Study on the association between malnutrition, early childhood caries and caries activity among children aged 3-5 years.

BACKGROUND: This study aims to investigate the association between malnutrition and early childhood caries (ECC) and caries activity among children aged 3-5 years, in order to provide a theoretical basis for preventing and blocking ECC and improving malnutrition. METHODS: Children aged 3-5 years from six kindergartens in Zhao Xian, China were enrolled in this study. The decayed, missing, filled teeth (dmft) of all children were examined and recorded. The Cariostat method was used to detect dental caries activity, collect anthropometric data and measure haemoglobin concentration. Parents were asked to complete a questionnaire on the general characteristics and oral health behaviour of the participants. The "Growth Standards for Chinese Children Under 7 Years Old" was used to assess the nutritional status of all participating children. Wilcoxon rank sum test and multivariate logistic regression analysis were used to analyse and evaluate the relationship between ECC, caries activity and malnutrition. RESULTS: A total of 635 children who met the criteria were included in this study. After adjusting for confounding factors, logistic regression showed that the risk of ECC was significantly increased in underweight children compared with normal children (OR = 5.43, P < 0. 05); compared with normal children, the risk of ECC decreased in overweight and obese children (OR = 0.31, P < 0.001); underweight children had higher caries severity than normal weight children, and the difference was statistically significant (OR = 2.69, P < 0. 05); stunted children had higher caries severity than normal weight children and the difference was statistically significant (OR = 2.28, P < 0.05); underweight was positively associated with caries activity and the association was statistically significant (OR = 2.33, P < 0. 05); stunting was positively associated with caries activity and the association was statistically significant (OR = 2.1, P < 0.05); overweight and obesity were negatively associated with caries activity and the association was statistically significant (OR = 0.61, P < 0.05). CONCLUSIONS: The risk of ECC among children aged 3-5 years was positively associated with undernutrition and negatively associated with overnutrition. The severity of ECC among children aged 3-5 years was positively associated with undernutrition. The caries activity among children aged 3-5 years was positively associated with undernutrition and negatively associated with overnutrition.

CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model.

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS images. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolution. When taking original MS and PAN images as inputs, they always obtain sub-optimal results due to the scale variation. In this paper, we propose to explore the self-supervised representation for pansharpening by designing a cross-predictive diffusion model, named CrossDiff. It has two-stage training. In the first stage, we introduce a cross-predictive pretext task to pre-train the UNet structure based on conditional Denoising Diffusion Probabilistic Model (DDPM). While in the second stage, the encoders of the UNets are frozen to directly extract spatial and spectral features from PAN and MS images, and only the fusion head is trained to adapt for pansharpening task. Extensive experiments show the effectiveness and superiority of the proposed model compared with state-of-the-art supervised and unsupervised methods. Besides, the cross-sensor experiments also verify the generalization ability of proposed self-supervised representation learners for other satellite datasets. Code is available at https://github.com/codgodtao/CrossDiff.

Scene-Dependent Prediction in Latent Space for Video Anomaly Detection and Anticipation.

Video anomaly detection (VAD) plays a crucial role in intelligent surveillance. However, an essential type of anomaly named scene-dependent anomaly is overlooked. Moreover, the task of video anomaly anticipation (VAA) also deserves attention. To fill these gaps, we build a comprehensive dataset named NWPU Campus, which is the largest semi-supervised VAD dataset and the first dataset for scene-dependent VAD and VAA. Meanwhile, we introduce a novel forward-backward framework for scene-dependent VAD and VAA, in which the forward network individually solves the VAD and jointly solves the VAA with the backward network. Particularly, we propose a scene-dependent generative model in latent space for the forward and backward networks. First, we propose a hierarchical variational auto-encoder to extract scene-generic features. Next, we design a score-based diffusion model in latent space to refine these features more compact for the task and generate scene-dependent features with a scene information auto-encoder, modeling the relationships between video events and scenes. Finally, we develop a temporal loss from key frames to constrain the motion consistency of video clips. Extensive experiments demonstrate that our method can handle both scene-dependent anomaly detection and anticipation well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, and the proposed NWPU Campus datasets.

