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Chlorine-doped MoS2quantum dots (Cl-MoS2QDs) embedded in a SiO2molecularly imprinted polymer (Cl-MoS2QDs@SiO2@MIP) have been successfully synthesized and can be used for highly selective and sensitive optosensing of quercetin. The novel environmentally friendly sensor integrated the advantages of the Cl-MoS2QDs and MIP, high sensitivity and specific recognition for quercetin. The as-fabricated sensor is used to detect trace amounts of quercetin, and its fluorescence intensity showed a good linear decline with the increasing concentration of quercetin from 2 ng mL−1to 200 ng mL−1with a detection limit of 1.2 ng mL−1(S/N = 3). The Cl-MoS2QDs@SiO2@MIP probe was employed to assay the content of quercetin of real onion extract with good performance, which is in fine agreement with the result obtained by high performance liquid chromatography. The developed Cl-MoS2QDs@SiO2@MIP sensor exhibits promising potential in the detection of quercetin.

High-resolution remote sensing (RS) image change detection (CD) focuses on ground surface changes. It has wide applications, including territorial spatial planning, urban region detection, and military operations. However, class imbalance and pseudochanges are caused by the unchanged areas far outnumbering the changed areas and lighting changes. To address these problems, we propose spatial–temporal attention with a difference enhancement-based network (STADE-CDNet). In STADE-CDNet, a CD difference enhancement module (CDDM) is proposed to extract important features from the difference map to detect changed regions. This module enhances the network with differential feature attributes through the training layer, improving the network’s learning ability and reducing the imbalance problem. A temporal memory module (TMM) is designed to extract temporal and spatial information. Inspired by the self-attention mechanism of the transformer, we propose a transformer and TMM (TTMM). Four encoding layers are designed to detect the semantic information from high to low levels of the multitemporal image pairs. The fusion and parallelism of multivariate data are achieved through collaborative modeling of deep learning and CD, compensating for the need for excessive human intervention in traditional algorithms. We evaluate our approach in two different datasets [LEVIR building CD (LEVIR-CD) and deeply supervised image fusion network for change detection (DSIFN-CD)]. Promising quantitative and qualitative results show that the STADE-CDNet can improve accuracy. In particular, the proposed CDDM significantly reduces false positive detection, with $F1$ scores at least 1.97% and 2.1% higher than other methods in the case of the LEVIR-CD and DSIFN-CD datasets, respectively. Our code is available at https://github.com/LiLisaZhi/STADE-CDNet.

Infrared small target detection (ISTD) is vital for civil and military applications. However, existing methods often face challenges in coping with complex scenes, discriminating targets from similar objects, or leveraging temporal information effectively. To tackle these limitations, we offer an innovative approach that exploits the spatiotemporal structure of infrared images. A 4-D infrared tensor is initially constructed from a sequence of infrared images and decomposed into lower dimensional tensors using the tensor train (TT) and its extension—tensor ring (TR) techniques. The ISTD problem is then formulated as a sparse plus low-rank decomposition problem, where the sparse part is the target and the low-rank part is the background. We factorize the composed tensors into matrices via TT and TR unfolding approaches, which mitigates the imbalance between different modes containing spatial and temporal information. By constraining the balanced unfolded components with the weighted sum of nuclear norm, we solve the problem using the alternating direction multiplier method (ADMM). Furthermore, we validate models on several datasets and benchmark them with state-of-the-art techniques in detection accuracy and background suppression. Comparison results demonstrate the superiority of our approach over the existing methods. Moreover, the results of an ablation study with 3-D tensor structures show the effectiveness and feasibility of the dimension expansion to 4-D.

The nucleus accumbens (NAc) plays an important role in motivation and reward processing. Recent studies suggest that different NAc subnuclei differentially contribute to reward-related behaviors. However, how reward is encoded in individual NAc neurons remains unclear. Using in vivosingle-cell resolution calcium imaging, we find diverse patterns of reward encoding in the medial and lateral shell subdivision of the NAc (NAcMed and NAcLat, respectively). Reward consumption increases NAcLat activity but decreases NAcMed activity, albeit with high variability among neurons. The heterogeneity in reward encoding could be attributed to differences in their synaptic inputs and transcriptional profiles. Specific optogenetic activation of Nts-positive neurons in the NAcLat promotes positive reinforcement, while activation of Cartpt-positive neurons in the NAcMed induces behavior aversion. Collectively, our study shows the organizational and transcriptional differences in NAc subregions and provides a framework for future dissection of NAc subregions in physiological and pathological conditions.