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Background: Calcific aortic valve disease (CAVD) is characterized by a phenotypic switch of valvular interstitial cells to bone-forming cells. Toll-like receptors (TLRs) are evolutionarily conserved pattern recognition receptors at the interface between innate immunity and tissue repair. Type I interferons (IFNs) are not only crucial for an adequate antiviral response but also implicated in bone formation. We hypothesized that the accumulation of endogenous TLR3 ligands in the valvular leaflets may promote the generation of osteoblast-like cells through enhanced type I IFN signaling. Methods: Human valvular interstitial cells isolated from aortic valves were challenged with mechanical strain or synthetic TLR3 agonists and analyzed for bone formation, gene expression profiles, and IFN signaling pathways. Different inhibitors were used to delineate the engaged signaling pathways. Moreover, we screened a variety of potential lipids and proteoglycans known to accumulate in CAVD lesions as potential TLR3 ligands. Ligand-receptor interactions were characterized by in silico modeling and verified through immunoprecipitation experiments. Biglycan ( Bgn ), Tlr3 , and IFN-α/β receptor alpha chain ( Ifnar1 )–deficient mice and a specific zebrafish model were used to study the implication of the biglycan (BGN)-TLR3-IFN axis in both CAVD and bone formation in vivo. Two large-scale cohorts (GERA [Genetic Epidemiology Research on Adult Health and Aging], n=55 192 with 3469 aortic stenosis cases; UK Biobank, n=257 231 with 2213 aortic stenosis cases) were examined for genetic variation at genes implicated in BGN-TLR3-IFN signaling associating with CAVD in humans. Results: Here, we identify TLR3 as a central molecular regulator of calcification in valvular interstitial cells and unravel BGN as a new endogenous agonist of TLR3. Posttranslational BGN maturation by xylosyltransferase 1 (XYLT1) is required for TLR3 activation. Moreover, BGN induces the transdifferentiation of valvular interstitial cells into bone-forming osteoblasts through the TLR3-dependent induction of type I IFNs. It is intriguing that Bgn −/− , Tlr3 −/− , and Ifnar1 −/− mice are protected against CAVD and display impaired bone formation. Meta-analysis of 2 large-scale cohorts with >300 000 individuals reveals that genetic variation at loci relevant to the XYLT1–BGN–TLR3–interferon-α/β receptor alpha chain (IFNAR) 1 pathway is associated with CAVD in humans. Conclusions: This study identifies the BGN-TLR3-IFNAR1 axis as an evolutionarily conserved pathway governing calcification of the aortic valve and reveals a potential therapeutic target to prevent CAVD.

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

With the continuous development of my country's economy and society, the number of elderly people in the population has reached the highest level in history. and it has become the general trend to solve the aging problem. Therefore, we deeply analyze the reasons for the aging of the population in China and Japan from the three aspects of low birth rate, long life expectancy and low desire, and analyze the countermeasures to cope with the aging phenomenon. We find that national economic factors are important factors affecting the aging problem. To deal with the aging problem, we need to look at it from a long-term perspective, grasp it from the overall situation, and find a good solution [1,2].

Inborn errors of TLR3-dependent type I IFN immunity in cortical neurons underlie forebrain herpes simplex virus-1 (HSV-1) encephalitis (HSE) due to uncontrolled viral growth and subsequent cell death. We report an otherwise healthy patient with HSE who was compound heterozygous for nonsense (R422*) and frameshift (P493fs9*) RIPK3 variants. Receptor-interacting protein kinase 3 (RIPK3) is a ubiquitous cytoplasmic kinase regulating cell death outcomes, including apoptosis and necroptosis. In vitro, the R422* and P493fs9* RIPK3 proteins impaired cellular apoptosis and necroptosis upon TLR3, TLR4, or TNFR1 stimulation and ZBP1/DAI-mediated necroptotic cell death after HSV-1 infection. The patient’s fibroblasts displayed no detectable RIPK3 expression. After TNFR1 or TLR3 stimulation, the patient’s cells did not undergo apoptosis or necroptosis. After HSV-1 infection, the cells supported excessive viral growth despite normal induction of antiviral IFN-β and IFN-stimulated genes (ISGs). This phenotype was, nevertheless, rescued by application of exogenous type I IFN. The patient’s human pluripotent stem cell (hPSC)–derived cortical neurons displayed impaired cell death and enhanced viral growth after HSV-1 infection, as did isogenic RIPK3-knockout hPSC-derived cortical neurons. Inherited RIPK3 deficiency therefore confers a predisposition to HSE by impairing the cell death–dependent control of HSV-1 in cortical neurons but not their production of or response to type I IFNs.

