לוח שנה משולב

Empathy, the recognition and sharing of affective states between individuals, is an adaptive response with ancient evolutionary roots. The experience of empathy rises from activation of subcortical neural circuits in the brain stem, thalamus and paralimbic areas that are highly conserved across mammalian species. Primarily, it is crucial for the survival of altricial mammals to be able to respond to the needs of offspring appropriately. More broadly, communication of emotions promotes group survival, by alerting against potential threats and, depending on context, inducing pro-social actions. Behavioral homologues of empathy have been observed in different non-human animals. For instance, it has been clearly established that rodents display emotional contagion of others’ distress, and are motivated to alleviate another rat’s distress. We found that rats intentionally released a cagemate trapped in a restrainer, even when social contact was prevented. When a second restrainer containing a highly palatable food (chocolate chips) was present, rats opened both restrainers and typically shared the chocolate. Since only cagemates were tested, it is unclear if these behaviors generalize to strangers. Helping others is costly and resource depleting, and should thus be discriminately extended. In humans, the expression of empathically motivated pro-social behavior is dependent on social context, where people are more motivated to help in-group members than out-group members. Correspondingly, emotional contagion is modulated by familiarity in rodents. Mice have been found to display heightened pain sensitivity when witnessing a cagemate in pain, but not a stranger in pain. To investigate these questions, we are currently exploring the effect of social parameters such as familiarity and relatedness on the expression of empathic helping in rats.

Traditionally, condensed matter physicists have classified phases of matter according to their symmetries. Over the last few decades, it became clear that near zero temperature, there are plenty of phases which lie beyond this classification scheme. These intrinsically quantum mechanical states of matter lack any ordinary order parameter; they can be thought of as a strongly fluctuating quantum liquids. Nevertheless, they posses a hidden underlying order, known as "topological order". The quantum Hall effect is a celebrated example of such a phase; several others have been discovered recently, and many more have been predicted theoretically. The elementary excitations of topologically ordered states can be thought of as emergent particles; intriguingly, these particles can obey unu-sual exchange statistics rules which resemble neither those of bosons nor of fermions. This property makes topological phases potentially useful as building blocks for future decoherence-free quantum processing devices. In this talk, I will describe some modern insights into the nature of these phases, and their characterization in term of their quantum entanglement. I will also discuss a new route to realize novel phases that arise on the boundaries of other, previously known topologically ordered states.

The hippocampus is made up of a diverse collection of neurons with complex physiological properties. I will describe our efforts to understand the functional diversity of these neurons. Most of our work has focused on principal neurons (pyramidal neurons in CA1 and subiculum), where we have described a role for dendritic excitability in synaptic integration and plasticity, as well as diversity in the structure, function, and plasticity in two distinct types of pyramidal neurons. In addition, I will describe recent work demonstrating the importance of the axon as an integrative structure in some inhibitory interneurons in the hippocampus.

Large volumes of high-throughput mass spectrometry proteomics studies are being routinely conducted nowadays. They are ultimately aimed at better understanding biological processes by studying proteomes’ profiles. However, the size and complexity of proteomics data hinders efforts to easily share, integrate, query, and compare such profiles. I will overview our recent developments addressing these challenges, including the creation of MOPED (Model Organism Protein Expression Database, moped.proteinspire.org). MOPED focuses on answering four fundamental questions:
1. What proteins are identified and where (organisms, tissues, localizations, pathways)?
2. How much of each protein is identified (relative and absolute expression)?
3. How does the knowledge of your current experiment compare to existing information? and
4. How does this knowledge guide your next (experimental or computational) study?

Proteomics is certainly not the only field with data challenges. The Data-Enabled Life Sciences Alliance (DELSA Global, delsaglobal.org) grew out of a need for solutions that was recognized by many; its mission is to “Accelerate the impact of Data-Enabled Life Sciences on the pressing needs of the global society”. DELSA’s purpose is to build and advance a sustainable ecosystem of professional societies, funding agencies, foundations, companies, and citizens together with life science researchers and innovators in computing, infrastructure, and analysis. Key priorities for DELSA include enabling collaborative work, promoting reproducible research, and translating new discoveries into tools, resources, and products. I will introduce DELSA Global and its Endorsed Projects and Working Groups.