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Abstract Background Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. Methods This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥ 50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient’s receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race/ethnicity, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. Results Among 2,710 eligible patients (mean age = 61 ± 8, female = 69%, White = 84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.4% vs. 11.8%), with an aOR of 1.36 (95% CI = 1.06–1.74). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were aged

Abstract Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

According to the corrosion damage of heat exchange tube plate of ammonia condense separator in coking ammonium sulfide production，the corrosion resistance characteristics of the surface passivation film of tube sheet TA9 alloy were studied by total immersion test and electrochemical analysis.Using scanning electron microscope (SEM) and X-ray energy dispersive spectrometer (EDS)，the corrosion products of samples after total immersion test were analyzed under different temperature conditions in ammonia solution containing S2-，Cl-，F-anions or a mixture of these anions，respectively.The corrosion rate of TA9 alloy and the corrosion resistance characteristics of the surface passivation film were investigated using an electrochemical workstation.Results showed that the general corrosion and pitting corrosion of TA9 alloy gradually increased with the increase of temperature，and there was no obvious difference in the macroscopic morphology of corrosion under the action of different anions at same temperature and the corrosion products were titanium oxides.The corrosion rate of the TA9 alloy was greatly affected by temperature.In an ammonia aqueous solution mixed with three anions，when the temperature was increased from 20 ℃to 90 ℃，the corrosion rate increased from 1.059 97 μm/a to 10.619 74 μm/a，so the corrosion rate increased nearly 10 times.As the temperature rose to 50 ℃，the protective performance of the passivation film to the material decreased.At the same temperature，when fluoride ions existed alone，the resistanceRof the passive film on the electrode surface changed significantly，and the redox reaction on the electrode surface was the most violent，resulting in the fastest dissolution rate of the passivation film on the electrode surface and the highest corrosion rate of TA9 alloy.

AimsThe current study aims to investigate the consistency between the surveyees’ self-reported disease diagnosis and clinical assessment of eight major chronic conditions using community-based survey data collected in Xi’an, China in 2017. With a focus on under-reporting patients, we aim to explore its magnitude and associated factors, to provide an important basis for disease surveillance, health assessment and resource allocation, and public health decision-making and services.MethodsQuestionnaires were administered to collect self-reported chronic condition prevalence among the study participants, while physical examinations and laboratory tests were conducted for clinical assessment. For each of the eight chronic conditions, the sensitivity, specificity, under-reporting, over-reporting, and agreement were calculated. Log-binomial regression analysis was employed to identify potential factors that may influence the consistency of chronic condition reporting.ResultsA total of 2,272 participants were included in the analysis. Four out of the eight chronic conditions displayed under-reporting exceeding 50%. The highest under-reporting was observed for goiter [85.93, 95% confidence interval (CI): 85.25–86.62%], hyperuricemia (83.94, 95% CI: 83.22–84.66%), and thyroid nodules (72.89, 95% CI: 72.02–73.76%). Log-binomial regression analysis indicated that senior age and high BMI were potential factors associated with the under-reporting of chronic condition status in the study population.ConclusionThe self-reported disease diagnosis by respondents and clinical assessment data exhibit significant inconsistency for all eight chronic conditions. Large proportions of patients with multiple chronic conditions were under-reported in Xi’an, China. Combining relevant potential factors, targeted health screenings for high-risk populations might be an effective method for identifying under-reporting patients.

Abstract Background Diabetic kidney disease (DKD) is a severe complication of diabetes. Currently, no effective measures are available to reduce the risk of DKD progression. This study aimed to establish a weighted risk model to determine DKD progression and provide effective treatment strategies. Methods This was a hospital-based, cross-sectional study. A total of 1104 patients with DKD were included in this study. The random forest method was used to develop weighted risk models to assess DKD progression. Receiver operating characteristic curves were used to validate the models and calculate the optimal cutoff values for important risk factors. Results We developed potent weighted risk models to evaluate DKD progression. The top six risk factors for DKD progression to chronic kidney disease were hemoglobin, hemoglobin A1c (HbA1c), serum uric acid (SUA), plasma fibrinogen, serum albumin, and neutrophil percentage. The top six risk factors for determining DKD progression to dialysis were hemoglobin, HbA1c, neutrophil percentage, serum albumin, duration of diabetes, and plasma fibrinogen level. Furthermore, the optimal cutoff values of hemoglobin and HbA1c for determining DKD progression were 112 g/L and 7.2%, respectively. Conclusion We developed potent weighted risk models for DKD progression that can be employed to formulate precise therapeutic strategies. Monitoring and controlling combined risk factors and prioritizing interventions for key risk factors may help reduce the risk of DKD progression.