La panencéphalite sclérosante subaiguë (PESS), une complication tardive de la rougeole, est encore présente lors d’épidémies de cette maladie dues aux insuffisances de la vaccination. Après un rappel historique, nous aborderons la physiopathologie de la PESS et l’importance des critères diagnostiques. De nombreux travaux portant sur les paramètres de l’immunité innée et sur ceux des réponses interféron tendent à montrer une baisse de l’activité de l’immunité cellulaire au cours de cette maladie. Nous formulons ici plusieurs hypothèses s’appuyant sur des publications concernant différentes formes de la maladie : congénitales, périnatales, formes à incubation courte, semblables à l’encéphalite aiguë à inclusions (EAI), formes d’évolution rapide, formes retrouvées chez les immunodéprimés ou chez l’adulte. Des formes familiales ont également été identifiées, suggérant une origine génétique. Selon la durée de la période de latence entre rougeole et la PESS, deux groupes de patients ont été individualisés, incitant à des analyses rétrospective et prospective des exomes de ces malades. La connaissance des gènes participant à la maladie devrait être utile pour la compréhension de la physiopathologie de la PESS mais aussi d’autres infections neurologiques tardives dues à des virus à ARN.

Brown adipose tissue (BAT) plays a key role in thermogenesis during acute cold exposure. However, it remains unclear how BAT is prepared to rapidly turn on thermogenic genes. Here, we show that damage-specific DNA binding protein 1 (DDB1) mediates the rapid transcription of thermogenic genes upon acute cold exposure. Adipose- or BAT-specific Ddb1 knockout mice show severely whitened BAT and significantly decreased expression of thermogenic genes. These mice develop hypothermia when subjected to acute cold exposure at 4 °C and partial lipodystrophy on a high-fat diet due to deficiency in fatty acid oxidation. Mechanistically, DDB1 binds the promoters of Ucp1 and Ppargc1a and recruits positive transcriptional elongation factor b (P-TEFb) to release promoter-proximally paused RNA polymerase II (Pol II), thereby enabling rapid and synchronized transcription of thermogenic genes upon acute cold exposure. Our findings have thus provided a regulatory mechanism of how BAT is prepared to respond to acute cold challenge.

STAT2 is a transcription factor activated by type I and III IFNs. We report 23 patients with loss-of-function variants causing autosomal recessive (AR) complete STAT2 deficiency. Both cells transfected with mutant STAT2 alleles and the patients' cells displayed impaired expression of IFN-stimulated genes and impaired control of in vitro viral infections. Clinical manifestations from early childhood onward included severe adverse reaction to live attenuated viral vaccines (LAV) and severe viral infections, particularly critical influenza pneumonia, critical COVID-19 pneumonia, and herpes simplex virus type 1 (HSV-1) encephalitis. The patients displayed various types of hyperinflammation, often triggered by viral infection or after LAV administration, which probably attested to unresolved viral infection in the absence of STAT2-dependent types I and III IFN immunity. Transcriptomic analysis revealed that circulating monocytes, neutrophils, and CD8+ memory T cells contributed to this inflammation. Several patients died from viral infection or heart failure during a febrile illness with no identified etiology. Notably, the highest mortality occurred during early childhood. These findings show that AR complete STAT2 deficiency underlay severe viral diseases and substantially impacts survival. ispartof: J Clin Invest vol:133 issue:12 pages:e168321- ispartof: location:United States status: Published online

