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The asymptotic stability of the electric vehicle-to-grid (V2G) system with actuator faults (AFs) is studied in this article. When control signals transmit from controllers to actuators, AFs may affect control laws and destroy the asymptotic stability of the V2G system. Thus, the asymptotic stability of the V2G system with AFs is worked out by the proposed methods in two steps. First, the effects of AFs are analyzed that break or reduce passivity of the V2G system. Second, structure and energy features of the V2G system with AFs are applied to stabilize the system asymptotically. The proposed methods not only avoid applying the observer of traditional fault-tolerant control (FTC) methods to design control laws but also avoid switching from one control law to traditional FTC control law. Finally, the validity and advantages of the proposed methods are shown in examples.

Background: Serum levels of inflammatory factors, such as C3, C4, C-reactive protein (CRP), immunoglobulin (Ig) G, IgA, and IgM, in patients with Alzheimer’s disease (AD) and their correlation with cognitive function remain unexplored. Objective: To investigate the expression of serum inflammatory factors in patients with AD and its correlation with cognitive function. Methods: Serum levels of C3, C4, CRP, IgG, IgA, and IgM in 200 patients with AD (mild, moderate, and severe) and those in 174 normal controls were assessed. Spearman’s rank correlation analysis was used to explore the relationships among biomarker levels, cognitive function, and activities of daily living (ADL). Results: Among these inflammatory factors, C3 and CRP levels were significantly lower, and IgG and IgA levels were significantly higher in the AD group than in the control group (p 0.05). In all participants, CRP level was positively correlated with the Mini-Mental State Examination and Montreal Cognitive Assessment scores (p 0.05). Conclusion: Inflammatory factors C3, CRP, IgG, and IgA have the potential to serve as biomarkers for AD. Furthermore, serum IgA was not only correlated with AD but also with ADL. These results support the hypothesis that inflammation is involved in the occurrence and development of AD.

The objective of this study was to investigate the effect of sedan wheelbase size on the kinematics and injury severity of left rear-seat occupants by using the finite element (FE) modeling method. A total of 270 cases with detailed accidental information records were analyzed to define the influence laws of wheelbase size and impact speed on the injury of left rear-seat occupants. First, the THUMS (Ver. 4.0.2) FE model was used to reconstruct two small offset collisions with different wheelbases size and unbelted left rear-seat occupants, and the effectiveness of the accident model was verified. Then, seatbelts were added to the left rear-seat occupant models. Finally, LS-DYNA software was used to study the correlation among head and chest injury and five sedan wheelbases sizes (2300, 2450, 2600, 2750, and 2905 mm) at three impact velocities (54, 64, and 74 km/h). The results showed that the occupants’ chest injuries showed an upward trend at the impact velocity of 64 and 74 km/h when the wheelbases sizes was reduced to 2300 mm. This research illustrated that at higher impact velocities, excessively small wheelbases might increase the chest injury severity of left rear-seat occupants.

White matter hyperintensities (WMHs) on magnetic resonance imaging are commonly found in older adults. The mechanisms underpinning the dose-dependent association between WMH severity and cognition are not well understood. This study aimed to investigate how brain activity changes with WMH severity, and if altered brain activity mediates the relationship between WMH and cognitive function. A total of 35 participants with moderate to severe WMHs (Fazekas grade 2 or 3) and 34 participants with mild WMHs (Fazekas grade 1), who were cognitively normal, were included. Resting-state brain function was analyzed using the amplitude of low-frequency fluctuation (ALFF). A mean fractional anisotropy (FA) value of 20 tract-specific regions of interest was calculated. Mediation analysis was used to assess whether ALFF values mediated the relationship between WMH and cognition. The results showed that compared to those with mild WMHs, participants with confluent WMHs had worse memory and naming ability and also had increased ALFF in the right middle frontal gyrus and decreased ALFF in the left middle occipital gyrus. After controlling for age, gender, education and apolipoprotein E (ApoE) e4 status, increased ALFF in the right prefrontal cortex was associated with worse immediate recall and recognition, and ALFF values mediated the relationships between both Fazekas scores and FA values and memory. In conclusion, our study suggests that cognitively normal adults with high WMH load exhibit subclinical cognitive dysfunction and altered spontaneous brain activity. The mediating effects of brain activity help to shed light on our understanding of the relationship between WMHs and cognition.

Targeting for flexible, broadband ultrasound sensors, a new breed of nanographene platelet (NGP)/polyimide (PI) film is inkjet printed with morphologically optimized NGP/poly (amic acid) hybrid-based nanocomposite ink. The ink is produced with high-shear liquid phase exfoliation from inexpensive bulk graphite, manifesting good printability and high graphene concentration as high as 13.1 mg mL−1. Featuring an ultra-thin thickness (∼1 μm only), the inkjet-printed NGP/PI film sensor is demonstrated to possess excellent thermal stability and high adhesive strength reaching the American Society for Testing and Materials 5B level. The highly uniform and consolidated NGP/PI nanostructure in the sensor enables the formation of π-π interaction between NGPs and PI polymer matrix, and the quantum tunneling effect is triggered among NGPs when ultrasound traverses the sensor. This sensing mechanism endows the NGP/PI sensor with good sensitivity, fidelity and accuracy, showing comparable performance as prevailing commercial ultrasound sensors such as piezoelectric sensors. The film sensor has a proven gauge factor as high as 739, when sensing ultrasound at 175 kHz, and an ultrabroad responsive spectrum up to 1.6 MHz.

Alzheimer’s disease (AD) research is entering a unique moment in which enormous information about the molecular basis of this disease is being translated into therapeutics. However, almost all drug candidates have failed in clinical trials over the past 30 years. These many trial failures have highlighted a need for the incorporation of biomarkers in clinical trials to help improve the trial design. Fluid biomarkers measured in cerebrospinal fluid and circulating blood, which can reflect the pathophysiological process in the brain, are becoming increasingly important in AD clinical trials. In this review, we first succinctly outline a panel of fluid biomarkers for neuropathological changes in AD. Then, we provide a comprehensive overview of current and future application of fluid biomarkers in clinical trials for AD. We also summarize the many challenges that have been encountered in efforts to integrate fluid biomarkers in clinical trials, and the barriers that have begun to be overcome. Ongoing research efforts in the field of fluid biomarkers will be critical to make significant progress in ultimately unveiling disease-modifying therapies in AD.