Phosphogypsum is an industrial waste generated in the production of fertilizers causing environmental problems. Calcined phosphogypsum (CPG) is obtained by low-temperature calcination and dehydration of phosphogypsum. CPG is mainly used to produce building materials. However, mechanical strength properties and water resistance of CPG materials is extremely poor, which leads to its relatively low utilization rate in construction industry. To address this issue, various amount of Ordinary Portland cement (OPC), fly ash (FA), rice husk (RH) and modified admixtures (AD) were mixed with the CPG to obtain CPG-based composite cementitious materials, which enhanced the water resistance and mechanical strength of the corresponding hardened materials. The influence of different contents of cement on the setting time, hydration heat, hydration products, mechanical properties, water absorption, heavy metal leaching and deterioration coefficient of the composite cementitious material is investigated. The results showed that the optimal mixing ratio of CPG: OPC: FA: RH was 57: 20: 10: 13 considering the water resistance and mechanical strength properties of CPG-based composite cementitious materials. On these conditions, the compressive strength, the flexural strength, water absorption and deterioration coefficient of the specimens at 28 days are 6.1 MPa, 4.6 MPa, 29.4% and 0.66 respectively, which were in line with the national standard of phosphorus building gypsum wallboard. It was demonstrated that two skeleton types are present in the samples prepared using CPG-based composite cementitious materials, which had a synergistic support effect on the structure. The first skeleton was formed by gypsum dihydrate crystals (CaSO4·2H2O) and provided strength to the specimens at an early stage, while the second skeleton was formed by ettringite (AFt) and C–S–H gels, which provided strength at a later stage. The skeletons make the hardened materials denser and more waterproof. Moreover, the composite materials exhibited a positive solidification effect on heavy metals present in CPG.

In order to increase the yield of L-lactic acid and reduce the production cost of L-lactic acid, a strain of Lactobacillus rhamnosus AK-0779 with alcohol tolerance and high yield of L-lactic acid was screened and domesticated. Corn distiller's grains were used as the nitrogen source for the fermentation medium of strain AK-0779 instead of part of yeast powder. On the basis of single factor experiments, three factors and three levels response surface optimization experiments were carried out on the addition of glucose, yeast powder and corn distiller's grains. The results showed that the optimal fermentation medium was glucose 9.80%, corn distiller's grains 0.98%, yeast powder 1.72%, L-lactic acid yield 78.91 g/L and sugar acid conversion rate 80.52%. There was no significant difference in the yield of L-lactic acid compared with yeast powder, which was 82.36 g/L, indicating that corn distiller's grains could effectively replace part of yeast powder as the nitrogen source of fermentation medium and reduce the production cost of L-lactic acid.

Background: Alzheimer’s disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed.Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD.Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690–0.790]) and full feature sets (0.730 [0.678–0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained.Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.

Introduction: Coupling the indoor and outdoor airflow of roadside buildings in a street canyon, the impact of flat and triangular roofs on indoor air pollutant concentration and ventilation rates of naturally ventilated buildings is studied using numerical simulation methods. Materials and methods: The flow and pollutant diffusion control equations are solved by using ANSYS Fluent. In simulation, RNG k-ε turbulence model is adopted. The numerical model is validated using the three-dimensional street canyon test data from the wind tunnel experiment at University of Karlsruhe. Results: The flow and pollutant concentration distributions under different roof shapes are obtained. The ventilation rates with different air flow resistances and pollution level indoors are provided. Conclusion: Ventilation direction through windows of roadside buildings determines the level of indoor air polluted by vehicle emissions in street canyon. When the building main height equals to the width of the street, flat roofs make the indoor concentration basically consistent with that near the external walls of the canyon. The higher the triangular roofs, the higher the ventilation rate and the lower the indoor concentration. The ventilation rate is influenced not only by roofs, but also by the floor location and indoor ventilation resistance.