Preterm cerebral white matter injury (WMI), a major form of prenatal brain injury, may potentially be treated by oligodendrocyte (OL) precursor cell (OPC) transplantation. However, the defective differentiation of OPCs during WMI seriously hampers the clinical application of OPC transplantation. Thus, improving the ability of transplanted OPCs to differentiate is critical to OPC transplantation therapy for WMI. We established a hypoxia–ischemia-induced preterm WMI model in mice and screened the molecules affected by WMI using single-cell RNA sequencing. We revealed that endothelin (ET)-1 and endothelin receptor B (ETB) are a pair of signaling molecules responsible for the interaction between neurons and OPCs and that preterm WMI led to an increase in the number of ETB-positive OPCs and premyelinating OLs. Furthermore, the maturation of OLs was reduced by knocking out ETB but promoted by stimulating ET-1/ETB signaling. Our research reveals a new signaling module for neuron–OPC interaction and provides new insight for therapy targeting preterm WMI.

Relation extraction (RE) involves identifying the relations between entities from unstructured texts. RE serves as the foundation for many natural language processing (NLP) applications, such as knowledge graph completion, question answering, and information retrieval. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art of RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including RE datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives (text representation, context encoding, and triplet prediction). Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.

Additional file 1: Table S1. Primers and probes used for RT-qPCR. Table S2. List of proteins pulled-down by recombinant MXRA7 from SVEC4-10 cells. Figure S1. The effect of genders on wound healing speed in both WT and MXRA7-/- mice. Figure S2. Archived MXRA7 expression in various cells of different organs in Human Protein Atlas. Figure S3. GST pull-down analysis of the interaction between MXRA7 and Vimentin proteins by SDS-PAGE. Figure S4. EdU proliferation assay analysis of the effect of different concentrations of rmMXRA7 on the growth of MXRA7-/- fibroblast cells.

The research community has shown increasing interest in designing intelligent embodied agents that can assist humans in accomplishing tasks. Despite recent progress on related vision-language benchmarks, most prior work has focused on building agents that follow instructions rather than endowing agents the ability to ask questions to actively resolve ambiguities arising naturally in embodied environments. To empower embodied agents with the ability to interact with humans, in this work, we propose an Embodied Learning-By-Asking (ELBA) model that learns when and what questions to ask to dynamically acquire additional information for completing the task. We evaluate our model on the TEACH vision-dialog navigation and task completion dataset. Experimental results show that ELBA achieves improved task performance compared to baseline models without question-answering capabilities.

Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In this paper, we construct causal graphs for abstractive text summarization and identify the intrinsic causes of the factual inconsistency, i.e., the language bias and irrelevancy bias, and further propose a debiasing framework, named CoFactSum, to alleviate the causal effects of these biases by counterfactual estimation. Specifically, the proposed CoFactSum provides two counterfactual estimation strategies, i.e., Explicit Counterfactual Masking with an explicit dynamic masking strategy, and Implicit Counterfactual Training with an implicit discriminative cross-attention mechanism. Meanwhile, we design a Debiasing Degree Adjustment mechanism to dynamically adapt the debiasing degree at each decoding step. Extensive experiments on two widely-used summarization datasets demonstrate the effectiveness of CoFactSum in enhancing the factual consistency of generated summaries compared with several baselines.

Chain-of-Thought prompting (CoT) enables large-scale language models to solve complex reasoning problems by decomposing the problem and tackling it step-by-step. However, Chain-of-Thought is a greedy thinking process that requires the language model to come up with a starting point and generate the next step solely based on previous steps. This thinking process is different from how humans approach a complex problem e.g., we proactively raise sub-problems related to the original problem and recursively answer them. In this work, we propose Socratic Questioning, a divide-and-conquer fashion algorithm that simulates the self-questioning and recursive thinking process. Socratic Questioning is driven by a Self-Questioning module that employs a large-scale language model to propose sub-problems related to the original problem as intermediate steps and Socratic Questioning recursively backtracks and answers the sub-problems until reaches the original problem. We apply our proposed algorithm to the visual question-answering task as a case study and by evaluating it on three public benchmark datasets, we observe a significant performance improvement over all baselines on (almost) all datasets. In addition, the qualitative analysis clearly demonstrates the intermediate thinking steps elicited by Socratic Questioning are similar to the human's recursively thinking process of a complex reasoning problem., Comment: 15 pages, 12 figure, 2 algorithms

Additional file 1: Table S1 Method validation. Table S2 Classification of the subjects according to their TC levels in adults (n=846). Table S3 Classification of the subjects according to their blood pressures in adults (n=846). Table S4 Classification of the subjects according to their BMI in adults (n=846).

It is intractable to evaluate the performance of Grammatical Error Correction (GEC) systems since GEC is a highly subjective task. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. Previous mainstream evaluation metrics, i.e., reference-based metrics, introduce bias into the multi-reference evaluation because they extract edits without considering the presence of multiple references. To overcome the problem, we propose Chunk-LEvel Multi-reference Evaluation (CLEME) designed to evaluate GEC systems in multi-reference settings. First, CLEME builds chunk sequences with consistent boundaries for the source, the hypothesis and all the references, thus eliminating the bias caused by inconsistent edit boundaries. Then, based on the discovery that there exist boundaries between different grammatical errors, we automatically determine the grammatical error boundaries and compute F$_{0.5}$ scores in a novel way. Our proposed CLEME approach consistently and substantially outperforms existing reference-based GEC metrics on multiple reference sets in both corpus-level and sentence-level settings. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of our designed evaluation metric., Comment: Rejected by ACL 2023 with Soundness 4/4/4 and Excitement 4/3.5/3.5 :(

Additional file 1: Supplementary Methods. Fig S1. Luciferase assay on HEK293T cells transfected with the pGL4.32 luciferase reporter construct and an expression vector for Renilla luciferase together with no vector (mock), EV, WT, or 4 TLR7 variants found in our cohort. After 24 h, transfected cells were left untreated or were treated by incubation with 1 μg/mL R848 for 24 h. These data were established from two independent experiments. The y-axis represents NF-κB transcriptional activity as a percentage of the WT. The x-axis indicates the alleles used for transfection. Fig S2. Age distribution as boxplot and violin plot of the critical COVID-19 cases according to the carrier status of pLOF/bLOF at 15 type I IFN-related loci. Mean age of the patients for each category is shown in red. T-test was used to compare the means, showing a significant difference between non-carriers and carriers of heterozygous or homozygous/hemizygous variants (P = 2.2 × 10−6) and between heterozygous carriers and homozygous/hemizygous carriers (P = 0.008). Fig S3. Empirical power of our sample to detect an association at the 2.5 × 10−6 exome-wide significance threshold for various relative risks and proportion of carriers of at least one disease causing variant in the general population (PD), as estimated by simulation study (N = 1000 replicates).

International audience; Type I interferons (IFNs) have broad and potent antiviral activity. We review the interplay between type I IFNs and SARS-CoV-2. Human cells infected with SARS-CoV-2 in vitro produce low levels of type I IFNs, and SARS-CoV-2 proteins can inhibit various steps in type I IFN production and response. Exogenous type I IFNs inhibit viral growth in vitro. In various animal species infected in vivo, type I IFN deficiencies underlie higher viral loads and more severe disease than in control animals. The early administration of exogenous type I IFNs improves infection control. In humans, inborn errors of, and auto-antibodies against type I IFNs underlie life-threatening COVID-19 pneumonia. Overall, type I IFNs are essential for host defense against SARS-CoV-2 in individual cells and whole organisms.

Background Subacute sclerosing panencephalitis (SSPE) is a rare, non-treatable and fatal neurological complication of measles , still present due to the return of the epidemic linked to the loosening of vaccination policies. Its mechanism remains unexplained. Objective The main objective was to investigate explanatory variables relating to the risk of developing SSPE and its pathophysiology . Methods Literature analysis was focused on different varieties of SSPE: perinatal forms, short-incubation forms similar to acute measles inclusion body encephalitis (MIBE), rapidly evolving forms, forms occurring in the immunosuppressed, adult forms, and family forms. In addition, several studies on the parameters of innate immunity and interferon responses of patients were analyzed. Results Two main data were highlighted: a relationship between the so-called fulminant forms and the prescription of corticosteroids was established. In familial SSPE, two groups were individualized according to the duration of the latency period, prompting an analysis of patient exomes . Conclusion Treatment with corticosteroids should be banned. Knowledge of the genes involved and epigenetics should be useful for understanding the pathophysiology of SSPE and other late-onset neurological infections with RNA viruses .

