Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection

We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge. Additionally, we developed a practical fingerprint recognition system with 94.68% accuracy, earning second place in LivDet 2023 Liveness Detection in Action. By investigating various methods, particularly style transfer, we demonstrate improvements in accuracy and generalization when faced with limited training data. As a result, our approach achieved state-of-the-art performance in LivDet 2023 Challenges.Comment: 1st Place in LivDet2023 Fingerprint Representation Challeng

Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-label. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.Comment: 8 pages,10 figure

Effect of different processing temperature on texture profile and flavor of the edible bird's nest

Objective: This study aimed to investigate the effects of different processing temperature on texture profile and flavor of dried edible bird's nest. Methods: With dried edible bird's nest as the main raw material, the texture profile analysis of edible bird's nest at different processing temperature was carried out by physical property analyzer. The volatile components of edible bird's nest at different processing temperature were determined by electronic nose and solid phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). Results: It was found that the adhesion of cooked edible bird's nest decreased significantly (P＜0.01), but its resilience increased significantly (P＜0.05) with the increase of temperature. 12 kinds of effective flavor substances were detected in the cooked edible bird's nest by SPME-GC-MS, including: 3 alcohols, 2 aldehydes, 2 esters, 3 ketones, 1 acid, and 1 ether, accounting for 6.23%, 49.34%, and 54.85% of the total detected substances in the cooked edible bird's nest at 95 ℃, 105 ℃ and 115 ℃, respectively. Conclusion: Different processing temperature affects the taste of edible bird's nest and the overall odor profile after processing. Moreover, higher cooking temperature tends to make the egg white-like flavor stronger. The volatile substances in the bird's nest stew at 95 ℃ are mainly acids, the bird's nest cooked at 105 ℃ mainly produces alcohols, and the edible bird's nest cooked at 115 ℃ mainly produces ketones and alcohols

An Autonomous Large Language Model Agent for Chemical Literature Data Mining

Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry

Flames: Benchmarking Value Alignment of Chinese Large Language Models

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames

BAM15 as a mitochondrial uncoupler: a promising therapeutic agent for diverse diseases

Subcellular organelles dysfunction is implicated in various diseases, including metabolic diseases, neurodegenerative diseases, cancer, and cardiovascular diseases. BAM15, a selective mitochondrial uncoupler, has emerged as a promising therapeutic agent due to its ability to enhance mitochondrial respiration and metabolic flexibility. By disrupting the coupling between electron transport and ATP synthesis, BAM15 dissipates the proton gradient, leading to increased mitochondrial respiration and energy expenditure. This review provides a comprehensive overview of BAM15, including its mechanism of action and potential therapeutic applications in diverse disease contexts. BAM15 has shown promise in obesity by increasing energy expenditure and reducing fat accumulation. In diabetes, it improves glycemic control and reverses insulin resistance. Additionally, BAM15 has potential in non-alcoholic fatty liver disease, sepsis, and cardiovascular diseases by mitigating oxidative stress, modulating inflammatory responses, and promoting cardioprotection. The safety profile of BAM15 is encouraging, with minimal adverse effects and remarkable tolerability. However, challenges such as its high lipophilicity and the need for alternative delivery methods need to be addressed. Further research is necessary to fully understand the therapeutic potential of BAM15 and optimize its application in clinical settings

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

BackgroundCoronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.MethodsSequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.ResultsThe analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.ConclusionThis research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza

Celecoxib ameliorates diabetic sarcopenia by inhibiting inflammation, stress response, mitochondrial dysfunction, and subsequent activation of the protein degradation systems

Aim: Diabetic sarcopenia leads to disability and seriously affects the quality of life. Currently, there are no effective therapeutic strategies for diabetic sarcopenia. Our previous studies have shown that inflammation plays a critical role in skeletal muscle atrophy. Interestingly, the connection between chronic inflammation and diabetic complications has been revealed. However, the effects of non-steroidal anti-inflammatory drug celecoxib on diabetic sarcopenia remains unclear.Materials and Methods: The streptozotocin (streptozotocin)-induced diabetic sarcopenia model was established. Rotarod test and grip strength test were used to assess skeletal muscle function. Hematoxylin and eosin and immunofluorescence staining were performed to evaluate inflammatory infiltration and the morphology of motor endplates in skeletal muscles. Succinate dehydrogenase (SDH) staining was used to determine the number of succinate dehydrogenase-positive muscle fibers. Dihydroethidium staining was performed to assess the levels of reactive oxygen species (ROS). Western blot was used to measure the levels of proteins involved in inflammation, oxidative stress, endoplasmic reticulum stress, ubiquitination, and autophagic-lysosomal pathway. Transmission electron microscopy was used to evaluate mitophagy.Results: Celecoxib significantly ameliorated skeletal muscle atrophy, improving skeletal muscle function and preserving motor endplates in diabetic mice. Celecoxib also decreased infiltration of inflammatory cell, reduced the levels of IL-6 and TNF-α, and suppressed the activation of NF-κB, Stat3, and NLRP3 inflammasome pathways in diabetic skeletal muscles. Celecoxib decreased reactive oxygen species levels, downregulated the levels of Nox2 and Nox4, upregulated the levels of GPX1 and Nrf2, and further suppressed endoplasmic reticulum stress by inhibiting the activation of the Perk-EIF-2α-ATF4-Chop in diabetic skeletal muscles. Celecoxib also inhibited the levels of Foxo3a, Fbx32 and MuRF1 in the ubiquitin-proteasome system, as well as the levels of BNIP3, Beclin1, ATG7, and LC3Ⅱ in the autophagic-lysosomal system, and celecoxib protected mitochondria and promoted mitochondrial biogenesis by elevating the levels of SIRT1 and PGC1-α, increased the number of SDH-positive fibers in diabetic skeletal muscles.Conclusion: Celecoxib improved diabetic sarcopenia by inhibiting inflammation, oxidative stress, endoplasmic reticulum stress, and protecting mitochondria, and subsequently suppressing proteolytic systems. Our study provides evidences for the molecular mechanism and treatment of diabetic sarcopenia, and broaden the way for the new use of celecoxib in diabetic sarcopenia

Evolutionary origin of genomic structural variations in domestic yaks

Yak has been subject to natural selection, human domestication and interspecific introgression during its evolution. However, genetic variants favored by each of these processes have not been distinguished previously. We constructed a graph-genome for 47 genomes of 7 cross-fertile bovine species. This allowed detection of 57,432 high-resolution structural variants (SVs) within and across the species, which were genotyped in 386 individuals. We distinguished the evolutionary origins of diverse SVs in domestic yaks by phylogenetic analyses. We further identified 334 genes overlapping with SVs in domestic yaks that bore potential signals of selection from wild yaks, plus an additional 686 genes introgressed from cattle. Nearly 90% of the domestic yaks were introgressed by cattle. Introgression of an SV spanning the KIT gene triggered the breeding of white domestic yaks. We validated a significant association of the selected stratified SVs with gene expression, which contributes to phenotypic variations. Our results highlight that SVs of different origins contribute to the phenotypic diversity of domestic yaks