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Video anomaly detection is a very important research topic, especially in the field of automatic driving, and timely acquisition of road conditions ahead is very important for safe driving. Past methods used convolution and other operations, resulting in severe delays or low detection accuracy. This paper introduces a cutting-edge video anomaly detection method that combines the advantages of image quality assessment and attention mechanisms while using image signal processing techniques to enhance the data. Our approach first uses image quality metrics to evaluate visual fidelity anomalies for each video frame, providing a computationally simple and effective method for initial anomaly recognition. Then, an attention mechanism is integrated into two deep learning models including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This integration enables the system to dynamically focus on significant regions in the video. This capability is unmatched by traditional analysis methods. The dual-structure system shows higher sensitivity to various anomalies. In addition, the system is also equipped with image signals, which makes the data feature extraction more precise. Through thorough testing of the benchmark dataset, the proposed approach has been shown to significantly outperform current state-of-the-art models. It especially excels in challenging monitoring scenarios. For example, in the UCSD dataset, we achieved an area under curve (AUC) value of 99.9%, which exceeds most current methods.

Abstract Background Insulin resistance is linked to an increased risk of frailty, yet the comprehensive relationship between the triglyceride glucose-body mass index (TyG-BMI), which reflects weight, and frailty, remains unclear. This relationship is investigated in this study. Methods Data from 9135 participants in the China Health and Retirement Longitudinal Study (2011–2020) were analysed. Baseline TyG-BMI, changes in the TyG-BMI and cumulative TyG-BMI between baseline and 2015, along with the frailty index (FI) over nine years, were calculated. Participants were grouped into different categories based on TyG-BMI changes using K-means clustering. FI trajectories were assessed using a group-based trajectory model. Logistic and Cox regression models were used to analyse the associations between the TyG-BMI and FI trajectory and frail incidence. Nonlinear relationships were explored using restricted cubic splines, and a linear mixed-effects model was used to evaluate FI development speed. Weighted quantile regression was used to identify the primary contributing factors. Results Four classes of changes in the TyG-BMI and two FI trajectories were identified. Individuals in the third (OR = 1.25, 95% CI: 1.10–1.42) and fourth (OR = 1.83, 95% CI: 1.61–2.09) quartiles of baseline TyG-BMI, those with consistently second to highest (OR = 1.49, 95% CI: 1.32–1.70) and the highest (OR = 2.17, 95% CI: 1.84–2.56) TyG-BMI changes, and those in the third (OR = 1.20, 95% CI: 1.05–1.36) and fourth (OR = 1.94, 95% CI: 1.70–2.22) quartiles of the cumulative TyG-BMI had greater odds of experiencing a rapid FI trajectory. Higher frail risk was noted in those in the fourth quartile of baseline TyG-BMI (HR = 1.42, 95% CI: 1.28–1.58), with consistently second to highest (HR = 1.23, 95% CI: 1.12–1.34) and the highest TyG-BMI changes (HR = 1.58, 95% CI: 1.42–1.77), and those in the third (HR = 1.10, 95% CI: 1.00-1.21) and fourth quartile of cumulative TyG-BMI (HR = 1.46, 95% CI: 1.33–1.60). Participants with persistently second-lowest to the highest TyG-BMI changes (β = 0.15, 0.38 and 0.76 respectively) and those experiencing the third to fourth cumulative TyG-BMI (β = 0.25 and 0.56, respectively) demonstrated accelerated FI progression. A U-shaped association was observed between TyG-BMI levels and both rapid FI trajectory and higher frail risk, with BMI being the primary factor. Conclusion A higher TyG-BMI is associated with the rapid development of FI trajectory and a greater frail risk. However, excessively low TyG-BMI levels also appear to contribute to frail development. Maintaining a healthy TyG-BMI, especially a healthy BMI, may help prevent or delay the frail onset.

The pathogenesis of alopecia areata, a common autoimmune non-scarring alopecia, remains unclear. Current studies have shown that the loss of immune privilege of hair follicles due to the up-regulation of MHC molecules and the decrease in immunosuppressive molecules leads to the exposure of self-antigens in the affected area. This triggers the infiltration of immune cells (such as CD8+ T cells, NK cells, plasmacytoma dendritic cells, and mast cells) around the hair bulb. Various immune cells and related cytokines participate in the pathogenesis and development of this disease through diverse signaling pathways. Due to its complex pathogenesis and diverse clinical manifestations, there are challenges in the diagnosis and treatment in clinic. This paper aims to provide a comprehensive review on recent advances in understanding the immunological pathogenesis of alopecia areata by focusing on mechanisms underlying immune privilege loss around hair follicles and the interaction between various immune cells in the microenvironment for a deeper understanding of its pathogenesis. It also seeks to explore new research directions and potential treatment strategies.

Abstract Algorithmic recommendation technology, including news apps, social networking services (SNSs), and video or short-video apps, and is widely embedded in various mobile applications (apps), has raised concerns about potential addiction. This study constructs a model linking the uses and gratifications of algorithmic recommendation apps with algorithmic dependence, with a particular focus on fatigue as a key mediator in explaining the psychological mechanism behind the formation of algorithmic app dependence. Snowball sampling was employed for data collection, and a total of 354 valid questionnaires were collected via the online survey tool (i.e., WJX platform). The results show that the use of algorithmic recommendation apps has a direct positive effect on algorithmic app dependence. While all three types of user gratification obtained (i.e., information gratification, entertainment gratification, and expression gratification) are positively related to algorithmic app dependence, the mediating role of fatigue varies: entertainment gratification indirectly reduces algorithmic app dependence by increasing fatigue, whereas expression gratification indirectly increases algorithmic app dependence by reducing fatigue. The theoretical contributions and practical implications of the research findings are discussed.

