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Sevoflurane induces developmental neurotoxicity in mice; however, the underlying mechanisms remain unclear. Triggering receptor expressed on myeloid cells 2 (TREM2) is essential for microglia-mediated synaptic refinement during the early stages of brain development. We explored the effects of TREM2 on dendritic spine pruning during sevoflurane-induced developmental neurotoxicity in mice. Mice were anaesthetized with sevoflurane on postnatal days 6, 8, and 10. Behavioral performance was assessed using the open field test and Morris water maze test. Genetic knockdown of TREM2 and overexpression of TREM2 by stereotaxic injection were used for mechanistic experiments. Western blotting, immunofluorescence, electron microscopy, three-dimensional reconstruction, Golgi staining, and whole-cell patch-clamp recordings were performed. Sevoflurane exposures upregulated the protein expression of TREM2, increased microglia-mediated pruning of dendritic spines, and reduced synaptic multiplicity and excitability of CA1 neurons. TREM2 genetic knockdown significantly decreased dendritic spine pruning, and partially aggravated neuronal morphological abnormalities and cognitive impairments in sevoflurane-treated mice. In contrast, TREM2 overexpression enhanced microglia-mediated pruning of dendritic spines and rescued neuronal morphological abnormalities and cognitive dysfunction. TREM2 exerts a protective role against neurocognitive impairments in mice after neonatal exposures to sevoflurane by enhancing microglia-mediated pruning of dendritic spines in CA1 neurons. This provides a potential therapeutic target in the prevention of sevoflurane-induced developmental neurotoxicity.

Introduction: Air pollution is a significant contributor to morbidity and mortality globally and has been linked to an increased risk of dementia. Previous studies within the Betula cohort in Northern Sweden have demonstrated associations between air pollution and dementia, as well as distinctive metabolomic profiles in dementia patients compared to controls. This study aimed to investigate whether air pollution is associated with quantitative changes in metabolite levels within this cohort, and whether future dementia status would modify this association. Methods: Both short-term and long-term exposure to air pollution were evaluated using high spatial resolution models and measured data. Air pollution from vehicle exhaust and woodsmoke were analyzed separately. Metabolomic profiling was conducted on 321 participants, including 58 serum samples from dementia patients and a control group matched for age, sex, and education level, using nuclear magnetic resonance spectroscopy. Results: No statistically significant associations were found between any metabolites and any measures of short-term or long-term exposure to air pollution. However, there were trends potentially suggesting associations between both long-term and short-term exposure to air pollution with lactate and glucose metabolites. Notably, these associations were observed despite the lack of correlation between long-term and short-term air pollution exposure in this cohort. There were also tendencies for associations between air pollution from woodsmoke to be more pronounced in participants that would later develop dementia, suggesting a potential effect depending on urban/rural factors. Discussion: While no significant associations were found, the trends observed in the data suggest potential links between air pollution exposure and changes in lactate and glucose metabolites. These findings provide some new insights into the link between air pollution and metabolic markers in a low-exposure setting. Howe

Graph Attention Network (GAT) is one of the most popular Graph Neural Network (GNN) architecture, which employs the attention mechanism to learn edge weights and has demonstrated promising performance in various applications. However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs. To address this limitation, we propose the Directional Graph Attention Network (DGAT) in this paper. DGAT is able to combine the feature-based attention with the global directional information extracted from the graph topology. To this end, a new class of Laplacian matrices is proposed which can provably reduce the diffusion distance between nodes. Based on the new Laplacian, topology-guided neighbour pruning and edge adding mechanisms are proposed to remove the noisy and capture the helpful long-range neighborhood information. Besides, a global directional attention is designed to enable a topological-aware information propagation. The superiority of the proposed DGAT over the baseline GAT has also been verified through experiments on real-world benchmarks and synthetic data sets. It also outperforms the state-of-the-art (SOTA) models on 6 out of 7 real-world benchmark datasets.

We consider the success probability of the $L_0$-regularized box-constrained Babai point, which is a suboptimal solution to the $L_0$-regularized box-constrained integer least squares problem and can be used for MIMO detection. First, we derive formulas for the success probability of both $L_0$-regularized and unregularized box-constrained Babai points. Then we investigate the properties of the $L_0$-regularized box-constrained Babai point, including the optimality of the regularization parameter, the monotonicity of its success probability, and the monotonicity of the ratio of the two success probabilities. A bound on the success probability of the $L_0$-regularized Babai point is derived. After that, we analyze the effect of the LLL-P permutation strategy on the success probability of the $L_0$-regularized Babai point. Then we propose some success probability based column permutation strategies to increase the success probability of the $L_0$-regularized box-constrained Babai point. Finally, we present numerical tests to confirm our theoretical results and to show the advantage of the $L_0$ regularization and the effectiveness of the proposed column permutation algorithms compared to existing strategies., Comment: 37 pages, 1 figure including 2 subfigures

In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramidal Information Funneling where attention is scattering widely in lower layers, progressively consolidating within specific contexts, and ultimately focusin on critical tokens (a.k.a massive activation or attention sink) in higher layers. Motivated by these insights, we developed PyramidKV, a novel and effective KV cache compression method. This approach dynamically adjusts the KV cache size across different layers, allocating more cache in lower layers and less in higher ones, diverging from traditional methods that maintain a uniform KV cache size. Our experimental evaluations, utilizing the LongBench benchmark, show that PyramidKV matches the performance of models with a full KV cache while retaining only 12% of the KV cache, thus significantly reducing memory usage. In scenarios emphasizing memory efficiency, where only 0.7% of the KV cache is maintained, PyramidKV surpasses other KV cache compression techniques achieving up to a 20.5 absolute accuracy improvement on TREC.

Recent studies reveal that Large Language Models (LLMs) face challenges in balancing safety with utility, particularly when processing long texts for NLP tasks like summarization and translation. Despite defenses against malicious short questions, the ability of LLMs to safely handle dangerous long content, such as manuals teaching illicit activities, remains unclear. Our work aims to develop robust defenses for LLMs in processing malicious documents alongside benign NLP task queries. We introduce a defense dataset comprised of safety-related examples and propose single-task and mixed-task losses for instruction tuning. Our empirical results demonstrate that LLMs can significantly enhance their capacity to safely manage dangerous content with appropriate instruction tuning. Additionally, strengthening the defenses of tasks most susceptible to misuse is effective in protecting LLMs against processing harmful information. We also observe that trade-offs between utility and safety exist in defense strategies, where Llama2, utilizing our proposed approach, displays a significantly better balance compared to Llama1., Comment: accepted to NAACL2024 TrustNLP workshop

Massive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for this task. Expectation propagation (EP) and its variants are widely used for MIMO detection and have achieved the best performance. However, EP-based solvers fail to capture the correlation between unknown variables, leading to loss of information, and in addition, they are computationally expensive. In this paper, we show that the real-valued system can be modeled as spectral signal convolution on graph, through which the correlation between unknown variables can be captured. Based on this analysis, we propose graph convolution-enhanced expectation propagation (GCEPNet), a graph convolution-enhanced EP detector. GCEPNet incorporates data-dependent attention scores into Chebyshev polynomial for powerful graph convolution with better generalization capacity. It enables a better estimation of the cavity distribution for EP and empirically achieves the state-of-the-art (SOTA) MIMO detection performance with much faster inference speed. To our knowledge, we are the first to shed light on the connection between the system model and graph convolution, and the first to design the data-dependent attention scores for graph convolution.

