Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.

Introduction: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable cohort.

Methods: We enrolled 199 older patients with coronavirus disease 2019 (COVID-19) from Zhejiang Provincial Hospital of Chinese Medicine (Hubin) between 16 December 2022 and 17 January 2023.

Zero-Shot Video Grounding With Pseudo Query Lookup and Verification.

Video grounding, the process of identifying a specific moment in an untrimmed video based on a natural language query, has become a popular topic in video understanding. However, fully supervised learning approaches for video grounding that require large amounts of annotated data can be expensive and time-consuming. Recently, zero-shot video grounding (ZS-VG) methods that leverage pre-trained object detectors and language models to generate pseudo-supervision for training video grounding models have been developed.

Collaborative Contrastive Refining for Weakly Supervised Person Search.

Weakly supervised person search involves training a model with only bounding box annotations, without human-annotated identities. Clustering algorithms are commonly used to assign pseudo-labels to facilitate this task. However, inaccurate pseudo-labels and imbalanced identity distributions can result in severe label and sample noise.

High expression of IL6 and decrease in immune cells in COVID-19 patients combined with myocardial injury.

Purpose: Myocardial injury, as a serious complication of coronavirus disease-2019 (COVID-19), increases the occurrence of adverse outcomes. Identification of key regulatory molecules of myocardial injury may help formulate corresponding treatment strategies and improve the prognosis of COVID-19 patients.

Methods: Gene Set Enrichment Analysis (GSEA) was conducted to identify co-regulatory pathways.

Divide and Retain: A Dual-Phase Modeling for Long-Tailed Visual Recognition.

This work explores visual recognition models on real-world datasets exhibiting a long-tailed distribution. Most of previous works are based on a holistic perspective that the overall gradient for training model is directly obtained by considering all classes jointly. However, due to the extreme data imbalance in long-tailed datasets, joint consideration of different classes tends to induce the gradient distortion problem; i.

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding.

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization).

Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition.

In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed for handling long-tailed multi-label recognition or label noise respectively, learning with noisy labels in long-tailed multi-label visual data has not been well-studied because of the complexity of long-tailed distribution entangled with multi-label correlation.

Bilaterally Normalized Scale-Consistent Sinkhorn Distance for Few-Shot Image Classification.

Few-shot image classification aims at exploring transferable features from base classes to recognize images of the unseen novel classes with only a few labeled images. Existing methods usually compare the support features and query features, which are implemented by either matching the global feature vectors or matching the local feature maps at the same position. However, few labeled images fail to capture all the diverse context and intraclass variations, leading to mismatch issues for existing methods.

Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification.

Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason is that the model overfits those hard examples in the training set, but does not learn to generalize to unseen examples in the test set.

Removal of p-Nitrophenol from simulated sewage using steel slag: Capability and mechanism.

The steel slag was investigated for the removal of p-nitrophenol (4-NP) from simulated sewage by batch adsorption and fixed-bed column absorption experiments. The results showed that the maximum adsorption capacity was 109.66 mg/g at 298 K, pH of 7, initial concentration 100 mg/L, and dose 0.

Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting.

RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows more robust results in complex scenes compared with RGB SOD. However, state-of-the-art RGB-D SOD approaches heavily rely on a large amount of pixel-wise annotated data for training. Such densely labeled annotations are often labor-intensive and costly.

Filter Pruning by Switching to Neighboring CNNs With Good Attributes.

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same.

Untargeted metabolomic approach to study the serum metabolites in women with polycystic ovary syndrome.

Background: Polycystic ovary syndrome (PCOS) is not only a kind of common endocrine syndrome but also a metabolic disorder, which harms the reproductive system and the whole body metabolism of the PCOS patients worldwide. In this study, we aimed to investigate the differences in serum metabolic profiles of the patients with PCOS compared to the healthy controls.

Material And Methods: 31 PCOS patients and 31 matched healthy female controls were recruited in this study, the clinical characteristics data were recorded, the laboratory biochemical data were detected.

Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes.

Object detection has gained great improvements with the advances of convolutional neural networks and the availability of large amounts of accurate training data. Though the amount of data is increasing significantly, the quality of data annotations is not guaranteed from the existing crowd-sourcing labeling platforms. In addition to noisy category labels, imprecise bounding box annotations are commonly existed for object detection data.

DIA proteomics analysis through serum profiles reveals the significant proteins as candidate biomarkers in women with PCOS.

Background: The aim of this study was to apply proteomic methodology for the analysis of proteome changes in women with polycystic ovary syndrome (PCOS).

Material And Methods: All the participators including 31 PCOS patients and 31 healthy female as controls were recruited, the clinical characteristics data was recorded at the time of recruitment, the laboratory biochemical data was detected. Then, a data-independent acquisition (DIA)-based proteomics method was performed to compare the serum protein changes between PCOS patients and controls.

Few-Shot Common-Object Reasoning Using Common-Centric Localization Network.

In the few-shot common-localization task, given few support images without bounding box annotations at each episode, the goal is to localize the common object in the query image of unseen categories. The few-shot common-localization task involves common object reasoning from the given images, predicting the spatial locations of the object with different shapes, sizes, and orientations. In this work, we propose a common-centric localization (CCL) network for few-shot common-localization.

Instance-Invariant Domain Adaptive Object Detection Via Progressive Disentanglement.

Most state-of-the-art methods of object detection suffer from poor generalization ability when the training and test data are from different domains. To address this problem, previous methods mainly explore to align distribution between source and target domains, which may neglect the impact of the domain-specific information existing in the aligned features. Besides, when transferring detection ability across different domains, it is important to extract the instance-level features that are domain-invariant.

Holistic LSTM for Pedestrian Trajectory Prediction.

Accurate predictions of future pedestrian trajectory could prevent a considerable number of traffic injuries and improve pedestrian safety. It involves multiple sources of information and real-time interactions, e.g.

Learning to Anticipate Egocentric Actions by Imagination.

Anticipating actions before they are executed is crucial for a wide range of practical applications, including autonomous driving and robotics. In this paper, we study the egocentric action anticipation task, which predicts future action seconds before it is performed for egocentric videos. Previous approaches focus on summarizing the observed content and directly predicting future action based on past observations.

Symbiotic Attention for Egocentric Action Recognition With Object-Centric Alignment.

In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e.

Label Independent Memory for Semi-Supervised Few-Shot Video Classification.

In this paper, we propose to leverage freely available unlabeled video data to facilitate few-shot video classification. In this semi-supervised few-shot video classification task, millions of unlabeled data are available for each episode during training. These videos can be extremely imbalanced, while they have profound visual and motion dynamics.

Survival stratification in childhood rhabdomyosarcoma of the extremities: a derivation and validation study.

The objective of this study was to estimate overall survival in children with extremity rhabdomyosarcoma (RMS). In addition, we attempted to construct a nomogram to predict the prognosis in such patients using a population-based cohort. The national Surveillance, Epidemiology, and End Results (SEER) registry was used to identify a cohort of childhood RMS patients.

Rap2B knockdown suppresses malignant progression of hepatocellular carcinoma by inactivating the PTEN/PI3K/Akt and ERK1/2 pathways.

Rap2B, belonging to the Ras superfamily of small guanosine triphosphate-binding proteins, is upregulated and contributes to the progression of several tumors by acting as an oncogene, including hepatocellular carcinoma (HCC). However, the mechanism underlying the functional roles of Rap2B in HCC remains unclear. In this study, the evaluation of Rap2B expression in HCC cells and tissues was achieved by qRT-PCR and western blot assays.

Circulating Klotho is linked to prognosis of acute intracerebral hemorrhage.

Background: Klotho is an anti-aging protein and its increased plasma concentrations were related to good functional outcome of acute ischemic stroke. This study was designed to ascertain the prognostic significance of plasma Klotho in intracerebral hemorrhage.

Methods: Plasma Klotho concentrations in 96 intracerebral hemorrhage patients and 96 healthy controls were quantified.