Neglected Time Intervals Before "1-3-7" Approach About Imported Malaria Cases - China, 2014-2021.

What Is Already Known About This Topic?: China's "1-3-7" approach outlines specific targets to guide and monitor the processes of case reporting, investigation, and response. However, few studies have examined the time intervals preceding the initial step, and the timeline from the arrival of imported malaria cases in China to their diagnosis has been largely overlooked.

What Is Added By This Report?: The study demonstrated that the median duration from arrival in China to the onset of symptoms for was 78 days, with 71.

Corrigendum: Accurate few-shot object counting with Hough matching feature enhancement.

[This corrects the article DOI: 10.3389/fncom.2023.

Corrigendum: An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

[This corrects the article DOI: 10.3389/fncom.2023.

Reemergence of Visceral Leishmaniasis in Henan Province, China.

Visceral leishmaniasis (VL) was widely prevalent in Henan Province in the 1950s. Through active efforts by the government, there were no local cases reported from 1984 to 2015. In 2016, local VL cases reoccurred, and there was an increasing trend of VL cases in Henan Province.

Insecticide resistance of Anopheles sinensis after elimination of malaria in Henan Province, China.

Background: Historically, malaria due to Plasmodium vivax has been epidemic in Henan Province, China, with Anopheles sinensis as the main vector. The most effective measures to prevent malaria transmission are based on vector control through the use of insecticides. However, insecticides exert a strong selective pressure on mosquito populations for insecticide resistance.

An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

Human motion prediction is one of the fundamental studies of computer vision. Much work based on deep learning has shown impressive performance for it in recent years. However, long-term prediction and human skeletal deformation are still challenging tasks for human motion prediction.

Accurate few-shot object counting with Hough matching feature enhancement.

Introduction: Given some exemplars, few-shot object counting aims to count the corresponding class objects in query images. However, when there are many target objects or background interference in the query image, some target objects may have occlusion and overlap, which causes a decrease in counting accuracy.

Methods: To overcome the problem, we propose a novel Hough matching feature enhancement network.

A Lysosome-Targeted Near-Infrared Fluorescent Probe with Excellent Water Solubility for Surgery Navigation in Breast Cancer.

To get a tumor-targeted contrast agent for imaging guide resection of tumors, we designed a novel fluorescent probe based on the heptamethine cyanine core, Cy7-MO, which has excellent water solubility and near-infrared photophysical and lysosomal targeting properties. The chemical structure of Cy7-MO was characterized by nuclear magnetic resonance spectroscopy and high-resolution mass spectrometry. The toxicity of Cy7-MO was evaluated by cell counting kit-8.

Contrastive Bayesian Analysis for Deep Metric Learning.

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting.

One Health approach to improve the malaria elimination programme in Henan Province.

One Health is a collaborative, multi-sectoral, trans-disciplinary approach with the goal of achieving optimal health outcomes by recognizing the interconnection between people, animals, plants, and the environment and determining how this relates to the control of infectious diseases such as malaria, schistosomiasis and so on. Malarias caused by Plasmodium that commonly infects female Anopheles mosquitoes, which feed on human blood and act as a disease vector. It has been a worldwide important public health problem from ancient times.

Epidemiological characteristics, routine laboratory diagnosis, clinical signs and risk factors for hand, -foot -and -mouth disease: A systematic review and meta-analysis.

Background: For the past few years, only a few monovalent EV71 vaccines have been developed, while other enterovirus vaccines are in short supply. We conducted a quantitative meta-analysis to explore the epidemiological characteristics, routine laboratory diagnosis, clinical signs and risk factors for hand, foot and mouth disease (HFMD).

Methods: PubMed, Embase and the Web of Science were searched for eligible reports published before April 16, 2021, with no publication time or language restrictions.

Two-Dimensional TiVC Solid-Solution MXene as Surface-Enhanced Raman Scattering Substrate.

Two-dimensional (2D) MXenes are attractive candidates as surface-enhanced Raman scattering (SERS) substrates because of their metallic conductivity and abundant surface terminations. Herein, we report the facile synthesis of bimetallic solid-solution TiVC (MXene) and its application in SERS. The few-layered MXene nanosheets with high crystallinity were successfully prepared using a one-step chemical etching method without ultrasonic and organic solvent intercalation steps.

MMDD-Ensemble: A Multimodal Data-Driven Ensemble Approach for Parkinson's Disease Detection.

Parkinson's disease (PD) is the second most common neurological disease having no specific medical test for its diagnosis. In this study, we consider PD detection based on multimodal voice data that was collected through two channels, i.e.

Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a serious infectious disease with a fatality of up to 30%. To identify the severity of SFTS precisely and quickly is important in clinical practice.

Methods: From June to July 2020, 71 patients admitted to the Infectious Department of Joint Logistics Support Force No.

Deformable adversarial registration network with multiple loss constraints.

Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation.

Changes in peripheral blood cytokines in patients with severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is recognized as an emerging infectious disease. This study aimed to investigate the pathogenic mechanism of SFTS. A total of 100 subjects were randomly included in the study.

Severe fever with thrombocytopenia syndrome: a systematic review and meta-analysis of epidemiology, clinical signs, routine laboratory diagnosis, risk factors, and outcomes.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with the high case-fatality rate, and lack of vaccines. We aimed to systematically analysed the epidemiological characteristics, clinical signs, routine laboratory diagnosis, risk factors, and outcomes.

Methods: Documents on SFTS were collected by searching the Chinese National Knowledge Infrastructure, Wan Fang Data, PubMed, Embase, and Web of Science databases from 2011 to 2018.

Overcoming Wnt-β-catenin dependent anticancer therapy resistance in leukaemia stem cells.

Leukaemia stem cells (LSCs) underlie cancer therapy resistance but targeting these cells remains difficult. The Wnt-β-catenin and PI3K-Akt pathways cooperate to promote tumorigenesis and resistance to therapy. In a mouse model in which both pathways are activated in stem and progenitor cells, LSCs expanded under chemotherapy-induced stress.

A Fast Projection-Based Algorithm for Clustering Big Data.

With the fast development of various techniques, more and more data have been accumulated with the unique properties of large size (tall) and high dimension (wide). The era of big data is coming. How to understand and discover new knowledge from these data has attracted more and more scholars' attention and has become the most important task in data mining.

Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising.

In this paper, we develop a new low-rank matrix recovery algorithm for image denoising. We incorporate the total variation (TV) norm and the pixel range constraint into the existing reweighted low-rank matrix analysis to achieve structural smoothness and to significantly improve quality in the recovered image. Our proposed mathematical formulation of the low-rank matrix recovery problem combines the nuclear norm, TV norm, and norm, thereby allowing us to exploit the low-rank property of natural images, enhance the structural smoothness, and detect and remove large sparse noise.

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure.

Protein structure Quality Assessment (QA) is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA. In this work, we developed a new Hidden Markov Model (HMM) to assess the compatibility of protein sequence and structure for capturing their complex relationship.

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

Background: Protein structure data in Protein Data Bank (PDB) are widely used in studies of protein function and evolution and in protein structure prediction. However, there are two main barriers in large-scale usage of PDB data: 1) PDB data are highly redundant in terms of sequence and structure similarity; and 2) many PDB files have issues due to inconsistency of data and standards as well as missing residues, so that automated retrieval and analysis are often difficult.

Description: To address these issues, we have created MUFOLD-DB http://mufold.