Ultrasound Spectral Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastatic in Breast Cancer.

OBJECTIVES: To explore the clinical value of the nomogram based on ultrasound spectral combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. METHODS: We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. RESULTS: Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). CONCLUSION: Axillary ultrasound combined with Ki-67 and ultrasound spectral parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

MAC: Maximal Cliques for 3D Registration.

This paper presents a 3D registration method with maximal cliques (MAC) for 3D point cloud registration (PCR). The key insight is to loosen the previous maximum clique constraint and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each representing a consensus set. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. In addition, we present a variant of MAC if given overlap prior, called MAC-OP. Overlap prior further enhances MAC from many technical aspects, such as graph construction with re-weighted nodes, hypotheses generation from cliques with additional constraints, and hypothesis evaluation with overlap-aware weights. Extensive experiments demonstrate that both MAC and MAC-OP effectively increase registration recall, outperform various state-of-the-art methods, and boost the performance of deep-learned methods. For instance, MAC combined with GeoTransformer achieves a state-of-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch. We perform synthetic experiments on 3DMatch-LIR / 3DLoMatch-LIR, a dataset with extremely low inlier ratios for 3D registration in ultra-challenging cases. Code will be available at: https://github.com/zhangxy0517/3D-Registration-with-Maximal-Cliques.

Hemorrhagic Shock Caused by Spontaneous Bleeding from Early Gastric Cancer Was Successfully Cured by Emergency Endoscopic Submucosal Dissection: A Case Report.

Cases and studies of protruding early gastric cancer (EGC) combined with spontaneous bleeding are relatively rare. The current study present a female patient aged 70 to 75 years old with hemorrhagic shock caused by spontaneous bleeding from EGC type 0-Isp, which was successfully cured by rapid emergency endoscopic submucosal dissection (ESD).

Relationship of dietary nutrients with early childhood caries and caries activity among children aged 3-5 years-a cross-sectional study.

BACKGROUND: Early childhood caries (ECC) is a challenge for pediatric dentists all over the world, and dietary factor is an important factor affecting the occurrence of ECC. Currently, there is limited research on the impact of dietary nutrient intake from Chinese diets on ECC. The purpose of this study is to explore the correlation of dietary nutrients intake with ECC and caries activity (CA) among children aged 3-5 years, and to provide dietary guidance to slow down the occurrence and development of ECC. METHODS: A cross-sectional study was conducted in 2022. A total of 155 children were divided into three groups: caries-free group, ECC group and Severe early childhood caries (SECC) group according to the caries statues. And according to the caries activity test (CAT) value, they were also divided into three group: low CA group (L-CA), middle CA group (M-CA) and high CA group (H-CA). The 24-hour dietary intake information was collected by mobile phone application (APP). The intake of children's daily dietary nutrients were calculated referring to "China Food Composition Tables". RESULTS: In this study, 17, 39,and 99 children were diagnosed with caries-free, ECC, and SECC. There were 33, 36, and 86 children diagnosed with L-CA, M-CA, and H-CA. The risk of ECC was increased with the intake of cholesterol(OR = 1.005) and magnesium (OR = 1.026) and decreased with the intake of iron (OR = 0.770). The risk of SECC was increased with the intake of cholesterol (OR = 1.003). The risk of high CA was increased with the intake of cholesterol (OR = 1.002). The combined application of dietary total calories, carbohydrate, cholesterol, sodium, magnesium and selenium in the diagnosis of ECC had an area under ROC curve of 0.741. CONCLUSIONS: The increased dietary cholesterol intake may be a common risk factor for ECC and high CA in children aged 3-5. The combined application of dietary intake of total calories, carbohydrate, cholesterol, sodium, magnesium and selenium has a higher predictive value for the occurrence of ECC.

Application of machine learning approaches to predict ammonium nitrogen transport in different soil types and evaluate the contribution of control factors.