Background: Methamphetamine use disorder (MUD) has become a global problem due to the highly addictive nature of methamphetamine. Earlier research have demonstrated that PROK2 functions as a compensatory and protective response against neurotoxic stress by stimulating astrocyte reactivity. The aim of our study was to evaluate the correlation between the PROK2 gene and both MUD risk susceptibility and craving scale in the Chinese Han population.Methods: A total of 5,282 participants (1,796 MUD patients and 3,486 controls) were recruited. Seven tag SNPs of the PROK2 gene were chosen and genotyped in the samples. Genetic association analyses were performed to capture the significant SNPs. To investigate the relationship between PROK2 levels and craving scores with the associated-SNP genotypes, we conducted a linear model.Results: SNP rs75433452 was significantly linked with MUD risk (p-value = 1.54 × 10−8), with the A allele being positively correlated with an increased risk of MUD. Moreover, the average serum level of PROK2 decreased when more copies of the A allele were presented in both MUD patients (p-value = 4.57 × 10−6) and controls (p-value = 1.13 × 10−5). Furthermore, the genotypes of SNP rs75433452 were strongly correlated with the craving scores in MUD patients (p-value = 4.05 × 10−4).Conclusion: Our study identified a significant association signal of the PROK2 gene with MUD risk susceptibility and methamphetamine craving scores in the Chinese Han population, providing potential valuable insights into the underlying mechanisms of METH dependence.

Glucocorticoid-induced osteoporosis (GIOP) is considered the third type of osteoporosis and is accompanied by high morbidity and mortality. Long-term usage of glucocorticoids (GCs) causes worsened bone quality and low bone mass via their effects on bone cells. Currently, there are various clinical pharmacological treatments to regulate bone mass and skeletal health. Pulsed electromagnetic fields (PEMFs) are applied to treat patients suffering from delayed fracture healing and non-unions. PEMFs may be considered a potential and side-effect-free therapy for GIOP. PEMFs inhibit osteoclastogenesis, stimulate osteoblastogenesis, and affect the activity of bone marrow mesenchymal stem cells (BMSCs), osteocytes and blood vessels, ultimately leading to the retention of bone mass and strength. However, the underlying signaling pathways via which PEMFs influence GIOP remain unclear. This review attempts to summarize the underlying cellular mechanisms of GIOP. Furthermore, recent advances showing that PEMFs affect bone cells are discussed. Finally, we discuss the possibility of using PEMFs as therapy for GIOP.

The purpose of this study was clarify the relationship between the differential expression of cyclins CCNB1 and CCNG1 and chondrocyte damage in Kashin-Beck disease. Systematic review and high-throughput sequencing of chondrocytes derived from Kashin-Beck disease patients were combined to identify the differentially expressed cyclins and cyclin-dependent kinase genes. In parallel, weaned SD rats were treated with low selenium for 4 weeks and then T-2 toxin for 4 weeks. Knee cartilage was collected to harvest chondrocytes for gene expression profiling. Finally, the protein expression levels of CCNB1 and CCNG1 were verified in knee cartilage tissue of Kashin-Beck disease patients and normal controls by immunohistochemical staining. The systematic review found 52 cartilage disease-related cyclins and cyclin-dependent kinase genes, 23 of which were coexpressed in Kashin-Beck disease, including 15 upregulated and 8 downregulated genes. Under the intervention of a low selenium diet and T-2 toxin exposure, CCNB1 (FC = 0.36) and CCNG1 (FC = 0.73) showed a downward expression trend in rat articular cartilage. Furthermore, compared to normal controls, CCNB1 protein in Kashin-Beck disease articular cartilage was 71.98% and 66.27% downregulated in the superficial and middle zones, respectively, and 12.06% upregulated in the deep zone. CCNG1 protein was 45.66% downregulated in the superficial zone and 12.19% and 9.13% upregulated in the middle and deep zones, respectively. The differential expression of cyclins CCNB1 and CCNG1 may be related to articular cartilage damage in Kashin-Beck disease.

