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BackgroundThe GSDM family includes six members, GSDMA, GSDMB, GSDMC, GSDMD, GSDME (DFNA5), and PJVK (Pejvakin, DFNB59), which can induce pyroptosis, thereby regulating the tumorigenesis of various cancers. However, the clinical characteristics and role of the GSDM family in LUAD are not well understood.MethodsIn this study, several important bioinformatics databases were used to integrate the analysis of the expression, prognostic value, and immune infiltration of GSDMs in LUAD. These databases include UALCAN, DiseaseMeth, GEPIA, THPA, cBioPortal, TIMER, WebGestalt, STRING database, and Cytoscape.ResultsThe findings from the UALCAN database revealed that the expression of all six GSDMs based on the tumor stage in LUAD was increased (particularly GSDMD). Our IHC results verified it. Additionally, the DiseaseMeth database showed that the methylation levels of GSDMA, GSDMB, GSDMC, and GSDMD were decreased. The expression of six GSDMs was related to shorter overall survival in patients with LUAD, according to the GEPIA database. The cBioPortal database was further used to explore the alteration rate and correlated genes in LUAD. Subsequently, these genes were subjected to functional enrichment and protein-protein interaction network analyses. We identified that the GSDM family regulate several signaling pathways, including immune-associated signaling pathways. According to tumor-infiltrating immune cell analysis from the TIMER database, GSDM family members are associated with the infiltration of important immune cells and their signature markers.ConclusionsGSDM family may be prognostic markers and novel strategies for the treatment of LUAD.

BackgroundThe purpose of this study was to define and analyze the characteristics of the top 100 most-cited articles and reviews on the topic of pheochromocytomas and paragangliomas (PPGLs) by using bibliometric methods.MethodsWe explored the Web of Science Core Collection database to gather the 100 top-cited original articles and reviews of PPGL from 1985 to 20 December 2020. We conducted a bibliometric study to identify the most influential journals, authors, countries, and institutions in the PPGL field.ResultsThe 100 top-cited papers were cited a total number of 25,723 times, ranging from 131 to 1,144 (mean, 257.23 ± 173.64). All of these 100 top-cited papers were published between 1999 and 2017, and the number of top-cited papers published before 2008 (1999–2008) was significantly higher than that after 2008 (2009–2017) (p = 0.043). The journal with the highest number of published papers is the Journal of Clinical Endocrinology & Metabolism (n = 23). The United States was the most productive country in this topic, which published about half of these publications (n = 51). The National Institutes of Health (NIH) had the largest number of publications (n = 17). Genes or genetics is still the hottest topic in the field of PPGLs.ConclusionsWe defined and analyzed the top 100 most-cited papers in the field of PPGLs by gathering detailed information. These data provided insights into the most influential studies related to PPGL. We hoped to inspire researchers and readers in this field to improve their understanding of PPGL research trends and provide ideas for future research from unique perspectives.

Preeclampsia (PE) is a pregnancy complication characterized by severe hypertension and multiple organ damage. Gut microbiota has been linked to PE by previous amplicon sequencing studies. To resolve the PE gut microbiota in a higher taxonomy resolution, we performed shotgun metagenomic sequencing on the fecal samples from 40 early-onset PE and 37 healthy pregnant women. We recovered 1,750 metagenome-assembled genomes (representing 406 species) from the metagenomic dataset and profiled their abundances. We found that PE gut microbiota had enriched in some species belonging to Blautia, Pauljensenia, Ruminococcus, and Collinsella and microbial functions such as the bacitracin/lantibiotics transport system, maltooligosaccharide transport system, multidrug efflux pump, and rhamnose transport system. Conversely, the gut microbiome of healthy pregnant women was enriched in species of Bacteroides and Phocaeicola and microbial functions including the porphyrin and chlorophyll metabolism, pyridoxal-P biosynthesis, riboflavin metabolism, and folate biosynthesis pathway. PE diagnostic potential of gut microbial biomarkers was developed using both species and function profile data. These results will help to explore the relationships between gut bacteria and PE and provide new insights into PE early warning.

