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With the development of automobile technology, intelligent vehicle and automatic driving technology will make due contributions to reducing traffic accidents. This paper aims to improve the dynamic identification and tracking technology in the current intelligent vehicle and automatic driving. First, it is improved based on the MobileNet V2 backbone network, and then a new tracking model framework is designed combining with the SiamRPN single target tracker. Secondly, it integrates space-time tracking clues to improve the stability and robustness of the algorithm. Finally, it constructs a pedestrian dynamic identification algorithm based on the dynamic pedestrian factors in the driving process. Through the training of data sets and video tracking experiments, the performance of the algorithm in this paper is proved quantitatively and qualitatively. &nbsp

The strain variations in Elm earlywood and latewood during drying were studied with digital image correlation method. Also, the moisture contents of different water states were quantified based on time-domain nuclear magnetic resonance technique to reveal the strain-water relationships. Although the shrinkage strains gradually increased with increasing drying time, there was a significant difference in the strain distribution of the earlywood and latewood. Owing to latewood large stress, the earlywood was pulled by it to shrink together, and the earlywood maximum shrinkage appeared in the early/latewood junction, while the maximum shrinkage of latewood happened in its middle part. Wood shrinkage strain increased fast in the early-drying stage, mainly influenced by moisture content variation; in the mid-drying stage, the shrinkage increased slowly due to the synergistic effect of free and bound water migration; in the late-drying stage, the bound water variation led to a mild shrinkage.

The Kinh people of China have historically migrated from Tushan and other areas of Vietnam since the 16th century. They are now settled in Fangchenggang City in the Beibu Gulf region of Guangxi, China. The local Kinh people have lived by fishing and have a rich fishing culture. Accordingly, the Kinh people of China have a long history of using traditional marine animal drugs for their daily medicinal needs. However, with the advent of modern medicine, there is a risk of losing the valuable traditional knowledge of marine animal drugs. Thus, this study aimed to document the types of marine animal drugs and related traditional knowledge of the Kinh people and explore the sustainability of their access to marine animal drugs. Ethnobiological methods, including snowball sampling and semi-structured interviews are used to collect information about the animal drugs used locally during the study. Through field investigations in the “Three Islands of Kinh,” we collected ethnozoological data on 61 marine animal drugs belonging to 52 families across six animal phyla. Chordata and Mollusca are the most represented phyla, while Arthropoda, Echinodermata, Sipuncula, and Cnidaria are less represented. According to the analysis, animal meat is the most frequent medicinal part; the Kinh prefer decoction or making soup, and internal administration is the most frequent use mode. Our statistical analysis revealed that nourishing was the most common purpose for the recorded marine animal drugs in the study area. Our study found that 6 documented species are listed as threatened on the IUCN Red List but receive little targeted protection. The article provides recommendations for protecting traditional knowledge, promoting sustainable development of marine resources, and conserving endangered species.

In this paper, the multi-user measurement has been done at 28 GHz with the handset antenna array for purpose of studying the user blockage. The measurement has been done in the anechoic chamber with one active and 3 passive users. The passive users are oriented in 6 different setups and the base station probe horn antenna has two different angles in order to replicate the most critical real life scenarios. It has been found that presence of multiple users will increase the overall handset antenna gain by adding multiple reflections, scattering and diffractions. In order to increase the power in the shadow region when the user is pointing away from the base station an extra one to three different scan angles on the handset can be added in order to gain 10 dB.

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive neuromodulation technique. Several studies have reported the effectiveness of taVNS in patients with disorders of consciousness (DOC); however, differences in the modulation paradigm have led to inconsistent treatment outcomes.Methods/designThis prospective exploratory trial will include 15 patients with a minimally conscious state (MCS) recruited according to the coma recovery scale-revised (CRS-R). Each patient will receive 5 different frequencies of taVNS (1, 10, 25, 50, and 100 Hz); sham stimulation will be used as a blank control. The order of stimulation will be randomized, and the patients’ CRS-R scores and resting electroencephalography (EEG) before and after stimulation will be recorded.DiscussionThe overall study of taVNS used in treating patients with DOC is still in the preliminary stage of exploration. Through this experiment, we aim to explore the optimal stimulation frequency parameters of taVNS for the treatment of DOC patients. Furthermore, we expect to achieve a stable improvement of consciousness in DOC patients by continuously optimizing the neuromodulation paradigm of taVNS for the treatment of DOC patients.Clinical trial registrationhttps://www.chictr.org.cn/index.aspx, identifier ChiCTR 2200063828.

