THE DYNAMIC LOAD ON HAMSTRING MUSCLES DURING SPRINTING

The purpose of this study was to analyze the load condition on hamstring muscles during stance and swing phase of sprinting. Three-dimensional videographic and ground reaction force data were collected and the intersegmental dynamics of lower limb was quantified while eight elite male sprint runners performed sprint running with maximum effort. During initial stance phase, the ground reaction torque (EXT) was the main passive torque at knee and hip. During late swing phase, it was motion-dependent torque (MDT) as well. The muscle torques counteracted the large effect of EXT and MDT. This result revealed that the hamstring muscles would suffer from tremendous loads in both initial stance phase and late swing phase. Thus it can be speculated that hamstring muscles were exposed to a higher risk of injury during these two phases in sprinting

An original model for multi-target learning of logical rules for knowledge graph reasoning

Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logical rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logical rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model able to fully use training data which also considers multi-target scenarios. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators

Liver Metabolome and Proteome Response of Turbot (Scophthalmus maximus) to Lysine and Leucine in Free and Dipeptide Forms

Omics approaches provide more metabolic information to explain the relationship between dietary nutrition and fish growth. This study aimed to explore the metabolome and proteome response of turbot (Scophthalmus maximus) fed diets containing lysine and leucine in free and dipeptide forms by the approaches of integrated liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics and isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics. Plant protein-based diets were formulated to contain the equivalent of lysine and leucine in free amino acid [crystalline amino acid (CAA)] and synthetic Lys-Leu (Lys-Leu) forms. The metabolome and proteome profiles of the liver were screened in fish fed either the CAA diet or the Lys-Leu diet after an 8-week feeding trial. Fish fed the Lys-Leu diet showed a significantly higher final body weight and a specific growth rate compared with fish fed the CAA diet. Protein- and amino acid-related metabolic processes in the liver were identified between the Lys-Leu and CAA groups based on differential metabolites and proteins. The proteolytic enzymes and amino acid transporters from differential proteins of the liver showed that the process of protein digestion and absorption may be affected by the different forms of lysine and leucine in the feed. A mechanistic target of rapamycin complex 1 and ubiquitin proteasome pathways were identified by differential proteins, which were involved in the processes of protein synthesis and degradation in the liver. Lysine degradation, tryptophan metabolism, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, arginine and proline metabolism, and glycine, serine, and threonine metabolism were identified based on differential metabolites and proteins, which showed that the metabolism of various amino acids, including lysine, had been affected by both the CAA and Lys-Leu groups. In conclusion, the data of integrated metabonomics and proteomics suggested that different forms of lysine and leucine in the feed may affect liver metabolic processes including protein digestion and absorption, protein synthesis and degradation, and amino acid metabolism. In addition, a good correlation between differential metabolites and proteins was observed in amino acid metabolism by using the approaches of integrated LC-MS/MS-based metabolomics and iTRAQ-based proteomics

How Joint Torques Affect Hamstring Injury Risk in Sprinting Swing-Stance Transition

Purpose: The potential mechanisms of hamstring strain injuries in athletes are not well understood. The study, therefore, was aimed at understanding hamstring mechanics by studying loading conditions during maximum-effort overground sprinting. Methods: Three-dimensional kinematics and ground reaction force data were collected from eight elite male sprinters sprinting at their maximum effort. Maximal isometric torques of the hip and knee were also collected. Data from the sprinting gait cycle were analyzed via an intersegmental dynamics approach, and the different joint torque components were calculated. Results: During the initial stance phase, the ground reaction force passed anteriorly to the knee and hip, producing an extension torque at the knee and a flexion torque at the hip joint. Thus, the active muscle torque functioned to produce flexion torque at the knee and extension torque at the hip. The maximal muscle torque at the knee joint was 1.4 times the maximal isometric knee flexion torque. During the late swing phase, the muscle torque counterbalanced the motion-dependent torque and acted to flex the knee joint and extend the hip joint. The loading conditions on the hamstring muscles were similar to those of the initial stance phase. Conclusions: During both the initial stance and late swing phases, the large passive torques at both the knee and hip joints acted to lengthen the hamstring muscles. The active muscle torques generated mainly by the hamstrings functioned to counteract those passive effects. As a result, during sprinting or high-speed locomotion, the hamstring muscles may be more susceptible to high risk of strain injury during these two phases

