Integrated Calendar

Membrane proteins (MPs) navigate challenging biogenesis. Errors in this process are rigorously surveilled by cellular quality control to eliminate faulty MPs. The first critical challenge of this surveillance is the accurate recognition of misfolded proteins. However, how this recognition is achieved for MPs remains poorly defined. Here we reveal the specificity mechanism of FtsH, the major quality control protease clearing faulty MPs in Escherichia coli. Analyzing the in vivo degradation of two substrates, we show that lipid-facing polar residues direct substrates to FtsH-mediated degradation. Such polar residues are typically buried in the structural cores of folded MPs, and their exposure to the membrane may thus signify misfolding and flag proteins for degradation. Remarkably, lipid-facing polar residues are sufficient for recognition and can target even folded MPs for degradation. The degradation depends on the FtsH transmembrane domain. Thus, MP misfolding is sensed within the membrane to maintain a healthy membrane proteome.

Over the last decade, several exciting directions have been initiated by laser-driven plasmas,
e.g., compact particle accelerators, inertial fusion and laboratory astrophysics. The first has
known rapid progress, in terms of current, energy, stability; fusion has gone through a historic
step, with the news of ignition being achieved at NIF in 2022; and laboratory astrophysics has
known also spectacular developments, demonstrating the possibility to perform fully scalable
experiments relevant to various objects such as forming stars and supernovae. A particularly
interesting aspect is that all these fields are strongly synergistic, i.e., that advances in one can
push the others as well. I will present examples of such synergies, through recent results
we have obtained in all these domains, and in particular how ultra-bright neutron beams
can be developed using latest generation multi-PW lasers [1,2]. These could open interesting
perspectives in terms of cargo inspection, but also for fusion plasma measurements.
I will also show how fusion can benefit from external magnetization [3]. Finally, I will discuss
advances in laboratory astrophysics, particularly the first-stage acceleration of ions leading to
cosmic rays [4,5], understanding the universal nature of collimated outflows in the Universe [6],
and probing the intricacy of 3D magnetic reconnection [7]
[1] High-flux neutron generation by laser-accelerated ions from single-and double-layer targets, V Horný et al.,
Scientific Reports 12 (1), 19767, 2022
[2] Numerical investigation of spallation neutrons generated from petawatt-scale laser-driven proton beams,
B Martinez et al., Matter and Radiation at Extremes 7 (2), 024401, 2022
[3] Dynamics of nanosecond laser pulse propagation and of associated instabilities in a magnetized underdense
plasma, W. Yao et al., https://doi.org/10.48550/arXiv.2211.06036
[4] Laboratory evidence for proton energization by collisionless shock surfing, W Yao et al.,
Nature Physics 17 (10), 1177-1182, 2021
[5] Enhancement of the Nonresonant Streaming Instability by Particle Collisions, A Marret et al.,
Physical Review Letters 128 (11), 115101, 2022
[6] Laboratory disruption of scaled astrophysical outflows by a misaligned magnetic field, G Revet et al.,
Nature communications 12 (1), 762, 2021
[7] Laboratory evidence of magnetic reconnection hampered in obliquely interacting flux tubes, S Bolaños et al.,
Nature Communications 13 (1), 6426, 2022

TBA

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