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BackgroundModerate-to-severe atopic dermatitis (AD) often has a profound impact on the quality of life of young children and their caregivers. One of the most burdensome symptoms reported by patients is skin pain.MethodsThis post hoc analysis focuses on the impact of dupilumab treatment on skin pain in young children using data from the LIBERTY AD PRESCHOOL part B (NCT03346434), a 16-week randomized, double-blind, placebo-controlled, phase 3 study in 162 children aged 6 months to 5 years with moderate-to-severe AD receiving dupilumab or placebo, plus topical corticosteroids (TCS). Analyses were performed on the full analysis set and subgroups of patients who did not achieve an Investigator's Global Assessment score of 0 or 1 (IGA >1 subgroup), or who did not achieve a 75% improvement from baseline in the Eczema Area and Severity Index (1 and

Abstract Background A subset of Graves’ disease (GD) patients develops refractory hyperthyroidism, posing challenges in treatment decisions. The predictive value of baseline characteristics and early therapy indicators in identifying high risk individuals is an area worth exploration. Methods A prospective cohort study (2018–2022) involved 597 newly diagnosed adult GD patients undergoing methimazole (MMI) treatment. Baseline characteristics and 3-month therapy parameters were utilized to develop predictive models for refractory GD, considering antithyroid drug (ATD) dosage regimens. Results Among 346 patients analyzed, 49.7% developed ATD-refractory GD, marked by recurrence and sustained Thyrotropin Receptor Antibody (TRAb) positivity. Key baseline factors, including younger age, Graves’ ophthalmopathy (GO), larger goiter size, and higher initial free triiodothyronine (fT3), free thyroxine (fT4), and TRAb levels, were all significantly associated with an increased risk of refractory GD, forming the baseline predictive model (Model A). Subsequent analysis based on MMI cumulative dosage at 3 months resulted in two subgroups: a high cumulative dosage group (average ≥ 20 mg/day) and a medium–low cumulative dosage group (average

Abstract Background Atopic dermatitis (AD) significantly impacts health‐related quality of life (HRQoL) in children and their caregiver(s)/family. Measures to assess HRQoL include the Children's Dermatology Life Quality Index (CDLQI), Infants' Dermatitis Quality of Life Index (IDQoL) and Dermatitis Family Impact (DFI) questionnaire. Currently, there are no established clinically meaningful within‐person change thresholds for these measures in young children (6 months to 5 years) and their caregiver(s)/family. Objectives To determine the clinically meaningful within‐person change thresholds for CDLQI (4–5 years), IDQoL (

Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks.

Abstract Cellular senescence serves as a fundamental and underlying activity that drives the aging process, and it is intricately associated with numerous age-related diseases, including Alzheimer's disease (AD), a neurodegenerative aging-related disorder characterized by progressive cognitive impairment. Although increasing evidence suggests that senescent microglia play a role in the pathogenesis of AD, their exact role remains unclear. In this study, we quantified the levels of lactic acid in senescent microglia, and hippocampus tissues of naturally aged mice and AD mice models (FAD4T and APP/PS1). We found lactic acid levels were significantly elevated in these cells and tissues compared to their corresponding counterparts, which increased the level of pan histone lysine lactylation (Kla). We aslo identified all histone Kla sites in senescent microglia, and found that both the H3K18 lactylation (H3K18la) and Pan-Kla were significantly up-regulated in senescent microglia and hippocampus tissues of naturally aged mice and AD modeling mice. We demonstrated that enhanced H3K18la directly stimulates the NFκB signaling pathway by increasing binding to the promoter of Rela (p65) and NFκB1(p50), thereby upregulating senescence-associated secretory phenotype (SASP) components IL-6 and IL-8. Our study provides novel insights into the physiological function of Kla and the epigenetic regulatory mechanism that regulates brain aging and AD. Specifically, we have identified the H3K18la/NFκB axis as a critical player in this process by modulating IL-6 and IL-8. Targeting this axis may be a potential therapeutic strategy for delaying aging and AD by blunting SASP.