Abstract Background The interplay between diabetes mellitus (DM), glycemic traits, and vascular and valvular calcifications is intricate and multifactorial. Exploring potential mediators may illuminate underlying pathways and identify novel therapeutic targets. Methods We utilized univariable and multivariable Mendelian randomization (MR) analyses to investigate associations and mediation effects. Additionally, the multivariable MR analyses incorporated cardiometabolic risk factors, allowing us to account for potential confounders. Results Type 2 diabetes mellitus (T2DM) and glycated hemoglobin (HbA1c) were positively associated with both coronary artery calcification (CAC) and calcific aortic valvular stenosis (CAVS). However, fasting glucose (FG) was only linked to CAVS and showed no association with CAC. Additionally, CAVS demonstrated a causal effect on FG. Calcium levels partially mediated the impact of T2DM on both types of calcifications. Specifically, serum calcium was positively associated with both CAC and CAVS. The mediation effects of calcium levels on the impact of T2DM on CAC and CAVS were 6.063% and 3.939%, respectively. The associations between T2DM and HbA1c with calcifications were influenced by body mass index (BMI) and smoking status. However, these associations were generally reduced after adjusting for hypertension. Conclusion Our findings suggest a genetically supported causal relationship between DM, glycemic traits, and vascular and valvular calcifications, with serum calcium playing a critical mediating role.

Abstract The advancement of Li-metal batteries is significantly impeded by the presence of unstable solid electrolyte interphase and Li dendrites upon cycling. Herein, we present an innovative approach to address these issues through the synergetic regulation of solid electrolyte interphase mechanics and Li crystallography using yttrium fluoride/polymethyl methacrylate composite layer. Specifically, we demonstrate the in-situ generation of Y-doped lithium metal through the reaction of composite layer with Li metal, which reduces the surface energy of the (200) plane, and tunes the preferential crystallographic orientation to (200) plane from conventional (110) plane during Li plating. These changes effectively passivate Li metal, thereby significantly reducing undesired side reactions between Li and electrolytes by 4 times. Meanwhile, the composite layer with suitable modulus (~1.02 GPa) can enhance mechanical stability and maintain structural stability of SEI. Consequently, a 4.2 Ah pouch cell with high energy density of 468 Wh kg−1 and remarkable capacity stability of 0.08% decay/cycle is demonstrated under harsh condition, such as high-areal-capacity cathode (6 mAh cm−2), lean electrolyte (1.98 g Ah−1), and high current density (3 mA cm−2). Our findings highlight the potential of reactive composite layer as a promising strategy for the development of stable Li-metal batteries.

Abstract Study objective To evaluate the efficacy and pregnancy outcomes of intrauterine balloon and intrauterine contraceptive devices in the prevention of adhesion reformation following hysteroscopic adhesiolysis in infertile women with moderate to severe intrauterine adhesion. Design A prospective, randomized, controlled trial study. Setting A tertiary university hospital. Patients A total of 130 patients with moderate (American Fertility Society [AFS] score of 5–8) and severe (AFS score of 9–12) intrauterine adhesions were recruited. Interventions 86 patients were evenly allocated to group treated with an IUD for 1 month and group treated with an IUD for 2 months. 44 patients were allocated to group treated with a Foley catheter balloon.(IUD: Yuangong IUD). Measurements and main results The primary outcome measures were the AFS score, endometrial thickness, and pregnancy outcome. After hysteroscopy, the AFS score was significantly decreased(P

Abstract Exploring efficient and low‐toxicity radiosensitizers to break through the bottleneck of radiation tolerance, immunosuppression and poor prognosis remains one of the critical developmental challenges in radiotherapy. Nanoheterojunctions, due to their unique physicochemical properties, have demonstrated excellent radiosensitization effects in radiation energy deposition and in lifting tumor radiotherapy inhibition. Herein, they doped selenium (Se) into prussian blue (PB) to construct a nano‐heterojunction (Se@PB), which could promote the increase of Fe2+/Fe3+ ratio and conversion of Se to a high valence state with Se introduction. The Fe2+‐Se‐Fe3+ electron transfer chain accelerates the rate of electron transfer on the surface of the nanoparticles, which in turn endows it with efficient X‐ray energy transfer and electron transport capability, and enhances radiotherapy physical sensitivity. Furthermore, Se@PB induces glutathione (GSH) depletion and Fe2+ accumulation through pro‐Fenton reaction, thereby disturbs the redox balance in tumor cells and enhances biochemical sensitivity of radiotherapy. As an excellent radiosensitizer, Se@PB effectively enhances X‐ray induced mitochondrial dysfunction and DNA damage, thereby promotes cell apoptosis and synergistic cervical cancer radiotherapy. This study elucidates the radiosensitization mechanism of Se‐doped nanoheterojunction from the perspective of the electron transfer chain and biochemistry reaction, which provides an efficient and low‐toxic strategy in radiotherapy.

