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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.

Network studies have played a central role for understanding many systems in nature - e.g., physical, biological, and social. So far, much of the focus has been static networks in isolation. Yet, many networks are dynamic, coupled to each other. We considered this issue, in the context of social networks. In particular, We introduce a simple model of adaptive networks, modeling a society in which an individual cuts/adds links based on whether he or she has more/less links than some "preferred number"($kappa$). For example, introverts/extroverts typically have small/large $kappa$'s. Evolving with detailed balance violating dynamics, the steady state distribution of this dynamic network is not known in general, though it displays reasonably understandable properties. I will begin with a brief summary of our findings for systems with a single $kappa$ (i.e., a homogeneous population), many of which are "mundane." Surprises arise when a system with just two $kappa$'s are simulated. I will present the details of a "society" consisting of extreme introverts and extroverts. In particular, we find a mapping to a 2-D Ising-like model, restoration of detailed balance, the exact steady state distribution, and an abrupt transition (in the total number of links, as the I-E composition crosses 50-50). Sharp contrasts between this phenomenon and typical phase transitions (e.g.,Lenz-Ising-Onsager) will be noted. Beyond this theoretically interesting limit of our system, we outline some potentially important applications, such as modeling the response to a spreading epidemic by a population with adaptive behavior.

In this talk I review recent experimental evidence presented by the FINUDA Collaboration in the e+e --> Phi --> K+K- DAFNE machine at Frascati, Italy, for a particle stable Lambda-6H, with one proton and four neutrons stabilized by a Lambda hyperon [1]. Ongoing few-body calculations of Lambda-6H as well as shell-model estimates for its stability will also be briefly reviewed. The Lambda-6H hypernucleus was highlighted by Akaishi [2] as a test ground for the significance of Lambda N Sigma N coupling in Lambda hypernuclei, spurred by the role it plays in s-shell hypernuclei and by the far-reaching consequences it might have for dense neutron-star matter with strangeness. The discovery of Lambda-6H has stirred renewed interest in charting domains of particle-stable neutron-rich Lambda hypernuclei, particularly for unbound nuclear cores.

Millener and I have studied within a shell-model approach several neutron rich Lambda hypernuclei in the nuclear p shell that could be formed in (pi-,K+) (ongoing experiment E10 at J-PARC, Japan) or in (K-,pi+) reactions on stable nuclear targets. The hypernuclear shell-model input was taken from a theoretically inspired successful fit of gamma-ray transitions in p-shell Lambda hypernuclei. Predictions for binding energies of Lambda-9He, Lambda-10Li, Lambda-12Be and Lambda-14B will be reviewed, concluding that none of the large effects conjectured by Akaishi to arise from Lambda N Sigma N coupling is borne out by our realistic shell-model calculations.