It is currently difficult to adequately characterize the probability distribution information of rolling bearings, and research on the reliability of rolling bearings based on plastic deformation is lacking. To address this problem, the Latin hypercube design, BP neural networks, and higher-order moment method are combined, fully considering the randomness of the load, geometry, and material parameters of the rolling bearing. The dynamics model of the rolling bearing was constructed by using the Hertz contact theory and Jones model, combined with a Latin hypercube design to obtain neural network training samples. Based on the rational construction of the neural network structure, the mapping relationship between the equivalent stress and the design variables is obtained. The state function of the rolling bearing is constructed according to the stress–strength interference model, and then, the reliability index and reliability of the rolling bearing are obtained using the higher-order moment method, followed by a reliability sensitivity analysis. Finally, the proposed method is applied to the reliability analysis of a certain type of angular contact ball bearing, and the calculation results are compared with the Monte Carlo method results to demonstrate the correctness and effectiveness of the proposed method.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that is rare in China. At present, there are no widespread quantitative imaging markers associated with disease severity in MS. Despite several previous studies reporting cerebral blood flow (CBF) changes in MS, no consensus has been reached. In this study, we enrolled 30 Eastern MS patients to investigate CBF changes in different brain regions using the arterial spin labeling technique and their relationship with disease severity. The average CBF in MS patients were higher than those in health controls in various brain regions except cerebellum. The results indicated that MS patients with strongly increased CBF showed worse disease severity, including higher Expanded Disability Status Scale (EDSS) scores and serum neurofilament light chain (sNfL) values than those with mildly increased CBF in the parietal lobes, temporal lobes, basal ganglia, and damaged white matter (DWM). From another perspective, MS patients with worse disease severity (higher EDSS score and sNfL values, longer disease duration) showed increased CBF in parietal lobes, temporal lobes, basal ganglia, normal-appearing white matter (NAWM), and DWM. Correlation analysis showed that there was a strong association among CBF, EDSS score and sNfL. MS patients with strongly increased CBF in various brain regions had more ratio in relapsing phase than patients with mildly increased CBF. And relapsing patients showed significantly higher CBF in some regions (temporal lobes, left basal ganglia, right NAWM) compared to remitting patients. In addition, MS patients with cognitive impairment had higher CBF than those without cognitive impairment in the right parietal lobe and NAWM. However, there were no significant differences in CBF between MS patients with and without other neurologic dysfunctions (e.g., motor impairment, visual disturbance, sensory dysfunction). These findings expand our understanding of CBF in MS and imply that CBF could be a potential quantitative imaging marker associated with disease severity.

Abstract Background Diabetic foot ulcer (DFU) is a serious chronic complication of diabetes. This study aimed to establish weighted risk models for determining DFU occurrence and severity in diabetic patients. Methods This was a multi-center hospital-based cross-sectional study. A total of 1488 diabetic patients with or without an ulcer from three tertiary hospitals were included in the study. Random forest method was used to develop weighted risk models for assessing DFU risk and severity. Receiver operating characteristic curves were used to validate the models and calculate the optimal cut-off values of the important risk factors. Results We developed potent weighted risk models for evaluating DFU occurrence and severity. The top eight important risk factors for DFU onset were plasma fibrinogen, neutrophil percentage and hemoglobin levels in whole blood, stroke, estimated glomerular filtration rate, age, duration of diabetes, and serum albumin levels. The top 10 important risk factors for DFU severity were serum albumin, neutrophil percentage and hemoglobin levels in whole blood, plasma fibrinogen, hemoglobin A1c, estimated glomerular filtration rate, hypertension, serum uric acid, diabetic retinopathy, and sex. Furthermore, the area under curve values in the models using plasma fibrinogen as a single risk factor for determining DFU risk and severity were 0.86 (sensitivity 0.74, specificity 0.87) and 0.73 (sensitivity 0.76, specificity 0.58), respectively. The optimal cut-off values of plasma fibrinogen for determining DFU risk and severity were 3.88 g/L and 4.74 g/L, respectively. Conclusions We have established potent weighted risk models for DFU onset and severity, based on which precise prevention strategies can be formulated. Modification of important risk factors may help reduce the incidence and progression of DFUs in diabetic patients.