The aorta is highly heterogeneous, containing many different types of cells that perform sophisticated functions to maintain aortic homeostasis. Recently, single-cell RNA sequencing studies have provided substantial new insight into the heterogeneity of vascular cell types, the comprehensive molecular features of each cell type, and the phenotypic interrelationship between these cell populations. This new information has significantly improved our understanding of aortic biology and aneurysms at the molecular and cellular level. Here, we summarize these findings, with a focus on what single-cell RNA sequencing analysis has revealed about cellular heterogeneity, cellular transitions, communications among cell populations, and critical transcription factors in the vascular wall. We also review the information learned from single-cell RNA sequencing that has contributed to our understanding of the pathogenesis of vascular disease, such as the identification of cell types in which aneurysm-related genes and genetic variants function. Finally, we discuss the challenges and future directions of single-cell RNA sequencing applications in studies of aortic biology and diseases. Graphic Abstract: A graphic abstract is available for this article.

Viral encephalitis is a major neglected medical problem. Host defense mechanisms against viral infection of the central nervous system (CNS) have long remained unclear. The few previous studies of CNS-specific immunity to viruses in mice in vivo and humans in vitro have focused on the contributions of circulating leukocytes, resident microglial cells and astrocytes, with neurons long considered passive victims of viral infection requiring protection from extrinsic antiviral mechanisms. The last decade has witnessed the gradual emergence of the notion that neurons also combat viruses through cell-intrinsic mechanisms. Forward genetic approaches in humans have shown that monogenic inborn errors of TLR3, IFN-α/β, or snoRNA31 immunity confer susceptibility to herpes simplex virus 1 (HSV-1) infection of the forebrain, whereas inborn errors of DBR1 underlie brainstem infections due to various viruses, including HSV-1. The study of human pluripotent stem cell (hPSC)-derived CNS-resident cells has unraveled known (i.e. TLR3-dependent IFN-α/β immunity) and new (i.e. snoRNA31-dependent or DBR1-dependent immunity) cell-intrinsic antiviral mechanisms operating in neurons. Reverse genetic approaches in mice have confirmed that some known antiviral mechanisms also operate in mouse neurons (e.g. TLR3 and IFN-α/β immunity). The search for human inborn errors of immunity (IEIs) underlying various forms of viral encephalitis, coupled with mouse models in vivo, and hPSC-based culture models of CNS and peripheral nervous system cells and organoids in vitro, should shed further light on the cell-specific and tissue-specific mechanisms of host defense against viruses in the human brain.

The search for candidate variants underlying human disease in massive parallel sequencing data typically focuses on coding regions and essential splice sites, mostly ignoring noncoding variants. The RNA spliceosome recognizes intronic branchpoint (BP) motifs at the beginning of splicing and operates mostly within introns to define the exon-intron boundaries; however, BP variants have been paid little attention. We established a comprehensive genome-wide database and knowledgebase of BP and developed BPHunter for systematic and informative genome-wide detection of intronic variants that may disrupt BP and splicing, together with an effective strategy for prioritizing BP variant candidates. BPHunter not only constitutes an important resource for understanding BP, but should also drive discovery of BP variants in human genetic diseases and traits. Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from and

Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.

BackgroundWe previously reported inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity in 1-5% of unvaccinated patients with life-threatening COVID-19, and auto-antibodies against type I IFN in another 15-20% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3,269 unvaccinated patients with life-threatening COVID-19 (1,301 previously reported and 1,968 new patients), and 1,373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. A quarter of the patients tested had antibodies against type I IFN (234 of 928) and were excluded from the analysis.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants wasTLR7, with an OR of 27.68 (95%CI:1.5-528.7,P=1.1×10−4), in analyses restricted to biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70 [95%CI:1.3-8.2],P=2.1×10−4). Adding the recently reportedTYK2COVID-19 locus strengthened this enrichment, particularly under a recessive model (OR=19.65 [95%CI:2.1-2635.4];P=3.4×10−3). When these 14 loci andTLR7were considered, all individuals hemizygous (n=20) or homozygous (n=5) for pLOF or bLOF variants were patients (OR=39.19 [95%CI:5.2-5037.0],P=4.7×10−7), who also showed an enrichment in heterozygous variants (OR=2.36 [95%CI:1.0-5.9],P=0.02). Finally, the patients with pLOF or bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years;P=1.68×10−5).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old.

