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Abstract Objective The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC). Methods Clinicopathological data (age, gender, Hashimoto’s thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People’s Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed. Results There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279–3.769, p = 0.004), age 1 cm (OR: 5.408, 95% CI: 3.233–9.046, p 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

Abstract Background Circulating tumor cell (CTC) detection is one form of liquid biopsy. It is a novel technique that is beginning to be applied in the field of thyroid cancer. The present study was designed to evaluate the diagnostic value of CTCs in patients with thyroid cancer. Methods A total of 1478 patients were retrospectively analyzed and divided into malignant group (n = 747) and benign group (n = 731). Peripheral blood was collected, and CTCs were enriched and quantified before surgery. The baseline data of the two groups were matched by Propensity Score Matching (PSM). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficiency of different indicators for thyroid cancer. The malignant group before PSM was further divided into subgroups according to the BRAF V600E mutation and lymphatic metastasis (N stage), and the number of CTCs in different subgroups was compared. Results After 1:1 PSM, baseline characteristics of the malignant group and benign group were matched and assigned 315 cases in each group. The number of CTCs and the TPOAb values were comparable in the two groups (p > 0.05). The TgAb values [1.890 (1.110 – 16.010) vs 1.645 (1.030 – 7.073) IU/mL, p = 0.049] were significantly higher in the malignant group than in the benign group. After PSM, ROC analyses showed that the areas under the curve (AUCs) of CTC, TgAb and ultrasound were 0.537 (sensitivity 65.6%, specificity 45.8%), 0.546 (sensitivity 40.0%, specificity 70.8%) and 0.705 (sensitivity 77.1%, specificity 63.2%), respectively. The AUCs of the combined detection of ‘CTC + ultrasound’ (combine 1) and the combined detection of ‘CTC + TgAb + ultrasound’ (combine 2) were 0.718 (sensitivity 79.3%, specificity 61.7%) and 0.724 (sensitivity 78.0%, specificity 63.3%), respectively. The AUC of ultrasound was significantly higher than CTC (p 0.05). The number of CTCs between the N0 and N1 subgroups, and between the BRAF mutant and BRAF wild subgroups was comparable (p > 0.05). Conclusions As an emerging and noninvasive testing tool, the efficacy of CTCs in diagnosing thyroid cancer is limited.

Axon projection is a spatial- and temporal-specific process in which the growth cone receives environmental signals guiding axons to their final destination. However, the mechanisms underlying changes in axonal projection direction without well-defined landmarks remain elusive. Here, we present evidence showcasing the dynamic nature of axonal projections in Drosophila’s small ventral lateral clock neurons (s-LNvs). Our findings reveal that these axons undergo an initial vertical projection in the early larval stage, followed by a subsequent transition to a horizontal projection in the early-to-mid third instar larvae. The vertical projection of s-LNv axons correlates with mushroom body calyx expansion, while the s-LNv-expressed Down syndrome cell adhesion molecule (Dscam1) interacts with Netrins to regulate the horizontal projection. During a specific temporal window, locally newborn dorsal clock neurons secrete Netrins, facilitating the transition of axonal projection direction in s-LNvs. Our study establishes a compelling in vivo model to probe the mechanisms of axonal projection direction switching in the absence of clear landmarks. These findings underscore the significance of dynamic local microenvironments in the complementary regulation of axonal projection direction transitions.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China’s second largest inland river basin. It is a typical oasis-type agricultural area in northwest China’s arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites’ line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

Background: osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model. Methods: we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency. Results: Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis. Conclusion: Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

The traditional networked radar power allocation is typically optimized with a given jamming model, while the jammer resource allocation is optimized with a given radar power allocation method; such research lack gaming and interaction. Given the rising seriousness of combat scenarios in which radars and jammers compete, this study suggests a deep game problem of networked radar power allocation under escort suppression jamming, in which intelligent target jamming is trained using Deep Reinforcement Learning (DRL). First, the jammer and the networked radar are mapped as two agents in this problem. Based on the jamming model and the radar detection model, the target detection model of the networked radar under suppressed jamming and the optimized objective function for maximizing the target detection probability are established. In terms of the networked radar agent, the radar power allocation vector is generated by the Proximal Policy Optimization (PPO) policy network. In terms of the jammer agent, a hybrid policy network is designed to simultaneously create beam selection and power allocation actions. Domain knowledge is introduced to construct more effective reward functions. Three kinds of domain knowledge, namely target detection model, equal power allocation strategy, and greedy interference power allocation strategy, are employed to produce guided rewards for the networked radar agent and the jammer agent, respectively. Consequently, the learning efficiency and performance of the agent are improved. Lastly, alternating training is used to learn the policy network parameters of both agents. The experimental results show that when the jammer adopts the DRL-based resource allocation strategy, the DRL-based networked radar power allocation is significantly better than the particle swarm-based and the artificial fish swarm-based networked radar power allocation in both target detection probability and run time metrics.

