Decentralized stochastic sharpness-aware minimization algorithm.

In recent years, distributed stochastic algorithms have become increasingly useful in the field of machine learning. However, similar to traditional stochastic algorithms, they face a challenge where achieving high fitness on the training set does not necessarily result in good performance on the test set. To address this issue, we propose to use of a distributed network topology to improve the generalization ability of the algorithms. We specifically focus on the Sharpness-Aware Minimization (SAM) algorithm, which relies on perturbation weights to find the maximum point with better generalization ability. In this paper, we present the decentralized stochastic sharpness-aware minimization (D-SSAM) algorithm, which incorporates the distributed network topology. We also provide sublinear convergence results for non-convex targets, which is comparable to consequence of Decentralized Stochastic Gradient Descent (DSGD). Finally, we empirically demonstrate the effectiveness of these results in deep networks and discuss their relationship to the generalization behavior of SAM.

Geographical traceability of flavor compounds in Chinese mitten crab (Eriocheir sinensis): Implications for quality differentiation, authenticity assessment, and mechanism research.

Geographical traceability plays a crucial role in ensuring quality assurance, brand establishment, and the sustainable development of the crab industry. In this study, we examined the possibility of using gas chromatography-ion mobility spectrometry with multivariate statistical authenticity analysis to identify the origin of crabs from five sites downstream of the Yangtze River. Significant variations were observed in the levels of alcoholic flavor compounds in the hepatopancreas and muscles of crabs from different geographical locations, and a support vector machine exhibited discriminant ability with 100% accuracy. These flavor variations exhibited significant correlations with the types and concentrations of elements within the crabs, as well as with free amino acids. This study offers a practical approach for determining the geographical traceability of Chinese mitten crabs and elucidates the role of elements in flavor modulation, thereby providing innovative strategies to enhance the efficiency of crab farming.

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar.

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.

Dietary arginine regulates the growth performance, antioxidant capacity, and immune response in Culter alburnus.

Culter alburnus is sensitive to stressors. Arginine is a precursor of nitric oxide, which can effectively relieve the level of oxidative stress and improve the antioxidant and immune capacity of fish. The effect of different arginine levels on topmouth culter (Culter alburnus) fry development performance, liver antioxidant capacity, and immune parameters were investigated in this study. Five diets (1.96%, ARG1, control group; 2.28%, ARG2; 2.52%, ARG3; 2.81%, ARG4; 3.09%, ARG5) were used to feed fry (initial weight 0.31 ± 0.01 g) for 8 weeks. The data showed that the final weight (FW), weight gain rate (WGR), and specific growth rate (SGR) of the ARG3 and ARG4 groups were significantly improved, while the feed conversion ratio (FCR) reduced significantly. Compared with the ARG1 group, all groups remarkably reduced the crude ash content of the whole body. The activity of hepatic superoxide dismutase (SOD) and the content of hepatic glutathione (GSH) were significantly increased in the ARG3 and ARG4 groups, while the malondialdehyde (MDA) content was significantly decreased. Compared with the ARG1 group, arginine levels in ARG2, ARG3, and ARG4 groups up-regulated the expression levels of Nrf2, down-regulated the gene expression level of Keap1 in the liver. And the expression of Nrf2/Keap1 pathway downstream genes Mn-SOD and CAT was up-regulated in ARG2 and ARG3 groups. Furthermore, the expression levels of MyD88 and IL-1ß were down-regulated, and the anti-inflammatory gene TGF-ß expression levels were up-regulated in the ARG2, ARG3, and ARG4 groups. Additionally, compared to the ARG1 group, there was a significant increase in the relative expression levels of the C3 and C4 genes in the ARG4 group. In conclusion, 2.28-2.81% dietary arginine levels improved the growth performance, promoted antioxidant capacity, and enhance immune response. The optimal level of arginine was determined by the quadratic regression analysis of SGR and FCR to be 2.55% of diet (5.43% of dietary protein) and 2.53% of diet (5.38% of dietary protein), accordingly.

The effect of bioturbation on the release behavior of polycyclic aromatic hydrocarbons from sediments: A sediment-seawater microcosm experiment combined with a fugacity model.

The effects of two benthonic species, Perinereis aibuhitensis and Matuta planipes Fabricius, on the release of polycyclic aromatic hydrocarbons (PAHs) from sediments were investigated using a sediment-seawater microcosm. A Level IV fugacity model was used to simulate the behavior and fate of PAHs in the environment. This study revealed that both benthos significantly influenced the release of PAHs, and Matuta planipes Fabricius had a stronger disturbance effect than another. The final concentrations of Matuta planipes Fabricius group, Perinereis aibuhitensis group and the control group in the seawater phase reached 10.8, 9.94 and 7.90 µg/L, respectively. There were certain differences in the behaviour of the two benthonic species. Matuta planipes Fabricius caused more sediment resuspension, while Perinereis aibuhitensis increased the total organic carbon (TOC) content in the environment. The vertical concentration distribution of sediment indicated that vertical mixing was slightly stronger in the Matuta planipes Fabricius group than that in the Perinereis aibuhitensis group. The fugacity model effectively simulated the release behavior of PAHs, providing insight into PAH transport and distribution. The results demonstrated that bioturbation could promote the release of PAHs from seawater. The amount of PAHs released was significantly correlated with the biological habits of the benthos.

A novel predefined-time neurodynamic approach for mixed variational inequality problems and applications.

In this paper, we propose a novel neurodynamic approach with predefined-time stability that offers a solution to address mixed variational inequality problems. Our approach introduces an adjustable time parameter, thereby enhancing flexibility and applicability compared to conventional fixed-time stability methods. By satisfying certain conditions, the proposed approach is capable of converging to a unique solution within a predefined-time, which sets it apart from fixed-time stability and finite-time stability approaches. Furthermore, our approach can be extended to address a wide range of mathematical optimization problems, including variational inequalities, nonlinear complementarity problems, sparse signal recovery problems, and nash equilibria seeking problems in noncooperative games. We provide numerical simulations to validate the theoretical derivation and showcase the effectiveness and feasibility of our proposed method.

An aptamerelectrochemical sensor based on functional carbon nanofibers for tetracycline determination.

Fe-Co@CNF was synthesized by electrospinning technology, and AuNPs was loaded onto Fe-Co@CNF by in-situ reduction to obtain Fe-Co@CNF@AuNPs composite material, which was used as the working electrode based on Au-S bond cooperation. The tetracycline electrochemical sensing interface Fe-Co@CNF@AuNPs@Apt was constructed by connecting mercaptoylated tetracycline (TC) aptamers on Fe-Co@CNF@AuNPs surface. The morphology and composition of Fe-Co@CNF@AuNPs composites were characterized by SEM, TEM, EDS, XRD and XPS, and the electrochemical properties of tetracycline were evaluated by CV and DPV. The results showed that the addition of Fe and Co did not destroy the structure of the original carbon nanofibers, and their synergistic effect enhanced the electrocatalytic performance, effective electrode area and electron transfer ability of carbon nanofibers. AuNPs are evenly distributed over the fibers, which effectively improves the electrical conductivity of the material. Under the optimal conditions, the theoretical detection limit of tetracycline was 0.213 nM, and the linear detection range was 5.12-10 mM, which could successfully detect tetracycline in milk.

Integrated analysis of physiological, transcriptome, and metabolome analyses of the gills in Solenaia oleivora under ammonia exposure.

Ammonia is a common toxicant in aquatic systems and one of the key factors affecting aquaculture. However, data on mollusks' toxic response and coping mechanisms to ammonia nitrogen, especially freshwater mollusks, are still lacking. In this study, we evaluated the tolerance of a freshwater mollusk Solenaia oleivora to ammonia and investigated its coping mechanisms by combining physiological, metabolic, and transcriptomic analyses in the gills. The acute toxicity test revealed that the LC50-96 h (temperature-20 â„ƒ, pH-7.4) of ammonia in S. oleivora was 63.29 mg/L. The physiological and TUNEL results showed that although 10 mg/L ammonia exposure increased the activities of antioxidant, immune and ammonia detoxification-related enzymes, it still caused oxidative damage and cell apoptosis of gill tissues. A total of 97 differential metabolites (DMs) and 3431 differential expressed genes (DEGs) were identified after ammonia stress. Among them, most DMs and DEGs were involved in immune response, antioxidant, cell apoptosis, carbohydrate metabolism, amino acid metabolism, and lipid metabolism. The enhancement of glycolysis and lipid metabolisms may provide energy for immune response and ammonia detoxification. In addition, glutamine synthesis, alanine synthesis and urea cycle were involved in ammonia nitrogen detoxification in the gill tissue of S. oleivora. Our results indicate that ammonia leads to individual death in S. oleivora, as wells as oxidative damage, cell apoptosis, immune response, and metabolic changes of gill tissues. The findings will provide valuable information to assess the potential ecological risk of environmental ammonia to freshwater mollusks and theoretical guidance for the healthy aquaculture of S. oleivora.