Abstract Background In a recent post hoc analysis of multinational clinical trial data, dupilumab treatment for patients with moderate or severe atopic dermatitis (AD) reduced all‐cause and AD‐related hospitalisations. Objectives We studied the impact of dupilumab on health care resource utilisation (HCRU) and AD medication by comparing use during the 6 months either side of starting dupilumab (index) in a real‐world setting. Methods This observational, retrospective, cohort study used Japanese hospital claims data (1 April 2018 to 28 February 2019). A total of 357 patients (mean [range] age 45 [15–92] years; 65.5% male) with ≥1 AD diagnosis and ≥1 dupilumab prescription were included. Outcome variables included AD treatments and HCRU during the 6 months pre‐ and post‐index. Results The number of all‐cause hospitalisations decreased by 43.1% (from 51 in the 6 months before to 29 in the 6 months after dupilumab initiation), with a 96.0% reduction in skin/subcutaneous tissue disease‐related hospitalisations (25 to 1). Use of nonsteroidal systemic immunosuppressants, phototherapy and systemic corticosteroids decreased (by 59.8%, 59.5% and 30.6%, respectively) after dupilumab initiation. At 180 days after dupilumab initiation, 86.8% of patients were still taking dupilumab. Conclusions Overall, these results suggest that dupilumab can reduce skin/soft tissue‐related hospitalisations and use of various other AD medications in a real‐world setting.

Abstract Introduction Atopic dermatitis (AD) can require long-term therapy. Few real-world studies have evaluated long-term effectiveness from the patients’ perspective. The aim of this study was to evaluate patient-reported outcomes (PROs) during long-term dupilumab treatment. Methods Adults with moderate-to-severe AD who initiated dupilumab through the US manufacturer patient support program and participated in RELIEVE-AD (a prospective patient survey study with a 12-month follow-up) were recontacted 30–36 months post-initiation regardless of current dupilumab use. The online questionnaire consisted of PROs, including the Atopic Dermatitis Control Tool (ADCT), use of concomitant AD therapies, satisfaction with current therapy, global change in itch relative to before dupilumab initiation, non-itch skin symptoms (skin pain/soreness, hot/burning feeling, and sensitivity to touch), flares, Dermatology Life Quality Index, sleep problems, and the AD-specific Work Productivity and Activity Impairment Questionnaire. Results Of 698 patients who initiated dupilumab (baseline) and were recontacted, 425 completed the 30–36-month survey. Significant reductions from baseline were reported in concomitant AD therapy use (P

Abstract In order to enhance the performance of recommendation systems that support attack detection algorithms, we have designed a novel approach based on deep learning. Specifically, our algorithm focuses on improving convergence, user scoring accuracy, algorithm efficiency, and detection accuracy. To achieve this, we first construct a preliminary user rating matrix, which is optimised by incorporating the user preference word matrix and the weight of the preference word. Additionally, we adjust the size of the matrix using principal component analysis. Next, we construct a deep, bidirectional RNN model based on the deep learning network. This model is then combined with the user scoring matrix to identify the type of user. In the case of abnormal or false users, our algorithm is able to identify the recommendation system’s support attack through the detection results. The experimental results demonstrate the effectiveness of our algorithm. Specifically, our approach achieves fast convergence speeds, with the loss value remaining low throughout the process. Moreover, we achieve high average accuracy in user scoring, with a score of 97.14%. The detection time of the recommendation system support attack is also consistently lower than 0.7 s. Furthermore, our approach achieves an average accuracy of 98.09% in the detection of recommendation system support attacks. Overall, our algorithm shows promising results for practical applications in the field of recommendation systems.

Abstract Doxorubicin (DOX) is a commonly used antitumor drug, but its application has been limited because of its strong cardiac damage. This study aims to explore the role of NSUN2 in DOX-induced heart injury. C57BL/6J mice were intraperitoneally injected with 20 mg/Kg DOX to induce heart injury. After 3 days, the cardiac function, cardiac histopathology, myocardial apoptosis, and the expression level of NSUN2 were detected. In vitro, H9C2 cells were transfected with NSUN2 siRNA or overexpressed lentivirus and then treated with 500 ng/ml DOX. After 24 h, the changes in reactive oxygen species (ROS), apoptosis, and NSUN2 expression were detected. After DOX treatment, both in vitro and in vivo experiments showed that the cardiac function decreased, the number of apoptotic cells increased, and the expression level of NSUN2 increased. Interfering the expression of NSUN2 by siRNA promoted DOX-induced heart injury, while overexpression of NSUN2 could inhibit DOX-induced heart injury. Further study showed that NSUN2 promoted antioxidative stress by upregulating the Nrf2 protein level. In addition, NSUN2 overexpression could increase the half-life of Nrf2 mRNA. m5C RNA methylation immunoprecipitation (MeRIP) also showed that the level of Nrf2 m5C mRNA was significantly increased in NSUN2 overexpressed group when compared to the GFP group. NSUN2 enhances the expression of Nrf2 by promoting Nrf2 mRNA m5C modification and enhances its antioxidative stress effect to alleviate DOX-induced myocardial injury.

