Search

ObjectiveEvidence-based review of epidemiological change trends of Salmonella serotype infections in the United States in recent 50 years.MethodsTo collect and analyze 30 dominant Salmonella serotypes（including enteral and parenteral dominant serotypes） infected in humans and the ecologically dominant Salmonella serotypes in farmed animals （animal disease and non-animal disease groups） in the United States from 1968 to 2011， and analyze the big Salmonella serotypes data of National Enteric Salmonella Surveillance in the United States from 2006 to 2016. To correlate the Salmonella serotypes based on the outbreaks of Multistate foodborne and pet-borne disease in the United States from 2006 to 2017.ResultsThe United States has caused the formation of colonization and pathogenic ecological characteristics between some Salmonella serotypes and cultured animals based on the more and more developed breeding industry in the past 50 years. There is an exposure response relationship between the dominant Salmonella serotypes of colonized animals and the human infected. The long-term interaction between human and animal makes the dominant Salmonella serotypes highly match. In the past 50 years， the baseline of Salmonella infection in the population in the United States has been gradually increasing， The patients are most common in children and the elderly， the highest incidence in children under 1 year old， and more cases in the south and Midwest. In 2008， enteritis replaced typhimurium as the first human infection of Salmonella in the United States. The foods attributed to Salmonella multistate foodborne outbreaks have the consumption characteristics of transition from traditional meat， eggs， and milk to vegetables， ready-to-eat foods， fruits， etc. The Salmonella multistate pet-borne outbreaks， which have gradually increased since 2006 and are related to live animals such as domesticated reptiles， belong to behavioral ecotype infectious diseases， with longer duration， more dispersed cases and higher concealability than foodborne outbreaks.ConclusionTo summarizes the disease ecological characteristics of Salmonella in the United States， which can provide reference for the comprehensive prevention and control of salmonellosis between veterinarians and humans in China.

ObjectiveThis study aimed to analyze the serotype and phenotype of human and non-human sourced Salmonella from 1975 to 2021 in Fujian Province retrospectively.MethodsSalmonella isolates were collected from clinical hospitals， public service employee health， circulating fresh food， and the environment from 1975 to 2021 in Fujian Province， and serotypes and variants were distinguished using WHO-recommended Salmonella serotyping methods. Two prediction software were used based on whole genome sequencing， while the same antigen but different isolates or phenotypic variants were identified using biochemical combinations and MALDI-TOF-MS flight mass spectrometry.ResultsThere were 4 693 human- and non-human-sourced Salmonella isolates that contained 21 serogroups of four subspecies （18 from patients， 9 from healthy employees， 17 from food， and 8 from the environment）. Salmonella paratyphoid A， S. typhoid， S. enteritis， S. cholerae， S. typhimurium， S. rosen， and S. stanley were dominant isolates in the 30 total extraintestinal invasive serotypes. There were 96 Salmonella serotypes other than S. typhoid， S. paratyphoid A， and S. paratyphoid B （63 from patients， 51 from healthy employees， 60 from food， and 20 from the environment）， and the top 10 clinical serotypes were S. typhimurium， S. typhoid， S. enteritis， S. derby， S. stanley， S. paratyphoid A， S. anatum， S. wetavreden， S. rosen， and S. london， while the top 10 from food were S. anatum， S. derby， S. typhimurium， S. stanley， S. wetavreden， S. newport， S. enteritis， S. tompson， S. agona， and S. london. Fourteen serotypes （including 10 diphasic serotypes and 4 monophasic serotypes） had monophasic variants. Several serotypes had phenotypic variants such as hydrogen sulfide-negative， fermenter of lactose or sucrose， colony mucus type， and chromogenic type.ConclusionSalmonella are characterized by their biodiversity and multiple sources in Fujian Province. Invasive cases are gradually dominated by non-typhoid Salmonella. Laboratories should enhance their ability to diagnose monophasic variants and phenotypic variants of Salmonella， as well as pet and environmental traceability for scattered outbreaks of infectious diseases.

