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Abstract High temperature and humidity in the environment are known to be associated with discomfort and disease, yet the underlying mechanisms remain unclear. We observed a decrease in plasma glucagon-like peptide-1 levels in response to high-temperature and humidity conditions. Through 16S rRNA gene sequencing, alterations in the gut microbiota composition were identified following exposure to high temperature and humidity conditions. Notably, changes in the gut microbiota have been implicated in bile acid synthesis. Further analysis revealed a decrease in lithocholic acid levels in high-temperature and humidity conditions. Subsequent in vitro experiments demonstrated that lithocholic acid increases glucagon-like peptide-1 secretion in NCI-H716 cells. Proteomic analysis indicated upregulation of farnesoid X receptor expression in the ileum. In vitro experiments revealed that the combination of lithocholic acid with farnesoid X receptor inhibitors resulted in a significant increase in GLP-1 levels compared to lithocholic acid alone. In this study, we elucidate the mechanism by which reduced lithocholic acid suppresses glucagon-like peptide 1 via farnesoid X receptor activation under high-temperature and humidity condition.

ProblemsPlant diseases significantly impact crop growth and yield. The variability and unpredictability of symptoms postinfection increase the complexity of image-based disease detection methods, leading to a higher false alarm rate.AimTo address this challenge, we have developed an efficient, weakly supervised agricultural disease localization model using Siamese neural networks.MethodsThis model innovatively employs a Siamese network structure with a weight-sharing mechanism to effectively capture the visual differences in plants affected by diseases. Combined with our proprietary Agricultural Disease Precise Localization Class Activation Mapping algorithm (ADPL-CAM), the model can accurately identify areas affected by diseases, achieving effective localization of plant diseases.Results and conclusionThe results showed that ADPL-CAM performed the best on all network architectures. On ResNet50, ADPL-CAM’s top-1 accuracy was 3.96% higher than GradCAM and 2.77% higher than SmoothCAM; the average Intersection over Union (IoU) is 27.09% higher than GradCAM and 19.63% higher than SmoothCAM. Under the SPDNet architecture, ADPL-CAM achieves a top-1 accuracy of 54.29% and an average IoU of 67.5%, outperforming other CAM methods in all metrics. It can accurately and promptly identify and locate diseased leaves in crops.

Despite advances in deep learning for plant leaf disease recognition, accurately distinguishing morphological features under varying environmental conditions continues to pose significant challenges. Traditional deep learning models often fail to effectively merge local and global information, especially in small-scale datasets, impairing performance and elevating training costs. Focusing on citrus diseases, we propose an improved FasterViT Model, an advanced hybrid CNN-ViT framework that builds upon the FasterViT model. The proposed model seamlessly integrates CNN's rapid local learning capabilities with ViT's global information processing strength, thereby effectively extracting complex textures and morphological features from images. Cross-stage alternating Mixup and Cutout methods are strategically employed to enhance model robustness and generalization capabilities, particularly valuable for fast learning on small-scale datasets by simulating a more diverse training environment. Triplet Attention and AdaptiveAvgPool mechanisms are utilized to reduce training costs and optimize training performance. The proposed model is tested on both our specially constructed small-scale citrus disease dataset called in-field small dataset and the comprehensive PlantVillage dataset. The experimental results demonstrated that the model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks, and demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Additionally, its exemplary performance in transfer learning scenarios underscores its adaptability and broad applicability. This study not only highlights the efficacy of the improved FasterViT model in addressing the complexities of plant disease image recognition but also pioneers a new paradigm for developing efficient, scalable, and robust classification systems.

