TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer.

Background: Resistance or even hyper-progression to immune checkpoint inhibitors (ICIs) manifesting as accelerated disease progression or death has impeded the clinical use of ICIs. The transforming growth factor beta (TGFβ) receptor pathway has been identified in contributing to immune dysfunction, which might be associated with resistance to ICIs. We aimed to explore the role of TGFβ in the resistance to ICIs in non-small cell lung cancer (NSCLC) in this study.

Model-Driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-Transformation.

Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both manually annotate and automatically segment cancers.

A prediction method of fire frequency: Based on the optimization of SARIMA model.

In the current study, based on the national fire statistics from 2003 to 2017, we analyzed the 24-hour occurrence regularity of fire in China to study the occurrence regularity and influencing factors of fire and provide a reference for scientific and effective fire prevention. The results show that the frequency of fire is low from 0 to 6 at night, accounting for about 13.48%, but the death toll due to fire is relatively high, accounting for about 39.

Optimization of Virtual Shack-Hartmann Wavefront Sensing.

Virtual Shack-Hartmann wavefront sensing (vSHWS) can flexibly adjust parameters to meet different requirements without changing the system, and it is a promising means for aberration measurement. However, how to optimize its parameters to achieve the best performance is rarely discussed. In this work, the data processing procedure and methods of vSHWS were demonstrated by using a set of normal human ocular aberrations as an example.

ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation.

Alzheimer's disease is a neurodegenerative disease that causes 60-70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately. We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms.

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes.

Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus (2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation.

Fms-like tyrosine kinase 3-internal tandem duplications epigenetically activates checkpoint kinase 1 in acute myeloid leukemia cells.

It is not clear how Fms-like tyrosine kinase 3-internal tandem duplications (FLT3-ITD) regulates checkpoint kinase 1 (CHK1) in acute myeloid leukemia (AML). In this study, we investigated the regulatory effect of FLT3-ITD on CHK1. Our results showed that CHK1 was highly expressed in FLT3-ITD positive AML.

Harnessing the Power of Social Media to Understand the Impact of COVID-19 on People Who Use Drugs During Lockdown and Social Distancing.

Objectives: This paper uses a social media platform, Reddit, to identify real-time experiences of people who use drugs during the COVID-19 lock-down.

Methods: Reddit is a popular and growing social media platform, providing a large, publicly available dataset necessary for high performance of machine learning and topic modeling techniques. We used opioid-related "subreddits," communities where Reddit users engage in conversations about drug use, to examine COVID-19-related content of posts and comments from March to May 2020.

Speckle-reduced reconstruction of a single-shot hologram by multiple tip-tilt modulations.

Speckle can be attenuated by averaging the reconstructed images of each sub-hologram or being filtered with different filters, at the expense of resolution. We propose a de-speckling method for a single-shot digital hologram while maintaining the resolution. Different tip-tilt phases are demonstrated to cause changes only for the speckle distributions of the reconstructed image.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer.

Background: Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods: The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists' interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms.

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition.

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous period time.

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study.

Purpose: A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)-based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI).

High-density mapping for gray leaf spot resistance using two related tropical maize recombinant inbred line populations.

Gray leaf spot (GLS) caused by Cercospora zeae-maydis or Cercospora zeina is one of the devastating maize foliar diseases worldwide. Identification of GLS-resistant quantitative trait loci (QTL)/genes plays an urgent role in improving GLS resistance in maize breeding practice. Two groups of recombinant inbred line (RIL) populations derived from CML373 × Ye107 and Chang7-2 × Ye107 were generated and subjected to genotyping-by-sequencing (GBS).

Pneumothorax Induced by Computed Tomography Guided Transthoracic Needle Biopsy: A Review for the Clinician.

Percutaneous computed tomography (CT)-guided transthoracic needle biopsy (TTNB) is a valuable procedure for obtaining tissue or cells for diagnosis, which is especially indispensable in thoracic oncology. Pneumothorax and hemoptysis are the most common complications of percutaneous needle biopsy of the lung. According to reports published over the past decades, pneumothorax incidence in patients who underwent TTNB greatly varies.

AVNC: Attention-Based VGG-Style Network for COVID-19 Diagnosis by CBAM.

(Aim) To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network.

Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes.

Background: Metabolic syndrome (MS) is a major global health concern comprising a cluster of co-occurring conditions that increase the risk of heart disease, stroke and type 2 diabetes. MS is usually diagnosed using a combination of physiochemical indexes (such as BMI, abdominal circumference and blood pressure) but largely ignores clinical symptoms when investigating prevention and treatment of the disease. Exploring predictors of MS using multiple diagnostic indicators may improve early diagnosis and treatment of MS.

A longitudinal investigation of young mothers' prenatal attachment, depressive symptoms, and early parenting behaviour.

Objectives: This study explores whether young, low-income mothers' prenatal attachment to their infants is related to attachment and parenting behaviour postnatally.

Background: A small literature has documented continuity in maternal attachment from pregnancy to postpartum and shown that early maternal attachment is associated with positive parenting behaviour. Less is known about whether prenatal attachment has a unique impact on parenting behaviour, or if it is primarily a step in the development of postnatal attachment, which in turn influences parenting.

Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine.

In this work, we propose a deep learning framework for the classification of COVID-19 pneumonia infection from normal chest CT scans. In this regard, a 15-layered convolutional neural network architecture is developed which extracts deep features from the selected image samples - collected from the Radiopeadia. Deep features are collected from two different layers, global average pool and fully connected layers, which are later combined using the max-layer detail (MLD) approach.

Multi-Stage Cortical Plasticity Induced by Visual Contrast Learning.

Perceptual learning, the improved sensitivity via repetitive practice, is a universal phenomenon in vision and its neural mechanisms remain controversial. A central question is which stage of processing is changed after training. To answer this question, we measured the contrast response functions and electroencephalography (EEG) before and after ten daily sessions of contrast detection training.