Knowledge graphs (KGs) serve as a crucial resource for numerous artificial intelligence tasks, significantly contributing to the advancement of the AI field. However, the incompleteness of existing KGs hinders their effectiveness in practical applications. Consequently, researchers have proposed the task of KG completion. Currently, embedding-based techniques dominate the field as they leverage the structural information within KGs to infer and complete missing parts. Nonetheless, these methods exhibit limitations. They are limited by the quality and quantity of structural information and are unable to handle the missing entities in the original KG. To overcome these challenges, researchers have attempted to integrate pretrained language models and textual data to perform KG completion. This approach utilizes the definition statements and description text of entities within KGs. The goal is to compensate for the latent connections that are difficult for traditional methods to obtain. However, text-based methods still lag behind embedding-based models in terms of performance. Our analysis reveals that the critical issue lies in the selection process of negative samples. In order to enhance the performance of the text-based methods, various types of negative sampling methods are employed in this study. We introduced prompt learning to fill the gap between the pre-training language model and the knowledge graph completion task, and to improve the model reasoning level. Simultaneously, a ranking strategy based on KG structural information is proposed to utilize KG structured data to assist reasoning. The experiment results demonstrate that our model exhibits strong competitiveness and outstanding inference speed. By fully exploiting the internal structural information of KGs and external relevant descriptive text resources, we successfully elevate the performance levels of KG completion tasks across various metrics.

Abstract Person reidentification rate has become a challenging research topic in the field of computer vision due to the fact that person appearance is easily affected by lighting, posture and perspective. In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential equation with the graph convolution network is proposed in this paper. First, a continuous time model is constructed by using the ordinary differential equation (ODE) network so as to capture hidden information between video frames. By simulating the hidden space of the hidden variables with the hidden time series model, the hidden information between frames that may be ignored in the discrete model can be obtained. Then, the features of the generated video frames are given to the graph convolution network to reconstruct them. Finally, weak supervision is used to classify the features. Experiments on PRID2011 datasets show that the proposed algorithm can significantly improve person reidentification performance.

Disaster monitoring is a primary task for wireless sensor networks. Systems for the rapid reporting of earthquake information are a crucial aspect of disaster monitoring. Furthermore, during emergency rescue after a large earthquake, wireless sensor networks can provide pictures and sound information to save lives. Therefore, when accompanied by multimedia data flow, the alert and seismic data sent by the seismic monitoring nodes must be sufficiently fast. We present herein the architecture of a collaborative disaster-monitoring system that can obtain seismic data in a highly energy-efficient manner. In this paper, a hybrid superior node token ring MAC scheme is proposed for disaster monitoring in wireless sensor networks. This scheme consists of set-up and steady-state stages. A clustering approach was proposed for heterogeneous networks during the set-up stage. The proposed MAC operates in the duty cycle mode at the steady-state stage and is based on the virtual token ring of ordinary nodes, the polling all the superior nodes in one period, and alert transmissions with a low-power listening and shortened preamble approach during the sleep state. The proposed scheme can simultaneously satisfy the requirements of three types of data in disaster-monitoring applications. Based on embedded Markov chains, a model of the proposed MAC was developed and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained. Using simulations under various conditions, the clustering approach performed better than the pLEACH approach, and the theoretical results of the proposed MAC were verified. We found that alerts and superior data have outstanding delay and throughput performances even under heavy traffic intensity, and the proposed MAC can provide a data rate of several hundred kb/s for superior and ordinary data. Considering all three types of data, the frame delay performances of the proposed MAC are better than those of the WirelessHART and DRX schemes, and the alert data of the proposed MAC have a maximum frame delay of 15 ms. These satisfy the application requirements of disaster monitoring.