Abstract Background Patients with post-stroke depression (PSD) usually experience anxiety, hopelessness, and insomnia, which have a negative impact on their daily activities and post-stroke rehabilitation. Acupuncture (AC), as a minimally invasive technique, has become a popular choice for improving depression symptoms. However, it is still unclear which therapy is associated with the best outcomes for PSD. In this review, we aimed to explore the impact of AC in alleviating symptoms of PSD and to evaluate the difference in effectiveness between AC combined with pharmacotherapies and various non-pharmacotherapies. Methods Six databases and three clinical trials registration platforms were searched from inception to March 2023. Randomized clinical trial comparing needle-based AC with pharmacotherapy, and other non-pharmacotherapy or invalid group were included. Two independent reviewers identified eligible studies, and collected data using a pre-made form. A Bayesian network meta-analysis was conducted to assess and compare different techniques using RStudio 3.6.0 with the package ‘GEMTC’ V.0.8.1. The primary outcome was the efficacy for PSD assessed by scales measuring depressive symptoms. The secondary outcomes were effectiveness for neurological function and the quality of life. The ranking probabilities for all treatment interventions was performed using the Surface Under the Cumulative Ranking curve (SUCRA). The risk of bias was assessed by using the Revised Cochrane Risk of Bias tool 2. Results Sixty-two studies, involving 5308 participants published from 2003 to 2022, were included. The results showed that compared with western medicine (WM) (defined as pharmacotherapy for PSD), AC alone or with repetitive transcranial magnetic stimulation (RTMS), Traditional Chinese medicine (TCM) alone or with WM, were superior for alleviating depression symptoms. Compared to Usual Care, AC alone or plus other therapies could significantly decrease scores on the Hamilton Depression Rating scale. According to result of SUCRA, AC plus RTMS had the highest probability of improving depressive symptoms with a probability of 49.43%. Conclusions The results of this study indicate that AC alone or combined with other therapies appears to be effective in improving depression symptoms of stroke survivors. Moreover, in comparison to WM, AC alone or plus RTMS, TCM, TCM with WM, or WM, were more effective in improving depression symptoms of PSD. Also, AC with RTMS seems to be the most effective with the highest probability. Registration This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database in November 2020 and updated in July 2021. The registration number is CRD42020218752.

Abstract Vortex acoustic waves (VAW) with helical phase wavefronts could provide a new dimension for the detection and imaging of targets in the ocean. To explore the propagation characteristics of VAW under the effect of ocean turbulence (OT) and the impact on imaging, the phase screen method is used to simulate the propagation of VAW generated by a phase‐modulated uniform circular array under the effect of OT and the imaging of the target. The phase change and detection probability under the influence of the acoustic wave parameters and OT parameters during the propagation process are studied. Second, the azimuth resolution performance and focussing performance of the target imaging are studied. Under the effect of OT, the VAW of different frequencies and topological modes become scattered, and the degree of scattering is different when the parameters are different; and the resolution performance and focussing performance have also deteriorated. However, the VAW still has a reliable resolution under the effect of medium and low‐intensity OT, which provides theoretical support for the practice of the VAW in underwater target imaging.

ProblemsPlant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models.AimThis paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample.MethodsTwo training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images.Results and conclusionThe test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.

BackgroundMeasurements of IgG antibodies to wild-type SARS-CoV-2 antigens can assess vaccine efficacy, but the absolute risk of Omicron symptomatic infection at different IgG levels for children and adolescents remains uncertain, as well as the minimum effective antibody level. We sought to determine the relationship between the tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens and children with symptomatic infection of the pandemic and duration to negative conversion in China for the first time.MethodsA retrospective study was conducted, including 168 participants under 18 years old from the No.2 People’s Hospital of Lanzhou, China, diagnosed with Omicron variant BA.2.38 between July 8, 2022, and August 2, 2022. We calculated odds ratios (OR) in univariate and multivariate regression to assess the association of symptomatic infection with the tertiles of IgG, respectively. Kaplan–Meier curves and Cox proportional hazards regression were used to evaluate the relationship between IgG level and negative conversion time.ResultsThe average age of the 168 children included in this study was 7.2 (4.7) years old, 133 (79.2%) were symptomatic patients, and the average negative conversion time was 12.2 (3.5) days. The participants with high IgG levels were less likely to become symptomatic, had a shorter turnaround time, and had higher values of IgM and nucleic acid CT. Compared to those with the lowest tertile of IgG, patients with the highest tertile had a 91% lower risk of developing a symptomatic infection after fully adjusting for confounders (OR = 0.09, 95% CI, 0.02-0.36, p = 0.001). There’s no robust relationship between IgG level and negative conversion time in multivariate Cox regression.ConclusionThe risk of developing a symptomatic infection can be predicted independently by tertiles of IgG antibodies to wild-type SARS-CoV-2 antigens. High IgG levels can inhibit viral replication, vastly reduce the risk of symptomatic infections and promote a virus-negative conversion, especially when IgG quantitative detection was ≥3.44 S/CO, a potential threshold for protection and booster strategy in the future. More data and research are needed in the future to validate the predictive models.

