Search

Abstract Background Refractory exit-site infections (ESIs) and tunnel infections (TIs) are challenging complications for patients undergoing peritoneal dialysis (PD). This study compared the outcomes of surgical intervention, notably the cuff-shaving (CS) procedure coupled with negative-pressure wound therapy (NPWT), and conservative management strategies for patients with refractory ESI and TI. Methods We retrospectively reviewed patients who underwent PD at our center, focusing on the incidence and management of ESI and TI. We evaluated and compared treatment outcomes, including ESI scores, frequency of ESI and/or TI, identification of causative microorganisms, and duration of catheter survival or time until removal. Results We identified 97 episodes of catheter-related ESI and/or TI across 71 patients with an incidence rate of 0.15 episodes per patient-year. Of the 23 patients with refractory ESI and/or TI, surgical intervention was performed in 8, while 15 chose conservative management. In the one-month follow-up, patients who underwent CS combined with NPWT showed no complications such as leakage, and their local symptoms resolved completely. The mean PD catheter survival time was significantly longer in the surgical group (29.38 ± 7.25 months) than in the conservative group (7.86 ± 2.13 months). Surgical intervention demonstrated a significantly higher therapeutic efficacy and extended catheter survival. Conclusions The combination of CS and NPWT as a surgical approach is crucial for eradicating infectious foci and significantly improving the longevity of PD catheter function. This integrated surgical strategy offers a promising solution for the management of refractory ESI and TI in patients undergoing PD.

A novel approach for detecting small targets of wind turbine blade surface damage, particularly minute defects within intricate scenes depicted in low-resolution images, is introduced through the utilization of an enhanced YOLO-v7 algorithm. The method incorporates the Ghost-Shuffle Convolution (GSConv) to improve detection accuracy without compromising inference speed, tailored to the specific characteristics of wind turbine blade defects. Furthermore, the integration of the Simple Attention Mechanism (SimAM) within the ELAN structure aims to enhance the identification of smaller wind turbine blades. To expedite model convergence, the Edge Intersection over Union (EIoU) is adopted as the edge loss function. Experimental findings demonstrate that the refined algorithm attains an average accuracy of 78.7%, surpassing the original YOLO-v7 algorithm by 4.2%. Moreover, it achieves a recognition speed of 105.1 frames per second, facilitating more effective detection of wind turbine blade defects while meeting real-time target detection requisites.

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

Abstract This study describes a novel stretchable and very sensitive strain gauge enabled by laser‐joining carbon nanotubes, resulting in unique architectures forming macroscopic web networks. The stiffness of the resistive strain sensor created by laser material patterning is reduced significantly, from 1.5824 to 0.142 kN m−1, corresponding to a change in laser power from 0 to 0.436 W. Many benefits of laser‐based bonding include precise and flexible local pattern production with minimal energy impact on the flexible substrate. Furthermore, the maximum Gauge factor of the sensor exhibits diverse trends with different strains, generating values of GF = 12.8 for strains less than 2.5% and GF = 165 for strains greater than 2.5% during extension‐retraction cycles. These disparities are the result of a dynamic fight between network degradation and regeneration.

Background Conscious sedation anesthesia (CSA) is an anesthetic method during peritoneal dialysis catheter implantation. However, lack of optimal CSA strategies for patients with end-stage renal disease (ESRD). This study aimed to evaluate the analgesic effects and safety of CSA using different doses of remifentanil combined with dexmedetomidine during peritoneal dialysis catheter insertion.Methods Patients who underwent peritoneal dialysis (PD) catheter placement via open surgical incision were retrospectively analyzed and divided into three groups based on the tertile dose of remifentanil. The bispectral index (BIS) was used to monitor the depth of anesthesia. Data regarding clinical findings, the effects of anesthesia, and the incidence of drug-related adverse effects were collected.Results In total, 102 patients completed the surgery successfully and safely. The dose of remifentanil was 0.02–0.07 μg/kg/min, 0.08–0.13 μg/kg/min, and 0.14–0.20 μg/kg/min in Groups A, B, and C, respectively. Only seven patients reported mild pain during the surgery. No significant differences were observed among the numeric rating scale scores of the three groups (p > 0.05). Intraoperative hemodynamics were stable. The incidence of respiratory depression was 8.3%, 20.0%, and 41.9% in Groups A, B, and C, respectively (p

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks.

