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Currently, whether patients with rare epidermal growth factor receptor (EGFR) mutations could benefit from EGFR tyrosine kinase inhibitors (TKIs) demands further studies. Limited clinical data are available regarding the molecular characteristics and clinical response in non-small-cell lung cancer (NSCLC) patients harboring EGFR E709-T710delinsX mutations, a rare mutation type in exon 18 of EGFR. In this study, we aimed to explore the molecular distribution and clinical outcome of EGFR E709-T710delinsX mutated Chinese patients.Next-generation sequencing (NGS) tests were performed in 15,078 NSCLC patients. A multicenter retrospective cohort involving 17 advanced lung cancer patients with EGFR E709-T710delinsX was collected to evaluate clinical responses to diverse therapies. In silico protein structure modeling was conducted to predict drug response.5905 EGFR mutant patients (39.2%, 5905/15078) were identified, with 26 cases (0.44%, 26/5905) harbored EGFR E709-T710delinsD. Afatinib showed a better overall objective response rate (ORR) compared with the first-generation (1G) EGFR TKIs and chemotherapy with significant difference. Superior progression free survival (PFS) was also observed in patients treated with afatinib (afatinib 10.85 m vs 1G EGFR TKIs 4.0 m vs chemotherapy 2.8 m, p = 0.0007). In silico protein structure modeling predicts better binding of afatinib with EGFR E709-T710delinsD compared with other EGFR TKIs.This is the largest studies for EGFR E709-T710delinsX, with 26 cases with EGFR E709-T710delinsX being identified (0.44% in EGFR mutant NSCLC, 0.17% in NSCLC patients). This study also firstly revealed that afatinib might exert superior antitumor activity to the 1G EGFR TKIs and chemotherapy in EGFR E709-T710delinsX mutant patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving with mutations in the spike protein, especially in the receptor-binding domain (RBD). The failure of public health measures in some countries to contain the spread of the disease has given rise to novel viral variants with increased transmissibility. However, key questions about how quickly the variants can spread remain unclear. Herein, we performed a structural investigation using molecular dynamics simulations and determined dissociation constant (

The aim of this study is to investigate the role of calcium-sensing receptor (CaSR) in the expression of inflammatory mediators of lipopolysaccharide (LPS)-treated human dental pulp cells (hDPCs). The expression profile of CaSR in LPS-simulated hDPCs was detected using immunofluorescence, real time quantitative PCR (RT-qPCR), and Western blot analyses. Then, its regulatory effects on the expression of specific inflammatory mediators such as interleukin (IL)-1β, IL-6, cyclooxygenase 2 (COX2)-derived prostaglandin E2 (PGE

The aim of this study was to investigate the influence of pigment epithelium-derived factor (PEDF) on periodontal homeostasis in mice and the osteogenic differentiation of human periodontal ligament fibroblasts (PDLFs).Micro-computed tomography and histology were performed to compare the alveolar bone volume, density, and bone-related markers between PEDF-deficient (PEDFThe alveolar bone volume and density of PEDFPEDF gene knockout suppresses periodontal homeostasis in mice, and PEDF treatment induces PDLF osteogenic differentiation

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. Existing distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework for distributed subgraph enumeration on static data graphs. Batch-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. To handle large-scale data graphs that may exceed the memory capacity of a single machine, Batch-BENU stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand, shuffling the data graph instead of partial matching results. To support incremental subgraph enumeration on dynamic data graphs, we propose the Streaming-BENU framework. Streaming-BENU turns the problem of enumerating incremental matching results into enumerating all matching results of incremental pattern graphs at each time step. We implement Batch-BENU and Streaming-BENU with the local database cache and the load balance optimization to improve their efficiency. Extensive experiments show that Batch-BENU and Streaming-BENU can scale to big graphs and complex pattern graphs. They outperform the state-of-the-art distributed methods by up to one and two orders of magnitude, respectively.