ObjectiveTo evaluate the equivalence of 5 Salmonella selective enrichment procedures in standard methods worldwide.MethodsThe growth rates（PR） and the colony characteristics of 5 selective enrichment broths（SC， TTB， RV， MKTTn， SBG） and 5 selective mediums（CAS， XLD， XLT4， HE， BS） were validated according to the GB4789.28—2013. The sensitivity（SE）， the relative trueness（RT） and the acceptable limit（AL） were estimated on highly contaminated foods through the use of 5 protocols combined with 5 selective enrichment broths and 2 selective mediums according to the method of ISO 16140—2：2016. The sensitivity（Se）， the specificity（Sp）， the positive predictive value（PPV） and the negative predictive value（NPV） were simultaneously determined through the use of 10 compound modes combined with selective enrichment broths and selective mediums according to the classical statistical methods.ResultsThe results showed that SC broth had no efficacy in enriching Salmonella enterica serotype Cholerae and Salmonella enterica serotype Cholerae var. Kunzendorf. The CAS and XLD were significantly better than XLT4， HE and BS in identifying typhoidal Salmonella and nontyphoidal Salmonella as well as the phenotype of hydrogen sulfide.Totally 84 samples（22 pieces of meat， 32 pieces of poultry and 30 pieces of aquatic products） were tested. 25 out of the 84（29.76%） were confirmed positive by 5 protocols， and 16 serotypes from 42 isolates were identified. The SE of 5 protocols were 84.00%， 100.00%， 88.00%，88.00% and 92.00%， respectively， but showed no statistically significant difference between each other. The AL of one protocol （SC+TTB） went beyond the limitation of the ISO 16140—2：2016 due to the ineffectiveness of SC broth. The compound mode of RV-CAS had the best comprehensive benefits， while the Se， the Sp， the PPV and the NPV of which were 68.00%， 95.00%， 85.00% and 88.00%， respectively.ConclusionThe ineffectiveness of SC broth reduces the sensitivity of the detection protocol for Salmonella， which could cause a negative deviation， resulting in the threshold exceeding of the Acceptability Limit （AL）. The combinational selective enrichment broth of RV-CAS is recommended to be the optimal mode， with CAS+XLD being recommended to be the optimal selective medium combination. The process of Salmonella serotyping is suggested to be improved in the standard of GB4789.4 to cope with the multi-serotype Salmonella contamination in food chain and be consistent with the international standardization for food microbiology.

Kazakh is one of the multilingual languages of China and is widely spoken in some areas of Xinjiang, China. However, due to the fact that Kazakh is a language in which several characters are glued together to form a continuous word with a unique shape and complex structural combinations of relationships. This paper explores a solution for offline image retrieval of handwritten Kazakh words, which is a challenging task because, due to the lack of relevant datasets and the special writing morphology of the Kazakh language, traditional text image retrieval algorithms often struggle to achieve satisfactory results when dealing with writing styles that are varied and adherent to the language. Therefore, a dataset of offline Kazakh handwritten document images was created in this paper. The dataset contains 300 pages of document images with 20 500 words. Then, a new model called the 'AENet' is proposed. The model utilizes an attention mechanism to focus more finely on focal regions such as centers, inflection points, and contours of handwritten word images and to capture important local features from different scales. Fusion space pyramid pooling, feature aggregation, encoding operations, and feature downscaling and reconstruction are used to extract and reconstruct more representative feature representations from local to global to capture the overall information in the word images. Through experimental evaluation on Kazak‐80, Zilla‐64, and HWDB1.1‐375 datasets, it is verified that the method significantly improves the mAP for image retrieval of handwritten words, which is especially applicable to adhesive languages like Kazakh. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A total of 265 Salmonella Enteritidis isolates collected from retail markets and children's hospitals in Shanghai were used to investigate the prevalence and molecular epidemiology of plasmid-mediated fosfomycin resistance genes. Nine of the isolates—7 from the 146 (4.79%) retail chicken-related samples and 2 from the 119 (1.68%) samples from clinical children—were fosfomycin-resistant (FosR). The fosA3 gene was detected in all of the nine FosR isolates, which were located on Inc F-type (8/9, 88.9%) and unknown-type (1/9, 11.1%) transferable plasmids. In total, five plasmid types, namely Inc HI2 (1/9, 11.1%), Inc I1 (3/9, 33.3%), Inc X (8/9, 88.9%), Inc FIIs (9/9, 100%), and Inc FIB (9/9, 100%), were detected in these FosR isolates, which possessed five S1 nuclease pulsed-field gel electrophoresis (S1-PFGE) profiles. The extended-spectrum β-lactamase determinant blaCTX-M-14 subtype was identified in one FosR S. Enteritidis isolate, which was located in a transferable unknown-type plasmid co-carrying fosA3 and tetR genes. Sequence homology analysis showed that this plasmid possessed high sequence similarity to previously reported blaCTX-M-14- and fosA3-positive plasmids from E. coli strains, implying that plasmids carrying the fosA3 gene might be disseminated among Enterobacterales. These findings highlight further challenges in the prevention and treatment of Enterobacteriaceae infections caused by plasmids containing fosA3. [ABSTRACT FROM AUTHOR]

