CUTS: Neural Causal Discovery from Irregular Time-Series Data

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from unstructured time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.Comment: https://openreview.net/forum?id=UG8bQcD3Em

QIDANTONGMAI PROTECTS ENDOTHELIAL CELLS AGAINST HYPOXIA-INDUCED DAMAGE THROUGH REGULATING THE SERUM VEGF-A LEVEL

Qidantongmai (QDTM) is a Traditional Chinese Medicine (TCM) preparation that has long been used in folk medicine for the treatment of cardiovascular diseases. However, the underlying mechanisms are poorly understood. The present study was designed to determine the effects of QDTM on endothelial cells under hypoxic conditions both in vitro and in vivo. Primary human umbilical vein endothelial cells (HUVECs) were isolated, pretreated with QDTM medicated serum or saline control, and then cultured under hypoxia (2% oxygen) for 24 h. Sprague-Dawley rats were administered 1 ml/100 g of QDTM or saline twice a day for 4 days and treated with hypoxia (6 hours/day, discontinuous hypoxia, 360 mm Hg). QDTM not only protected HUVECs from hypoxia-induced damage by significantly retaining cell viability (P < 0.05) and decreasing apoptosis (P < 0.05) in vitro, but also protected liver endothelial cells from hypoxia-induced damage in vivo. Moreover, QDTM increased the serum VEGF-A level (P < 0.05) in rats treated with hypoxia for 7 days but suppressed the upregulation of serum VEGF-A in rats treated with hypoxia for 14 days. QDTM is a potent preparation that can protect endothelial cells against hypoxia-induced damage. The ability of QDTM to modulate the serum VEGF-A level may play an important role in its effects on endothelial cells

Red blood cell distribution width combined with age as a predictor of acute ischemic stroke in stable COPD patients

AimThis retrospective study aimed to investigate the independent clinical variables associated with the onset of acute cerebral ischemic stroke (AIS) in patients with stable chronic obstructive pulmonary disease (COPD).MethodA total of 244 patients with COPD who had not experienced a relapse within 6 months were included in this retrospective study. Of these, 94 patients hospitalized with AIS were enrolled in the study group, and the remaining 150 were enrolled in the control group. Clinical data and laboratory parameters were collected for both groups within 24 h after hospitalization, and the data of the two groups were statistically analyzed.ResultsThe levels of age, white blood cell (WBC), neutrophil (NEUT), glucose (GLU), prothrombin time (PT), albumin (ALB), and red blood cell distribution width (RDW) were different in the two groups (P &lt; 0.01). Logistic regression analysis showed that age, WBC, RDW, PT, and GLU were independent risk factors for the occurrence of AIS in patients with stable COPD. Age and RDW were selected as new predictors, and the receiver operating characteristic curves (ROC) were plotted accordingly. The areas under the ROC curves of age, RDW, and age + RDW were 0.7122, 0.7184, and 0.7852, respectively. The sensitivity was 60.5, 59.6, and 70.2%, and the specificity was 72.4, 86.0, and 60.0%, respectively.ConclusionThe combination of RDW and age in patients with stable COPD might be a potential predictor for the onset of AIS

Multikingdom interactions govern the microbiome in subterranean cultural heritage sites

