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Self-healing anti-corrosion materials are widely regarded as a promising long-term corrosion protection strategy, and this is even more significant if the damage can be monitored in real-time and consequently repaired. Inspired by the hierarchical structure of human skin, self-healing, solvent-free polyurethane/carbon nanotubes composites (SFPUHE-HTF-CNTs) with a skin-like bilayer structure were constructed. The SFPUHE-HTF-CNTs were composed of two layers, namely, a hydrophobic solvent-free polyurethane (SFPUHE-HTF) containing disulfide bonds and fluorinated polysiloxane chain segments consisting of a self-healing layer and CNTs with good electrical conductivity consisting of a corrosion protection layer, which also allowed for the real-time monitoring of damage. The results of corrosion protection experiments indicated that the SFPUHE-HTF-CNTs had a low corrosion current density (8.94 × 10−9 A·cm−2), a positive corrosion potential (−0.38 V), and a high impedance modulus (|Z| = 4.79 × 105 Ω·cm2). The impedance modulus could still reach 4.54 × 104 Ω·cm2 after self-healing, showing excellent self-healing properties for anti-corrosion protection. Synchronously, the SFPUHE-HTF-CNTs exhibited a satisfactory damage sensing performance, enabling the real-time monitoring of fractures at different sizes. This work realized the effective combination of self-healing with corrosion protection and damage detection functions through a bionic design, and revealed the green, and low-cost preparation of advanced composites, which have the advantage of scale production.

Abstract Background The methylation of lysine residues has been involved in the multiple biological and diseases processes. Recently, some particular non‐histone proteins have been elucidated to be methylated by SMYD2, a SET and MYND domain protein with lysine methyltransferase activity. Methods SMYD2 was evaluated in synovial tissue and cells derived from rheumatoid arthritis patients. We confirmed TRAF2 could be methylated by SMYD2 using Mass spectrometry, pull‐down, immunoprecipitation, methyltransferase assay, ubiquitination assay, luciferase reporter assays, and western blot analyses. Using loss‐ and gain‐of function studies, we explored the biological functions of SMYD2 in vitro and in vivo. Using acute and chronic inflammation with different mice models to determine the impact of SMYD2. Results Here, we first time confirmed that the cytoplasmic protein TRAF2 as the kernel node for NF‐κB signaling pathway could be methylated by SMYD2. SMYD2‐mediated TRAF2 methylation contributed to the durative sensitization of NF‐κB signaling transduction through restraining its own proteolysis and enhancing the activity. In addition, we found knocking down of SMYD2 has different degrees of mitigation in acute and chronic inflammation mice models. Furthermore, as the lysine‐specific demethylase, LSD1 could resist methylation on TRAF2 induced by SMYD2. Conclusions Our data uncovered an unprecedented cytoplasmic protein network that employed methylation of TRAF2 for the maintenance of NF‐κB activation during inflammatory diseases.

Summary: An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.

Pilots are highly susceptible to anxiety symptoms due to the uncertainty of the dangers of aviation operations and the potential terror and insecurity that pilots experience over time. This research aims to understand the complex relationship between the occupational stress and group anxiety symptoms of Chinese civil aviation pilots, and to analyze the type A behavior pattern (TABP) as a mediator for both issues. The occupational stress, anxiety symptoms and TABP of Chinese civil aviation pilots are investigated by using questionnaires based on the effort-reward-imbalance (ERI) scale, the Zung Self-Rating Anxiety Scale (SAS) and a TABP scale established based on Chinese people’s inherent characteristics. The study revealed that higher occupational stress experienced by Chinese civil aviation pilots exacerbated their anxiety symptoms, and that overcommitment and TABP mediated the relationship between occupational stress and anxiety symptoms.

The safety issue of lithium-ion batteries is a great challenge for the applications of EVs. The internal short circuit (ISC) of lithium-ion batteries is regarded as one of the main reasons for the lithium-ion batteries failure. However, the online ISC diagnosis algorithm for real vehicle data remains highly imperfect at present. Based on the onboard data from the cloud battery management system (BMS), this work proposes an ISC diagnosis algorithm for battery packs with high accuracy and high robustness via voltage anomaly detection. The mean-difference model (MDM) is applied to characterize large battery packs. A diagram of the adaptive integrated prediction algorithm combining MDM and a bi-directional long short-term memory (Bi-LSTM) neural network is firstly proposed to approach the voltage prediction of each cell. The diagnosis of an ISC is realized based on the residual analysis between the predicted and the actual state. The experimental data in DST conditions evaluate the proposed algorithm by comparing it with the solo equivalent circuit-based prediction algorithm and the Bi-LSTM based prediction algorithm. Finally, through the practical vehicle data from the cloud BMS, the diagnosis and pre-warn ability of the proposed algorithm for an ISC and thermal runaway (TR) in batteries are verified. The ISC diagnosis algorithm that is proposed in this paper can effectively identify the gradual ISC process in advance of it.

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.

It is essential to determine the running state of a production line to monitor the production status and make maintenance plans. In order to monitor the real-time running state of an A-class insulation board production line conveniently and accurately, a novel state prediction method based on deep learning and long short-term memory (LSTM) network is proposed. The multiple layers of the Res-block are introduced to fuse local features and improve hidden feature extraction. The transfer learning strategy is studied and the improved loss function is proposed, which makes the model training process fast and stable. The experimental results show that the proposed Res-LSTM model reached 98.9% prediction accuracy, and the average R2-score of the industrial experiments can reach 0.93. Compared with other mainstream algorithms, the proposed Res-LSTM model obtained excellent performance in prediction speed and accuracy, which meets the needs of industrial production.

To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.

