RELATIONSHIP BETWEEN TRPM4 RS8104571 GENOTYPE, CIRCULATING TRPM4 AND SUR1, AND CLINICAL OUTCOME FOLLOWING TRAUMATIC BRAIN INJURY.

ABSTRACT: The variant single nucleotide polymorphism rs8104571 has been associated with poor outcomes following traumatic brain injury (TBI) and is most prevalent in those of African ancestry. This single nucleotide polymorphism (SNP) resides within a gene coding for the TRPM4 protein, which complexes with SUR1 protein to create a transmembrane ion channel and is believed to contribute to cellular swelling and cell death in neurological tissue. Our study evaluates the relationship between circulating TRPM4 and SUR1, rs8104571 genotype, and clinical outcome in TBI patients. Trauma patients with moderate to severe TBI were included in this retrospective study. rs8104571 genotyping and admission plasma TRPM4 and SUR1 quantification was performed with real-time PCR and enzyme-linked immunosorbent assay (ELISA), respectively. Adequate plasma for TRPM4 and SUR1 ELISA quantification was available for 289 patients, 54 of whom were African American (AA). Plasma TRPM4 concentration was increased in those with a variant rs8104571 allele compared to wild type when controlling for demographics and injury characteristics in the overall cohort (P = 0.04) and within the AA subgroup (P = 0.01). There was no significant association between plasma TRPM4 or SUR1 and clinical outcome (each P > 0.05). Plasma TRPM4 abundance increased with acute kidney injury severity (P = 0.02). The association between increased plasma TRPM4 and variant rs810457 supports an underlying mechanism involving increased neuroinflammation with a subsequent increase in the leakage of TRPM4 from the central nervous system into circulation. Alternative sources of plasma TRPM4 including the kidney cannot be excluded and may play a significant role in the pathophysiology of trauma as well.

Multidimensional Measure Matching for Crowd Counting.

This article addresses the challenge of scale variations in crowd-counting problems from a multidimensional measure-theoretic perspective. We start by formulating crowd counting as a measure-matching problem, based on the assumption that discrete measures can express the scattered ground truth and the predicted density map. In this context, we introduce the Sinkhorn counting loss and extend it to the semi-balanced form, which alleviates the problems including entropic bias, distance destruction, and amount constraints. We then model the measure matching under the multidimensional space, in order to learn the counting from both location and scale. To achieve this, we extend the traditional 2-D coordinate support to 3-D, incorporating an additional axis to represent scale information, where a pyramid-based structure will be leveraged to learn the scale value for the predicted density. Extensive experiments on four challenging crowd-counting datasets, namely, ShanghaiTech A, UCF-QNRF, JHU ++ , and NWPU have validated the proposed method. Code is released at https://github.com/LoraLinH/Multidimensional-Measure-Matching-for-Crowd-Counting.

Early clinical outcomes of bipolar hemiarthroplasty for femoral neck fractures in elderly patients using the OCM approach: a retrospective study.

Objective: This study aims to assess the early clinical outcomes of bipolar hemiarthroplasty for treating femoral neck fractures in elderly patients aged 75 and above using the Orthopädische Chirurgie München (OCM) approach. Methods: A retrospective analysis was conducted on a cohort of 95 elderly patients who underwent bipolar hemiarthroplasty for Garden Type III and IV femoral neck fractures between January 2020 and December 2022. The participants were categorized into two groups according to the surgical approach used: the OCM approach and the posterior-lateral approach (PLA). The average follow-up duration was 11.20 ± 2.80 months for the OCM group and 11.12 ± 2.95 months for the PLA group, with both groups ranging from 6 to 18 months. Clinical outcomes assessed included surgical duration, incision length, postoperative hospital stay, time to ambulation, hemoglobin levels, serum creatine kinase (CK) levels, C-reactive protein (CRP) levels, pain (assessed using the Visual Analogue Scale, VAS), and functional recovery (evaluated through Harris hip scores). Additionally, complications such as intraoperative and postoperative fractures, deep vein thrombosis, wound infection, nerve injury, postoperative dislocation, leg length discrepancy, and Trendelenburg gait were monitored. Results: There was no significant difference in the surgical duration between the OCM and PLA groups. However, the OCM group exhibited shorter incision lengths, reduced postoperative hospital stays, and earlier ambulation times compared to the PLA group. Significantly lower intraoperative blood loss, smaller decreases in hemoglobin levels on postoperative days 1 and 3, lesser hidden blood loss, and decreased levels of CK and CRP were observed in the OCM group. Pain levels, measured by VAS scores, were lower, and Harris hip scores, indicating functional recovery, were higher at 2 and 6 weeks postoperatively in the OCM group than in the PLA group. The incidence of complications, such as periprosthetic fractures, intramuscular venous thrombosis, hip dislocations, Trendelenburg gait, and leg length discrepancies, showed no significant differences between the groups. Conclusion: The OCM approach for bipolar hemiarthroplasty in patients aged 75 and above with femoral neck fractures offers significant early clinical benefits over the traditional PLA, including faster recovery, reduced postoperative pain, and enhanced early functional recovery.

Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration.

We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation models and downstream tasks is minimal. iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT). 2) Multi-stage supervision to the feature pyramid using masked feature modeling (MFM) . iTPN is updated to Fast-iTPN, reducing computational memory overhead and accelerating inference through two flexible designs. 1) Token migration: dropping redundant tokens of the backbone while replenishing them in the feature pyramid without attention operations. 2) Token gathering: reducing computation cost caused by global attention by introducing few gathering tokens. The base/large-level Fast-iTPN achieve 88.75%/89.5% top-1 accuracy on ImageNet-1K. With 1× training schedule using DINO, the base/large-level Fast-iTPN achieves 58.4%/58.8% box AP on COCO object detection, and a 57.5%/58.7% mIoU on ADE20K semantic segmentation using MaskDINO. Fast-iTPN can accelerate the inference procedure by up to 70%, with negligible performance loss, demonstrating the potential to be a powerful backbone for downstream vision tasks. The code is available at github.com/sunsmarterjie/iTPN.

Lattice-Strained Bimetallic Nanocatalysts: Fundamentals of Synthesis and Structure.

Bimetallic nanostructured catalysts have shown great promise in the areas of energy, environment and magnetics. Tunable composition and electronic configurations due to lattice strain at bimetal interfaces have motivated researchers worldwide to explore them industrial applications. However, to date, the fundamentals of the synthesis of lattice-mismatched bimetallic nanocrystals are still largely uninvestigated for most supported catalyst materials. Therefore, in this work, we have conducted a detailed review of the synthesis and structural characterization of bimetallic nanocatalysts, particularly for renewable energies. In particular, the synthesis of Pt, Au and Pd bimetallic particles in a liquid phase has been critically discussed. The outcome of this review is to provide industrial insights of the rational design of cost-effective nanocatalysts for sustainable conversion technologies.

CIRF: Coupled Image Reconstruction and Fusion Strategy for Deep Learning Based Multi-Modal Image Fusion.

Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network's feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods.

Reliability-Guided Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability. In this work, we propose a reliability-guided hierarchical memory network (RHMNet) for this task, which segments the target in a stepwise expanding strategy w.r.t. the memory reliability level. To be specific, RHMNet maintains a reliability-guided memory bank. It first uses the high-reliability memory to locate the region with high reliability belonging to the target, i.e., highly similar to the initial target scribble. Then, it expands the located high-reliability region to the entire target conditioned on the region itself and all existing memories. In addition, we propose a scribble-supervised learning mechanism to facilitate the model learning for dense prediction. It exploits the pixel-level relations within a single frame and the instance-level variations across multiple frames to take full advantage of the scribble annotations in sequence training samples. The favorable performance on four popular benchmarks demonstrates that our method is promising. Our project is available at: https://github.com/mkg1204/RHMNet-for-SSVOS.

Structure and function of the Arabidopsis ABC transporter ABCB19 in brassinosteroid export.

Brassinosteroids are steroidal phytohormones that regulate plant development and physiology, including adaptation to environmental stresses. Brassinosteroids are synthesized in the cell interior but bind receptors at the cell surface, necessitating a yet to be identified export mechanism. Here, we show that a member of the ATP-binding cassette (ABC) transporter superfamily, ABCB19, functions as a brassinosteroid exporter. We present its structure in both the substrate-unbound and the brassinosteroid-bound states. Bioactive brassinosteroids are potent activators of ABCB19 ATP hydrolysis activity, and transport assays showed that ABCB19 transports brassinosteroids. In Arabidopsis thaliana, ABCB19 and its close homolog, ABCB1, positively regulate brassinosteroid responses. Our results uncover an elusive export mechanism for bioactive brassinosteroids that is tightly coordinated with brassinosteroid signaling.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods.

Three-Year Point Prevalence Survey of Antimicrobial Use in a Chinese University Hospital.