Abstract The current SARS-CoV-2 variants strikingly evade all authorized monoclonal antibodies and threaten the efficacy of serum-neutralizing activity elicited by vaccination or prior infection, urging the need to develop antivirals against SARS-CoV-2 and related sarbecoviruses. Here, we identified both potent and broadly neutralizing antibodies from a five-dose vaccinated donor who exhibited cross-reactive serum-neutralizing activity against diverse coronaviruses. Through single B-cell sorting and sequencing followed by a tailor-made computational pipeline, we successfully selected 86 antibodies with potential cross-neutralizing ability from 684 antibody sequences. Among them, PW5-570 potently neutralized all SARS-CoV-2 variants that arose prior to Omicron BA.5, and the other three could broadly neutralize all current SARS-CoV-2 variants of concern, SARS-CoV and their related sarbecoviruses (Pangolin-GD, RaTG13, WIV-1, and SHC014). Cryo-EM analysis demonstrates that these antibodies have diverse neutralization mechanisms, such as disassembling spike trimers, or binding to RBM or SD1 to affect ACE2 binding. In addition, prophylactic administration of these antibodies significantly protects nasal turbinate and lung infections against BA.1, XBB.1, and SARS-CoV viral challenge in golden Syrian hamsters, respectively. Importantly, post-exposure treatment with PW5-5 and PW5-535 also markedly protects against XBB.1 challenge in these models. This study reveals the potential utility of computational process to assist screening cross-reactive antibodies, as well as the potency of vaccine-induced broadly neutralizing antibodies against current SARS-CoV-2 variants and related sarbecoviruses, offering promising avenues for the development of broad therapeutic antibody drugs.

Abstract Viruses within the Polerovirus genus in the family of Solemoviridae have a single-stranded positive-sense RNA genome of about 5.6–6.2 kb in length. In this study, the bitter gourd leaves showing yellowing and crumple symptoms were collected for small RNAs (sRNAs) sequencing. Analysis of the contigs de novo assembled from sRNA-sequencing data, followed by RT-PCR and cloning, determined the complete viral genome to be 5665 nucleotides. This virus isolate contains conserved ORF3a, ORF3, ORF4, and other typical features of poleroviruses. The P0 protein of this virus isolate shares less than 74.80% amino acid sequence identity with any of the previously characterized poleroviruses, indicating that it should be a novel polerovirus. We name this virus as bitter gourd yellowing crumple virus (BYCV). We further revealed the dynamic subcellular localization and protein accumulation of seven proteins encoded by BYCV in Nicotiana benthamiana plants over time, and these viral proteins displayed specific subcellular localization. Expressing the BYCV P0 protein using a potato virus X vector caused severe symptoms in N. benthamiana plants, while P0 exhibited weak RNA silencing suppression activity. These findings provide an example for investigating the dynamic subcellular localizations of viral proteins and demonstrate that P0 is a critical protein potentially playing an important role in virus infection.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD.

The early afterglow of a gamma-ray burst (GRB) can provide critical information on the jet and progenitor of the GRB. The extreme brightness of GRB 221009A allows us to probe its early afterglow in unprecedented detail. In this Letter, we report comprehensive observation results of the early afterglow of GRB 221009A (from T _0 +660 s to T _0 +1860 s, where T _0 is the Insight-HXMT/HE trigger time) in X-ray/gamma-ray energy band (from 20 keV to 20 MeV) by Insight-HXMT High Energy X-ray Telescope, GECAM-C, and Fermi/Gamma-ray Burst Monitor. We find that the spectrum of the early afterglow in 20 keV–20 MeV can be well described by a cutoff power law with an extra power law that dominates the low- and high-energy bands, respectively. The cutoff power law E _peak is ∼30 keV, and the power-law photon index is ∼1.8 throughout the early afterglow phase. By fitting the light curves in different energy bands, we find that a significant achromatic break (from keV to TeV) is required at T _0 + ${1246}_{-26}^{+27}$ s (i.e., 1021 s since the afterglow starting time T _AG = T _0 +225 s), providing compelling evidence of a jet break. Interestingly, both the pre-break and post-break decay slopes vary with energy, and these two slopes become closer in the lower energy band, making the break less identifiable. Intriguingly, the spectrum of the early afterglow experienced a slight hardening before the break and a softening after the break. These results provide new insights into the physics of this remarkable GRB.

Hydrogen peroxide (H2O2) is an environmentally friendly oxidant with a wide range of applications, and the two-electron pathway (2e−) of the oxygen reduction reaction (ORR) for H2O2 production has attracted much attention due to its eco-friendly nature and operational simplicity in contrast to the conventional anthraquinone process. The challenge is to design electrocatalysts with high activity and selectivity and to understand their structure–activity relationship and catalytic mechanism in the ORR process. Covalent organic frameworks (COFs) provide an efficient template for the construction of highly efficient electrocatalysts due to their designable structure, excellent stability, and controllable porosity. This review firstly outlines the design principles of COFs, including the selection of metallic and nonmetallic active sites, the modulation of the electronic structure of the active sites, and the dimensionality modulation of the COFs, to provide guidance for improving the production performance of H2O2. Subsequently, representative results are summarized in terms of both metallic and metal-free sites to follow the latest progress. Moreover, the challenges and perspectives of 2e− ORR electrocatalysts based on COFs are discussed.

The rapid mutation of SARS-CoV-2 has led to multiple rounds of large-scale breakthrough infection and reinfection worldwide. However, the dynamic changes of humoral and cellular immunity responses to several subvariants after infection remain unclear. In our study, a 6-month longitudinal immune response evaluation was conducted on 118 sera and 50 PBMC samples from 49 healthy individuals who experienced BA.5/BF.7/XBB breakthrough infection or BA.5/BF.7-XBB reinfection. By studying antibody response, memory B cell, and IFN-γ secreting CD4+/CD8+ T cell response to several SARS-CoV-2 variants, we observed that each component of immune response exhibited distinct kinetics. Either BA.5/BF.7/XBB breakthrough infection or BA.5/BF.7-XBB reinfection induces relatively high level of binding and neutralizing antibody titers against Omicron subvariants at an early time point, which rapidly decreases over time. Most of the individuals at 6 months post-breakthrough infection completely lost their neutralizing activities against BQ.1.1, CH.1.1, BA.2.86, JN.1 and XBB subvariants. Individuals with BA.5/BF.7-XBB reinfection exhibit immune imprinting shifting and recall pre-existing BA.5/BF.7 neutralization antibodies. In the BA.5 breakthrough infection group, the frequency of BA.5 and XBB.1.16-RBD specific memory B cells, resting memory B cells, and intermediate memory B cells gradually increased over time. On the other hand, the frequency of IFN-γ secreting CD4+/CD8+ T cells induced by WT/BA.5/XBB.1.16 spike trimer remains stable over time. Overall, our research indicates that individuals with breakthrough infection have rapidly declining antibody levels but have a relatively stable cellular immunity that can provide some degree of protection from future exposure to new antigens.