Abstract Electrohydrodynamic (EHD) printing has great potential to be used in the field of electronic industry, precision optics and biological engineering. However, it is still a challenge to achieve a high scaling ratio for line patterns production by EHD jet printing technique, which can effectively prevent the needles to be blocked. In this paper, the influence of the key process parameters on the line width of EHD jet printing was investigated. The printing needle with the inner diameter of 890 μm was used, and the line with width of 3.02 μm was printed by optimizing the parameters. The scaling ratio between the needle inner diameter and the printed line width was up to 295 times. At the same time, the function model between the flow rate and printed line width of PVP/PEO composite was deduced. The error was found to be less than 1% by the verification of experiments. By utilizing this model, the printed line width can be predicted by controlling the flow rate, which will greatly improve the work efficiency. By controlling the flow rate, the goal of printing on demand can be realized. The proposed method and model can provide support for the fabrication of micro‐nano scale structure.

BackgroundSmoking and secondhand smoke (SHS) exposure rates are much higher in China than in other countries. A smoke-free policy was implemented in Xi'an, a city in Shaanxi Province, China, on November 1, 2018. This study aimed to evaluate the effect of the smoke-free policy on changes in hospital admissions for acute ischemic heart disease (AIHD) and stroke in Xi'an.MethodsAll subjects had been hospitalized for AIHD or stroke from February 9, 2017 to December 25, 2019 (study period: 150 weeks) in six randomly selected public hospitals out of 36 tertiary hospitals in Xi'an. A generalized additive model developed using an interrupted time series design was used to compare immediate and annual percent changes in hospital admissions before and after policy implementation.ResultsThe study included 31,400 cases (16,656 cases of AIHD and 14,744 cases of stroke) from 6 hospitals in Xi'an. Immediately after implementation of the smoke-free policy, AIHD admissions were reduced significantly (−31.66%, 95% CI: – 39.45 to −22.86%), but stroke admissions were not (−4.94%, 95% CI: −13.26 to 4.17%). The annual reduction in stroke-related admissions (−14.54%, 95% CI: −23.53 to −4.49%) and the annual increase in AIHD-related admissions (40.58%, 95% CI: 22.08 to 61.87%) were significant. Although there was no significant reduction in AIHD admissions, stroke admissions were significantly reduced by −15.73% (from 7,350 to 6,194) after implementation of the smoke-free policy in Xi'an.ConclusionThe smoke-free policy had different effects on hospital admissions for AIHD and stroke in Xi'an. Xi'an should improve its smoke-free legislation and expand the measures to maintain or achieve additional significant health benefits. These findings can guide the formulation and implementation of regional and national smoke-free policies.

ObjectivesMethamphetamine (METH) is a central nervous psychostimulant and one of the most frequently used illicit drugs. Numerous genetic loci that influence complex traits, including alcohol abuse, have been discovered; however, genetic analyses for METH dependence remain limited. An increased histone deacetylase 3 (HDAC3) expression has been detected in Fos-positive neurons in the dorsomedial striatum following withdrawal after METH self-administration. Herein, we aimed to systematically investigate the contribution of HDAC3 to the vulnerability to METH dependence in a Han Chinese population.MethodsIn total, we recruited 1,221 patients with METH dependence and 2,328 age- and gender-matched controls. For genotyping, we selected 14 single nucleotide polymorphisms (SNPs) located within ± 3 kb regions of HDAC3. The associations between genotyped genetic polymorphisms and the vulnerability to METH dependence were examined by single marker- and haplotype-based methods using PLINK. The effects of expression quantitative trait loci (eQTLs) on targeted gene expressions were investigated using the Genotype-Tissue Expression (GTEx) database.ResultsThe SNP rs14251 was identified as a significant association signal (χ2 = 9.84, P = 0.0017). An increased risk of METH dependence was associated with the A allele (minor allele) of rs14251 [odds ratio (95% CI) = 1.25 (1.09–1.43)]. The results of in silico analyses suggested that SNP rs14251 could be a potential eQTL signal for FCHSD1, PCDHGB6, and RELL2, but not for HDAC3, in various human tissues.ConclusionWe demonstrated that genetic polymorphism rs14251 located at 5q31.3 was significantly associated with the vulnerability to METH dependence in Han Chinese population.