The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH4+-N) transport in soil and increasing the fixation of NH4+-N after N application are effective methods for improving N use efficiency. However, the main factors are not easily identified because of the complicated transport and retardation factors in different soils. This study employed machine learning (ML) to identify the main influencing factors that contribute to the retardation factor (Rf) of NH4+-N in soil. First, NH4+-N transport in the soil was investigated using column experiments and a transport model. The Rf (1.29 - 17.42) was calculated and used as a proxy for the efficacy of NH4+-N transport. Second, the physicochemical parameters of the soil were determined and screened using lasso and ridge regressions as inputs for the ML model. Third, six machine learning models were evaluated: Adaptive Boosting, Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting Regression, Multilayer Perceptron, and Support Vector Regression. The optimal ML model of the XGB model with a low mean absolute error (0.81), mean squared error (0.50), and high test r2 (0.97) was obtained by random sampling and five-fold cross-validation. Finally, SHapely Additive exPlanations, entropy-based feature importance, and permutation characteristic importance were used for global interpretation. The cation exchange capacity (CEC), total organic carbon (TOC), and Kaolin had the greatest effects on NH4+-N transport in the soil. The accumulated local effect offered a fundamental insight: When CEC > 6 cmol+ kg-1, and TOC > 40 g kg-1, the maximum resistance to NH4+-N transport within the soil was observed. This study provides a novel approach for predicting the impact of the soil environment on NH4+-N transport and guiding the establishment of an early-warning system of nutrient loss.

The association between preoperative serum cholinesterase and all-cause mortality in geriatric patients with hip fractures: a cohort study of 2387 patients.

OBJECTIVE: This study is to evaluate the association between preoperative cholinesterase levels and all-cause mortality in geriatric hip fractures. METHODS: Elderly patients with hip fractures were screened between Jan 2015 and Sep 2019. Demographic and clinical characteristics of patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between preoperative cholinesterase levels and mortality in these patients. Analyses were performed using EmpowerStats and the R software. RESULTS: Two thousand three hundred eighty-seven patients were included in this study. The mean follow-up period was 37.64 months. Seven hundred eighty-seven (33.0%) patients died due to all-cause mortality. Preoperative cholinesterase levels were 5910 ± 1700 U/L. Linear multivariate Cox regression models showed that preoperative cholinesterase level was associated with mortality (HR = 0.83, 95% CI: 0.78-0.88), P < 0.0001) for every 1000 U/L. However, the linear association was unstable, and nonlinearity was identified. A cholinesterase concentration of 5940 U/L was an inflection point. When preoperative cholinesterase level < 5940 U/L, the mortality decreased by 28% for every 1000 U/L increase in cholinesterase (HR = 0.72, 95%CI: 0.66-0.79, P < 0.0001). When cholinesterase was > 5940 U/L, the mortality was no longer decreased with the rise of cholinesterase (HR = 1.01, 95%CI: 0.91-1.11, P = 0.9157). We found the nonlinear association was very stable in the propensity score-matching sensitive analysis. CONCLUSIONS: Preoperative cholinesterase levels were nonlinearly associated with mortality in elderly hip fractures, and cholinesterase was a risk indicator of all-cause mortality. TRIAL REGISTRATION: This study is registered on the website of the Chinese Clinical Trial Registry (ChiCTR: ChiCTR2200057323) (08/03/2022).

Interface States in Gate Stack of Carbon Nanotube Array Transistors.

A deep understanding of the interface states in metal-oxide-semiconductor (MOS) structures is the premise of improving the gate stack quality, which sets the foundation for building field-effect transistors (FETs) with high performance and high reliability. Although MOSFETs built on aligned semiconducting carbon nanotube (A-CNT) arrays have been considered ideal energy-efficient successors to commercial silicon (Si) transistors, research on the interface states of A-CNT MOS devices, let alone their optimization, is lacking. Here, we fabricate MOS capacitors based on an A-CNT array with a well-designed layout and accurately measure the capacitance-voltage and conductance-voltage (C-V and G-V) data. Then, the gate electrostatics and the physical origins of interface states are systematically analyzed and revealed. In particular, targeted improvement of gate dielectric growth in the A-CNT MOS device contributes to suppressing the interface state density (Dit) to 6.1 × 1011 cm-2 eV-1, which is a record for CNT- or low-dimensional semiconductors-based MOSFETs, boosting a record transconductance (gm) of 2.42 mS/µm and an on-off ratio of 105. Further decreasing Dit below 1 × 1011 cm-2 eV-1 is necessary for A-CNT MOSFETs to achieve the expected high energy efficiency.