Small object detection (SOD) is significant for many real-world applications, including criminal investigation, autonomous driving and remote sensing images. SOD has been one of the most challenging tasks in computer vision due to its low resolution and noise representation. With the development of deep learning, it has been introduced to boost the performance of SOD. In this paper, focusing on the difficulties of SOD, we analyze the deep learning-based SOD research papers from four perspectives, including boosting the resolution of input features, scale-aware training, incorporating contextual information and data augmentation. We also review the literature on crucial SOD tasks, including small face detection, small pedestrian detection and aerial image object detection. In addition, we conduct a thorough performance evaluation of generic SOD algorithms and methods for crucial SOD tasks on four well-known small object datasets. Our experimental results show that network configuring to boost the resolution of input features can enable significant performance gains on WIDER FACE and Tiny Person. Finally, several potential directions for future research in the area of SOD are provided.

Objective To analyze the current situation of human parvovirus B19 infection in blood donors in different regions of China, so as to provide basis for formulating reasonable screening programs of B19 virus for blood donors in various cities and regions. Methods The literatures related to human parvovirus B19 infection in whole blood and plasma donors published from 1998 to 2021 were searched in the database, and meta-analysis of literatures that satisfied the inclusion criteria was conducted by R4.1.0 software. Results A total of 35 literatures were obtained, 20 literatures involving 56 846 blood donor samples and 8 literatures involving 1 608 pooled raw plasma samples were subjected to Meta analysis of the positive rates of B19 DNA; 17 literatures involving 12 308 blood sample were subjected to the Meta analysis of the positive rate of B19 IgG antibody.The positive rates of B19 DNA in blood donors(I2=96%, τ2=0.026 0, P

Pt/TiO2 nanoparticles were prepared by a simple two-step aqueous solution method, which consists of a low temperature hydrothermal step and a photoreduction deposition step. The as-prepared samples were characterized by XRD, TEM, XPS, UV-vis, and BET techniques. The as-prepared samples exhibited enhanced photocatalytic activity towards the degradation of rhodamine B (RhB) under visible light irradiation. With increasing Pt content from 0.25 to 1 wt.%, the band gap energies shift from 2.32 to 1.64 eV. The 0.75 wt.% Pt/TiO2 sample showed the best photocatalytic activity, which could be ascribed to the formation of a Schottky barrier and the localized surface plasmon resonance effect of Pt nanoparticles. And the as-prepared samples also displayed excellent stability and reusability in multiple experimental cycles.

Rumor detection is an important research topic in social networks, and lots of rumor detection models are proposed in recent years. For the rumor detection task, structural information in a conversation can be used to extract effective features. However, many existing rumor detection models focus on local structural features while the global structural features between the source tweet and its replies are not effectively used. To make full use of global structural features and content information, we propose Source-Replies relation Graph (SR-graph) for each conversation, in which every node denotes a tweet, its node feature is weighted word vectors, and edges denote the interaction between tweets. Based on SR-graphs, we propose an Ensemble Graph Convolutional Neural Net with a Nodes Proportion Allocation Mechanism (EGCN) for the rumor detection task. In experiments, we first verify that the extracted structural features are effective, and then we show the effects of different word-embedding dimensions on multiple test indices. Moreover, we show that our proposed EGCN model is comparable or even better than the current state-of-art machine learning models.

Network dismantling is one of the important NP-hard problems in the field of social network analysis. It aims to break down networks into many small components of limited size by only removing a small group of nodes. One feasible way is to decycle (eliminating all the cycles) the network first and then break the acyclic graph. However, existing decycling-based algorithms mainly concentrate on the decycling step, ignoring the importance of the tree breaking process. Besides, none of the algorithms try to pre-process the network, which may bring improvement in both effectiveness and efficiency. In this paper, we fill these two gaps by proposing a novel network dismantling algorithm that combines skeleton extraction and greedy tree breaking (SEGTB). Network skeleton supports the whole network structure, whose removal would leave a much looser structure and serves as an effective pre-processing for the dismantling problem. The presented tree breaking method is provided with theoretical proofs on its lower bound. Experiments on ten real-world datasets show that our proposed SEGTB algorithm is both effective and efficient, outperforming the state-of-the-art.