Introduction: The impacts of building layout on pedestrian level wind comfort and gas pollutant diffusion are simulated using computational fluid dynamics method. Materials and methods: The control equations of flow and pollutant diffusion are solved by using ANSYS Fluent. The SIMPLE algorithm is selected for the pressure-velocity coupling. The data from wind tunnel experiment at Tokyo Polytechnic University is employed in the validation case. Results: The velocity field and turbulence intensity at pedestrian level under different building layouts are obtained. The distribution and evaluation of wind comfort grade and pollutant concentration are given. Conclusion: Building layouts have significant impacts on flow and pollutant diffusion at pedestrian level. The outward staggered layout of building group can improve both wind comfort grade and air quality, but the inward staggered layout has the adverse effect. Non-staggered layouts are the worst in terms of the wind comfort grade in this paper.

BackgroundRecently, NUS1 and GP2 genes were reported to be associated with the risk of type 2 diabetes (T2D) in a Japanese population. Given the sharing of pathogenic contribution from genetic factors between T2D and gestational diabetes mellitus (GDM), we conducted the study to systematically examine the relationship of NUS1 and GP2 genes with the susceptibility to GDM in Chinese Han population.MethodsA total of 4,250 subjects comprised of 1,282 patients with GDM and 2,968 controls were recruited, and 20 tag single nucleotide polymorphisms (SNPs) (10 from NUS1 and 10 from GP2) were selected for genotyping. Association analyses were conducted for GDM and its related biomedical indexes including fasting glucose and HbA1c levels.ResultsTwo SNPs, rs80196932 from NUS1 (P=2.93×10-5) and rs117267808 from GP2 (P=5.68×10-5), were identified to be significantly associated with the risk of GDM. Additionally, SNP rs80196932 was significantly associated with HbA1c level in both patients with GDM (P=0.0009) and controls (P=0.0003), while SNP rs117267808 was significantly associated with fasting glucose level in both patients with GDM (P=0.0008) and controls (P=0.0007). Serum levels of protein NUS1 and GP2 were measured for the study subjects, and significant differences were identified among groups with different genotypes of SNP rs80196932 and rs117267808, respectively.ConclusionsOur findings indicate that NUS1 and GP2 genes contribute to the risk of GDM, which would help to offer the potential to improve our understanding of the etiology of GDM and, in turn, could facilitate the development of novel medicines and treatments for GDM.

Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize the fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple while effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives.

The vibrations in hydraulic pipeline systems inevitably involve the interaction between the two-phase media of a solid and fluid. The fluid flowing in the pipeline generates pressure on the pipeline wall and thus causes vibration and deformation in the pipeline, which in turn changes the fluid flow condition, thereby cyclically affecting the deformation and motion of the solid and fluid and making the pipeline system vulnerable to vibration damage. Therefore, it is of great theoretical and practical value to investigate the vibration behavior and characteristics of hydraulic pipeline systems. In this study, the fluid flow-induced vibrations in a pipeline system are investigated based on Housner’s differential vibration equation of fluid pipelines. Through the application of relevant mathematical theories and methods, the derivation of the inherent characteristics and dynamic behavior of a hydraulic pipeline system with two hinged ends is simplified, and the corresponding equations for the natural frequencies and dynamic response of the system are obtained. The analytical method for analyzing the vibration behavior and characteristics of the system is presented, and the analytical results of the vibration analysis of the system are obtained through computer simulation, providing a theoretical and technical basis for the safe and reliable operation of the hydraulic pipeline system.

With the development of internet service and IaaS cloud,the PaaS cloud was discussed in the construction and application of IT support systems.How to take into technological evolution and how to build PaaS architecture,were analyzed.How to implement a system of centralized management,supervision,sharing and scheduling was introduced.How to achieve unity of application deployment and how to achieve high availability for data center were discussed.