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

Purpose To explore the effect of image-guided systems in phacoemulsification with intraocular lens (IOL) implantation.Methods We searched Pubmed, Embase and China National Knowledge Infrastructure (inception to January 20, 2021). Two researchers extracted data and assessed paper quality independently. Uncorrected distance visual acuity (UDVA) before and after surgery, best corrected visual acuity (BCVA) before and after surgery, preoperative cylinder, postoperative residual refractive cylinder, postoperative corneal cylinder, IOL misalignment, and intraocular pressure (IOP) were compared.Results We included 14 studies with 885 cataract eyes. All data were performed using Review Manager 5.3 (RevMan 5.3) (https://revman.cochrane.org/). Cases of all preoperative outcomes showed no significant difference between image-guided group and manual group. There was no significant difference in postoperative UDVA (Standard mean difference (SMD: −0.11, 95% CI: −0.32 to 0.11, I2 = 59%, p = 0.33)), BCVA (SMD: 0.03, 95% CI: −0.12 to 0.18, I2 = 36%, p = 0.72), corneal cylinder (Weighted mean difference WMD: 0.13, 95% CI: −0.06 to −0.32, I2 = 0%, p = 0.17), IOP (WMD: −0.37, 95% CI: −1.36 to −0.62, I2 = 9%, p = 0.46) between two groups. There was less residual refractive cylinder in image-guided group than in manual group (WMD: −0.20, 95% CI: −0.26 to −0.14, I2 = 59%, p＜0.00001). It is more accurate in IOL alignment when combined with image-guided systems (WMD: −1.20, 95% CI: −1.43 to −0.96, I2 = 14%, p

Abstract Ionic liquids (ILs) have attracted intensive research interest due to their outstanding physiochemical properties. However, comprehensive design is necessary for targeted applications and has rarely been conducted. As a result, the industry‐scale application of ILs is still very limited. In this academia–industry collaborative research among the University of Pittsburgh, Virginia Tech. University, and Seagate Technology LLC, we report the design, synthesis, molecular dynamics (MD) simulation, and characterization of a nanometer‐thick IL, which contains abundant fluorinated segments and a hydroxyl endgroup, as the next‐generation nano‐lubricant for hard disk drives (HDDs). The lab‐ and industry‐level testing results indicate that the IL lubricant performs significantly better than the state‐of‐the‐art lubricant, that is, perfluoropolyether (PFPE) that has been utilized for three decades in the HDD industry in two key functions: thermal stability and fly clearance. Meanwhile, the IL lubricant also shows excellent lubricity and durability. The outstanding performance of the IL has been attributed to its unique molecular structure on the solid substrate, which is supported by MD simulation results. Our work establishes the IL as a promising candidate among the next‐generation media lubricants in HDD industry. Meanwhile, the finding obtained here has important implications in many other applications involving nano‐lubricants.

Abstract Background Distal clavicular fracture is a shoulder joint injury that is common in clinical settings and is generally surgically treated using the clavicular hook plate technique with a confirmed curative effect. However, symptoms, such as shoulder abduction limitation, shoulder discomfort, and postoperative joint pain, may occur in some patients. To overcome these problems, after a previous study we developed an acromial height-measuring device and a new type of clavicular hook plate. This study aimed to investigate whether an acromial height-measuring device combined with an improved new-type clavicular hook plate can better reduce the incidence of complications and improve postoperative function. To provide patients with better treatment effects, an acromion gauge and clavicular hook plate are used. Methods A retrospective analysis was performed on 81 patients with distal clavicular fractures admitted to our hospital. They were divided into experimental and control groups according to different plates, and the Constant–Murley score, visual analogue scale score, incidence of acromion osteolysis, and incidence of subacromial impingement syndrome were compared. Results Compared with the standard clavicular hook plate, the acromial height-measuring device combined with the new-type clavicular hook plate in the treatment of distal clavicle fractures has a lower incidence of subacromial impingement syndrome with better postoperative functional recovery and quality of life. Conclusions We considered the acromial height-measuring device combined with the new clavicular hook plate to be a safe and promising alternative to distal clavicular fractures.

Dewatering of fine coal is a significant industrial challenge with economic and environmental implications. Due to the lack of suitable dewatering technologies, fine coal particles are often discarded to waste impoundments, leading to substantial loss of valuable natural resources while creating environmental problems. The hydrophobic-hydrophilic separation (HHS) process is a unique solution to this problem. In this process, a recyclable solvent is used to simultaneously remove inorganic impurities (ash) and water from a run-of-mine fine coal slurry. A small amount of recyclable oil (or solvent) is added to a fine coal slurry so that the solvent can spontaneously displace the water from the surface of coal particles. The spent solvent is subsequently recovered and recycled in a closed loop. Here, we report the results obtained using two different solvents, i.e., pentane and hexane, to de-ash and dewater ultrafine coal and recover the spent solvent by filtration, followed by steam stripping. Most of the spent solvent can be recovered during the filtration step at 20 psig N₂ and at a 60 s filtration time. The residual solvent left in the cake was then recovered using steam under different conditions. The results showed that the residual solvent concentration could be reduced to

Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.