To evaluate the prevalence and quality of antimicrobial prescriptions using a Global Point Prevalence Survey (PPS) tool and help identify targets for improvement of antimicrobial prescribing and inform the development of antimicrobial stewardship activities. Antimicrobial prescriptions for inpatients staying at a hospital overnight were surveyed on one weekday in October 2018, November 2019, and November 2020. Data including basic patient information, antimicrobial drugs, quality evaluation of antimicrobial drug prescription, and the risk factors of nosocomial infection were collected from doctor network workstation. Patient information was anonymized and entered in the PPS Web application by physicians. A total of 720 patients (median age, 62 years) were surveyed. Of them, 246 (34.2%) were prescribed antimicrobials on the survey days. Hospital-wide antimicrobial use had a significantly decreasing trend (P < 0.001). The most commonly prescribed antimicrobial drugs were third-generation cephalosporins (40.5%), followed by quinolones (21.8%) and second-generation cephalosporin (12.5%). In our study, cefoperazone/sulbactam, ceftazidime, and levofloxacin were the most commonly used antimicrobials. The most common indication for antimicrobial use was pneumonia or lower respiratory tract infection (159/321, 49.5%). Antimicrobial for surgical prophylaxis represented 16.2% of the total antibiotic doses. Of those, 67.3% were administered for more than 24 h. The rate of adherence to antibiotic guidelines was 61.4%. The indications for antimicrobials were not documented in 54.5% of the prescriptions. Stop/review date was documented for 36.8% of prescriptions. The PPS tool is useful in identifying targets to enhance the quality of antimicrobial prescriptions to improve the adherence rate in hospitals. This survey can be used as a control to assess the rational application quality of antimicrobial after regular application of antimicrobial intervention.

Fine-Grained Accident Detection: Database and Algorithm.

This paper presents a novel fine-grained task for traffic accident analysis. Accident detection in surveillance or dashcam videos is a common task in the field of traffic accident analysis by using videos. However, common accident detection does not analyze the specific particulars of the accident, only identifies the accident's existence or occurrence time in a video. In this paper, we define the novel fine-grained accident detection task which contains fine-grained accident classification, temporal-spatial occurrence region localization, and accident severity estimation. A transformer-based framework combining the RGB and optical flow information of videos is proposed for fine-grained accident detection. Additionally, we introduce a challenging Fine-grained Accident Detection (FAD) database that covers multiple tasks in surveillance videos which places more emphasis on the overall perspective. Experimental results demonstrate that our model could effectively extract the video features for multiple tasks, indicating that current traffic accident analysis has limitations in dealing with the FAD task and that further research is indeed needed.

Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting.

Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a simple-yet-effective modulating domain-specific knowledge network (MDKNet) to handle the domain bias issue in multidomain crowd counting. MDKNet is achieved by employing the idea of "modulating", enabling deep network balancing and modeling different distributions of diverse datasets with little bias. Specifically, we propose an instance-specific batch normalization (IsBN) module, which serves as a base modulator to refine the information flow to be adaptive to domain distributions. To precisely modulating the domain-specific information, the domain-guided virtual classifier (DVC) is then introduced to learn a domain-separable latent space. This space is employed as an input guidance for the IsBN modulator, such that the mixture distributions of multiple datasets can be well treated. Extensive experiments performed on popular benchmarks, including Shanghai-tech A/B, QNRF, and NWPU validate the superiority of MDKNet in tackling multidomain crowd counting and the effectiveness for multidomain learning. Code is available at https://github.com/csguomy/MDKNet.

VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows.

Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination (LI). Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking. In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under LI, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and source code have been released on: https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark.

HQG-Net: Unpaired Medical Image Enhancement With High-Quality Guidance.

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this article, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multiencoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets verify that our method outperforms existing techniques in terms of both enhancement quality and downstream task performance. The code and the newly collected datasets are publicly available at https://github.com/ChunmingHe/HQG-Net.

Catalysis of Synergistic Reactions by Host-Guest Assemblies: Reductive Carbonylation of Nitrobenzenes.