Implementation of excellent spin-filtering effect in half-metallic electrode-based single-molecule optoelectronic devices.

The application of half-metallic materials in single-molecule optoelectronic devices opens a promising way in advancing device performance and functionality, thus addressing a research question of significance. Here we propose a series of single-molecule devices with half-metallic FeN4-doped armchair graphene nanoribbon as electrodes and metalloporphyrin (MPr) molecules as photoresponsive materials for photon harvesting, which are driven by photogalvanic effects (PGEs). Through the quantum transport simulations, we systematically investigated the spin-polarized photocurrents under the linearly polarized light illumination in these devices. Since the exclusive opening only exists in the spin-up channel of the half-metallic nanoribbons, these devices can generate a large photocurrent in the spin-up direction whereas suppressing the spin-down photocurrent. Consequently, they exhibit an effective spin-filtering effect at numerous photon energies. Our study unveils the excellent spin-filtering effect achieved in single-molecule optoelectronic devices with half-metallic electrodes, showing instructive significance for the future design of new optoelectronic devices.

Thermally assisted optical processes in InP/ZnS quantum dots.

The utilization of InP-based biocompatible quantum dots (QDs) necessitates a comprehensive understanding of the structure-dependent characteristics influencing their optical behavior. The optimization of core/shell QDs for practical applications is of particular interest due to their reduced toxicity, enhanced photostability, and improved luminescence efficiency. This optimization involves analyzing thermally activated processes involving exciton and defect-related energy levels. This study investigates water-soluble colloidal InP/ZnS QDs with varying shell thicknesses and stabilizing coatings using temperature-dependent optical absorption (OA) and photoluminescence (PL). Our results indicate that all samples experience temperature-induced shifts in exciton absorption and luminescence peaks due to interactions with acoustic phonons. Despite the wide size distribution of nanocrystals, the halfwidth of the bands remains constant. We observe a temperature-dependent Stokes shift in InP/ZnS QDs, revealing the fine structure of exciton states across different configurations. Furthermore, our findings demonstrate common mechanisms underlying PL thermal quenching in these QDs, regardless of the shell thickness or coating type. Specifically, defect-related emissions arise from localized energy levels at the core/shell interface. At the same time, exciton PL quenching primarily occurs through thermally activated electron migration from the InP core to the ZnS shell. Overall, our study highlights the potential for tailoring the temperature response of InP/ZnS QDs by adjusting shell thickness, offering opportunities to optimize their performance for specific applications.

circRNA-PTPN4 mediated regulation of FOXO3 and ZO-1 expression: implications for blood-brain barrier integrity and cognitive function in uremic encephalopathy.

Uremic encephalopathy (UE) poses a significant challenge in neurology, leading to the need to investigate the involvement of non-coding RNA (ncRNA) in its development. This study employed ncRNA-seq and RNA-seq approaches to identify fundamental ncRNAs, specifically circRNA and miRNA, in the pathogenesis of UE using a mouse model. In vitro and in vivo experiments were conducted to explore the circRNA-PTPN4/miR-301a-3p/FOXO3 axis and its effects on blood-brain barrier (BBB) function and cognitive abilities. The research revealed that circRNA-PTPN4 binds to and inhibits miR-301a-3p, leading to an increase in FOXO3 expression. This upregulation results in alterations in the transcriptional regulation of ZO-1, affecting the permeability of human brain microvascular endothelial cells (HBMECs). The axis also influences the growth, proliferation, and migration of HBMECs. Mice with UE exhibited cognitive deficits, which were reversed by overexpression of circRNA-PTPN4, whereas silencing FOXO3 exacerbated these deficits. Furthermore, the uremic mice showed neuronal loss, inflammation, and dysfunction in the BBB, with the expression of circRNA-PTPN4 demonstrating therapeutic effects. In conclusion, circRNA-PTPN4 plays a role in promoting FOXO3 expression by sequestering miR-301a-3p, ultimately leading to the upregulation of ZO-1 expression and restoration of BBB function in mice with UE. This process contributes to the restoration of cognitive abilities.