The Hepatoprotective Effect of Sodium Nitrite on Cold Ischemia-Reperfusion Injury

Liver ischemia-reperfusion injury is a major cause of primary graft non-function or initial function failure post-transplantation. In this study, we examined the effects of sodium nitrite supplementation on liver IRI in either Lactated Ringer's (LR) solution or University of Wisconsin (UW) solution. The syngeneic recipients of liver grafts were also treated with or without nitrite by intra-peritoneal injection. Liver AST and LDH release were significantly reduced in both nitrite-supplemented LR and UW preservation solutions compared to their controls. The protective effect of nitrite was more efficacious with longer cold preservation times. Liver histological examination demonstrated better preserved morphology and architecture with nitrite treatment. Hepatocellular apoptosis was significantly reduced in the nitrite-treated livers compared their controls. Moreover, liver grafts with extended cold preservation time of 12 to 24 hours demonstrated improved liver tissue histology and function post-reperfusion with either the nitrite-supplemented preservation solution or in nitrite-treated recipients. Interestingly, combined treatment of both the liver graft and recipient did not confer protection. Thus, nitrite treatment affords significant protection from cold ischemic and reperfusion injury to donor livers and improves liver graft acute function post-transplantation. The results from this study further support the potential for nitrite therapy to mitigate ischemia-reperfusion injury in solid organ transplantation

D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field

Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple ``value to distribution'' transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at https://github.com/psyai-net/D-IF_release

Turning a CLIP Model into a Scene Text Detector

The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at https://github.com/wenwenyu/TCM.Comment: CVPR202

KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation

Knowledge graph completion is a task that revolves around filling in missing triples based on the information available in a knowledge graph. Among the current studies, text-based methods complete the task by utilizing textual descriptions of triples. However, this modeling approach may encounter limitations, particularly when the description fails to accurately and adequately express the intended meaning. To overcome these challenges, we propose the augmentation of data through two additional mechanisms. Firstly, we employ ChatGPT as an external knowledge base to generate coherent descriptions to bridge the semantic gap between the queries and answers. Secondly, we leverage inverse relations to create a symmetric graph, thereby creating extra labeling and providing supplementary information for link prediction. This approach offers additional insights into the relationships between entities. Through these efforts, we have observed significant improvements in knowledge graph completion, as these mechanisms enhance the richness and diversity of the available data, leading to more accurate results

High-throughput screening of rare metabolically active tumor cells in pleural effusion and peripheral blood of lung cancer patients

Malignant pleural effusion (MPE), the presence of malignant cells in pleural fluid, is often the first sign of many cancers and occurs in patients with metastatic malignancies. Accurate detection of tumor cells in pleural fluid is crucial because the presence of MPE denotes an advanced stage of disease and directs a switch in clinical managements. Cytology, as a traditional diagnostic tool, has limited sensitivity especially when tumor cells are not abundant, and may be confounded by reactive mesothelial cells in the pleural fluid. We describe a highly sensitive approach for rapid detection of metabolically active tumor cells in MPE via exploiting the altered glucose metabolism of tumor cells relative to benign cells. Metabolically active tumor cells with high glucose uptake, as evaluated by a fluorescent glucose analog (2-NBDG), are identified by high-throughput fluorescence screening within a chip containing 200,000 addressable microwells and collected for malignancy confirmation via single-cell sequencing. We demonstrate the utility of this approach through analyzing MPE from a cohort of lung cancer patients. Most candidate tumor cells identified are confirmed to harbor the same driver oncogenes as their primary lesions. In some patients, emergence of secondary mutations that mediate acquired resistance to ongoing targeted therapies is also detected before resistance is manifested in the clinical imaging. The detection scheme can be extended to analyze peripheral blood samples. Our approach may serve as a valuable complement to cytology in MPE diagnosis, helping identify the driver oncogenes and resistance-leading mutations for targeted therapies