Spiral ganglion neurons (SGNs) transmit sound signals received by hair cells to the auditory center to produce hearing. The quantity and function are important for maintaining normal hearing function. Limited by the regenerative capacity, SGNs are unable to regenerate spontaneously after injury. Various neurotrophic factors play an important role in the regeneration process. Neuritin, a neurite outgrowth factor, is a neurotrophic factor closely related to neural plasticity. In this study, we used bioinformatics analysis to show that neuritin was negatively correlated with cochlear damage. Then, we aimed to establish a cochlear spiral ganglion-specific sensorineural deafness model in gerbils using ouabain and determine the effects of exogenous neuritin protein in protecting damaged cochlear SGNs and repairing damaged auditory nerve function. The provides a new research strategy and scientific basis for the prevention and treatment of sensorineural deafness caused by the loss of SGNs. We found that neuritin is expressed throughout the development of the gerbil cochlea, primarily in the SGNs and Corti regions. The expression of neuritin was negatively correlated with the sensorineural deafness model. In vitro and in vivo revealed that neuritin significantly maintained the number and arrangement of SGNs and nerve fibers in the damaged cochlea and effectively protected the high-frequency listening function of gerbils.

Medical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.

Epidermal growth factor (EGF) has many important biological functions. It plays an important role in regulating the growth, survival, migration, apoptosis, proliferation, and differentiation of intestinal tissues and cells. However, until now, the effect of inflammation on the biological activity of EGF in intestinal cells or tissues is still unclear. For this reason, in the current research, we have conducted a detailed study on this issue. Using the rat small intestinal crypt epithelial cell line (IEC6) was used as an in vitro model, and Confocal laser scanning microscope (CLSM), Flow cytometry (FCM), Indirect immunofluorescence assay (IFA), Western-blotting (WB), and Quantitative real-time RT-PCR (QRT-PCR) methods were used to explore the effects of inflammation on EGF/EGFR biological activity and signal transduction profiles. We found that the EGF/EGFR nuclear signal almost disappeared in the inflammatory state, and the phosphorylation levels of EGFR, AKT, and STAT3 were all significantly down-regulated. In addition, we also studied the effect of β-carotene on the biological activity of EGF, and found that when cells were pretreated with β-carotene, the cellular behavior, biological activity, and nuclear signal of EGF/EGFR under inflammation stimulation were partially restored. In summary, the current study shows that inflammation can disrupt EGF/EGFR-mediated signaling in IEC6 cells, suggesting that inflammation negatively regulates the biological activity of EGF/EGFR. Furthermore, we found that β-carotene not only attenuated lipopolysaccharide (LPS)-induced inflammation but also partially restored the biological activity of EGF in IEC6 cells, laying a solid foundation for studying the biological functions of EGF and β-carotene.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression. METHODS: We obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated. RESULTS: A nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression. CONCLUSIONS: The novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.

PurposeTo evaluate the correlation between microvascular density (MVD) and intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) parameters and the effect of glycolytic flux after transarterial chemoembolization (TACE) in a rabbit VX2 liver tumor.Materials and methodsVX2 liver tumor allografts in 15 New Zealand white rabbits were treated with sterile saline (control group, n = 5) or lipiodol-doxorubicin emulsion (experimental group, n = 10). MRI was performed 2 weeks after the procedure to evaluate IVIM parameters, including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF). All animal samples were taken of the tumor and surrounding liver. Immunostaining for CD31, CD34, CD105, and VEGF was used to evaluate MVD. The protein expression of Glut4, HK2, PKM2, LDHA, and MCT1 was determined using western blotting. Pearson correlation tests were used to analyze the relationship between MVD and IVIM parameters.ResultsD* value in the peritumoral region was negatively correlated with CD34 (r = –0.71, P = 0.01). PF value positively correlated with CD34 (r = 0.68, P = 0.015), CD105 (r = 0.76, P = 0.004) and VEGF (r = 0.72, P = 0.008) in the peritumoral region. Glut4, HK2, PKM2, and MCT1 in the peritumoral regions were higher in the experimental group than in the control group (all P < 0.05).ConclusionIVIM parameters were correlated with MVD in the intratumoral and peritumoral regions after TACE in a rabbit liver tumor model. The angiogenesis reflected by MVD may be related to changes of glycolytic flux.