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.

Objectives: To evaluate a two-test strategy for HIV screening in the low-prevalence population and to assess the feasibility of utilizing the optimal signal-to-cutoff (S/CO) threshold on the chemiluminescence immunoassay(CMIA) and an additional rapid test on the gold immune-chromatography assay (GICA) for screening positive patients and optimization of clinical management. Methods: We conducted a retrospective study of samples analyzed by the fourth-generation Architect HIV Ag/Ab combo assay (CMIA) in a large medical center between June 2017 and August 2020. Reactive samples underwent a second screening test using the rapid test GICA, followed by Western blot (WB) as the confirmatory test. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal S/CO. We calculated sensitivity, specificity, and predictive value based on our population. The performance of the single-test strategy (CMIA) was compared with that of the two-test strategy (CMIA and GICA). Logistic regression was used to analyze the factors of clinical characteristics leading to false positive results. Results: A total of 220558 samples were screened by CMIA, and 429 patients met the inclusion criteria. Of these, CMIA produced 199 false-positive results with a median S/CO of 1.93(IQR1.45–3.68) and 230 positive results with a median S/CO of 455.1 (IQR169.3–709.7). The optimal S/CO of the single-test strategy was 8.82, which achieved a sensitivity of 100% and a positive predictive value (PPV) of 90.9%. The two-test strategy (CMIA and GICA) provided a sensitivity of 100% and a PPV of 98.7%, which best correlated with the confirmatory test WB. The combination of S/CO 8.82 on the CMIA assay and additional test results of GICA can be defined as four types used to interpret HIV serostatus. The false positive rate (FPR) was high in the female, the age≤18 group, the pre-operative patients, and the patients from the clinical departments of Pediatrics, Gynecology and Obstetrics, and Oncology, etc. Conclusions: The false positive rate is high in the low-prevalence setting by using CMIA. The two-test strategy (CMIA and GICA) is recommended for HIV screening in hospitals. Hopefully, the clinicians will be able to interpret HIV serostatus and facilitate clinical decision-making while waiting for the confirmatory results.

Due to their soft structure and covering material, plastic greenhouses are vulnerable to wind disasters, causing large-scale damage and huge economic losses. The wind load of greenhouses depends on the surface wind pressure distribution, which is different for greenhouses located in valleys from those in plain areas. To study the wind pressure distribution law for various regions of greenhouses built in valleys, mountain and greenhouse models have been built by Computational Fluid Dynamics, in which the length direction of the greenhouse is perpendicular to the valley and the wind direction is parallel to the valley. In the analysis, the verified turbulence model and grid division method are both introduced, and the effect of the height and distance of mountains is considered. According to the distribution law of wind pressure, the greenhouse’s surface is partitioned, and the variation law of the shape factor of wind load on a plastic tunnel is analyzed. Then, the calculation model for the shape factor of the wind load on the greenhouse located in a valley is proposed. The conclusions show that: (a) When the wind inflow direction angle is parallel to the valley, the distribution pattern of wind pressure on the surface of the greenhouse is similar to that on the plain regardless of the distance and height of the mountains, while the values of the wind pressure are greatly affected by the mountain height and distance. The distance between mountains has greater influence than the effect of mountain height. (b) The shape factor of wind load on the suction area of the greenhouse decreases as the distance of mountains increases, while the shape factor on the pressure area of the greenhouse increases with the increase in the distance. It can be seen that the valley effect is non-negligible. The narrower and deeper the valley, the greater the wind pressure effect. (c) When the ratio of the distance between the foot of the mountain and the greenhouse d to the height of the mountain H is less than 5, i.e., d/H < 5, the ratio of the distance to the height has a significant impact on the shape factor of wind load on the greenhouse. When d/H is close to 10, the shape factor of the wind load in the valley area is close to that in the plain area, and the effect of the ratio between the height and the distance is negligible. (d) The proposed calculation model can be used to calculate the effect of mountain height and distance on the shape factor of wind load. The research results can be used in the wind resistance design of plastic greenhouses in valley areas, and can also provide some data support for the revision of the greenhouse structural load code.