Many deep learning models for fault diagnosis have not considered the prior diagnosis knowledge of the rolling bearing. Moreover, some measuring locations cannot collect adequate data to diagnose due to equipment size or installation space problems. This paper proposes a wavelet convolutional deep transfer learning model for rolling bearing fault detection on cross-measurement points. A new convolution layer includes a redesigned convolution kernel and a new energy pooling layer. The convolution kernel is designed based on wavelet construction for mining time-frequency characteristics. The energy pooling layer is proposed to extract the energy of different frequency bands. The associated location’s fault information has been transferred to domain knowledge to enhance the target features. Different domain features have adaptive matches based on multiple kernel variants of maximum mean discrepancy. The experimental results demonstrate that the accuracy of fault detection can reach 99.73%, and the robustness of the proposed method is also verified.

Increasing service time makes the axlebox bearing of railway vehicle vulnerable to develop a fault in inner or outer races, which can cause some serious adverse effects on a railway vehicle’s safe operation. To tackle this problem, we established a railway vehicle vertical-longitudinal dynamic model with inner/outer races faults of axlebox bearing and validated it by experimental data. We utilized the time-synchronous average (TSA) technology to filter the raw signals and studied their vibration features. The results show that the longitudinal vibration features are more sensitive for inner race fault identification, while the vertical vibration features are more suitable for outer race fault identification. For inner race fault identification, the indicator peak-to-peak value (PPV) that increases 1056% relative to the healthy state at the most severe fault performs the best sensitivity. For outer race fault identification, the indicator skewness value (SV) that increases 518% relative to the healthy state at the most severe fault exhibits the best performance. The research work can provide meaningful guidance for accurate diagnosis of axlebox bearing faults of railway vehicles.

The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. First, GAN was used to track the distribution of rail fastener failure data. To study the noise distribution, the mapping relationship between image data was established. Additional real fault samples were then generated to balance and extend the existing data sets, and these data sets were used as input to ResNet for recognition and detection training. Finally, the average accuracy of multiple experiments was used as the evaluation index. The experimental results revealed that the fault diagnosis of rail fastener based on GAN and ResNet could improve the fault detection accuracy in the case of a serious shortage of fault data.

Introduction The genetic risk effects of apolipoprotein E (APOE) on familial Alzheimer's disease (FAD) with or without gene mutations, sporadic AD (SAD), and normal controls (NC) remain unclear in the Chinese population. Methods In total, 15 119 subjects, including 311 FAD patients without PSEN1, PSEN2, APP, TREM2, and SORL1 pathogenic mutations (FAD [unknown]); 126 FAD patients with PSENs/APP mutations (FAD [PSENs/APP]); 7234 SAD patients; and 7448 NC were enrolled. The risk effects of APOE ε4 were analyzed across groups. Results The prevalence of the APOE ε4 genotype in FAD (unknown), FAD (PSENs/APP), SAD, and NC groups was 56.27%, 26.19%, 36.23%, and 19.54%, respectively. Further, the APOE ε4 positive genotype had predictive power for FAD (unknown) risk (odds ratio: 4.51, 95% confidence interval: 3.57–5.45, P < .001). Discussion APOE ε4 positive genotype may cause familial aggregation, and the investigation of multiple interventions targeting APOE pathological function to reduce the risk for this disease warrants attention.

Differential gene analyses on gastric cancer usually focus on expression change of single genes between tumor and adjacent normal tissues. However, besides changes on single genes, there are also coexpression and expression network module changes during the development of gastric cancer. In this study, we proposed a pipeline to investigate various levels of changes between gastric cancer and adjacent normal tissues, which were used to repurpose potential drugs for treating gastric cancer. Specifically, we performed a series of analyses on 242 gastric cancer samples (33-normal, 209-cancer) downloaded from the cancer genome atlas (TCGA), including data quality control, differential gene analysis, gene coexpression network analysis, module function enrichment analysis, differential coexpression analysis, differential pathway analysis, and screening of potential therapeutic drugs. In the end, we discovered some genes and pathways that are significantly different between cancer and adjacent normal tissues (such as the interleukin-4 and interleukin-13 signaling pathway) and screened perturbed genes by 2703 drugs that have a high overlap with the identified differentially expressed genes. Our pipeline might be useful for understanding cancer pathogenesis as well as gastric cancer treatment.

In the 5G era, massive devices need to be securely connected to the edge of communication networks, while emerging quantum computers can easily crack the traditional public-key ciphers. Lattice-based cryptography (LBC) is one of the most promising types of schemes in all post-quantum cryptography (PQC) due to its security and efficiency. To meet the requirements of high-throughput and diverse application scenarios of 5G, we investigate the vectorization of kernel algorithms of several LBC candidates and thus present a domain-specific vector processor, VPQC, leveraging the extensible RISC-V architecture. To support the parallel computation of number theoretic transform (NTT) of different dimensions (from 64 to 2048), a vector NTT unit is implemented in VPQC. Besides, a vector sampler executing both uniform sampling and binomial sampling is also employed. Evaluated under TSMC 28nm technology, the vector coprocessor of VPQC consumes 942k equivalent logic gates and 12KB memories. Experimental results show that VPQC can speed up several typical key encapsulation mechanisms (NewHope, Kyber and LAC) by an order of magnitude compared with previous state-of-the-art hardware implementations.

We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of ∼4.4 and ∼7.3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987–2018. The mean unbiased RMSE and bias were 7.1 and −0.05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and −1.20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (>20 cm), with biases of −10.4, −8.9, and −34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai–Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of −0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC.