Shirashi's double door laminoplasty method was a popular decompression procedure for cervical myelopathy. In this paper, we introduced a modified double door laminoplasty based on Shirashi's method with preliminary results. This study retrospectively analyzed 22 patients who underwent modified double door laminoplasty. During procedure, a single segment of the unilateral lamina was separated from the cervical semispinalis muscle and the multifidus muscle space for the preparation of lamina groove. A self-developed mini titanium plate was used to fix the inner side of the spinous process to complete the fixation after open-door process. The VAS, JOA scores and QoL scale were recorded for pain assessment, neurological and functional recovery. The overall curvature and range of motion of C2–C7 were measured with x-ray images. Changes in sagittal diameter of spinal canal were measured by CT scans. MRI was used to measure the cross-sectional area of cervical paravertebral muscles. All 22 patients successfully recovered with this procedure. The mean operation time, blood loss and follow-up durations were 117 ± 25 min, 149 ± 32 ml and 16.1 ± 3.6 months respectively. The preoperative, 3-month postoperative and 12-month postoperative JOA scores were 9.35 ± 3.25, 13.74 ± 4.86 and 15.73 ± 5.19 respectively. with improvement rates of 57.4% and 83.4%. Mean VAS scores before, 3 months after and 12 months after surgery were 1.81 ± 0.79, 2.82 ± 1.56 and 2.18 ± 1.34 respectively. The C2–7 lordotic angle and overall range of motion shows no statistical difference preoperatively and 12 post-surgery. The average sagittal diameter of the cervical spinal canal was enlarged from 9.15 ± 1.55 mm to 14.25 ± 1.46 mm. The average area of cervical paravertebral volume measured preoperatively and 3 months post operation was 84% of pre-operative value respectively. This value was improved to 93% of the preoperative value at 12 months post-surgery. This paper introduced initial experience on a modified posterior cervical double-door laminoplasty that was based on Shirashi's method, featuring creating bilateral laminar grooves on both sides and fixing central gap with self-developed mini plates. This procedure prevented obvious axial symptoms and improved patients' quality of life, which provided a baseline for further research with larger cohorts.

BackgroundEarly identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.MethodsThis retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.ResultsTen features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.ConclusionsThe CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

Symmetry breaking in quantum materials is of great importance and leads to novel nonreciprocal charge transport. The topological insulator system provides a unique platform to study nonreciprocal charge transport due to the exotic surface state. But it is typically small in magnitude because the contributions from the top and bottom surface of topological insulator are usually opposite. Here, we report the observation of giant nonreciprocal charge transport mediated by the quantum Hall state in intrinsic topological insulator Sn-Bi1.1Sb0.9Te2S devices, which is attributed to the coexistence of quantum Hall states and Dirac surface states. A giant nonreciprocal coefficient of up to 2.26*10^5 A^-1 is found, because only a single surface of topological insulator contributes to the nonreciprocal charge transport. Our work not only reveals the intrinsic properties of nonreciprocal charge transport in topological insulators, but also paves the way for future electronic devices.

