לוח שנה משולב

We show how the distribution of skills or phenotypes in a population acts as collective memory or "distributed information
store" serving individual so that individuals with varying innate abilities are able to
attain the mature fully skilled phenotype. We show how information moves "in" and "out" of genomes, relative to this memory system, elucidating how evolution determines where best to store information. This question applies to understanding diverse biological systems in which individuals acquire capacities from the population, including immunity, the microbiome, and social learning. Using Agent Based Modeling we investigate how properties of the
population and social aspects of the acquisition process affect the behavior of the system. We show
that the genetic properties of the population react predictably to changes in population properties that affect selection
pressures, without any group level selective processes. Specifically, parameter changes that make
acquisition slower lead to skills becoming increasingly innate while changes in parameters that improve
the results of acquisition (e.g., making acquisition reliant on abundant left-over tools) lead
to an increased reliance on acquisition, all while the average phenotype remains constant. The dynamics
we study contribute to understanding how individuals can evolve to become more or less reliant on
social learning and cultural information, how this depends on population properties (e.g., group
size), and how this manifests demographically. The more information stored externally, the stronger
the selection pressure on traits that support acquisition. Finally, we contrast our model and the Baldwin
Effect and relate out results to the study of the evolution of human social learning.

The modern information era is built on silicon nanoelectronic devices. The future quantum information era might be built on silicon too, if we succeed in controlling the interactions between individual spins hosted in silicon nanostructures.
Spins in silicon constitute excellent solid-state qubits, because of the weak spin-orbit coupling and the possibility to remove nuclear spins from the environment through 28Si isotopic enrichment. Substitutional 31P atoms in silicon behave approximately like hydrogen in vacuum, providing two spin 1/2 qubits -- the donor-bound electron and the 31P nucleus -- that can be coherently controlled [1,2], read out in single-shot [2,3], and are naturally coupled through the hyperfine interaction.
In isotopically-enriched 28Si, these single-atom qubits have demonstrated outstanding coherence times, up to 35 seconds for the nuclear spin [4], and 1-qubit gate fidelities well above 99.9% for both the electron and the nucleus [5]. The hyperfine coupling provides a built-in interaction to entangle the two qubits within one atom. The combined initialization, control and readout fidelities result in a violation of Bell’s inequality with S = 2.70, a record value for solid-state qubits [6].
Despite being identical atomic systems, 31P atoms can be addressed individually by locally modifying the hyperfine interaction through electrostatic gating [7]. Multi-qubit logic gates can be mediated either by the exchange interaction [8] or by electric dipole coupling [9].
Scaling up beyond a single atom presents formidable challenges, but provides a pathway to building quantum processors that are compatible with standard semiconductor fabrication, and retain a nanometric footprint, important for truly large-scale quantum computers.

[1] J.J. Pla et al., Nature 489, 541 (2012)
[2] J.J. Pla et al., Nature 496, 334 (2013)
[3] A. Morello et al., Nature 467, 687 (2010)
[4] J.T. Muhonen et al., Nature Nanotech. 9, 986 (2014)
[5] J.T. Muhonen et al., J. Phys.: Condens. Matt. 27, 154205 (2015)
[6] J.P. Dehollain et al., Nature Nanotech. 11, 242 (2016)
[7] A. Laucht et al., Science Advances 1, e1500022 (2015)
[8] R. Kalra et al., Phys. Rev. X 4, 021044 (2014)
[9] G. Tosi et al., Nature Communications 8:450 (2017)

: In this special event, motivated by the 2017 Physics Nobel prize and the recent first
detection of a neutron star merger via both gravitational waves and electromagnetic radiation,
we will review the recent discovery and its implications.

Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns. These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: “the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid”. While this dynamical systems perspective can seem horribly abstract, it has immediate applications to the study of large-scale atmospheric patterns and extreme weather events. I will first show that we can compute measures of the stability and complexity (dimension) of instantaneous atmospheric fields in a (relatively) easy way. Next, I hope to convince you that these two quantities are actually useful! Their extreme values correspond to specific large-scale atmospheric patterns, and match extreme weather occurrences. They can also be used to identify "maximum predictability" states of the atmosphere, where the flow at positive lags of up to one week is particularly stable and with a small number of degrees of freedom. Finally, there is a significant correlation between the time series of instantaneous stability and complexity of an atmospheric field and the mean spread at lead times of over two weeks of an operational ensemble weather forecast initialised from that state.

Genome sequencing projects have revealed that eukaryotic and prokaryotic organisms universally possess a huge number of uncharacterized proteins. The functional annotation of these proteins should enrich our knowledge of the biochemical pathways that support human physiology and disease, as well as lead to the discovery of new therapeutic targets. To address these problems, we have introduced chemical proteomic technologies that globally profile the functional state of proteins in native biological systems. Prominent among these methods is activity-based protein profiling (ABPP), which utilizes chemical probes to map the activity state of large numbers of proteins in parallel. In this lecture, I will describe the application of ABPP to discover and functionally annotate proteins in mammalian physiology and disease. I will also discuss the generation and implementation of advanced ABPP platforms for proteome-wide ligand discovery.