For sudden drinking water pollution event, reasonable opening or closing valves and hydrants in a water distribution network (WDN), which ensures the isolation and discharge of contaminant as soon as possible, is considered as an effective emergency measure. In this paper, we propose an emergency scheduling algorithm based on evolutionary reinforcement learning (ERL), which can train a good scheduling policy by the combination of the evolutionary computation (EC) and reinforcement learning (RL). Then, the optimal scheduling policy can guide the operation of valves and hydrants in real time based on sensor information, and protect people from the risk of contaminated water. Experiments verify our algorithm can achieve good results and effectively reduce the impact of pollution events.

Abstract Tomato yellow leaf curl virus (TYLCV), belonging to the genus Begomovirus in the family Geminiviridae, is the most damaging virus for global tomato production. This virus has become one of the most studied plant viral pathogens because of its huge economic importance. Since it was firstly identified in Shanghai, China, in 2006, it has been spread to most parts of the country. The widespread occurrence, rapid spread to new regions, and enormous damage to tomato production, makes it an important agricultural pathogen in China. TYLCV has been characterized extensively at the molecular level. This review is focused on the occurrence and distribution of all TYLCV isolates in China, providing valuable information for further epidemiological studies. In addition, management strategies for TYLCV are also proposed, with the ultimate goal to prevent and control the further occurrence of this viral disease.

Abstract Fluorescent probes for detection of CN− still have many limitations, such as small Stokes shift, irreversible, and background interference, which hamper their applications for on‐site detection and bioimaging of CN−. In this work, we design a new CN−‐activatable fluorescent probe (named AHMM) containing an ESIPT (excited‐state intramolecular proton transfer) and hydrogen bond features, which show a large Stokes shift (225 nm) and molecular structural reversible detection. The probe AHMM exhibits an excellent selectivity for CN− without any interference from other anions in aqueous DMSO system. Furthermore, the mechanism of the interaction of AHMM with CN− is concluded by various experiments. The limit of detection of AHMM for CN− is calculated as low as 4.47 × 10−8 M, lower than the concentration of CN− deemed acceptable by WHO (World Health Organization). AHMM can recognize CN− in tap water quantitatively and on‐site by a smartphone APP. Moreover, food samples such as almond and cassava including CN− are visualized by fluorescence imaging. In addition, the probe shows practical applications of CN− imaging in cells and mice. This concept can be applied for designing multifunctional fluorescent probes with ESIPT and reversible characteristics for detection of CN−.

Immunopeptidomics benefits from highly sensitive mass spectrometry (MS). Here, the authors present a computational platform for integrating data-dependent and -independent acquisition MS approaches, and demonstrate its utility for deeper immunopeptidome profiling.

Abstract Objective To explore the technical specifications and clinical outcomes of thrombosed aneurysmal haemodialysis arteriovenous fistula (AVF) treated with ultrasound-guided percutaneous transluminal angioplasty combined with minimal aneurysmotomy. Methods This case series study included 11 patients who had thrombosed aneurysmal AVF and underwent salvage procedures over a 13-month period. All procedures were performed under duplex guidance. Minimal aneurysmotomy was performed, along with manual thrombectomy and thrombolytic agent infusion, followed by angioplasty to macerate the thrombus and sufficiently dilate potential stenoses. A successful procedure was defined as immediate restoration of flow through the AVF. Results The 11 patients (four males and seven females) had a mean age of 49.6 years ± 11.9 years. Six patients (54.5%) had two or more aneurysms. The mean aneurysm maximal diameter was 21.5 mm (standard deviation: ± 5.0 mm), and the mean thrombus length was 12.9 cm (8–22 cm). Ten (83.3%) of the 12 procedures were technically successful. The mean duration of operation was 150.9 minutes (standard deviation: ± 34.2 minutes), and mean postoperative AVF blood flow was 728.6 ml/min (standard deviation: ± 53.7 mi/min). The resumption of hemodialysis was successful in all 11 cases, with a clinical success rate of 100%. The primary patency rates were 90.0% and 75.0% at three and four months over a mean follow-up time of 6.3 months (3–12 months). The secondary patency rates were 90.4% at three and four months. Conclusion A hybrid approach combining ultrasound-guided percutaneous transluminal angioplasty and minimal aneurysmotomy might be a safe and effective method for thrombosed aneurysmal AVF salvage.

The brightest gamma-ray burst, GRB 221009A, has spurred numerous theoretical investigations, with particular attention paid to the origins of ultrahigh-energy TeV photons during the prompt phase. However, analyzing the mechanism of radiation of photons in the ∼MeV range has been difficult because the high flux causes pileup and saturation effects in most GRB detectors. In this Letter, we present systematic modeling of the time-resolved spectra of the GRB using unsaturated data obtained from the Fermi Gamma-ray Burst Monitor (precursor) and SATech-01/GECAM-C (main emission and flare). Our approach incorporates the synchrotron radiation model, which assumes an expanding emission region with relativistic speed and a global magnetic field that decays with radius, and successfully fits such a model to the observational data. Our results indicate that the spectra of the burst are fully in accordance with a synchrotron origin from relativistic electrons accelerated at a large emission radius. The lack of thermal emission in the prompt emission spectra supports a Poynting flux–dominated jet composition.

Inflammatory breast cancer (IBC) is an aggressive form of breast cancer with a poor prognosis. Here the authors report the characterization of a human IBC cell line recapitulating the clinical and histopathological features of the human disease, and implicating its high level of CCL2 in macrophage infiltration and tumor progression.