Fluorescence in situ hybridization (FISH) is a widely used tool for quantifying gene expression and determining the location of RNA molecules in cells. We present an improved method for FISH probe production that yields high-purity probes with a wide range of fluorophores using standard laboratory equipment at low cost. The method modifies an earlier protocol that uses terminal deoxynucleotidyl transferase to add fluorescently labeled nucleotides to synthetic deoxyoligonucleotides. In our protocol, amino-11-ddUTP is joined to an oligonucleotide pool prior to its conjugation to a fluorescent dye, thereby generating pools of probes ready for a variety of modifications. This order of reaction steps allows for high labeling efficiencies regardless of the GC content or terminal base of the oligonucleotides. The degree of labeling (DOL) for spectrally distinct fluorophores (Quasar, ATTO, and Alexa dyes) was mostly >90%, comparable with commercial probes. The ease and low cost of production allowed the generation of probe sets targeting a wide variety of RNA molecules. Using these probes, FISH assays in C2C12 cells showed the expected subcellular localization of mRNAs and pre-mRNAs for Polr2a (RNA polymerase II subunit 2a) and Gapdh, and of the long noncoding RNAs Malat1 and Neat1 Developing FISH probe sets for several transcripts containing retained introns, we found that retained introns in the Gabbr1 and Noc2l transcripts are present in subnuclear foci separate from their sites of synthesis and partially coincident with nuclear speckles. This labeling protocol should have many applications in RNA biology.

Background: The mechanisms underlying myocardial ischemia/reperfusion (I/R) injury are not fully understood. This study aims to explore key candidate genes and potential therapeutic targets for treatment of myocardial I/R injury. Methods: The transcriptional profiles of ventricular myocardium during cardiac arrest, ischemia, and reperfusion were obtained from the Gene Expression Omnibus database. Based on the transcriptional data of GSE6381, functional pathway and process enrichment analyses, protein-protein interaction network, and gene set enrichment analyses were conducted. In the animal experiments, we established the myocardial I/R injury model in mice. We validated the mRNA and protein expression of the key genes using the qPCR and western blots. We further assessed the expression and localization of CCL21 and its receptors using immunofluorescence staining experiments. Results: The microarray analyses identified five key genes (CCL21, XCR1, CXCL13, EDN1, and CASR). Myocardial I/R process in mice resulted in significant myocardial infraction, histological damage, and myocardial apoptosis. The results of qPCR and western blots showed that the expression of CCL21 and CXCL13 were increased following myocardial I/R injury in mice. Furthermore, the immunofluorescence staining results revealed that the expression of GPR174/CCR7 (CCL21 receptors), but not CXCR5 (CXCL13 receptor), was elevated following myocardial I/R injury. Moreover, the activated CCL21-GPR174/CCR7 signaling was located on the cardiac fibroblasts of the myocardium with I/R injury. Conclusion: This study revealed several key factors underlying myocardial I/R injury. Of these, the activation of CCL21-GPR174/CCR7 signaling on cardiac fibroblasts was highlighted, which provides potential therapeutic targets for cardioprotection.

Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.Recent research suggests that, even in the absence of homophily, the advantage of GNNs still exists as long as nodes from the same class share similar neighborhood patterns [38].However, this argument only considers intra-class Node Distinguishability (ND) but neglects inter-class ND, which provides incomplete understanding of homophily on GNNs.In this paper, we first demonstrate such deficiency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND.To formulate this idea and study ND deeply, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and define two metrics, Probabilistic Bayes Error (PBE) and negative generalized Jeffreys divergence, to quantify ND.With the metrics, we visualize and analyze how graph filters, node degree distributions and class variances influence ND, and investigate the combined effect of intra- and inter-class ND.Besides, we discovered the mid-homophily pitfall, which occurs widely in graph datasets.Furthermore, we verified that, in real-work tasks, the superiority of GNNs is indeed closely related to both intra- and inter-class ND regardless of homophily levels.Grounded in this observation, we propose a new hypothesis-testing based performance metric beyond homophily, which is non-linear, feature-based and can provide statistical threshold value for GNNs' the superiority.Experiments indicate that it is significantly more effective than the existing homophily metrics on revealing the advantage and disadvantage of graph-aware modes on both synthetic and benchmark real-world datasets. © 2023 Neural information processing systems foundation. All rights reserved.

BackgroundMultiple neonatal exposures to sevoflurane induce neurocognitive dysfunctions in rodents. The lack of cell type-specific information after sevoflurane exposure limits the mechanistic understanding of these effects. In this study, the authors tested the hypothesis that sevoflurane exposures alter the atlas of hippocampal cell clusters and have neuronal and nonneuronal cell type-specific effects in mice of both sexes.MethodsNeonatal mice were exposed to 3% sevoflurane for 2 h at postnatal days 6, 8, and 10 and analyzed for the exposure effects at postnatal day 37. Single-nucleus RNA sequencing was performed in the hippocampus followed by in situ hybridization to validate the results of RNA sequencing. The Morris Water Maze test was performed to test neurocognitive function.ResultsThe authors found sex-specific distribution of hippocampal cell types in control mice alongside cell type- and sex-specific effects of sevoflurane exposure on distinct hippocampal cell populations. There were important changes in male but not in female mice after sevoflurane exposure regarding the proportions of cornu ammonis 1 neurons (control vs. sevoflurane, males: 79.9% vs. 32.3%; females: 27.3% vs. 24.3%), dentate gyrus (males: 4.2% vs. 23.4%; females: 36.2% vs. 35.8%), and oligodendrocytes (males: 0.6% vs. 6.9%; females: 5.9% vs. 7.8%). In male but not in female mice, sevoflurane altered the number of significantly enriched ligand-receptor pairs in the cornu ammonis 1, cornu ammonis 3, and dente gyrus trisynaptic circuit (control vs. sevoflurane, cornu ammonis 1-cornu ammonis 3: 18 vs. 42 in males and 15 vs. 21 in females; cornu ammonis 1-dentate gyrus: 21 vs. 35 in males and 12 vs. 20 in females; cornu ammonis 3-dentate gyrus: 25 vs. 45 in males and 17 vs. 20 in females), interfered with dentate gyrus granule cell neurogenesis, hampered microglia differentiation, and decreased cornu ammonis 1 pyramidal cell diversity. Oligodendrocyte differentiation was specifically altered i

