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The nucleus accumbens (NAc) is the most promising target for drug use disorder treatment. Deep brain stimulation (DBS) of NAc is effective for drug use disorder treatment. However, the mechanisms by which DBS produces its therapeutic effects remain enigmatic. Here, we define a behavioral cutoff criterion to distinguish depressive-like behaviors and nondepressivelike behaviors in mice after morphine withdrawal. We identified a basolateral amygdala (BLA) to NAc D1 medium spiny neuron (MSN) pathway that controls depressive-like behaviors after morphine withdrawal. Furthermore, the paraventricular nucleus of thalamus (PVT) to NAc D2 MSN pathway controls naloxone-induced acute withdrawal symptoms. Optogenetically induced long-term potentiation with K-opioid receptor (KOR) antagonism enhanced BLA to NAc D1 MSN signaling and also altered the excitation/inhibition balance of NAc D2 MSN signaling. We also verified that a new 50 Hz DBS protocol reversed morphine withdrawal-evoked abnormal plasticity in NAc. Importantly, this refined DBS treatment effectively alleviated naloxone-induced withdrawal symptoms and depressive-like behaviors and prevented stress-induced reinstatement. Taken together, the results demonstrated that input- and cell type-specific synaptic plasticity underlies morphine withdrawal, which may lead to novel targets for the treatment of opioid use disorder., Introduction Opioid drugs are powerful analgesics, but they are also highly addictive due to the elicitation of powerful euphoria. Opioid use disorder depends not only on its positive reinforcement effects, [...]

Electrocatalysts based on high‐valent indium are promising for formate production via CO2 electroreduction. However, reconstruction often occurs during the reaction progress, resulting in a decline in catalytic performance. Here, a composite of In2O3/In2S3 is developed, and its catalytic performance exceeds that of either individual phase, particularly in stability. Analysis of morphology, valence state, and in situ Raman spectroscopy reveals that In2O3 is well preserved during the reaction. Theoretical calculations suggest that the desorption energy of lattice oxygen on In2O3 can be strengthened due to In2O3‐In2S3 bonding within the composite. This reinforcement facilitates the formation of more active sites and promotes CO2 adsorption, further decreasing the energy barrier of formate production to only 0.12 eV. As a result, the composite exhibits a formate selectivity over 95.05% at –1.13 V vs reversible hydrogen electrode accompanied by a partial current density of 434.4 mA cm–2. Notably, the selectivity of formate maintains over 95% even after 50 h at an industrial‐level current density of 200 mA cm–2, 17 times longer than the individual phase. Furthermore, 18.33% solar‐to‐formate and 19.49% solar‐to‐fuel are obtained when coupled with III‐V solar cells, demonstrating its feasibility. [ABSTRACT FROM AUTHOR]

During underground coal seam mining, changes in the working face advancement rate can easily affect the compaction state of granules in the collapse zone. This is an important factor in the induction of gas disasters and surface subsidence in mining areas. In this work, a cyclic loading and unloading mechanical test of granules under different loading rates was carried out. The changes in mechanical parameters of the granules at various stages were investigated. It is shown that the strain of each group of specimens under cyclic loading shows an increasing trend and the final strain increases with the loading rate. The input energy of the granules increases under cyclic loading, and under a low loading rate, the compaction force needs to overcome interparticle friction to destroy the relatively stable structure, which results in a need for more energy to achieve the same level of deformation. The acoustic emission ringing counts of each group of granules specimens show an overall increasing trend, with the highest proportion of ringing counts in the first loading stage. The compaction of pores and filling of particles under cyclic loading is a "uniform compaction, stable change, slow adjustment" dynamic process. When the loading is slow, the relative positions of the granule particles in each stress gradient are more adequately adjusted. The results of this study provide important theoretical support for the scientific formulation of gas control strategies and the prevention of surface subsidence in air-mining zones under different mining speeds. [ABSTRACT FROM AUTHOR]

Aerial object detection (AOD) aims at locating and categorizing aerial targets in remote sensing image, which has wide practical applications. Mainstream feature pyramid network (FPN) has made great progresses in multi-scale natural objects detection, which is also successfully extended into AOD. However, existing FPNs still have some shortcomings in discriminative feature extraction and fusion for aerial objects due to diverse object scales and dense small objects. To address these limitations, this letter proposes a novel mixed local-global attention FPN (MAFPN). The core components of MAFPN are cross-scale transformer (CST) module and multi-scale spatial fusion (MSSF) module, which are equipped at the feature extraction and fusion of FPN, respectively. Specifically, the CST module is devised to capture global semantic information and cross-scale similarity by exploiting self-attention in multi-scale features. The MSSF module utilizes the spatial attention maps as fusion weights, and adaptively fuses multi-scale features between adjacent two levels. Such mixed attention approach enhances the multi-scale feature learning in dense small objects. Besides, to further balance the influences of different object scales, we propose an area-guided dot-IOU (ADIOU) regression loss, which takes into account the size of objects and enables the model to prioritize the centre position of small objects. Experimental results on public datasets demonstrate the superiority of proposed method. [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide guidance for CBM production and prevention of coal mine disasters. In this research, a hybrid neural network prediction model integrating a genetic algorithm, an adaptive boosting algorithm, and a back propagation neural network was developed to predict coal seam permeability. Additional momentum and variable learning rate algorithms were used to improve the learning rate and accuracy of the model, and the model structure was optimized, including the number of hidden layer nodes and the transfer function. The input parameters of the prediction model included gas pressure, compressive strength, reservoir temperature, and effective stress. The corresponding output parameter was coal seam permeability. The correlation between the parameters was calculated. Additionally, a comparative analysis between the proposed prediction model and four other prediction models was carried out to demonstrate the advantages of the proposed model. The results indicated that the correlations between compressive strength, gas pressure, reserve temperature, effective stress, and coal seam permeability were 0.334, −0.148 , −0.406 , and −0.785 , respectively. The proposed prediction model had high accuracy compared with the other prediction models, and its coefficient of determination and root mean squared error were 0.999 and 0.021. Thus, the model can predict coal seam permeability more accurately. [ABSTRACT FROM AUTHOR]

To refine the grain size and improve the mechanical properties of ultrahigh-strength aluminum alloy (Al-10Zn-1.9Mg-1.6Cu-0.12Zr), the Al-Ti-B-Er grain refiner was prepared by the melt reaction method using the aluminum melt and Al + Ti + B precursor. The results exhibit that the Al-Ti-B-Er grain refiner is mainly composed of a block TiAl3 phase, and loose agglomerated nano-sized TiB2 and Al3Er phases. The microstructure of ultrahigh-strength aluminum is significantly affected by the Al-Ti-B-Er refiner, which changes from dendrite to equiaxial grain with increasing Al-Ti-B-Er content, and the size of the eutectic phase is significantly refined. The high-efficiency refinement of Al-Ti-B-Er is due to Er promoting the uniform distribution of TiAl3 particles and the formation of loose agglomerated nano-sized TiB2 particles. The optimal addition content of Al-Ti-B-Er into ultrahigh-strength aluminum alloys is 1 wt%, whose grain size is approximately 40 µm. Additionally, the strength and ductility of ultrahigh-strength aluminum alloys are simultaneously improved by adding 1wt% Al-Ti-B-Er after the T6 treatment, reaching 756 MPa and 20%, respectively. This enhancement in strength and ductility is mainly attributed to grain refinement and the eutectic phase refinement. [ABSTRACT FROM AUTHOR]