Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction

Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image (MRI) processing and achieves accurate MRI reconstruction from under-sampled k-space data. According to the current research, there are still several problems with dynamic MRI k-space reconstruction based on CS. 1) There are differences between the Fourier domain and the Image domain, and the differences between MRI processing of different domains need to be considered. 2) As three-dimensional data, dynamic MRI has its spatial-temporal characteristics, which need to calculate the difference and consistency of surface textures while preserving structural integrity and uniqueness. 3) Dynamic MRI reconstruction is time-consuming and computationally resource-dependent. In this paper, we propose a novel robust low-rank dynamic MRI reconstruction optimization model via highly under-sampled and Discrete Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition Model (RDLEDM). Our method mainly includes linear decomposition, double Total Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear image domain error analysis, the noise is reduced after under-sampled and DFT processing, and the anti-interference ability of the algorithm is enhanced. Double TV and NN regularizations can utilize both spatial-temporal characteristics and explore the complementary relationship between different dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and non-convexity of TV and NN terms, it is difficult to optimize the unified objective model. To address this issue, we utilize a fast algorithm by solving a primal-dual form of the original problem. Compared with five state-of-the-art methods, extensive experiments on dynamic MRI data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity

DPHANet: Discriminative Parallel and Hierarchical Attention Network for Natural Language Video Localization

Natural Language Video Localization (NLVL) has recently attracted much attention because of its practical significance. However, the existing methods still face the following challenges: 1) When the models learn intra-modal semantic association, the temporal causal interaction information and contextual semantic discriminative information are ignored, resulting in the lack of intra-modal semantic context connection; 2) When learning fusion representations, existing cross-modal interaction modules lack hierarchical attention function to extract intermodal similarity information and intra-modal self-correlation information, resulting in insufficient cross-modal information interaction; 3) When the loss function is optimized, the existing models ignore the correlation of causal inference between the start and end boundaries, resulting in inaccurate start and end boundary calibrations. To conquer the above challenges, we proposed a novel NLVL model, called Discriminative Parallel and Hierarchical Attention Network (DPHANet). Specifically, we emphasized the importance of temporal causal interaction information and contextual semantic discriminative information and correspondingly proposed a Discriminative Parallel Attention Encoder (DPAE) module to infer and encode the above critical information. Besides, to overcome the shortcomings of the existing cross-modal interaction modules, we designed a Video-Query Hierarchical Attention (VQHA) module, which can perform cross-modal interaction and intra-modal self-correlation modeling in a hierarchical manner. Furthermore, a novel deviation loss function was proposed to capture the correlation of causal inference between the start and end boundaries and force the model to focus on the continuity and temporal causality in the video. Finally, extensive experiments on three benchmark datasets demonstrated the superiority of our proposed DPHANet model, which has achieved about 1.5% and 3.5% average performance improvement and about 2.5% and 7.5% maximum performance improvement on the Charades-STA and TACoS datasets respectively

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Autophagy-related gene model as a novel risk factor for schizophrenia

Abstract Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, we cataloged 232 ARGs and pinpointed 80 autophagy-related DEGs (AR-DEGs) after intersecting them with DEGs. Subsequent analyses, including metascape gene annotation, pathway and process enrichment, and protein-protein interaction enrichment, were performed on the 80 AR-DEGs to delve deeper into their biological roles and associated molecular pathways. From this, we identified 34 candidate risk AR-DEGs (RAR-DEGs) and honed this list to final RAR-DEGs via a constructed and optimized logistic regression model. These genes include VAMP7, PTEN, WIPI2, PARP1, DNAJB9, SH3GLB1, ATF4, EIF4G1, EGFR, CDKN1A, CFLAR, FAS, BCL2L1 and BNIP3. Using these findings, we crafted a nomogram to predict SCZ risk for individual samples. In summary, our study offers deeper insights into SCZ’s molecular pathogenesis and paves the way for innovative approaches in risk prediction, gene-targeted diagnosis, and community-based SCZ treatments

Superpoint Transformer for 3D Scene Instance Segmentation

Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer

Studies on the extraction performance of phorate by aptamer-functionalized magnetic nanoparticles in plasma samples

Phorate, a highly toxic organophosphorus pesticide, poses significant risks due to its efficiency, versatility, and affordability. Therefore, studying pretreatment and detection methods for phorate in complex samples is crucial. In this study, we synthesized core-shell phorate aptamer-functionalized magnetic nanoparticles using solvothermal and self-assembly techniques. Subsequently, we developed a magnetic dispersive solid-phase extraction and detection method to identifying phorate in plasma samples. Under optimal conditions, we achieved quantitation of phorate within a range of 2–700 ng·mL−1 using gas chromatography-mass spectrometry. The detection limit (S/N = 3) was 0.46 ng·mL−1, and the intraday and interday relative standard deviation were 3.4% and 4.1%, respectively. In addition, the material exhibited excellent specificity, an enrichment capacity (EF = 416), and reusability (≥15). During phorate extraction from real plasma samples, spiked recoveries ranged from 86.1% to 101.7%. These results demonstrate that our method offers superior extraction efficiency and detection capability for phorate in plasma samples

Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area

The detailed analysis of the initial ground stress distribution law is an important work for the safety of tunnel construction and operation. Especially, the high ground stress phenomenon in the deep-buried tunnel area is common, which has a great impact on the tunnel construction. Based on the on-site measured ground stress data, the analysis of the initial ground stress field by numerical simulation and multiple linear regression is mainly described in this study. Following the comparison and selection of three coefficient estimation methods for the regression equation, the best regression method is selected for inversion and verification. The distribution characteristic of the initial ground stress at different buried depths of the tunnel line is obtained. The inversion results of the initial ground stress in a tunnel area, in China, show that the lateral pressure coefficient gradually decreases with the buried depth increasing, while the overall lateral pressure coefficient is in the range of 1.0–2.0, showing a more significant horizontal tectonic. At the area where the tunnel passes through the fault, a small amount of horizontal tectonic stress is released. The ratio of horizontal principal stress to vertical principal stress is smaller than that on both sides, which is different from the distribution characteristic of lateral pressure coefficient without the impact of fault. It shows that faults have a great influence on ground stress. The lateral pressure coefficient in the area near the fault must be determined according to the on-site measured results

Modification and characterization of an aptamer-based surface plasmon resonance sensor chip

Recently, aptamer-based surface plasmon resonance (SPR) sensors have become increasingly popular due to their high specificity, high sensitivity, real-time detection capabilities, and label-free features. The core component of an aptamer-based SPR sensor is a chip. This paper presents the modification steps and the characterization results of a sensor chip for the construction of a 2, 4, 6-trinitrotoluene-targeted, aptamer-based, SPR sensor. After cleaning the aptamer-based SPR sensor chip, polyethylene glycol (PEG) with functional thiol groups at one end was added to the chip surface by Au-S covalent bonds to form a self-assembled film. Then, the carboxyl groups at the other end of PEG and the carboxyl groups of trinitrophenyl-glycine (TNP-Gly) were activated and connected via ethylenediamine (EDA). This effectively completed the chip’s modification. During the modification process, relevant experimental conditions were optimized. The chip’s surface elements, as well as their chemical states, were characterized by X-ray photoelectron spectroscopy (XPS). The results, outlined in the following study, demonstrate that this modification of an aptamer-based SPR sensor chip adhered to normative expectations. Thus, the modification process proposed here establishes an important foundation for subsequent study of TNT detection