In many graph analytical applications, the local structural vertex similarity calculation is an essential prerequisite for advanced graph mining. The similarity calculation finds out all the similar vertex pairs whose local structural similarity scores (like the number of common neighbors, and the Jaccard index of adjacency sets) are above a given threshold. The real-world applications use a wide range of similarity thresholds. However, the existing distributed methods for the problem only optimize for either high thresholds (> 0.7) or low thresholds (

Decision trees have favorable properties, including interpretability, high computational efficiency, and the ability to learn from little training data. Learning a decision tree is known to be NP-complete. The researchers have proposed many greedy algorithms such as CART to learn approximate solutions. Inspired by the current popular neural networks, soft trees that support end-to-end training with back-propagation have attracted more and more attention. However, existing soft trees either lose the interpretability due to the continuous relaxation or employ the two-stage method of end-to-end building and then pruning. In this paper, we propose One-Stage Tree to build and prune the decision tree jointly through a bilevel optimization problem. Moreover, we leverage the reparameterization trick and proximal iterations to keep the tree discrete during end-to-end training. As a result, One-Stage Tree reduces the performance gap between training and testing and maintains the advantage of interpretability. Extensive experiments demonstrate that the proposed One-Stage Tree outperforms CART and the existing soft trees on classification and regression tasks.

In the big data era, large amounts of data are under generation and accumulation in various industries. However, users usually feel hindered by the data quality issues when extracting values from the big data. Thus, data quality issues are gaining more and more attention from data quality management analysts. Cutting-edge solutions like data ETL, data cleaning, and data quality monitoring systems have many deficiencies in capability and efficiency, making it difficult to cope with complicated situations on big data. These problems inspire us to build SparkDQ, a generic distributed data quality management model and framework that provides a series of data quality detection and repair interfaces. Users can quickly build custom tasks of data quality computing for various needs by utilizing these interfaces. In addition, SparkDQ implements a set of algorithms that in a parallel manner with optimizations. These algorithms aim at various data quality goals. We also propose several system-level optimizations, including the job-level optimization with multi-task execution scheduling and the data-level optimization with data state caching. The experimental evaluation shows that the proposed distributed algorithms in SparkDQ run up to 12 times faster compared to the corresponding stand-alone serial and multi-thread algorithms. Compared with the cutting-edge distributed data quality solution Apache Griffin, SparkDQ has more features, and its execution time is only around half of Apache Griffin on average. SparkDQ achieves near-linear data and node scalability.

Lipopolysaccharide (LPS) and lipoprotein, two essential components of the outer membrane (OM) in Gram-negative bacteria, play critical roles in bacterial physiology and pathogenicity. LPS translocation to the OM is mediated by LptDE, yet how lipoproteins sort to the cell surface remains elusive. Here we report the identification of an inventory of lipoproteins that are transported to the cell surface via LptDE. Notably, we determined crystal structures of LptDE fromPseudomonas aeruginosaand its complex with an endogenousEscherichia colilipoprotein YifL. ThepaLptDE-YifL structure demonstrates that YifL translocates to the OM via LptDE, in a manner similar to LPS transport. The β-barrel domain serves as a passage for the proteinaceous moiety while its acyl chains are transported outside. Our finding has been corroborated by results from native mass spectrometry, immunofluorescence, and photocrosslinking assays, revealing a unique mechanism through which lipoproteins are translocated across the OM in an ATP- and LPS-dependent manner. Moreover, our study expands the scope of current knowledge of lipoprotein sorting by disclosing a crosstalk between the Lpt and Lol pathways.