ObjectiveTo investigate the genomic and virulence characteristics of seven invasive non-typhoidal Salmonella (iNTS) isolates.MethodsBased on the collection of seven iNTS strains, whole genome sequencing were performed. The serotypes, genomes and virulence factors were identified, annotated and analyzed according to the comparison of genome data and relevant databases.ResultsAmong the seven tested iNTS isolates, six were monophasic, including one Salmonella Typhimurium and two Salmonella I 4, [5], 12∶i∶-(ST34 type), two Salmonella Enteritidis, one Salmonella Corvallis and one unknown serotype with the antigenic formulae of I 4, [5], 12∶d∶- (ST279 type). No consistent rule was found in genomic components and relevant parameter differences varies much between different serotypes than those of the same serotype from different sources. The distribution of virulence factors (VF) was significantly different among different Salmonella serotypes, but the common VF characteristics in iNTS strains were not found, though Capsule/Immune evasion, toxin, peritrichous_flagella and Salmochelin may be associated with high invasiveness. All tested isolates contained type III and type VI secretion systems. The number of secretion protein, type III for S. Corvallis and type VI for Salmonella I 4, [5], 12∶d∶-was the highest among the seven isolates. Pseudogenization of the secreted effector SseI may exist among some Salmonella serotypes which may contribute to the enhancement of Salmonella invasiveness.ConclusionThe common characteristic differences of virulence factor between iNTS and gastroenteritis NTS were not observed at the moment, and highly invasive pathogenic mechanisms still require further analysis and research at the functional transcriptome and proteomic levels to determine the genetic basis of iNTS occurrence and transmission.