The search for new physics beyond the Standard Model is one of the central problems of current high energy physics interest. As the luminosities of current and near-future colliders continue to increase, the search for new physics has increased the requirements for processing large amounts of data. Mean while, quantum computing which is rapidly evolving, has great potential to become a powerful tool to help search for new physics signals. Since the k-means algorithm is known to be able to be speeded up with the help of quantum computing, we investigate and propose an event selection strategy based on k-means algorithm to search for new physics signals. Taking the case of tri-photon processes at the muon colliders as an example, the event selection strategy is shown to be effective in helping to search for the signal of dimension-8 operators contributing to anomalous quartic gauge couplings., Comment: 24 pages, 6 figures

Existing fake audio detection systems perform well in in-domain testing, but still face many challenges in out-of-domain testing. This is due to the mismatch between the training and test data, as well as the poor generalizability of features extracted from limited views. To address this, we propose multi-view features for fake audio detection, which aim to capture more generalized features from prosodic, pronunciation, and wav2vec dimensions. Specifically, the phoneme duration features are extracted from a pre-trained model based on a large amount of speech data. For the pronunciation features, a Conformer-based phoneme recognition model is first trained, keeping the acoustic encoder part as a deeply embedded feature extractor. Furthermore, the prosodic and pronunciation features are fused with wav2vec features based on an attention mechanism to improve the generalization of fake audio detection models. Results show that the proposed approach achieves significant performance gains in several cross-dataset experiments., Comment: Interspeech2023

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure.} FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11% on CIFAR-10 and CINIC-10 datasets.

Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerful cross-modal contrastive learning methods for fake news detection has not been well exploited. Besides, how to aggregate features from different modalities to boost the performance of the decision-making process is still an open question. To address that, we propose COOLANT, a cross-modal contrastive learning framework for multimodal fake news detection, aiming to achieve more accurate image-text alignment. To further improve the alignment precision, we leverage an auxiliary task to soften the loss term of negative samples during the contrast process. A cross-modal fusion module is developed to learn the cross-modality correlations. An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations. Finally, we evaluate the COOLANT and conduct a comparative study on two widely used datasets, Twitter and Weibo. The experimental results demonstrate that our COOLANT outperforms previous approaches by a large margin and achieves new state-of-the-art results on the two datasets., Comment: 9 pages, 3 figures

Communication success relies heavily on reading participants' reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client's progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors' intervention strategies rather than their clients' reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors' strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study shows how clients react to counselors' strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients' states., Comment: Accept to ACL 2023, oral. For code and data, see https://github.com/dll-wu/Client-React

Supplemental material, sj-pdf-6-jcb-10.1177_0271678X231156981 for Selenium supplementation provides potent neuroprotection following cerebral ischemia in mice by Zhan Zhuo, Huimei Wang, Shuai Zhang, Perry F Bartlett, Tara L Walker and Sheng-Tao Hou in Journal of Cerebral Blood Flow & Metabolism

Although recent approaches aiming for video instance segmentation have achieved promising results, it is still difficult to employ those approaches for real-world applications on mobile devices, which mainly suffer from (1) heavy computation and memory cost and (2) complicated heuristics for tracking objects. To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices. Firstly, MobileInst adopts a mobile vision transformer to extract multi-level semantic features and presents an efficient query-based dual-transformer instance decoder for mask kernels and a semantic-enhanced mask decoder to generate instance segmentation per frame. Secondly, MobileInst exploits simple yet effective kernel reuse and kernel association to track objects for video instance segmentation. Further, we propose temporal query passing to enhance the tracking ability for kernels. We conduct experiments on COCO and YouTube-VIS datasets to demonstrate the superiority of MobileInst and evaluate the inference latency on a mobile CPU core of Qualcomm Snapdragon-778G, without other methods of acceleration. On the COCO dataset, MobileInst achieves 30.5 mask AP and 176 ms on the mobile CPU, which reduces the latency by 50% compared to the previous SOTA. For video instance segmentation, MobileInst achieves 35.0 AP on YouTube-VIS 2019 and 30.1 AP on YouTube-VIS 2021. Code will be available to facilitate real-world applications and future research., Comment: Preprint. 12 pages, 7 figures

Additional file 2: Table S1. Independent genome-wide significant lead SNPs associated with pulmonary embolism (PE) in the discovery stage and replication stage. Table S2. The allele frequency of identified loci in 1000Genomes. Table S3. Replication of associations for the known loci in our cohort. Table S4. Association results for genes that were significant in FUMA gene-based analysis. Table S5. Polygenic risk score variants. Table S6. Polygenic risk score (PRS) quantile and odds ratio (OR).