Numerous chemical transformations require two or more catalytically active sites that act in a concerted manner; nevertheless, designing heterogeneous catalysts with such multiple functionalities remains an overwhelming challenge. Herein, it is shown that by the integration of acidic flexible polymers and Pd-metallated covalent organic framework (COF) hosts, the merits of both catalytically active sites can be utilized to realize heterogeneous synergistic catalysis that are active in the conversion of nitrobenzenes to carbamates via reductive carbonylation. The concentrated catalytically active species in the nanospace force two catalytic components into proximity, thereby enhancing the cooperativity between the acidic species and Pd species to facilitate synergistic catalysis. The resulting host-guest assemblies constitute more efficient systems than the corresponding physical mixtures and the homogeneous counterparts. Furthermore, this system enables easy access to a family of important derivatives such as herbicides and polyurethane monomers and can be integrated with other COFs, showing promising results. This study utilizes host-guest assembly as a versatile tool for the fabrication of multifunctional catalysts with enhanced cooperativity between different catalytic species.

AAformer: Auto-Aligned Transformer for Person Re-Identification.

In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. Most of the existing CNN-based methods only locate the human parts coarsely, or rely on pretrained human parsing models and fail in locating the identifiable nonhuman parts (e.g., knapsack). In this article, we introduce an alignment scheme in transformer architecture for the first time and propose the auto-aligned transformer (AAformer) to automatically locate both the human parts and nonhuman ones at patch level. We introduce the "Part tokens (PARTs)", which are learnable vectors, to extract part features in the transformer. A PART only interacts with a local subset of patches in self-attention and learns to be the part representation. To adaptively group the image patches into different subsets, we design the auto-alignment. Auto-alignment employs a fast variant of optimal transport (OT) algorithm to online cluster the patch embeddings into several groups with the PARTs as their prototypes. AAformer integrates the part alignment into the self-attention and the output PARTs can be directly used as part features for retrieval. Extensive experiments validate the effectiveness of PARTs and the superiority of AAformer over various state-of-the-art methods.

Update of Contrast-enhanced Ultrasound in Musculoskeletal Medicine: Clinical Perspectives - A Review.

Contrast-enhanced ultrasound (CEUS) uses an intravascular contrast agent to enhance blood flow signals and assess microcirculation in different parts of the human body. Over the past decade, CEUS has become more widely applied in musculoskeletal (MSK) medicine, and the current review aims to systematically summarize current research on the application of CEUS in the MSK field, focusing on 67 articles published between January 2001 and June 2021 in online databases including PubMed, Scopus, and Embase. CEUS has been widely used for the clinical assessment of muscle microcirculation, tendinopathy, fracture nonunions, sports-related injuries, arthritis, peripheral nerves, and tumors, and can serve as an objective and quantitative evaluation tool for prognosis and outcome prediction. Optimal CEUS parameters and diagnostic cut off values for each disease category remain to be confirmed.

Entity-Graph Enhanced Cross-Modal Pretraining for Instance-Level Product Retrieval.

Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets and define two real practical instance-level retrieval tasks that enable evaluations on price comparison and personalized recommendations. For both instance-level tasks, accurately identifying the intended product target mentioned in visual-linguistic data and mitigating the impact of irrelevant content are quite challenging. To address this, we devise a more effective cross-modal pretraining model capable of adaptively incorporating key concept information from multi-modal data. This is accomplished by utilizing an entity graph, where nodes represented entities and edges denoted the similarity relations between them. Specifically, a novel Entity-Graph Enhanced Cross-Modal Pretraining (EGE-CMP) model is proposed for instance-level commodity retrieval, which explicitly injects entity knowledge in both node-based and subgraph-based ways into the multi-modal networks via a self-supervised hybrid-stream transformer. This could reduce the confusion between different object contents, thereby effectively guiding the network to focus on entities with real semantics. Experimental results sufficiently verify the efficacy and generalizability of our EGE-CMP, outperforming several SOTA cross-modal baselines like CLIP Radford et al. 2021, UNITER Chen et al. 2020 and CAPTURE Zhan et al. 2021.

Plasmodesmata mediate cell-to-cell transport of brassinosteroid hormones.

Brassinosteroids (BRs) are steroidal phytohormones that are essential for plant growth, development and adaptation to environmental stresses. BRs act in a dose-dependent manner and do not travel over long distances; hence, BR homeostasis maintenance is critical for their function. Biosynthesis of bioactive BRs relies on the cell-to-cell movement of hormone precursors. However, the mechanism of the short-distance BR transport is unknown, and its contribution to the control of endogenous BR levels remains unexplored. Here we demonstrate that plasmodesmata (PD) mediate the passage of BRs between neighboring cells. Intracellular BR content, in turn, is capable of modulating PD permeability to optimize its own mobility, thereby manipulating BR biosynthesis and signaling. Our work uncovers a thus far unknown mode of steroid transport in eukaryotes and exposes an additional layer of BR homeostasis regulation in plants.