Transcriptome-Wide Identification of Dark- and Salt-Induced Senescence-Related NAC Gene Family Members in Alfalfa.

Leaves are a key forage part for livestock, and the aging of leaves affects forage biomass and quality. Preventing or delaying premature leaf senescence leads to an increase in pasture biomass accumulation and an improvement in alfalfa quality. NAC transcription factors have been reported to affect plant growth and abiotic stress responses. In this study, 48 NAC genes potentially associated with leaf senescence were identified in alfalfa under dark or salt stress conditions. A phylogenetic analysis divided MsNACs into six subgroups based on similar gene structure and conserved motif. These MsNACs were unevenly distributed in 26 alfalfa chromosomes. The results of the collinearity analysis show that all of the MsNACs were involved in gene duplication. Some cis-acting elements related to hormones and stress were screened in the 2-kb promoter regions of MsNACs. Nine of the MsNAC genes were subjected to qRT-PCR to quantify their expression and Agrobacterium-mediated transient expression to verify their functions. The results indicate that Ms.gene031485, Ms.gene032313, Ms.gene08494, and Ms.gene77666 might be key NAC genes involved in alfalfa leaf senescence. Our findings extend the understanding of the regulatory function of MsNACs in leaf senescence.

On simplicity and complexity in the brave new world of large-scale neuroscience.

Technological advances have dramatically expanded our ability to probe multi-neuronal dynamics and connectivity in the brain. However, our ability to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of theoretically principled data analytic procedures, as well as theoretical frameworks for how circuit connectivity and dynamics can conspire to generate emergent behavioral and cognitive functions. We review and outline potential avenues for progress, including new theories of high dimensional data analysis, the need to analyze complex artificial networks, and methods for analyzing entire spaces of circuit models, rather than one model at a time. Such interplay between experiments, data analysis and theory will be indispensable in catalyzing conceptual advances in the age of large-scale neuroscience.

A superposable silicon synapse with programmable reversal potential.

We present a novel log-domain silicon synapse designed for subthreshold analog operation that emulates common synaptic interactions found in biology. Our circuit models the dynamic gating of ion-channel conductances by emulating the processes of neurotransmitter release-reuptake and receptor binding-unbinding in a superposable fashion: Only a single circuit is required to model the entire population of synapses (of a given type) that a biological neuron receives. Unlike previous designs, which are strictly excitatory or inhibitory, our silicon synapse implements-for the first time in the log-domain-a programmable reversal potential (i.e., driving force). To demonstrate our design's scalability, we fabricated in 180nm CMOS an array of 64K silicon neurons, each with four independent superposable synapse circuits occupying 11.0×21.5 µm(2) apiece. After verifying that these synapses have the predicted effect on the neurons' spike rate, we explored a recurrent network where the synapses' reversal potentials are set near the neurons' threshold, acting as shunts. These shunting synapses synchronized neuronal spiking more robustly than nonshunting synapses, confirming that reversal potentials can have important network-level implications.