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Advances in brain-imaging design and analysis have allowed investigators to use neural activity to predict individual choice, while emerging Internet markets have opened up new opportunities for forecasting aggregate choice. Here, we review emerging research that bridges these levels of analysis by attempting to use group neural activity to forecast aggregate choice. A survey of initial findings suggests that components of group neural activity might forecast aggregate choice, in some cases even beyond traditional behavioral measures. In addition to demonstrating the plausibility of neuroforecasting, these findings raise the possibility that not all neural processes that predict individual choice forecast aggregate choice to the same degree. We propose that although integrative choice components may confer more consistency within individuals, affective choice components may generalize more broadly across individuals to forecast aggregate choice.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Predicting has necessary implications as part of air pollution alerts and the air quality management system. In recent years, air quality studies and observations in Vietnam have shown that pollution is increasing, especially the concentration of PM 2.5 . There are warnings about excessively high concentrations of PM 2.5 in the two major cities of Vietnam as Ho Chi Minh City and Hanoi. Projections for PM 2.5 concentrations in these cities will provide short-term predictive data on air quality. Using the WRF model to forecast PM 2.5 in Ho Chi Minh City is new research for providing forecast information on air pollution. Experiments with six machine learning algorithms show that the Extra Trees Regression model gives the best forecast with statistical evaluation indicators including RMSE = 7.68 µg m −3 , MAE = 5.38 µg m −3 , R-squared = 0.68, and the confusion matrix accuracy of 74%. The experimental setting of the Extra Trees Regression algorithm to predict PM 2.5 for the next two days with WRF’s simulated meteorological data compared with the forecast with observed data showing high accuracy of over 80%. The results show that machine learning with the WRF model can predict PM 2.5 concentration, suitable for early warning of pollution and information provision for air quality management system in large cities as Ho Chi Minh City.

This paper addresses the integrated problem of production, distribution, and maintenance planning based on a data-driven approach, within the supply chain context, involving multiple production sites and multi-demand. Traditionally, these activities have been treated as separate topics in the literature. We aim to predict an optimal production plan for each site to meet its specific demand. Additionally, we aim to establish a collaborative distribution plan among the sites, bridging the gap between production and demand for needy sites, while minimizing the production-distribution costs and adhering to a service level for each production site. Given that demand forecasting presents a significant challenge, we analyze historical demand data for each site. This analysis aims to accurately forecast demand for each manufacturing site using various deep learning models. We also aim to predict preventive maintenance actions for each site, taking into account the dependency of the failure rate and the production cadence. We propose a data-driven approach that employs deep learning methods, namely the long short-term memory model for forecasting multi-demand and the NeuroEvolution of augmenting topologies model for predicting the three joint plans. The resulting integrated method is assessed using reference datasets. The proposed framework is evaluated using reference datasets, with results being compared across various approaches to highlight the advantages of the proposed framework.

Background The disease burden of non-melanoma skin cancer (NMSC) has become a significant public health threat. We aimed to conduct a comprehensive analysis to mitigate the health hazards of NMSC. Methods This study had three objectives. First, we reported the NMSC-related disease burden globally and for different subgroups (sex, socio-demographic index (SDI), etiology, and countries) in 2019. Second, we examined the temporal trend of the disease burden from 1990 to 2019. Finally, we used the Bayesian age-period-cohort (BAPC) model integrated nested Laplacian approximation to predict the disease burden in the coming 25 years. The Norpred age-period-cohort (APC) model and the Autoregressive Integrated Moving Average (ARIMA) model were used for sensitivity analysis. Results The disease burden was significantly higher in males than in females in 2019. The results showed significant differences in disease burden in different SDI regions. The better the socio-economic development, the heavier the disease burden of NMSC. The number of new cases and the ASIR of basal cell carcinoma (BCC) were higher than that of squamous cell carcinoma (SCC) in 2019 globally. However, the number of DALYs and the age-standardized DALYs rate were the opposite. There were statistically significant differences among different countries. The age-standardized incidence rate (ASIR) of NMSC increased from 54.08/100,000 (95% uncertainty interval (UI): 46.97, 62.08) in 1990 to 79.10/100,000 (95% UI: 72.29, 86.63) in 2019, with an estimated annual percentage change (EAPC) of 1.78. Other indicators (the number of new cases, the number of deaths, the number of disability-adjusted life years (DALYs), the age-standardized mortality rate (ASMR), and the age-standardized DALYs rate) showed the same trend. Our predictions suggested that the number of new cases, deaths, and DALYs attributable to NMSC would increase by at least 1.5 times from 2020 to 2044. Conclusions The disease burden attributable to NMSC will continue to increase or remain stable at high levels. Therefore, relevant policies should be developed to manage NMSC, and measures should be taken to target risk factors and high-risk groups.