Adventitious root (AR) formation is the key to asexual reproduction; however, cytokinin (CK) hampers AR formation. But the mechanism by which CK inhibits it is still unknown. In this study, we used Malus prunifolia var. ringo apple stem cuttings that were treated with exogenous 6-benzyl adenine (6-BA) at 1 mg/L and lovastatin (CK biosynthesis inhibitor) at 1 mg/L to compare with control (untreated) cuttings. The results indicated that the control and 6-BA-treated cuttings failed to produce ARs; however, lovastatin-treated cuttings successfully produced a few ARs after 20 days (d) of treatments by increasing indole-3-acetic acid (IAA) and reducing zeatin riboside (ZR) content at several time points. The 6-BA treatment induced the expression of CK-related genes, such as MdARR3, MdARR5, MdARR5-2, MdAKH4, and MdCKX5, at most time points. However, lovastatin-treated cuttings reduced their expression, which favors AR formation. Furthermore, the expression of auxin-related genes, including MdIAA23, MdARF7, and MdARF19, was induced by lovastatin treatment. Like auxin-related genes, several root-development-related genes (MdWOX5, MdWOX11, MdLB29, and MdARRO1) were also promoted in response to lovastatin treatment that were repressed by 6-BA and control cuttings. In conclusion, lovastatin treatment supports AR formation by inhibiting CK biosynthesis inside the cuttings, as compared to the control and 6-BA-treated cuttings. This study laid the foundation for future studies on the relationship of CK biosynthesis inhibitors with adventitious rooting in apples and other crops.

Gearbox fault deterioration can significantly impact the safety, reliability, and efficiency of wind turbines, resulting in substantial economic losses for wind farms. However, current condition monitoring methods face challenges in effectively mining the hidden spatio-temporal features within SCADA data and establishing reasonable weight allocations for model input variables. To tackle these issues, we proposed a novel condition monitoring method for wind turbine gearboxes called HBCE, which integrated a feature-time hybrid attention mechanism (HA), the bidirectional convolutional long short-term memory networks (BiConvLSTM), and an improved exponentially weighted moving-average (iEWMA). Specifically, utilizing historical health SCADA data acquired through the modified Thompson tau data-cleaning algorithm, a normal behavior model (HA-BiConvLSTM) of gearbox was constructed to effectively extract the spatio-temporal features and learn normal behavior patterns. An iEWMA-based outlier detection approach was employed to set dynamic adaptive thresholds, and real-time monitor the prediction residuals of HA-BiConvLSTM to identify the early faults of gearbox. The proposed HBCE method was validated through actual gearbox faults and compared with conventional spatio-temporal models (i.e., CNN-LSTM and CNN&LSTM). The results illustrated that the constructed HA-BiConvLSTM model achieved superior prediction precision in terms of RMSE, MAE, MAPE, and R2, and the proposed method HBCE can effectively and reliably identify early anomalies of a wind turbine gearbox in advance.

Summary: A high point of Tibetan Plateau (TP) civilization, the expansive Tubo Empire (618–842 AD) wielded great influence across ancient western China. However, whether the Tubo expansion was cultural or demic remains unclear due to sparse ancient DNA sampling. Here, we reported ten ancient genomes at 0.017- to 0.867-fold coverages from the Dulan site with typical Tubo archaeological culture dating to 1308–1130 BP. Nine individuals from three different grave types have close relationship with previously reported ancient highlanders from the southwestern Himalayas and modern core-Tibetan populations. A Dulan-related Tubo ancestry contributed overwhelmingly (95%–100%) to the formation of modern Tibetans. A genetic outlier with dominant Eurasian steppe-related ancestry suggesting a potential population movement into the Tubo-controlled regions from Central Asia. Together with archeological evidence from burial styles and customs, our study suggested the impact of the Tubo empire on the northeast edge of the TP involved both cultural and demic diffusion.