Abstract We derive the ALOS‐2 coseismic interferograms, pixel‐offsets and Sentinel‐2 sub‐pixel offsets of the 2023 Mw7.8 and Mw7.7 Kahramanmaras, Turkey earthquake sequence. Offset maps show that the sequence ruptured ∼300 km along the East Anatolian Fault (EAF) and ∼180 km along the secondary Cardak and Dogansehir faults. We infer the coseismic slip distribution and interseismic fault motion by inverting the co‐ and inter‐seismic observations. Inversion results show that the coseismic slip (∼8.0 m) and interseismic strike‐slip rate (∼4.6 mm/yr) on the main rupture of the Mw7.8 event are basically consistent with the ∼8.4 m and ∼3.9 mm/yr of the Mw7.7 event. Most coseismic slips of the Mw7.8 and Mw7.7 events occur within 10 and 12 km at depth, respectively, in keeping with the interseismic locking depth of 10.4 ± 3.3 km and 11.1 ± 3.1 km. This implies that the coseismic rupture kinematics correlate with the interseismic strain accumulation. Moreover, static stress changes show that the Mw7.7 event is likely promoted by ∼2 bar stress increase from the Mw7.8 event on the central section of its main rupture.

Abstract Intestinal stem cells (ISCs) are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. Through the establishment of ionizing radiation (IR) induced intestinal injury model, we found that a TLR2 agonist, Zymosan-A, promoted the regeneration of ISCs in vivo and in vitro. Zymosan-A improved the survival of abdominal irradiated mice (81.82% of mice in the treated group vs. 30% of mice in the PBS group), inhibited the radiation damage of intestinal tissue, increased the survival rate of intestinal crypts and the number of ISCs after lethal IR in vivo. Through organoid experiments, we found that Zymosan-A promoted the proliferation and differentiation of ISCs after IR. Remarkably, the results of RNA sequencing and Western Blot (WB) showed that Zymosan-A reduced IR-induced intestinal injury via TLR2 signaling pathway and Wnt signaling pathway and Zymosan-A had no radioprotection on TLR2 KO mice, suggesting that Zymosan-A may play a radioprotective role by targeting TLR2. Moreover, our results revealed that Zymosan-A increased ASCL2, a transcription factor of ISCs, playing a core role in the process of Zymosan-A against IR-induced intestinal injury and likely contributing to the survival of intestinal organoids post-radiation. In conclusion, we demonstrated that Zymosan-A promotes the regeneration of ISCs by upregulating ASCL2.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.MethodsWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.ResultFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.ConclusionAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%.

Abstract Background Severe ionizing radiation (IR)-induced intestinal injury associates with high mortality, which is a worldwide problem requiring urgent attention. In recent years, studies have found that the PHD-HIF signaling pathway may play key roles in IR-induced intestinal injury, and we found that FG-4592, the PHD inhibitor, has significant radioprotective effects on IR-induced intestinal injury. Methods In the presence or absence of FG-4592 treatment, the survival time, pathology, cell viability, cell apoptosis, and organoids of mice after irradiation were compared, and the mechanism was verified after transcriptome sequencing. The data were analyzed using SPSS ver. 19 software. Results Our results show that FG-4592 had significant radioprotective effects on the intestine. FG-4592 improved the survival of irradiated mice, inhibited the radiation damage of intestinal tissue, promoted the regeneration of intestinal crypts after IR and reduced the apoptosis of intestinal crypt cells. Through organoid experiments, it is found that FG-4592 promoted the proliferation and differentiation of intestinal stem cells (ISCs). Moreover, the results of RNA sequencing and Western blot showed that FG-4592 significantly upregulated the TLR4 signaling pathway, and FG-4592 had no radioprotection on TLR4 KO mice, suggesting that FG-4592 may play protective role against IR by targeting TLR4. Conclusion Our work proves that FG-4592 may promote the proliferation and regeneration of ISCs through the targeted regulation of the TLR4 signaling pathway and ultimately play radioprotective roles in IR-induced injury. These results enrich the molecular mechanism of FG-4592 in protecting cells from IR-induced injury and provide new methods for the radioprotection of intestine.