Ferroptosis is a newly discovered way of programmed cell death, mainly caused by the accumulation of iron-dependent lipid peroxides in cells, which is morphologically, biochemically and genetically different from the previously reported apoptosis, necrosis and autophagy. Studies have found that ferroptosis plays a key role in the occurrence and development of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and vascular dementia, which suggest that ferroptosis may be involved in regulating the progression of neurodegenerative diseases. At present, on the underlying mechanism of ferroptosis in neurodegenerative diseases is still unclear, and relevant research is urgently needed to clarify the regulatory mechanism and provide the possibility for the development of agents targeting ferroptosis. This review focused on the regulatory mechanism of ferroptosis and its various effects in neurodegenerative diseases, in order to provide reference for the research on ferroptosis in neurodegenerative diseases.

Entity Set Expansion (ESE) is a promising task which aims to expand entities of the target semantic class described by a small seed entity set. Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities. In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities. Extensive experiments and detailed analyses on three datasets show that our method outperforms previous state-of-the-art methods., Accepted by SIGIR 2022 (Full Paper)

The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment for data-scarce domain BERT knowledge distillation, by learning a cross-domain manipulation scheme that automatically augments the target with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced selector to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on four different tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ${\sim}13.3\%$) when only a few labeled examples available., Comment: AAAI2021

Life-threatening 'breakthrough' cases of critical COVID-19 are attributed to poor or waning antibody response to the SARS-CoV-2 vaccine in individuals already at risk. Pre-existing autoantibodies (auto-Abs) neutralizing type I IFNs underlie at least 15% of critical COVID-19 pneumonia cases in unvaccinated individuals; however, their contribution to hypoxemic breakthrough cases in vaccinated people remains unknown. Here, we studied a cohort of 48 individuals (age 20-86 years) who received 2 doses of an mRNA vaccine and developed a breakthrough infection with hypoxemic COVID-19 pneumonia 2 weeks to 4 months later. Antibody levels to the vaccine, neutralization of the virus, and auto-Abs to type I IFNs were measured in the plasma. Forty-two individuals had no known deficiency of B cell immunity and a normal antibody response to the vaccine. Among them, ten (24%) had auto-Abs neutralizing type I IFNs (aged 43-86 years). Eight of these ten patients had auto-Abs neutralizing both IFN-α2 and IFN-ω, while two neutralized IFN-ω only. No patient neutralized IFN-β. Seven neutralized 10 ng/mL of type I IFNs, and three 100 pg/mL only. Seven patients neutralized SARS-CoV-2 D614G and the Delta variant (B.1.617.2) efficiently, while one patient neutralized Delta slightly less efficiently. Two of the three patients neutralizing only 100 pg/mL of type I IFNs neutralized both D61G and Delta less efficiently. Despite two mRNA vaccine inoculations and the presence of circulating antibodies capable of neutralizing SARS-CoV-2, auto-Abs neutralizing type I IFNs may underlie a significant proportion of hypoxemic COVID-19 pneumonia cases, highlighting the importance of this particularly vulnerable population.

Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.

Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall.