Objective. Autoimmune thyroid diseases (AITD), mainly Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), are common organ-specific autoimmune diseases characterized by circulating antibodies and lymphocyte infiltration. Follicular helper T (Tfh) cell dysregulation is involved in the development of autoimmune pathologies. We aimed to explore the role of intrathyroidal and circulating Tfh cells in patients with GD and HT. Methods. Ultrasound-guided thyroid fine-needle aspiration (FNA) was conducted in 35 patients with GD, 40 patients with HT, and 22 patients with nonautoimmune thyroid disease (nAITD). Peripheral blood samples were also obtained from 40 patients with GD, 40 patients with HT, and 40 healthy controls. The frequencies of intrathyroidal and circulating Tfh cells from FNA and peripheral blood samples were assessed by flow cytometry. Additionally, the correlations between the frequencies of the Tfh cells and the levels of autoantibodies and hormones or disease duration were analyzed. Results. The frequency of intrathyroidal CD4+CXCR5+ICOShigh Tfh cells was higher in HT patients than in GD patients. Significant correlations were identified between the percentages of circulating and intrathyroidal Tfh cells and the serum concentrations of thyroid autoantibodies, especially thyroglobulin antibodies (TgAb), in AITD. Intrathyroidal CD4+CXCR5+ICOShigh Tfh cells were positively correlated with free triiodothyronine (FT3) in HT patients but negatively correlated with FT3 in GD patients. In addition, HT patients with a longer disease duration had an increased frequency of intrathyroidal CD4+CXCR5+ICOShigh and CD4+CXCR5+PD-1+ Tfh cells. In contrast, in the GD patients, a longer disease duration did not affect the frequency of intrathyroidal CD4+CXCR5+ICOShigh but was associated with a lower frequency of CD4+CXCR5+PD-1+ Tfh cells. Conclusions. Our data suggest that intrathyroidal Tfh cells might play a role in the pathogenesis of AITD and they are potential immunobiomarkers for AITD.

Abstract Background Qiliqiangxin (QLQX) is a preparation refined from a traditional Chinese medicine compound. It plays an important role in protecting cardiac function after myocardial infarction (MI). However, the underline mechanism of QLQX action is not clear. The purpose of this study was to detect the effects of QLQX on mitophagy after MI. Methods Male FVB/NJ mice aged 8–10 weeks were underwent left coronary artery ligation and were orally administered either QLQX (0.25 g/kg/d) or saline. Twenty-eight days after surgical operation, the cardiac function of mice was detected by echocardiography. Electron Microscopy was used to observe the microstructure of cardiomyocytes. Myocardial apoptosis was examined by TdT-mediated dUTP Nick-End Labeling (TUNEL) and western blot. H9c2 cells were cultured in a hypoxic incubator chamber (5% CO2, 1% O2, 94% N2) for 12 h and pretreated with or without QLQX (0.5 mg/mL). The cell apoptosis, reactive oxygen species (ROS), mitochondrial membrane potential and mitophagy were detected. Results When compared to sham group, the cardiac function of MI mice decreased significantly, and their cardiomyocyte apoptosis and mitochondrial damage were more serious. These MI-induced cardiac changes could be reversed by QLQX treatment. In vitro experiments also confirmed that QLQX could protect cardiomyocytes from hypoxia-induced apoptosis and mitochondrial damage. Further study indicated that QLQX could increase the expression of Pink1 and Parkin in cardiomyocytes. Conclusion Qiliqiangxin could reduce cardiomyocytes apotosis and improved heart function in infarcted heart through Pink1-mediated mitochondrial autophagy.

Rumor stance classification is a task for identifying different stances about specific social media posts, and it is considered as an important step to prevent rumors from spreading. However, most previous studies for the rumor stance classification, which focus on content features of posts, ignore the textual information granularity and reading (or writing) habits of the public. In social networks, people may have different stances towards unverified posts with different reading habits. Based on this observation, we propose a Stochastic Attention Convolutional Neural Net (SACNN) under different textual information granularity to catch different habits of the public for the rumor stance classification task. Specifically, being treated as view-windows of readers, convolutional kernels in a SACNN contain trainable convolutional kernels and stochastic untrainable convolutional kernels, and different sizes of the stochastic convolutional kernels are used to simulate casual online reading habits and to extract course-grained features such as phrase features. After the convolutional layer, fine-grained features such as keywords can be extracted by a pooling layer. The experiments show that the average accuracy and F1 score of our proposed model are respectively 0.26% and 0.46% higher than the state-of-the-art results on the rumor stance classification task on PHEME dataset.

Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network’s ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms.

Using a dataset of 114 meteorological stations in the Yangtze River Basin from 1980–2019, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the Penman-Monteith evapotranspiration model for multiple time scales, and the spatial and temporal evolution characteristics and driving factors of drought in the Yangtze River Basin were analyzed by combining spatial and temporal analysis methods as well as geodetector. The main results obtained are as follows: (1) The climate of the Yangtze River Basin is an overall wet trend, and the trend of summer drought is more similar to the annual scale trend. (2) Most areas in the Yangtze River Basin showed mild drought or no drought, and there is little difference in drought condition among the Yangtze River Basin regions. The areas with drought conditions are mainly distributed in the southwest and east of the Yangtze River Basin. (3) There are significant seasonal differences in drought conditions in all regions, and the drought condition is more different in autumn compared to spring, summer and winter. (4) The average annual precipitation and elevation factors are the dominant driving factors of drought in the Yangtze River Basin, and the double-factor interaction has a greater influence on the drought variation in the Yangtze River Basin than the single-factor effect, indicating that the difference of drought condition in the Yangtze River Basin is the result of the combination of multiple factors.

Abstract Background A clinical diagnosis model include thyroid functions, thyroid antibodies and radioactive iodine uptake (RAIU) of patients with hyperthyroidism were established and as new evaluation indicators for the differentiation of the Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). Methods Clinical data of patients with newly diagnosed hyperthyroidism including gender, age, thyroid function, thyroid antibodies (FT3, FT4, TSH, TPOAb, TGAb, TRAb), RAIU (2 h, 6 h, 24 h) were collected. A stepwise regression analysis was performed to establish a model based on these variables. Results Model 1 was subjected to stepwise regression analysis. After screening, the variables that entered the model included FT3, TGAb, TPOAb, TRAb, 2-h RAIU, 24-h RAIU and gender, in which the variables FT3, TGAb, TRAb, 2-h RAIU, 24-h RAIU, and gender were significantly different. Model 2 without RAIU was also subjected to stepwise regression analysis. After screening, the variables that entered the model included FT4, TGAb, TPOAb, TRAb and gender were statistical significant. The larger value of each variable in the two models indicated the higher probability to diagnose GD. The area under the receiver operating characteristic (ROC) curve of model 1 was 0.843 (95% CI 0.779–0.894), and the area under the ROC curve of model 2 was 0.806 (95% CI 0.685–0.824), which showed good differential diagnostic value. Conclusions GD and HT diagnosis model was established according to the variables including gender, FT3, TGAb, TRAb, the 2-h RAIU, the 24-h RAIU in the model 1, and the variables FT4, TGAb, TPOAb, TRAb and gender in the model 2 that did not include RAIU. These models had high value to differentiate GD and HT for patients with early hyperthyroidism.

Abstract Introduction Real‐world data are critical to demonstrate the consistency of evidence and external generalizability of randomized controlled trials (RCTs). This study examined characteristics and outcomes of metastatic triple‐negative breast cancer (mTNBC) patients treated with eribulin mesylate at community oncology practices in the United States. Methods Physicians identified mTNBC patients initiating eribulin between 1 January 2011 and 1 January 2014 and abstracted data into an electronic case report form (eCRF). Eribulin treatment in the metastatic setting was categorized as early use (EU, first‐/second‐line) and late use (LU, third‐line or later). Patient characteristics, overall survival (OS), disease response (per treating physician), and adverse events (AEs) rates in each group, respectively, are reported. Results Overall 252 eCRFs were completed: 125 (49.6%) EU and 127 (50.4%) LU. The median age at metastatic diagnosis was 53 years and 42.1% were stage IV at their initial diagnosis. The median duration of follow‐up from the initiation of first‐line treatment was 24 months. Rates of disease response (complete or partial per treating physician) were 69.9% in the EU group and 48.8% in the LU group. The five most commonly reported adverse events during eribulin were as follows: fatigue (65.1%), weakness (40.1%), decreased appetite (32.5%), neutropenia (31.0%), and leukopenia (27.4%). Discontinuation of eribulin due to AE was observed in 4.0% of patients. Median OS from initiation of eribulin was 23.0 months (95% CI: 18.7‐27.3) among EU and 14.7 (95% CI: 12.6‐16.9) among LU. Conclusion In the real‐world eribulin‐treated mTNBC, patients have more sites of metastatic disease and exposure to greater numbers of prior therapies compared to RCTs. The median OS of 14.7 months among LU patients is consistent with, and slightly longer than the 13.1 months and 14.4 months reported in the EMBRACE and Study 301 clinical trials, respectively.

