Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach.

BACKGROUND: During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. OBJECTIVE: The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. METHODS: We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. RESULTS: Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. CONCLUSIONS: Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations.

Tracing air pollution changes (CO, NO2, SO2, and HCHO) using GEE and Sentinel 5P images in Ahvaz, Iran.

The first case of COVID-19 in Iran was reported on February 25, 2020, leading in the implementation of a government-mandated lockdown as the virus gradually spread to different cities. The objective of this study was to evaluate the impact of the COVID-19 pandemic on air quality in Ahvaz city by utilizing Sentinel 5 images and the Google Earth Engine (GEE) platform. Specifically, the concentrations of air pollutants, including CO, NO2, SO2, and HCHO, during the COVID-19 pandemic from May 10 to June 01, 2021, were examined. Also, they were compared to the same period in 2019. Additionally, the influence of meteorological parameters, such as wind speed and precipitation, on pollutant concentrations during the pandemic and in the pre-pandemic year of 2019 were investigated. The results revealed a significant decrease in the concentrations of NO2 (13.7%), CO (6.1%), SO2 (28%), and HCHO (9.5%) in Ahvaz during the study period in 2021 compared to the same period in 2019. Statistical analyses indicated no significant changes in wind speed and precipitation between the COVID-19 pandemic and the pre-pandemic period in 2019. Therefore, the impact of these parameters on the observed changes in pollutant concentrations can be disregarded. It is clear that the COVID-19 epidemic and the subsequent lockdown measures, including traffic restrictions and business closures, played a crucial role in significantly reducing air pollutant concentrations in Ahvaz.

Changes in NO2 and O3 levels due to the pandemic lockdown in the industrial cities of Tehran and Arak, Iran using Sentinel 5P images, Google Earth Engine (GEE) and statistical analysis.

Air pollution has very damaging effects on human health. In recent years the Coronavirus disease (COVID-19) pandemic has created a worldwide economic disaster. Although the consequences of the COVID-19 lockdowns have had severe effects on economic and social conditions, these lockdowns also have also left beneficial effects on improving air quality and the environment. This research investigated the impact of the COVID-19 lockdown on NO2 and O3 pollutants changes in the industrial and polluted cities of Arak and Tehran in Iran. Based on this, the changes in NO2 and O3 levels during the 2020 lockdown and the same period in 2019 were investigated in these two cities. For this purpose, the Sentinel-5P data of these two pollutants were used during the lockdown period from November 19 to December 05, 2020, and at the same time before the pandemic from November 19 to December 05, 2019. For better results, the effect of climatic factors such as rain and wind in reducing pollution was removed. The obtained results indicate a decrease in NO2 and O3 levels by 3.5% and 6.8% respectively in Tehran and 20.97% and 5.67% in Arak during the lockdown of 2020 compared to the same time in 2019. This decrease can be caused by the reduction in transportation and socio-economic and industrial activities following the lockdown measures. This issue can be a solid point to take a step toward controlling and reducing pollution in non-epidemic conditions by implementing similar standards and policies in the future.

NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction.

Prediction of drug-target interactions (DTIs) plays a significant role in drug development and drug discovery. Although this task requires a large investment in terms of time and cost, especially when it is performed experimentally, the results are not necessarily significant. Computational DTI prediction is a shortcut to reduce the risks of experimental methods. In this study, we propose an effective approach of nonnegative matrix tri-factorization, referred to as NMTF-DTI, to predict the interaction scores between drugs and targets. NMTF-DTI utilizes multiple kernels (similarity measures) for drugs and targets and Laplacian regularization to boost the prediction performance. The performance of NMTF-DTI is evaluated via cross-validation and is compared with existing DTI prediction methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR). We evaluate our method on four gold standard datasets, comparing to other state-of-the-art methods. Cross-validation and a separate, manually created dataset are used to set parameters. The results show that NMTF-DTI outperforms other competing methods. Moreover, the results of a case study also confirm the superiority of NMTF-DTI.

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach.

BACKGROUND: Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficial in reducing this burden and improving the quality of life for affected individuals. To this end, the abundance of data exemplified by X (formerly Twitter) presents an invaluable resource for discerning insights into individuals' mental states and enabling timely detection of these transitory depressive feelings. OBJECTIVE: The objective of this study was to automate the detection of momentary depressive feelings in posts using contextual language approaches. METHODS: First, we identified terms expressing momentary depressive feelings and depression, scaled their relevance to depression, and constructed a lexicon. Then, we scraped posts using this lexicon and labeled them manually. Finally, we assessed the performance of the Bidirectional Encoder Representations From Transformers (BERT), A Lite BERT (ALBERT), Robustly Optimized BERT Approach (RoBERTa), Distilled BERT (DistilBERT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and machine learning (ML) algorithms in detecting momentary depressive feelings in posts. RESULTS: This study demonstrates a notable distinction in performance between binary classification, aimed at identifying posts conveying depressive sentiments and multilabel classification, designed to categorize such posts across multiple emotional nuances. Specifically, binary classification emerges as the more adept approach in this context, outperforming multilabel classification. This outcome stems from several critical factors that underscore the nuanced nature of depressive expressions within social media. Our results show that when using binary classification, BERT and DistilBERT (pretrained transfer learning algorithms) may outperform traditional ML algorithms. Particularly, DistilBERT achieved the best performance in terms of area under the curve (96.71%), accuracy (97.4%), sensitivity (97.57%), specificity (97.22%), precision (97.30%), and F1-score (97.44%). DistilBERT obtained an area under the curve nearly 12% points higher than that of the best-performing traditional ML algorithm, convolutional neural network. This study showed that transfer learning algorithms are highly effective in extracting knowledge from posts, detecting momentary depressive feelings, and highlighting their superiority in contextual analysis. CONCLUSIONS: Our findings suggest that contextual language approaches-particularly those rooted in transfer learning-are reliable approaches to automate the early detection of momentary depressive feelings and can be used to develop social media monitoring tools for identifying individuals who may be at risk of depression. The implications are far-reaching because these approaches stand poised to inform the creation of social media monitoring tools and are pivotal for identifying individuals susceptible to depression. By intervening proactively, these tools possess the potential to slow the progression of depressive feelings, effectively mitigating the societal load of depression and fostering improved mental health. In addition to highlighting the capabilities of automated sentiment analysis, this study illuminates its pivotal role in advancing global public health.

Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review.

BACKGROUND: Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. METHODS: A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. RESULTS: Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. CONCLUSION: The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.

NTD-DR: Nonnegative tensor decomposition for drug repositioning.

Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and target-disease networks, NTD-DR uses these pairwise associations to construct a three-dimensional tensor representing drug-target-disease triplet associations and integrates them with similarity information of drugs, targets, and disease to make a prediction. We compare NTD-DR with recent state-of-the-art methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR) and find that our method outperforms competing methods. Moreover, case studies with five diseases also confirm the reliability of predictions made by NTD-DR. Our proposed method identifies more known associations among the top 50 predictions than other methods. In addition, novel associations identified by NTD-DR are validated by literature analyses.

Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni.

Land surface temperature (LST) and vegetation cover changes are two indicators of landscapes in a region. The relationship between LST anomalies, elevation, vegetation, and urban growth is significant to conservation. This study addresses this issue using night-time satellite imagery, kernel methods (points aggregation), and the trend analysis for a long-term period (2001-2017) in Iran. Variables for two seasons (summer and winter) in urban and natural land uses were derived using the Google Earth Engine (GEE) and NASA's Giovanni. Point data derived from raster maps were quantified using statistical kernel and trend analysis. As result, it was observed that LST rise in various elevations, seasons, and land uses. The LST was analyzed through kernels (point aggregation in scatter graphs), which shifted to the right. The LST anomaly in the daytime had the highest maximum value (>4 °C) and lowest minimum value (<-5 °C) in forests and mountains and metropolises with the highest population growth rate. Summer and winter seasons had positive trends in LST for forest and mountain land uses. All seasons had positive trends in EVI in the mountain, and desert land uses. This warming and increasing LST can increase vulnerability to drought, dust storms, floods, avalanches, and natural fires. The EVI is increasing over the years due to government projects in green spaces and urban parks. There is a need to protect urban and natural environments to prevent natural disasters and unplanned population growth.

Social-ecological system resilience of debris flow alluvial fans in the Awang basin, China.

Debris flow alluvial fans (DFAFs) are vulnerable, although they can be used as a natural resource. The relationships between different factors related to DFAF systems and between these factors and systems are important both for identifying the risks and opportunities presented by DFAFs and for tracking system status. In this regard, resilience may be used to characterize the status of a DFAF. This study aimed to explore the processes and mechanisms of interactions among the social, economic, and ecological factors related to DFAF with respect to resilience, and to discuss potential problems in a representative DFAF. Based on the site condition and characteristics of the Awang DFAF (China) in the period 1996-2017, we formed a comprehensive indicator evaluation framework by analyzing disturbance, function, and feedback. We also established a model for evaluating resilience by integrating the analytic hierarchy process (AHP) - an entropy evaluation method (EEM) and set pair analysis (SPA). The results showed that the system of the studied DFAF was dynamically stable. The domination of the ecological system was subsequently superseded by social and economic resilience. While disturbance had direct and immediate effects, coping ability was cumulative and characterized by hysteresis at a particular response time. Overall, resilience fluctuated within an acceptable range rather than linearly increasing or decreasing. This analysis illuminated the dynamic processes of DFAFs and contributed to the understanding and planning of system trade-offs and degraded-land utilization.

MDIPA: a microRNA-drug interaction prediction approach based on non-negative matrix factorization.

MOTIVATION: Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA-drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA-drug interactions. RESULTS: In this study, a matrix factorization-based method, called the microRNA-drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA-drug interactions. AVAILABILITY AND IMPLEMENTATION: All code and data are freely available from https://github.com/AliJam82/MDIPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org.

Applications of diatoms as potential microalgae in nanobiotechnology.

INTRODUCTION: Diatoms are single cell eukaryotic microalgae, which present in nearly every water habitat make them ideal tools for a wide range of applications such as oil explora-tion, forensic examination, environmental indication, biosilica pattern generation, toxicity testing and eutrophication of aqueous ecosystems. METHODS: Essential information on diatoms were reviewed and discussed towards impacts of diatoms on biosynthesis and bioremediation. RESULTS: In this review, we present the recent progress in this century on the application of diatoms in waste degradation, synthesis of biomaterial, biomineraliza-tion, toxicity and toxic effects of mineral elements evaluations. CONCLUSION: Diatoms can be considered as metal toxicity bioindicators and they can be applied for biomineralization, synthesis of biomaterials, and degradation of wastes.