Spreading digital literacy via Minimal English. The concept of ‘class’ in Swift programming language

The article aims at providing explications of the concept of a class, as it is implemented in the Swift programming language offered by Apple. The explications are framed in Minimal English, which is based on the theory of Natural Semantic Metalanguage. Detailed analysis of the Swift concept of class leads to four distinct core explications of the programming construct in question and the related feature that Swift classes possess, namely the concept of property. The article’s primary purpose is to offer a more smooth experience with programming, especially with beginners in mind. Their initial exposure to programming might face several challenges due to the complicated digital jargon of the documentation. Minimal English is implemented to ease the learning curve and promote digital literacy as one of the most fundamental skills in today’s world.

Multi-Label Feature Selection Combining Three Types of Conditional Relevance

With the rapid growth of the Internet, the curse of dimensionality caused by massive multi-label data has attracted extensive attention. Feature selection plays an indispensable role in dimensionality reduction processing. Many researchers have focused on this subject based on information theory. Here, to evaluate feature relevance, a novel feature relevance term (FR) that employs three incremental information terms to comprehensively consider three key aspects (candidate features, selected features, and label correlations) is designed. A thorough examination of the three key aspects of FR outlined above is more favorable to capturing the optimal features. Moreover, we employ label-related feature redundancy as the label-related feature redundancy term (LR) to reduce unnecessary redundancy. Therefore, a designed multi-label feature selection method that integrates FR with LR is proposed, namely, Feature Selection combining three types of Conditional Relevance (TCRFS). Numerous experiments indicate that TCRFS outperforms the other 6 state-of-the-art multi-label approaches on 13 multi-label benchmark data sets from 4 domains.

Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer’s and Parkinson’s Diseases

Neurodegenerative diseases, including Alzheimer’s (AD) and Parkinson’s diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

Endoplasmic Reticulum Stress-related Classification for Prognosis Prediction in Hepatocellular Carcinoma

Background: Cancer cells under ER stress are common in hepatocellular carcinoma (HCC) and ER stress is strongly associated with poor prognosis. The aim of this study was to discover credible biomarkers for predicting prognosis of HCC based on ER stress-related genes (ERSRGs). Methods: Univariate Cox regression was performed to calculate the association between ERSRGs and survival outcomes of HCC patients in TCGA. Then LASSO-Cox regression strategy and stepwise Cox regression examination were performed to investigate the quality and establish the prognostic characteristics associated with prognosis. Finally, the model was subsequently validated in two additional independent HCC cohorts.Results: A novel seven-gene prognostic risk model based on ERSRGs was constructed and exhibited superior accuracy in forecasting the survival outcomes and 1-, 2-, 3- year survival rate of HCC patients. qRT-PCR was performed to validate the prognostic risk model in an independent clinical cohort containing 59 HCC patients and the results revealed that this signature had a good prognostic performance. Moreover, we found ER stress could affect the immune microenvironment in HCC and immune checkpoint inhibitors (ICIs) treatment was more effective for patients in high-risk subgroup. In addition, we identified 103 tumor-sensitive drugs in the CellMiner database that may be available for the treatment of HCC patients targeting ER stress and constructed a nomogram combining ER stress-related feature, TNM stage, age and gender. Conclusions: Our seven genetic risk model associated with ER stress can accurately predict survival outcome in HCC patients and facilitate the selection of the best individualized treatment targeting ER stress.

Identification of a 15 DNA Damage Repair–Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

Background: Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment. Introduction: To identify a reliable DDR-related gene signature as an independent predictor of LUAD. Methods: DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan–Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis. Results: Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 – 4, T2–T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

Systems analysis reveals ageing-related perturbations in retinoids and sex hormones in Alzheimer's and Parkinson's diseases

Neurodegenerative diseases (NDDs), including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analyse transcriptomic data from AD and PD patients, and stratify these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identify retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrate that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

Deep learning for typhoon intensity classification using satellite cloud images

Tropical cyclone, also known as typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damages to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to the disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant pre-processing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on convolutional neural network (CNN). Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud band (SCB) more effectively and reduces over-fitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10,000 multiple sensor satellite cloud images of National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3 and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas and SCBs, which facilitates classify and forecast typhoon intensity.

Stenotic remodeling of coronary vessels in the elderly patients with high cardiovascular risk and the presence of multifocal atherosclerosis

Aim. Identifying persons who are distinguished by an increased frequency of stenosing atherosclerosis of the coronary arteries also affects the process of identifying the features of the analyzed disease. Material and methods. The main research method was the method of hierarchical analysis of categorical variables. At the same time, they differentiated into 3 clusters depending on the age and duration of the disease. The age of the analyzed patients is from 61 to 75 years. For diagnostics, a general assessment of the clinical condition, coronary angiography, and echodopleroscopy of a number of carotid and lower limb arteries were performed. The research base is the Regional Vascular Center of Ufa. Results. The work revealed that the age-related feature of 65+ is a stable lesion of the coronary vessels. At the same time, it was determined that both men and women suffer from this. Conclusion. It has been shown that this is characterized by an increased frequency of stenosing-type remodeling.