Search

Abstract Transgenic mice expressing human major histocompatibility complex class II (MHCII) risk alleles are widely used in autoimmune disease research, but limitations arise due to non‐physiologic expression. To address this, physiologically relevant mouse models are established via knock‐in technology to explore the role of MHCII in diseases like rheumatoid arthritis. The gene sequences encoding the ectodomains are replaced with the human DRB1*04:01 and 04:02 alleles, DRA, and CD74 (invariant chain) in C57BL/6N mice. The collagen type II (Col2a1) gene is modified to mimic human COL2. Importantly, DRB1*04:01 knock‐in mice display physiologic expression of human MHCII also on thymic epithelial cells, in contrast to DRB1*04:01 transgenic mice. Humanization of the invariant chain enhances MHCII expression on thymic epithelial cells, increases mature B cell numbers in spleen, and improves antigen presentation. To validate its functionality, the collagen‐induced arthritis (CIA) model is used, where DRB1*04:01 expression led to a higher susceptibility to arthritis, as compared with mice expressing DRB1*04:02. In addition, the humanized T cell epitope on COL2 allows autoreactive T cell‐mediated arthritis development. In conclusion, the humanized knock‐in mouse faithfully expresses MHCII, confirming the DRB1*04:01 alleles role in rheumatoid arthritis and being also useful for studying MHCII‐associated diseases.

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

Salidroside (SAL), belonging to a kind of the main active ingredient of Rhodiola rosea, is extensively utilized for anti-hypoxia and prevention of altitude sickness in the plateau region of China. However, the research on the systemic changes induced by SAL at intracellular protein level is still limited, especially at protein phosphorylation level. These limitations hinder a comprehensive understanding of the regulatory mechanisms of SAL. This study aimed to investigate the potential molecular mechanism of SAL in ameliorating the acute myocardial hypoxia induced by cobalt chloride using integrated proteomics and phosphoproteomics. We successfully identified 165 differentially expressed proteins and 266 differentially expressed phosphosites in H9c2 cells following SAL treatment under hypoxic conditions. Bioinformatics analysis and biological experiment validation revealed that SAL significantly antagonized CoCl2-mediated cell cycle arrest by downregulating CCND1 expression and upregulating AURKA, AURKAB, CCND3 and PLK1 expression. Additionally, SAL can stabilize the cytoskeleton through upregulating the Kinesin Family (KIF) members expression. Our study systematically revealed that SAL had the ability to protect myocardial cells against CoCl2-induced hypoxia through multiple biological pathways, including enhancing the spindle stability, maintaining the cell cycle, relieving DNA damage, and antagonizing cell apoptosis. This study supplies a comprehension perspective on the alterations at protein and protein phosphorylation levels induced by SAL treatment, thereby expanded our knowledge of the anti-hypoxic mechanisms of SAL. Moreover, this study provides a valuable resource for further investigating the effects of SAL.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model’s optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

Abstract Rheumatoid arthritis (RA) involves several classes of pathogenic autoantibodies, some of which react with type-II collagen (COL2) in articular cartilage. We previously described a subset of COL2 antibodies targeting the F4 epitope (ERGLKGHRGFT) that could be regulatory. Here, using phage display, we developed recombinant antibodies against this epitope and examined the underlying mechanism of action. One of these antibodies, R69-4, protected against cartilage antibody- and collagen-induced arthritis in mice, but not autoimmune disease models independent of arthritogenic autoantibodies. R69-4 was further shown to cross-react with a large range of proteins within the inflamed synovial fluid, such as the complement protein C1q. Complexed R69-4 inhibited neutrophil FCGR3 signaling, thereby impairing downstream IL-1β secretion and neutrophil self-orchestrated recruitment. Likewise, human isotypes of R69-4 protected against arthritis with comparable efficiency. We conclude that R69-4 abrogates autoantibody-mediated arthritis mainly by hindering FCGR3 signaling, highlighting its potential clinical utility in acute RA.