Single-cell transcriptomic platforms provide an opportunity to map an organisms response to environmental cues with high resolution. Here, we applied single-nucleus RNA sequencing (snRNA-seq) to establish the tissue and cell type-resolved transcriptome of the adult C. elegans and characterize the inter- and trans-generational transcriptional impact of ethanol. We profiled the transcriptome of 41,749 nuclei resolving into 31 clusters, representing a diverse array of adult cell types including syncytial tissues. Following exposure to human-relevant doses of alcohol, several germline, striated muscle, and neuronal clusters were identified as being the most transcriptionally impacted at the F1 and F3 generations. The effect on germline clusters was confirmed by phenotypic enrichment analysis as well as by functional validation, which revealed a remarkable inter- and trans-generational increase in germline apoptosis, aneuploidy, and embryonic lethality. Together, snRNA-seq represents a valuable approach for the detailed examination of an adult organisms response to environmental exposures.

Robust, high-precision global localization is fundamental to a wide range of outdoor robotics applications. Conventional fusion methods use low-accuracy pseudorange based GNSS measurements ($>>5m$ errors) and can only yield a coarse registration to the global earth-centered-earth-fixed (ECEF) frame. In this paper, we leverage high-precision GNSS carrier-phase positioning and aid it with local visual-inertial odometry (VIO) tracking using an extended Kalman filter (EKF) framework that better resolves the integer ambiguity concerned with GNSS carrier-phase. %to achieve centimeter-level accuracy in the ECEF frame. We also propose an algorithm for accurate GNSS-antenna-to-IMU extrinsics calibration to accurately align VIO to the ECEF frame. Together, our system achieves robust global positioning demonstrated by real-world hardware experiments in severely occluded urban canyons, and outperforms the state-of-the-art RTKLIB by a significant margin in terms of integer ambiguity solution fix rate and positioning RMSE accuracy.

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.

Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we propose a tri-agent generation pipeline comprising a generator, an instructor, and an editor to enhance output personalization. The generator produces an initial output, the instructor automatically generates editing instructions based on user preferences, and the editor refines the output to align with those preferences. The inference-only large language model (ChatGPT) serves as both the generator and editor, with a smaller model acting as the instructor to guide output generation. We train the instructor using editor-steered reinforcement learning, leveraging feedback from a large-scale editor model to optimize instruction generation. Experimental results on two abstractive summarization datasets demonstrate the effectiveness of our approach in generating outputs that better meet user expectations. Code is available at \url{https://github.com/Wendy-Xiao/chatgpt_editing_summ}, Comment: Accepted at EACL 2024 Findings

Xuegang Wang,1,* Weiquan Li,2,* Ning Lou,3,* Weiwei Han,3 Bo Hai,2 Wen Xiao,2 Xiaoping Zhang2 1Department of Urology, The First Affiliated Hospital, School of Medicine, Xiamen University, Xiamen, Fujian, People’s Republic of China; 2Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 3Department of Urology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen Xiao; Bo, Hai, Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, People’s Republic of China, Tel +86-17088353610, Fax +86 85776343, Email xiaowenx11@163.com; Haibo2022@hust.edu.cnBackground: Invasion and metastasis led to poor prognosis and death of clear cell renal cell carcinoma (ccRCC) patients. The deoxynucleotidyl transferase terminal interacting protein 1 (DNTTIP1) was reported to promote multiple tumor progression. However, there is no research about DNTTIP1 in ccRCC.Methods: Kaplan–Meier survival analysis, multivariate analysis demonstrated the prognostic indicator in overall survival (OS) and disease-free survival (DFS) of ccRCC with DNTTIP1 expression in the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC). Receiver operator characteristic (ROC) curve analyzed diagnostic ability of DNTTIP1 in TCGA-KIRC and validation dataset. The quantitative real-time polymerase chain reaction (qRT-PCR) detected the DNTTIP1 expression in renal cancer tissues, and the Office of Cancer Clinical Proteomics Research (CPTAC) verified the protein expression of DNTTIP1. Moreover, nomogram predicted the role of DNTTIP1 in ccRCC patient. Single-sample Gene Set Enrichment Analysis (SsGSEA) and GSEA evaluated the pathoge

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. For instance, LLMs can effectively summarize malicious long documents but often refuse to translate them. This discrepancy highlights a previously unidentified vulnerability: attacks exploiting tasks with weaker safety alignment, like summarization, can potentially compromise the integraty of tasks traditionally deemed more robust, such as translation and question-answering (QA). Moreover, the concurrent use of multiple NLP tasks with lesser safety alignment increases the risk of LLMs inadvertently processing harmful content. We demonstrate these vulnerabilities in various safety-aligned LLMs, particularly Llama2 models and GPT-4, indicating an urgent need for strengthening safety alignments across a broad spectrum of NLP tasks., Comment: 17 pages,10 figures

While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpretability. In this work, we present a novel visual analytical framework to support the analysis of transformer-based generative networks. In contrast to previous work, which has mainly focused on encoder-based models, our framework is one of the first dedicated to supporting the analysis of transformer-based encoder-decoder models and decoder-only models for generative and classification tasks. Hence, we offer an intuitive overview that allows the user to explore different facets of the model through interactive visualization. To demonstrate the feasibility and usefulness of our framework, we present three detailed case studies based on real-world NLP research problems., Comment: 6 pages (reference excluded), 7 figures

Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks. It is usually expected that satisfactory overall accuracy can be achieved across numerous domains through well-designed textual prompts. However, we found that their performance in the worst categories is significantly inferior to the overall performance. For example, on ImageNet, there are a total of 10 categories with class-wise accuracy as low as 0\%, even though the overall performance has achieved 64.1\%. This phenomenon reveals the potential risks associated with using CLIP models, particularly in risk-sensitive applications where specific categories hold significant importance. To address this issue, we investigate the alignment between the two modalities in the CLIP model and propose the Class-wise Matching Margin (\cmm) to measure the inference confusion. \cmm\ can effectively identify the worst-performing categories and estimate the potential performance of the candidate prompts. We further query large language models to enrich descriptions of worst-performing categories and build a weighted ensemble to highlight the efficient prompts. Experimental results clearly verify the effectiveness of our proposal, where the accuracy on the worst-10 categories on ImageNet is boosted to 5.2\%, without manual prompt engineering, laborious optimization, or access to labeled validation data.