Background The diagnosis of myasthenia gravis (MG) in children remains difficult. Circulating small extracellular vesicle (sEV)-derived miRNAs (sEV-miRNAs) have been recognized as biomarkers of various diseases and can be excreted by different cell types. These biomarker candidates also play a vital role in autoimmune diseases via intercellular communication. Methods In the present study, we used sEV isolation and purification methods to extract the plasma-derived sEV-miRNAs from children with MG and healthy controls. A small RNA sequencing analysis confirmed the miRNA expression features in plasma-derived sEVs from MG patients. The miRNA expression analysis in vitro was determined using microarray analysis. The enrichment and network analyses of altered sEV-miRNAs were performed using miRNA databases and Database for Annotation, Visualization, and Integrated Discovery website. Quantitative real-time polymerase chain reaction was performed for validation of sEV-miRNA. The diagnostic power of altered sEV-miRNAs was evaluated using receiver operating characteristic curve analyses. Results Twenty-four sEV-miRNAs with altered expression level were identified between groups by DESeq2 method. The miRNAs were extracted from the sEVs, which were isolated from human primary skeletal muscle cell culture treated with mAb198. The target genes and enriched pathways of sEV-miRNAs partially overlapped between cell supernatant and plasma samples. The significantly downregulated miR-143-3p was validated in quantitative real-time polymerase chain reaction analysis. Conclusions For the first time, we report that plasma-derived sEV-miRNAs may act as novel circulating biomarkers and therapeutic targets in pediatric MG. What is already known on this subject: The miRNA expression signature can potentially be used as a biomarker to diagnose a wide variety of diseases. Small extracellular vesicles (sEVs) can provide a stable environment for miRNA cargo to avoid degradation. What this study adds: Twenty-four sEV-miRNA candidates were differentially expressed in pediatric myasthenia gravis (MG) patients compared to healthy controls. Plasma-derived sEV-miR-143-3p has potential value in the diagnosis of pediatric autoantibody seronegative myasthenia gravis. How this study might affect research, practice, or policy: Our findings provide a set of plasma-derived sEV-miRNAs candidates for the diagnosis of pediatric myasthenia gravis, especially autoantibody seronegative patients. [ABSTRACT FROM AUTHOR]

Background: This study aimed to provide epidemiological features of Salmonella enterica serovar Rissen, determine antimicrobial susceptibility, virulence gene profiles, and describe the potential association of S. Rissen from different sources in China. Methods: During 2008– 2019, a total of non-repetitive 228 S. Rissen isolates were collected from human, animals and environment in China. The antimicrobial susceptibility test, screening of antimicrobial and virulence genes by PCR, and pulsed-field gel electrophoresis (PFGE) were performed. Results: Among the 154 isolates from human, the majority of the cases (80.5%) occurred in summer, and S. Rissen was mainly detected in people aged 21– 40 (37.7%) and 41– 60 (28.6%) years old, and 74 non-human source S. Rissen strains were identified, with pork being the most common source. About 93.4% isolates were resistant to at least one of the 12 tested antimicrobial agents, and high frequencies of resistance were observed for tetracyclines (91.2%), trimethoprim-sulfamethoxazole (74.1%) and ampicillin (67.5%). A total of 171 (75%) isolates were resistant to at least three categories of antimicrobials, and the most common resistance profile was Tetracycline(s)-β-Lactams-Sulfonamides. The resistance rates to chloramphenicol, quinolones and sulfafurazole were significantly higher in strains isolated from human compared to non-human source strains. Among these isolates, the β-Lactams resistance was mainly associated with gene blaTEM (54.7%), sulfonamide resistance with sul2 (45.7%) and sul3 (54.3%), tetracycline resistance with tetA (81.3%). All the isolates harbored virulence genes hilA, sopB, sciN, stn and ssrB, and most of them harbored ssaQ (98.7%), mgtC (98.7%) and invA (98.2%). The majority (91.7%) of S. Rissen isolates showed high similarity (> 80%) with each other in PFGE patterns and came from human, animals and environment. Conclusion: The high frequencies of multidrug resistance and probable clonal dissemination in this serovar call for the necessity of systematic surveillance on S. Rissen in China. [ABSTRACT FROM AUTHOR]

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users' privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user's privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems. [ABSTRACT FROM AUTHOR]

Lung segmentation is an essential step in a computer‐aided diagnosis system for chest radiographs. The lung parenchyma is first segmented in pulmonary computer‐aided diagnosis systems to remove the interference of non‐lung regions while increasing the effectiveness of the subsequent work. Nevertheless, most medical image segmentation methods nowadays use U‐Net and its variants. These variant networks perform poorly in segmentation to detect smaller structures and cannot accurately segment boundary regions. A multi‐interaction feature fusion network model based on Kiu‐Net is presented in this paper to address this problem. Specifically, U‐Net and Ki‐Net are first utilized to extract high‐level and detailed features of chest images, respectively. Then, cross‐residual fusion modules are employed in the network encoding stage to obtain complementary features from these two networks. Second, the global information module is introduced to guarantee the segmented region's integrity. Finally, in the network decoding stage, the multi‐interaction module is presented, which allows to interact with multiple kinds of information, such as global contextual information, branching features, and fused features, to obtain more practical information. The performance of the proposed model was assessed on both the Montgomery County (MC) and Shenzhen datasets, demonstrating its superiority over existing methods according to the experimental results. [ABSTRACT FROM AUTHOR]

