It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general ...computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. First, our SNN model could successfully describe all the experimental findings in fly visual reinforcement learning and action selection among multiple conflicting choices as well. Second, our computational modeling shows that dopaminergic neuron-GABAergic neuron-mushroom body (DA-GABA-MB) works in a recurrent loop providing a key circuit for gain and gating mechanism of nonlinear decision making. Compared with existing models, our model shows more biologically plausible on the network design and working mechanism, and could amplify the small differences between two conflicting cues more clearly. Finally, based on the proposed model, the UAV could quickly learn to make clear-cut decisions among multiple visual choices and flexible reversal learning resembling to real fly. Compared with linear and uniform decision-making methods, the DA-GABA-MB mechanism helps UAV complete the decision-making task with fewer steps.
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Metal nanoclusters whose surface ligands are removable while keeping their metal framework structures intact are an ideal system for investigating the influence of surface ligands on catalysis of ...metal nanoparticles. We report in this work an intermetallic nanocluster containing 62 metal atoms, Au34Ag28(PhCC)34, and its use as a model catalyst to explore the importance of surface ligands in promoting catalysis. As revealed by single-crystal diffraction, the 62 metal atoms in the cluster are arranged as a four-concentric-shell Ag@Au17@Ag27@Au17 structure. All phenylalkynyl (PA) ligands are linearly coordinated to the surface Au atoms with staple “PhCC–Au–CCPh” motif. Compared with reported thiolated metal nanoclusters, the surface PA ligands on Au34Ag28(PhCC)34 are readily removed at relatively low temperatures, while the metal core remains intact. The clusters before and after removal of surface ligands are used as catalysts for the hydrolytic oxidation of organosilanes to silanols. It is, for the first time, demonstrated that the organic-capped metal nanoclusters work as active catalysts much better than those with surface ligands partially or completely removed.
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A general strategy, using mixed ligands, is utilized to synthesize atomically precise, intrinsically chiral nanocluster Ag78(DPPP)6(SR)42 (Ag78) where DPPP is the achiral ...1,3-bis(diphenyphosphino)propane and SR = SPhCF3. Ag78 crystallizes as racemates in a centric space group. Using chiral diphosphines BDPP = 2,4-bis(diphenylphosphino)pentane, the enantiomeric pair Ag78(R/S-BDPP)6(SR)42 can be prepared with 100% optical purity. The chiral diphosphines gives rise to, separately, two asymmetric surface coordination motifs composed of tetrahedral R3PAg(SR)3 moieties. The flexible nature of C–C–C angles between the two phosphorus atoms restricts the relative orientation of the tetrahedral R3PAg(SR)3 moieties, thereby resulting in the enantiomeric selection of the intrinsic chiral metal core. This proof-of-concept strategy raises the prospect of enantioselectively synthesizing optically pure, atomically precise chiral noble metal nanoclusters for specific applications.
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An intermetallic nanocluster containing 44 metal atoms, Au24Ag20(2-SPy)4(PhCC)20Cl2, was successfully synthesized and structurally characterized by single-crystal analysis and density funtional ...theory computations. The 44 metal atoms in the cluster are arranged as a concentric three-shell Au12@Ag20@Au12 Keplerate structure having a high symmetry. For the first time, the co-presence of three different types of anionic ligands (i.e., phenylalkynyl, 2-pyridylthiolate, and chloride) was revealed on the surface of metal nanoclusters. Similar to thiolates, alkynyls bind linearly to surface Au atoms using their σ-bonds, leading to the formation of two types of surface staple units (PhCC-Au-L, L = PhCC– or 2-pyridylthiolate) on the cluster. The co-presence of three different surface ligands allows the site-specific surface and functional modification of the cluster. The lability of PhCC– ligands on the cluster was demonstrated, making it possible to keep the metal core intact while removing partial surface capping. Moreover, it was found that ligand exchange on the cluster occurs easily to offer various derivatives with the same metal core but different surface functionality and thus different solubility.