Additional file 1: Fig. S1. Regional association plot at genome-wide association study (GWAS) genome-wide significant loci. Fig. S2. Principal component analysis (PCA) plot of Han Chinese PE cohort. Fig. S3. FUMA Manhattan plot and QQ plot of genome-wide association study (GWAS) meta-analysis. Fig. S4. The transfection efficiency of cellular experiments for FABP2. Fig. S5. Low-density lipoprotein cholesterol (LDL-C) levels of patients with different genotypes of rs1799883. Fig. S6. Forest plot for the association of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) with PE. Fig. S7. Ancestry-specific polygenic risk score (PRS) ROC plot. Fig. S8. Performance of different PRSVTE in the CURES testing set.

Despite the recent development in machine learning, most learning systems are still under the concept of "black box", where the performance cannot be understood and derived. With the rise of safety and privacy concerns in public, designing an explainable learning system has become a new trend in machine learning. In general, many machine learning problems are formulated as minimizing (or maximizing) some loss function. Since real data are most likely generated from non-linear models, the loss function is non-convex in general. Unlike the convex optimization problem, gradient descent algorithms will be trapped in spurious local minima in solving non-convex optimization. Therefore, it is challenging to provide explainable algorithms when studying non-convex optimization problems. In this thesis, two popular non-convex problems are studied: (1) low-rank matrix completion and (2) neural network learning., Comment: PhD thesis

supporting information for the publication

Additional file 1: Table S1. Radiomics feature selection results

Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include \textit{graph sparsification} that samples a subgraph to reduce the amount of data aggregation and \textit{model sparsification} that prunes the neural network to reduce the number of trainable weights. Despite the empirical successes in reducing the training cost while maintaining the test accuracy, the theoretical generalization analysis of sparse learning for GNNs remains elusive. To the best of our knowledge, this paper provides the first theoretical characterization of joint edge-model sparse learning from the perspective of sample complexity and convergence rate in achieving zero generalization error. It proves analytically that both sampling important nodes and pruning neurons with the lowest-magnitude can reduce the sample complexity and improve convergence without compromising the test accuracy. Although the analysis is centered on two-layer GNNs with structural constraints on data, the insights are applicable to more general setups and justified by both synthetic and practical citation datasets.

The bulk photovoltaic effect (BPVE) is a second-order optical process in noncentrosymmetric materials that converts the light into DC currents. BPVE is classified into shift current and injection current according to the generation mechanisms, whose dependence on the polarization of light is sensitive to the spatial and time-reversal symmetry of materials. In this work, we present a comprehensive study on the BPVE response of $EuSn_2As_2$ with different magnetic structures through symmetry analysis and first-principles calculation. We demonstrate that the interlayer antiferromagnetic (AFM) $EuSn_2As_2$ of even-layer breaks the inversion symmetry and has the second-order optical responses. Moreover, the bilayer AFM $EuSn_2As_2$ not only displays distinct BPVE responses when magnetic moments align in different directions, but also shows symmetry-related responses in two phases which have mutually perpendicular in-plane magnetic moments. Due to the dependence of BPVE responses on the polarization of light and magnetic symmetry, these magnetic structures can be distinguished by the circular polarized light with well-designed experiments. Our work demonstrates the feasibility of the BPVE response as a tool to probe the magnetic structure.

Additional file 1: Table S1. Baseline characteristics between excluded and included participants. Table S2. Sensitivity analysis for the association between the METS-IR and MACE. Table S3. Subgroup and interaction between the METS-IR (Per SD) and non-fatal MI and across various subgroups.

Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents. As performance demands for vector search systems surge, accelerated hardware offers a promising solution in the post-Moore's Law era. We introduce \textit{FANNS}, an end-to-end and scalable vector search framework on FPGAs. Given a user-provided recall requirement on a dataset and a hardware resource budget, \textit{FANNS} automatically co-designs hardware and algorithm, subsequently generating the corresponding accelerator. The framework also supports scale-out by incorporating a hardware TCP/IP stack in the accelerator. \textit{FANNS} attains up to 23.0$\times$ and 37.2$\times$ speedup compared to FPGA and CPU baselines, respectively, and demonstrates superior scalability to GPUs, achieving 5.5$\times$ and 7.6$\times$ speedup in median and 95\textsuperscript{th} percentile (P95) latency within an eight-accelerator configuration. The remarkable performance of \textit{FANNS} lays a robust groundwork for future FPGA integration in data centers and AI supercomputers., Comment: 11 pages

Supplemental material, sj-pdf-8-jcb-10.1177_0271678X231156981 for Selenium supplementation provides potent neuroprotection following cerebral ischemia in mice by Zhan Zhuo, Huimei Wang, Shuai Zhang, Perry F Bartlett, Tara L Walker and Sheng-Tao Hou in Journal of Cerebral Blood Flow & Metabolism

The current work experimentally studies the complex interaction between underwater explosion (UNDEX) bubbles and a free surface. We aim to reveal the dependence of the associated physics on the key factor, namely, the dimensionless detonation depth $\gamma$ (scaled by the maximum equivalent bubble radius). Four typical bubble behavior patterns are identified with the respective range of $\gamma$: (i) bubble bursting at the free surface, (ii) bubble jetting downward, (iii) neutral collapse of the bubble, and (iv) quasi-free-field motion. By comparison of the jet direction and the migration of the bubble centroid, a critical value of $\gamma$ is vital for ignoring the effects of the free surface on UNDEX bubbles. Good agreements are obtained between the experimental data and the unified theory for bubble dynamics by Zhang et al. Additionally, the dependence of the pressure signals in the flow field on $\gamma$ is investigated. The peak pressure, impulse, and energy dissipation in the UNDEX are investigated.

By applying auxiliary-field quantum Monte Carlo, we calculate the equation of state (EOS) and B1-B2 phase transition of magnesium oxide (MgO) up to 1 TPa. The results agree with available experimental data at low pressures and are used to benchmark the performance of various exchange-correlation functionals in density functional theory calculations. We determine PBEsol is an optimal choice for the exchange-correlation functional and perform extensive phonon and quantum molecular-dynamics calculations to obtain the thermal EOS. Our results provide a preliminary reference for the EOS and B1-B2 phase boundary of MgO from zero up to 10,500 K., Comment: 41 pages (single column), 16 figures, 4 tables

Additional file 1: Figure 1. PPI network and univariate Cox regression analysis. Figure 2. Survival analysis curve of TLR7, Mrc1, CCL19, cd3e, CD1C, CD1e, IL2 in BRCA. Figure 3. Relationship between TLR7, Mrc1, CCL19, cd3e, CD1C, CD1e, IL2 and clinical characteristics of BRCA. Table 1. The primer sequences of CD1C.

This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification, semantic segmentation, and object detection, to boost recognition performances in a zero-shot manner. Empirical evaluation shows that POMP achieves state-of-the-art performances on 21 downstream datasets, e.g., 67.0% average accuracy on 10 classification dataset (+3.1% compared to CoOp) and 84.4 hIoU on open-vocabulary Pascal VOC segmentation (+6.9 compared to ZSSeg)., Comment: Code is available at https://github.com/amazon-science/prompt-pretraining

Supplemental material, sj-pdf-6-jcb-10.1177_0271678X231156981 for Selenium supplementation provides potent neuroprotection following cerebral ischemia in mice by Zhan Zhuo, Huimei Wang, Shuai Zhang, Perry F Bartlett, Tara L Walker and Sheng-Tao Hou in Journal of Cerebral Blood Flow & Metabolism

Exciton-polaritons resulting from the strong exciton-photon interaction stimulate the development of novel coherent light sources with low threshold, long range spatial, and temporal coherence to circumvent the ever increasing energy demands of optical communications. Polaritons from bound states in the continuum (BICs) are promising for Bose-Einstein condensation owing to their infinite quality factors that enlarge photon lifetimes and benefit polariton accumulations. However, BIC polariton condensation remains limited to cryogenic temperatures ascribed to the small exciton binding energies of conventional material platforms. Herein, we demonstrated a room-temperature BIC polaritonic platform based on halide perovskite air-hole photonic crystals, profiting from the non-radiative BIC states and stable excitons of cesium lead bromide. BIC polariton condensation was achieved near the dispersion minimum that generates directional vortex beam emission with long-range coherence. Our work provides a significant basis for the applications of polariton condensates for integrated photonic and topological circuits., Comment: 16 pages,4 figures