Facial expression recognition technology has become a powerful tool for conveying human emotions and intentions and is widely used in areas such as assisted driving and intelligent medical care. Due to the limited computing power of current hardware devices and the real‐time requirements of application scenarios, this paper proposes a high‐performance and lightweight framework for real‐time facial expression recognition framework to solve the problem of real‐time completion of expression recognition tasks under low hardware costs. To address these issues, this paper first designs a RepVGG and mobileNetV2 dual‐channel structure in the feature extraction. It is then input into the MobileViT Block for global feature modelling. Finally, the position vector of the capsule network is used to replace the output of the global pooling, preserving the spatial relationship of the salient features and enhancing the classification effect. Compared with the mainstream facial expression recognition algorithm that cannot get good classification results under low complexity conditions, the model has a significant accuracy improvement while ensuring lightweight. With only 294.60M FLOPS and 0.95M parameters, it achieved an accuracy of 97.53% on the KDEF dataset and 85.56% on the RAF‐DB, demonstrating the advanced nature of the algorithm. [ABSTRACT FROM AUTHOR]

Understanding changes in pathogen behavior (e.g. increased virulence, a shift in transmission channel) is critical for the public health management of emerging infectious diseases. Genome degradation via gene depletion or inactivation is recognized as a pathoadaptive feature of the pathogen evolving with the host. However, little is known about the exact role of genome degradation in affecting pathogenic behavior, and the underlying molecular detail has yet to be examined. Using large-scale global avian-restricted Salmonella genomes spanning more than a century, we projected the genetic diversity of Salmonella Pullorum (bvSP) by showing increasingly antimicrobial-resistant ST92 prevalent in Chinese flocks. The phylogenomic analysis identified three lineages in bvSP, with an enhancement of virulence in the two recently emerged lineages (L2/L3), as evidenced in chicken and embryo infection assays. Notably, the ancestor L1 lineage resembles the Salmonella serovars with higher metabolic flexibilities and more robust environmental tolerance, indicating stepwise evolutionary trajectories towards avian-restricted lineages. Pan-genome analysis pinpointed fimbrial degradation from a virulent lineage. The later engineered fim -deletion mutant, and all other five fimbrial systems, revealed behavior switching that restricted horizontal fecal–oral transmission but boosted virulence in chicks. By depleting fimbrial appendages, bvSP established persistent replication with less proinflammation in chick macrophages and adopted vertical transovarial transmission, accompanied by ever-increasing intensification in the poultry industry. Together, we uncovered a previously unseen paradigm for remodeling bacterial surface appendages that supplements virulence-enhanced evolution with increased vertical transmission. [ABSTRACT FROM AUTHOR]

Chest X-ray (CXR) images can be used to diagnose a variety of lung diseases, such as tuberculosis, pneumonia, and lung cancer. However, the variation in lung morphology due to differences in age, gender, and the severity of pathology makes high-precision lung segmentation a challenging task. Traditional segmentation networks, such as U-Net, have become the standard architecture and have achieved remarkable results in lung field image segmentation tasks. However, because traditional convolutional operations can only explicitly capture local semantic information, it is difficult to obtain global semantic information, resulting in difficult performance in terms of accuracy requirements in medical practical applications. In recent years, the introduction of Transformer technology to natural language processing has achieved great success in the field of computer vision. In this paper, a new network architecture called TransCotANet is proposed. The network architecture is based on the U-Net architecture with convolutional neural networks (CNNs) as the backbone and extracts global semantic information through symmetric cross-layer connections in the encoder structure, where the encoder stage includes an upsampling module to improve the resolution of the feature map, and uses the dynamic aggregation module CotA to dynamically aggregate multi-scale feature maps and finally obtain more accurate segmentation results. The experimental results show that the method outperformed other methods for lung field image segmentation datasets. [ABSTRACT FROM AUTHOR]

