Related Features Research Articles

Landscape and Landform Mapping in the Rongdo Basin, Eastern Karakoram, Ladakh, India

ABSTRACT Eastern Karakoram is one of the least studied areas with respect to glacial geomorphology due to its remoteness and inaccessibility and hostile climatic conditions, hence the use of remotely sensed data is indispensable in studying glaciers and related features of the region. In the present article, we present a comprehensive description of the glacial landforms and landscape evolution in the Rongdo basin Eastern Karakoram Ladakh, India. The region is dominated by well-preserved glacial features which offer an excellent opportunity to map and evaluate past glacial fluctuations. The mapping was carried out using high-resolution satellite data (Planet images), and Google Earth, along with ASTER GDEM scenes supplemented by field-based observations to delineate and map various glacial-geomorphological features in the region. The geomorphological landforms mapped include various types of erosional and depositional features. Based on the position of frontal and lateral moraines and glacial trimlines it is evident that the region has witnessed multiple episodes of glacial advances and experienced huge glacial thickness in the recent past. The study highlights role of a number of variables, including topography, climate, and geology, that have contributed to the formation and evolution of landscapes in study area. However detailed dating of the landforms in the region is vital in understanding the past glacial fluctuations and the impact of the climatic variations on the glaciers of the region.

Predictive Factors of Neoadjuvant Chemotherapy Response in Breast Cancer Validated by Three Anatomopathological Scores: Residual Cancer Burden, Chevallier System, and Tumor-Infiltrating Lymphocytes.

The aim of this retrospective, observational, descriptive study was to identify predictors of response to neoadjuvant therapy in breast cancer patients and to validate them using three anatomopathological scores, such as residual cancer burden (RCB) score, Chevallier system, and tumor-infiltrating lymphocytes (TIL) score. We conducted a study on 88 female patientsaged 37 to 78 years with confirmed breast cancerwho were indicated for neoadjuvant chemotherapy. We analyzed different individual variables (such as age, menarche, and menopause), clinical/imaging characteristics of the breast tumor and axillary nodes, immunohistochemical biomarkers (such as ER/PR/HER2 and Ki67), and histopathological features (such as subtype and grading) in relation to three anatomopathological scores calculated based on the surgical resection specimen. The percentage of patients who could have benefited from conservative surgery increased from 6% at the time of diagnosis to 20% post-primary systemic therapy (PST). Age under 49 (p = 0.01), premenopausal status (p < 0.01), no special type (NST) (p = 0.04), high Ki67 (p < 0.01), triple-negative breast cancer (TNBC) (p = 0.02) are positive predictive factors of neoadjuvant therapy, whilelobular/mixt carcinoma-type (p = 0.04), luminal A (p = 0.01), positive lymph node (p < 0.01), and low differentiation grade (p = 0.01) are negative predictive factors for the response to PST. There is a strong correlation between the RCB score and the Chevallier system for quantifying the response to PST, with most predictive factors being confirmed through appropriate statistical analysis for both.TIL score values correlated with only some of the predictors, most likely due to the importance of calculating this score on both biopsy specimens at diagnosis and resection specimens after chemotherapy.

Gene Expression Profiling to Unfolded Proteins Response as a Risk Modulator of Patients with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory disease. Despite new methods of diagnostics and treatment as well as extensive biological and immunosuppressive treatment, the etiology of RA is not fully understood. Moreover, the problem of diagnosis and treatment of RA patients is still current and affects a large group of patients. It is suggested that endoplasmic reticulum (ER)-related features may impair adaptation to chronic stress, inferring the risk of rheumatoid arthritis. The main goal in this study was evaluation of changes in mRNA translation to determine chronic ER stress conditions in rheumatoid arthritis patients. The study group consist of 86 individuals including a total of 56 rheumatoid arthritis patients and 30 healthy controls. The expression level of mRNA form blood samples of RA patients as well as controls of the unfolded protein response (UPR)-associated genes (p-eIF2, BCL-2, PERK, ATF4, and BAX) were investigated using real-time qPCR. GAPDH expression was used as a standard control. Considering the median, the expression levels of PERK, BCL-2, p-eIF2, ATF4, and BAX were found to be significantly increased in the blood of RA patients compared with the control group. The p-value for the PERK gene was 0.0000000036, the p-value for the BCL-2 gene was 0.000000014, the p-value for the p-eIF2 gene was 0.006948, the p-value for the ATF4 gene was 0.0000056, and the p-value for the BAX gene was 0.00019, respectively. Thus, it can be concluded that the targeting of the components of the PERK-dependent UPR signaling pathway via small-molecule PERK inhibitors may contribute to the development of novel, innovative treatment strategies against rheumatoid arthritis.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data