IntroductionA hot cell is generally deployed with a teleoperation power manipulator to complete tests, operations, and maintenance. The position and pose of the manipulator are mostly acquired through radiation-resistant video cameras arranged in the hot cell. In this paper, deep learning-based target detection technology is used to establish an experimental platform to test the methods for target detection and pose estimation of teleoperation power manipulators using two cameras.MethodsIn view of the fact that a complex environment affects the precision of manipulator pose estimation, the dilated-fully convolutional one-stage object detection (dilated-FCOS) teleoperation power manipulator target detection algorithm is proposed based on the scale of the teleoperation power manipulator. Model pruning is used to improve the real-time performance of the dilated-FCOS teleoperation power manipulator target detection model. To improve the detection speed for the key points of the teleoperation power manipulator, the keypoint detection precision and model inference speed of different lightweight backbone networks were tested based on the SimpleBaseline algorithm. MobileNetv1 was selected as the backbone network to perform channel compression and pose distillation on the upsampling module so as to further optimize the inference speed of the model.Results and discussionCompared with the original model, the proposed model was experimentally proven to reach basically the same precision within a shorter inference time (only 58% of that of the original model). The experimental results show that the compressed model basically retains the precision of the original model and that its inference time is 48% of that of the original model.

Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a health concern for both unvaccinated and vaccinated individuals against coronavirus disease 2019 (COVID-19). To date, the humoral immune response following vaccination and natural infection remains uncharacterized in children ages 17 years and younger. To address this concern, we performed clinical and immunological analyses of IgM and IgG antibody responses to SARS-CoV-2 Omicron BA.2.38 infection in 64 pediatric patients. COVID-19 symptom severity decreased with age in pediatric patients, from 70.8% (17/24) in patients 0–2 years of age to 50% (6/12) and 50% (14/28) in patients 3–5 years and 6–17 years of age, respectively. Furthermore, fewer patients experienced symptoms when vaccinated with the CoronaVac or BBIBP-CorV vaccine (50%, 13/26) than unvaccinated patients (71%, 22/31). Using a protein array, we found that the Omicron BA.2.38 infection induced antibody responses to other Omicron variants (Omicron BA.1-BA.5), which increased with vaccination. Notably, non-Omicron and Omicron variants showed distinct serotypes. Altogether, our results provide insight into the clinical and immunological characteristics of pediatric patients with COVID-19 Omicron BA.2.38 who have and have not been vaccinated against COVID-19. These data may help develop more effective diagnostic tests and vaccines in the future.

Phytophthora blight of pepper is a notorious disease caused by the oomycete pathogen Phytophthora capsici, which poses a great threat to global pepper production. MicroRNA (miRNA) is a class of non-coding small RNAs that regulate gene expressions by altering the translation efficiency or stability of targeted mRNAs, which play important roles in the regulation of a plant’s response to pathogens. Herein, time-series mRNA-seq libraries and small RNA-seq libraries were constructed using pepper roots from the resistant line CM334 and the susceptible line EC01 inoculated with P. capsici at 0, 6, 24, and 48 h post-inoculation, respectively. For mRNA-seq analysis, a total of 2159 and 2971 differentially expressed genes (DEGs) were identified in CM334 and EC01, respectively. For miRNA-seq analysis, 491 pepper miRNAs were identified, including 330 known miRNAs and 161 novel miRNAs. Among them, 69 and 88 differentially expressed miRNAs (DEMs) were identified in CM334 and EC01, respectively. Examination of DEMs and their targets revealed 22 regulatory networks, predominantly featuring up-regulated miRNAs corresponding to down-regulated target genes. Notably, these DEM-DEG regulatory networks exhibited significant overlap between CM334 and EC01, suggesting that they might contribute to pepper’s basal defense against P. capsici. Furthermore, five selected DEMs (miR166, miR1171, miR395, miR530 and miRN2) and their target genes underwent qRT-PCR validation, confirming a consistent negative correlation in the expression patterns of miRNAs and their targets. This comprehensive analysis provides novel insights into the regulatory networks of miRNAs and their targets, offering valuable contributions to our understanding of pepper’s defense mechanisms against P. capsici.