BackgroundViral tracers are important tools for mapping brain connectomes. The feature of predominant anterograde transneuronal transmission offers herpes simplex virus-1 (HSV-1) strain H129 (HSV1-H129) as a promising candidate to be developed as anterograde viral tracers. In our earlier studies, we developed H129-derived anterograde polysynaptic tracers and TK deficient (H129-dTK) monosynaptic tracers. However, their broad application is limited by some intrinsic drawbacks of the H129-dTK tracers, such as low labeling intensity due to TK deficiency and potential retrograde labeling caused by axon terminal invasion. The glycoprotein K (gK) of HSV-1 plays important roles in virus entry, egress, and virus-induced cell fusion. Its deficiency severely disables virus egress and spread, while only slightly limits viral genome replication and expression of viral proteins. Therefore, we created a novel H129-derived anterograde monosynaptic tracer (H129-dgK) by targeting gK, which overcomes the limitations of H129-dTK.MethodsUsing our established platform and pipeline for developing viral tracers, we generated a novel tracer by deleting the gK gene from the H129-G4. The gK-deleted virus (H129-dgK-G4) was reconstituted and propagated in the Vero cell expressing wildtype H129 gK (gKwt) or the mutant gK (gKmut, A40V, C82S, M223I, L224V, V309M), respectively. Then the obtained viral tracers of gKmut pseudotyped and gKwt coated H129-dgK-G4 were tested in vitro and in vivo to characterize their tracing properties.ResultsH129-dgK-G4 expresses high levels of fluorescent proteins, eliminating the requirement of immunostaining for imaging detection. Compared to the TK deficient monosynaptic tracer H129-dTK-G4, H129-dgK-G4 labeled neurons with 1.76-fold stronger fluorescence intensity, and visualized 2.00-fold more postsynaptic neurons in the downstream brain regions. gKmut pseudotyping leads to a 77% decrease in retrograde labeling by reducing axon terminal invasion, and thus dramatic

Background: The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI. Objectives: In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy. Methods: Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016–December 2018), and mortality (January 2017–December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality. Results: Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO2 and O3 to the final AQHI were prominent, while the contribution of SO2 and PM2.5 were relatively small. Conclusions: The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective

Single-cell RNA transcriptomic platforms have significantly contributed to our understanding of tissue heterogeneity as well as of developmental and cellular differentiation trajectories. They also provide an opportunity to map an organism’s response to environmental cues with high resolution and unbiasedly identify the cell types that are the most transcriptionally sensitive to exposures. Here, we applied single nucleus RNA-seq experimental and computational approaches to C. elegans to establish the transcriptome of the adult nematode and comprehensively characterize the transcriptional impact of ethanol as a model environmental exposure on the entire organism at cell type-resolution over several generations. Clustering, tissue and phenotype enrichment, and gene ontology analyses identified 31 clusters representing a diverse number of adult cell types, including those from syncytial and multi-nucleated tissues which are difficult to assess by single cell RNA-seq, such as the mitotic and meiotic germline, hypodermal cells, and the intestine. We applied this method to identify the impact of inter- and trans-generational exposure to two human-relevant doses of alcohol. Cell type proportions were not significantly altered by ethanol. However, Euclidean distance analysis identified several germline, striated muscle, and neuronal clusters as being major transcriptional targets of ethanol at both the F1 and F3 generations although the relative order of clusters changed between generations. The impact on germline clusters was further confirmed by phenotypic enrichment analysis as well as functional validation, namely a remarkable inter- and trans-generational increase in germline apoptosis, aneuploidy, and embryonic lethality. Together, snRNA-seq of the adult C. elegans represents a powerful approach for the detailed examination of an adult organism’s response to environmental cues.

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN models for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks., Comment: Accepted as Oral Presentation at NeurIPS 2022 New Frontiers in Graph Learning Workshop (NeurIPS GLFrontiers 2022)

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i.i.d.) samples. Though GNNs are believed to outperform basic NNs in real-world tasks, it is found that in some cases, GNNs have little performance gain or even underperform graph-agnostic NNs. To identify these cases, based on graph signal processing and statistical hypothesis testing, we propose two measures which analyze the cases in which the edge bias in features and labels does not provide advantages. Based on the measures, a threshold value can be given to predict the potential performance advantages of graph-aware models over graph-agnostic models., Comment: Accepted by 12th International Conference on Complex Networks and Their Applications

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared to NNs is not satisfactory. Heterophily has been considered the main cause of this empirical observation and numerous works have been put forward to address it. In this paper, we first revisit the widely used homophily metrics and point out that their consideration of only graph-label consistency is a shortcoming. Then, we study heterophily from the perspective of post-aggregation node similarity and define new homophily metrics, which are potentially advantageous compared to existing ones. Based on this investigation, we prove that some harmful cases of heterophily can be effectively addressed by local diversification operation. Then, we propose the Adaptive Channel Mixing (ACM), a framework to adaptively exploit aggregation, diversification and identity channels node-wisely to extract richer localized information for diverse node heterophily situations. ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers. When evaluated on 10 benchmark node classification tasks, ACM-augmented baselines consistently achieve significant performance gain, exceeding state-of-the-art GNNs on most tasks without incurring significant computational burden., Comment: Published at 36th Conference on Neural Information Processing Systems (NeurIPS 2022). arXiv admin note: substantial text overlap with arXiv:2109.05641

Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In this paper, we focus on entity-level factual inconsistency, i.e. reducing the mismatched entities between the generated summaries and the source documents. We therefore propose a novel entity-based SpanCopy mechanism, and explore its extension with a Global Relevance component. Experiment results on four summarization datasets show that SpanCopy can effectively improve the entity-level factual consistency with essentially no change in the word-level and entity-level saliency. The code is available at https://github.com/Wendy-Xiao/Entity-based-SpanCopy

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent surge of interest in researching hypergraphs, the centrality problem has attracted abundant attention due to the challenge of how to utilize the higher-order structure for the definition of centrality metrics. In this paper, we propose a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC (LHGC) which can achieve a balance between accuracy and computational complexity. Meanwhile, two comprehensive evaluation metrics, i.e., a complex contagion model in hypergraphs that mimics the group influence during the spreading process and network s-efficiency based on the higher-order distance between nodes, are first proposed to evaluate the effectiveness of our methods. The results show that our methods can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.

This paper presents a two-step method for damage localization in beams by combining natural frequencies and mode shapes. The general locations of the damage are first identified from an indicator developed using relative natural frequency change (RNFC) curves and the values of RNFCs. A curvature-mode-shape-based method is then utilized to determine the specific location of the damage in the second step. The proposed two-step method is verified by detecting damage in a simulated simply-supported beam. The identified damage location agrees well with the actual damage location. A strategy for fast and accurate damage localization based on general localization using natural frequencies and specific localization using mode shapes is the main novelty of the paper.

The Korkine--Zolotareff (KZ) reduction, and its generalisations, are widely used lattice reduction strategies in communications and cryptography. The KZ constant and Schnorr's constant were defined by Schnorr in 1987. The KZ constant can be used to quantify some useful properties of KZ reduced matrices. Schnorr's constant can be used to characterize the output quality of his block $2k$-reduction and is used to define his semi block $2k$-reduction, which was also developed in 1987. Hermite's constant, which is a fundamental constant lattices, has many applications, such as bounding the length of the shortest nonzero lattice vector and the orthogonality defect of lattices. Rankin's constant was introduced by Rankin in 1953 as a generalization of Hermite's constant. It plays an important role in characterizing the output quality of block-Rankin reduction, proposed by Gama et al. in 2006. In this paper, we first develop a linear upper bound on Hermite's constant and then use it to develop an upper bound on the KZ constant. These upper bounds are sharper than those obtained recently by the authors, and the ratio of the new linear upper bound to the nonlinear upper bound, developed by Blichfeldt in 1929, on Hermite's constant is asymptotically 1.0047. Furthermore, we develop lower and upper bounds on Schnorr's constant. The improvement to the lower bound over the sharpest existing one developed by Gama et al. is around 1.7 times asymptotically, and the improvement to the upper bound over the sharpest existing one which was also developed by Gama et al. is around 4 times asymptotically. Finally, we develop lower and upper bounds on Rankin's constant. The improvements of the bounds over the sharpest existing ones, also developed by Gama et al., are exponential in the parameter defining the constant., Comment: to appear in Designs, Codes and Cryptography. arXiv admin note: text overlap with arXiv:1904.09395

With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km^2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk., Comment: Accepted by IJCV 2022

Hai-Ying Chen,1,&ast; Ming-Chao Li,2,&ast; Dan Liao,1 Chi Li,1 Qiu-Ming Ji,1 Ping Guo,1 Zou Su,1 Yu-Hong Yang,1 Wen-Hao Xiao,1 Wen-Hui Zhai1 1Department of Psychiatry, Wuhan Wudong Hospital, Wuhan, People’s Republic of China; 2Department of Psychiatry, Wuhan Mental Health Center, Wuhan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dan Liao; Chi Li, Department of Psychiatry, Wuhan Wudong Hospital, No. 46 of Wudong Street, Qingshan District, Wuhan, 430084, People’s Republic of China, Tel +86 27 5052 8367, Fax +86 27 8643 8247, Email danliaodr@21cn.comObjective: Our study aimed to investigate the effects of computer-assisted cognitive remediation therapy (CCRT) on cognitive function, social function and quality of life in patients with vascular dementia (VD).Methods: Ninety-eight patients with VD were treated with CCRT in four 45-minute sessions per week over a course of 40 sessions to exercise four cognitive functions, including flexibility, working memory, plan execution and social cognition. The Mini-Mental State Examination (MMSE), Social Disability Screening Schedule (SDSS), Personal and Social Performance Scale (PSP), and Generic Quality of Life Inventory-74 (GQOL-74) were used to assess before and after treatment.Results: (1) The scores of orientation (5.60 ± 1.35), calculation (2.20 ± 0.79), verbal ability (7.10 ± 0.36), spatial ability (0.78 ± 0.42), immediate memory (2.42 ± 0.53), short-term memory (1.17 ± 0.78) and MMSE (23.36 ± 2.98) were all improved after treatment (P < 0.05) compared with those before treatment; (2) The scores of SDSS, PSP and Activities of Daily Living (ADL) after treatment were 8.23 ± 0.94, 81.36 ± 14.23, and 32.7 ± 12.1, and all of which improved (P < 0.05); (3) The scores of physical health were 68.24 ± 7.44, mental health were 69.75 ± 7.15, social function were 69.08 ± 7.43, material life were 37.46 ± 4.85 and the total score were 230.79 ± 9.56, a

Wei Liu,1,&ast; Di-Jun Lu,1,&ast; Xiao-Wen Li,1 Yu-Liang Zhang,2 Ming-Hua Shi,2 Xu-Dong Wang1 1Department of Emergency, Aerospace Center Hospital, Beijing, 100049, People’s Republic of China; 2Department of Emergency, Keyouqianqi People’s Hospital, Inner Mongolia, 137400, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xu-Dong Wang, Department of Emergency, Aerospace Center Hospital, No. 15 of Yuquan Street, Haidian District, Beijing, 100049, People’s Republic of China, Tel +86 10 59971968, Fax +86 10 59971199, Email xudongwang59@163.comObjective: To summarize the characteristics of patients calling the 120 emergency number for pre-hospital first aid in Keyouqian Banner, Hinggan League.Methods: The clinical data of adult patients with pre-hospital first aid from 2016 to 2018 were retrospectively analyzed.Results: There were 2711 cases with pre-hospital first aid. Males significantly outnumbered females. Young and middle-aged patients comprised 81.5%. Patients were mainly Han and Mongolians. Most injuries and illnesses occurred at home and on the road. The time to arrival of medical services was 30.34 ± 28.29 minutes. The proportion of pre-hospital first aid for trauma was the highest, followed by diseases concerning the cardiovascular and neurological systems. The proportion of patients with improved medical conditions after onsite first aid was 43.3%, the proportion with unchanged conditions was 51.7%, and the total mortality rate was 3.9%.Conclusion: The disease spectrum, ethnic distribution, age at onset, and pre-hospital first aid capabilities for outpatients were analyzed. These results may facilitate the establishment of a pre-hospital first aid system for the local prevention and control of acute and critical illnesses, increase the success rate of the region’s pre-hospital first aid services, and improve the prognosis.Keywords: Keyouqian Banner, Inner Mongolia, pre-hospital first aid, emerge

