregation, off-target interactions, and protein corona formation, appropriate interface design is essential. This talk will present general nanoparticle design strategies and specific examples including nanodiamonds and lipid nanoparticles.

TBA