Supplemental material, sj-pdf-1-imr-10.1177_03000605231173839 for Epstein–Barr virus-associated poorly differentiated nasopharyngeal adenocarcinoma: a case report and literature review by Shuai Zhang, Xiao-Peng Huang, Hai-Hong Li and Ze-Tan Chen in Journal of International Medical Research

Exploring the hydrophobicity of robust conductors is significant for electronic devices to simultaneously be used in a wet environment and extreme conditions. However, a combination of conductivity, strong mechanical properties, and hydrophobicity in one material is hindered by the inherent features of the materials. A new kind of robust hydrophobic conductor is designed in transition-metal diborides (TMdBs: TiB2, ZrB2, and HfB2) to break through this challenge. The results calculated by density functional theory indicate that high hardness comes from high shear and bulk modulus, which is consistent with experimental results (TiB2, 25.0 GPa; ZrB2, 17.5 GPa; HfB2, 21.5 GPa). The theoretical calculated results reveal that edge sides have a lower surface energy than basal plane (001) in TMdBs. Hence, the edge sides are exposed with a needle-like morphology in TMdBs. Moreover, needle-like surfaces exhibiting hydrophobicity have water contact angles of 132.0° (TiB2), 116.8° (ZrB2), and 114.0° (HfB2). The hydrophobicity arises from a lower surface free energy of edge sides in TMdBs and a rough surface that reduces the contact area of water and a solid. This work develops a new kind of robust functional material in TMdBs.

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations., Proceedings of the VLDB Endowment, 15 (3), ISSN:2150-8097

DAL-1/4.1B is frequently absent in lung cancer tissues, which is significantly related to the occurrence and development of lung cancer. In this research, we found that DAL-1/4.1B affected the uptake of exosomes by lung cancer cells. When the expression of DAL-1/4.1B increased and decreased, the ability of exosome uptake enhanced and attenuated correspondingly. And we found that when cells were treated with different vesicles uptake inhibitors (chlorpromazine, methyl-β-cyclodextrin (MβCD), cytochalasin D, chloroquine and heparin) and heparinase (HSPE), only heparin and HSPE counteracted the uptake enhancement effect caused by DAL-1/4.1B. Therefore, we speculated that DAL-1/4.1B might promote the uptake of exosomes through the heparan sulfate proteoglycans (HSPGs) pathway. After screening the expression of HSPGs and HSPE in H292 cells, the expression of heparan sulfate proteoglycan 2 (HSPG2) increased with overexpression of DAL-1/4.1B and decreased with knockdown of DAL-1/4.1B. Meanwhile, exosome uptake decreased with HSPG2 knockdown in H292 and DAL-1/4.1B-overexpressing H292 cells. Moreover, knockdown of DAL-1/4.1B and HSPG2 in lung cancer A549 cells resulted in a similar decrease in exosome uptake, and the expression of HSPG2 was also decreased with DAL-1/4.1B knockdown. These results indicated that HSPG2 directly affected the uptake of exosomes, while DAL-1/4.1B positively affected the expression of HSPG2. Therefore, DAL-1/4.1B may promote cellular adhesion and inhibit migration in cancer cells.

Hot compression behaviors of CrCoNi medium entropy alloy are were performed on the Gleeble-3800 thermal simulator in the strain rate range from 0.01 to 10 s−1 and the temperature range from 800 to 1100 °C. According to the true stress-true strain curves, the true stress decreases with the decreasing of strain rate and the increasing of deformation temperature. The experimental parameters were fitted, and a modified model based on Arrhenius hyperbolic sine was established. Among them, material constants such as, n, A, and Q are functions of strain. Meanwhile, the processing map of CrCoNi medium entropy alloy under different strain conditions are established. Importantly, the optimal domain of apparent flow stability can be determined as the deformation temperature from 900 to 1100 °C and the strain rate from 0.01 to 10 s−1. The results indicate that the stability domain of the CrCoN medium entropy alloy has a wider range, and the strain has little effect on its processing performance.

