TLR7 gain-of-function genetic variation causes human lupus

Although circumstantial evidence supports enhanced Toll-like receptor 7 (TLR7) signalling as a mechanism of human systemic autoimmune disease evidence of lupus-causing TLR7 gene variants is lacking. Here we describe human systemic lupus erythematosus caused by a TLR7 gain-of-function variant. TLR7 is a sensor of viral RNA and binds to guanosine. We identified a de novo, previously undescribed missense TLR7Y264H variant in a child with severe lupus and additional variants in other patients with lupus. The TLR7Y264H variant selectively increased sensing of guanosine and 2',3'-cGMP1 and was sufficient to cause lupus when introduced into mice. We show that enhanced TLR7 signalling drives aberrant survival of B cell receptor (BCR)-activated B cells, and in a cell-intrinsic manner, accumulation of CD11c+ age-associated B cells and germinal centre B cells. Follicular and extrafollicular helper T cells were also increased but these phenotypes were cell-extrinsic. Deficiency of MyD88 (an adaptor protein downstream of TLR7) rescued autoimmunity, aberrant B cell survival, and all cellular and serological phenotypes. Despite prominent spontaneous germinal-centre formation in Tlr7Y264H mice, autoimmunity was not ameliorated by germinal-centre deficiency, suggesting an extrafollicular origin of pathogenic B cells. We establish the importance of TLR7 and guanosine-containing self-ligands for human lupus pathogenesis, which paves the way for therapeutic TLR7 or MyD88 inhibition

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Training Quality Evaluation of Innovative and Entrepreneurial Talents for Smart Tourism

 The smart tourism industry is developing at an incredible pace and has posed requirements for persons with related skills and talents, especially those with creative thinking, innovation ability, and entrepreneur quality. The training quality evaluation of innovative and entrepreneurial talents could provide guidance for the decision-making of higher educational institutions and enterprises so that proper adjustments can be made to current education system and curriculum setting. However, little existing research has addressed this issue, so this study attempts to develop a method for evaluating the training quality of innovative and entrepreneurial talent responding to the requirements of smart tourism. The quality control of innovative and entrepreneurial talents for the smart tourism industry is discussed to ensure that the evaluation criteria can be implemented during the training process, then an evaluation index system (EIS) is created with weight values assigned to each index by the entropy method, and the gray comprehensive evaluation method is employed for the said evaluation task. Finally, linear regression and other experiments are carried out to prove the scientific validity of the proposed method. Research conclusions attained in this study provide useful evidence for optimizing the training strategies of innovative and entrepreneurial talents

Construction and Application of a Complex Network-Based Case Knowledge Base in an Assisted Instruction System

With the development of the business administration major, case teaching has become an effective teaching method. However, a lot of case resources exist in the business administration field, and the correlation between these cases is complex, which poses challenges to teachers and students in terms of information overload and knowledge organization. Existing case knowledge bases in assisted instruction systems often have shortcomings in terms of knowledge relationship strength calculation, text semantic similarity calculation, hierarchical knowledge clustering, and propagation evolution. To solve these problems, this study proposed a method of constructing and applying a case knowledge base in an assisted instruction system based on a complex network. This method mainly includes three aspects: calculation and reasoning of ontology relationship strength, calculation of ontology text semantic similarity, and hierarchical knowledge clustering and propagation evolution in complex networks. Through a comprehensive study of these three aspects, a more efficient and intelligent case knowledge base in an assisted instruction system was constructed, which not only improved the teaching efficiency and quality of the business administration major but also had the potential to promote teaching in other disciplines and fields. In addition, this study also provided new perspectives and methods in related fields, which is of great significance for efficiently organizing and utilizing knowledge

Multi-mode switching control of hybrid electromagnetic suspension based on road conditions adaptation

In this research, a hybrid electromagnetic actuator is proposed to coordinate the contradictions between dynamic performance and energy consumption of an electromagnetic suspension. The hybrid electromagnetic suspension (HEMS) is configured to operate in the passive energy regeneration mode, active control ride comfort mode or active control driving safety mode depending on the road excitation frequency. Then, the HEMS system is modeled. The simulation results show that the HEMS can automatically switch between different modes, and realize an effective coordination between dynamic performance and energy saving. Finally, a quarter car test is conducted, which verifies the effectiveness of the multi-mode switching control

MMG/EMG Mapping with Reservoir Computing

Magnetomyography(MMG) is the method that measures the magnetic field around the human muscle as an informative bio-signal that has received considerable attention in recent years. However, the noise compositions of MMG signals are complex and difficult to be removed, thus hindering the application of MMG. To extract muscle movement information for MMG and attenuate the effect of noise, this paper proposes a method to convert noisy MMG to clean electromyography (EMG) that also stems from muscle activities. The conversion is done by using a recently proposed electronic Rotating Neuron Reservoir (eRNR) model with high efficiency and strong system approximation ability. This model is trained with our self-collected MMG data as input and the corresponding EMG as target output. After training, the model can successfully map the MMG signal to EMG with acceptable normalised root mean square error (0.3894), offering a new pathway for extracting desirable information from the noisy bio-signal

Microstructure and Properties of Copper–Graphite Composites Fabricated by Spark Plasma Sintering Based on Two-Step Mixing

The microstructure and properties of Copper-Graphite Composites (CGC) prepared by spark plasma sintering (SPS) based on two-step mixing and wet milling were investigated. The results showed that Cu powders were rolled into Cu flakes during milling, and their size significantly decreased from 23.2 to 10.9 μm when the graphite content increased from 1.0 wt.% to 2.5 wt.%. The oxidation of Cu powder was avoided during two-step mixing and wet milling. After spark plasma sintering, the graphite powders of the composites were mainly distributed at Cu grain boundaries in granular and flake shapes. The mean size of Cu grains was 9.4 um for 1.0 wt.% graphite content and reduced slightly with the increasing of graphite content. Compared with other conventional methods, the composite prepared by two-step mixing and SPS achieved higher relative density, electrical conductivity, and micro-hardness, which, respectively, reduced from 98.78%, 89.7% IACS (International annealed copper standard), and 64 HV (Vickers-hardness) to 96.56%, 81.3% IACS, and 55 HV when the graphite content increased from 1.0 wt.% to 2.5 wt.%. As the graphite content increases, the friction coefficient and wear rate of the composite decreases. When the graphite content of CGC is 1.0 wt.%, the main wear mechanism was plastic deformation, delamination, adhesive, and fatigue wear. The adhesive and fatigue wear disappeared gradually with the increasing of graphite content