This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on barrier Lyapunov function. Considering practical limitations such as communication distance and bandwidth constraints during operation, not all trains can directly communicate with the leader and obtain the expected trajectory it sends, making it difficult to maintain formation consistency as per the predetermined ideal state. Furthermore, to address the challenge of unknown external disturbances encountered by trains during operation, this paper designs a distributed observer deployed on each train in the formation. This observer can estimate and dynamically compensate for unknown reference trajectories and disturbances solely based on the states of adjacent trains. Additionally, to ensure that the spacing between adjacent trains remains within a predefined range, a safety hard constraint, this paper encodes the spacing hard constraint using barrier Lyapunov function. By integrating nonlinear adaptive control theory to handle model parameter uncertainties, a barrier Lyapunov function-based adaptive control method is proposed, which enables all trains to track the reference trajectory while ensuring that the spacing between them remains within the preset interval, therefore guaranteeing the asymptotic stability of the closed-loop system. Finally, a practical example using data from the Guangzhou Metro Line 22, specifically the route from Shiguang Road Station to Chentougang Station over three stations and two sections, is utilized to validate the effectiveness and robustness of the proposed algorithm.

Song singing interpretation is one of the people’s favorite entertainment pastimes, but there are some abnormal problems in the process of song singing. Traditional song singing problems need to be analyzed and judged by professionals, but the traditional way requires offline face-to-face teaching and is time-consuming and laborious. In this paper, we hope to realize the automatic judgment classification of abnormal vocal problems of song singing and provide some guidance help to online teaching. In this paper, we propose a deep learning-based method for classifying abnormal vocal interpretation problems in music songs, using a computer to record the singers’ voices, and then analyzing and judging them with a trained method model to point out the main problems that exist in the process of singing songs. In this paper, more than 300 singers’ audio were collected and the data were calibrated and classified by researchers specialized in the music field into seven main categories. Short-time Fourier transform (STFT), Mel frequency cepstrum coefficient (MFCC) and spectral mass center methods were used to extract the features of song audio and produce the corresponding datasets. The dataset is trained using residual neural network and EfficientNet. The experimental results of the model in this paper show that the data training accuracy is about 90.1%, which achieves a good result.

Schematic diagrams of SD-PQLED based on light interaction layer and light-emitting layer and proof-of-concept diagram of the light interactive display by a four-pixel SD-PQLEDs.

Abstract Background Primary generalized glucocorticoid hypersensitivity (PGGH) is a very rare disease caused by terminal organ hypersensitivity to glucocorticoids for which the aetiology is unknown. The incidence of PGGH is extremely rare, especially in children. To date, the literatures about the etiology, prognosis and treatment of PGGH are scarce. Aim of the study is describing the cases of two Chinese children with infantile-onset PGGH in one family, one of whom died and one who was treated with mifepristone. They are the two youngest children with PGGH reported in the literature. Case presentation Two siblings with infantile-onset PGGH were affected in this family. The main manifestations of patient 1 were typical Cushing’s syndrome-like manifestations, significantly aggravated symptoms after physiological doses of glucocorticoids and very low levels of serum cortisol and adrenocorticotropin hormone (ACTH) during attacks. After being diagnosed with PGGH, he was given guidance to avoid glucocorticoids and took mifepristone therapy for 5 months, and his symptoms improved. Patient 2 was the younger brother of patient 1, with similar manifestations to his brother at the age of 4 months. Patient 2 ultimately died at the age of 9 months. Conclusion PGGH is a very rare disease that can lead to death if not diagnosed and treated in a timely manner. This article describes the cases of the two youngest children with PGGH reported in the literature, one of whom improved after mifepristone treatment, and increases the knowledge of the clinical manifestations of and the treatment experience in PGGH.

