Genomic variations in the counterpart normal controls of lung squamous cell carcinomas

Lung squamous cell carcinoma （LUSC） causes approximately 400 000 deaths each year worldwide. The occurrence of LUSC is attributed to exposure to cigarette smoke, which induces the development of numerous genomic abnormalities. However, few studies have investigated the genomic variations that occur only in normal tissues that have been similarly exposed to tobacco smoke as tumor tissues. In this study, we sequenced the whole genomes of three normal lung tissue samples and their paired adjacent squamous cell carcinomas. We then called genomic variations specific to the normal lung tissues through filtering the genomic sequence of the normal lung tissues against that of the paired tumors, the reference human genome, the dbSNP138 common germline variants, and the variations derived from sequencing artifacts. To expand these observations, the whole exome sequences of 478 counterpart normal controls （CNCs） and paired LUSCs of The Cancer Genome Atlas （TCGA） dataset were analyzed. Sixteen genomic variations were called in the three normal lung tissues. These variations were confirmed by Sanger capillary sequencing. A mean of 0.5661 exonic variations/Mb and 7.7887 altered genes per sample were identified in the CNC genome sequences of TCGA. In these CNCs, C：G→T：A transitions, which are the genomic signatures of tobacco carcinogen N-methyl-N-nitro-N-nitrosoguanidine, were the predominant nucleotide changes. Twenty five genes in CNCs had a variation rate that exceeded 2%, including ARSD （18.62%）, MUC4 （8.79%）, and RBMX（7.11%）. CNC variations in CTAGE5 and USP17L7were associated with the poor prognosis of patients with LUSC. Our results uncovered previously unreported genomic variations in CNCs, rather than LUSCs, that may be involved in the develooment of LUSC.

A REVERSED-FRAME NORMALIZATION DESIGN OF ROBUST FLIGHT CONTROL SYSTEM

In this paper, a discussion is devoted to the theory and method of a reversed-frame normalization design to robust flight control system. The robust stability theory of the normal transfer function matrix with the same characteristic gain loci is proved. An example of flight control system design shows the application and advantage of this method.

Role of Soluble Factors in Immune and Normal Sera in the Control of Giardiasis Due to Giardia Iamblia

Hyperimmune sera (HIS), raised against crude giardia antigen, on in vitro interaction, caused more agglutination of Giardia lamblia trophozoites. Heat inactivated HIS possessed a comparable agglutinating activity as the non-inactivated controls. Non-inactivated normal (unimmunized) serum caused immobilization of Giardia trophozoite, which was checked on heat inactivation. Antibodies in immune sera are mainly responsible for agglutination, whereas the heat labile non-immune components control the mobility of the intestinal parasite.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Infection Prevention Strategy in General Hospital under Regular Epidemic Prevention and Control

New coronavirus pneumonia spread rapidly all over the world in the first half of 2020, which is a new respiratory disease with strong infectiousness. At present, the epidemic situation in China has been effectively controlled, but the global epidemic situation remains grim, and the National Health Commission has instructed that the prevention and control of new coronavirus pneumonia should become normalized. As outposts for surveillance of public health events, hospitals are an important front in the fight against the epidemic. Therefore, it is very crucial to construct infection prevention and control system actively to contain the outbreak. This paper analyzes and summarizes the normal prevention and control management strategy of Infection Control Branch Management (hereinafter referred to as Hospital Infection-Control Dept) in a certain designated comprehensive third-grade hospital in Guangdong Province and transforms the prevention and control means in emergency state into measures for sustainable development, which also provides basis and reference for Hospital Infection-Control Dept of medical institutions to formulate prevention and control guidelines.

ABSOLUTE OBSERVABILITIES OF NONLINEAR CONTROL SYSTEMS AND THEIR NORMAL FORMS

In this paper the concept of absolute observability of nonlinear control systems is advanced.Different from the linear time-invariant version, there are different definitions of absolute observability for nonlinear control systems.Two algorithms for maximal absolutely observable subsystems are given.Correspondingly,there are two relevant normal forms.The relations with the largest controlled invariant distribution contained in kerdh,zero dynamics etc.,are discussed from the view point of maximal absolute observabilities.

Integrated VP-Based Control Strategies for ATM Survivable Networks

Nowadays the self-healing techniques in ATM networks are highly considered. However, in mostof networks, the normal control and sell-healing control are separately considered. In this paper, intenttedATM VP-based control strategies for ATM survivable networks are prOPosed, which combine the normalcontrol and self-healing control in a unified wad, being able to operate under both normal conditions andcatastrophic failure situations. These strategies allow a cost-effective network plan and design.

Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study

The aging population is primarily affected by Alzheimer’s disease(AD)that is an incur-able neurodegenerative disorder.There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage.Various techniques are used to diagnose AD.EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis.Parkinson’s disease(PD)is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year.The voice features are majorly used as the main means to diag-nose PD.The major symptoms of PD are loss of intensity,the monotony of loudness and pitch,reduction in stress,unidentified silences,and dysphonia.Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classifi-cation diseases,still there is a need for efficient learning methodologies and techniques.This paper provides a review on using machine learning(ML)together with several fea-ture extraction techniques that is helpful in the early detection of AD with Parkinson’s.The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD.The proposed technique depends on the nonlinear process for data dimension reduction,feature removal,and classification using kernel-based support vector machine(SVM)classifiers.The dimension of the input space is radically diminished with kernel methods.As the learning set is labeled,it creates sense to utilize this information to make a dependable method of dropping the input space dimension.The different techniques of ML are explained under the major approaches viz.SVM,artificial neural network(ANN),deep learning(DL),and ensemble methods.A comprehensive assessment is presented at SVM,ANN,and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.

THE ORIGIN AND TRANSFORMATION OF JUDICIAL YUAN： FROM THE COMPARATIVE AND HISTORICAL PERSPECTIVE

The Judicial Yuan, a model of supreme judicial organization peculiar to China＇s recent history, was one of the outcomes of several decades of evolution from the legal reform in the late Qing dynasty to the passage of Constitution of the Republic of China （ROC） in 1947. Its predecessor, the Dali Yuan established in 1906 by the Qing government, not only had the supreme judicial power and the power of abstract interpretation, but also enjoyed the power of interior administration independently from the outset. The Judicial Yuan, established in 1928, inherited the judicial administrative power of Dali Yuan and further expanded it. The Judicial Yuan also inherited Dali Yuan＇s power to unify the interpretation of law and regulations, and expanded the power of abstract normal control （Normenkontrolle） to constitutional interpretation. The Council of the Grand Justices of the Judicial Yuan had developed the paradigm of constitutional court and shared similarities with the Federal Constitutional Court of Germany. It is safe to conclude that even before the promulgation of the Constitution of the ROC, the Judicial Yuan was fairly well-developed in institutional terms. The key characters of the ROC Judicial Yuan include the special arrangement of judicial administration, a centralized judicial review by the Council of the Grand Justices and a diverse judicial trial mode.