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

For location-aware applications in wireless sensor networks (WSNs), it is important to ensure that sensor nodes can get correct locations in a hostile WSNs. Sybil attacks, which are vital threats in WSNs, especially in the distributed WSNs. They can forge one or multiple identities to decrease the localization accuracy, or sometimes to collapse the whole localization systems. In this paper, a novel lightweight sybilfree (SF)-APIT algorithm is presented to solve the problem of sybil attacks in APIT localization scheme, which is a popular range-free method and performs at individual node in a purely distributed fashion. The proposed SF-APIT scheme requires minimal overhead for wireless devices and works well based on the received signal strength. Simulations demonstrate that SF-APIT is an effective scheme in detecting and defending against sybil attacks with a high detection rate in distributed wireless localization schemes.

As an extremely complicated task, road detection is of vital importance for the traveling comfort and driving safety. While high-end automobiles are already equipped with road detection function, most mid-range cars can only detect and evaluate road conditions leveraging remodeled or additional hardware devices built on vehicles, thereby constraining the road quality detection. With the growing popularity of smartphones, detections based on built-in sensors emerge. Most detections on built-in sensors, nevertheless, are on the basis of Euclidean distance, thus neglecting the correlation between characteristics in road quality, i.e., the acceleration sensor and gyroscope have obvious fluctuations when the vehicle passes through the larger pothole, and there is a connection between them. In this paper, we propose a novel road detection approach based on Mahalanobis-Taguchi system (MTS), leveraging smartphones for data collection and involving the correlation between characteristics. We develop an application to collect and process the data, and then classify road quality conditions. The experimental test was carried out on city roads in Xi'an, Shaanxi. Experiment results reveal that the road surface conditions, including manhole cover, pothole, and speed bump, can be well differentiated with the method based on MTS. To a certain extent, the strategy of marking road conditions to the navigation map can effectively improve not only driving experience and traveling comfort but also driving safety, thereby providing more supports for the maintenance units.

Elastic fibers, a major component of the extracellular matrix of the skin, are often exposed to ultraviolet (UV) radiation throughout mammalian life. We report on an in vitro study of the alterations in bovine nuchal ligament elastic fibers resulting from continuous UV-A exposure by the use of transmission electron microscopy (TEM), histology, mass spectrometry, and solid state 13C NMR methodologies. TEM images reveal distinct cracks in elastic fibers as a result of UV-A irradiation and histological measurements show a disruption in the regular array of elastic fibers present in unirradiated samples; elastic fibers appear shorter, highly fragmented, and thinner after UV-A treatment. Magic angle spinning 13C NMR was applied to investigate possible secondary structural changes or dynamics in the irradiated samples; our spectra reveal no differences between UV-A irradiated and non-irradiated samples. Lastly, MALDI mass spectrometry indicates that the concentration of desmosine, which forms cross-links in elastin, is observed to decrease by 11 % following 9 days of continuous UV-A irradiation, in comparison to unirradiated samples. These alterations presumably play a significant role in the loss of elasticity observed in UV exposed skin.

Network intrusion detection is a relatively mature research topic, but one that remains challenging particular as technologies and threat landscape evolve. Here, a semi-supervised tri-Adaboost (STA) algorithm is proposed. In the algorithm, three different Adaboost algorithms are used as the weak classifiers (both for continuous and categorical data), constituting the decision stumps in the tri-training method. In addition, the chi-square method is used to reduce the dimension of feature and improve computational efficiency. We then conduct extensive numerical studies using different training and testing samples in the KDDcup99 dataset and discover the flows demonstrated that (1) high accuracy can be obtained using a training dataset which consists of a small number of labeled and a large number of unlabeled samples. (2) The algorithm proposed is reproducible and consistent over different runs. (3) The proposed algorithm outperforms other existing learning algorithms, even with only a small amount of labeled data in the training phase. (4) The proposed algorithm has a short execution time and a low false positive rate, while providing a desirable detection rate.