Abstract For the practical applications of wearable electronic skin (e‐skin), the multifunctional, self‐powered, biodegradable, biocompatible, and breathable materials are needed to be assessed and tailored simultaneously. Integration of these features in flexible e‐skin is highly desirable; however, it is challenging to construct an e‐skin to meet the requirements of practical applications. Herein, a bio‐inspired multifunctional e‐skin with a multilayer nanostructure based on spider web and ant tentacle is constructed, which can collect biological energy through a triboelectric nanogenerator for the simultaneous detection of pressure, humidity, and temperature. Owing to the poly(vinyl alcohol)/poly(vinylidene fluoride) nanofibers spider web structure, internal bead‐chain structure, and the collagen aggregate nanofibers based positive friction material, e‐skin exhibits the highest pressure sensitivity (0.48 V kPa−1) and high detection range (0–135 kPa). Synchronously, the nanofibers imitating the antennae of ants provide e‐skin with short response and recovery time (16 and 25 s, respectively) to a wide humidity range (25–85% RH). The e‐skin is demonstrated to exhibit temperature coefficient of resistance (TCR = 0.0075 °C−1) in a range of the surrounding temperature (27–55 °C). Moreover, the natural collagen aggregate and the all‐nanofibers structure ensure the biodegradability, biocompatibility, and breathability of the e‐skin, showing great promise for practicability.

Many carcinomas feature hypoxia, a condition has long been associated with tumor progression and poor prognosis, as well as resistance to chemoradiotherapy. Here, we report that the F-box protein JFK promotes mammary tumor initiation and progression in MMTV-PyMT murine model of spontaneous breast cancer. We find that JFK is inducible under hypoxic conditions, in which hypoxia-inducible factor HIF-1α binds to and transcriptionally activates JFK in breast cancer cells. Consistently, analysis of public clinical datasets reveals that the mRNA level of JFK is positively correlated with that of HIF-1α in breast cancer. We show that JFK deficiency leads to a decrease in HIF-1α-induced glycolysis in breast cancer and sensitizes hypoxic breast cancer cells to ionizing radiation and chemotherapeutic treatment. These results indicate that JFK is an important player in hypoxic response, supporting the pursuit of JFK as a potential therapeutic target for breast cancer intervention.

Abstract In order to explore the application of multi‐source data from different observational systems (including meteorological stations, radar, and airport observational reports) and high‐resolution numerical model results in the analysis of severe convective weather affecting airports, a local severe convective weather event that affected Beijing Capital International Airport (ZBAA) on 26 June 2018 was studied in this work. Although there was no significant triggering of synoptic systems, the process produced a violent thunderstorm with strong wind gusts, poor prevailing visibility, and hail in just a 30‐min period, resulting in severe impacts on the airport operations. In addition to the routine analysis of weather using observations from meteorological stations and radar, high spatial and temporal resolution data from instruments at the airport were used to reveal the fine‐scale characteristics of the development and evolution of various associated meteorological parameters, which demonstrably served as a useful auxiliary dataset for the analysis of this event. Furthermore, the reliability of the high‐resolution numerical (Weather Research and Forecasting) model simulation was evaluated by using these 1‐min‐scale airport observations. Based on what was judged to be a reliable simulation of the meteorological variables by the model, the trigger and strengthening mechanisms of the storm formed in this region of complex topography, as well as the interaction with another cell, were explored. The model results suggested that convections were initially triggered by the interaction of southerly flow from the plain and northwesterly flow from the mountains, in conjunction with the effect of orographic uplift. The outflow of the cold pool from the former cell prevented the later cell (affecting ZBAA) from moving east. Due to the strong vertical wind shear in the lower levels, the echo tilted to a certain extent and formed an inner secondary circulation, contributing to the maintenance and strengthening for the storm.

Objective: The aim of this study was to evaluate thyroid functions in Cushing’s syndrome (CS), the dynamic changes of thyroid hormones and antithyroid antibodies in Cushing’s disease (CD) pre- and postoperatively. Design and methods: This is a retrospective study enrolling 118 patients with CS (102 CD, 10 adrenal CS and 6 ectopic adrenocorticotropic syndrome (EAS)). Thyroid functions (thyroid-stimulation hormone (TSH), T3, free T3 (FT3), T4 and free T4 (FT4)) were measured in all CS at the time of diagnosis and in all CD 3 mon ths after transsphenoidal pituitary tumor resection. Postoperative hormone monitoring within 3 months was conducted in 9 CD patients completing remission. Twenty-eight remitted CD patients experienced hormone and antithyroid antibody evaluation preoperatively and on the 3rd, 6th and 12th month after surgery. Results: TSH, T3 and FT3 were below the reference range in 31%, 69% and 44% of the 118 CS patients. Remitted CD patients (81/102) had significantly higher TSH (P = 0.000), T3 (P = 0.000) and FT3 (P = 0.000) than those in the non-remission group (21/102). After remission of CD, TSH, T3 and FT3 showed a significant increase, with a few cases above the reference range. By 12 months, most CD patients’ thyroid fu nctions returned to normal. Thyroid hormones (including TSH, T3 and FT3) were negatively associated with serum cortisol levels both before and after surgery. No signific ant changes of antithyroid autoantibodies were observed. Conclusions: TSH, T3 and FT3 are suppressed in endogenous hypercortisolemia. After remission of CD, TSH, T3 and FT3 increased significantly, even a bove the reference range, but returned to normal 1 year after surgery in most cases. Anti thyroid antibodies did not change significantly after remission of CD.

Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients’ OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients’ prognosis.