Background Genetic alterations involving the SWItch/Sucrose NonFermentable (SWI/SNF) family are highly prevalent but understudied in non-small cell lung cancer (NSCLC), especially its controversial roles in the implication of immunotherapy. We aimed to comprehensively characterize the clinicopathologic features of SWI/SNF mutation and to investigate its impact on immunotherapy outcomes among NSCLC patients. Methods Mutational landscapes of the SWI/SNF family in the Chinese or Western NSCLC population were depicted and compared using large public cohorts with sequencing data. Meanwhile, 519 NSCLC patients who received at least one dose of ICIs were enrolled from Sun Yat-sen University Cancer Center (SYSUCC) to explore the impact of SWI/SNF family mutation on immunotherapy and to discover distinct subpopulations. Multiple publicly available cohorts were then used for external validation. Results SWI/SNF family mutation was strongly associated with EGFR wild-type NSCLC and its prevalence was significantly lower in Chinese compared to Western population (14.7% vs. 24.5%, p

With the development of the intelligent manufacturing, a large amount of data will be generated at the edge of the network. Industrial data have unprecedented requirements for transmission speed and quality. Edge computing makes full use of the computing power of the terminal, which realizes real-time processing of data. As the first step of the edge computing, the deployment of edge servers is the foundation and key. However, unreasonable edge server deployment strategies cannot meet the requirements of data processing in intelligent manufacturing. In this paper, we establish an edge server deployment optimization model to optimize deployment cost and load balance. Reliability is an important point in intelligent manufacturing, so we propose a fault-tolerant server deployment scheme. When the edge server fails, the fault-tolerant server can replace the fault server in time to ensure the normal workflow. To solve the above model, we propose a binary-based gray wolf genetic strategy algorithm, which improves the global optimal solution of the algorithm. Simulations reveal that up to 10.97 $$\%$$ of the total load can be saved by using the gray wolf genetic algorithm and an average of 16.15 $$\%$$ time savings can be achieved using our proposed method during hours.

B4C-TiB2-SiC composites with W addition were successfully prepared by spark plasma sintering with B4C, TiB2, SiC, and W as raw materials. The XRD results indicate that the main phases of the composites are B4C, TiB2, SiC, and W2B5. A large number of core-shell structures were observed in the composites with the introduction of W. It is determined that the core is the TiB2 phase and the shell is a (Ti, W) B2 solid solution by EDS, EBSD, and TEM characterization methods. The core-shell structure is conducive to the deflection of cracks, thereby improving the fracture toughness of the composites. As a result, with the increase of W addition from 0 to 5 vol%, the fracture toughness of composites increased from 3.96±0.25 to 5.14±0.23 MPa m1/2, the Vickers hardness improved from 20.24±0.22 to 28.35±0.22 GPa.

The graph neural network (GNN) has become a popular research area for its state-of-the-art performance in many graph analysis tasks. Recently, various graph neural network libraries have emerged. They make developing GNNs convenient, but the performance bottlenecks of GNNs on large datasets are not well studied. In this work, we analyze the performance bottlenecks in GNN training and inference with GPUs empirically. A GNN layer can be decomposed into two parts: the vertex and the edge calculation parts. We select four representative GNNs (GCN, GGNN, GAT, GaAN) for evaluation according to their computational complexity. We decompose their running time and memory usage, evaluate the effects of hyper-parameters and assess the efficiency of the sampling techniques. The experimental evaluation with PyTorch Geometric indicates that the edge-related calculation is the performance bottleneck for most GNNs, dominating the training/inference time and memory usage. The sampling techniques are essential for GNN training and inference on big graphs with GPUs, but their current implementation still has non-trivial overheads in sampling and data transferring.

Accumulated hard tissue debris (AHTD) is an inevitable by-product during endodontic treatment and is difficult to remove completely using traditional syringe and needle irrigation (SNI). Adjunctive irrigation is proposed to assist the clean-up of AHTD. This systematic review and meta-analysis aimed to evaluate the AHTD removal efficacy of different root canal irrigation devices using micro-computed tomography (Micro-CT). A literature search was carried out within the main scientific databases until 20 June 2022. All results were screened with detailed eligibility criteria. Eleven studies were included for analysis. SNI, passive ultrasonic irrigation (PUI), negative pressure systems, sonically activated irrigation (SAI), mechanical-activated system and laser-activated irrigation (LAI) were assessed. PUI is superior to SNI for debris removal and LAI has better AHTD removal performance than PUI. The negative pressure system and mechanical-activated system were proved to be less effective. Registration: PROSPERO (CRD42021273892).