Convergence Analysis of Online Gradient Method for High-Order Neural Networks and Their Sparse Optimization.

In this article, we investigate the boundedness and convergence of the online gradient method with the smoothing group L1/2 regularization for the sigma-pi-sigma neural network (SPSNN). This enhances the sparseness of the network and improves its generalization ability. For the original group L1/2 regularization, the error function is nonconvex and nonsmooth, which can cause oscillation of the error function. To ameliorate this drawback, we propose a simple and effective smoothing technique, which can effectively eliminate the deficiency of the original group L1/2 regularization. The group L1/2 regularization effectively optimizes the network structure from two aspects redundant hidden nodes tending to zero and redundant weights of surviving hidden nodes in the network tending to zero. This article shows the strong and weak convergence results for the proposed method and proves the boundedness of weights. Experiment results clearly demonstrate the capability of the proposed method and the effectiveness of redundancy control. The simulation results are observed to support the theoretical results.

Full-length transcriptome analysis provides new insights into the diversity of immune-related genes in the threatened freshwater shellfish Solenaia oleivora.

Solenaia oleivora, a valuable and rare bivalve endemic to China, is becoming a threatened freshwater sepcies. However, the lack of research on its genome and immune system will hinder advances in its conservation and artificial breeding. In this study, we obtained the full-length transcriptome of S. oleivora using PacBio sequencing. A total of 21,415 transcripts with an average length of 1,726 bp were generated. Among these transcripts, 12,084 had coding sequences (CDS), of which 8,639 were annotated in 6 databases. The structure analysis identified 625 transcript factors (TFs), 8,005 long non-coding RNAs (lncRNAs), and 5,288 simple sequences repeat (SSRs). Meanwhile, massive immune genes were identified from the transcriptome of S. oleivora. In terms of non-self-identification, 97 transcripts of pattern recognition receptors (PRRs) were discovered, including peptidoglycan recognition proteins (PGRPs), gram-negative bacteria binding proteins (GNBPs), toll-like receptors (TLRs), scavenger receptors (SRs), galectins (GALs), C-type lectins (CLTs), and fibrinogen-related protein (FREPs). For pathogen elimination, 7 transcripts related to antimicrobial peptides, lysozymes, and lysosomal enzymes were identified. Moreover, 33 complement-associated transcripts were found. This study enriched the genome resources of S. oleivora and provided new insights for the study of the immune system of S. oleivora.

Stochastic momentum methods for non-convex learning without bounded assumptions.

Stochastic momentum methods are widely used to solve stochastic optimization problems in machine learning. However, most of the existing theoretical analyses rely on either bounded assumptions or strong stepsize conditions. In this paper, we focus on a class of non-convex objective functions satisfying the Polyak-Lojasiewicz (PL) condition and present a unified convergence rate analysis for stochastic momentum methods without any bounded assumptions, which covers stochastic heavy ball (SHB) and stochastic Nesterov accelerated gradient (SNAG). Our analysis achieves the more challenging last-iterate convergence rate of function values under the relaxed growth (RG) condition, which is a weaker assumption than those used in related work. Specifically, we attain the sub-linear rate for stochastic momentum methods with diminishing stepsizes, and the linear convergence rate for constant stepsizes if the strong growth (SG) condition holds. We also examine the iteration complexity for obtaining an ϵ-accurate solution of the last-iterate. Moreover, we provide a more flexible stepsize scheme for stochastic momentum methods in three points: (i) relaxing the last-iterate convergence stepsize from square summable to zero limitation; (ii) extending the minimum-iterate convergence rate stepsize to the non-monotonic case; (iii) expanding the last-iterate convergence rate stepsize to a more general form. Finally, we conduct numerical experiments on benchmark datasets to validate our theoretical findings.

Antibody-assisted MIL-53(Fe)/Pt-based electrochemical biosensor for the detection of the nicotine metabolite cotinine.

Antibody-assisted MIL-53(Fe)/Pt was used as an electrochemical biosensor, and a rapid detection method for analysing cotinine content in smokers' saliva was developed based on this sensor. In this sensor, Pt-modified MIL-53(Fe) was modified as an electrode material onto the surface of the working electrode. The amino group was activated with glutaraldehyde and antibodies to cotinine were modified onto the surface and closed with 1% BSA. Cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) were used to study the electrode assembly and cotinine detection. The DPV results showed an ideal linear relationship between the current value and the logarithm of the concentration. The detection limit was 0.0092 ng/mL. It has good selectivity and cycling stability. The proposed Abs-MIL-53(Fe)/Pt can effectively and sensitively detect cotinine in saliva and has promisingapplications.

Integrated transcriptome and miRNA sequencing analyses reveal that hypoxia stress induces immune and metabolic disorders in gill of genetically improved farmed tilapia (GIFT, Oreochromis niloticus).

The survival and growth of fish are significantly impacted by a hypoxic environment (low dissolved oxygen). In this study, we compared tissue structure, physiological changes, and mRNA/miRNA transcriptome, in gills of genetically improved farmed tilapia (GIFT, Oreochromis niloticus) between the hypoxic group (DO: 0.55 mg/L, HG) and the control group (DO: 5 mg/L, CG). The results showed that the gill filaments in the hypoxic group showed curling, engorgement, and apoptotic cells increased, and that exposure for 96 h resulted in a reduction in the antioxidant capacity. We constructed and sequenced miRNA and mRNA libraries from gill tissues of GIFT at 96 h of hypoxia stress. Between the HG and CG, a total of 14 differentially expressed (DE) miRNAs and 1557 DE genes were obtained. GO and KEGG enrichment showed that DE genes were mainly enriched in immune and metabolic pathways such as natural killer cell mediated cytotoxicity, steroid biosynthesis, primary immunodeficiency, and synthesis and degradation of ketone bodies. Based on the results of mRNA sequencing and screening for miRNA-mRNA pairs, we selected and verified six DE miRNAs and their probable target genes. The sequencing results were consistent with the qRT-PCR validation results. The result showed that under hypoxia stress, the innate immune response was up-regulated, and the adaptive immune response was down-regulated in the gill of GIFT. The synthesis of cholesterol in gill cells is reduced, which is conducive to the absorption of solvent oxygen. These findings offer fresh information about the processes of fish adaptation to hypoxic stress.

Magnetically separable laccase-biochar composite enable highly efficient adsorption-degradation of quinolone antibiotics: Immobilization, removal performance and mechanisms.

The tremendous potential of hybrid technologies for the elimination of quinolone antibiotics has recently attracted considerable attention. This current work prepared a magnetically modified biochar (MBC) immobilized laccase product named LC-MBC through response surface methodology (RSM), and LC-MBC showed an excellent capacity in the removal of norfloxacin (NOR), enrofloxacin (ENR) and moxifloxacin (MFX) from aqueous solution. The superior pH, thermal, storage and operational stability demonstrated by LC-MBC revealed its potential for sustainable application. The removal efficiencies of LC-MBC in the presence of 1 mM 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) for NOR, ENR and MFX were 93.7 %, 65.4 % and 77.0 % at pH 4 and 40 °C after 48 h reaction, respectively, which were 1.2, 1.3 and 1.3 times higher than those of MBC under the same conditions. The synergistic effect of adsorption by MBC and degradation by laccase dominated the removal of quinolone antibiotics by LC-MBC. Pore-filling, electrostatic, hydrophobic, π-π interactions, surface complexation and hydrogen bonding contributed in the adsorption process. The attacks on the quinolone core and piperazine moiety were involved in the degradation process. This study underscored the possibility of immobilization of laccase on biochar for enhanced remediation of quinolone antibiotics-contaminated wastewater. The proposed physical adsorption-biodegradation system (LC-MBC-ABTS) provided a novel perspective for the efficient and sustainable removal of antibiotics in actual wastewater through combined multi-methods.

PacBio Full-Length and Illumina Transcriptomes of the Gill Reveal the Molecular Response of Corbicula fluminea under Aerial Exposure.