Alzheimer disease (AD) is a progressive neurodegenerative disease resulting from the accumulation of extracellular amyloid beta (Aβ) and intracellular neurofibrillary tangles. There are currently no objective diagnostic measures for AD. The aim of this study was to identify potential diagnostic markers for AD and evaluate the role of immune cell infiltration in disease pathogenesis. AD expression profiling data for human hippocampus tissue (GSE48350 and GSE5281) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software and the Human Protein Atlas database was used to screen AD-related DEGs. We performed functional enrichment analysis and established a protein–protein interaction (PPI) network to identify disease-related hub DEGs. The fraction of infiltrating immune cells in samples was determined with the Microenvironment Cell Populations-counter method. The random forest algorithm was used to develop a prediction model and receiver operating characteristic (ROC) curve analysis was performed to validate the diagnostic utility of the candidate AD markers. The correlation between expression of the diagnostic markers and immune cell infiltration was also analyzed. A total of 107 AD-related DEGs were screened in this study, including 28 that were upregulated and 79 that were downregulated. The DEGs were enriched in the Gene Ontology terms GABAergic synapse, Morphine addiction, Nicotine addiction, Phagosome, and Synaptic vesicle cycle. We identified 10 disease-related hub genes and 20 candidate diagnostic genes. Synaptophysin (SYP) and regulator of G protein signaling 4 (RGS4) (area under the ROC curve = 0.909) were verified as potential diagnostic markers for AD in the GSE28146 validation dataset. Natural killer cells, B lineage cells, monocytic lineage cells, endothelial cells, and fibroblasts were found to be involved in AD; additionally, the expression levels of both SYP and RGS4 were negatively correlated with the infiltration of these immune cell types. These results suggest that SYP and RGS4 are potential diagnostic markers for AD and that immune cell infiltration plays an important role in AD development and progression.

Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective.

Evidence of antibody-dependent enhancement (ADE) of other viruses has raised concerns about the safety of SARS-CoV-2 vaccines and antibody therapeutics. In vitro studies have shown ADE of SARS-CoV-2 infection. In this study, we also found that vaccination/convalescent sera and some approved monoclonal antibodies can enhance SARS-CoV-2 infection of FcR-expressing B cells in vitro. However, the enhancement of SARS-CoV-2 infection can be prevented by blocking Fc–FcR interaction through the addition of human serum/IgG or the introduction of mutations in the Fc portion of the antibody. It should be noted that ADE activity observed on FcR-expressing cells in vitro may not necessarily reflect the situation in vivo; therefore, animal and clinical data should be included for ADE evaluation.

Recent years have seen an increase in scientific interest in the El Nio/La Nia Southern Oscillation (ENSO), a quasiperiodic climate phenomenon that takes place throughout the tropical Pacific Ocean over five years and causes significant harm. It is associated with the warm oceanic stage known as El Nio and the cold oceanic stage known as La Nia. In this research, the ENSO model is considered under a fractional operator, which is defined via a nonsingular and nonlocal kernel. Some theoretical features, such as equilibrium points and their stability, bifurcation maps, the existence of a unique solution via the Picard–Lindelof approach, and the stability of the solution via the Ulam–Hyres stability approach, are deliberated for the proposed ENSO model. The Adams–Bashforth numerical method, associated with Lagrangian interpolation, is used to obtain a numerical solution for the considered ENSO model. The complex dynamics of the ENSO model are displayed for a few fractional orders via MATLAB-18.

In the field of computer vision, edge line segment detection in images is widely used in tasks such as 3D reconstruction and simultaneous localization and mapping. Currently, there are many algorithms that primarily focus on detecting straight line segments in undistorted images, but they do not perform well in detecting edge line segments in distorted images. To address this quandary, the present study introduces a novel method of line segment identification founded on the principles of quadratic fitting. The method proposed utilizes the inherent property of a linear projection in a three-dimensional space, whereby it appears as a quadratic curve in a distorted two-dimensional image. This approach applies an iterative estimation process to ascertain the optimal parameters of the quadratic form that aligns with the edge contour. This process is facilitated by implementing an assumption and validation mechanism. Upon deriving the optimal model, it is then employed to identify the line segments that are encompassed within the edge contour. The experimental assessment of this novel method incorporates its application to both distorted and distortion-free image datasets. The method eliminates the necessity for preliminary processing to discarding distortions, thereby making it universally applicable to both distorted and non-distorted images. In addition to this, the experimental results based on the dataset indicate that the proposed algorithm in this paper achieves an average computational efficiency that is 27 times faster than traditional ones. Thus, this research will contribute to line segment detection in computer vision.

Since the insulation performance of air is not as good as that of SF6 and other gases, it is necessary to conduct an in-depth study on the insulation characteristics of the fuse tube in order to meet the requirements of normal breaking and to design a structural improvement scheme for a 12 kV environmental protection cabinet fuse tube that is suitable for using air as the insulating medium. In this study, the insulation performance of the fuse tube before improvement was studied, and the electric-field distribution of the 12 kV fuse tube placed in the air-insulated switchgear was analyzed. The results showed that the electric-field was heavily concentrated in the air gap of the fuse tube plug and around the ground rod. In this study, the structure of the fuse tube was specifically designed. By spraying conductive paint on the intersection surface of different media, equipotentiality was achieved, and a reasonable metal shielding structure was added to the site where the field intensity was concentrated so as to improve the distribution of the electric-field and avoid the breakdown caused by the concentration of field intensity. Through several improved designs, the maximum electric-field strength of the fuse tube of an environmental protection cabinet can be effectively reduced, and the insulation requirements of relevant standards can be met. A partial discharge test for the improved fuse tube was carried out, and the local discharge quantity of the improved fuse tube met the industry requirements, which verified the rationality of the fuse-tube-improvement scheme.