International audience; Ancient genomics can directly detect human genetic adaptation to environmental cues. However, it remains unclear how pathogens have exerted selective pressures on human genome diversity across different epochs and affected present-day inflammatory disease risk. Here, we use an ancestry-aware approximate Bayesian computation framework to estimate the nature, strength, and time of onset of selection acting on 2,879 ancient and modern European genomes from the last 10,000 years. We found that the bulk of genetic adaptation occurred after the start of the Bronze Age

Interferometry has been widely used in biosensing due to its ability to acquire molecular affinity and kinetics in real-time. However, interferometric-based sensors are susceptible to environmental disturbances, including temperature and non-specific binding of target molecules, which reduces their detection robustness. To address this shortcoming, this paper proposes a self-referencing interference sensor based on coherence multiplexing to resist environmental disturbances. The proposed sensor can address temperature and non-specific binding, but it is not limited only to these types of disturbances. In the proposed sensor design, each sensor signal is encoded using a specific optical path difference determined by the optical thickness of a sensor chip. In addition, two sensor signals for disturbances tracking and biomolecule detection are detected simultaneously without additional cost to the second spectrometer and then differenced to achieve real-time self-reference. The temperature fluctuations experiments and specific binding experiments of protein A to IgG are performed to verify the performance of the proposed sensor. The results demonstrate that the proposed sensor can eliminate non-specific binding and temperature disturbances in real-time during biomolecule detection, achieving higher detection robustness. The proposed sensor is suitable for applications that require large-scale testing of biomolecular interactions, such as drug screening.

International audience; ObjectiveTo describe neurologic, radiologic and laboratory features in children with central nervous system (CNS) inflammatory disease complicating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.Study designWe focused on CNS inflammatory diseases in children referred from 12 hospitals in the Paris area to Necker-Sick Children Reference Centre.ResultsWe identified 19 children who had a history of SARS-CoV-2 infection and manifest a variety of CNS inflammatory diseases: encephalopathy, cerebellar ataxia, acute disseminated encephalomyelitis, neuromyelitis optica spectrum disorder, or optic neuritis. All patients had a history of SARS-CoV-2 exposure, and all tested positive for circulating antibodies against SARS-CoV-2. At the onset of the neurologic disease, SARS-CoV-2 PCR results (nasopharyngeal swabs) were positive in 8 children. Cerebrospinal fluid was abnormal in 58% (11/19) and magnetic resonance imaging was abnormal in 74% (14/19). We identified an autoantibody co-trigger in 4 children (myelin-oligodendrocyte and aquaporin 4 antibodies), representing 21% of the cases. No autoantibody was found in the 6 children whose CNS inflammation was accompanied by a multisystem inflammatory syndrome in children. Overall, 89% of patients (17/19) received anti-inflammatory treatment, primarily high-pulse methylprednisolone. All patients had a complete long-term recovery and, to date, no patient with autoantibodies presented with a relapse.ConclusionsSARS2-CoV-2 represents a new trigger of postinfectious CNS inflammatory diseases in children.

Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on tasks specified through instructions, has shown promising zero-shot performance on various natural language processing tasks. However, it has yet to be explored for vision and multimodal tasks. In this work, we introduce MUL-TIINSTRUCT, the first multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. The tasks are derived from 21 existing open-source datasets and each task is equipped with 5 expert-written instructions. We take OFA as the base pre-trained model for multimodal instruction tuning, and to further improve its zero-shot performance, we explore multiple transfer learning strategies to leverage the large-scale NATURAL INSTRUCTIONS dataset. Experimental results demonstrate strong zero-shot performance on various unseen multimodal tasks and the benefit of transfer learning from a text-only instruction dataset. We also design a new evaluation metric - Sensitivity, to evaluate how sensitive the model is to the variety of instructions. Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task., Comment: ACL 2023, dataset url: https://github.com/VT-NLP/MultiInstruct

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the development of CGEC research. However, there are two major limitations in the CGEC field: First, the lack of high-quality annotated training corpora prevents the performance of existing CGEC models from being significantly improved. Second, the grammatical errors in widely used test sets are not made by native Chinese speakers, resulting in a significant gap between the CGEC models and the real application. In this paper, we propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. Additionally, we present a challenging CGEC benchmark derived entirely from errors made by native Chinese speakers in real-world scenarios. Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field., Comment: Long paper, accepted at the Findings of EMNLP 2022

Supplemental material, sj-doc-6-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.

Supplemental material, sj-doc-4-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers

Supplemental material, sj-doc-5-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models. Our dataset and source codes are available at \url{https://github.com/huangshulin123/Blessing-Generation}., Comment: Accepted to EMNLP 2022 GEM Workshop