Aimed at improving the resistance against power analysis attacks, a systematic and architectural design approach for multicore processors is proposed in this article and is demonstrated in an eight-core prototype platform with low performance overhead and hardware cost. In order to introduce randomness in both the time dimension and the amplitude dimension and make realignment extremely difficult, the proposed multicore platform leverages several methods together, such as random task scheduling (RTS), random insertion of operations (RIO), and frequency and phase randomization (FPR). Moreover, a power state monitoring and control (PSMC) scheme is proposed to defend against power analysis attacks by keeping enough background noises. A test chip of the proposed multicore processor is fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS LP technology and can operate at up to 800 MHz with a 1.2-V supply. The Advanced Encryption Standard (AES) algorithm with these randomization methods is implemented on the processor. Measurement results show that the correlation power analysis (CPA) attacks and the power analysis attacks based on convolutional neural networks (CNNs) are unsuccessful even with 2 000 000 power traces when all the countermeasures are used.

Vascular cognitive impairment, no dementia (VCIND) refers to cognitive deficits associated with underlying vascular causes that are insufficient to confirm a diagnosis of dementia. The default mode network (DMN) is a large-scale brain network of interacting brain regions involved in attention, working memory and executive function. The role of DMN white matter integrity in cognitive deficits of VCIND patients is unclear. Using diffusion tensor imaging (DTI), this study was carried out to investigate white matter microstructural changes in the DMN in VCIND patients and their contributions to cognitive deficits. Thirty-one patients with subcortical VCIND and twenty-two healthy elderly subjects were recruited. All patients underwent neuropsychological assessments and DTI examination. Voxel-based analyses were performed to extract fractional anisotropy (FA) and mean diffusivity (MD) measures in the DMN. Compared with the healthy elderly subjects, patients diagnosed with subcortical VCIND presented with abnormal white matter integrity in several key hubs of the DMN. The severity of damage in the white matter microstructure in the DMN significantly correlated with cognitive dysfunction. Mediation analyses demonstrated that DTI values could account for attention, executive and language impairments, and partly mediated global cognitive dysfunction in the subcortical VCIND patients. DMN integrity is significantly impaired in subcortical VCIND patients. The disrupted DMN connectivity could explain the attention, language and executive dysfunction, which indicates that the white matter integrity of the DMN may be a neuroimaging marker for VCIND.

Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only on the intensity information, which makes it sensitive to noise. Lots of GMM-based segmentation algorithms have the low segmentation accuracy because of the influence caused by noise and intensity inhomogeneity. To further improve the segmentation accuracy, a robust spatial information-theoretic GMM algorithm is proposed in this paper to simultaneously estimate the intensity inhomogeneity and segment brain MR images. First, a novel spatial factor containing the non-local spatial information is incorporated to reduce the impact of noise. The proposed spatial factor not only takes into account the local neighboring information, but also considers the spatial structure information of pixels. Thus, our algorithm can retain more image details while reducing the influence of noise. Second, the mutual information (MI) maximization method is used to identify and eliminate outliers. Finally, to overcome the impact of intensity inhomogeneity, we use a linear combination of a set of orthogonal polynomials to approximate the bias field. The objective function is integrated with the bias field estimation model to segment the images and estimate the bias field simultaneously. The experimental results on both synthetic and clinical brain MR images show that the proposed algorithm can overcome the influence of noise and intensity inhomogeneity, and achieve more accurate segmentation results.

The intelligent diagnosis of wheel flat based on vibration image classification is a promising research subject for performance maintenance of railway vehicles. However, the image representation method of vibration signal and classification network construction under small samples have become two obstacles to intelligent diagnosis of wheel flat. This paper presents a novel frequency-domain Gramian angular field (FDGAF) algorithm to encode the vibration signal of wheel flat to featured images. Furthermore, a modified transfer learning network is introduced to classify these featured images under small samples without any involvement of prior knowledge. The proposed FDGAF can calculate the Gramian angular matrix of axle box acceleration signal in frequency domain and assign frequency position dependence to the featured images to preserve original characteristic information. Then, these featured images can be intelligent classified by a transfer learning network under the condition of 30 sample without require of prior knowledge. To verify the efficiency of this proposed method, 12 cases of artificial wheel flats are processed on a scaled railway test rig, and their axle box acceleration signals are collected to obtain visual diagnosis results. The verfication proves that FDGAF is able to obtain accurate diagnostic results with high separability, for separability indexes of FDGAF reaches 10.8, 8.7, 14.9, and 5.8. We anticipate that this method will find use in the performance maintenance of railway vehicles and the improvement of industrial condition monitoring.

Background The most common biomarkers of Alzheimer’s disease (AD) are amyloid β (Aβ) and tau, detected in cerebrospinal fluid (CSF) or with positron emission tomography imaging. However, these procedures are invasive and expensive, which hamper their availability to the general population. Here, we report a panel of microRNAs (miRNAs) in serum that can predict P-tau/Aβ42 in CSF and readily differentiate AD from other dementias, including vascular dementia (VaD), Parkinson disease dementia (PDD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy body (DLB). Methods RNA samples were extracted from the participant’s blood. P-tau/Aβ42 of CSF was examined for diagnostic purposes. A pilot study (controls, 21; AD, 23), followed by second (controls, 216; AD, 190) and third groups (controls, 153; AD, 151), is used to establish and verify a predictive model of P-tau/Aβ42 in CSF. The test is then applied to a fourth group of patients with different dementias (controls, 139; AD,155; amnestic mild cognitive impairment [aMCI], 55; VaD, 51; PDD, 53; bvFTD, 53; DLB, 52) to assess its diagnostic capacity. Results In the pilot study, 29 upregulated and 31 downregulated miRNAs in the AD group were found. In Dataset 2, these miRNAs were then included as independent variables in the linear regression model. A seven-microRNA panel (miR-139-3p, miR-143-3p, miR-146a-5p, miR-485-5p, miR-10a-5P, miR-26b-5p, and miR-451a-5p) accurately predicted values of P-tau/Aβ42 of CSF. In Datasets 3 and 4, by applying the predicted P-tau/Aβ42, the predictive model successfully differentiates AD from controls and VaD, PDD, bvFTD, and DLB. Conclusions This study suggests that the panel of microRNAs is a promising substitute for traditional measurement of P-tau/Aβ42 in CSF as an effective biomarker of AD.