Kang-Da Zhang,1 Lin-Yu Wang,1 Zhi-Hua Zhang,1 Dan-Xu Zhang,1 Xiao-Wen Lin,2 Tao Meng,1 Feng Qi1 1Department of Anesthesiology, Qilu Hospital of Shandong University, Jinan, People’s Republic of China; 2Department of Pain Management, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of ChinaCorrespondence: Tao Meng; Feng Qi, Department of Anesthesiology, Qilu Hospital of Shandong University, No.107 Wenhua Xilu, Jinan, Shandong, 250012, People’s Republic of China, Email mengtao@sdu.edu.cn; 198962001111@sdu.edu.cnObjective: Several guidelines highlight the beneficial impact of exercise on the management of symptoms and health-related quality of life (HRQOL) in patients with fibromyalgia syndrome (FMS). However, few analyses have compared different types of exercise. We, therefore, intent to compare the effects of different exercise types on improving the overall HRQOL and typical symptoms in patients with FMS.Methods: Medline, Embase, the Cochrane Register of Clinical Trials, and NIH ClinicalTrials.gov were searched from inception to April 21, 2022. Randomized clinical trials (RCTs) were included to assess the impact of exercise intervention on health parameters in adult FMS patients. Data were extracted independently and a frequentist network meta-analyses (NMA) was performed to rank the effects of interventions according to P-scores. The NMA evidence certainty was assessed using the method recommended by Grading of Recommendations Assessment, Development, and Evaluation Working Group.Results: A total of 57 RCTs were identified, including 3319 participants, involving 9 interventions (7 types of exercise, 2 controls). Of all treatments compared with usual care in efficacy outcomes, Mind-body exercise was associated with the best HRQOL (SMD, − 12.12; 95% CI, − 15.79 to − 8.45). On the other characteristic symptom dimensions, based on moderate quality evidence, sensorimotor training was associated with minim

BackgroundTP53 mutations occur in more than 50% of cancers. We sought to determine the effect of the intragenic P72R single nucleotide polymorphism (SNP; rs1042522) on the oncogenic properties of mutant p53.MethodsP72R allelic selection in tumors was determined from genotype calls and a Gaussian distributed mixture model. The SNP effect on mutant p53 was determined in p53-negative cancer cell lines. RNA-sequencing, chromatin immunoprecipitation, and survival analysis were performed to describe the SNP effect. All statistical tests were 2-sided.ResultsAmong 409 patients with germline heterozygous P72R SNP who harbored somatic mutations in TP53, we observed a selection bias against missense TP53 mutants encoding the P72 SNP (P = 1.64 x 10-13). Exogenously expressed hotspot p53 mutants with the P72 SNP were negatively selected in cancer cells. Gene expression analyses showed the enrichment of p53 pathway genes and inflammatory genes in cancer cells transduced with mutants encoding P72 SNP. Immune gene signature is enriched in patients harboring missense TP53 mutations with homozygous P72 SNP. These patients have improved overall survival as compared with those with the R72 SNP (P = .04).ConclusionThis is the largest study demonstrating a selection against the P72 SNP. Missense p53 mutants with the P72 SNP retain partial wild-type tumor-suppressive functions, which may explain the selection bias against P72 SNP across cancer types. Ovarian cancer patients with the P72 SNP have a better prognosis than with the R72 SNP. Our study describes a previously unknown role through which the rs1042522 SNP modifies tumor suppressor activities of mutant p53 in patients.

Background: Evidence of maternal exposure to ambient air pollution on congenital heart defects (CHD) has been mixed and are still relatively limited in developing countries. We aimed to investigate the association between maternal exposure to air pollution and CHD in China. Method: This longitudinal, population-based, case-control study consecutively recruited fetuses with CHD and healthy volunteers from 21 cities, Southern China, between January 2006 and December 2016. Residential address at delivery was linked to random forests models to estimate maternal exposure to particulate matter with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm, and ≤10 µm as well as nitrogen dioxides, in three trimesters. The CHD cases were evaluated by obstetrician, pediatrician, or cardiologist, and confirmed by cardia ultrasound. The CHD subtypes were coded using the International Classification Diseases. Adjusted logistic regression models were used to assess the associations between air pollutants and CHD and its subtypes. Results: A total of 7055 isolated CHD and 6423 controls were included in the current analysis. Maternal air pollution exposures were consistently higher among cases than those among controls. Logistic regression analyses showed that maternal exposure to all air pollutants during the first trimester was associated with an increased odds of CHD (e.g., an interquartile range [13.3 µg/m3] increase in PM1 was associated with 1.09-fold ([95% confidence interval, 1.01-1.18]) greater odds of CHD). No significant associations were observed for maternal air pollution exposures during the second trimester and the third trimester. The pattern of the associations between air pollutants and different CHD subtypes was mixed. Conclusions: Maternal exposure to greater levels of air pollutants during the pregnancy, especially the first trimester, is associated with higher odds of CHD in offspring. Further longitudinal well-designed studies are warranted to confirm our

Aim: To characterize gingival metabolome in high-fat diet (HFD)-induced obesity in mice with/without periodontitis. Methods: HFD-induced obesity mouse model was established by 16-week feeding, and a lean control group was fed with low-fat diet (n = 21/group). Both models were induced for periodontitis on the left sides by molar ligation for 10 days, whereas the right sides were used as controls. Gingival metabolome and arginine metabolism were analysed by non-targeted/targeted liquid chromatography–mass spectrometry. Results: Of 2247 reference features, presence of periodontitis altered 165 in lean versus 885 in HFD mice; and HFD altered 525 in absence versus 1435 in presence of periodontitis. Compared with healthy condition, periodontitis and HFD had distinct effects on gingival metabolome. Metabolomic impacts of periodontitis were generally greater in HFD mice versus lean controls. K-medoids clustering showed that HFD amplified the impacts of periodontitis on gingival metabolome in both intensity and extensity. Ten metabolic pathways were enriched, including 2 specific to periodontitis, 5 specific to HFD and 3 shared ones. Targeted validation on arginine metabolism confirmed the additive effects between HFD and periodontitis. Conclusion: The obese population consuming excessive HFD display amplified metabolic response to periodontitis, presenting a metabolic susceptibility to exacerbated periodontal destruction.

