Generating Stylized Features for Single-Source Cross-Dataset Palmprint Recognition With Unseen Target Dataset.

As a promising topic in palmprint recognition, cross-dataset palmprint recognition is attracting more and more research interests. In this paper, a more difficult yet realistic scenario is studied, i.e.

CytoGAN: Unpaired staining transfer by structure preservation for cytopathology image analysis.

With the development of digital pathology, deep learning is increasingly being applied to endometrial cell morphology analysis for cancer screening. And cytology images with different staining may degrade the performance of these analysis algorithms. To address the impact of staining patterns, many strategies have been proposed and hematoxylin and eosin (H&E) images have been transferred to other staining styles.

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.

Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency.

Intelligent classification of cardiotocography based on a support vector machine and convolutional neural network: Multiscene research.

Objective: To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently.

Methods: We retrospectively collected 2542 CTG records of singleton pregnancies delivered at the maternity ward of the First Affiliated Hospital of Xi'an Jiaotong University from October 10, 2020, to August 7, 2021. CTG records were divided into five categories (baseline, variability, acceleration, deceleration, and normality).

Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks.

Objectives: The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs).

Methods: We used Li Brush to acquire endometrial cells from patients.

Combining Radiology and Pathology for Automatic Glioma Classification.

Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network.

Context.—: The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges.

Objective.

3D Dense Volumetric Network for Accurate Automated Pancreas Segmentation.

Pancreatic cancer poses a great threat to our health with an overall five-year survival rate of 8%. Automatic and accurate segmentation of pancreas plays an important and prerequisite role in computer-assisted diagnosis and treatment. Due to the ambiguous pancreas borders and intertwined surrounding tissues, it is a challenging task.

Towards open-set touchless palmprint recognition via weight-based meta metric learning.

Touchless biometrics has become significant in the wake of novel coronavirus 2019 (COVID-19). Due to the convenience, user-friendly, and high-accuracy, touchless palmprint recognition shows great potential when the hygiene issues are considered during COVID-19. However, previous palmprint recognition methods are mainly focused on close-set scenario.

Towards Cross-Dataset Palmprint Recognition Via Joint Pixel and Feature Alignment.

Deep learning-based palmprint recognition algorithms have shown great potential. Most of them are mainly focused on identifying samples from the same dataset. However, they may be not suitable for a more convenient case that the images for training and test are from different datasets, such as collected by embedded terminals and smartphones.

A computational method for prediction of matrix proteins in endogenous retroviruses.

Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in group-specific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far.

A computational model for predicting integrase catalytic domain of retrovirus.

Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far.

A novel method for in silico identification of regulatory SNPs in human genome.

Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found.

A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology.

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously.

CarSPred: a computational tool for predicting carbonylation sites of human proteins.

Protein carbonylation is one of the most pervasive oxidative stress-induced post-translational modifications (PTMs), which plays a significant role in the etiology and progression of several human diseases. It has been regarded as a biomarker of oxidative stress due to its relatively early formation and stability compared with other oxidative PTMs. Only a subset of proteins is prone to carbonylation and most carbonyl groups are formed from lysine (K), arginine (R), threonine (T) and proline (P) residues.

A practical application combining wireless sensor networks and Internet of Things: Safety Management System for Tower Crane Groups.

The so-called Internet of Things (IoT) has attracted increasing attention in the field of computer and information science. In this paper, a specific application of IoT, named Safety Management System for Tower Crane Groups (SMS-TC), is proposed for use in the construction industry field. The operating status of each tower crane was detected by a set of customized sensors, including horizontal and vertical position sensors for the trolley, angle sensors for the jib and load, tilt and wind speed sensors for the tower body.

Analysis of interleukin-8 gene variants reveals their relative importance as genetic susceptibility factors for chronic periodontitis in the Han population.

Interleukin (IL)-8, an important chemokine that regulates the inflammatory response, plays an important role in periodontitis. Previous studies indicate that certain IL-8 gene polymorphisms are associated with periodontitis susceptibility in some populations. However, the literature is somewhat contradictory, and not all IL-8 polymorphisms have been examined, particularly in Han Chinese individuals.

Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome.

Long noncoding RNAs (lncRNAs) play essential regulatory roles in the human cancer genome. Many identified lncRNAs are transcribed by RNA polymerase II in which they are polyadenylated, whereby the long intervening noncoding RNAs (lincRNAs) have been widely used for the researches of lncRNAs. To date, the mechanism of lincRNAs polyadenylation related to cancer is rarely fully understood yet.

ISDTool 2.0: a computational model for predicting immunosuppressive domain of retroviruses.

Immunosuppressive domain (ISD) is a conserved region of transmembrane proteins (TM) in envelope gene (env) of retroviruses. in vitro and vivo, a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. Evidence has shown that ISD suppresses lymphocyte proliferation and allows escape from immune effectors of the innate and adaptive arms in mouse immune system.

ISDTool: a computational model for predicting immunosuppressive domain of HERVs.

Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function.

A hybrid model for the prediction of mRNA polyadenylation signals.

The mRNA polyadenylation is the cellular process that adds adenosine tails to mature mRNAs. Malfunction of polyadenylation has been implicated in several human diseases. In this paper, we proposed a novel feature extraction approach which employs the K-gram nucleotide acid pattern, the position weight matrix (PWM) and the increment of diversity (ID) to represent the original features.

Analysis of selective constraints on mitochondrial DNA, flight ability and physiological index on avian.

For most of the birds in the word, they can be divided into two main groups, i.e. resident birds and migratory ones.

Fingerprint identification using SIFT-based minutia descriptors and improved all descriptor-pair matching.

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios.

A novel computational method for the identification of plant alternative splice sites.

Alternative splicing (AS) increases protein diversity by generating multiple transcript isoforms from a single gene in higher eukaryotes. Up to 48% of plant genes exhibit alternative splicing, which has proven to be involved in some important plant functions such as the stress response. A hybrid feature extraction approach which combing the position weight matrix (PWM) with the increment of diversity (ID) was proposed to represent the base conservative level (BCL) near splice sites and the similarity level of two datasets, respectively.