The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.

Can we automatically learn discriminative embedding features from images when human-annotated labels are absent? The problem of unsupervised embedded learning remains a significant and open challenge in image and vision community. A joint online deep embedded clustering and hard samples mining framework are proposed to improve the representation ability of embedded learning. In addition, to enhance the discriminability of feature representations, a structure-level pair-based loss is introduced to take full advantage of structure correlation between all the mined hard samples. Finally, the quantitative results of exhaustive experiments on three benchmarks show that our proposed method performs better than existing state-of-the-art methods.

INTRODUCTION Alzheimer's disease (AD) is associated with altered metabolites. This study aimed to determine the validity of using circulating metabolites to differentiate AD from other dementias. METHODS Blood metabolites were measured in three data sets. Data set 1 (controls, 27; AD, 28) was used for analyzing differential metabolites. Data set 2 (controls, 93; AD, 92) was used to establish a diagnostic AD model with use of a metabolite panel. The model was applied to Data set 3 (controls, 76; AD, 76; other dementias, 205) to verify its capacity for differentiating AD from other dementias. RESULTS Data set 1 revealed 7 upregulated and 77 downregulated metabolites. In Data set 2, a panel of 11 metabolites was included in a model that could distinguish AD from controls. In Data set 3, this panel was used to successfully differentiate AD from other dementias. DISCUSSION This study revealed an AD-specific panel of 11 metabolites that may be used for AD diagnosis.

Background Larotinib is a new first-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. This open-label, phase 1b study is aimed to evaluate the efficacy, safety of larotinib in patients with advanced esophageal squamous cell carcinoma (ESCC) with EGFR overexpression or amplification pretreated with one or more system regimens, and to recommend an appropriate dose for its further study. Methods Patients received larotinib orally at 3 doses (250, 300, 350 mg), once daily. Clinical response was evaluated every 8 weeks according to RECIST v1.1 criteria by both investigators and independent radiology review (IRC). Results 81 patients were enrolled. The investigator-assessed overall response rate (ORR) was 13.7% (10/73), all responses were observed in the 350 mg group of which ORR up to 20.0% (10/50), with 10 of them having EGFR overexpression and 4 having EGFR amplification. Per IRC assessment, ORR for all patients and 350 mg group were 13.9% (10/72) and 16.3% (8/50). In the 350 mg group, median overall survival (OS) and progression-free survival (PFS) were 8.0 (95% CI 4.9–10.2) months and 3.4 (95% CI 2.4–3.7) months, respectively. The most common treatment-related adverse events (TRAEs) were diarrhea, rash, and palmar-plantar erythrodysesthesia syndrome, elevated AST/ALT, vomiting, similarly with other EGFR TKIs. Conclusions Larotinib demonstrated promising antitumor activity and manageable safety profiles in patients with pre-treated advanced ESCC with EGFR overexpression or amplification, especially at the dose of 350 mg, which showed better efficacy and acceptable safety. A phase 3 study is underway on 350 mg larotinib in ESCC patients with EGFR overexpression. Trial registration This trial was retrospectively registered on 25/03/2019, NCT03888092. https://clinicaltrials.gov/ct2/show/NCT03888092.

Tiewei Li,1,&ast; Xiaojuan Li,1,&ast; Yulei Wei,1 Geng Dong,1 Jianwei Yang,1 Junmei Yang,1 Panpan Fang,1 Minglu Qi2 1Zhengzhou Key Laboratory of Children’s Infection and Immunity, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, People’s Republic of China; 2General Hospital of Taiyuan Steel (Group) Co., Ltd., Taiyuan, 030000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Panpan FangZhengzhou Key Laboratory of Children’s Infection and Immunity, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, 33M Waihuan East Road, Longhu, Zhengzhou, 450018, People’s Republic of ChinaTel +8637185515779Email fang_panpan@163.comMinglu QiGeneral Hospital of Taiyuan Steel (Group) Co., Ltd., No. 7, South 2nd Lane, Yingxin Street, Jiancaoping District, Taiyuan City, 030008, People’s Republic of ChinaTel +863512139774Email feiyuxiaotian109@163.comPurpose: Previous studies have reported that C-reactive protein-to-albumin ratio (CAR) was a risk factor for sepsis in adults. However, little is known regarding the role of CAR in neonates with sepsis. The aim of this study was to explore the relationship between CAR and neonatal sepsis.Patients and Methods: In this research, from January 2016 to February 2020, a total of 1076 neonates were enrolled at Henan Children’s Hospital in China. Complete clinical and laboratory data were collected. To identify the potential independent risk factor for neonatal sepsis, multivariate logistic regression analysis was performed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy of CAR in identifying neonatal sepsis.Results: CAR levels were higher in neonates with sepsis and showed a gradual increase among the control group, mild sepsis group and severe sepsis group. The prevalence of neonates with overall sepsis, mild sepsis and severe sepsis increased significantly from CAR tertile 1 to tertile 3. Multiple logistic regression analysis showed that CAR was an independent risk factor for the presence of sepsis (OR = 10.144, 95% CI 4.151– 24.790, P < 0.001) and severe sepsis (OR = 1.876, 95% CI 1.562– 2.253, P < 0.001). ROC curve analysis showed that CAR had a well discriminatory power in predicting sepsis (area under curve (AUC) = 0.74, 95% CI, 0.71– 0.77, P < 0.001) and severe sepsis (AUC = 0.70, 95% CI, 0.67– 0.74, P < 0.001).Conclusion: CAR was an independent predictor for the presence and severity of neonatal sepsis.Keywords: C-reactive protein-to-albumin ratio, neonatal sepsis, risk factor, severe sepsis

Skeleton-based human action recognition (HAR) has been extensively studied these years because body skeleton has the simple but informative representation of human action, which greatly reduces the computation complexity compared with the image-based HAR. As a result, it is suitable for low power implementation in embedded platforms. In this brief, we present a systematic approach to developing a hardware-efficient and low-power processor for real-time skeleton-based HAR. First, a lightweight HAR algorithm only using the one-dimensional convolutional neural network (1D-CNN) is proposed. Second, the singular value decomposition is employed to compress the weights in the fully connected (FC) layers of the proposed convolutional neural network. Third, a hardware processor implementing the proposed algorithm is presented. Aimed at optimizing area and energy, this processor utilizes a flexible structure supporting different kernel sizes of the 1D-CNN and reuses hardware in both convolution layers and FC layers. The proposed processor is implemented under SMIC 65-nm CMOS technology and consumes a total area of 1.016 mm2. Experimental results show that the proposed processor can achieve state-of-the-art classification accuracy in NTU RGB+D dataset and SBU dataset while outperforming previous solutions in area and energy efficiency.

In order to smooth the system frequency disturbances caused by wind power fluctuation, this study proposes a novel load frequency control (LFC) strategy based on model predictive control (MPC) to regulate the system frequency. The proposed strategy mainly aims to improve the system frequency responses by considering the dynamics of wind turbine (WT) and forecasting the output power of WT. First, the frequency response model (FRM) of system including the power system LFC and WT is established. Second, through discretisation of the FRM, a predictive model of system is obtained. Then, system state and response in the next predictive period are forecasted by the predictive model. After obtaining the prediction information, the control signal of system is optimised for improving system frequency response. On this basis, a novel MPC-based LFC strategy is proposed based on the predictive model and the optimisation of control sequence. With the information of wind speed, this control strategy is able to forecast the system states and responses easily and adjust the control signal according to wind power fluctuation. Finally, numerical simulations are demonstrated to visualise the effectiveness and feasibility of the proposed MPC-based LFC strategy and its advantages over existing methods.

PurposeThe purpose of this paper is to study the wear evolution of metro wheels under the conditions of different track sequences, track composition and vehicle load and then to predict wheel wear and to guide its maintenance.MethodologyBy using the SIMPACK and MATLAB software, numerical simulation analysis of metro wheel wear is carried out based on Hertz theory, the FASTSIM algorithm and the Archard model. First of all, the vehicle dynamics model is established to calculate the motion relationship and external forces of wheel-rail in the SIMPACK software. Then, the normal force of wheel-rail is solved based on Hertz theory, and the tangential force of wheel-rail is calculated based on the FASTSIM algorithm through the MATLAB software. Next, in the MATLAB software, the wheel wear is calculated based on the Archard model, and a new wheel profile is obtained. Finally, the new wheel profile is re-input into the vehicle system dynamics model in the SIMPACK software to carry out cyclic calculation of wear.FindingsThe results show that the setting order of different curves has an obvious influence on wear when the proportion of the straight track and the curve is fixed. With the increase in running mileage, the severe wear zone is shifted from tread to flange root under the condition of the sequence-type track, but the wheel wear distribution is basically stable for the unit-type track, and their wear growth rates become closer. In the tracks with different straight-curved ratio, the more proportion the curved tracks occupy, the closer the severe wear zone is shifted to flange root. At the same time, an increase in weight of the vehicle load will aggravate the wheel wear, but it will not change the distribution of wheel wear. Compared with the measured data of one city B type metro in China, the numerical simulation results of wheel wear are nearly the same with the measured data.Practical implicationsThese results will be helpful for metro tracks planning and can predict the trend of wheel wear, which has significant importance for the vehicle to do the repair operation. At the same time, the security risks of the vehicle are decreased economically and effectively.Originality/valueAt present, many scholars have studied the influence of metro tracks on wheel wear, but mainly focused on a straight line or a certain radius curve and neglected the influence of track sequence and track composition. This study is the first to examine the influence of track sequence on metro wheel wear by comparing the sequence-type track and unit-type track. The results show that the track sequence has a great influence on the wear distribution. At the same time, the influence of track composition on wheel wear is studied by comparing different straight-curve ratio tracks; therefore, wheel wear can be predicted integrally under different track conditions.

Traditional machine fault diagnosis techniques are labor-intensive and hard for nonexperts to use. In this paper, a novel three-stage intelligent fault diagnosis approach is proposed for practical industrial process monitoring. A new feature processing technique is developed to enhance the identification accuracy and reduce the computation burden, which incorporates variational mode decomposition-based trend detection and self-weight algorithm. Furthermore, an adaptive density peaks search (ADPS) algorithm has been primarily proposed for adaptive clustering, whose effectiveness is verified in comparison with the original DPS, affinity propagation clustering, and K-medoids. The three-stage intelligent fault diagnosis approach is subsequently applied to three specific industrial cases. Results of bearing and gear fault diagnosis have well demonstrated that the proposed method is able to reliably and accurately identify different faults with less prior knowledge and diagnostic expertise. Moreover, the proposed technique can be adopted to adaptively monitor different conditions using unlabeled bearing run-to-failure testing data, which also shows it is well suitable for industrial online applications.