Nonreciprocal and Nonvolatile Electric-Field Switching of Magnetism in van der Waals Heterostructure Multiferroics.

Multiferroic materials provide robust and efficient routes for the control of magnetism by electric fields, which have been diligently sought after for a long time. Construction of two-dimensional (2D) vdW multiferroics is a more exciting endeavor. To date, the nonvolatile manipulation of magnetism through ferroelectric polarization still remains challenging in a 2D vdW heterostructure multiferroic. Here, we report a van der Waals (vdW) heterostructure multiferroic comprising the atomically thin layered antiferromagnet (AFM) CrI3 and ferroelectric (FE) α-In2Se3. We demonstrate anomalously nonreciprocal and nonvolatile electric-field control of magnetization by ferroelectric polarization. The nonreciprocal electric control originates from an intriguing antisymmetric enhancement of interlayer ferromagnetic coupling in the opposite ferroelectric polarization configurations of α-In2Se3. Our work provides numerous possibilities for creating diverse heterostructure multiferroics at the limit of a few atomic layers for multistage magnetic memories and brain-inspired in-memory computing.

VIRMA promotes the progression of head and neck squamous cell carcinoma by regulating UBR5 mRNA and m6A levels.

Head and neck squamous cell carcinoma (HNSCC) is a globally prevalent and lethal cancer form which precise mechanisms remain incompletely understood. Increasing evidence suggests that N6-methyladenosine (m6A) plays a crucial role in cancer progression. This study aimed to explore the biological function of m6A modification and vir-like m6A methyltransferase associated (VIRMA) in HNSCC. We conducted an analysis of VIRMA expression in HNSCC cells using The Cancer Genome Atlas (TCGA) database and employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting to assess its expression levels in HNSCC cell lines. Additionally, m6A levels in HNSCC cells were quantified, and the correlation between VIRMA expression levels and the clinical and pathological features of other genes was analyzed. Upon knocking down VIRMA levels, we assessed HNSCC cell proliferation, migration, and invasion and validated downstream genes using RT-qPCR and western blot. Our findings suggested that VIRMA, as an m6A-related regulator, may significantly influence HNSCC progression by regulating ubiquitin protein ligase E3 component N-recognin 5 (UBR5) through m6A modification. Therefore, VIRMA may serve as a prognostic biomarker.

Molecular-Scale Visualization of Steric Effects of Ligand Binding to Reconstructed Au(111) Surfaces.

Direct imaging of single molecules at nanostructured interfaces is a grand challenge with potential to enable new, precise material architectures and technologies. Of particular interest are the structural morphology and spectroscopic signatures of the adsorbed molecule, where modern probes are only now being developed with the necessary spatial and energetic resolution to provide detailed information at the molecule-surface interface. Here, we directly characterize the adsorption of individual m-terphenyl isocyanide ligands on a reconstructed Au(111) surface through scanning tunneling microscopy and inelastic electron tunneling spectroscopy. The site-dependent steric pressure of the various surface features alters the vibrational fingerprints of the m-terphenyl isocyanides, which are characterized with single-molecule precision through joint experimental and theoretical approaches. This study provides molecular-level insights into the steric-pressure-enabled surface binding selectivity as well as its effect on the chemical properties of individual surface-binding ligands.

Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection.

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framework based on context recovery and knowledge retrieval, where the two streams can complement each other. For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame. Furthermore, we propose a maximum local error mechanism to alleviate the problem of large recovery errors caused by complex foreground objects. For the knowledge retrieval stream, we propose an improved learnable locality-sensitive hashing, which optimizes hash functions via a Siamese network and a mutual difference loss. The knowledge about normality is encoded and stored in hash tables, and the distance between the testing event and the knowledge representation is used to reveal the probability of anomaly. Finally, we fuse the anomaly scores from the two streams to detect anomalies. Extensive experiments demonstrate the effectiveness and complementarity of the two streams, whereby the proposed two-stream framework achieves state-of-the-art performance on ShanghaiTech, Avenue and Corridor datasets among the methods without object detection. Even if compared with the methods using object detection, our method reaches competitive or better performance on the ShanghaiTech, Avenue, and Ped2 datasets.