Background: Clinically, there are no clear guidelines on the extent of lymphadenectomy in patients with T1 esophageal cancer. Studying the minimum number of lymph nodes for resection may increase cancer-specific survival. Methods: Patients who underwent esophagectomy and lymphadenectomy at T1 stage were selected from the Surveillance, Epidemiology and End Results Program (United States, 1998–2014). Maximally selected rank and Cox proportional hazard models were used to examine three variables: the number of lymph nodes examined, the number of negative lymph nodes and the lymph node ratio. Results: Approximately 18% had lymph node metastases, where the median values were 10, 10 and 0 for the number of lymph nodes examined, the number of negative lymph nodes and the lymph node ratio, respectively. All three examined variables were statistically associated with cancer-specific survival probability. Dividing patients into two groups shows a clear difference in cancer-specific survival compared to four or five groups for all three variables: there was a 29% decrease in the risk of death with the number of lymph nodes examined ≥14 vs < 14 (hazard ratio 0.71, 95% confidence interval: 0.57–0.89), a 35% decrease in the risk of death with the number of negative lymph nodes ≥13 vs < 13 (hazard ratio 0.65, 95% confidence interval: 0.52–0.81), and an increase of 1.21 times in the risk of death (hazard ratio 2.21, 95% confidence interval: 1.76–2.77) for the lymph node ratio > 0.05 vs ≤ 0.05. Conclusions: The extent of lymph node dissection is associated with cancer-specific survival, and the minimum number of lymph nodes that need to be removed is 14. The number of negative lymph nodes and the lymph node ratio also have prognostic value after lymphadenectomy among T1 stage patients.

Steps of mRNA maturation are important gene regulatory events that occur in distinct cellular locations. However, transcriptomic analyses often lose information on the subcellular distribution of processed and unprocessed transcripts. We generated extensive RNA-seq data sets to track mRNA maturation across subcellular locations in mouse embryonic stem cells, neuronal progenitor cells, and postmitotic neurons. We find disparate patterns of RNA enrichment between the cytoplasmic, nucleoplasmic, and chromatin fractions, with some genes maintaining more polyadenylated RNA in chromatin than in the cytoplasm. We bioinformatically defined four regulatory groups for intron retention, including complete cotranscriptional splicing, complete intron retention in the cytoplasmic RNA, and two intron groups present in nuclear and chromatin transcripts but fully excised in cytoplasm. We found that introns switch their regulatory group between cell types, including neuronally excised introns repressed by polypyrimidine track binding protein 1 (PTBP1). Transcripts for the neuronal gamma-aminobutyric acid (GABA) B receptor, 1 (Gabbr1) are highly expressed in mESCs but are absent from the cytoplasm. Instead, incompletely spliced Gabbr1 RNA remains sequestered on chromatin, where it is bound by PTBP1, similar to certain long noncoding RNAs. Upon neuronal differentiation, Gabbr1 RNA becomes fully processed and exported for translation. Thus, splicing repression and chromatin anchoring of RNA combine to allow posttranscriptional regulation of Gabbr1 over development. For this and other genes, polyadenylated RNA abundance does not indicate functional gene expression. Our data sets provide a rich resource for analyzing many other aspects of mRNA maturation in subcellular locations and across development.

Yu-fan Yang,1,* Hui Wang,1,2,* Nan Song,1,* Ya-hui Jiang,1 Jun Zhang,1 Xiao-wen Meng,1 Xiao-mei Feng,3,4 Hong Liu,5 Ke Peng,1 Fu-hai Ji1 1Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Department of Anesthesiology, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, People’s Republic of China; 3Department of Anesthesiology, University of Utah Health, Salt Lake City, UT, USA; 4Transitional Residency Program, Intermountain Medical Center, Murray, UT, USA; 5Department of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, CA, USA*These authors contributed equally to this workCorrespondence: Ke Peng; Fu-hai JiDepartment of Anesthesiology, First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, Jiangsu, 215006, People’s Republic of ChinaTel +86-159-6215-5989; +86-512-6797-2352Email pengke0422@163.com; jifuhaisuda@163.comBackground: Endoplasmic reticulum stress (ERS)-mediated myocardial inflammation and apoptosis plays an important role in myocardial ischemia/reperfusion (I/R) injury. Dexmedetomidine has been used clinically with sedative, analgesic, and anti-inflammatory properties. This study aimed to determine the effects of dexmedetomidine pretreatment on inflammation, apoptosis, and the expression of ERS signaling during myocardial I/R injury.Methods: Rats underwent myocardial ischemia for 30 min and reperfusion for 6 h, and H9c2 cardiomyocytes were subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) injury (OGD for 12 h and reoxygenation for 3 h). Dexmedetomidine was administered prior to myocardial ischemia in rats or ODG in cardiomyocytes. In addition, the α 2-adrenergic receptor antagonist (yohimbine) or the PERK activator (CCT020312) was given prior to dexmedetomidine treatment.Results: Dexmedetomidine pretreatment decreased serum levels of cardiac

Meng-yuan Zhou,1,* Li Cai,1,2,* Xiao-wen Feng,1 Yu-rong Mu,1 Bo Meng,1 Fang-yuan Liu,1 Rong Li1 1Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, School of Pharmacy, Anhui Medical University, Hefei, 230032, Anhui Province, People’s Republic of China; 2Department of Pathology, School of Basic Medicine, Anhui Medical University, Hefei, 230032, Anhui Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Rong LiSchool of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui Province, People’s Republic of ChinaTel +86 551-65172132Email aydlirong@163.comIntroduction: Previous studies have confirmed the pathologic role of synovial aquaporin 1 (AQP1) in rheumatoid arthritis (RA), but its associations with the abnormal biologic behaviors of fibroblast-like synoviocytes (FLS) remain unclear. Herein, we examined the roles of AQP1 in the proliferation, migration and invasion of TNF-α-stimulated RA FLS (MH7A cells) and explored the underlying mechanisms.Materials and Methods: Lentivirus-mediated AQP1 overexpression or silencing MH7A cells was constructed. Assays of MTT, flow cytometry (PI staining and Annexin V-PE/7-AAD staining), TMRM staining, wound-healing, transwell and phalloidin staining were performed to detect cell proliferation, cycle distribution, apoptosis, migration and invasion. The involvement of Wnt/β-catenin pathway was revealed by Western blot and β-catenin immunofluorescence staining.Results: AQP1 overexpression promoted cell proliferation of TNF-α-stimulated MH7A by facilitating transformation from G0/G1 to S phase and inhibiting cell apoptosis (ie, reduced apoptosis rates, raised mitochondrial membrane potential, increased Bcl-2 protein level and decreased levels of Bax and cleaved caspase 3 protein). Also, AQP1 overexpression increased the migration index as well as the numbers of migrated and invasive ce

The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns. Then those patterns are injected, not only to smaller models, but also to the original model. The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation. After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency., Comment: 16 pages, 4 figures, Camera-Ready for EMNLP 2022 (Long Paper)

We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. PRIMERA uses our newly proposed pre-training objective designed to teach the model to connect and aggregate information across documents. It also uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on zero-shot, few-shot and full-supervised settings, PRIMERA outperforms current state-of-the-art dataset-specific and pre-trained models on most of these settings with large margins. The code and pre-trained models can be found at \url{https://github.com/allenai/PRIMER}., Comment: 19 pages, accepted at the main conference of ACL 2022