BackgroundMonoclonal antibodies targeting programmed death ligand 1 (PD-L1) signaling currently approved for defective mismatch repair (dMMR)/microsatellite instability high (MSI-H) tumors must be delivered by intravenous infusion. Envafolimab, a humanized single-domain anti-PD-L1 antibody fused to an Fc fragment, represents a potential advance because it can be conveniently administered subcutaneously.MethodsThis open-label, single-arm, phase 2 study evaluated the efficacy and safety of envafolimab in patients with previously treated advanced dMMR/MSI-H tumors from 25 clinical sites across China. Adults with histologically confirmed locally advanced or metastatic malignant dMMR/MSI-H solid tumors received weekly 150 mg subcutaneous envafolimab injections in a 28-day treatment cycle. The primary efficacy endpoint was the objective response rate (assessed by a blinded independent review committee). Secondary efficacy outcomes were disease control rate, duration of response, progression-free survival, and overall survival.ResultsOne hundred and three patients (65 with colorectal cancer, 18 with gastric cancer, and 20 with other solid tumors) were enrolled. Median follow-up was 11.5 months. The objective response rate was 42.7% (95% confidence interval [CI] 33.0–52.8), and the disease control rate was 66.0% (95% CI 56.0–75.1). Median duration of response was not reached; the duration of response rate at 12 months was 92.2% (95% CI 77.5–97.4). Median progression-free survival was 11.1 months (95% CI 5.5 to not evaluable). Overall survival at 12 months was 74.6% (95% CI 64.7–82.1). Sixteen patients (16%) had at least one grade 3 or 4 related treatment-emergent adverse event. No grade 5 treatment-emergent adverse events related to envafolimab were reported. Injection site reactions, all grade 1–2, were reported in nine patients (9%), but there were no infusion reactions. Eight patients (8%) had grade 3 or 4 immune-related adverse events.ConclusionsThis is the first pivotal phase 2 study to examine the efficacy and safety of a single-domain immune checkpoint antibody in the treatment of cancer. Envafolimab was effective and had acceptable safety in the treatment of previously treated advanced dMMR/MSI-H solid tumors. As the first single-domain PD-L1-targeting antibody administered by rapid subcutaneous injection, envafolimab has the potential to be a significant advance in the treatment of cancer.Trial registrationClinicalTrials.gov, NCT03667170. Registered 10 September 2018—Retrospectively registered,https://clinicaltrials.gov/ct2/show/NCT03667170.

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \emph{bottom-up and top-down} model \cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.

Older patients with confluent white matter hyperintensities (WMHs) on magnetic resonance imaging have an increased risk for the onset of vascular cognitive impairment (VCI). This study investigates the predictive effects of the white matter (WM) fractional anisotropy (FA) and brain volumes on cognitive impairment for those with confluent WMHs. This study enrolled 77 participants with confluent WMHs (Fazekas grade 2 or 3), including 44 with VCI-no dementia (VCIND) and 33 with normal cognition (NC). The mean FA of 20 WM tracts was calculated to evaluate the global WM microstructural integrity, and major WM tracts were reconstructed using probabilistic tractography. Voxel-based morphometry was used to calculate brain volumes for the total gray matter (GM), the hippocampus, and the nucleus basalis of Meynert (NbM). All volumetric assays were corrected for total intracranial volume. All regression analyses were adjusted for age, gender, education, and apolipoprotein E (ApoE) gene ε4 status. Logistic regression analysis revealed that the mean FA value for global WM was the only independent risk factor for VCI (z score of FA: OR = 4.649, 95%CI 1.576–13.712, p = 0.005). The tract-specific FAs were not associated with the risk of cognitive impairment after controlling the mean FA for global WM. The mean FA value was significantly associated with scores of Mini-Mental State Examination (MMSE) and Auditory Verbal Learning Test. A lower FA was also associated with smaller volumes of total GM, hippocampus, and NbM. However, brain volumes were not found to be directly related to cognitive performances, except for an association between the hippocampal volume and MMSE. In conclusion, the mean FA for global WM microstructural integrity is a superior predictor for cognitive impairment than tract-specific FA and brain volumes in people with confluent WMHs.

Traditionally, multi-object tracking is a technique for tracking points of interest through space and time. With the rise of powerful deep networks, this situation has changed. Nowadays, tracking mainly consists of pipelines that perform object detection and time correlation (also called tracking by detection). However, in order to extract more representative functions, many State-Of-The-Art (SOTA) models use a lot of complex backbones, which hindered the speed of inference. We proposed a new multi-object tracking model named Squeezed-Excited-Center-Based tracking (SECB-tracking), and replace the original IoU loss with the new Complete IoU loss, which can help the model converge faster. We evaluated our proposed method on the MOT17 dataset. Compared with CenterTrack, we have increased MOTA by 7 points (a relative increase of 10.3%) and reduced inference time by 20%.

With the increasing number of electric vehicles in urban power grid, the random characteristics of charging and discharging behavior make the distribution network power flow distribution complex and changeable. At the same time, the electric vehicle has the characteristics of discreteness and randomness in the space-time distribution, which makes the electric vehicle's discharge behavior difficult to describe. This paper proposes a distributed generation equivalent method based on the discharge behavior of electric vehicles. Firstly, the vehicle transfer matrix based on Markov chain is used to simulate the travel behavior of electric vehicles; Secondly, combined with the travel behavior and charging and discharging rules of electric vehicles, the charge and discharge state of electric vehicles is studied, and the discharge behavior of electric vehicles is equivalent to a distributed generation with a change in output; Finally, the validity of the proposed equivalent method is verified by an example analysis, and the influence of electric vehicle discharge behavior on distribution network power flow distribution is discussed.

Background Accelerated long-term forgetting has been identified in preclinical Alzheimer’s disease (AD) and is attributed to a selective impairment of memory consolidation in which the hippocampus plays a key role. As blood may contain multiple senescence-related factors that involved in neurogenesis and synaptic plasticity in the hippocampus, we tested whether there is an association between blood-borne factors and accelerated long-term forgetting in asymptomatic individuals from families with autosomal dominant AD (ADAD). Methods We analyzed data of 39 asymptomatic participants (n = 18 ADAD mutation carriers, n = 21 non-carriers) from the Chinese Familial Alzheimer’s Disease Network (CFAN) study. Long-term forgetting rates were calculated based on recall or recognition of two materials (word list and complex figure) at three delays comprising immediate, 30 min, and 7 days. Peripheral blood concentrations of candidate pro-aging factors (CC chemokine ligand 11 [CCL11] and monocyte chemotactic protein 1 [MCP1]) and rejuvenation factors (growth differentiation factor 11 [GDF11], thrombospondin-4 [THBS4], and secreted protein acidic and rich in cysteine like 1 [SPARCL1]) were evaluated in all participants. Results Despite normal performance on standard 30-min delayed testing, mutation carriers exhibited accelerated forgetting of verbal and visual material over 7 days in comparison with matched non-carriers. In the whole sample, lower plasma THBS4 was associated with accelerated long-term forgetting in list recall (β = −0.46, p = 0.002), figure recall (β = −0.44, p = 0.004), and list recognition (β = −0.37, p = 0.010). Additionally, higher plasma GDF11 and CCL11 were both associated with accelerated long-term forgetting (GDF11 versus figure recall: β = 0.39, p = 0.007; CCL11 versus list recognition: β = 0.44, p = 0.002). Conclusions Accelerated long-term forgetting is a cognitive feature of presymptomatic AD. Senescence-related blood-borne factors, especially THBS4, GDF11, and CCL11, may be promising biomarkers for the prediction of accelerated long-term forgetting.