In order to reduce the potential safety hazards in the operation of rail vehicles, a new parameter table optimization algorithm based on the particle swarm optimization algorithm is proposed to carry on the failure assessment and fatigue life calculation of welded joints. This algorithm reduces the number of iterations by adding connection pools to connect the parameter selection database. In addition, based on this algorithm, combined with Python language and BS7910 standard, a calculation program for failure assessment and fatigue crack growth life is written. The algorithm program is validated by taking the commonly used T-type, butt, and lap joints as examples. In terms of failure assessment, the calculation results of the algorithm program are securer and more conservative, and the safety accuracy of the algorithm program is improved by 8%–12%. In terms of fatigue crack growth life, the error between the calculated value of the algorithm program and the test data is within the engineering allowable range. At the same time, the algorithm program can give the evaluation calculation results more intuitively and quickly, which provides a reference for the failure assessment and life calculation of the welded structure.

On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.

Abstract Background SARS-CoV-2 can remain transiently viable on surfaces. We examined if use of shared chairs in outpatient hemodialysis associates with a risk for indirect patient-to-patient transmission of SARS-CoV-2. Methods We used data from adults treated at 2,600 hemodialysis facilities in United States between February 1st and June 8th, 2020. We performed a retrospective case-control study matching each SARS-CoV-2 positive patient (case) to a non-SARS-CoV-2 patient (control) treated in the same dialysis shift. Cases and controls were matched on age, sex, race, facility, shift date, and treatment count. For each case-control pair, we traced backward 14 days to assess possible prior exposure from a ‘shedding’ SARS-CoV-2 positive patient who sat in the same chair immediately before the case or control. Conditional logistic regression models tested whether chair exposure after a shedding SARS-CoV-2 positive patient conferred a higher risk of SARS-CoV-2 infection to the immediate subsequent patient. Results Among 170,234 hemodialysis patients, 4,782 (2.8 %) tested positive for SARS-CoV-2 (mean age 64 years, 44 % female). Most facilities (68.5 %) had 0 to 1 positive SARS-CoV-2 patient. We matched 2,379 SARS-CoV-2 positive cases to 2,379 non-SARS-CoV-2 controls; 1.30 % (95 %CI 0.90 %, 1.87 %) of cases and 1.39 % (95 %CI 0.97 %, 1.97 %) of controls were exposed to a chair previously sat in by a shedding SARS-CoV-2 patient. Transmission risk among cases was not significantly different from controls (OR = 0.94; 95 %CI 0.57 to 1.54; p = 0.80). Results remained consistent in adjusted and sensitivity analyses. Conclusions The risk of indirect patient-to-patient transmission of SARS-CoV-2 infection from dialysis chairs appears to be low.

The development of InSAR satellite hardware and data processing technology enables us to rapidly obtain massive high spatial and temporal resolution surface deformation results. The abundant information helps geohazard detection, but also brings challenges for geohazard interpretation. InSAR geohazards intelligent detection methods based on deep learning can greatly improve the efficiency and precision of geohazards interpretation. However, these methods are usually limited by quality of the InSAR result that have various errors, such as decoherence error, topography residue and atmospheric delay. This study proposes a convolutional neural network incorporated an attention mechanism, referred to as DeforNet, which can effectively reduce the influences of the atmospheric delay and topography noises by introducing convolutional block attention module and depth-wise separable convolution. The new method can efficiently and accurately detect small and medium-scale geohazards from InSAR results. The comparison between the DeforNet with FCN, U-net and SegNet, using both synthetic and real samples, show that DeforNet has significant superiority in noise suppression and deformation identification. In the application of the whole Shanxi province, China, the DeforNet detected 1,553 geohazards with the minimum area of 0.21 km2. Our result shows that a strong spatial correlation between the location of geohazards and coal mining in this region. Within the coalfield, the number of identified geohazard accounts for 64.6 % of the total number in Shanxi. We also found that the identification accuracy of DeforNet is affected by the quality of the InSAR results, the scale of the geohazard and the wrap interval. DeforNet can serve to refine the detailed investigation of geohazards and promote the application of InSAR technology in geohazards prevention and mitigation.

In the small sample fatigue test space, in order to obtain the master S-N curves of each reliability of the titanium alloy welded structures. Firstly, the Bootstrap method is combined with the equivalent structural stress to determine the minimum number of samples under each level of equivalent structural stress, and the appropriate resampling times are obtained through residual analysis. Secondly, based on the fatigue test data of titanium alloy welded joints with different plate thicknesses, materials, types and stress ratios, the linear regression model of master S-N curves are determined by using the traditional group method and Bootstrap method, and its reliability is 99%, 95%, 84%, and 50% respectively. Finally, the comparative analysis shows that the titanium alloy fatigue test samples obtained by Bootstrap method tends to be more stable, which improves the fitting degree of the titanium alloy fatigue life test data and the accuracy of the fatigue life prediction when the sample is small. A more accurate regression model of the master S-N curves of titanium alloy welded structures was obtained and verified.