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities. [ABSTRACT FROM AUTHOR]

Using citric acid, ethylene glycol, ZrOCl2·8H2O, HfOCl2·8H2O, TiCl4, NbCl5, and TaCl5 as raw materials, the (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C high‐entropy ceramic precursor was prepared by Pechini coordination polymerization method. The precursor and pyrolysis products were analyzed and characterized by different methods. The results showed that the precursor formed a chelate structure, which greatly improved the stability of the precursor, and was clear and transparent with moderate viscosity (30–50 mPa·s). At a low pyrolysis temperature of 1600°C, single‐phase (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C high‐entropy ceramic nanopowders with a ceramic yield of 50.8% can be obtained. The high‐entropy ceramic powder has high purity, a particle size of about 140–200 nm, and uniform element distribution. It is suitable for introduction into fiber‐reinforced ceramic matrix composite by precursor infiltration and pyrolysis (PIP) process. [ABSTRACT FROM AUTHOR]

The recycling of cathode materials for spent NCM has always been a major concern for the energy industry. However, among the current processing methods, the general leaching efficiency of Li is between 85% and 93%, with much room for improvement. The recovery of Ni, Co and Mn requires a high cost of secondary purification. In this study, to recycle the NCM cathode material, a route of sulphated reduction roasting – selective Li water leaching – efficiency acid leaching of Ni, Co, Mn – extraction separation – crystallisation was adopted. The results showed that after roasting (a temperature of 800 °C, a reaction time of 90 min, a carbon content of 26%, and a sulphuric acid addition of nH2SO4:nLi = 0.85), Li water leaching efficiency was 98.6%, followed by acid leaching of Ni, Co and Mn at around 99%. Mn, Co were extracted with Di-(2-ethylhexyl) phosphoric acid and 2-Ethylhexyl phosphonic acid mono-2-ethylhexyl ester respectively to obtain Ni, Co, Mn solutions, which eventually were crystallized for manganese sulphate, cobalt sulphate, lithium carbonate and nickel sulphate products, with high purity of 99.40%, 98.95%, 99.10%, and 99.95%. The results of this study improved the leaching efficiency of Li and were closely linked to the actual industrial preparation of Ni, Co and Mn sulphates, providing a feasible and promising basis for spent NCM cathode materials industrial recovery. [ABSTRACT FROM AUTHOR]

The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems. [ABSTRACT FROM AUTHOR]

Abstract Background Treatment of recurrent retinal detachment (re-RD) following vitrectomy (post-gas/air tamponade and post-silicone oil removal) is challenging. Previously reported treatment is commonly revision pars plana vitrectomy (PPV) combined with tamponade, which is invasive and a burden both economically and emotionally when compared with scleral buckling (SB). The purpose of this study is to report anatomical and functional outcomes of SB with or without gas tamponade in eyes with recurrent retinal detachment (re-RD) that previously underwent PPV at least once. Methods We retrospectively reviewed the medical records of 14 patients (14 eyes) who underwent PPV at least once and were treated with SB after re-RD. Preoperative characteristics, intraoperative complications, and postoperative data were assessed. The final anatomical and functional outcomes were analyzed. Results The original PPV was performed for primary rhegmatogenous retinal detachment in 11 eyes, macular hole retinal detachment in 2 eyes, and myopic foveoschisis in 1 eye. Previously, 3 eyes underwent one PPV with gas tamponade, and the remaining 11 (79%) eyes underwent 2–5 operations. Seven eyes underwent the procedure with gas injection. At the last follow-up, 13 eyes achieved total retinal attachment and 1 eye had re-RD. The postoperative intraocular pressure was within the normal range, except in 1 eye (6 mmHg). The finest postoperative best-corrected visual acuity (BCVA) was 20/25. There was a significant improvement in BCVA from 20/160 ± 20/63 at baseline to 20/80 ± 20/50 at the last visit in the 13 successfully treated eyes (P = 0.025). Conclusions SB can be effective for re-RD after PPV in specific cases.