Salmonella enterica subsp. enterica serovar Typhimurium (S. typhimurium) is an important zoonotic pathogen with important public health significance. To understand S. typhimurium's epidemiological characteristics in China, multi-locus sequence typing, biofilm-forming ability, antimicrobial susceptibility testing, and resistant genes of isolates from different regions and sources (human, food) were investigated. Among them, ST34 accounted for 82.4% (243/295), with ST19 ranking second (15.9%; 47/295). ST34 exhibited higher resistance levels than ST19 (p < 0.05). All colistin, carbapenem, and ciprofloxacin-resistant strains were ST34, as were most cephalosporin-resistant strains (88.9%; 32/36). Overall, 91.4% (222/243) ST34 isolates were shown to have multidrug resistance (MDR), while 53.2% (25/47) ST19 isolates were (p < 0.05). Notably, 97.8% (45/46) of the MDR-ACSSuT (resistance to Ampicillin, Chloramphenicol, Streptomycin, Sulfamethoxazole, and Tetracycline) isolates were ST34, among which 69.6% (32/46) of ST34 isolates were of human origin, while 30.4% (14/46) were derived from food (p < 0.05). Moreover, 88.48% (215/243) ST34 showed moderate to strong biofilm-forming ability compared with 10.9% (5/46) ST19 isolates (p < 0.01). This study revealed the emergence of high-level antibiotic resistance S. typhimurium ST34 with strong biofilm-forming ability, posing concerns for public health safety. [ABSTRACT FROM AUTHOR]

Ear images are easy to capture, and ear features are relatively stable and can be used for identification. The ear images are all asymmetric, and the asymmetry of the ear images collected in the unconstrained environment will be more pronounced, increasing the recognition difficulty. Most recognition methods based on hand-crafted features perform poorly in terms of recognition performance in the face of ear databases that vary significantly in terms of illumination, angle, occlusion, and background. This paper proposes a feature fusion human ear recognition method based on channel features and dynamic convolution (CFDCNet). Based on the DenseNet-121 model, the ear features are first extracted adaptively by dynamic convolution (DY_Conv), which makes the ear features of the same class of samples more aggregated and different types of samples more dispersed, enhancing the robustness of the ear feature representation. Then, by introducing an efficient channel attention mechanism (ECA), the weights of important ear features are increased and invalid features are suppressed. Finally, we use the Max pooling operation to reduce the number of parameters and computations, retain the main ear features, and improve the model's generalization ability. We performed simulations on the AMI and AWE human ear datasets, achieving 99.70% and 72.70% of Rank-1 (R1) recognition accuracy, respectively. The recognition performance of this method is significantly better than that of the DenseNet-121 model and most existing human ear recognition methods. [ABSTRACT FROM AUTHOR]

This paper proposes an improved selective kernel network-temporal convolutional (SKResNet-TCN) network-based video recognition model for isolated word sign language with too large parameters, large computation, and difficult to extract effective features. SKResNet uses grouped convolution to save computational cost while dynamically selecting feature information of different perceptual fields to improve the feature extraction ability of the model for video frame images, and TCN introduces causal and inflation convolution to take full advantage of computer parallel computing and reduce memory overhead during computation. The introduction of causal convolution and dilation convolution allows the network to take full advantage of computer parallel computing and reduce memory overhead during computation, and it can capture the feature information between consecutive frames. In this paper, we design a hybrid SKResNet-TCN network model based on these two networks, and propose a solution of hybrid inflated convolution for the problem of losing information between data features in inflated convolution, using adaptive maximum pooling to preserve significant features of sign language instead of adaptive average pooling, and using Mish activation function to improve the generalization ability and accuracy of the model. The accuracy is 100% on the Argentine LSA64 dataset, and the experimental results show that the model in this paper has the advantages of fewer model parameters, smaller operations, and higher accuracy in sign language recognition compared with traditional 3D convolutional networks and long–short term memory, which effectively saves computational cost and time cost. [ABSTRACT FROM AUTHOR]

Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing stress in society comes with a growing surge in conditions associated with sleep disorders. Studies have shown that sleep stages are essential for the body's memory, immune system, and brain functioning. Therefore, automatic sleep stage classification is of great medical practice importance as a basis for monitoring sleep conditions. Although previous research into the classification of sleep stages has been promising, several challenges remain to be addressed: (1) The EEG signal is a non-smooth signal with harrowing feature extraction and high requirements for model accuracy. (2) Some existing network models suffer from overfitting and gradient descent. (3) Correlation between long time sequences is challenging to capture. This paper proposes NAMRTNet, a deep model architecture based on the original single-channel EEG signal to address these challenges. The model uses a modified ResNet network to extract features from sub-epochs of individual epochs, a lightweight attention mechanism normalization-based attention module (NAM) to suppress insignificant features, and a temporal convolutional network (TCN) network to capture dependencies between features of long time series. The recognition rate of 20-fold cross-validation with the NAMRTNet model for Fpz-cz channel data in the public sleep dataset Sleep-EDF was 86.2%. The experimental results demonstrate the network's superiority in this paper, surpassing some state-of-the-art techniques in different evaluation metrics. Furthermore, the total time to train the network was 5.1 h, which was much less than the training time of other models. [ABSTRACT FROM AUTHOR]

Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a new Res2Net-based improved script identification method, namely FAS-Res2Net. In the feature extraction part, the feature pyramid network (FPN) module is introduced, which is beneficial to aggregate the geometric feature information extracted by the shallow network and the semantic feature information extracted by the deep network. Integrating the Adaptive Spatial Feature Fusion (ASFF) module is beneficial to obtain local feature information for optimal weight fusion. In addition, the global feature information of the image is extracted by introducing the swin transformer coding block, which makes the extracted feature information more abundant. In the classification part, the convolutional classifier is used to replace the traditional Linear classification, and the classification confidence of each category is output, which improves the identification efficiency. The improved algorithm achieved identification rates of 94.7% and 96.0% on public script identification datasets SIW-13 and CVSI-2015, respectively, which verified the superiority of the method. [ABSTRACT FROM AUTHOR]

Salmonella enterica is one of the most common bacterial pathogens in humans and animals. Systematic studies on the trends and geographical distribution of antimicrobial-resistant Salmonella and dominant serovars have been well studied in European and American countries while not in China. Here, taking the One-Health strategy, we used >35 000  Salmonella enterica isolates to explore the temporal and spatial dynamics of dominant serovars in China. We found that Salmonella Typhimurium was the dominant serovar causing human infection in China, which was consistent with Australia but inconsistent with North American and European countries. The proportion of Salmonella serovars Typhimurium, London, Rissen, Corvallis, Meleagridis, Kentucky, and Goldcoast showed an increasing trend during 2006–2019. We randomly selected 1962 isolates for comparative genomics and antimicrobial resistance studies and found that the number of antibiotic resistance genes (ARGs) per isolate increased 1.84 and 2.69 times of human and non-human origins, respectively, spanning 14 years. The proportion of antimicrobial-resistant Salmonella isolates had an increasing trend during 2006–2019, especially beta-lactam, quinolone, tetracycline, and rifampicin resistance. Moreover, we found that higher diversity of sequence types (STs) in S. Typhimurium than in other serovars, ST34 from pig and ST19 from chicken origin, were mainly associated with isolates causing child and adult gastro-infection, respectively. Our results fill in the data gap on the trends of dominant serovars and antimicrobial resistance of Salmonella enterica in China. These data provide useful information for public health decision-makers prioritizing interventions for foodborne diseases and food safety. [ABSTRACT FROM AUTHOR]