Establishment of transgenic fluorescent mice for labeling synapses and screening synaptogenic adhesion molecules.

Synapse is the fundamental structure for neurons to transmit information between cells. The proper synapse formation is crucial for developing neural circuits and cognitive functions of the brain. The aberrant synapse formation has been proved to cause many neurological disorders, including autism spectrum disorders and intellectual disability. Synaptic cell adhesion molecules (CAMs) are thought to play a major role in achieving mechanistic cell-cell recognition and initiating synapse formation via trans-synaptic interactions. Due to the diversity of synapses in different brain areas, circuits and neurons, although many synaptic CAMs, such as Neurexins (NRXNs), Neuroligins (NLGNs), Synaptic cell adhesion molecules (SynCAMs), Leucine-rich-repeat transmembrane neuronal proteins (LRRTMs), and SLIT and NTRK-like protein (SLITRKs) have been identified as synaptogenic molecules, how these molecules determine specific synapse formation and whether other molecules driving synapse formation remain undiscovered are unclear. Here, to provide a tool for synapse labeling and synaptic CAMs screening by artificial synapse formation (ASF) assay, we generated synaptotagmin-1-tdTomato (Syt1-tdTomato) transgenic mice by inserting the tdTomato-fused synaptotagmin-1 coding sequence into the genome of C57BL/6J mice. In the brain of Syt1-tdTomato transgenic mice, the tdTomato-fused synaptotagmin-1 (SYT1-tdTomato) signals were widely observed in different areas and overlapped with synapsin-1, a widely-used synaptic marker. In the olfactory bulb, the SYT1-tdTomato signals are highly enriched in the glomerulus. In the cultured hippocampal neurons, the SYT1-tdTomato signals showed colocalization with several synaptic markers. Compared to the wild-type (WT) mouse neurons, cultured hippocampal neurons from Syt1-tdTomato transgenic mice presented normal synaptic neurotransmission. In ASF assays, neurons from Syt1-tdTomato transgenic mice could form synaptic connections with HEK293T cells expressing NLGN2, LRRTM2, and SLITRK2 without immunostaining. Therefore, our work suggested that the Syt1-tdTomato transgenic mice with the ability to label synapses by tdTomato, and it will be a convenient tool for screening synaptogenic molecules.

pH-Responsive Pesticide-Loaded Hollow Mesoporous Silica Nanoparticles with ZnO Quantum Dots as a Gatekeeper for Control of Rice Blast Disease.

Nanotechnology-enabled pesticide delivery systems have been widely studied and show great prospects in modern agriculture. Nanodelivery systems not only achieve the controlled release of agrochemicals but also possess many unique characteristics. This study presents the development of a pH-responsive pesticide nanoformulation utilizing hollow mesoporous silica nanoparticles (HMSNs) as a nanocarrier. The nanocarrier was loaded with the photosensitive pesticide prochloraz (Pro) and then combined with ZnO quantum dots (ZnO QDs) through electrostatic interactions. ZnO QDs serve as both the pH-responsive gatekeeper and the enhancer of the pesticide. The results demonstrate that the prepared nanopesticide exhibits high loading efficiency (24.96%) for Pro. Compared with Pro technical, the degradation rate of Pro loaded in HMSNs@Pro@ZnO QDs was reduced by 26.4% after 24 h ultraviolet (UV) exposure, indicating clearly improved photostability. In a weak acidic environment (pH 5.0), the accumulated release of the nanopesticide after 48 h was 2.67-fold higher than that in a neutral environment. This indicates the excellent pH-responsive characteristic of the nanopesticide. The tracking experiments revealed that HMSNs can be absorbed by rice leaves and subsequently transported to other tissues, indicating their potential for effective systemic distribution and targeted delivery. Furthermore, the bioactivity assays confirmed the fungicidal efficacy of the nanopesticide against rice blast disease. Therefore, the constructed nanopesticide holds great prospect in nanoenabled agriculture, offering a novel strategy to enhance pesticide utilization.

Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model.

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://github.com/Roc-Ng/VAR.