Air exposure is a common stress for Corbicula fluminea, an economically important freshwater shellfish consumed in China, during aquaculture and transportation. However, little is known about its molecular responses to air exposure. Therefore, this study used a combination of PacBio full-length and Illumina transcriptomes to investigate its molecular responses to air exposure. A total of 36,772 transcripts were obtained using PacBio sequencing. Structural analysis identified 32,069 coding sequences, 1906 transcription factors, 8873 simple sequence repeats, and 17,815 long non-coding RNAs. Subcellular localization analysis showed that most transcripts were located in the cytoplasm and nucleus. After 96-h of air exposure, 210 differentially expressed genes (DEGs) in the gill were obtained via Illumina sequencing. Among these DEGs, most of the genes related to glycolysis, tricarboxylic acid cycle, lipid metabolism, and amino acid metabolism were upregulated. Additionally, many DEGs associated with immunity, cytoskeleton reorganization, autophagy, and ferroptosis were identified. These findings indicated that metabolic strategy change, immune response, cytoskeleton reconstruction, autophagy, and ferroptosis might be the important mechanisms that C. fluminea use to cope with air exposure. This study will enrich the gene resources of C. fluminea and provide valuable data for studying the molecular mechanisms coping with air exposure in C. fluminea and other freshwater mollusks.

Recent advances in morphology, aperture control, functional control and electrochemical sensors applications of carbon nanofibers.

Among many nanomaterials, electrospun carbon nanofibers (CNFs) have become one of the hot spots in nanoscience research because of their interesting physicochemical and biological properties such as large specific surface area, easy functionalization and biocompatibility. Polyacrylonitrile（PAN) has also become the most widely used precursor fiber for CNF manufacturing. In this paper, the latest advances in the synthesis of CNF by electrospinning were reviewed, including using template method, heat treatment, coaxial spinning technology to control the morphology and aperture, as well as the functionalization of electrospinning doped with chemical substances such as heteroatoms, nanoparticles (NPs), carbon nanotubes (CNTs) and grapheme (Gr), in order to further expand its application scope. The application of electrospun CNFs as electrochemical sensing platform for toxic and harmful substances in food and environment was also briefly introduced.

Surface Plasmon Resonance (SPR) biosensor for detection of mycotoxins: A review.

Mycotoxin is one of the most important natural pollutants, which poses a global threat to food safety. However, the pollution of mold in food production is inevitable. The detection technology of mycotoxins in food production is an important means to prevent the damage of mycotoxins, so rapid detection and screening to avoid pollution diffusion is essential. The focus of this review is to update the literature on the detection of mycotoxins by surface plasmon resonance (SPR) technology, rather than just traditional chromatographic methods. As a relatively novel and simple analytical method, SPR has been proved to be fast, sensitive and label-free, and has been widely used in real-time qualitative and quantitative analysis of various pollutants. This paper aims to give a broad overview of the sensors for detection and analysis of several common mycotoxins.

Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction.

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical-chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Target Detection Based on Improved Hausdorff Distance Matching Algorithm for Millimeter-Wave Radar and Video Fusion.

The intelligent transportation system (ITS) is inseparable from people's lives, and the development of artificial intelligence has made intelligent video surveillance systems more widely used. In practical traffic scenarios, the detection and tracking of vehicle targets is an important core aspect of intelligent surveillance systems and has become a hot topic of research today. However, in practical applications, there is a wide variety of targets and often interference factors such as occlusion, while a single sensor is unable to collect a wealth of information. In this paper, we propose an improved data matching method to fuse the video information obtained from the camera with the millimetre-wave radar information for the alignment and correlation of multi-target data in the spatial dimension, in order to address the problem of poor recognition alignment caused by mutual occlusion between vehicles and external environmental disturbances in intelligent transportation systems. The spatio-temporal alignment of the two sensors is first performed to determine the conversion relationship between the radar and pixel coordinate systems, and the calibration on the timeline is performed by Lagrangian interpolation. An improved Hausdorff distance matching algorithm is proposed for the data dimension to calculate the similarity between the data collected by the two sensors, to determine whether they are state descriptions of the same target, and to match the data with high similarity to delineate the region of interest (ROI) for target vehicle detection.