BackgroundThyroid cancer is the most common malignant tumor of the endocrine system. Most patients with thyroid cancer have a good prognosis, although a small proportion experience recurrence and metastasis and have a poor prognosis. Ferroptosis is a novel form of regulated cell death (RCD); previous studies have confirmed that ferroptosis was associated with thyroid cancer. The purpose of this study was to investigate the key ferroptosis-related genes in thyroid cancer and their relationship with prognosis and immune cell infiltration.MethodsIn this study, 497 thyroid cancer RNA expression datasets were downloaded from the cancer genome atlas (TCGA) cohort and a prognostic risk model for eight ferroptosis-related genes (FRGs) was constructed by Lasso-Cox regression. The prognostic value of the risk model and the correlation of prognostic features with immune scores and tumor immune cell infiltration were systematically analyzed.ResultsThe prognostic risk model for eight FRGs (DPP4, TYRO3, TIMP1, CDKN2A, SNCA, NR4A1, IL-6 and FABP4) were constructed and validated in training and testing cohorts. Kaplan-Meier curve and receiver operating characteristic (ROC) curve analysis confirmed that that the ferroptosis-related eight gene signature had good predictive value for the prognosis of thyroid cancer (THCA) patients. Multivariate regression analysis further showed that the risk score of the prognostic model could be used as an independent prognostic factor for THCA patients. Functional enrichment analysis showed that DEGs in high risk and low risk groups were involved in immune-related biological processes and that there were significant differences in immune cell infiltration between the two risk groups.ConclusionWe identified eight key genes related to ferroptosis in THCA patients. Further studies are now needed to investigate the mechanisms involved; these genes may represent clinical diagnostic and prognostic biomarkers.

BackgroundFerroptosis is widely involved in the occurrence and development of various cancers, but a specific mechanism involving ferroptosis in cervical cancer is still unclear.MethodsBased on the expressions of ferroptosis-related genes, a prognostic model was constructed using lasso regression, and the overall predictive performance of this model was verified. An in-depth analysis of the prognostic model was then conducted.ResultsThe prognostic model showed good predictive performance in both the validation and test sets. Mechanism analysis indicated that differences in the tumor microenvironment were the basis of the predictive ability of the model. Notably, CA9 mRNA was significantly overexpressed in cervical carcinoma, tissues but not in normal cervix tissues. A pair of ceRNAs (CA9/ULBP2) could be involved in the carcinogenesis and development of cervical cancer, and the potential target might be hsa-miR-34a. In addition, predicted miRNAs and drugs for these DEGs were identified.ConclusionsWe constructed a prognostic model with good predictive performance, based on the expression of ferroptosis-related genes. Further research found that the ceRNA pairs of ULBP2/CA9 could regulate cervical cancer through hsa-miR-34a. These results identified the mechanism of ferroptosis in cervical cancer, and might provide novel therapeutics for cervical cancer patients.

Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&M) cost and improving their reliability. In this study, based on the sparrow search algorithm (SSA), bidirectional long short-term memory networks with a self-attention mechanism (SABiLSTM), and a binary segmentation changepoint detection algorithm (BinSegCPD), a condition-monitoring method (SSA-SABiLSTM-BinSegCPD, SSD) used for wind turbines is proposed. Specifically, the self-attention mechanism, which can mine the nonlinear dynamic characteristics and spatial–temporal features inherent in the SCADA time series, was introduced into a two-layer BiLSTM network to establish a normal-behavior model for wind turbine key components. Then, as a result of the advantages of searching precision and convergence rate methods, the sparrow search algorithm was employed to optimize the constructed SABiLSTM model. Moreover, the BinSegCPD algorithm was applied to the predicted residual sequence to achieve the automatic identification of deterioration conditions for wind turbines. Case studies conducted on multiple wind turbines located in south China showed that the established SSA-SABiLSTM model was superior to other contrast models, achieving a better prediction precision in terms of RMSE, MAE, MAPE, and R2. The MAE, RMSE, and MAPE of SSA-SABiLSTM were 0.2543 °C, 0.3412 °C, and 0.0069, which were 47.23%, 42.19%, and 53.38% lower than those of SABiLSTM, respectively. The R2 of SABiLSTM was 0.9731, which was 4.6% higher than that of SABiLSTM. The proposed SSD method can detect deterioration conditions 47–120 h in advance and trigger fault alarm signals approximately 36 h ahead of the actual failure time.