Abstract Background Osteoporosis is a common phenomenon in HIV patients on tenofovir treatment, but its underlying mechanisms remain to be explored. Methods Quantitative real-time PCR was performed to analyze the expression of miR-302, miR-101, miR-145 and osteoclast-specific genes in the serum of HIV patients treated with tenofovir and ZOL. ELISA was used to evaluate the expression of RANKL, SMAD3 and PRKACB in the serum of these patients. Luciferase assay was carried out to explore the inhibitory effects of miR-302, miR-101 and miR-145 on the expression of PRKACB, RANKL and SMAD3, respectively. Western blot was used to examine the expression of genes involved in NF‑κB and JNK signaling pathways. Results ZOL treatment significantly suppressed the expression of CTx and osteocalcin in HIV patients treated with tenofovir. The BMD loss of HIV patients treated with tenofovir was effectively hindered by ZOL treatment. Mechanistically, the expression of miR-302, miR-101, miR-145, RANKL, SMAD3 and PRKACB in the serum was remarkably activated by ZOL treatment. Luciferase assays showed that miR-302, miR-101 and miR-145 effectively suppressed the expression of PRKACB, RANKL and SMAD3, respectively, through binding to their 3′ UTR. Furthermore, ZOL treatment notably restored the normal expression of osteoclast‑specific genes while activating NF‑κB and JNK signaling pathways. Conclusion The findings of this study demonstrated that administration of ZOL suppressed the expression of RANKL via modulating signaling pathways of miR-101-3p/RANKL, miR-302/PRKACB/RANKL and miR-145/SMAD3/RANKL. Furthermore, down-regulated expression of RANKL by ZOL treatment alleviated osteoporosis in HIV-positive subjects treated with tenofovir.

The triplet-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.

Abstract Background Self-harm and drinking are both serious problems in adolescents and many studies presented evidence of their association. However, gender differences in this association are seldom deeply discussed. Our study aimed to evaluate the prevalence of self-harm and explore its association with drinking behaviors by gender and investigate the extent to which the gender differences exist in the association between self-harm and drinking. Methods A total of 32,362 students in grades 7 to 12 in Beijing, China were anonymously surveyed and included in our study using two-stage, stratified probability proportion sampling. Self-harm, drinking behaviors and other basic information were obtained from an anonymous questionnaire. Demographic variables, self-harm and drinking behaviors were analyzed using the Chi-square test and the Gamma test between genders and the gender differences in this association were analyzed by Log-binomial regression. Results The total prevalence of self-harm was 13.7% with no significant gender difference (χ 2 =0.352, P = 0.553). The prevalence of self-harm in girls decreased with age (G = -0.163, P

The financial risk spreads widely on the financial network and the risk information diffuses broadly on the social networks. How the information diffusion affects the financial risk spreading still lacks mathematical study. This paper proposes a model to describe the coevolution of financial information diffusion and financial risk spreading on a two-layered network. We use a mean-field approach to describe the spreading dynamics and adopt extensive numerical simulations to simulate the dynamics. We find that financial information diffusion can suppress the financial risk generally. Notably, there exists an optimal information diffusion probability at which the financial risk is greatly suppressed. Our results may shed some light on controlling the financial risk spreading dynamics.

Recently, cartilage tissue engineering has become a cornerstone to treat cartilage degeneration and osteoarthritis (OA). Fibronectin1 (FN1) is described as multiple functional glycoproteins that play an essential role in chondrogenic and osteogenic differentiation. Few studies reported the potential of FN1 to enhance tissue engineering and reduce the death of chondrocytes in OA. Further, FN1 possesses multiple binding domains including collagen, integrin, and heparin that can interact with heparan sulfate proteoglycans at the surface of chondrocyte leading to promote cell signaling and differentiation. Recent studies suggested that FN1 can promote chondrocyte differentiation by upregulating TGF-β/PI3K/Akt pathways. Further, FN1 can inhibit the apoptosis of chondrocytes by preventing the release of metalloproteinases through lowering the expression of p-PI3K/PI3K and p-AKT/AKT pathways. However, the use of FN1 in cartilage repair studies using animal models or clinical trials was rarely reported. Therefore, this article provides new insights into the importance of FN1 in cartilage tissue engineering to encourage more studies concerning FN1 in cartilage repair studies. Further, we provided new suggestions for advanced applications of FN1 to treat OA and cartilage degeneration.