Objective To study the historical global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035. Methods Incidence data were retrieved from the Cancer Incidence in Five Continents (CI5) volumes I-XI, and mortality data were obtained from the latest update of the World Health Organization (WHO) mortality database. We used join-point regression analysis to examine historical incidence and mortality trends and used the package NORDPRED in R to predict the number of deaths and mortality rates by 2035 by country and sex. Results More than 1,089,000 new cases of gastric cancer and 769,000 related deaths were reported in 2020. The average annual percent change (AAPC) in the incidence of gastric cancer from 2003 to 2012 among the male population, South Korea, Japan, Malta, Canada, Cyprus, and Switzerland showed an increasing trend ( P  > 0.05); among the female population, Canada [AAPC, 1.2; (95%Cl, 0.5–2), P  < 0.05] showed an increasing trend; and South Korea, Ecuador, Thailand, and Cyprus showed an increasing trend ( P  > 0.05). AAPC in the mortality of gastric cancer from 2006 to 2015 among the male population, Thailand [3.5 (95%cl, 1.6–5.4), P  < 0.05] showed an increasing trend; Malta Island, New Zealand, Turkey, Switzerland, and Cyprus had an increasing trend ( P  > 0.05); among the male population aged 20–44, Thailand [AAPC, 3.4; (95%cl, 1.3–5.4), P  < 0.05] showed an increasing trend; Norway, New Zealand, The Netherlands, Slovakia, France, Colombia, Lithuania, and the USA showed an increasing trend ( P  > 0.05). It is predicted that the mortality rate in Slovenia and France’s female population will show an increasing trend by 2035. It is predicted that the absolute number of deaths in the Israeli male population and in Chile, France, and Canada female population will increase by 2035. Conclusion In the past decade, the incidence and mortality of gastric cancer have shown a decreasing trend; however, there are still some countries showing an increasing trend, especially among populations younger than 45 years. Although mortality in most countries is predicted to decline by 2035, the absolute number of deaths due to gastric cancer may further increase due to population growth.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

Diabetes is a long-term chronic medical condition with the potential to evolve into a global healthcare crisis, glycemic control is fundamental for the effective management of diabetes and the prevention of its associated complications. Forecasting future blood glucose levels (BGLs) for diabetic patients can help them avoid serious health problems. This study investigates the application of the KNN regression algorithm to predict future (BGLs), utilizing historical blood glucose measurements from twelve patients (six patients from the Ohio dataset version 2018 and six patients from the Ohio dataset version 2020) as the only input feature. Our proposed approach employed a methodology that utilized historical measurements to train predictive models. Specifically, we leveraged the following historical data points - (BGLs) at 4-hours, 8-hours, 12-hours, 16-hours, 20-hours, and 24-hours intervals - as input features to predict (BGLs) 30 minutes into the future. This study explores the impact of varying parameters of the KNN algorithm, such as the K value= [2,3,5,7,11], weights= ['uniform', 'distance'] and distance metric= ['euclidean', 'manhattan', 'minkowski'], on the performance of the model. Furthermore, we compared the obtained results of the KNN algorithm with other machine learning methods, including linear regression, Random Forests, Support Vector Machines, CatBoostRegressor, LightGBM, XGBoost, artificial neural networks and previous studies. Among these, KNN yielded the best results with optimal hyperparameters (k=2, Weights='distance', Metric='manhattan') in the tow version of datasets OhioT1DM V2018 and OhioT1DM V2020. The OhioT1DM V2018 dataset yielded optimal performance with an RMSE of 5.09 ± 0.91 mg/dl using a 24-hour window size, and an MAE of 2.42 ± 0.34 mg/dl with a 12-hour window size. For the OhioT1DM V2020 dataset, the best results were an RMSE of 5.56 ± 1.14 mg/dl with a 12-hour window size, and an MAE of 2.47 ± 0.34 mg/dl achieved using an 8-hour window size. This research confirms that KNN algorithm with optimal hyperparameters (k=2, Weights='distance', Metric='manhattan') can effectively predict blood glucose events, which will help prevent and reduce the occurrence of serious complications such as hypoglycemia and hyperglycemia.

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in \"established use.\" Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.