BackgroundDeafness-dystonia-optic neuronopathy (DDON) syndrome, a condition that predominantly affects males, is caused by mutations in translocase of mitochondrial inner membrane 8A (TIMM8A)/deafness dystonia protein 1 (DDP1) gene and characterized by progressive deafness coupled with other neurological abnormalities. In a previous study, we demonstrated the phenotype of male mice carrying the hemizygous mutation of Timm8a1-I23fs49X. In a follow-up to that study, this study aimed to observe the behavioral changes in the female mutant (MUT) mice with homologous mutation of Timm8a1 and to elucidate the underlying mechanism for the behavioral changes.Materials and methodsHistological analysis, transmission electron microscopy (EM), Western blotting, hearing measurement by auditory brainstem response (ABR), and behavioral observation were compared between the MUT mice and wild-type (WT) littermates.ResultsThe weight of the female MUT mice was less than that of the WT mice. Among MUT mice, both male and female mice showed hearing impairment, anxiety-like behavior by the elevated plus maze test, and cognitive deficit by the Morris water maze test. Furthermore, the female MUT mice exhibited coordination problems in the balance beam test. Although the general neuronal loss was not found in the hippocampus of the MUT genotype, EM assessment indicated that the mitochondrial size showing as aspect ratio and form factor in the hippocampus of the MUT strain was significantly reduced compared to that in the WT genotype. More importantly, this phenomenon was correlated with the upregulation of translation of mitochondrial fission process protein 1(Mtfp1)/mitochondrial 18 kDa protein (Mtp18), a key fission factor that is a positive regulator of mitochondrial fission and mitochondrial size. Interestingly, significant reductions in the size of the uterus and ovaries were noted in the female MUT mice, which contributed to significantly lower fertility in the MUT mice.ConclusionTogether, a homologous mutation in the Timm8a1 gene caused the hearing impairment and psychiatric behavioral changes in the MUT mice; the latter phenotype might be related to a reduction in mitochondrial size regulated by MTP18.

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to the security and privacy concerns, it is more practical to perform adversarial attacks under the black-box setting, where the architecture/parameters and training data of target systems cannot be easily accessed by attackers. However, generating high-quality fake user profiles under black-box setting is rather challenging with limited resources to target systems. To address this challenge, in this work, we introduce a novel strategy by leveraging items' attribute information (i.e., items' knowledge graph), which can be publicly accessible and provide rich auxiliary knowledge to enhance the generation of fake user profiles. More specifically, we propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies through deep reinforcement learning techniques, in which knowledge graph is seamlessly integrated into hierarchical policy networks to generate fake user profiles for performing adversarial black-box attacks. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework under the black-box setting., Comment: Accepted in the KDD'22

Reaction-bonded silicon carbide (RBSC) has become an important structural ceramic with the benefit of being capable of preparing complex-shaped products. In order to fabricate high-performance RBSC, particle gradation of raw SiC combined with slip casting was used to prepare the porous preform before liquid silicon infiltration (LSI). The microstructural and mechanical properties of RBSC were compared by adding different amounts of carbon black (CB) content from 4 wt% to 10 wt%. Two pore structures with submicron and nano pores formed in the preform. As the amounts of carbon black increased, the mechanical properties improved and then suddenly weakened due to residual silicon initiating a nonuniform microstructure. The elastic modulus of the preform with 8 wt%CB after LSI was 389 ± 4 GPa and the flexural strength was 340 ± 17 MPa, which improved by about 150% compared to other rapid prototyping methods and has attractive application prospects.