Repurposing clinically approved drugs to construct novel nanomedicines is currently a very attractive therapeutic approach. Selective enrichment of anti-inflammatory drugs and reactive oxygen species (ROS) scavenging at the region of inflammation by stimuli-responsive oral nanomedicine is an effective strategy for the treatment of inflammatory bowel disease (IBD). This study reports a novel nanomedicine, which is based on the excellent drug loading and free radical scavenging ability of mesoporous polydopamine nanoparticles (MPDA NPs). By initiating polyacrylic acid（PAA）polymerization on its surface, a “core-shell” structure nano-carrier with pH response is constructed. Then, under alkaline conditions, using the π-π stacking and hydrophobic interaction between the anti-inflammatory drug sulfasalazine (SAP) and MPDA, the nanomedicines (PAA@MPDA-SAP NPs) loaded efficiently (928 μ g mg−1) of SAP was successfully formed. Our results reveal that PAA@MPDA-SAP NPs can pass through the upper digestive tract smoothly and finally accumulate in the inflamed colon. Through the synergistic effect of anti-inflammation and antioxidation, it can effectively reduce the expression of pro-inflammatory factors and enhance the intestinal mucosal barrier, and finally significantly alleviate the symptoms of colitis in mice. Furthermore, we confirmed that PAA@MPDA-SAP NPs have good biocompatibility and anti-inflammatory repair ability under inflammation induction through human colonic organoids. In summary, this work provides a theoretical basis for the development of nanomedicines for IBD therapy.

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily utilize vectorized monitoring data, interpreted either through vector-based models or distance-based measurements. However, these methods exhibit limited interpretability or are heavily reliant on standard curves, which impairs their performance or restricts their generalizability. To address these limitations, a railway turnouts fault diagnosis method with monitoring signal images and support matrix machine is proposed herein. In addition, a pinball loss-based multiclass support matrix machine (PL-MSMM) is designed to address the noise sensitivity limitations of the multiclass support matrix machine (MSMM). First, the time-series monitoring signals in one dimension are transformed into images in two dimensions. Subsequently, the image-based feature matrix is constructed. Then, the PL-MSMM model is trained using the feature matrix to facilitate the fault diagnosis. The proposed method is evaluated using a real-world operational current dataset, achieving a fault identification accuracy rate of 98.67%. This method outperforms the existing method in terms of accuracy, precision, and F1-score, demonstrating its superiority.

As the amount of data generated by marine acoustic observation signals grows, efficient information acquisition of non-stationary observation signals has become a major challenge in marine observation platform technology. In this paper, an optimized algorithm is proposed for the non-stationary marine acoustic signals. This algorithm can increase the effective data acquisition rate while decreasing the observation platform’s algorithm energy consumption. To constantly enhance the processing of the observation signal through the self-feedback, the optimized algorithm is based on the sign function, the adjustable coefficient, the adaptive step size, and the frequency domain threshold. The simulation verification experiment and the application experiment based on the optimized algorithm are shown in this study. The experimental results indicate that the optimized algorithm efficiency is 78.16% in the simulation conditions and reaches 89.89% in the application experiment. And the data compression rates for the simulation conditions and the application experiment are 74.65% and 69.32%, respectively. As a result, the optimized algorithm’s performance has significantly improved.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

Abstract Background In isolated premature thelarche (IPT) girls, bone age (BA) is considered consistent with chronological age. However, some IPT girls confirmed by gonadotropin-releasing hormone (GnRH) stimulation test could show another trend. We analysed BA and possible potentiating factors in a selected group of girls aged 4–8 years with IPT. Methods IPT girls confirmed by GnRH stimulation test aged 4–8 years hospitalized from January 2015 to April 2018 at Shenzhen Children’s Hospital were included in this retrospective study. They were divided into two groups with advanced BA of 2 years as the cut-off. Body mass index (BMI) and hormone levels were the main outcome measures, and regression analysis was used to identify independent risk factors. IPT girls were divided into subgroups according to the levels of BMI standard deviation score (SDS), insulin-like growth factor-1 (IGF-1) SDS and dehydroepiandrosterone sulfate (DHEAS) SDS for comparisons of advanced BA. Results Overall, 423 subjects were included and classified into the advanced BA group (48.7%, n = 206) and control group (51.3%, n = 217). The advanced BA group had significantly higher BMI SDS, serum DHEAS SDS, IGF-1 SDS, androstenedione and fasting insulin and significantly lower sex hormone binding globulin (all p