Objective: Synaptic degeneration is the pathologic foundation of cognitive decline in the Alzheimer’s disease (AD) continuum. We aimed to determine whether cerebrospinal fluid (CSF) synaptic marker...

Molecular heterogeneity determines the differences in the pathological features, prognosis and survival after relapse when comparing left-sided colon cancer (LCC) and right-sided colon cancer (RCC). At present, the discrepancy in the underlying molecular events between the two types of colon cancer has not been thoroughly investigated. The present study aimed to explore novel targets to predict the disease stage and prognosis of LCC and RCC. Expression analysis of guanine nucleotide binding-protein γ subunit 4 (GNG4) was performed using the Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine databases. Survival and association analyses were performed using GEPIA and the colon adenocarcinoma dataset from The Cancer Genome Atlas database. GNG4-positive cells in a tissue microarray were examined using immunohistochemistry. According to the GNG4 expression data from Caucasian patients included in the TCGA dataset, GNG4 was highly expressed and positively associated with pathological stage and overall survival (OS) rates in colon cancer. GNG4 expression was higher in LCC than in RCC. Patients with LCC with high GNG4 expression exhibited higher pathological stage and lower survival rates, whereas this was not observed in patients with RCC. In addition, the clinical tissues used in the microarray showed that GNG4 expression was increased in Chinese patients with LCC compared with that in patients with RCC. Consistently, GNG4 expression was negatively associated with OS in patients with LCC, but not in patients with RCC. However, no association was observed between GNG4 expression and the disease stage of colon cancer in both patients with LCC and RCC. Overall, the molecular heterogeneity of GNG4 in LCC and RCC suggests that GNG4 may be used as a diagnostic and prognostic biomarker in patients with LCC.

Rotating machinery has a complicated structure and interaction of multiple components, which usually results in coupling faults with complex dynamic characteristics. Fault diagnosis methods based on vibration signals have been widely used, however, these methods are intricate when identifying coupling faults, especially in the situation where coupling faults share similar patterns. As a noncontact and nonintrusive temperature-measuring technique, methods by infrared images can recognize multiple faults through temperature variations; however, it is not effective if the faults are temperature-insensitive. In this paper, an improved machinery fault diagnosis technique based on information fusion of infrared images and vibration signals is studied, to have better utilization of multisource sensors and to solve the problems when one single type of data is separately used. Firstly, data enhancement for infrared images and data visualization for vibration are performed on the dataset by using the principle of graphics and Short-Term Fourier Transform, which increases the diversity of the dataset and enhances the generalization ability of the model. Then, a multichannel convolution neural network-based method is constructed to achieve data-level information fusion and improve the fault diagnosis accuracy. The effectiveness of the presented method is validated by the experimental studies on a rotor test stand, the results illustrate that the coupling faults can be effectively identified by the information fusion method, and the fault diagnosis accuracy is higher in comparison with the method by a signal from single-source sensors.

Liquid biopsy, which generally refers to the analysis of biological components such as circulating nuclear acids and circulating tumor cells in body fluids, particularly in peripheral blood, has shown good capacity to overcome several limitations faced by conventional tissue biopsies. Emerging evidence in recent decades has confirmed the promising role of liquid biopsy in the clinical management of various cancers, including colorectal cancer, which is one of the most prevalent cancers and the second leading cause of cancer-related deaths worldwide. Despite the challenges and poor clinical outcomes, patients with metastatic colorectal cancer can expect potential clinical benefits with liquid biopsy. Therefore, in this review, we focus on the clinical prospects of liquid biopsy in metastatic colorectal cancer, specifically with regard to the recently discovered various biomarkers identified on liquid biopsy. These biomarkers have been shown to be potentially useful in multiple aspects of metastatic colorectal cancer, such as auxiliary diagnosis of metastasis, prognosis prediction, and monitoring of therapy response.

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.

In this paper, a self-adaptive Network-on-chip (NoC) with reconfigurable rings is designed to reduce the communication latency of the multi-core system. In our design NoC, we use reconfigurable ring clusters as an express optional path to send packages, at the same time the different ring clusters can reconfigure and various interconnect manners can be created according to the communication conditions. Because of the low latency of ring clusters and flexible reconfigure manners, the proposed NoC can achieve about 38.4% latency reduction in various benchmarks and the extra area overhead is only about 8.2%.

Background: Lung Cancer is one of the most common cancers with high degree of malignancy, is a devastating disease with a poor prognosis worldwide. prognostic prediction for patients with non small-cell lung cancer (NSCLC) is still challenge. Material and methods: The cohort consisted of 64 consecutive patients with NSCLC identified from June1, 2014, to June 30, 2018. Liquid biopsy samples were collected. Genomic mutation DNA was calculated by including all substitutions and indels over the entire somatic, coding, sequencing length. statistical evaluations were carried out using SPSS software. Results: Quantity of total ctDNA was successfully determined in all 64 patients from whom baseline circulating DNA was available. ctDNA concentration ranged from 4000 to 3,562,000 genome equivalents per milliliter. Treatments induced a significant decrease in cancer specific markers in most patients with response to treatments, while the methylated DNA demonstrated favorable prediction efficiency regardless of the response status. Patients with ctDNA mutation and methylated DNA decreasing have favorable overall survival (P