Considering that the modification parameters and performance parameters of gear are affected by various influencing factors during manufacturing and operation process， a robust modification design method for EMU transmission gears based on improved tolerance model and fuzzy theory is proposed. The controllable and uncontrollable factors in gear robust modification are firstly determined， and then the sample points are obtained by combining orthogonal experimental design and Romax motion simulation. The polynomial response surface model is used to fit the response surface function of peak-to-peak value of transmission error. On this basis， the fuzzy theory is introduced， the mathematical model of gear modification robust optimization is established based on improved tolerance model， the NSGA-III algorithm is used to find the optimal solution， and the comparison with other modification schemes is completed. Finally， the simulation verification of each modification scheme is carried out. The results show that the robust modification result based on the improved tolerance model is better than the traditional tolerance model. Considering the influence of fuzzy factors in the design of gear robust modification based on the improved tolerance model， it is not only improves the offset load condition of the tooth surface， but also improves the robustness and rationality of the modification results.

Abstract Background Dialysis recovery time (DRT) surveys capture the perceived time after HD to return to performing regular activities. Prior studies suggest the majority of HD patients report a DRT > 2 h. However, the profiles of and modifiable dialysis practices associated with changes in DRT relative to the start of dialysis are unknown. We hypothesized hemodialysis (HD) dose and rates of intradialytic hypotension (IDH) would associate with changes in DRT in the first years after initiating dialysis. Methods We analyzed data from adult HD patients who responded to a DRT survey ≤180 days from first date of dialysis (FDD) during 2014 to 2017. DRT survey was administered with annual KDQOL survey. DRT survey asks: “How long does it take you to be able to return to your normal activities after your dialysis treatment?” Answers are: 4 h. An adjusted logistic regression model computed odds ratio for a change to a longer DRT (increase above DRT > 2 h) in reference to a change to a shorter DRT (decrease below DRT 4 h). Changes in DRT were calculated from incident (≤180 days FDD) to first prevalent (> 365-to- ≤ 545 days FDD) and second prevalent (> 730-to- ≤ 910 days FDD) years. Results Among 98,616 incident HD patients (age 62.6 ± 14.4 years, 57.8% male) who responded to DRT survey, a higher spKt/V in the incident period was associated with 13.5% (OR = 0.865; 95%CI 0.801-to-0.935) lower risk of a change to a longer DRT in the first-prevalent year. A higher number of HD treatments with IDH episodes per month in the incident period was associated with a 0.8% (OR = 1.008; 95%CI 1.001-to-1.015) and 1.6% (OR = 1.016; 95%CI 1.006-to-1.027) higher probability of a change to a longer DRT in the first- and second-prevalent years, respectively. Consistently, an increased in incidence of IDH episodes/months was associated to a change to a longer DRT over time. Conclusions Incident patients who had higher spKt/V and less sessions with IDH episodes had a lower likelihood of changing to a longer DRT in first year of HD. Dose optimization strategies with cardiac stability in fluid removal should be tested.

The use of fire-resistant steel is an effective way to alleviate fire safety issues in steel structures. Considering the limited research on bolted joints fabricated from fire-resistant steel, this study presents the results of the experimental investigation on the mechanical behavior of 40 specimens of bolted joints at room temperature and elevated temperatures (20°C∼700°C). These specimens are assembled with fire-resistant steel plates and fire-resistant high-strength bolts (or ordinary high-strength bolts). The mechanical behavior of these specimens was investigated, which included the failure mode, deformation performance, and degradation of bearing capacity. The experimental results show that the treatment of the friction surface (specifically, impeller blasting and sprayed hard quartz sand) has a negligible effect on the ultimate strength of specimens and a relatively significant effect on the slip strength. In addition, reduction factors of the tensile strength (KN) and slip strength (KS) for both fire-resistant bolt specimens and ordinary high-strength bolt specimens decrease with the increase in temperature, especially in the case of elevated temperatures ranging from 400°C to 700°C. Prediction models with different reliability degrees are proposed to calculate the degradation of tensile strength under elevated temperatures, which has the potential to be used for the fire-resistant design of steel connections and structures.

Torrential rain is one of the most important disaster severe weather in China. Forecasting it is not only the key point but also the difficulty in operation. The heavy rain forecasting operation in National Meteorological Center has been developed from the traditional method of subjective extrapolation based on synoptics and statistics on weather map analysis to the modern method of numerical prediction. It is emphasized that the scientific problems of rainstorm forecast are extracted from the practice of forecasting, not from literatures. It is highlighted that forecasters are the initiators of the study on scientific problems of rainstorm and the first participants in solving practical problems. The close combination of forecast and service is an important part of meteorological development with Chinese characteristics, and the important role of National Meteorological Center and the Institute of Heavy Rain and other units in the CMA in rainstorm forecast and research and China's response to sudden natural disasters are fully affirmed. It is clear that the forecast of rainstorm and other disasters is aimed at serving the people, society, economic development and national security. Some perspectives are discussed on how to improve the accuracy of rainstorm forecast from the following aspects:training and improving forecaster's scientific research and practical ability, multi-source observation data application, technology development of mesoscale numerical prediction model and ensemble model, and validation and improvement of revision ability, artificial intelligence technology application.

Sleep is a fundamental, evolutionarily conserved, plastic behavior that is regulated by circadian and homeostatic mechanisms as well as genetic factors and environmental factors, such as light, humidity, and temperature. Among environmental cues, temperature plays an important role in the regulation of sleep. This review presents an overview of thermoreception in animals and the neural circuits that link this process to sleep. Understanding the influence of temperature on sleep can provide insight into basic physiologic processes that are required for survival and guide strategies to manage sleep disorders.

The incidence of multiple primary malignant tumors (MPMTs) has increased greatly with the progress of tumor diagnosis and therapy technology. However, triple primary cancer is still very rare, and its genetic change is not clear yet. This case report described a 70-year-old Chinese male patient with triple primary cancers of the esophagus, stomach and right-sided colon. Pathological examination confirmed that each malignant tumor developed independently. Next-generation sequencing (NGS) using a 599-gene panel revealed five TP53 mutations in three tumor tissues. These variations might contribute to development of the triple primary malignant tumors in the patient. The patient underwent laparoscopic feeding jejunostomy and postoperative radiotherapy for synchronous esophageal and gastric carcinomas. Then, he underwent laparoscopic-assisted resection of right-sided colonic cancer and lysis of abdominal adhesions. By the time of submitting this manuscript, the patient had been well and no sign of recurrence or metastasis had been observed. To the best of our knowledge, this case is the first one to clarify the genetic abnormalities of triple primary cancers of esophagus, stomach and colon in a Chinese patient. It may contribute to understanding the molecular pathogenesis of multiple primary digestive malignancies and providing valuable treatment strategies for the similar patients in the future.

The Sentinel-1A/B satellite launched by European Space Agency (ESA) in 2014 provides a huge amount of free Terrain Observation by Progressive Scans (TOPS) data with global coverage to the public. The TOPS data have a frame width of 250 km and have been widely used in surface deformation monitoring. However, traditional Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) methods require large computer memory and time when processing full resolution data with large width and long strips. In addition, they hardly correct atmospheric delays and orbital errors accurately over a large area. In order to solve these problems, this study proposes a data partition strategy based on MT-InSAR methods. We first process the partitioned images over a large area by traditional MT-InSAR method, then stitch the deformation results into a complete deformation result by correcting the offsets of adjacent partitioned images. This strategy is validated in a flat urban area (Changzhou City in Jiangsu province, China), and a mountainous region (Qijiang in Chongqing City, China). Compared with traditional MT-InSAR methods, the precision of the results obtained by the new strategy is improved by about 5% for Changzhou city and about 15% for Qijiang because of its advantage in atmospheric delay correction. Furthermore, the proposed strategy needs much less memory and time than traditional methods. The total time needed by the traditional method is about 20 h, and by the proposed method, is about 8.7 h, when the number of parallel processing is 5 in the Changzhou city case. The time will be further reduced when the number of parallel processes increases.

The azimuth displacement derived by pixel offset tracking (POT) or multiple aperture InSAR (MAI) measurements is usually used to characterize the north-south coseismic deformation caused by large earthquakes (M > 6.5), but its application in the source parameter inversion of moderate-magnitude earthquakes (~M 6.0) is rare due to the insensitive observation accuracy. Conventional line-of-sight (LOS) displacements derived by the Interferometric Synthetic Aperture Radar (InSAR) have limited ability to constrain the source parameters of the earthquake with near north-south striking. On 21 May 2021, an Mw 6.1 near north-south striking earthquake occurred in Yangbi County, Yunnan Province, China. In this study, we derive both the coseismic LOS displacement and the burst overlap interferometry (BOI) displacement from the Sentinel-1 data to constrain the source model of this event. We construct a single-segment fault geometry and estimate the coseismic slip distribution by inverting the derived LOS and BOI-derived azimuth displacements. Inversion results show that adding the BOI-derived azimuth displacements to source modeling can improve the resolution of the slip model by ~15% compared with using the LOS displacements only. The coseismic slip is mainly distributed 2 to 11 km deep, with a maximum slip of approximately 1.1 m. Coulomb stress calculation shows a maximum Coulomb stress increment of ~0.05 Mpa at the north-central sub-region of the Red River Fault. In addition, there is a small Coulomb stress increase at the Southern end of the Weixi-Weishan fault. The potential seismic risks on the Weixi-Weishan and Northwest section of the Red River faults should be continuously monitored.

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

In this paper, a new fatigue life evaluation method, namely augmented-reverse notch equivalent stress method, is proposed for titanium alloy welded structures with stress singularities. First, a new three-parameter power function model is proposed in this paper, and the notch stress equivalent value method with correction factor is deduced. Combining the two, the theoretical framework of the augmented-reverse notch equivalent stress method is obtained. Within this framework, the fatigue test data of four titanium alloy welded joints with the same grade were used for analysis, and a new three-parameter power function model of titanium alloy welded structure was established. According to the calculation method of the notch equivalent stress correction factor, the correction factor suitable for the titanium alloy welded structure was obtained. Finally, the fatigue test data, different from the above titanium alloy welded structure, are used to verify the augmented-reverse notch equivalent stress method. The verification results show that the augmented-reverse notch equivalent stress method improves the effectiveness and accuracy of fatigue life evaluation and establishes a new life evaluation method for titanium alloy welded structures with stress singularities.

Summary: The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19. The Bigger Picture: In this study, we integrate longitudinal symptoms reports and continuous data from commercial wearables to compare and contrast flu and COVID-19 presentations. We found that, while symptoms constellation between COVID-19 and flu have large overlap, symptoms are significantly more prolonged and severe for COVID-19 than for flu. Similarly, physiological data from commercial wearables showed increased resting heart rate around symptoms onset date that were more severe for COVID-19, but present in milder form for flu as well.Person-generated health data (PGHD), including data from smartphones and other connected sensors, has the potential to enable applications ranging from individual-level early warnings or population-level hotspot detection for COVID-19. However, for these applications to become a reality, our findings suggest that it is crucial to develop and validate them in the context of other potentially confounding respiratory illnesses, such as the flu.

Background Hypertension remains a leading global cause for premature death and disease. Most treatment guidelines emphasize the importance of risk factors, but not all are known, modifiable, or easily avoided. Population blood pressure correlates with latitude and is lower in summer than winter. Seasonal variations in sunlight exposure account for these differences, with temperature believed to be the main contributor. Recent research indicates that UV light enhances nitric oxide availability by mobilizing storage forms in the skin, suggesting incident solar UV radiation may lower blood pressure. We tested this hypothesis by exploring the association between environmental UV exposure and systolic blood pressure (SBP) in a large cohort of chronic hemodialysis patients in whom SBP is determined regularly. Methods and Results We studied 342 457 patients (36% black, 64% white) at 2178 US dialysis centers over 3 years. Incident UV radiation and temperature data for each clinic location were retrieved from the National Oceanic and Atmospheric Administration database. Linear mixed effects models with adjustment for ambient temperature, sex/age, body mass index, serum Na+/K+ and other covariates were fitted to each location and combined estimates of associations calculated using the DerSimonian and Laird procedure. Pre‐dialysis SBP varied by season and was ≈4 mm Hg higher in black patients. Temperature, UVA and UVB were all linearly and inversely associated with SBP. This relationship remained statistically significant after correcting for temperature. Conclusions In hemodialysis patients, in addition to environmental temperature, incident solar UV radiation is associated with lower SBP. This raises the possibility that insufficient sunlight is a new risk factor for hypertension, perhaps even in the general population.