9 páginas.- 5 figuras.- 66 referencias.- Data Availability. The amplicon sequences, shotgun metagenomics, and screened Actinobacteria strain sequences reported in this article have been deposited in the NCBI BioProject and GenBank databases (accession nos. PRJNA721777, PRJNA745276, and OL444665 to OL444682, respectively). All other study data are included in the article and/or supporting informationMicrobial biodeterioration is a major concern for the conservation of historical cultural relics worldwide. However, the ecology involving the origin, composition, and establishment of microbiomes on relics, once exposed to external environments, is largely unknown. Here, we combined field surveys with physiological assays and biological interaction experiments to investigate the microbiome in the Dahuting Han Dynasty Tomb, a Chinese tomb with more than 1,800 y of history, and its surrounding environments. Our investigation finds that multikingdom interactions, from mutualism to competition, drive the microbiome in this subterranean tomb. We reveal that Actinobacteria, Pseudonocardiaceae are the dominant organisms on walls in this tomb. These bacteria produce volatile geosmin that attracts springtails (Collembola), forming an interkingdom mutualism, which contributes to their dispersal, as one of the possible sources into the tomb from surrounding environments. Then, intrakingdom competition helps explain why Pseudonocardiaceae thrive in this tomb via the production of a mixture of cellulases, in combination with potential antimicrobial substances. Together, our findings show that multikingdom interactions play an important role in governing the microbiomes that colonize cultural relics. This knowledge is integral to understanding the ecological and physiological features of relic microbiomes and to supporting the relics’ long-term conservation.This work was supported by the National Key R&D Program (2019YFC1520700), the National Natural Science Foundation of China (42177297), Chinese Academy of Sciences (CAS) Strategic Priority Research Program Grant XDA28010302, and the Youth Innovation Promotion Association, CAS (Member No. 2014271). M.D.-B. is supported by a Ramón y Cajal Grant (RYC2018-025483-I), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and Project Plan Andaluz de Investigación, Desarrollo e Innovación 2020 from the Junta de Andalucía (P20_00879).Peer reviewe

DualTrans: A Novel Glioma Segmentation Framework Based on a Dual-Path Encoder Network and Multi-View Dynamic Fusion Model

Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity

DenseTrans: Multimodal Brain Tumor Segmentation Using Swin Transformer

Aiming at the task of automatic brain tumor segmentation, this paper proposes a new DenseTrans network. In order to alleviate the problem that convolutional neural networks(CNN) cannot establish long-distance dependence and obtain global context information, swin transformer is introduced into UNet++ network, and local feature information is extracted by convolutional layer in UNet++. then, in the high resolution layer, shift window operation of swin transformer is utilized and self-attention learning windows are stacked to obtain global feature information and the capability of long-distance dependency modeling. meanwhile, in order to alleviate the secondary increase of computational complexity caused by full self-attention learning in transformer, deep separable convolution and control of swin transformer layers are adopted to achieve a balance between the increase of accuracy of brain tumor segmentation and the increase of computational complexity. on BraTs2021 data validation set, model performance is as follows: the dice dimilarity score was 93.2&#x0025;,86.2&#x0025;,88.3&#x0025; in the whole tumor,tumor core and enhancing tumor, hausdorff distance(95&#x0025;) values of 4.58mm,14.8mm and 12.2mm, and a lightweight model with 21.3M parameters and 212G flops was obtained by depth-separable convolution and other operations. in conclusion, the proposed model effectively improves the segmentation accuracy of brain tumors and has high clinical value

CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling

Nanostructured Ferroelectric‐Polymer Composites for Capacitive Energy Storage

The introduction of inorganic components into a polymer matrix to form polymer composites is an emerging and promising approach to dielectric materials for capacitive energy storage. Ferroelectric polymers are particularly attractive as matrices for dielectric polymer composites owing to their highest dielectric constant (≥10) among the known polymers. Here, the important aspects and recent advances in the development of the ferroelectric-polymer-based dielectric polymer composites for high-energy-density capacitor applications are summarized. The preparation methods of ferroelectric-polymer composites with 0D, 1D, and 2D nanostructured fillers, surface-modified nanofillers, and hierarchically structured fillers, and their comprehensive impacts on the dielectric properties, breakdown strength, and energy density of the resulting composites are described. The most recent progress on the incorporation of multiple nanofillers with complementary functionalities into ferroelectric polymers and the design of layer-structured ferroelectric-polymer composites is also highlighted. A discussion of the scientific and technological issues that remain to be addressed and an outlook for the future of ferroelectric polymer-based dielectric composites are also presented

CUTS+: High-dimensional Causal Discovery from Irregular Time-series

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling