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Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. “Train” and “control” neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

 

For domains in R d \mathbb {R}^d , d ≥ 2 d\geq 2 , we prove universal upper and lower bounds on the product of the bottom of the spectrum for the Laplacian to the power p > 0 p>0 and the supremum over all starting points of the p p -moments of the exit time of Brownian motion. It is shown that the lower bound is sharp for integer values of p p and that for p ≥ 1 p \geq 1 , the upper bound is asymptotically sharp as d → ∞ d\to \infty . For all p > 0 p>0 , we prove the existence of an extremal domain among the class of domains that are convex and symmetric with respect to all coordinate axes. For this class of domains we conjecture that the cube is extremal. 

An often-quoted statement attributed to Wolfgang Pauli is that God made the bulk, but the surface was invented by the devil. Although humorous, the statement really reflects frustration in developing a detailed picture of a surface. In the last several decades, that frustration has begun to abate with numerous techniques providing clues to interactions and reactions at surfaces. Often these techniques require considerable prior knowledge. Complex mixtures on irregular or soft surfaces—complex interfaces—thus represent the last frontier. Two optical techniques: sum frequency generation (SFG) and second harmonic generation (SHG) are beginning to lift the veil on complex interfaces. Of these techniques, SFG with one excitation in the infrared has the potential to provide exquisite molecular- and moiety-specific vibrational data. This Perspective is intended both to aid newcomers in gaining traction in this field and to demonstrate the impact of high-phase resolution. It starts with a basic description of light-induced surface polarization that is at the heart of SFG. The sum frequency is generated when the input fields are sufficiently intense that the interaction is nonlinear. This nonlinearity represents a challenge for disentangling data to reveal the molecular-level picture. Three, high-phase-resolution methods that reveal interactions at the surface are described.

 

Both thin (55μm) composite and thick (350μm) all active material battery porous electrodes were prepared for estimating the diffusion coefficient of Li⁺($D_{{\mathit{Li}}^{+}})$in tellurium (Te) during electrochemical lithiation. Galvanostatic intermittent titration technique (GITT), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) were applied to quantify the chemical lithium solid-state diffusion coefficient within the Te active material in the electrodes. Multiple methods of GITT and EIS were assessed. For the composite Te electrodes, the$D_{{\mathit{Li}}^{+}}$was on the order of 10⁻¹¹cm²s⁻¹from both CV and GITT methods, but 10⁻⁹cm²s⁻¹from EIS. For the thick tellurium electrodes, both GITT and EIS resulted in lithium diffusion coefficient estimates in the range of 10⁻¹¹–10⁻¹²cm²s⁻¹. The general trend across all methods that quantified the diffusion coefficient as a function of lithiation of tellurium was that the$D_{{\mathit{Li}}^{+}}$decreased rapidly when the Te material was initially lithiated. The$D_{{\mathit{Li}}^{+}}$at the phase transition voltage plateau (∼1.7 V, vs Li/Li⁺, where both Te and Li₂Te were expected) had the lowest$D_{{\mathit{Li}}^{+}},$while the$D_{{\mathit{Li}}^{+}}$both before and after the plateau was generally higher. Among all the electrochemical measurements of$D_{{\mathit{Li}}^{+}},$the modified GITT method with modelling the relaxation region resulted in relatively low scatter in the data, provided values as a function of lithiation, and was well suited to thick electrodes with a flat discharge plateau as was the case herein.

 

This paper is a write-up of the ideas that were presented, developed and discussed at the fourth International Workshop on QCD Challenges from pp to AA, which took place in February 2023 in Padua, Italy. The goal of the workshop was to focus on some of the open questions in the field of high-energy heavy-ion physics and to stimulate the formulation of concrete suggestions for making progresses on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.