Objective: Several studies have shown the significance of neuroinflammation in the pathological progress of cerebral palsy (CP). However, the etiology of CP remains poorly understood. Spastic CP is the most common form of CP, comprising 80% of all cases. Therefore, identifying the specific factors may serve to understand the etiology of spastic CP. Our research aimed to find some relevant factors through protein profiling, screening, and validation to help understand the pathogenesis of cerebral palsy.Materials and methods: In the current study, related clinical parameters were assessed in 18 children with spastic CP along with 20 healthy individuals of the same age. Blood samples of the spastic CP children and controls were analyzed with proteomics profiling to detect differentially expressed proteins. On the other hand, after hypoxic-ischemic encephalopathy (HIE) was induced in the postnatal day 7 rat pups, behavioral tests were performed followed by detection of the differentially expressed markers and inflammatory cytokines in the peripheral blood and cerebral cortex of the CP model rats by Elisa and Western blot. Independent sample t-tests, one-way analysis of variance, and the Pearson correlation were used for statistical analysis.Results: Through proteomic analysis, differentially expressed proteins were identified. Among them, tissue-nonspecific alkaline phosphatase (TNAP), the gene expression product of alkaline phosphatase (ALPL), was downregulated in spastic CP. In addition, significantly lower TNAP levels were found in the children with CP and model rats. In contrast, compared with the sham rats, the model rats demonstrated a significant increase in osteopontin and proinflammatory biomarkers in both the plasma and cerebral cortex on the ischemic side whereas serum 25 hydroxyvitamin D and IL-10 were significantly decreased. Moreover, serum TNAP level was positively correlated with serum CRP and IL-10 in model rats.Conclusion: These results suggest that TNAP is the potential molecule playing a specific and critical role in the neuroinflammation in spastic CP, which may provide a promising target for the diagnosis and treatment of spastic CP.

Genetic and acquired risk factors are extremely important mechanisms in the development of venous thromboembolism (VTE). Inherited antithrombin (AT) deficiency due to mutations in the SERPINC1 gene is a well-known risk factor for genetic thrombophilia. In this case, we reported a 28-year young abroad student who presented with refractory and recurrent VTE in-hospital. This patient presented with a 2-month history of right lower limb pain and 1 week of fever. The ultrasound showed deep venous thrombosis in the right common and superficial femoral veins. The CTPA confirmed acute pulmonary embolism with multiple filling defects in both pulmonary arteries. He was diagnosed with “pulmonary embolism, pneumonia, lower extremity venous thrombosis”. The level of serum antithrombin was normal, yet gene sequencing revealed a heterozygous missense mutation of SERPINC1, c.1277C>T (p.Ser426Leu). The patient underwent anticoagulant therapy of heparin and inferior vena cava filter implantation. The patient had undergone recurrent VTE despite adequate anticoagulation with heparin during the first 2 weeks. The swelling, pain, and thrombosis of lower extremity veins got resolved from warfarin and rivaroxaban. Inherited antithrombin deficiency due to mutations in the SERPINC1 gene is the genetic basis of this patient, and warfarin/rivaroxaban, other than heparin, is beneficial.

The role of teachers in developing positive traits in EFL students has been widely endorsed in the literature. Two such traits that have pivotal roles in language teaching and learning are grit and academic engagement. Despite the proliferation of correlational studies on these constructs, theoretical and systematic review studies on the role of EFL teachers’ approaches in strengthening and enhancing these variables are scant. To fill this wide gap, the present study aimed to review the theoretical and empirical underpinnings of students’ grit and engagement and their teachability. In so doing, the definitions, conceptualizations, and dimensions of the two variables were presented. Moreover, a number of practical teaching approaches were suggested to EFL teachers in various contexts. Finally, implications, research gaps, and future directions of this research strand are provided to enhance EFL stakeholders’ knowledge of teachability of grit, engagement, and many other positive psychological traits.

Abstract Background Acromioclavicular joint dislocation is a shoulder joint injury common in the clinical setting and is generally surgically treated with clavicular hook plate technique with confirmed curative effect. However, symptoms such as shoulder abduction limitation, shoulder discomfort and joint pain postoperatively may occur in some patients. Therefore, this study aimed to explore whether the existing clavicular hook plate can be reasonably selected to reduce the incidence of subacromial impingement syndrome (SIS) and provide a reference for clinical diagnosis and treatment. Materials and methods Patients with SIS admitted from March 2018 to June 2020 were selected as the experimental group and asymptomatic patients postoperatively, as the control group. The hook end depth and acromial height of the hook plate used in patients were recorded, and the difference between them was calculated. Results The difference between the hook plate depth and acromial height was 7.500±1.912 mm and 6.563±1.537 mm in the experimental and control groups, respectively, with statistically significant difference (t=3.021, P=0.006). A difference of >0.6 mm as a grouping index is required to perform a single factor analysis, with statistically significant difference (t=3.908, P=0.048). Conclusions The occurrence of SIS after placing the clavicular hook plate may be related to the difference between its depth and the acromial height. A difference of >6 mm may be a factor affecting the occurrence of SIS. Pre-imaging measurement of the acromial height can provide suggestions for selecting the type of hook plate intraoperatively.

To solve the problem of automatic generation of access control policies, an access control policy generation framework based on deep learning was proposed.Access control policy based on attributes could be generated from natural language texts.This technology could significantly reduce the time cost of access control policy generation and provide effective support for the implementation of access control.The policy generation problem was decomposed into two core tasks, identification of access control policy sentence and access control attribute mining.Neural network models such as BiGRU-CNN-Attention and AM-BiLSTM-CRF were designed respectively to realize identification of access control policy sentence and access control attribute mining, so as to generate readable and executable access control policies.Experimental results show that the proposed method has better performance than the benchmark method.In particular, the average F1-score index can reach 0.941 in the identification task of access control policy sentence, which is 4.1% better than the current state-of-the-art method.