In order to more accurately analyze the fatigue reliability of motor hanger for high-speed train and reduce the influence of uncertain factors, a Bayesian statistical method is introduced to propose a novel fatigue reliability analysis method based on Bayesian updating and subset simulation. First, considering the influence of various uncertain parameters on the first principal stress (FPS) of motor hanger, the ANSYS parametric design language (APDL) is used to establish the parametric model. The D-optimal design of experiment is carried out to calculate the FPS of the motor hanger. Second, the experimental data is fitted by the least square method to establish a polynomial response surface function which characterizes the FPS of the motor hanger, and analysis of variance (ANOVA) is carried out. On this basis, the variation trend of the FPS under parameter fluctuation is calculated, and its probability distribution characteristics are obtained. Based on the MATLAB platform, the Bayesian updating method is adopted to correct the probability and statistical characteristics of the FPS to improve the accuracy of prediction. Finally, the subset simulation (SS) method is used to calculate the fatigue failure probability of the motor hanger. The research results show that the proposed method helps to improve the accuracy and efficiency of fatigue reliability analysis.

Cardiovascular (CV) events are increased 36-fold in patients with end-stage renal disease. However, randomized controlled trials to lower LDL cholesterol (LDL-C) and serum total cholesterol (TC) have not shown significant mortality improvements. An inverse association of TC and LDL-C with all-cause and CV mortality has been observed in patients on chronic dialysis. Lipoproteins also may protect against infectious diseases. We used data from 37,250 patients in the international Monitoring Dialysis Outcomes (MONDO) database to evaluate the association between lipids and infection-related or CV mortality. The study began on the first day of lipid measurement and continued for up to 4 years. We applied Cox proportional models with time-varying covariates to study associations of LDL-C, HDL cholesterol (HDL-C), and triglycerides (TGs) with all-cause, CV, infectious, and other causes of death. Overall, 6,147 patients died (19.2% from CV, 13.2% from infection, and 67.6% from other causes). After multivariable adjustment, higher LDL-C, HDL-C, and TGs were independently associated with lower all-cause death risk. Neither LDL-C nor TGs were associated with CV death, and HDL-C was associated with lower CV risk. Higher LDL-C and HDL-C were associated with a lower risk of death from infection or other non-CV causes. LDL-C was associated with reduced all-cause and infectious, but not CV mortality, which resulted in the inverse association with all-cause mortality.

Abstract Background Evidence indicates favorable effects of dialysate (DNa+) to serum sodium concentration (SNa+) alignment, however, results from larger sample populations are needed. For this reason, we conducted a retrospective propensity score-matched cohort study from a quality improvement project to investigate the effects of alignment on population of maintenance hemodialysis patients. Methods At 4 participating hemodialysis (HD) clinics, patients with SNa+ lower than the standard DNa+ of 137 mEq/L who received HD with DNa+ aligned to the average of the last 4 SNa+ measurements were evaluated (clinicaltrials.gov #NCT01825590). In this retrospective data analysis, an intention-to-treat (primary) and an as-treated “intervention” (secondary) cohort were created. “Aligned” patients from both cohorts (N = 163 for the primary and N = 137 for the secondary) were then propensity-score matched in a 1:1 fashion to “unaligned” patients from the Renal Research Institute database. The propensity score was generated based on age, gender, white race, Hispanic ethnicity, absence or presence of diabetes, hemodialysis vintage, interdialytic weight gain (IDWG; as a percentage of postdialysis body weight), catheter as primary dialysis access, predialysis systolic blood pressure, serum sodium concentration, hospitalization count during baseline. T-Test was employed for group comparisons of changes to the primary (volume-related and hemodynamic parameters) and tertiary outcomes. All-cause and fluid overload-related hospitalization admission rates were compared using Wilcoxon Rank Sum test and Cox regression analysis for repeated events. Results In the primary analysis, aligned and unaligned subjects showed comparable demographics at baseline. Treatment effects were significant for IDWG [−0.12 (95% CI −0.24 to 0) L] and showed decreasing non-significant trends for pre-dialysis hemodynamic parameters. Count comparison and Cox regression analysis showed no clear advantage of alignment in terms of all-cause and fluid overload-related hospitalization. Conclusions Results from the largest sodium alignment program to date suggest positive treatment effects on volume-related and hemodynamic parameters, but no clear effect on risk of hospitalization. Well-matched control patients minimized confounding effects. Small effects and lack of significant differences may be explained by a low baseline DNa+ limiting the interventional change.