Phytophthora capsici is a broad–host range oomycete pathogen that can cause severe phytophthora blight disease of pepper and hundreds of other plant species worldwide. Natural resistance against P. capsici is inadequate, and it is very difficult to control by most of existing chemical fungicides. Therefore, it is urgent to develop alternative strategies to control this pathogen. Recently, host-induced or spray-induced gene silencing of essential or virulent pathogen genes provided an effective strategy for disease controls. Here, we demonstrate that P. capsici can effectively take up small interfering RNAs (siRNAs) from the environment. According to RNA-seq and quantitative reverse transcription PCR analysis, we identified four P. capsici RXLR effector genes that are significantly up-regulated during the infection stage. Transient overexpression and promote-infection assays indicated that RXLR1 and RXLR4 could promote pathogen infection. Using a virus-induced gene silencing system in pepper plants, we found that in planta–expressing RNA interference (RNAi) constructs that target RXLR1 or RXLR4 could significantly reduce pathogen infection, while co-interfering RXLR1 and RXLR4 could confer a more enhanced resistance to P. capsici. We also found that exogenously applying siRNAs that target RXLR1 or RXLR4 could restrict growth of P. capsici on the pepper and Nicotiana benthamiana leaves; when targeting RXLR1 and RXLR4 simultaneously, the control effect was more remarkable. These data suggested that RNAi-based gene silencing of RXLR effectors has great potential for application in crop improvement against P. capsici and also provides an important basis for the development of RNA-based antioomycete agents.[Graphic: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Despite its importance, interaction remains limited in MOOC-based flipped classroom (MBFC) Grounded in social learning theory, we proposed an MBFC approach supported by social media to facilitate students’ interaction with peers and learning performance. A quasi-experiment was conducted to compare the MBFC approach (N = 58) based on WeChat with the conventional MBFC approach (N = 52). The results revealed that the use of WeChat in an MBFC approach led to better performance in terms of watching video lectures and completing online exercises before the class; however, it did not significantly enhance student learning performance compared to the conventional MBFC approach. In addition, the study found that students were moderately satisfied with the MBFC approach supported by WeChat. According to a WeChat interaction quantity and quality analysis, students’ non-substantive postings are much higher than students’ substantive postings in WeChat interaction groups, but students’ contributions to the postings have no significant effect on the final marks. Findings from this study could be of valuable reference for practitioners and researchers who plan to leverage social media tools such as WeChat to support student MOOC learning.

The etiology of severe acute hepatitis (SAH) in children is various. We describe the first Chinese case of severe acute hepatitis in a 22-month-old boy with the mild illness of Omicron sub-variant BA.2.38. With the application of Compound Glycyrrhizin Injection (CGI), the patient gradually recovered from acute liver injury (ALI). This case highlights the possibility of severe ALI in children with the non-critical illness of SARS-CoV-2. The management of SAH associated with the pandemic presents challenges for clinicians, and follow-up is in need. The method of differential diagnosis using limited laboratory results is of great value to the clinicians.

ObjectiveIn this study, we aimed to explore the alterations in gut microbiota composition and cytokine responses related to disease progression, severity, and outcomes in patients with hypertensive intracerebral hemorrhage (ICH).MethodsFecal microbiota communities of 64 patients with ICH, 46 coronary heart disease controls, and 23 healthy controls were measured by sequencing the V3-V4 region of the 16S ribosomal RNA (16S rRNA) gene. Serum concentrations of a broad spectrum of cytokines were examined by liquid chips and ELISA. Relationships between clinical phenotypes, microbiotas, and cytokine responses were analyzed in the group with ICH and stroke-associated pneumonia (SAP), the major complication of ICH.ResultsIn comparison with the control groups, the gut microbiota of the patients with ICH had increased microbial richness and diversity, an expanded spectrum of facultative anaerobes and opportunistic pathogens, and depletion of anaerobes. Enterococcus enrichment and Prevotella depletion were more significant in the ICH group and were associated with the severity and functional outcome of ICH. Furthermore, Enterococcus enrichment and Prevotella depletion were also noted in the SAP group in contrast to the non-SAP group. Enterococci were also promising factors in the prognosis of ICH. The onset of ICH induced massive, rapid activation of the peripheral immune system. There were 12 cytokines (Eotaxin, GM-CSF, IL-8, IL-9, IL-10, IL-12p70, IL-15, IL-23, IL-1RA, IP-10, RANTES, and TNF-α) changed significantly with prolongation of ICH, and the Th2 responses correlated with the 90-day outcomes. Cytokines TNF-α, IP-10, IL-1RA, IL-8, IL-18, and MIP-1β in SAP group significantly differed from non-SAP group. Among these cytokines, only IP-10 levels decreased in the SAP group. Enterococcus was positively associated with IL-1RA and negatively associated with IP-10, while Prevotella was inversely associated in both the ICH and SAP groups.ConclusionThis study revealed that gut dysbiosis with enriched Enterococcus and depleted Prevotella increased the risk of ICH and subsequently SAP. The altered gut microbiota composition and serum cytokine profiles are potential biomarkers that reflect the inciting physiologic insult/stress involved with ICH.