BackgroundPlant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on discovering novel metabolites and their biosynthetic genes, the resolution of metabolic pathways and identified biosynthetic genes was limited by rudimentary analysis approaches and enormous number of candidate genes.ResultsHere we integrated state-of-the-art automated machine learning (ML) frame AutoGluon-Tabular and multi-omics data from Arabidopsis to predict genes encoding enzymes involved in biosynthesis of plant specialized metabolite (PSM), focusing on the three main PSM categories: terpenoids, alkaloids, and phenolics. We found that the related features of genomics and proteomics were the top two crucial categories of features contributing to the model performance. Using only these key features, we built a new model in Arabidopsis, which performed better than models built with more features including those related with transcriptomics and epigenomics. Finally, the built models were validated in maize and tomato, and models tested for maize and trained with data from two other species exhibited either equivalent or superior performance to intraspecies predictions.ConclusionsOur external validation results in grape and poppy on the one hand implied the applicability of our model to the other species, and on the other hand showed enormous potential to improve the prediction of enzymes synthesizing PSM with the inclusion of valid data from a wider range of species.

Local Interpretations for Explainable Natural Language Processing: A Survey

As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term interpretability and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are specifically divided into three categories: (1) interpreting the model’s predictions through related input features; (2) interpreting through natural language explanation; (3) probing the hidden states of models and word representations.

Recent progress in piezoelectric thin films as self-powered devices: material and application

Piezoelectric materials have become a key component in sensors and actuators in many industrial fields, such as energy harvesting devices, self-powered structures, biomedical devices, nondestructive testing, owing to the novel properties including high piezoelectric coefficient and electromechanical coupling factors. Piezoelectric thin films integrated on silicon substrates are widely investigated for their high performance and low manufacturing costs to meet the requirement of sensor networks in internet of things (IoT). The aim of this work is to clarify the application and design structure of various piezoelectric thin films types, synthesis methods, and device processes. Based on latest literature, the process of fabricating thin film sensors is outlined, followed by a concise overview of techniques used in microelectromechanical systems (MEMS) processing that can integrate more complex functions to obtain relevant information in surrounding environment. Additionally, by addressing piezoelectric thin films sensors as a cutting-edge technology with the ability to produce self-powered electronic devices, this work delivers incisive conclusions on all aspects of piezoelectric sensor related features. A greater understanding of piezoelectricity is necessary regarding the future development and industry challenges.

Cervical wear pathobiology by robot-simulated 3-year toothbrushing – New methodological approach

ObjectivesAn ex-vivo study was aimed at (i) programming clinically validated robot three-year random toothbrushing, (ii) evaluating cervical macro- and microwear patterns on all tooth groups of different functional age, (iii) documenting and codificating wear related morphological features at the cemento-enamel junction in young teeth and on roots in older teeth. DesignFollowing ethical approval random toothbrushing (44 strokes per tooth horizontally, rotating, vertically; 2x/d) with manual toothbrushes and low-abrasive dentifrice was performed in an artificial oral cavity with brushing-force 3.5 N on 14 extracted human teeth. Morphological features were examined by SEM at baseline and after simulated 3 years using the replication technique. 3D-SEM analyses were carried out with a four-quadrant back scattered electron detector. Wilcoxon-Mann-Whitney-test was used for statistical analyses. Results3-year random toothbrushing with horizontal, rotating and vertical brushing movements revealed morphological features classified as four enamel patterns, one dentin pattern and three cervical patterns. Negative impacts were enamel, cementum and dentin loss. Positive impact on oral health was removing dental calculus and straightening cervical traumatic and iatrogenic damages. The volume loss varied from x̅=34.25nl to x̅=87.75nl. Wear extended apically from 100 to 1500 micrometres. ConclusionRobot simulated toothbrushing in an artificial oral cavity, with subsequent SEM and 3D-SEM assessment, elucidated both negative and oral health-contributing micromorphology patterns of cervical wear after simulated 3-year random toothbrushing. Cervical macro- and microwear of cementum revealed, for the first time, what we describe as overhanging enamel peninsulas and enamel islands on roots in young teeth, but no enamel islands on roots from older teeth after root cementum loss. In contrast, many older teeth exhibited enamel peninsulas.