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Soil heavy metals pollution can cause many serious environment problems because of involving a very complex pollution process for soil health. Therefore, it is very important to explore methods that ...can effectively evaluate heavy metal pollution. Researchers were actively looking for new ideas and new methods for evaluating and predicting levels of soil heavy metal pollution. The study on microbial communities is one of the effective methods using gene chip technology. Gene chip technology, as a high-throughput metagenomics analysis technique, has been widely used for studying the structure and function of complex microbial communities in different polluted environments from different pollutants, including the soil polluted by heavy metals. However, there is still a lack of a systematic summarization for the polluted soil by heavy metals. This paper systematically analyzed soil heavy metals pollution via reviewing previous studies on applying gene chip technology, including single species, tolerance mechanisms, enrichment mechanisms, anticipation and evaluation of soil remediation, and multi-directional analysis. The latest gene chip technologies and corresponding application cases for discovering critical species and functional genes via analyzing microbial communities and evaluating heavy metal pollution of soil were also introduced in this paper. This article can provide scientific guidance for researchers actively investigating the soil polluted by heavy metals.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Demonstrated herein are the preparation and crystallographic characterization of the family of fcc silver nanoclusters from Nichol’s cube to Rubik’s cube and beyond via ligand-control (thiolates and ...phosphines in this case). The basic building block is our previously reported fcc cluster Ag14(SPhF2)12(PPh3)8 (1). The metal frameworks of Ag38(SPhF2)26(PR′3)8 (2 2 ) and Ag63(SPhF2)36(PR′3)8+ (2 3 ), where HSPhF2 = 3,4-difluorothiophenol and R′ = alkyl/aryl, are composed of 2 × 2 = 4 and 2 × 2 × 2 = 8 metal cubes of 1, respectively. All serial clusters share similar surface structural features. The thiolate ligands cap the six faces and the 12 edges of the cube (or half cube) while the phosphine ligands are terminally bonded to its eight corners. On the basis of the analysis of the crystal structures of 1, 2 2 , and 2 3 , we predict the next “cube of cubes” to be Ag172(SR)72(PR′3)8 (3 3 ), in the evolution of growth of this cluster sequence.
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Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet ...the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a "morphological experiment", while an "algorithm comparison experiment" confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.
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Air-source heat pumps (ASHPs), functioning as thermally controlled loads, possess significant adjustable capabilities and controllability when aggregated, establishing them as premium resources for ...demand-response engagement. This paper proposes a control strategy for the aggregation of ASHP loads to participate in demand response across multiple scenarios, framed within a three-tier architecture: electric power system, Load Aggregator (LA), and thermal load. Load Aggregators, considering the user-comfort temperature ranges and the thermal storage characteristics of buildings, aim to minimize heating costs through time-of-use electricity pricing, while assessing the adjustability of the load. Upon receiving control directives from the power system’s dispatch department, the strategy allocates load adjustments by considering user comfort and system regulatory needs, thereby addressing issues like aggregated power oscillations and significant rebound loads. The effectiveness of the proposed strategy is corroborated through simulation, demonstrating its potential to enhance demand-response participation and ameliorate associated power stability challenges.
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The function of the central nervous system to regulate food intake can be disrupted by sustained metabolic challenges such as high-fat diet (HFD), which may contribute to various metabolic disorders. ...Previously, we showed that a group of octopaminergic (OA) neurons mediated starvation-induced hyperactivity, an important aspect of food-seeking behavior (Yu et al., 2016). Here we find that HFD specifically enhances this behavior. Mechanistically, HFD increases the excitability of these OA neurons to a hunger hormone named adipokinetic hormone (AKH), via increasing the accumulation of AKH receptor (AKHR) in these neurons. Upon HFD, excess dietary lipids are transported by a lipoprotein LTP to enter these OA
AKHR
neurons via the cognate receptor LpR1, which in turn suppresses autophagy-dependent degradation of AKHR. Taken together, we uncover a mechanism that links HFD, neuronal autophagy, and starvation-induced hyperactivity, providing insight in the reshaping of neural circuitry under metabolic challenges and the progression of metabolic diseases.
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