Purpose An anatomical and histological study of the conjoint fascial sheath of the levator and superior rectus (CFS) was carried out by using the cadavers for teaching. Methods Three adult Asian cadaver heads fixed in formalin were used. The CFS was exposed by the same surgeon in each case. Then the CFS was observed and measured in vivo and ex vivo. And the CFS, the levator and the frontal muscle were removed from the same eye for histological study. Results The CFS was located 2.1 ± 0.4 mm posterior to the fornix. A special muscle sheath of the levator was observed. The special muscle sheath and the tendon of the superior rectus were fused to the CFS through loose connective tissue. Hematoxylin-Eosin (HE) staining showed a large amount of connective tissue on examination of the CFS by microscopy. Double staining with Victoria-blue and Masson trichrome staining confirmed elastic fibers and collagen fibers in the CFS tissues. Conclusions If ptosis correction surgery is performed by looking for the CFS from the upper edge of the conjunctiva, in fact, only a special part of the muscle sheath of the levator in the CFS, but not the integral CFS, is used in the surgery. The histological results confirm that the CFS is a fibrous tissue membrane with both elasticity and toughness. Perhaps the best choice is to recombine the special muscle sheath of the levator in the CFS with the levator muscle tissue during ptosis correction surgery to suspend the eyelids.

In this paper, the influence of the toolset size on the formability of the high strength steel formability was analyzed. The formability indexes include the thinning rate and springback. The toolset size analyzed in this study includes die corner size and punch corner size. To analyze the above relationship efficiently, the test design and the FE simulation were employed. At the same time, the optimization method was also used. The response surface method can be used to show the effect of the tool size on the formability clearly. It can be seen from the results that the springback increases with the increase of the radius of the toolset punch corner. And the effect of the radius of the toolset die corner shows slight effect on the springback. The radius of the toolset punch corner is more sensitive for the springback than the radius of the toolset die corner. The thinning increases with the decrease of the radius of the toolset punch corner. And the effect of the radius of the toolset die corner shows slight effect on the thinning, the thinning increases with the increase of the radius of the toolset die corner. In terms of the thinning, the punch corner is more sensitive than the die corner, especially when the radius of the toolset die corner is high.

Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods.

Nowadays, profile shift is one of the most powerful techniques to achieve high-performance spur gear transmission system. Although this technique has been widely used in industry, its influence on time-varying meshing stiffness (TVMS) and dynamic characteristics is still unclear. This paper presents a novel analytical model of TVMS for profile-shifted spur gears. The modification coefficient and transition curve revise the proposed model. Afterward, numerical simulations are carried out to analyze individual and compound shifts based on a six-degree of freedom gear model. For individual shifts, the positive shift can decrease the gear tooth's stiffness and TVMS so that it causes stronger vibrations in the gear system. On the contrary, the negative shift can improve the gear tooth's stiffness and TVMS, thereby mitigating the gear system's vibration. For compound shift, S0-gearing poses a slight impact on TVMS, but an unneglectable effect on dynamic characteristics. Besides, the S-gearing with the positive compound shift can decrease TVMS and increase vibration. In contrast, the S-gearing with the negative compound shift leads to TVMS rising and significantly reducing vibration. The insightful findings derived from the study can help engineers and manufacturers better understand the operation performance and dynamic features of profile-shifted spur gears.

Purpose Cetuximab in combination with chemotherapy is a standard-of-care first-line treatment regimen for patients with RAS wild-type (wt) metastatic colorectal cancer (mCRC); however, the efficacy of cetuximab plus leucovorin, fluorouracil, and oxaliplatin (FOLFOX) has never before been proven in a controlled and randomized phase III trial. To our knowledge, the TAILOR trial ( ClinicalTrials.gov identifier: NCT01228734) is the first randomized, multicenter, phase III study of the addition of cetuximab to first-line FOLFOX prospectively choosing a RAS wt population and thus providing confirmative data for the efficacy and safety of cetuximab plus FOLFOX versus FOLFOX alone. Patients and Methods TAILOR is an open-label, randomized (1:1), multicenter, phase III trial in patients from China comparing FOLFOX-4 with or without cetuximab in RAS wt ( KRAS/ NRAS, exons 2 to 4) mCRC. The primary end point of TAILOR was progression-free survival time; secondary end points included overall survival time, overall response rate, and safety and tolerability. Results In the modified intent-to-treat population of 393 patients with RAS wt mCRC, adding cetuximab to FOLFOX-4 significantly improved the primary end point of progression-free survival time compared with FOLFOX-4 alone (hazard ratio, 0.69; 95% CI, 0.54 to 0.89; P = .004; median, 9.2 v 7.4 months, respectively), as well as the secondary end points of overall survival time (current assessment after 300 events: hazard ratio, 0.76; 95% CI, 0.61 to 0.96; P = .02; median, 20.7 v 17.8 months, respectively) and overall response rate (odds ratio, 2.41; 95% CI, 1.61 to 3.61; P < .001; 61.1% v 39.5%, respectively). Treatment was well tolerated, and there were no new or unexpected safety findings. Conclusion The TAILOR study met all of its objectives and relevant clinical end points, confirming cetuximab in combination with FOLFOX as an effective standard-of-care first-line treatment regimen for patients with RAS wt mCRC.

A new stress-based high-cycle fatigue damage criterion for multiaxial load cases, D = αDσβDτγ is presented. This criterion is based on a critical plane approach that the damage parameter is a function of normal stress amplitude and shear stress amplitude on critical plane of maximum shear stress range. The coefficient α, β and γ in the damage function are material parameters. Tensions with torsion test data are required to ascertain these coefficients. This criterion matches the test results well and shows accurate predictions of fatigue failure life compared to some present methods.

In this letter, solution-processed black phosphorus quantum dots (BPQDs) were applied to enhance hole extraction of Si/poly-(3, 4-ethylenedioxythiophene): poly (styrenesulfonic acid) (PEDOT:PSS) heterojunction based hybrid solar cells. A remarkable improvement of power conversion efficiency (PCE) from 10.03% to 13.60% was achieved by the PEDOT:PSS/BPQDs-incorporated devices. Detailed investigations discover the influence of BPQDs on the hole extraction effect and work mechanism of BPQDs. Our work presents an avenue in using solution processed BPQDs for high performance hybrid solar cells.