Integrated Calendar

Increasing concentrations of atmospheric carbon dioxide are expected to affect photosynthesis, evapotranspiration (ET) and ultimately plant growth. Numerical tools that simulate land-surface and vegetation dynamics are typically used to represent future scenarios of terrestrial carbon and water cycles. However, these tools are rarely tested to perform well in conditions different from the historical climate. A combination of numerical modeling and observations from flux-towers and free air CO2 enrichment (FACE) experiments is adopted to illustrate strengths and weaknesses of the mechanistic modeling approach in simulating hydrology and vegetation behavior of different ecosystems exposed to elevated CO2 concentrations. Additionally, an ecosystem model (T&C) is used to investigate the relative contributions of direct (through carbon assimilation) and indirect (via soil moisture savings due to stomatal closure, and changes in leaf area index) effects of elevated CO2. The simulations suggest that the indirect effects of elevated CO2 on net primary productivity are large and variable, ranging from less than 10% to more than 100% of the size of direct effects. For ET, indirect effects are on average 65% of the size of direct effects. Indirect effects tend to be considerably larger in water-limited sites, portraying a critical response of semi-arid ecosystems to elevated CO2. A further analysis demonstrates that introducing subtle changes in plant physiological traits in the simulations can also explain the unexpectedly large increase in water use efficiency (WUE) observed during the last two decades in forests across the north hemisphere.
These results have major implications for our understanding of the CO2-response of ecosystems and for global projections of CO2 fertilization because they emphasize the role of indirect effects and the importance of ecosystem adaptability in controlling water, carbon and energy fluxes with potential consequences for climate change and supply of ecosystem services.

Proteases are enzymes that often play pathogenic roles in many common human diseases such as cancer, asthma, arthritis, atherosclerosis and infection by pathogens. Therefore tools that can be used to dynamically monitor their activity can be used as diagnostic agents, as imaging contrast agents for intraoperative image guidance and for the identification of novel classes of protease-targeted drugs. In the first part of this presentation, I will describe our efforts to design and synthesize small molecule probes that produce a fluorescent signal upon binding to a protease target. We have identified probes that show tumor-specific retention, fast activation kinetics, and rapid systemic distribution making them useful for real-time fluorescence guided tumor resection and other diagnostic imaging applications. In the second half of the presentation, I will present our recent advances using chemical probes to target the proteasome in the parasite pathogen Plasmodium falciparum, the causative agent of malaria. The proteasome is a multi-component protease complex responsible for regulating key processes such as the cell cycle and antigen presentation. Proteasome inhibitors have been shown to be toxic for the parasite at all stages of its life cycle, including the transmissive gametocyte stages. However, all compounds that have been tested also inhibit the mammalian proteasome resulting in toxicity. We used a recently developed substrate profiling method to uncover differences in the specificities of the human and parasite 20S proteasome cores. We designed inhibitors based on amino acid preferences specific to the P. falciparum proteasome, and found that they preferentially inhibit the tryptic-like subunit β2. We determined the structure of the P. falciparum 20S proteasome bound to our inhibitor using cryo-EM and single particle analysis, to a resolution of 3.6 Å. These data reveal the unusually open P. falciparumβ2 active site and provide valuable information regarding active site architecture that can be used to further refine inhibitor design. Furthermore, we observed growth inhibition synergism with low doses of this β2 selective inhibitor in artemisinin (ART) sensitive and resistant parasites. Finally, we demonstrated that a parasite selective inhibitor attenuates parasite growth in vivo without significant toxicity to the host. Thus, the Plasmodium proteasome is a chemically tractable target for next generation anti-malarial agents.

Inhibitory neurons are critically important for the adaptation of neural circuits to sensory experience, but the molecular mechanisms by which experience controls the connectivity between different types of inhibitory neurons to regulate cortical plasticity are largely unknown. In this talk, I will present studies demonstrating that sensory experience induces in cortical vasoactive intestinal peptide (VIP)-expressing neurons a gene program that is markedly distinct from that induced in excitatory neurons and other subtypes of inhibitory neuron. I will show that is Igf1 one of several activity-regulated genes that are specific to VIP neurons, that IGF1 functions cell-autonomously in VIP neurons to increase inhibitory synaptic input onto these neurons and that VIP neuron-derived IGF1 regulates visual acuity in an experience-dependent manner, likely by promoting the inhibition of disinhibitory neurons and affecting inhibition onto cortical pyramidal neurons. I will discuss how our findings support a model by which experience-induced transcriptional networks regulate the synaptic connectivity of each type of neuron according to a circuit-wide homeostatic logic and I will propose that the analysis of the genomic mechanisms regulating these transcriptional networks will allow us to evaluate the extent to which cell-type-specific homeostatic mechanisms contribute to the function of cortical circuits.

We propose an inclusive search for dark photons A' at the LHCb experiment based on both prompt and displaced di-muon resonances.
Because the couplings of the dark photon are inherited from the photon via kinetic mixing, the dark photon A' -> mu+mu- rate can be directly inferred from the off-shell photon gamma* -> mu+mu- rate, making this a fully data-driven search. For Run 3 of the LHC, we estimate that LHCb will have sensitivity to large regions of the unexplored dark-photon parameter space, especially in the 210-520 MeV and 10-40 GeV mass ranges. This search leverages the excellent invariant-mass and vertex resolution of LHCb, along with its unique particle-identification and real-time data-analysis capabilities.