Abstract Background Plant transcription factors (TFs) are key transcriptional regulators to manipulate the regulatory network of host immunity. However, the globally transcriptional reprogramming of plant TF families in response to pathogens, especially between the resistant and susceptible host plants, remains largely unknown. Results Here, we performed time-series RNA-seq from a resistant pepper line CM334 and a susceptible pepper line EC01 upon challenged with Phytophthora capsici, and enrichment analysis indicated that WRKY family most significantly enriched in both CM334 and EC01. Interestingly, we found that nearly half of the WRKY family members were significantly up-regulated, whereas none of them were down-regulated in the two lines. These induced WRKY genes were greatly overlapped between CM334 and EC01. More strikingly, most of these induced WRKY genes were expressed in time-order patterns, and could be mainly divided into three subgroups: early response (3 h-up), mid response (24 h-up) and mid-late response (ML-up) genes. Moreover, it was found that the responses of these ML-up genes were several hours delayed in EC01. Furthermore, a total of 19 induced WRKY genes were selected for functional identification by virus-induced gene silencing. The result revealed that silencing of CaWRKY03–6, CaWRKY03–7, CaWRKY06–5 or CaWRKY10–4 significantly increase the susceptibility to P. capsici both in CM334 and EC01, indicating that they might contribute to pepper’s basal defense against P. capsici; while silencing of CaWRKY08–4 and CaWRKY01–10 significantly impaired the disease resistance in CM334 but not in EC01, suggesting that these two WRKY genes are prominent modulators specifically in the resistant pepper plants. Conclusions These results considerably extend our understanding of WRKY gene family in pepper’s resistance against P. capsici and provide potential applications for genetic improvement against phytophthora blight.

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China's desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.

Background: Post-traumatic stress disorder (PTSD) is the most common psychiatric sequelae among novel coronavirus disease (COVID-19) patients. The aim of this study was to determine the prevalence of PTSD symptoms, PTSD-related factors, and its relationship with quality of life at long-term follow-up in hospitalized COVID-19 survivors.Methods: A cross-sectional study was undertaken to evaluate the health consequences of hospitalized COVID-19 survivors. All participants were interviewed face-to-face through a series of questionnaires: a researcher-developed symptom questionnaire, the Post-traumatic Stress Disorder Checklist–Civilian Version, the Generalized Anxiety Disorder 7-item, and the 36-item Short Form.Results: A total of 574 participants were enrolled with an average age of 57 years. The median follow-up time post-discharge was 193.9 days (SD = 15.32). Among the participants, 77.9% of survivors presented with at least one symptom, where fatigue or muscle weakness (47.9%) was reported the most frequently, followed by chest distress (29.4%) and sleep difficulty (29.4%). The prevalence of PTSD was 11.15% [95% confidence interval (CI): 8.56, 13.73] with a cut-off score of 44. Factors such as respiratory symptoms [odds ratio (OR): 3.53; 95% CI: 1.68–7.42], anxiety (OR: 14.64; 95% CI: 7.09–30.21), and sleep difficulty (OR: 2.17; 95% CI: 1.14–4.16) were positively related to PTSD. Those COVID-19 survivors with potential PTSD had significantly lower quality of life than those without (P < 0.05).Conclusion: Our study illustrated that a significant number of COVID-19 survivors were suffering from physical or mental distress to varying degrees at 6 months post-discharge. People with PTSD were more likely to experience persistent respiratory symptoms and sleep difficulty, as well as anxiety and a decreased quality of life. Such survivors require greater attention to their mental health, particularly the PTSD symptoms at the early phase, which may play an important role in the recovery of both the physical and psychological health of COVID-19 survivors.

Background and Purpose: Coronavirus disease 2019 (COVID-19) rapidly resulted in a pandemic. Information on patients with a history of cerebrovascular disease (CVD) infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is limited. This study investigated the clinical features and the risk factors of developing adverse outcomes in patients with COVID-19 and with previous CVD.Methods: This was a single-center retrospective clinical study including all the confirmed cases of COVID-19 at Wuhan Huoshenshan Hospital from February 4 to April 7, 2020. Differences in clinical characteristics were compared between patients with and without a history of CVD. The incidences of severe events comprising all-cause death, intensive care unit admission, shock, and mechanical ventilation usage during hospitalization in two groups were compared using propensity score matching analysis and multivariate logistic regression analyses. Besides, the risk factors of developing severe events in patients with COVID-19 who also have history of CVD were analyzed.Results: A total of 2,554 consecutive patients were included in our study, of whom 109 (4.27%) had a medical history of CVD. Patients with CVD tend to be older and with more comorbidities, including hypertension, diabetes, coronary heart disease, and chronic obstructive pulmonary disease. The levels of white blood cell, neutrophil, C-reactive protein, creatine kinase isoenzymes, and lactate dehydrogenase were higher, whereas the levels of lymphocyte and albumin were lower in the CVD group. Compared to those without CVD, patients with CVD were more likely to have severe events after age matching (12.8 vs. 5.7%, P = 0.012). After adjusting for the confounding effects of age, sex, smoking, and comorbidities, the odds ratio for developing severe events with a history of CVD was 2.326 (95% CI, 1.168–4.630; P = 0.016). Besides, patients with CVD, either with decreased lymphocyte count (OR 9.192, 95% CI, 1.410–59.902, P = 0.020) or increased blood urea nitrogen (OR 5.916, 95% CI, 1.072–32.641, P = 0.041), had a higher risk of developing severe events during hospitalization.Conclusions: Patients with CVD history tend to have adverse clinical outcomes after being infected with SARS-COV-2. Decreased lymphocyte counts and increased blood urea nitrogen levels may be risk factors for adverse outcomes in patients with COVID-19, and had CVD.