In this work, quinoa straw (QS) is considered as a sustainable biomass resource to produce adsorbent materials for wastewater treatment. Two materials, a porous carbon material derived from QS (PCQS) and a Fe _3 O _4 -containing composite material based on the PCPS (Fe _3 O _4 @PCQS), were prepared. PCQS was prepared via carbonization and subsequent chemical activation of the QS using NaOH. Thereafter, PCQS was characterized by SEM, TEM, XRD, IR, XPS, and N _2 adsorption-desorption analysis. As a carbon material with heterogeneous pores, PCQS has a BET specific surface area of 3435.21 m ^2 g ^−1 , which is about 175 times higher than that of the precursor QS (19.60 m ^2 g ^−1 ). The PCQS had an adsorption capacity of 1778.1 mg g ^−1 toward rhodamine B (RhB), and the adsorption followed pseudo-second-order kinetics and the Freundlich isotherm model. The PCQS was further modified by synthesizing Fe _3 O _4 magnetic nanoparticles on the surface of PCQS to give Fe _3 O _4 @PCQS. The adsorption capacity of Fe _3 O _4 @PCQS toward RhB reached 1156.2 mg g ^−1 , and it could be rapidly separated from water by applying an external magnetic field. The PCQS and Fe _3 O _4 @PCQS exhibited acceptable reusability which was evaluated through ten successive adsorption/desorption cycles. In summary, the adsorption capacities of PCQS and Fe _3 O _4 @PCQS toward RhB are comparable with most current adsorbents, including the graphene-based materials, which shows that QS is a promising biomass feedstock to prepare carbon-based materials and composites.

One of the most significant researches in location-based services is the development of effective indoor localization. In this work, we propose a novel model of fingerprint localization, which divides location area into different subareas by fuzzy C-means and calculates location via relative distance fuzzy localization. In offline training stage, fuzzy C-means algorithm is used in localization model to divide localization area into different subareas and then to select the useful access points in subareas to reduce the dimensions of fingerprint. In online location stage, we use the nearest neighbor algorithm to select the subareas and to calculate the coordinate of the target point according to relative distance fuzzy localization algorithm, which converts traditional fingerprint of reference points into distance fingerprint and calculates the coordinate of the target point by fuzzy C-means algorithm. The noise and non-linear attenuation between the wireless signal and distance are taken into full consideration in relative distance fuzzy localization algorithm, which eliminates the random environmental noise. Experiments show that our proposed model is able to save the calculation time and improve the localization accuracy.

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important. Although some algorithms using both node attributes and network structure information have been proposed in recent years, the complex hierarchical coupling relationships within and between attributes, nodes and network structure have not been considered. Such hierarchical couplings are driving factors in community formation. This paper introduces a novel coupled node similarity (CNS) to involve and learn attribute and structure couplings and compute the similarity within and between nodes with categorical attributes in a network. CNS learns and integrates the frequency-based intra-attribute coupled similarity within an attribute, the co-occurrence-based inter-attribute coupled similarity between attributes, and coupled attribute-to-structure similarity based on the homophily property. CNS is then used to generate the weights of edges and transfer a plain graph to a weighted graph. Clustering algorithms detect community structures that are topologically well-connected and semantically coherent on the weighted graphs. Extensive experiments verify the effectiveness of CNS-based community detection algorithms on several data sets by comparing with the state-of-the-art node similarity measures, whether they involve node attribute information and hierarchical interactions, and on various levels of network structure complexity.

Cluster of differentiation (CD)4+CD25+ regulatory T cells (Tregs) exert a suppressive activity on atherosclerosis, but the underlying mechanism remains unclear. Here, we investigated whether and how Tregs affect macrophages foam-cell formation. Tregs were isolated by magnetic cell sorting-column and analyzed by flow cytometry. Macrophages were cultured with or without Tregs in the presence of oxidized LDL (oxLDL) for 48 h to transform foam cells. After co-culture with Tregs, macrophages showed a decrease in lipid accumulation, which was accompanied by a significantly downregulated expression of CD36 and SRA but no obvious difference in ABCA1 expression. Tregs can inhibit the proinflammatory properties of macrophages and steer macrophage differentiation toward an anti-inflammatory cytokine producing phenotype. Mechanistic studies reveal that both cell-to-cell contact and soluble factors are required for Treg-mediated suppression on macrophage foam-cell formation. Cytokines, interleukin-10 (IL-10), and transforming growth factor-β (TGF-β) are the key factors for these suppressive functions.