In the title compound, [NiFe2(C5H5)(C9H5F3O2)2(C2H6O)2], the central NiII ion is observed in an octahedral coordination environment. The chelating β-diketonate ligands are substituted by ferrocene, a lipophilic organometallic moiety. The ferrocene groups have the normal geometry, with eclipsed cyclopentadiene rings. Coordinated ethanol molecules are engaged in intermolecular hydrogen bonds, and the crystal is further stabilized by weak C—H...F and C—H...π contacts.

Anti-counterfeiting technologies should ideally be unclonable, yet simple to fabricate and decode. Here, the authors develop an inkjet-printable and unclonable security label based on random patterning of quantum dot inks, and accompany it with an artificial intelligence decoding mechanism capable of authenticating the patterns.

Cancer is one of the leading causes of death worldwide. Recent advances in cellular therapy have demonstrated that this platform has the potential to give patients with certain cancers a second chance at life. Unlike chemical compounds and proteins, cells are living, self-replicating drugs that can be engineered to possess exquisite specificity. For example, T cells can be genetically modified to express chimeric antigen receptors (CARs), endowing them with the capacity to recognize and kill tumor cells and form a memory pool that is ready to strike back against persisting malignant cells. Anti-CD19 chimeric antigen receptor T cells (CART19s) have demonstrated a remarkable degree of clinical efficacy for certain malignancies. The process of developing CART19 essentially follows the conventional “one gene, one drug, one disease” paradigm derived from Paul Ehrlich’s “magic bullet” concept. With major players within the pharmaceutical industry joining forces to commercialize this new category of “living drugs,” it is useful to use CART19 as an example to examine the similarities and differences in its development, compared with that of a conventional drug. In this way, we can assimilate existing knowledge and identify the most effective approach for advancing similar strategies. This article reviews the use of biomarker-based assays to guide the optimization of CAR constructs, preclinical studies, and the evaluation of clinical efficacy; adverse effects (AEs); and CART19 cellular kinetics. Advanced technologies and computational tools that enable the discovery of optimal targets, novel CAR binding domains, and biomarkers predicting clinical response and AEs are also discussed. We believe that the success of CART19 will lead to the development of other engineered T cell therapies in the same manner that the discovery of arsphenamine initiated the era of synthetic pharmaceuticals. Keywords: Engineered T cell therapies, Chimeric antigen receptor, Drug development process, Biomarkers, CD19-specific chimeric antigen receptor, Anti-CD19 chimeric antigen receptor T cells

With a steadily growing number of vehicles, our roads are getting more and more crowded. As a consequence, traffic jams are becoming common. Vehicle platoon systems form a possible solution in the short term. It consists of a number of vehicles automatically following a leader vehicle, in-line, one after another at a short but safe distance. Ideally, all vehicles have to maintain the same speed, so as to have a better usage of the road by minimizing the distance between two vehicles. In this paper we present a timed automata model of a vehicle platoon system with the goal of finding a minimal but guaranteed safe distance between two vehicles under variable speed. Contrary to other models based on cooperative adaptive cruise control, we assume no (Internet) communication among different vehicles or road system. Instead of such global perspective we rather take a local point of view: each vehicle relies on its own sensors to dynamically calculate and maintain a safe distance with the preceding member of the platoon. We use the model checker UPPAAL to verify that the system does not deadlock, and most importantly, that it is safe, avoiding crashes at all time.

In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.