Some conjugated benzene ring molecules were prepared using the Sonogashira reaction, and the molecules were post-functionally modified using click chemistry. The optical and electrical band gaps were measured using UV-VIS absorption spectroscopy and the three-electrode method, and the results of both were verified against each other to prove the accuracy of the characterization. In addition, the optical performances of the material were studied by z-scan; almost all materials exhibited good nonlinear optical properties and interconversion between saturable and anti-saturable absorption due to the invocation of click reagents.

Owing to some of its specific advantages, magnetorheological fluid (MRF) has drawn significant attention in a broad range of modern precision machining fields. With the diversification and increase in demand, many novel structural configurations and processing methods have been applied to mechanical machining equipment. Although different applications using MRF have been proposed in the existing literature, the classification, latest approaches, and further trend are not understood clearly for the machining field. Therefore, the current applications such as machining auxiliary equipment and surface polishing equipment that used MRF are summarized from 2016 to 2020, in this article. Especially, some detailed structures of equipment are investigated, and relative limitations are analyzed based on the characteristics of MRF. Finally, in view of the current equipment, advantages and defects are briefly reported; the developing trends of modern precision machining with MRF are discussed. Therefore, in the state-of-the-art review, the significant role of MRF in the machining field is emphasized, which paves the way to innovative development and market selection.

This study aimed to investigate molecularly targeted therapy to revive bone remodeling and prevent BRONJ by local adipose-derived stem cells (ADSCs) transplantation. Clinical samples of BRONJ and healthy jawbones were used to examine the bone coupling-related cells and TGF-β1 expression. Bone coupling-related cells and TGF-β1 expression were also assessed in BRONJ-like animal model to confirm the results in clinical samples. ADSCs were locally administered in vivo and the therapeutic effects were evaluated by gross observation, radiological imaging, and histological examination. Furthermore, ADSCs-conditioned medium (ADSCs-CM) and neutralizing antibody were applied to assess the effects of ADSCs-derived TGF-β1 on restoring bone coupling in vivo. Osteoclast formation and resorption assays were performed to evaluate the effects of ADSCs-derived TGF-β1 on ZA-treated pre-osteoclasts. Cell migration was performed to assess the effects of ADSCs-derived TGF-β1 on patients’ bone marrow stem cells (BMSCs). The number of osteoclasts, Runx2-positive bone-lining cells (BLCs) and TGF-β1 expression were decreased in BRONJ and animal model jaw bone samples. These reductions were significantly rescued and necrotic jawbone healing was effectively promoted by local ADSCs administration in BRONJ-like animal models. Mechanistically, ADSCs-CM mainly contributed to promoting bone coupling, while TGF-β1 neutralizing antibody in the conditioned medium inhibited these effects. Besides, osteoclastogenesis and patients’ BMSCs migration were also rescued by ADSCs-derived TGF-β1. Furthermore, bone resorption-released bone matrix TGF-β1, together with ADSCs-derived TGF-β1, synergistically contributed to rescuing BMSCs migration. Collectively, ADSCs promoted bone healing of BRONJ by TGF-β1-activated osteoclastogenesis and BMSCs migration capacities.

Background: Essential tremor (ET), one of the most common neurological diseases, is associated with cognitive impairment. Surprisingly, predictors of cognitive decline in ET remain largely unidentified, as longitudinal studies are rare. In the general population, however, lower physical activity has been linked to cognitive decline.Objectives: To determine whether baseline physical activity level is a predictor of cognitive decline in ET.Methods: One hundred and twenty-seven ET cases (78.1 ± 9.5 years, range = 55–95), enrolled in a prospective, longitudinal study of cognition. At baseline, each completed the Physical Activity Scale for the Elderly (PASE), a validated, self-rated assessment of physical activity. Cases underwent an extensive battery of motor-free neuropsychological testing at baseline, 1.5 years, and 3 years, which incorporated assessments of cognitive subdomains. Generalized estimating equations (GEEs) were used to assess the predictive utility of baseline physical activity for cognitive change.Results: Mean follow-up was 2.9 ± 0.4 years (range = 1.3–3.5). In cross-sectional analyses using baseline data, lower physical activity was associated with lower overall cognitive function as well as lower cognitive scores in numerous cognitive domains (memory, language, executive function, visuospatial function and attention, all p < 0.05). In adjusted GEE models, lower baseline physical activity level significantly predicted overall cognitive decline over time (p=0.047), and declines in the subdomains of memory (p = 0.001) and executive function (p = 0.03).Conclusions: We identified reduced physical activity as a predictor of greater cognitive decline in ET. The identification of risk factors often assists clinicians in determining which patients are at higher risk of cognitive decline over time. Interventional studies, to determine whether increasing physical activity could modify the risk of developing cognitive decline in ET, may be warranted.

Sulfonyl drugs are widely used to treat various types of diseases, in which sultones have several desirable properties that have led to their increased use, including improving its simple and effective synthetic methods and/or potentially enhancing potency of drugs. This review focuses on the recent progress in typical synthesis of sultones and the latest developments in various therapeutic applications, including carbonic anhydrase inhibitory activity, HIV-I and HIV-II inhibitory activity, human cytomegalovirus inhibitory activity, and cholinesterase inhibitory activity and their structure–activity relationship. We hope that this review will encourage medicinal chemists to consider the potential benefits to incorporating sultone scaffold into future drug discovery.

Most herbal polyphenols and flavonoids reveals multiple ameliorative benefits for obesity caused by chronic metabolic disorders. Accumulated studies have revealed that preferable therapeutic effects can be obtained through clinical combination of these two kinds of natural compounds for obesity improvement. The typical representative research was the combination of quercetin and resveratrol (CQR), in which the ratio of quercetin and resveratrol is 2:1, demonstrating a synergistic effect in anti-obesity process. Although there exists reports clarifying the mechanism of the combination of two to improve obesity from the perspective of improving adipose tissue inflammation or modulating the composition of intestinal flora, there are few further studies on the mechanism of drug action from the perspective of metabolites transformation. In this research, we mainly focused on the alterations of endogenous metabolites in rats, and analyzed the urine metabolites of obese and intervention model. Therefore, a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was applied to assess the potential effects and mechanisms of CQR at different dosages (45, 90, and 180 mg/kg) in high fat diet (HFD)-induced obesity rats. Body weight gain and visceral fat weight were reduced by CQR, as well as blood lipid and inflammatory factor levels were increased by CQR in a dose-related manner. Urinary metabolomics revealed 22 differential metabolites related to the HFD-induced obesity, which were reversed in a dose-dependent manner by CQR, of which 8 were reversed in the 45 mg/kg CQR group, 15 were reversed in the 90 mg/kg CQR group, and 18 were reversed in the 180 mg/kg CQR group. Combined with bioinformatics and pattern recognition, the results demonstrated that the key differential metabolites were basically involved in amino acid metabolism, galactose metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism and lysine degradation. In summary, our results showed significant therapeutic action by CQR administration and remarkable metabolomic changes after HFD feeding and CQR intervention. Urinary metabolomic analysis was highlighted on account of providing holistic and comprehensive insights into the pathophysiological mechanisms of the HFD-induced obesity, which also supplied clues for the future mechanism studies of CQR’s anti-obesity effects.

Pterygium is a common ocular surface disease characterized by abnormal fibrovascular proliferation and invasion, similar to tumorigenesis. The formation of tumors is related to a change in the expression of various RNAs; however, whether they are involved in the formation and development of pterygium remains unclear. In this study, transcriptome analysis of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) of paired pterygium and normal conjunctiva was performed to explore key genes regulating the development of pterygium. In total, 579 mRNAs, 275 lncRNAs, and 21 circRNAs were differentially expressed (DE) in pterygium compared with paired conjunctival tissues. Functional enrichment analysis indicated that DE RNAs were associated with extracellular matrix organization, blood vessel morphogenesis, and focal adhesion. Furthermore, through protein-protein interaction network and mRNA-lncRNA co-expression network analysis, key mRNAs including FN1, VCAM1, and MMP2, and key lncRNAs including MIR4435-2HG and LINC00968 were screened and might be involved in the pathogenesis of pterygium. In addition, several circRNAs including hsa_circ_0007482 and hsa_circ_001730 were considered to be involved in the pterygium development. This study provides a scientific basis for elucidating the pathogenesis of pterygium and will be beneficial for the development of preventive and therapeutic strategies.

Magnetorheological fluid (MRF) is a kind of smart materials with rheological behavior change by means of external magnetic field application, which has been widely adopted in many complex systems of different technical fields. In this work, the state-of-the-art MRF based devices are reviewed according to structural configurations reported from 2018 to 2020. Based on the rheological characteristic, the MRF has a variety of operational modes, such as flow mode, shear mode, squeeze mode and pinch mode, and has unique advantages in some special practical applications. With reference to these operational modes, improved engineering mechanical devices with MRF are summarized, including brakes, clutches, dampers, and mounts proposed over these 3 years. Furthermore, some new medical devices using the MRF are also investigated, such as surgical assistive devices and artificial limbs. In particular, some outstanding advances on the structural innovations and application superiority of these devices are introduced in detail. Finally, an overview of the significant issues that occur in the MRF based devices is reported, and the developing trends for the devices using the MRF are discussed.

Cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1) are well-known key immune checkpoints that play a crucial dampening effect on regulating T-cell homeostasis and self-tolerance. In this study, we aimed to evaluate the association between immune checkpoints (CTLA-4 and PD-1) and Posner-Schlossman syndrome (PSS) in a southern Chinese population. A total of 137 patients with PSS and 139 healthy controls from a southern Chinese population were recruited. Five single nucleotide polymorphisms (SNPs) of CTLA-4 (rs733618, rs4553808, rs5742909, rs231775, and rs3087243) and five SNPs of PD-1 (rs10204525, rs2227981, rs2227982, rs41386349, and rs36084323) were genotyped by SNaPshot technique. Soluble CTLA-4 (sCTLA-4) and soluble PD-1 (sPD-1) were determined by ELISA and antibody array assay, respectively. The frequencies of T allele at rs733618 and A allele at rs231775 of CTLA-4 were significantly higher in PSS patients than in healthy controls (corrected p (Pc) = 0.037; Pc = 0.044, respectively). The haplotype frequencies of CACGG haplotype (rs733618-rs4553808-rs5742909-rs231775-rs3087243) of CTLA-4 and TGAGC haplotype (rs10204525-rs2227981-rs2227982-rs41386349-rs36084323) of PD-1 in the PSS group was significantly lower than those in the control group (Pc = 0.015, p = 0.034, respectively). Circulating plasma levels of sCTLA-4 and sPD-1 in PSS patients were significantly higher than those in controls (all p < 0.001). The present study suggests that CTLA-4 and PD-1 genetic polymorphisms are associated with the susceptibility to PSS in a southern Chinese population. The upregulated circulating plasma protein levels of sCTLA-4 and sPD-1 might provide some hints regarding the dysfunction of immune checkpoints in PSS during the active status.

Cathode electrolyte interphase (CEI) has obtained increasing attention due to the importance in sustaining full cell life. However, it has rarely been overviewed. Therefore, it is timely and necessary to write this mini-review on CEI. This mini-review will involve the formation mechanism and modification methods of CEI. The perspective on CEI is also given out. We expect that this mini-review will provide rational guidelines for the CEI design of highly-performance lithium-ion batteries (LIBs).

The cracking of concrete in the negative moment region for a composite beam subjected to a negative bending moment reduces the beam’s strength and stiffness. To improve the cracking performance of composite beams, this paper presents an experimental investigation on applying ultrahigh-performance concrete (UHPC) instead of conventional concrete. Three steel-UHPC composite beams with different forms of joints were designed and tested through a unique rotation angle loading method using a spring displacement control testing setup. The crack distribution, rotation versus crack width, load versus spring displacement, and strains in the UHPC slab and steel girders were measured and studied. Nonlinear finite element analysis using ABAQUS based on the damaged plasticity model of concrete was carried out for comparison with the test results. The experimental and numerical results showed that the use of a UHPC slab can enhance the cracking performance of composite beams. Considering the convenience of construction, a reasonable joint form was suggested, and the appropriate UHPC longitudinal laying length in the negative moment region was proposed to be 0.1 L. Furthermore, a simplified formula for calculating the UHPC crack width was developed based on bond-slip theory.

Objective. The primary aim of this investigation was to analyze the microbiome in patients with combined periodontal-endodontic lesions. Method. Patients with loose and/or painful teeth referred for treatment from March 2020 to December 2020 in the First People’s Hospital of Jinzhong were recruited. Samples were collected from teeth diagnosed as chronic periodontics (PE), ulcerative pulpitis (PU), and retrograde pulpitis (RE). Genomic DNA was extracted. The quantitative polymerase chain reaction, targeting the 16S ribosomal RNA (rRNA), was adopted for the quantification of bacteria. Then, the V3-V4 hypervariable regions of the 16S rRNA gene were amplified and subjected to next-generation sequencing. The statistical analysis was performed by R software (V3.5.1). Results. A total of 57 qualified samples were collected from 48 patients and analyzed (7 PE, 21 PU, and 19 RE). By linear discriminant analysis effect size, Kingella and Barnesiella were significantly increased in the periodontal pocket of retrograde pulpitis (RE-PE), compared with PE. The relative abundance of Clostridiales Incertae Sedis XI, Fusobacteriaceae, Fusobacterium, Parvimonas, Micrococcaceae, and Rothia was significantly increased in the pulp of retrograde pulpitis (RE-PU) than PU and RE-PE. Prevotella, Leptotrichia, Porphyromonas, Streptococcus, and Fusobacterium are consistently at a high abundance, across PU, RE-PE, and RE-PU. Conclusion. The current study highlighted the evidence that a specific microbial community is associated with the occurrence of retrograde pulpitis. The microenvironment of the root canal and pulp chamber will select microbiota. This study offered insights into the pathogenesis of retrograde pulpitis.

Objective. Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, leading to disability and decreased quality of life. In past research and clinical studies, the lower limb function of DPN patients was often the principal subject of research, with little attention given to the upper limb and hand. Our goal was to assess and compare hand function between elderly diabetic patients with DPN and without DPN. Methods. A total of 52 diabetic patients were registered and underwent hand function assessments and electrodiagnostic tests. Dynamometer, pinch meter, Semmes Weinstein monofilaments, and the Purdue Pegboard Test (PPT) were used to assess the patients’ grip strength, pinch strength, tactile sensory threshold, and hand dexterity. Results. Compared with the non-DPN group, the elderly DPN group showed worse thumb-middle fingertip pinch strength and thumb-little fingertip pinch strength in the dominant hand (3.50 (2.50, 4.25) vs. 4.50 (3.00, 5.00), p=0.019; 1.50 (1.00, 2.00) vs. 2.50 (2.00, 3.00), p

Abstract Introduction This study evaluated acute change in odor identification following atropine nasal spray challenge, and 8‐week change in odor identification ability, as a predictor of long‐term improvement in patients with mild to moderate Alzheimer's disease (AD) who received open‐label cholinesterase inhibitor treatment. Methods In patients with clinical AD, the University of Pennsylvania Smell identification Test (UPSIT) was administered before and after an anticholinergic atropine nasal spray challenge. Patients were then treated with donepezil for 52 weeks. Results In 21 study participants, acute atropine‐induced decrease in UPSIT was not associated with change in the Alzheimer's Disease Assessment Scale ‐ Cognitive Subscale (ADAS‐Cog) or Selective Reminding Test (SRT). Decline in odor identification performance from baseline to week 8 was indicative of a future decline in cognitive performance over 52 weeks. Discussion Change in odor identification with atropine challenge is not a useful predictor of treatment response to cholinesterase inhibitors. Short‐term change in odor identification performance needs further investigation as a potential predictor of cognitive improvement with cholinesterase inhibitor treatment.

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.

OBJECTIVES/GOALS: Exposure to arsenic, cadmium, manganese, and lead have been linked to adverse neurocognitive outcomes in adults/children, but effects in adolescents are not fully characterized. This study aims to examine the association between exposure to a mixture of metals (As, Cd, Mn, Pb, Se) and cognitive function in adolescents. METHODS/STUDY POPULATION: The Metals, Arsenic, & Nutrition in Adolescents Study (MANAS) is a cross-sectional study of 572 Bangladeshi adolescents. Blood levels of As, Cd, Mn, Pb, and Se were measured via ICP-MS. An abbreviated Cambridge Neuropsychological Test Automated Battery (CANTAB) was administered, with subtests assessing cognitive function and executive function tasks. Linear regression and Bayesian kernel machine regression (BKMR) were used to examine associations between individual metals, the overall mixture of metals, and cognitive function as measured by the CANTAB. RESULTS/ANTICIPATED RESULTS: Linear regression showed that As (B=−2.40) and Mn (B=−5.31) were negatively associated with Spatial Working Memory (p

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence’s current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round’s loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.

The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning.

Background: Metastatic melanoma (MM) represents a common malignancy with poor prognosis. Immune checkpoint inhibition (ICI), including PD-1 blockade, has been emerging as the popular therapeutic in MM for its durable treatment effect, but its response rate is still limiting. Methods: We comprehensively analyzed the associations between KMT2C somatic mutation and the tumor microenvironment as well as the ICI response of MM patients based on three published cohorts. Gene differential expression analysis between tumor samples with mutated and wild-type KMT2C was performed by DESeq2 package. Functional enrichment analysis was conducted by using clusterProfiler package. Kaplan-Meier was used to perform overall survival probability estimate through survival package and rms package was applied for the construction of nomogram model. Results: We report here that KMT2C is a potential biomarker for anti-PD-1 treatment in MM. This biomarker can be used for comprehensively analyzing its association with patients’ prognosis, tumor microenvironment and genomic features. Mutations of KMT2C profoundly altered expression of immune- and DNA replication-related genes in MM tumors. MM patients harboring KMT2C mutations showed significantly better overall survival (OS) after treatment with PD-1 monoclonal antibody as compared to wild-type KMT2C. Although KMT2C mutation has no significant influence on immune cell infiltration into MM tumors, the tumor mutation load and neoantigen load are indeed elevated in KMT2C mutated MM samples. This might represent a possible pathway through which KMT2C regulates the response of MM patients to anti-PD-1 treatment. Finally, we constructed a nomogram model by combing the independent prognostic factors, including KMT2C mutation, which could effectively predict the 1-year survival probability of MM patients after anti-PD-1 treatment. Conclusions: In conclusion, we report the role of KMT2C in anti-PD-1 treatment response regulation in MM for the first time. This may consequently be helpful for KMT2C personalized application.

Background: Emerging evidence suggests that phthalate exposure may be associated with behavior problems in children and that these associations may be sex specific. Methods: In a follow up study of 411 inner-city minority mothers and their children, mono-n-butyl phthalate (MnBP), monobenzyl phthalate (MBzP), monoisobutyl phthalate (MiBP), monethyl phthalate (MEP) and four di-2-ethylhexyl phthalate metabolites (DEHP) were quantified in maternal urine samples collected during the third trimester and in child urine samples at ages 3 and 5 years. The Conners’ Parent Rating Scale-Revised: Long Form (CPRS) and Child Behavior Checklist (CBCL) were administered to the mothers to assess children’s behavior problems at 7 years of age. The analysis included children with available measures of CBCL, CPRS and phthalates measured in maternal urine. We performed both Quasi-Poisson regression and a mixture analysis using Weighted Quantile Sum(WQS) regression to assess the risk for CPRS scores and for internalizing and externalizing behaviors (from the CBCL) following intra-uterine exposure to the phthalate metabolites for boys and girls separately. Results: Among boys, increases in in anxious-shy behaviors were associated with prenatal exposure to MBzP (Mean Ratio [MR] = 1.20, 95%CI 1.05–1.36) and MiBP (Mean Ratio (MR) = 1.22, 95%CI 1.02–1.47). Among girls, increases in perfectionism were associated with MBzP (MR = 1.15, 95%CI 1.01–1.30). In both boys and girls, increases in psychosomatic problems were associated with MiBP (MR = 1.28, 95%CI 1.02–1.60), and MnBP (MR = 1.28, 95%CI 1.02–1.59), respectively. Among girls, decreased hyperactivity was associated with two DEHP metabolites, mono(2-ethyl-5-oxohexyl) phthalate (MR = 0.83, 95%CI 0.71–0.98) and mono(2-ethyl-5-hydroxyhexyl) phthalate (MR = 0.85, 95%CI 0.72–0.99). Using weighted Quantile Sum logistic regression, no associations were found between the Weighted Quantile Sum (WQS) of phthalate metabolites and CPRS scores or externalizing and internalizing behaviors. Nonetheless, when the analysis was performed separately for DEHP and non-DEHP metabolites significant associations were found between the WQS of DEHP metabolites and social problems in boys (OR = 2.15, 95%CI 1.13–4.06, p-value = 0.02) anxious-shy problems in girls (OR = 2.19, 95%CI 1.15–4.16, p = 0.02), and emotional lability problems in all children (OR = 0.61, 95%CI 0.38–0.97, p = 0.04). MEHP and MEOHP were the most highly weighted DEHP metabolites in WQS mixture. The analysis performed with CBCL scale corroborated these associations. Conclusion: Concentration of non-DEHP metabolites was associated with anxious-shy behaviors among boys. DEHP phthalate metabolites were associated with decreased hyperactivity and impulsivity among girls on CPRS scores. These findings lend further support to the adverse associations between prenatal phthalate exposure and childhood outcomes, and clearly suggest that such associations are sex and mixture specific.

Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.

Introduction: Phthalates are a group of high production chemicals, generally used as plasticizers and odor enhancers. Phthalates cross the blood-placenta barrier and are associated with deficits in cognitive functions and behavior problems in offspring. We previously reported sex-specific associations with motor function when phthalates are considered singly. Because exposure to phthalates usually occurs as mixtures, here we assess these associations between a mixture of phthalates and motor function at age 11 years. Methods: Data come from the prospective cohort study of mothers and offspring who participated in the Columbia Center for Children’s Environmental Health birth cohort (CCCEH). Seven phthalate metabolites were measured in maternal spot urine obtained during the third trimester and motor function was evaluated using the short form of the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2) at the age of 11 years. We used Weighted Quartile Sum (WQS) regression models to examine the effect of phthalate metabolites in males and females separately. The models were adjusted for child age in months, child BMI, maternal race (African-American vs. Dominican), prenatal alcohol consumption, maternal demoralization score, HOME score, and urine specific gravity. In a secondary analysis we used linear regression models to examine the association between the sum of molar concentrations of both DEHP and non-DEHP metabolites, and outcomes of gross and fine motor functions. Results: 209 mother-child pairs were eligible for this analysis. A significant decrease in fine-motor functions was observed among females, but not among males, following exposure to high levels of weighted quartile sum of seven phthalate metabolites (Covariates-adjusted coefficient estimate B = −2.7, 95% Confidence Interval [CI] −4.64 to −0.75, p = 0.01 for females [n = 116] and B = −1.63, 95%CI −3.94 to 0.69, p = 0.16 for males [n = 93]). The most highly weighted phthalate metabolites, associated with fine-motor functions among females, were MBP, MBZP, and MIBP, all non-DEHP phthalates.No significant associations were found between the weighted quartile sum of seven phthalate metabolites and gross-motor functions at the age of 11 years for males (B = −0.81, 95%CI −1.17 to 1.96, p = 0.23).With the molar sum of four non-DEHP phthalates as main predictor of linear regression models, we found significant decrease in gross and fine motor functions among females prenatally exposed to non-DEHP phthalates B = −0.98, 95%CI −1.98 to 0.03, p = 0.05 and B = −0.85, 95%CI −1.49 to −0.20, p = 0.01, respectively). Conclusions: Phthalate exposure during pregnancy was associated with decreased motor functions among 11-year-old girls. Keywords: Phthalates, Motor functions

There are limited data examining the consequences of environmental exposure to arsenic on the immune system in adults, particularly among smokers. Smoking has been shown to exacerbate or contribute to impaired immune function in men chronically exposed to arsenic. In contrast, vitamin D (VitD) is known to have a positive influence on innate and adaptive immune responses. The effect of circulating VitD on arsenic-associated immune dysfunction is not known. Here we examine the relationship of arsenic exposure and T cell proliferation (TCP), a measure of immune responsiveness, and circulating VitD among adult men and women in Bangladesh. Arsenic exposure was assessed using total urinary arsenic as well as urinary arsenic metabolites all adjusted for urinary creatinine. TCP was measured ex vivo in cryopreserved peripheral blood mononuclear cells from 614 adult participants enrolled in the Bangladesh Health Effects of Arsenic Longitudinal Study; serum VitD was also evaluated. The influence of cigarette smoking on arsenic-induced TCP modulation was assessed only in males as there was an inadequate number of female smokers. These studies show that arsenic suppressed TCP in males. The association was significantly strong in male smokers and to a lesser extent in male non-smokers. Interestingly, we found a strong protective effect of high/sufficient serum VitD levels on TCP among non-smoking males. Furthermore, among male smokers with low serum VitD (⊔20 ng/ml), we found a strong suppression of TCP by arsenic. On the other hand, high VitD (>20 ng/ml) was found to attenuate effects of arsenic on TCP among male-smokers. Overall, we found a strong protective effect of VitD, when serum levels were >20 ng/ml, on arsenic-induced inhibition of TCP in men, irrespective of smoking status. To our knowledge this is the first large study of immune function in healthy adult males and females with a history of chronic arsenic exposure.

This paper proposes 3D nonlinear finite element (FE) models to predict the response of corroded reinforced concrete (RC) beam strengthened using a steel plate. Five FE models are developed based on the tests carried out by the authors in a previous investigation, in which three models are used to simulate the corroded RC beams with different schemes. The FE models use the coupled damaged-plasticity constitutive law for concrete in tension and compression and consider the bond-slip between the corroded tensile steel bar and concrete. The cohesive element is also used to model the cohesive bond between the steel plate and concrete. The FE results of load-deflection and the crack distribution are compared with the test data. The FE results are consistent with the test results. The influence of the thickness of the steel plate, the thickness, and location of the U-shaped steel strip on the bearing capacity of the strengthened corroded beam is analyzed through FE models. The results show that the thickness of the steel plate on the bottom surface should not exceed 4 mm for the flexure-strengthened and combined strengthened beams with a 10% corrosion rate. It is most reasonable to improve the bearing capacity using the 3 mm and 2 mm of thick U-shaped steel strips for the shear-strengthened and combined strengthened beams, respectively. The most reasonable location of the U-shaped steel plate is at the end of the steel plate for beams with a 10% corrosion rate.

Aiming to study the effect of drive plate with different surface topographies on the wear property of magnetorheological fluid (MRF), some specific experiments are carried out and analyzed in this paper. Firstly, experiment materials and test methods for MRF are elaborated, and four different surface topography drive plates are designed and manufactured. MRF test-bed is built up to test the rheological properties of MRF. Moreover, the surface morphology of particles is taken using a scanning electron microscope (SEM). Finally, experimental results show that there are remarkable influences on tribological properties of carbonyl iron-based MRF with different drive plates. Therefore, the drive plate with specific surface topography can meet the needs of different MRF transmission systems.

Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods.

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.

Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the rPPG signal. In this paper, we propose a 3D central difference convolutional network (CDCA-rPPGNet) to measure heart rate, with an attention mechanism to combine spatial and temporal features. First, we crop and stitch the region of interest together through facial landmarks. Next, the high-quality regions of interest are fed to CDCA-rPPGNet based on a central difference convolution, which can enhance the spatiotemporal representation and capture rich relevant time contexts by collecting time difference information. In addition, we integrate the attention module into the neural network, aiming to strengthen the ability of the neural network to extract video channels and spatial features, so as to obtain more accurate rPPG signals. In summary, the three main contributions of this paper are as follows: (1) the proposed network base on central difference convolution could better capture the subtle color changes to recover the rPPG signals; (2) the proposed ROI extraction method provides high-quality input to the network; (3) the attention module is used to strengthen the ability of the network to extract features. Extensive experiments are conducted on two public datasets—the PURE dataset and the UBFC-rPPG dataset. In terms of the experiment results, our proposed method achieves 0.46 MAE (bpm), 0.90 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the PURE dataset, and 0.60 MAE (bpm), 1.38 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the UBFC dataset, which proves the effectiveness of our proposed approach.

Studies have shown that ordinary color cameras can detect the subtle color changes of the skin caused by the heartbeat cycle. Therefore, cameras can be used to remotely monitor the pulse in a non-contact manner. The technology for non-contact physiological measurement in this way is called remote photoplethysmography (rPPG). Heart rate variability (HRV) analysis, as a very important physiological feature, requires us to be able to accurately recover the peak time locations of the rPPG signal. This paper proposes an efficient spatiotemporal attention network (ESA-rPPGNet) to recover high-quality rPPG signal for heart rate variability analysis. First, 3D depth-wise separable convolution and a structure based on mobilenet v3 are used to greatly reduce the time complexity of the network. Next, a lightweight attention block called 3D shuffle attention (3D-SA), which integrates spatial attention and channel attention, is designed to enable the network to effectively capture inter-channel dependencies and pixel-level dependencies. Moreover, ConvGRU is introduced to further improve the network’s ability to learn long-term spatiotemporal feature information. Compared with existing methods, the experimental results show that the method proposed in this paper has better performance and robustness on the remote HRV analysis.

Conservation tillage technology (CTT) provides a new solution to the problem of cultivated land protection. Using effective policy tools to ensure that farmers adopt conservation tillage technology is crucial to the sustainable utilization of cultivated land resources and the development of agricultural modernization. This study aims to explore the decision-making and the dynamic influence mechanism involved in using policy tools to influence farmers’ technology adoption behaviors by constructing a theoretical framework. Based on survey data of farmers in Liaoning Province, China, the Agent Belief–Desire–Intention (BDI) model is applied. The results show that the implementation of policy tools promotes farmers’ adoption of CTT, but different types of policy tools have different impacts on the decision-making behavior of farmers; a change in the intensity of the policy tools will also cause differences in farmers’ behavioral responses. In addition, policy tools must be implemented in a timely manner, as the number of farmers adopting CTT reaches the maximum within 2–3 years. Based on the above research results, in order to effectively promote farmers’ adoption of CTT, the government should pay attention to the role of information-inducing policy and set flexible policy subsidies and punishment standards.

In the operation process of a power battery pack, the inconsistency among lithium-ion cells may seriously restrict the pack’s capacity, power capability and lifetime, which may bring hidden danger to the use of electric vehicles. Equalization management systems (EMSs) are crucial to alleviate such inter-cell inconsistency, whose performance such as accuracy and stability, mainly depends on the setting of equalization control strategies. This paper proposes an equalization strategy aimed at throughput maximization of series battery in the whole life cycle based on Model Prediction Control (MPC). In this paper, a Mean-plus-difference model (M+D) model is selected as the series battery model and the parameters are identified by Recursive Least Squares (RLS). Based on the model predictive control theory, the control model of series battery pack is established and the objective function of maximizing the throughput in the whole life cycle is derived. At the end of the paper, the simulation results show that the proposed equalization strategy can achieve greater life cycle throughput compared with the traditional SOC equalization strategy, which verifies the guiding significance of the equilibrium strategy proposed in this paper.

Abstract Background Melanoma, originated from melanocytes located on the basal membrane of the epithelial tissue, is the most aggressive form of skin cancer that accounts for 75% of skin cancer-related death. Although it is believed that BRAF mutation and the mitogen-activated protein kinase (MAPK) pathway play critical roles in the pathogenesis of melanoma, how the MAPK signaling is regulated in melanoma carcinogenesis is still not fully understood. Methods We characterized JMJD6 expression in melanoma tissue array by immunohistochemistry analysis. We used human melanoma A375, 451Lu and SK-MEL-1 cell lines for in vitro proliferation and invasion experiments, and xenograft transplanted mice using murine melanoma B16F10 cells by bioluminescence imaging for in vivo tumor growth and pulmonary metastasis assessments. Endothelial tube formation assay, chicken yolk sac membrane assay and matrigel plug assay were performed to test the effect of JMJD6 on the angiogenic potential in vitro and in vivo. Results Here we report that the jumonji C domain-containing demethylase/hydroxylase JMJD6 is markedly up-regulated in melanoma. We found that high expression of JMJD6 is closely correlated with advanced clinicopathologic stage, aggressiveness, and poor prognosis of melanoma. RNA-seq showed that knockdown of JMJD6 affects the alternative splicing of a panel of transcripts including that encoding for PAK1, a key component in MAPK signaling pathway. We demonstrated that JMJD6 enhances the MAPK signaling and promotes multiple cellular processes including melanogenesis, proliferation, invasion, and angiogenesis in melanoma cells. Interestingly, JMJD6 is transcriptionally activated by c-Jun, generating a feedforward loop to drive the development and progression of melanoma. Conclusions Our results indicate that JMJD6 is critically involved in melanoma carcinogenesis, supporting the pursuit of JMJD6 as a potential biomarker for melanoma aggressiveness and a target for melanoma intervention.

EGFR is a well-known oncogene; however, the mechanisms regulating its expression are still unclear. Here, analysing genome-wide chromatin associations, the authors show that in breast cancer cells ZNF516 represses EGFR transcription through the interaction with the CtBP/LSD1/CoREST complex.

USP9X is a deubiquitinating enzyme with many known substrates and functions; it has been linked to cancer but the mechanisms remain unclear. Here Liet al. report that USP9X stabilizes the centrosomal protein CEP131 leading to centrosome amplification and breast cancer development.