Xin-Yue Zhang,1,2,&ast; Hai-Ping Dai,1,2,&ast; Ling Zhang,1,2 Si-Ning Liu,1,2 Yin Dai,1,2 De-Pei Wu,1,2 Xiao-Wen Tang1,2 1National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 2Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, 215123, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: De-Pei Wu; Xiao-Wen TangDepartment of Hematology, The First Affiliated Hospital of Soochow University, Jiangsu Institute of Hematology, Suzhou, People’s Republic of ChinaTel +8651267781856Fax +8651265113556Email drwudepei@163.com; xwtang1020@163.comAbstract: EP300-ZNF384-positive B cell acute lymphoblastic leukemia (B-ALL) patients are reported to have a unique immunophenotype with high expression of CD19 and CD22, weak expression of CD20 and aberrant expression of CD13 and/or CD33, sensitivity to chemotherapy and a favorable outcome. To date, the cases of only 53 patients have been reported, albeit few reports on salvage therapy when conventional chemotherapies failed. Here, we describe two relapsed and refractory adult B-ALL patients with EP300-ZNF384 who achieved second remission through tandem CD19/CD22 CAR T-cell therapy. Grade 3 and 2 cytokine release syndrome were observed in cases 1 and 2, respectively. No immune effector cell-associated neurotoxicity syndrome was detected. Both patients underwent consolidate haploidentical hematopoietic stem cell transplantation (HSCT), and each maintained measurable residual disease-negative remission for 14 and 13 months, respectively. Our study suggests that CD19/CD22 CAR T-cell therapy bridging to allogeneic HSCT may be a viable option for EP300-ZNF384-positive B-ALL.Keywords: chimeric antigen receptor T-cells, CD19/CD22, EP300-ZNF384, acute lymphoblastic leukemia, relapsed/re

Junwei Tong,1– 3,&ast; Xiangui Meng,1– 3,&ast; Qingyang Lv,1– 3,&ast; Hongwei Yuan,1– 3 Weiquan Li,1– 3 Wen Xiao,1– 3 Xiaoping Zhang1– 3 1Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 2Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518000, People’s Republic of China; 3Institute of Urology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaoping Zhang; Wen XiaoDepartment of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, People’s Republic of ChinaFax +86 85776343Email xzhang@hust.edu.cn; xiaowenx11@163.comBackground: Increasing evidence indicated that the aberrant expression of the cytoplasmic FMR1-interacting protein (CYFIP) family might possess critical role and potential functions in cancer. But the role of CYFIP2 in clear cell renal cell carcinoma (ccRCC) is still uncharacteristic.Methods: We investigated the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database for the expression profile, clinicopathological variables, clinical prognosis information, and promoter methylation levels of CYFIPs in ccRCC. The aberrant CYFIP2 protein expression was validated by the Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to uncover CYFIP2 mRNA levels in 28 pairs of ccRCC cancer tissues. Kaplan–Meier analysis, univariate and multivariate Cox proportional hazard regression were performed to assess CYFIPs’ prognosis value. Gene set enrichment analysis (GSEA) was used to determined hallmark functions, gene ontology of CYFIP2. TIMER database was utilize

Xiangui Meng,1– 3,&ast; Hongwei Yuan,1– 3,&ast; Weiquan Li,1– 3,&ast; Wen Xiao,1– 3 Xiaoping Zhang1– 3 1Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 2Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518000, People’s Republic of China; 3Institute of Urology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaoping Zhang; Wen XiaoDepartment of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, People’s Republic of ChinaTel +86-18602752025Fax +86 2785776343Email xzhang@hust.edu.cn; xiaowenx11@163.comBackground: Studies report that conventional treatment of clear cell renal cell carcinoma (ccRCC) is effective, but several advanced patients present with poor prognosis. The current study explored potential new tumor markers and therapeutic targets in advanced ccRCC.Methods: Biomarker gene expression of ccRCC was retrieved from GEO database and the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. Gene ontology (GO) analysis and protein–protein interaction (PPI) networks of biomarker genes were constructed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) analysis were performed to explore the prognostic and diagnostic roles of these genes. Gene set enrichment analysis (GSEA) analysis was used to determine hallmark functions of the biomarker genes. qRT-PCR was used to verify the reliability of the analysis results in tumor tissues.Results: A total of 21 upregulated genes were identified between advanced ccRCC and early ccRCC (grade III+IV vs

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believed to outperform NNs in real-world tasks, performance advantages of GNNs over graph-agnostic NNs seem not generally satisfactory. Heterophily has been considered as a main cause and numerous works have been put forward to address it. In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation. Then, we propose new metrics based on a similarity matrix which considers the influence of both graph structure and input features on GNNs. The metrics demonstrate advantages over the commonly used homophily metrics by tests on synthetic graphs. From the metrics and the observations, we find some cases of harmful heterophily can be addressed by diversification operation. With this fact and knowledge of filterbanks, we propose the Adaptive Channel Mixing (ACM) framework to adaptively exploit aggregation, diversification and identity channels in each GNN layer to address harmful heterophily. We validate the ACM-augmented baselines with 10 real-world node classification tasks. They consistently achieve significant performance gain and exceed the state-of-the-art GNNs on most of the tasks without incurring significant computational burden.

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model's intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements., Comment: 10 pages, 4 figures, accepted to EMNLP 2021 System Demonstration

This paper is concerned with detecting an integer parameter vector inside a box from a linear model that is corrupted with a noise vector following the Gaussian distribution. One of the commonly used detectors is the maximum likelihood detector, which is obtained by solving a box-constrained integer least squares problem, that is NP-hard. Two other popular detectors are the box-constrained rounding and Babai detectors due to their high efficiency of implementation. In this paper, we first present formulas for the success probabilities (the probabilities of correct detection) of these three detectors for two different situations: the integer parameter vector is deterministic and is uniformly distributed over the constraint box. Then, we give two simple examples to respectively show that the success probability of the box-constrained rounding detector can be larger than that of the box-constrained Babai detector and the latter can be larger than the success probability of the maximum likelihood detector when the parameter vector is deterministic, and prove that the success probability of the box-constrained rounding detector is always not larger than that of the box-constrained Babai detector when the parameter vector is uniformly distributed over the constraint box. Some relations between the results for the box constrained and ordinary cases are presented, and two bounds on the success probability of the maximum likelihood detector, which can easily be computed, are developed. Finally, simulation results are provided to illustrate our main theoretical findings., Comment: 12 pages, to appear in IEEE Transactions on Communications