Static program analysis has been widely applied along the whole process of the program development for bug detection, code optimization, testing, etc. Although researchers have made significant work in static program analysis, it is still challenging to perform sophisticated interprocedural analysis on large-scale modern software. The underlying reason is that interprocedural analysis for large-scale modern software is highly computation- and memory-intensive, leading to poor efficiency and scalability. In this article, we introduce an efficient distributed and scalable solution for sophisticated static analysis. Specifically, we propose a data-parallel algorithm and a join-process-filter computation model for the CFL-reachability-based interprocedural analysis. Based on that, an efficient distributed static analysis engine called BigSpa is developed, which is composed of an offline batch static program analysis system and an online incremental static program analysis system. The BigSpa system has high generality and can support all kinds of static analysis tasks that can be expressed as CFL reachability problems. The performance of BigSpa is evaluated on real-world large-scale software datasets. Our experiments show that the offline batch system can exceed an order of magnitude compared with the most advanced analysis tools available on performance, and for incremental analysis with small batch updates on the same data sets, the online analysis system can achieve near real-time response, which is very fast and flexible.

Deafness-dystonia-optic neuronopathy (DDON) syndrome, a condition that predominantly affects males, is caused by mutations inHistological analysis, transmission electron microscopy (EM), Western blotting, hearing measurement by auditory brainstem response (ABR), and behavioral observation were compared between the MUT mice and wild-type (WT) littermates.The weight of the female MUT mice was less than that of the WT mice. Among MUT mice, both male and female mice showed hearing impairment, anxiety-like behavior by the elevated plus maze test, and cognitive deficit by the Morris water maze test. Furthermore, the female MUT mice exhibited coordination problems in the balance beam test. Although the general neuronal loss was not found in the hippocampus of the MUT genotype, EM assessment indicated that the mitochondrial size showing as aspect ratio and form factor in the hippocampus of the MUT strain was significantly reduced compared to that in the WT genotype. More importantly, this phenomenon was correlated with the upregulation of translation of mitochondrial fission process protein 1(Mtfp1)/mitochondrial 18 kDa protein (Mtp18), a key fission factor that is a positive regulator of mitochondrial fission and mitochondrial size. Interestingly, significant reductions in the size of the uterus and ovaries were noted in the female MUT mice, which contributed to significantly lower fertility in the MUT mice.Together, a homologous mutation in the

Background and PurposeAn increasing number of autoimmune encephalitis (AE)-associated autoantibodies have been successfully characterized. However, many cases of AE remain unexplained on account of unknown antibodies. The aim of the present study was to identify a novel antibody against collapsin response mediator protein 2 (CRMP2) in suspected AE patients.MethodsA patient’s serum and cerebrospinal fluid samples tested negative for known AE antibodies; however, strong immunolabel signals were observed in the neuronal cytoplasm of the cortex, hippocampus, and Purkinje cells on rat brain sections. Immunoprecipitation from the rat brain protein lysate, followed by mass spectrometry analysis, was used to identify the targeting antigen. Western blotting and cell-based assay with antigen-overexpressing HEK293T cells were used for antibody specificity, epitope, IgG subtype determination, and retrospective study.ResultsAn antibody against CRMP2, a synaptic protein involved in axon guidance, was identified. The immunostains of the patient’s samples on rat brain sections were eliminated by pre-absorption with HEK293T cells overexpressing CRMP2. The samples specifically immunoreacted with CRMP2, but not with CRMP1, CRMP3, CRMP4, and CRMP5. The C-terminus of CRMP2 with 536 amino acids contained the epitope for antibody binding. The subtype analysis showed that the anti-CRMP2 antibody was IgG4. Furthermore, a screening of 46 patients with neurological disoders and neuro-cytoplasm immunostainings on rat brain sections resulted in the identification of anti-CRMP2 antibodies in a case of encephalomyelitis. The two patients responded well to immunotherapies.ConclusionsThis study discovered that a novel anti-CRMP2 antibody was associated with suspected AE and thus should be included in the testing list for AE.

This paper considers a new task of detecting heterogeneous parallel steganography (HPS) on streaming media. This task is to detect the existence of the confidential messages hidden in the frames of streaming media with multiple kinds of orthogonal steganographic methods. We target on detecting HPS in this work for low bit-rate Voice over Internet Protocol (VoIP) speech streams, which is a widely-used streaming medium. Specifically, two steganographic methods, i.e., Quantization Index Modulation and Pitch Modulation Steganography, are utilized to form the HPS. Detecting HPS on low bit-rate VoIP speech streams is challenging for existing steganalysis methods. To accomplish the target, we propose a novel deep model named as Steganalysis Feature Fusion Network (SFFN). SFFN consists of three sub-networks, i.e., a feature learning network, a feature fusion network and a classification network. With the three sub-networks, SFFN can effectively extract steganalysis features for the steganographic methods used in HPS and can fuse the features to make credible prediction. The experimental results demonstrate that our method is superior to the state-of-the-art steganalysis methods when detecting HPS. Besides, our method meets the requirement of real-time detection.

The IP core mapping optimization problem is an NP-hard problem in network-on-chip design. Because of the computational complexity of an IP core mapping, the MPNN-Ptr networks composed of the graph networks and the pointer networks are proposed to model the IP core mapping. The neural network IP core mapping model (NNMM) can effectively evaluate the probability of each mapping solution as the optimal solution to an IP core mapping optimization problem. Then, an IP core mapping algorithm, the neural mapping algorithm (NMA), is proposed. In this algorithm, the global search is realized by sampling the mapping solutions with a high probability evaluated by NNMM. The sampled candidate mapping solutions according to probability can effectively decrease the candidate mapping solution space, which can reduce invalid searches and avoid getting stuck at local minima. Then, the 2-opt algorithm is used as a local search to improve the quality of mapping further. Simulation results show that the MPNN-Ptr networks can effectively model the IP core mapping. Compared with the state-of-the-art mapping algorithms, NMA produces better solutions for both specific applications and random applications. NMA achieves a 7.90% communication cost reduction on average than the classic mapping algorithm: NMAP.

FUN14 domain‑containing 1 (FUNDC1) is a receptor that has been previously reported to activate hypoxia‑induced mitophagy. However, the potential role of FUNDC1 in the pathophysiology of dental pulp diseases remains unknown. Therefore, present study first collected tissue specimens from patients with pulpitis and from healthy individuals. The results of reverse transcription‑quantitative PCR and immunohistochemical staining revealed markedly increased FUNDC1 and hypoxia‑inducible factor‑1α expression in pulpitis tissue specimens compared with those from healthy individuals. To provide a theoretical basis for the study of the occurrence, development and reparative mechanisms in the dental pulp after tissue injury, the present study then investigated the role of hypoxia‑induced mitophagy in the regulation of proliferation, migration and odontoblastic differentiation in human dental pulp cells (HDPCs), in addition, to the possible involvement of FUNDC1. The surface markers and multipotent differentiation capabilities of HDPCs were performed by flow cytometry (surface markers), alizarin red (osteogenic capabilities), alcian blue (chondrogenic capabilities) and oil red O (adipogenic capabilities). Following culture under hypoxia conditions (1% O

Recent reports have demonstrated that Sox9+HNF4α+ hepatocytes are involved in liver regeneration after chronic liver injury; however, little is known about the origin of Sox9+HNF4α+ hepatocytes and the regulatory mechanism. Employing a combination of chimeric lineage tracing, immunofluorescence, and immunohistochemistry, we demonstrate that Sox9+HNF4α+ hepatocytes, generated by transition from mature hepatocytes, play an important role in the initial phase after partial hepatectomy (PHx). Additionally, knocking down the expression of Sox9 suppresses hepatocyte proliferation and blocks the recovery of lost hepatic tissue. In vitro and in vivo assays demonstrated that Bcl3, activated by LPS, promotes hepatocyte conversion and liver regeneration. Mechanistically, Bcl3 forms a complex with and deubiquitinates YAP1 and further induces YAP1 to translocate into the nucleus, resulting in Sox9 upregulation and mature hepatocyte conversion. We demonstrate that Bcl3 promotes Sox9+HNF4α+ hepatocytes to participate in liver regeneration, and might therefore be a potential target for enhancing regeneration after liver injury.