Background: Real-world evidence on the effectiveness of inactivated vaccines against the Delta and Omicron (BA.2.38) variants remains scarce. Methods: A retrospective cohort study was conducted to estimate the adjusted vaccine effectiveness (aVE) of one, two, and three doses of inactivated vaccines in attenuating pneumonia, severe COVID-19, and the duration of viral shedding in Delta and Omicron cases using modified Poisson and linear regression as appropriate. Results: A total of 561 COVID-19 cases were included (59.2% Delta and 40.8% Omicron). In total, 56.4% (184) of Delta and 12.0% (27) of Omicron cases had COVID-19 pneumonia. In the two-dose vaccinated population, 1.4% of Delta and 89.1% of Omicron cases were vaccinated for more than 6 months. In Delta cases, the two-dose aVE was 52% (95% confidence interval, 39–63%) against pneumonia and 61% (15%, 82%) against severe disease. Two-dose vaccination reduced the duration of viral shedding in Delta cases, but not in booster-vaccinated Omicron cases. In Omicron cases, three-dose aVE was 68% (18%, 88%) effective against pneumonia, while two-dose vaccination was insufficient for Omicron. E-values were calculated, and the E-values confirmed the robustness of our findings. Conclusions: In Delta cases, two-dose vaccination within 6 months reduced pneumonia, disease severity, and the duration of viral shedding. Booster vaccination provided a high level of protection against pneumonia with Omicron and should be prioritized.

Recent evidence suggests that inflammation was participated in the pathogenesis of PD, thus, to understand the potential mechanism of gut microbiota in the pathogenesis of Parkinson’s disease (PD), we performed a metagenomic analysis of fecal samples from PD patient and controls. Using a two-stage metagenome-wide association strategy, fecal DNA samples from 69 PD patients and 244 controls in three groups (comprising 66 spouses, 97 age-matched, and 81 normal samples, respectively) were analyzed, and differences between candidate gut microbiota and microbiota-associated epitopes (MEs) were compared. In the study, 27 candidate bacterial biomarkers and twenty-eight candidate epitope peptides were significantly different between the PD patients and control groups. Further, enriched 4 and 13 MEs in PD were positively associated with abnormal inflammatory indicators [neutrophil percentage (NEUT.1), monocyte count/percentage (MONO/MONO.1), white blood cell count (WBC)] and five candidate bacterial biomarkers (c_Actinobacteria, f_Bifidobacteriaceae, g_Bifidobacterium, o_Bifidobacteriales, p_Actinobacteria) from Actinobacteria phylum, and they were also positively associated with histidine degradation and proline biosynthesis pathways, respectively. Additionally, enriched 2 MEs and 1 ME in PD were positively associated with above inflammatory indicators and two bacteria (f_Lactobacillaceae, g_Lactobacillus) from Firmicutes phylum, and they were also positively associated with pyruvate fermentation to propanoate I and negatively associated with isopropanol biosynthesis, respectively. Of these MEs, two MEs from GROEL2, RPSC were derived from Mycobacterium tuberculosis, triggered the T cell immune response, as previously reported. Additionally, other candidate epitope peptides derived from Mycobacterium tuberculosis and Mycobacterium leprae may also have potential immune effects in PD. In all, the altered MEs in PD may relate to abnormalities in immunity and glutamate and propionate metabolism, which furthers our understanding of the pathogenesis of PD.

In traditional machine learning algorithms, more labeled data are required in model training for better classification accuracy. However, collecting labeled data is an expensive task for real applications. In this paper, a transfer support vector machine (TSVM) technique is introduced to tackle this problem with application to the fault diagnosis of delta 3D printers. Transfer component analysis is first proposed to capture shared features for representing the source and target domains, by cross-domain feature extraction from less labeled data in the source domain and massive unlabeled data in the target domain. Support vector machine is then applied to the fault diagnosis using the transferred features of attitude signals. In the experiments, fault classification rate achieves 83.79% using only 6.7% of the dataset for model training. Compared with peer methods, the results show that the present TSVM exhibits the best performance in extracting the cross-domain feature and improves the accuracy of the 3D printer fault diagnosis.

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.