Wavelet-driven differentiable architecture search for planetary gear fault diagnosis

With the advancement of artificial intelligence and the accumulation of industrial big data, intelligent diagnosis methods based on deep learning have become the mainstream for diagnosing mechanical faults in manufacturing systems. Despite this, developing high-performance neural network models for specific tasks necessitates a substantial amount of expert knowledge. This consumes a considerable amount of time in the trial-and-error process, thereby constraining the progress in neural network development. Neural architecture search (NAS) offers a solution to address this problem. However, the feature extraction operators in the existing NAS search space are primarily imported from other fields, leading to domain bias and a lack of interpretability. To address these challenges, we present a NAS method driven by wavelets. To be specific, the wavelet operators that can extract fault related features from vibration signals is added to the search space, and the gradient optimization strategy is utilized to search the optimal architecture from the hypernet. The effectiveness of the proposed method is validated through a dataset specific to planetary gears. Upon comparison with other models, it is evident that the proposed method exhibits superior performance across all added noise levels.

New to Russia records of lichens and a lichenicolous fungus from the Kolguev Island (Russian Arctic)

Two species of lichens, Buellia insularis and Polyblastia cataractae, as well as a lichenicolous fungus Lasiosphaeriopsis lecanorae, were identified as new for Russia during the comprehensive studies of vegetation on the Kolguev Island. For each species, a brief description is given based on the specimens studied, differences from closely related species, ecological features, and global distribution are shown.

Content bias in deep learning image age approximation: A new approach towards better explainability

In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g., belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method.

Bilateral adrenal neuroblastoma: peculiar pattern of a rare pediatric presentation

BackgroundBilateral suprarenal neuroblastoma (BSN) is a rare presentation. Few previously published literature showed BSN patients to have favorable pattern and prognosis. This study aim was to evaluate clinical and biological features in relation to outcome of Egyptian patients with BSN.MethodsIncluded patients were diagnosed from 2007 to 2017, retrospectively. Tissue biopsy, imaging and bone marrow were evaluated at presentation. Clinical, demographic, biological variables and risk group were determined and analyzed in relation to overall (OS) and event-free-survival (EFS).ResultsBSN patients (n = 33) represented 2% of hospital patients with neuroblastoma during the 10-year study period, 17 were males and 16 were females. Twenty-four patients (72.7%) were infants, and 9 patients (27.3%) were above 1 year of age (range: 1 month to 3 years). Metachronous disease was present in only one patient. Amplified MYCN was found in 10 patients. Initially, most patients (n = 25) had distant metastasis, 6 had stage 3 versus 2 stage 2. Fifteen were high risk (HR), 15 intermediate (IR), 1 low risk (LR) and 2 were undetermined due to inadequate tissue biopsy. Three-year OS for HR and IR patients were 40.5% and 83.9% versus 23.2% and 56.6% EFS; respectively.ConclusionBSN treatment is similar to unilateral disease. A more conservative surgical approach with adrenal tissue preservation on less extensive side should be considered. Biological variables and extent of disease are amongst the most important prognostic determinants. Future studies are warranted to further address the biologic profiling of BSN and highlight prognostic significance of size difference between both adrenal sides.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PurposeThis study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.ResultsSUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.ConclusionOur nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

Lifelong psychological and lifestyle features in relation to cardiovascular disease development: A narrative review

Despite the extensive literature on individual risk factors, such as diet, physical activity, body weight, smoking habits, and mental health characteristics, the literature on different lifestyle and mental health patterns, their evolution over time, and how these factors interact and synergistically affect cardiovascular health is sparse. The need for longitudinally studying these characteristics is currently being highlighted by the limited studies with repeated measurements. This review summarizes the literature concerning lifestyle and psychological characteristics, both well-studied (e.g., diet, physical activity, smoking habits, chronic stress and depression), as well as newly proposed characteristics (e.g., sleep habits, positive psychology characteristics) and their longitudinal association with cardiovascular disease. Studies with data on lifelong characteristics are not always comparable and have found mixed results, especially concerning changes in characteristics such as quitting smoking or starting to be physically active later in life. Therefore, further research is warranted for the implementation of future preventive strategies for cardiovascular disease.

Multi-feature Learning Adaptive Network for Underwater Image Enhancement

Underwater image enhancement faces variety of challenges owing to the diversity of underwater scenes (viewed as water types) and the rich multi-frequency information. To deal with these challenges, this paper proposes a multi-feature learning adaptive underwater image enhancement network comprising an adaptive module and a dual-layer synchronous enhancement network. First, we design an adaptive module which enables the determination of water type inside the model and eliminates the negative effect of water type diversity by building water type related features. Then, the model learns high-frequency and low-frequency features through a dual-layer synchronous enhancement network to extract more comprehensive information. Finally, the outputs of the dual-layer network are merged to obtain more realistic underwater enhanced images. Numerous experiments have shown that the proposed method outperforms the comparison method for visual perception and assessment metrics.