In this article, an autotransformer coil for domestic IH adopting single-ended resonant inverter (SE-IH) is proposed. The proposed SE-IH can simultaneously meet the impedance requirements of ZVS and high induction efficiency at a higher frequency, resulting in a higher efficiency, increased output power and improved compatibility with nonferromagnetic vessels. Based on the proposed accurate wide-frequency impedance model of the coil-vessel system, the induction efficiency of the proposed SE-IH is analyzed, as well as the impedance condition for ZVS. The selection of the operating frequency and the design of the proposed coil for induction efficiency optimization are then given. Finally, a prototype of the proposed domestic SE-IH is fabricated, achieving 2269-W output power with 99.26% induction efficiency for GB45 steel vessel and 932-W output power with 90.46% induction efficiency for aluminum vessel.

BackgroundApart from myasthenia gravis (MG), thymoma is associated with a wide spectrum of autoimmune paraneoplastic syndromes (PNSs). Here, we report on a rare case presenting with four different PNSs, namely, MG, membranous nephropathy, cutaneous amyloidosis, and Morvan’s syndrome associated with thymoma.Case presentationA middle-aged man was frequently hospitalized because of nephrotic syndrome (stage I membranous nephropathy), cutaneous amyloidosis, and MG with acetylcholine receptor (AChR) antibody and titin antibody positivity. Chest CT showed a thymic mass in the left anterior mediastinum, and he received intravenous immunoglobulin (IVIG), methylprednisolone pulse therapy, thoracoscopic thymoma resection, and radiotherapy. Postoperative pathological examination revealed a type B2 thymoma. During the perioperative stage, his electrocardiogram (ECG) showed myocardial infarction-like ECG changes; however, his levels of cardiac enzymes and troponin were normal, and he had no symptoms of precardiac discomfort. Six months after thymectomy, his nephrotic syndrome and MG symptoms were relieved; however, he presented with typical manifestations of Morvan’s syndrome, including neuromyotonia, severe insomnia, abnormal ECG activity, and antibodies against leucine-rich glioma-inactivated 1 (LGI1) and γ-amino-butyric acid-B receptor (GABABR). His symptoms did not improve after repeated IVIG and steroid therapies. Finally, he received low-dose rituximab, and his symptoms gradually resolved.ConclusionThis case serves to remind us that apart from MG, thymoma is also associated with other autoimmune PNSs such as membranous nephropathy, cutaneous amyloidosis, and Morvan’s syndrome. Autoimmune PNSs can present concurrently with or after surgical or medical therapy for thymoma. For Morvan’s syndrome post-thymectomy with LGI1 antibody positivity, B-cell depletion therapy such as intravenous rituximab is an effective treatment.

Formaldehyde (FA) is widely used in industry and also common in daily life. Finding an efficient method to determine FA is quite an industrial challenge. Herein, a novel method based on a resonance light scattering (RLS) technique was developed for the detection of FA with high sensitivity. Carbon dots (CDs) were used as RLS probes. CDs were obtained via one-pot solvothermal treatment from o-phenylenediamine. CDs showed yellow fluorescence with a quantum yield of 0.41. Due to the multiple amino groups on the surface of CDs, FA can be captured easily by formation of a covalent C = N bond based on the Schiff-base reaction. Owing to the covalent crosslinking, CD nanoparticles aggregated, and even formed precipitate. The aggregation of CDs induced RLS enhancement, where the RLS increment was linearly related to the concentration of FA ranging from 4 nM to 1.6 mM, with a limit of detection (LOD) of 1.6 nM. In comparison with many previous reports, the present RLS method showed a wider linear range and lower LOD. Furthermore, the RLS system was successfully used to detect FA in real food samples. The proposed system has prospective applicability in the detection of FA in food fields.

The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).