In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

Swarm robotics refers to artificial swarm systems composed of a large number of autonomous mobile robots with relatively simple structures and functions. One of the basic problems of swarm robotics involves the target search process, which entails a cooperative search using limited perception and local interaction of robots under a self-organizing mechanism. When communication is limited, the connectivity of swarm robotics may decrease, leading to the failure of the target search task. This article describes a new target search method based on the robot chain model and the elimination mechanism. The proposed method allows the target search task to be completed efficiently and reliably while maintaining the connectivity of the robots. Experimental results show that the proposed algorithm offers better performance than conventional techniques in terms of search speed and success rate, and provides an effective method for solving the target search problem using swarm robotics in limited-communication environments.

Investigation of joint torque constraint compliance is of significance for robot manipulators especially working in complex environments. A lot of which is attributed to that, on the one hand, it is beneficial to the improvement of both safety and reliability of the mission execution. On the other hand, the energy consumption required by the robot to complete the desired mission can be reduced. Most existing schemes do not take the joint torque limit and other inherent physical structure limits in a manipulator into account at the same time. In addition, many unavoidable uncertainties such as the external environmental disturbance and/or electromagnetism interferences in the circuit system may influence the accuracy and effectiveness of the task execution for a robot. In this study, we cast light on the acceleration level control of redundant robot manipulators considering both four physical constraint limits and interference rejection. A robust unified quadratic-programming-based hybrid control scheme is proposed, where the joint torque constraints are converted as two inequality constraints based on the robots’ dynamics equation. A recurrent-neural-network-based controller is designed for solving the control variable. Numerical experiments performing in PUMA 560 manipulator and planer manipulator illustrate that a rational torque distribution is obtained among the joints and the considered physical structural vectors are all restricted to the respective constraint range. In addition, even disturbed by the noise, the manipulator still successfully tracks the desired trajectory under the proposed control scheme.

River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system’s overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.

Abstract Protein‐polymer conjugates show improved pharmacokinetics but reduced bioactivity and tumor penetration as compared to native proteins, resulting in limited antitumor efficacy. To address this dilemma, genetic engineering of a body temperature‐responsive and matrix metalloproteinase (MMP)‐cleavable conjugate of interferon alpha (IFNα) and elastin‐like polypeptide (ELP) is reported with spatiotemporally programmed two‐step release kinetics for tumor therapy. Notably, the conjugate could phase separate to form a depot postsubcutaneous injection, leading to 1‐month zero‐order release kinetics. Furthermore, it could selectively be cleaved by MMPs that are overexpressed in tumors to release IFNα from ELP and thus to recover the bioactivity of IFNα. Consequently, it exhibits dramatically enhanced tumor accumulation, tumor penetration, and antitumor efficacy as compared to free IFNα in two mouse models of melanoma and ovarian tumor. These findings may provide an intelligent technology of thermoresponsive and protease‐cleavable protein‐polymer conjugates with spatiotemporally programmed two‐step release kinetics for tumor treatment.

Texture evolution of as-annealed hot rolled Al-Cu-Mg sheets with initial R-Cube {001}〈110〉, R-Cu {112} 〈110〉, and m-Brass {211}〈0,−1,1〉 texture has been studied by X-ray diffraction. Continuous overall texture intensity weakening effect and micro-mechanism, during the isochronal annealing at 280–480 °C for 2 h, was investigated using electron backscatter diffraction and scanning electron micrograph. A new texture fibre {φ1 = 0° − 90°, φ = 65°, φ2 = 63°} has been found. The results show that obvious overall texture intensity weakening started at 400 °C. The texture intensity ratio can be used to depict such weakening trend. Moreover, the overall texture intensity was determined by the reduction of R-Cube at 400–420 °C, but related to the decomposition of Brass at 420–480 °C. Such decomposition of Brass and induced rebound of orientations on the m-fibre, as well as R-Cu texture, due to the near 30°〈111〉 relationship, led to the further overall texture weakening and severe randomization. Keywords: Aluminium alloy, R-Cube, M-fibre, Texture intensity ratio

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time−frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