Background The effect of strabismus surgery on the quality of life of strabismus patients with thyroid-associated ophthalmopathy is unclear, The aim of the study is to explore the effect of strabismus surgery on the quality of life of strabismus patients with quiescent thyroid-associated ophthalmopathy (TAO).Methods This study is a prospective case-series study. Strabismus patients with TAO who were admitted in the First Affiliated Hospital of Guangxi Medical University from October 2011 to April 2016 were included in this study. The patients completed the Graves' ophthalmopathy quality of life questionnaire (GO-QOL) before the operation and 6 weeks after the operation. Preoperative and postoperative quality of GO-QOL scores were compared using paired samples t-test. The correlation between the angle of misalignment and the quality of life was analyzed using Spearman correlation analysis.Results We recruited 23 patients for this study, including 11 males and 12 females. Patients were 31-67 years old with a mean age of 49.04 years. Compared to preoperative GO-QOL scores, postoperative GO-QOL scores for visual function (43.04 vs. 73.50, pConclusions Strabismus surgery significantly improves the quality of life of the strabismus patients with quiescent TAO.

Bacterial lipoproteins perform a diverse array of functions including bacterial envelope biogenesis and microbe–host interactions. Lipoproteins in gram-negative bacteria are sorted to the outer membrane (OM) via the localization of lipoproteins (Lol) export pathway. The ATP-binding cassette (ABC) transporter LolCDE initiates the Lol pathway by selectively extracting and transporting lipoproteins for trafficking. Here, we report cryo-EM structures of LolCDE in apo, lipoprotein-bound, and AMPPNP-bound states at a resolution of 3.5 to 4.2 Å. Structure-based disulfide crosslinking, photo-crosslinking, and functional complementation assay verify the apo-state structure and reveal the molecular details regarding substrate selectivity and substrate entry route. Our studies snapshot 3 functional states of LolCDE in a transport cycle, providing deep insights into the mechanisms that underlie LolCDE-mediated lipoprotein sorting in E. coli.

Session-based recommendation is a challenging problem in real-world scenes, e.g., e-commerce, short video platforms, and music platforms,which aims to predict the next click action based on the anonymous session. Recently, graph neural networks (GNNs) have emerged as the state-of-the-art methods for session-based recommendation. However, we find that there exist some limitations in these methods. One is the important transition relations between items are not fully exploited. Another is the graph structure noise and long-range dependency problems when introducing GNNs in session-based recommendation. Considering these problems, we propose a novel approach based on self-attention mechanism for session-based recommendation, called Transition Relation Aware Self-Attention (TRASA). TRASA encodes the shortest path between items in a session graph through the gated recurrent unit as their transition relations. And uses a self-attention mechanism to explore the potential relations between items, which does not rely on the graph structure and can capture the long-range dependencies intrinsically. Besides, the transition relations are incorporated explicitly when computing the attention scores to keep the transition semantics. Extensive experiments on four real-word datasets demonstrate that TRASA outperforms the existing state-of-the-art methods consistently.

Boron carbide ceramics have broad application prospects due to their excellent performance. However, their poor sinterability and low fracture toughness limit their application, especially in the field of bulletproof armor. In this work, B4C–TiB2–SiC composites were successfully prepared by spark plasma sintering (SPS) with B4C, TiB2, and SiC powder mixtures. The effects of different SiC contents on the mechanical properties and microstructure of B4C–TiB2–SiC composites were investigated. The B4C–TiB2–SiC composites with 15 wt% SiC combined a relative density of 98.43%, a fracture toughness of 4.17 MPa m1/2, as well as Vickers hardness of 30.07 GPa. The addition of SiC can increase the toughness of B4C–TiB2–SiC composites while maintaining high hardness. The main reason for this behavior is grain refinement of B4C and TiB2 due to the pinning effect of the SiC phase.