Background/Aims: Few studies examine the impact of systolic blood pressure (SBP) on mortality in the incident hemodialysis (HD) period, and throughout the first HD year. This large retrospective observational study analyzes the impact of “current” and cumulative low preSBP =110 mmHg (H) for each patient. A generalized linear model (GLM) was used to compute the probability of death in the following week. The model includes age, gender, race and three preSBP-related parameters: (a) percent of prior weeks with L preSBP; (b) percent of prior weeks with switching between L to H; (c) “current” week’s preSBP as a binary variable. Separate models were constructed that include demographics and BP-related parameters (a), (b), and (c) separately. Results: In a model combining (a), (b), and (c) above, “current” week L preSBP is associated with increased odds ratio for following week mortality throughout the first HD year. The percent of prior week’s L and more switching between L and H are less significantly associated with short-term mortality. In models including (a), (b), and (c) separately, “current” L preSBP is associated with higher mortality. Conclusion: This study confirms an association of L preSBP with increased short-term mortality which is maintained over the first HD year. Percent of L preSBP in prior weeks, switching between L and H, and “current” week L are all associated with short-term mortality risk, but “current” week L preSBP is most significant.

This paper proposes a hybrid feature extraction-based intrusion discrimination scheme for an optical fiber perimeter security system, which concurrently possesses high classification rate and high efficiency. The high classification rate lies in two aspects: On one hand, plentiful contents (including bandwidth segmentation in frequency domain, kurtosis in statistics, and the zero-crossing rate in time domain) are incorporated into the proposed hybrid feature vector; on the other hand, a configurable filter bank is adopted to reduce the intercoupling between features in the hybrid vector. The high efficiency also arises for two reasons: For one thing, the configurable filter bank works in a pipeline stream; for another, an efficient support vector machine is employed to classify hybrid vectors. Experiments demonstrated that the proposed scheme can accurately identify four common intrusions (fence climbing, knocking the cable, waggling, and fence cutting) with an average recognition rate higher than 94%. Moreover, the recognition efficiency is also high.

To meet the growing demand of event discrimination in a dual Mach-Zehnder Interferometry (DMZI) vibration sensing system, this paper proposes a novel scheme of distinguishing invasion events. This scheme consists of three stages: endpoint detection, filter-bank-based feature extraction, and radial-basis-function neural network classification. As the hard core, the proposed filter bank, which is derived from the classical frequency-sampling-based filter design method, greatly suppresses the interchannel interference and, thus, provides accurate feature description for the radial basis function (RBF)-based classifier. Moreover, we also derive the closed-form formula of the filter coefficients and, thus, design a pipeline structure for the filter bank, which brings the advantages of high accuracy, high flexibility, great rapidity, and low cost. Simulation verifies the proposed filter bank's superiority in separating different frequency bands. Field experiments also show that the proposed event discrimination scheme cannot only eliminate some false alarms caused by noninvasion events but can discriminate two common invasion events (climbing the fence and knocking the cable) with high recognition accuracy as well.

This study aims to evaluate the fatigue durability of a cowcatcher and optimize its structure. The cowcatcher, which serves as the crucial component of electric multiple units, generally influences the electric multiple units operating security and stability. A finite element model considering material properties and geometry sizes of the cowcatcher is established for structural strength analysis. Based on the P-S-N curve, the nominal stress method is introduced to analyze the fatigue durability of the cowcatcher. Considering influences of input variables such as elastic modulus, Poisson ratio, loads, and plate thickness on the fatigue durability, the parametric model of cowcatcher is built. In addition, three-level fractional factorial design is chosen to ascertain the location of sampling points in the above variables’ sampling space. The response surface of the cowcatcher is fitted using Kriging model. Then, a non-linear programming by quadratic Lagrangian algorithm is proposed for optimizing the cowcatcher structure. With a comparison of fatigue durability pre-optimization and post-optimization, conclusions on cowcatcher design and application are made. The proposed method not only prolongs the fatigue life of the cowcatcher but also improves its structure reliability.

Interfacial interactions, including interfacial charge transfer and strain, play important roles in the applications of 2D layered materials. Recently, MoO2 have been widely used as precursors or substrates for growth of high-quality MoS2. Herein, photoluminescence (PL) and Raman spectroscopy are used to investigate the dependence of epitaxial monolayer MoS2 PL properties on MoO2 nanoflakes in different thicknesses on c-sapphire. On all MoO2 nanoflakes, the trion exciton of MoS2 disappears due to the interfacial charge transfer from MoS2 to MoO2. The neutral exciton of MoS2 exhibits a -60meV redshift on thick MoO2 nanoflakes compared to those either on c-sapphire or on thin MoO2 nanoflakes, which is attributed to the relaxation of c-sapphire-induced compressive strain in MoO2 over a critical thickness. Temperature-dependent PL measurements reveal a stronger electron-phonon interaction in MoS2 on MoO2 than on c-sapphire. The findings provide an easy way to tune the properties of MoS2 via tuning the thickness of MoO2 buffer layer on c-sapphire. [ABSTRACT FROM AUTHOR]