Identifying suicide attempter in major depressive disorder through machine learning: the importance of pain avoidance, event-related potential features of affective processing.

How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.

The Neighbourhood–Health Nexus: Design, Behaviour and Futures

Over the last decade, and more recently with the coronavirus disease 2019 (COVID- 19) pandemic, increased attention has been given to the dynamic between health and urbanism. Features such as city design, the environment, and socioeconomic factors have been studied worldwide. Most studies have focused on a single element of the urban environment, making it difficult to understand the possible influence of related urban features. Furthermore, studies have addressed the issue of urbanism and health on different international, national, urban, and local scales, resulting in multiple inconsistencies. With the enhanced growth of cities, it is argued that the neighbourhood scale is the ideal scale to understand the built environmental–health nexus. More specifically, the paper reviews studies that focus on neighbourhood design and its influence on health, and studies that focus on residents' health-related behaviour. In addition, it maps the key developments in e-health and its expected influence on health services in neighbourhoods. Insights from these reviews are used to offer a preliminary conceptual framework for addressing health in neighbourhoods.

Fast Multilabel Feature Selection via Global Relevance and Redundancy Optimization.

Information theoretical-based methods have attracted a great attention in recent years and gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the existing methods consider a heuristic way to the grid search of important features, and they may also suffer from the issue of fully utilizing labeling information. Thus, they are probable to deliver a suboptimal result with heavy computational burden. In this article, we propose a general optimization framework global relevance and redundancy optimization (GRRO) to solve the learning problem. The main technical contribution in GRRO is a formulation for MLFS while feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, which can avoid repetitive entropy calculations to obtain a global optimal solution efficiently. To further improve the efficiency, we extend GRRO to filter out inessential labels and features, thus facilitating fast MLFS. We call the extension as GRROfast, in which the key insights are twofold: 1) promising labels and related relevant features are investigated to reduce ineffective calculations in terms of features, even labels and 2) the framework of GRRO is reconstructed to generate the optimal result with an ensemble. Moreover, our proposed algorithms have an excellent mechanism for exploiting the inherent properties of multilabel data; specifically, we provide a formulation to enhance the proposal with label-specific features. Extensive experiments clearly reveal the effectiveness and efficiency of our proposed algorithms.

Classification of the Cardiac Arrhythmia Using Combined Feature Selection Algorithms

The prediction of heart disease has gained great importance in recent years. Efficient monitoring of cardiac patients can save tremendous number of lives. This paper presents a method for classification and prediction of electrocardiogram data obtained from 452 patients representing the risk of cardiac arrhythmia. The aim of the study is to select highly related features with arrhythmia risk by using three different feature selection algorithms. In addition, various machine learning models are utilized for the classification task such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM) and Decision Tree (DT). The experimental results show that combination of a purposed feature selection method which later is called “Matched Selection” using SVM classifier outperforms other combinations and have an accuracy of 81.27% while k-NN and DT classifiers have an accuracy of 69.66% and 73.50% respectively. The study, in which detailed analyses are presented comparatively, is promising for the future studies.

An MRI radiomics approach to discriminate hemorrhage prone intracranial tumors before stereotactic biopsy.

To explore imaging biomarkers predictive of intratumoral hemorrhage for lesions intended for elective stereotactic biopsy. This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset (n=121) and a test dataset (n=54) at a ratio of 7:3. 34 lesions were defined as "hemorrhage-prone tumors" as hemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1WI and T2WI images. Features informative of hemorrhage were then selected by the LASSO algorithm and an SVM model was built with selected features. The SVM model was further simplified by discarding features with low importance calculated using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and AUC value on the independent test dataset. Nine radiomics features were selected as hemorrhage related features of intracranial tumors by the LASSO algorithm. The simplified model's sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95%CI: 0.865-1.000) on the test dataset in the discrimination of "hemorrhage-prone tumors". The permutation method rated feature "T2_gradient_firstorder_10Percentile" as the most important, the absence of which decreased the model's accuracy by 10.9%. Radiomics features extracted on contrast-enhanced T1WI and T2WI sequences were predictive of future hemorrhage of intracranial tumors with favorable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.