Summary: Ubiquitin chain specificity has been described for some deubiquitinases (DUBs) but lacks a comprehensive profiling in vivo. We used quantitative proteomics to compare the seven lysine-linked ubiquitin chains between wild-type yeast and its 20 DUB-deletion strains, which may reflect the linkage specificity of DUBs in vivo. Utilizing the specificity and ubiquitination heterogeneity, we developed a method termed DUB-mediated identification of linkage-specific ubiquitinated substrates (DILUS) to screen the ubiquitinated lysine residues on substrates modified with certain chains and regulated by specific DUB. Then we were able to identify 166 Ubp2-regulating substrates with 244 sites potentially modified with K63-linked chains. Among these substrates, we further demonstrated that cyclophilin A (Cpr1) modified with K63-linked chain on K151 site was regulated by Ubp2 and mediated the nuclear translocation of zinc finger protein Zpr1. The K48-linked chains at non-K151 sites of Cpr1 were mainly regulated by Ubp3 and served as canonical signals for proteasome-mediated degradation. : Molecular Biology; Omics Subject Areas: Molecular Biology, Omics

The Boolean Satisfiability problem (SAT) is a prototypical NP-complete problem, which has been widely studied due to its significant importance in both theory and applications. Stochastic local search (SLS) algorithms are among the most efficient approximate methods available for solving certain types of SAT instances. The quantitative configuration checking (QCC) heuristic is an effective approach for improving SLS algorithms on solving the SAT problem, resulting in an efficient SLS solver for SAT named Swqcc. In this paper, we focus on combining the QCC heuristic with an aspiration mechanism, and then design a new heuristic called QCCA. On the top of Swqcc, we utilize the QCCA heuristic to develop a new SLS solver dubbed AspiSAT. Through extensive experiments, the results illustrate that, on random 3-SAT instances, the performance of AspiSAT is much better than that of Swqcc and Sparrow, which is an influential and efficient SLS solver for SAT. In addition, we further enhance the original clause weighting schemes employed in Swqcc and AspiSAT, and thus obtain two new SLS solvers called Ptwqcc and AspiPT, respectively. The eperimental results present that both Ptwqcc and AspiPT outperform Swqcc and AspiSAT on random 5-SAT instances, indicating that both QCC and QCCA heuristics are able to cooperate effectively with different clause weighting schemes.

Because of tumour dependence on angiogenesis, anti-angiogenic therapy has become the most attractive area of basic and clinical study in the field of cancer research. In order to create a synergistic effect on angiogenesis and immune regulation, we designed and constructed a new type of DNA vaccine that can express VEGFR2 (vascular endothelial growth factor receptor 2) and the prostate cancer antigen IL-12 (interleukin 12) in the same reading frame. The aim of this study was to investigate the anti-tumour activity of a eukaryotic expression plasmid carrying a fusion gene of human VEGFR2 and IL-12. According to the gene sequences in GenBank, we synthesized the human VEGFR2 and IL-12 genes. VEGFR2 and IL-12 were joined by a sequence encoding a Furin recognition site and a 2A cleavage site, and the resulting fusion gene was cloned into the eukaryotic expression vector pVAX1 to construct the expression plasmid pVAX1-VEGFR2-F2A-IL-12. The expression of VEGFR2 and IL-12 could be detected in 293T cells transfected with pVAX1-VEGFR2-F2A-IL-12 by enzyme-linked immunosorbent assay. Each of these proteins, and in particular co-expression of both proteins, can result in humoral and cellular immune responses in C57BL/6 mice. After injection into the tumour-bearing mouse model, the plasmid showed stronger inhibition of tumour growth than a plasmid expressing VEGFR2 alone. Our results demonstrate that a DNA vaccine carrying a fusion gene of human VEGFR2 and IL-12 could represent a promising approach for tumour immunotherapy.

Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data's limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDE-COMP/PARAFAC (CP) decomposition method. Then, the tensor learning-based model is built using the extracted fusion feature tensor. The developed fault diagnosis scheme is valid with the field three-phase current dataset. The experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach, particularly in terms of recall, precision, and F1-score. [ABSTRACT FROM AUTHOR]