Research on land surface processes at the catchment scale has drawn much attention over the past few decades, and a number of watershed observatories have been established worldwide. The Heihe River Basin (HRB), which contains the second largest inland river in China, is an ideal natural field experimental area for investigation of land surface processes involving diverse landscapes and the coexistence of cold and arid regions. The Heihe Integrated Observatory Network was established in 2007. For long-term observations, a hydrometeorological observatory, ecohydrological wireless sensor network, and satellite remote sensing are now in operation. In 2012, a multiscale observation experiment on evapotranspiration over heterogeneous land surfaces was conducted in the midstream region of the HRB, which included a flux observation matrix, wireless sensor network, airborne remote sensing, and synchronized ground measurements. Under an open data policy, the datasets have been publicly released following careful data processing and quality control. The outcomes highlight the integrated research on land surface processes in the HRB and include observed trends, scaling methods, high spatiotemporal resolution remote sensing products, and model–data integration in the HRB, all of which are helpful to other endorheic basins in the “Silk Road Economic Belt.” Henceforth, the goal of the Heihe Integrated Observatory Network is to develop an intelligent monitoring system that incorporates ground-based observatory networks, unmanned aerial vehicles, and multi-source satellites through the Internet of Things technology. Furthermore, biogeochemical processes observation will be improved, and the study of integrating ground observations, remote sensing, and large-scale models will be promoted further.

Intracerebral hemorrhage (ICH) is a significant cause of morbidity and mortality worldwide. Several recent controlled trials have reported that deferoxamine (DFX) therapy appears to be effective for ICH. The aim of this study was to perform a systematic review of DFX therapy for ICH patients and evaluate the efficacy and safety of DFX therapy for ICH patients. We searched Medline, Embase, the Cochrane Database of Systematic Reviews, clinicaltrials.gov, all Chinese databases and the reference lists of all included studies and review articles. We then performed a systematic review of studies involving the administration of DFX following ICH. Only two studies were included, a prospective, randomized clinical trial and a prospective,observational cohort study with concurrent groups. Qualitative analysis of each study revealed one randomized controlled trial of moderate quality with a moderate risk of bias and one observational cohort study of moderate quality with a moderate risk of bias. DFX may be an effective treatment for edema in patients with ICH. However, due to the small number of trials and small sample sizes of these trials, insufficient evidence exists to determine the effect of DFX on neurologic outcomes after ICH and the safety of this intervention. Further investigation is required before DFX can become a routine treatment for ICH.

Considering dangerous environmental conditions, maintenance of radioactive equipment can be performed by remote handling maintenance (RHM) system. The RHM system is a sophisticated man-machine system. Therefore, human factors analysis is an inevitable aspect considered in guaranteeing successful and safe task performance. This study proposes an approach for integrated analysis of human factors in RHM so as to make the evaluating process more practical. In the approach, indicators of accessibility, health safety, and fatigue are analyzed using virtual human simulation technologies. The human error factors in the maintenance process are analyzed using the human error probability (HEP) based on the success likelihood index method- (SLIM-) analytic hierarchy process (AHP). The psychological factors level of maintenance personnel is determined with an expert scoring. The human factors for the entire RHM system are then evaluated using the interval method. An application example is present, and the application results show that the approach can support the evaluation of the human factors in RHM.

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

Based on the requirements for remote handling maintenance (RHM) of China Spallation Neutron Source (CSNS) multilayered pipes, pipes cutting tests were performed under remote handling maintenance conditions. In this study, the results were obtained from different cutting directions and supporting intensities of pipe baseplates comparisons: When enough power was provided and the blade gripper did not slip, the cutting direction had little impact on the cutting capacity but more on the fault surface; the clearance between the blades caused the rotating torque; for remote handling maintenance, good horizontal support of the long-handled lever of the hydraulic cutter was required. Significant conclusions were made for multilayered pipe cutting that are crucial for auxiliary tools development for remote handling maintenance.

Assembly sequence planning (ASP) of remote handling maintenance in radioactive environment is a combinatorial optimization problem. This study proposes an improved shuffled frog leaping algorithm (SFLA) for the combinatorial optimization problem of ASP. An ASP experiment is conducted to verify the feasibility and stability of the improved SFLA. Simultaneously, the improved SFLA is compared with SFLA, genetic algorithm, particle swarm optimization, and adaptive mutation particle swarm optimization in terms of efficiency and capability of locating the best global assembly sequence. Experiment results show that the proposed algorithm exhibits outstanding performance in solving the ASP problem. The application of the proposed algorithm should increase the level of ASP in a radioactive environment.

Melting-casting is the first process in copper alloy strip production. The schedule scheme on this process affects the subsequent processes greatly. In this paper, we build the multiobjective model of melting-casting scheduling problem, which considers minimizing the makespan and total weighted earliness and tardiness penalties comprehensively. A novel algorithm, which we called Multiobjective Artificial Bee Colony/Decomposition (MOABC/D) algorithm, is proposed to solve this model. The algorithm combines the framework of Multiobjective Evolutionary Algorithm/Decomposition (MOEA/D) and the neighborhood search strategy of Artificial Bee Colony algorithm. The results on instances show that the proposed MOABC/D algorithm outperforms the other two comparison algorithms both on the distributions of the Pareto front and the priority in the optimal selection results.