The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection. To address such challenges, in this study, a condition monitoring approach was designed to detect early faults of wind turbines. Specifically, based on a GRU network with a self-attention mechanism, a SAGRU normal behavior model for wind turbines was constructed, which can learn temporal features and mine complicated nonlinear correlations within different status parameters. Additionally, based on the residual sequence obtained using a well-trained SAGRU, a binary segmentation changepoint detection algorithm (BinSegCPD) was introduced to automatically identify deterioration conditions in a wind turbine. A case study of a main bearing fault collected from a 50 MW windfarm in southern China was employed to evaluate the proposed method, which validated its effectiveness and superiority. The results showed that the introduction of a self-attention mechanism significantly enhanced the model performance, and the adoption of a changepoint detection algorithm improved detection accuracy. Compared to the actual fault time, the proposed approach could automatically identify the deterioration conditions of main bearings 72.47 h in advance.

DNA barcoding technology is becoming an increasingly powerful tool in resolving issues of detailed species identification based on morphology, as commonly employed by museums. In the present study, we aimed to identify a stranded Bryde’s whale on Hainan Island, China by extracting DNA from a vertebra pre-treated by physical and/or chemical processes. Based on morphological characteristics, this Bryde’s whale was initially determined as Balaenoptera edeni. Then, DNA was efficiently extracted using ancient DNA techniques. The mitochondrial gene (COI) phylogenetic analysis further revealed that this museum whale specimen belonged to the sub-species B. e. edeni. This study provides a testable and rapid method for museum species verification, by using ancient DNA extraction methods to compensate the disadvantage of traditional DNA extraction methods that are difficult to extract valid DNA.

The wind pressure coefficient is essential for calculating the wind loads on greenhouses. The wind pressure on single-span arched greenhouses built in valleys differs from those in plain regions. To promote our understanding of wind characteristics and ensure the structural safety of greenhouses in valley areas, an analysis of the distribution law of wind pressure on greenhouses is required. Firstly, we carried out a survey on greenhouse distribution and undulate terrain distribution near greenhouses in Tibet and measured the air density in Lhasa, Tibet. Then, employing the validated realizable k-ε turbulence model and the verification of grid independence, the wind pressure on greenhouses with different greenhouse azimuths was investigated. According to the survey results, values, such as the distance between the greenhouse and the mountain in addition to the greenhouse azimuth, were also obtained for calculating the wind pressure on greenhouses placed in valleys. A calculation model considering the relationship between the mountain distance and the wind pressure coefficient is proposed, whose results fit well with the results from computational fluid dynamics. The relative errors between the two different results are within 15%. Research shows that there is a canyon wind effect in the valley area, and its effect on wind pressure should be considered in greenhouse design. This research is valuable for the design of plastic greenhouses built in Tibet or other valley regions.

Steering vector mismatch rapidly degrades the performance of the minimum variance distortionless response beamformer. To solve this problem, a robust beamforming method based on weighted vector norm regularization is proposed. First, the factors affecting the robustness of the beamformer are analyzed. Second, by introducing the weighted vector norm, an optimization problem is constructed to increase the robustness of the beamformer. Furthermore, the regularization coefficient is provided to achieve a balance between the output power and the robustness of the beamformer. Meanwhile, a method of finding the appropriate regularization coefficient is provided. Then, simulations of an irregular arc array are carried out, showing that the proposed method is robust to the snapshot number. Finally, the results and data analysis indicate the effectiveness of the proposed method.

A robust adaptive beamformer based on the support vector machines is proposed to solve the rapid degradation of the performance of minimum variance distortionless response beamformer when the steering vector is mismatched. The proposed beamformer assumes that the angle error range in the steering vector can be estimated; the array only responds within this range, and the array response outside the angle error range is considered as the abnormal response. The proposed method is expressed in the form of support vector regression, and Vapnik’s $\varepsilon $ -insensitive quadratic loss function which can improve the robustness of beamformer is used as a regular term to punish the abnormal response. The assumption that the array only responds within a certain angle range reflects the sparsity of support vector regression and improves the ability of the algorithm against steering vector mismatch because the energy conservation theorem holds that the response is large. The regular term ensures that a deep null exists in the direction of interference, the interference can be effectively suppressed. The iterative reweighted least square procedure can converge to the real solution of the SVM regression problem and is easy to calculate; thus, it is used to solve the algorithm proposed in this study. The effectiveness of the algorithm is verified through computer simulations.