In smart systems, attackers can use botnets to launch different cyber attack activities against the Internet of Things. The traditional methods of detecting botnets commonly used machine learning algorithms, and it is difficult to detect and control botnets in a network because of unbalanced traffic data. In this article, we present a novel and highly efficient botnet detection method based on an autoencoder neural network in cooperation with decision trees on a given network. The deep flow inspection method and statistical analysis are first applied as a feature selection technique to select relevant features, which are used to characterize the communication-related behavior between network nodes. Then, the autoencoder neural network for feature selection is used to improve the efficiency of model construction. Finally, Tomek-Recursion Borderline Synthetic Minority Oversampling Technique generates additional minority samples to achieve class balance, and an improved gradient boosting decision tree algorithm is used to train and establish an abnormal traffic detection model to improve the detection of unbalanced botnet data. The results of experiments on the ISCX-botnet traffic dataset show that the proposed method achieved better botnet detection performance with 99.10% recall, 99.20% accuracy, 99.1% F1 score, and 99.0% area under the curve.

Low detection rates of Mycobacterium tuberculosis (MTB) by culture and smear microscopy prevent early diagnosis of tuberculosis (TB) in children. Therefore, developing rapid and accurate diagnostic techniques are critical to achieving the global aim of minimizing childhood TB. The present study was performed to evaluate the diagnostic effectiveness of the novel cross-priming amplification-based EasyNAT MTB complex assay (EasyNAT) in childhood TB. Five hundred and six children with suspected TB were enrolled from January 2018 to October 2021. Gastric aspirate (GA) samples were tested by bacterial culture, acid-fast bacillus microscopy, EasyNAT, Xpert MTB/RIF (Xpert), or Xpert MTB/RIF Ultra (Xpert Ultra). Among 239 children simultaneously tested by EasyNAT and Xpert methods, both assays showed similar sensitivities in total active TB cases [22.6% (31/137) vs. 26.3% (36/137), p = 0.441] and in bacteriologically confirmed TB cases [both 60.0% (9/15)]. The two assays presented similar specificities of 98.0% (100/102) and 99.0% (101/102), respectively (p = 1.000). Among 267 children who were simultaneously tested with EasyNAT and Xpert Ultra, Xpert Ultra demonstrated higher sensitivity than EasyNAT in total active TB cases [50.9% (89/175) vs. 30.3% (53/175), p

Abstract To forecast the spread tendency of the COVID-19 in China and provide effective strategies to prevent the disease, an improved SEIR model was established. The parameters of our model were estimated based on collected data that were issued by the National Health Commission of China (NHCC) from January 10 to March 3. The model was used to forecast the spread tendency of the disease. The key factors influencing the epidemic were explored through modulation of the parameters, including the removal rate, the average number of the infected contacting the susceptible per day and the average number of the exposed contacting the susceptible per day. The correlation of the infected is 99.9% between established model data in this study and issued data by NHCC from January 10 to February 15. The correlation of the removed, the death and the cured are 99.8%, 99.8% and 99.6%, respectively. The average forecasting error rates of the infected, the removed, the death and the cured are 0.78%, 0.75%, 0.35% and 0.83%, respectively, from February 16 to March 3. The peak time of the epidemic forecast by our established model coincided with the issued data by NHCC. Therefore, our study established a mathematical model with high accuracy. The aforementioned parameters significantly affected the trend of the epidemic, suggesting that the exposed and the infected population should be strictly isolated. If the removal rate increases to 0.12, the epidemic will come to an end on May 25. In conclusion, the proposed mathematical model accurately forecast the spread tendency of COVID-19 in China and the model can be applied for other countries with appropriate modifications.

Abstract Cartilage matrix remodelling homeostasis is a crucial factor in maintaining cartilage integrity. Loss of cartilage integrity is a typical characteristic of osteoarthritis (OA). Strategies aimed at maintaining cartilage integrity have attracted considerable attention in the OA research field. Recently, a series of studies have suggested dual functions of microRNA-140 (miR-140) in cartilage matrix remodelling. Here, we discuss the significance of miR-140 in promoting cartilage formation and inhibiting degeneration. Additionally, we focused on the role of miR-140 in the chondrogenesis of mesenchymal stem cells (MSCs). Of note, we carefully reviewed recent advances in MSC exosomes for miRNA delivery in OA treatment.