Response properties of BS and NS R-neurons in the cue and delay periods.

<p>(A) Population histograms of BS (red curves) and NS (blue curves) R-neurons identified in the cue period. (B) Population histograms of BS and NS R-neurons identified only in the delay period not in the cue period. The activity of each neuron was sorted by the two reward conditions: the preferred reward condition (solid curves) and the non-preferred reward condition (dashed curves). The shaded areas around the curves indicate SEM. The gray area in (A) is the cue period, and the gray area in (B) is the delay period. (C)-(D) Scatterplots of the baseline activity of BS R-neurons against the late cue activity in the preferred (C) and non-preferred (D) reward conditions. (E)-(F) Scatterplots of the baseline activity of BS R-neurons against the delay activity in the preferred (E) and non-preferred reward conditions. (G)-(H) The activity of NS R-neurons in the late cue period against the baseline activity in the preferred (G) and non-preferred (H) reward conditions. (I)-(J) The activity of NS R-neurons in the delay period against the baseline activity in the preferred (I) and non-preferred (J) reward conditions. Filled circles and triangles indicate statistical significance (sig., Mann Whitney <i>U</i> test, P < 0.05) and open ones indicate no statistical significance (n.s., P > 0.05).</p

Classification of LPFC broad-spike (BS) neurons in the four monkeys.

<p>Classification of LPFC broad-spike (BS) neurons in the four monkeys.</p

Response properties of BS and NS S-neurons in the cue period.

<p>(A) Population histograms of BS (red curves) and NS (blue curves) neurons that were identified as the stimulus type in the cue period. The neuronal activity was sorted by the preferred stimulus (solid curves) and the non-preferred stimulus (dashed curves). The shaded areas around the curves indicate SEM. The gray area indicates the cue period. (B)-(C) Scatterplots of the baseline activity of each BS S-neurons against the late cue activity to the preferred stimulus (B) and to the non-preferred stimulus (C). (D)-(E) Scatterplots of the activity of NS S-neurons to the preferred stimulus (D) and to the non-preferred stimulus (E). Filled circles indicate statistical significance (sig., Mann Whitney <i>U</i> test, P < 0.05) and open ones indicate no statistical significance (n.g., P > 0.05).</p

Response properties of BS and NS SR-neurons in the cue period.

<p>(A) Population histograms of BS SR-neurons sorted by four conditions: the preferred reward with preferred stimulus (thick solid curve), the preferred reward with non-preferred stimulus (thick dashed curve), the non-preferred reward with preferred stimulus (thin solid curve) and the non-preferred reward with non-preferred stimulus (thin dashed curve). (B) Population histograms of NS SR-neurons. The activity was sorted with the same four conditions as in (A). (C) Averaged activities of BS SR-neurons in the cue period. Four bars indicate the activities in the four conditions. (D) Average activities of NS SR-neurons in the cue period.</p

Characteristic responses of BS and NS neurons.

<p>(A)-(C) Population histograms of BS (red curves) and NS (blue curves) neurons aligned at the first cue onset (A), the second set of cues onset (B) and the third set of cues onset (C). The shaded areas around the curves indicate SEM. The two gray areas in (A) indicate the cue and delay periods, respectively. The NS neurons show significantly higher firing rates in all task periods than did the BS neurons (Mann Whiney <i>U</i> test, P < 0.001). (D) Cumulative curves of visual response latencies for BS neurons (red curve) and NS neurons (blue curve). NS cells showed faster visual responses than did BS neurons (Mann Whitney <i>U</i> test, <i>P</i> < 0.001).</p

Microphase Diffusion-Controlled Interfacial Polymerization for an Ultrahigh Permeability Nanofiltration Membrane

The key to improving nanofiltration membrane permeance is reducing its thickness while maintaining high rejection. Herein, a 25 nm thick ultrathin polyamide layer was prepared by a microphase diffusion-controlled interfacial polymerization (MDC-IP) of poly­(ethyleneimine) and trimesoyl chloride, which is much thinner than the conventional interfacial polymerization (CIP) polyamide layer. A new formation mechanism for such an ultrathin layer is presented, which included a microphase interfacial reaction and eliminated loose layers due to the confinement of microphase diffusion and the termination of stepwise diffusion. Moreover, the polyamide layer was post-cross-linked to form a stable dual-cross-linked interwoven structure. Such a membrane showed an ultrahigh permeance of 1246 kg/(m² h MPa), which was 23 times that of CIP membranes. MDC-IP could efficiently control the microinterface between two immiscible phases, which provided a facile way to regulate the membrane at nanoscale

Synthesis of Copper Graphene Materials Functionalized by Amino Acids and Their Catalytic Applications

Graphene oxide and its derivative have attracted extensive interests in many fields, including catalytic chemistry, organic synthesis, and electrochemistry, recently. We explored whether the use of graphene after chemical modification with amino acids to immobilize copper nanoparticles could achieve a more excellent catalytic activity for N-arylation reactions. A facile and novel method to prepare copper supported on amino-acid-grafted graphene hybrid materials (A–G–Cu) was first reported. The as-prepared hybrid materials were characterized by a variety of techniques, including Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and inductively coupled plasma–atomic emission spectrometry. The results showed that the morphology, distribution, and loading of copper nanoparticles could be well-adjusted by controlling the type of amino acids grafted on graphene. Moreover, most A–G–Cu hybrid materials could catalyze N-arylation of imidazole with iodobenzene with yields more than 90%, while the copper supported on graphene (G–Cu) displayed a yield of just 65.8%. The high activity of A–G–Cu can be ascribed to the good synergistic effects of copper nanoparticles (Cu NPs) and amino-acid-grafted graphene

Toward High-Performance Map-Recovery of Air Pollution Using Machine Learning

Mobile and pervasive sampling of urban air pollution has been increasingly valued as a sustainable method, in terms of economic and operational factors, for surveying atmospheric environment with high space-time resolution. Specifically, fine-granular air quality (AQ) inference provides fundamental progress toward data-driven urban management, as it estimates grid-level pollutant concentrations constantly using pollutant measurement data collected from fixed and mobile sensors. In this paper, we propose a tree-based multicascade space-time learning model (MCST-Tree) for AQ inference to recover pollution maps by exploiting multisource AQ samples (fixed and mobile) and heterogeneous urban feature sets (land-use, meteorology, population, traffic, etc.). This is implemented and evaluated in a study case of Chengdu (4900 km2, 14 June to 14 July 2018), which achieves map-recovery of PM2.5 distribution based upon the sparse measurements (ca. 16.2% space-time coverage) with high-performance (symmetric mean average percentage error (SMAPE) (%) = 14.13%; R2 = 0.94). Detailed evaluations are presented through the analysis of model performance, space-time coverage of mobile sampling, and AQ inference. We conduct a series of sensitivity analyses of mobile sampling coverage, and the experimental results show that it is a critical issue to enhance the model trust, which contributes to improve the R-square from 0.81 (fixed data + 10% mobile data) to 0.94 (fixed data + 100% mobile data). The results show that the mobile sampling significantly improves the space-time modeling capability, and our proposed model has great potential to achieve map-recovery for air pollution at high spatial-temporal resolution with high performance

Covalent Organic Framework–Covalent Organic Framework Bilayer Membranes for Highly Selective Gas Separation

Covalent organic frameworks (COFs) have been proposed as alternative candidates for molecular sieving membranes due to their chemical stability. However, developing COF membranes with narrowed apertures close to the size of common gas molecules is a crucial task for selective gas separation. Herein, we demonstrate a new type of a two-dimensional layered-stacking COF–COF composite membrane in bilayer geometry synthesized on a porous support by successively regulating the growth of imine-based COF-LZU1 and azine-based ACOF-1 layers via a temperature-swing solvothermal approach. The resultant COF-LZU1–ACOF-1 bilayer membrane has much higher separation selectivity for H₂/CO₂, H₂/N₂, and H₂/CH₄ gas mixtures than the individual COF-LZU1 and ACOF-1 membranes due to the formation of interlaced pore networks, and the overall performance surpasses the Robeson upper bounds. The COF-LZU1–ACOF-1 bilayer membrane also shows high thermal and long-time stabilities

Mechanical Degradation of CHF₃ by Graphite To Achieve Greenhouse Gas Disposal and the Resource Utilization Thereof

CHF3 is a greenhouse gas with little reactivity, ultrahigh global warming potential, and large output as a byproduct of CHF2Cl, a feedstock of the tetrafluoroethene series polymers. Its economical disposal is vital for greenhouse gas abatement and carbon neutrality. Herein, CHF3 is used as a fluorinating agent to prepare graphite fluoride (FG) via a mechanochemical reaction. The reaction arises from high colliding pressure, as verified by the molecular dynamics simulation and thermodynamic analysis, hotspot temperature, increased surface area, and ample radicals of graphite. The structure and properties of the FG were characterized by X-ray diffraction, inductively coupled plasma optical emission spectrometry, X-ray absorption fine structure, IR, Raman, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy. The FG is a porous material with varying F-content up to 44%, high specific area (∼668 m2 g–1), and superhydrophobicity and shows various potential applications, such as adsorbent, lubricant, electrochemical and oil–water separation materials. This article discovered a sustainable approach for the simultaneous degradation and resource utilization of CHF3 and a greener production process of FGs