Ag nanowires (AgNWs) have shown great application value in the field of flexible electronics due to their excellent optical and electrical properties, and the quality of its joints of AgNWs in the thin film network directly plays a key role in its performance. In order to further improve the joint quality of AgNWs under thermal excitation, the thermal welding process and atomic evolution behavior of AgNWs were investigated through a combination of in situ experimental and molecular dynamics simulations. The influence of processing time, temperature, and stress distribution due to spatial arrangement on nanojoints was systematically explored. What is more, the failure mechanisms and their atomic interface behavior of the nanojoints were also investigated.

Abstract Background Caffeine is one of the most abundant methylxanthines in tea, and it remains stable in processing of general teas. In the secondary metabolism of microorganism, theophylline is the main conversion product in caffeine catabolism through demethylation. Microorganisms, involved in the solid-state fermentation of pu-erh tea, have a certain impact on caffeine level. Inoculating an appropriate starter strain that is able to convert caffeine to theophylline would be an alternative way to obtain theophylline in tea. The purpose of this study was to isolate and identify the effective strain converting caffeine to theophylline in pu-erh tea, and discuss the optimal conditions for theophylline production. Results Caffeine content was decreased significantly (p

The MGISEQ-2000 sequencer is widely used in various omics studies, but the performance of this platform for paleogenomics has not been evaluated. We here compare the performance of MGISEQ-2000 with the Illumina X-Ten on ancient human DNA using four samples from 1750BCE to 60CE. We found there were only slight differences between the two platforms in most parameters (duplication rate, sequencing bias, θ, δS, and λ). MGISEQ-2000 performed well on endogenous rate and library complexity although X-Ten had a higher average base quality and lower error rate. Our results suggest that MGISEQ-2000 and X-Ten have comparable performance, and MGISEQ-2000 can be an alternative platform for paleogenomics sequencing.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) appeared recently and now presents a particularly critical problem to hospitalized patients worldwide. We aim to investigate the epidemiology and the risk factors for CRKP colonization and infections, and to evaluate the application performance of MALDI-TOF MS in clustering CRKP.Results: CRKP colonization and infections incidence was 2.7 (35/1,319,427) per 100,000 patient-days. Inpatients in CRKP group had higher medical expense than CSKP group. Inpatients with underlying conditions, particularly with pulmonary diseases, and with antimicrobial use prior to culture within 30 days, especially with carbapenem use, were risk factors for CRKP acquisition. All CRKP isolates were detected producing KPC-2. The MALDI-TOF MS system and PFGE system provided similar results, with a good concordance between the two methods (adjusted Rand's coefficient, 0.846) and a high probability of MALDI-TOF MS to predict PFGE results (Wallace coefficient, 0.908).Conclusions: Underlying conditions, particularly pulmonary diseases, and antimicrobial use prior to culture within 30 days, especially carbapenem use, are risk factors for CRKP acquisition. BlaKPC−2 is the mainstream gene of CRKP in our geographic area of analysis. As only simple sample preparation is needed and the results can be obtained in a short time, MALDI-TOF MS may be considered a probable alternative to PFGE in clustering KPC-2-producing CRKP.

Background: As a complex subjective experience, pain processing may be related to functional integration among intrinsic connectivity networks of migraine patients without aura. However, few study focused on the pattern alterations in the intrinsic connectivity networks of migraine patients without aura. Results: Thirty-one migraine patients without aura and 31 age- and education-matched healthy controls participated in this study. After identifying the default mode network, central executive network and salience network as core intrinsic connectivity networks by using independent component analysis, functional connectivity, and effective connectivity during the resting state were used to investigate the abnormalities in intrinsic connectivity network interactions. Migraine patients without aura showed decreased functional connectivity among intrinsic connectivity networks compared with healthy controls. The strength of causal influences from the right frontoinsular cortex to the right anterior cingulate cortex became weaker, and the right frontoinsular cortex to the right medial prefrontal cortex became stronger in migraine patients without aura. Conclusions: These changes suggested that the salience network may play a major role in the pathophysiological features of migraine patients without aura and helped us to synthesize previous findings into an aberrant network dynamical framework. [ABSTRACT FROM AUTHOR]