Background. A recent study has reported that high circulating 25-hydroxyvitamin D [25(OH)D] is associated with low circulating thyroid-stimulating hormone (TSH) levels, but only in younger individuals. The goal of the present study was to explore the relationship between vitamin D status and circulating TSH levels with thyroid autoimmunity and thyroid hormone levels taken into consideration in a population-based health survey of middle-aged and elderly individuals. Methods. A total of 1,424 Chinese adults, aged 41–78 years, were enrolled in this cross-sectional study. Serum levels of 25(OH)D, TSH, thyroid hormones, and thyroid autoantibodies were measured. Results. The prevalence of vitamin D insufficiency was 94.29% in males and 97.22% in females, and the prevalence of vitamin D deficiency was 55.61% in males and 69.64% in females. Vitamin D status was not associated with positive thyroid autoantibodies after controlling for age, gender, body mass index, and smoking status. Higher 25(OH)D levels were associated with lower TSH levels after controlling for age, FT4 and FT3 levels, thyroid volume, the presence of thyroid nodule(s), and smoking status in males. Conclusion. High vitamin D status in middle-aged and elderly males was associated with low circulating TSH levels independent of thyroid hormone levels.

Purpose. To determine the relationship between Hashimoto’s thyroiditis (HT) and all stages of papillary thyroid carcinoma (PTC) with or without local lymph node metastasis (LNM). Methods. We conducted a retrospective study of thyroidectomies from 2008–2013 in First Affiliated Hospital of Nanjing Medical University. We categorized patients according to the presence of histopathologically proven HT. The prevalence of mPTC (maximum diameter ≤ 10 mm) and crPTC (clinical relevant PTC) and local LNM rates were compared. Results. We evaluated 6,432 consecutive thyroidectomies. In total, 1,328 specimens were confirmed as HT. The prevalence of PTC in this HT cohort was 43.8%, significantly higher than non-HT group. After adjustment of gender and age, the prevalence of PTC was still higher in HT group. HT was a risk factor for PTC in multivariate analysis with odds ratio 2.725 (95% CI, 2.390–3.109) (P

The relationship between obesity and cardiovascular disease (CVD) remains unclear. This study aims to describe the relationship between arterial stiffness and obesity in order to investigate the effects of obesity on CVD.We collected data from 5,158 individuals over 40 years of age from a cross-sectional study in Nanjing, China. Anthropometric, demographic, hemodynamic measurements and arterial stiffness measured through brachial-ankle pulse wave velocity (baPWV) were obtained. Subjects were grouped by body mass index (BMI), waist circumference (WC) and visceral adiposity index (VAI), a sex-specific index based on BMI, WC, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C).The multivariate regression analysis revealed a negative but weak effect of BMI (β = -0.047, P0.05), it was still obtained between baPWV and VAI quartile (P0.05). However, baPWV significantly increased across groups with higher VAI categories even in the same metabolic category (P

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods.

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

Aim. We investigated the relationship between brachial-ankle pulse wave velocity (baPWV) and glucose levels, insulin sensitivity, and beta-cell function in Chinese individuals with or without hypertension. Methods. We recruited 3137 nondiabetic individuals whose age, body mass index (BMI), glucose levels, blood pressure (BP), lipids, hemoglobin A1C (HbA1c), baPWV, and insulin levels were measured. Results. In normotensive group, 2 h glucose levels (β=0.046, P

Size-controllable monodisperse CdSe nanocrystals with different organic capping were prepared based on the hot-injection method. The effective separation of nucleation and growth was achieved by rapidly mixing two highly reactive precursors. As a contrast, we prepared CdSe/CdS nanocrystals (NCs) successfully based on the selective ion layer adsorption and reaction (SILAR) technique. This inorganic capping obtained higher photoluminescence quantum yield (PLQY) of 59.3% compared with organic capping of 40.8%. Furthermore, the CdSe-epoxy resin (EP) composites were prepared by adopting a flexible ex situ method, and showed excellent stability in the ambient environment for one year. So the composites with both high PLQY of nanocrystals and excellent stability are very promising to device application.