The nuclear localization signal (NLS) and nuclear export signal (NES) are key signatures of proteins for controlling nuclear import and export. The NIb protein of turnip mosaic virus (TuMV) is an RNA-dependent RNA polymerase (RdRP) that is absolutely required for viral genome replication. Previous studies have shown that NIb is a nucleocytoplasmic shuttling protein and contains four putative NES and four putative NLS motifs. Here, we analyzed the function of these NESs and NLSs, and identified two functional NESs and one functional NLS. Mutation of the identified functional NESs or NLS inhibited viral RNA accumulation and systemic infection. Exportin 1 (XPO1) is a nuclear export receptor that binds directly to cargo proteins harboring a leucine-rich NES and translocates them to the cytoplasm. We found that XPO1 contains two NIb-binding domains, which recognize the NLS and NES of NIb, respectively, to mediate the nucleocytoplasmic transport of NIb and promote viral infection. Taken together, these data suggest that the nucleocytoplasmic transport of NIb is modulated by XPO1 through its interactions with the functional NLS and NES of NIb to promote viral infection.

As a new member of the Gravitational-wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) after GECAM-A and GECAM-B, GECAM-C (originally called HEBS), which was launched on board the SATech-01 satellite on 2022 July 27, aims to monitor and localize X-ray and gamma-ray transients from ∼6 keV to 6 MeV. GECAM-C utilizes a similar design to GECAM but operates in a more complex orbital environment. In this work, we utilize the secondary particles simultaneously produced by the cosmic-ray events on orbit and recorded by multiple detectors to calibrate the relative timing accuracy between all detectors of GECAM-C. We find the result is 0.1 μ s, which is the highest time resolution among all GRB detectors ever flown and very helpful in timing analyses such as minimum variable timescale and spectral lags, as well as in time delay localization. Besides this, we calibrate the absolute time accuracy using the one-year Crab Pulsar data observed by GECAM-C and Fermi’s Gamma-ray Burst Monitor, as well as GECAM-C and GECAM-B. The results are 2.02 ± 2.26 μ s and 5.82 ± 3.59 μ s, respectively. Finally, we investigate the spectral lag between the different energy bands of the Crab Pulsar observed by GECAM and the Gamma-ray Burst Monitor, which is ∼ −0.2 μ s keV ^−1 .

The Gravitational Wave High-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e., GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including the high-energy electromagnetic counterparts of gravitational waves, such as gamma-ray bursts, soft gamma-ray repeaters, solar flares, and terrestrial gamma-ray flashes. Since launch in 2020 December, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multiple wavelengths. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the distribution of burst counts in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in the literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is ∼2.°5. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors.

The Perspective-n-Point problem is usually addressed by means of a projective imaging model of 3D points, but the spatial distribution and quantity of 3D reference points vary, making it difficult for the Perspective-n-Point algorithm to balance accuracy, robustness, and computational efficiency. To address this issue, this paper introduces Hidden PnP, a hidden variable method. Following the parameterization of the rotation matrix by CGR parameters, the method, unlike the existing best matrix synthesis technique (Gröbner technology), does not require construction of a larger matrix elimination template in the polynomial solution phase. Therefore, it is able to solve CGR parameter rapidly, and achieve an accurate location of the solution using the Gauss–Newton method. According to the synthetic data test, the PnP algorithm solution, based on hidden variables, outperforms the existing best Perspective-n-Point method in accuracy and robustness, under cases of Ordinary 3D, Planar Case, and Quasi-Singular. Furthermore, its computational efficiency can be up to seven times that of existing excellent algorithms when the spatially redundant reference points are increased to 500. In physical experiments on pose reprojection from monocular cameras, this algorithm even showed higher accuracy than the best existing algorithm.

BackgroundsBreast cancer is a heterogeneous disease without clear pathogenesis and effective primary prevention. The “anti-cancer” effects of several trace elements have received increasing attention in recent years. The main purpose of current study is to explore the differences of three potential “anti-cancer” trace elements selenium (Se), molybdenum (Mo), and strontium (Sr) between patients with malignant breast tumors and healthy controls.MethodsWe conducted a case–control study in 45 patients with malignant breast tumors as cases and 95 healthy volunteers as controls from Peking University Third Hospital, Beijing, China. The serum concentrations of trace elements were evaluated by using inductively coupled plasma mass spectrometry (ICP-MS).ResultsThe cases may have a lower Se levels when compared with controls (cases: 106.22 ng/ml, SD: 20.95 ng/ml; controls: 117.02 ng/ml, IQR: 22.79 ng/ml, p = 0.014). High levels of Se were a protective factor from breast cancer after adjusting the potential confounders of age, BMI, smoking, drinking, and menopause status (OR = 0.395, 95% CI, 0.178, 0.877, p = 0.023). The levels of Sr were lower in cases with high histologic grade when compared to low histologic grade (low histologic grade: 49.83 ng/ml, IQR: 41.35–62.60 ng/ml; high histologic grade: 40.19 ng/ml, IQR: 39.24–47.16 ng/ml, p < 0.05).ConclusionsOur findings herein supported that Se has protective effects to avoid malignant breast tumors and Sr has protective effects to avoid poorly differentiated malignant breast tumors. Exploring “anti-cancer” related trace elements and their associations with breast cancer will assist for the early prevention and intervention for the disease.

Double helix structures appear widely in nature, but only rarely in synthetic non-chiral macromolecules. Here the authors describe a double helix in a densely charged aromatic polyamide, which exhibits an axial rigidity persistence length of ~ 1 μm, much higher than that of DNA (~ 50 nm).

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy.