Change Prediction Model Research Articles

Integrated multi-omics predictive analysis of atherosclerosis: a sub-study from the Mineralocorticoid Receptor Antagonism in Diabetic Atherosclerosis (MAGMA) trial

Abstract Background/Introduction Mineralocorticoid receptor (MR) antagonists (MRA) are beneficial in cardiorenal outcomes in randomized controlled trials but the mechanisms are unclear. MAGMA was an NHLBI sponsored randomized, double-blind, placebo controlled, 12-month trial comparing Spironolactone (n = 37) vs. placebo (n = 42) in Type 2 diabetics with CKD stages 3-4 on maximal renin-angiotensin system (RAS) blockade and a prior atherosclerotic event and/or left ventricular (LV) hypertrophy. The primary outcome of percent (%) change in total aortic wall volume (TWV) at 12 months, measured by magnetic resonance imaging (MRI), was significantly reduced by Spironolactone. Purpose/Originality The purpose of this analysis was to understand mechanistic pathways of MRAs using a multi-omics approach that could help tease out molecular mechanisms of benefit. Revealing for the first time which of these integrated pathways are predictive of the primary outcome will help design treatments for targeted interventions. Methods Plasma and peripheral blood mononuclear cells from patients randomized to Spironolactone or placebo at baseline and 3-months were measured for aptamer-based proteomic biomarkers (7,596 proteins) and 10-X platform-based single-cell RNA-sequencing (scRNAseq), respectively. Pre-selected candidate predictors were used as inputs of a predictive model of changes of TWV. We fit a Mixed-Multivariate Random Forest (RF) model, a variation of the RF supervised tree-based machine learning method to take the experimental design into account in the regression formulation. The two sources of "omics predictors" were integrated into a supervised multi-omics model to jointly explain the outcome using an extension of Sparse Generalized Canonical Correlation Analysis (SGCCA). Integrated multiome functional analyses with graphical visualizations were carried out by statistical enrichment analysis using Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) against databases of gene ontologies, biological pathways, putative regulatory motifs, proteins, or disease annotations. Results The plasma proteome in response to Spironolactone revealed downregulation of MR targets including fibrosis, immune activation/inflammation, leukocyte activation, proliferation and pathways involved in cytokine stimulation. scRNAseq pathways revealed negative regulation of cytokines production such as IL-2 and redistribution of multiple cell types. Predictors of plaque progression involved cytokine-receptor, complement-coagulation, cell adhesion and axonal guidance targets. Multiome integrated functional analyses results of predictive pathways will be presented. Conclusions The changes in plasma proteomic profile with Spironolactone were consistent with the phenotype of reduced atherosclerosis and downregulation of multiple inflammatory, immune response and profibrotic pathways.

IPREDICT: proof-of-concept study to develop a predictive model of changes in asthma control.

The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden. To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored. This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States. Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels. Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h. This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.

The concept of a neural predictive model of changes in range of the electric refrigerated vehicle extended by a knowledge base

The article presents the concept of a predictive model of electric range changes of a refrigerated vehicle. The authors presented a literature analysis of the parameters affecting the range of an electric vehicle in terms of its drive. The analysis was supplemented with parameters influencing the energy consumption of the cooling device of the vehicle for the transport of refrigerated loads. On this basis, the main parameters that limit the range were selected and associated with geographic coordinates and the season and time of day. The proposed predictive model is implemented through a neural network extended by a knowledge base containing information on the topography of the area as well as weather and traffic statistics.

WDBSTF: A Weighted Dual-Branch Spatiotemporal Fusion Network Based on Complementarity between Super-Resolution and Change Prediction

Spatiotemporal fusion (STF) is a solution to generate satellite images with both high-spatial and high-temporal resolutions. The deep learning-based STF algorithms focus on spatial dimensions to build a super-resolution (SR) model or the temporal dimensions to build a change prediction (CP) model, or the task itself to build a data-driven end-to-end model. The multi-source images used for STF usually have large spatial scale gaps and temporal spans. The large spatial scale gaps lead to poor spatial details based on a SR model; the large temporal spans make it difficult to accurately reconstruct changing areas based on a CP model. We propose a weighted dual-branch spatiotemporal fusion network based on complementarity between super-resolution and change prediction (WDBSTF), which includes the SR branch and CP branch, and a weight module representing the complementarity of the two branches. The SR branch makes full use of edge information and high-resolution reference images to obtain high-quality spatial features for image reconstruction. The CP branch decomposes complex problems via a two-layer cascaded network, changes features from the difference image, and selects high-quality spatial features through the attention mechanism. The fusion result of the CP branch has rich image details, but the fusion accuracy in the changing area is low due to the lack of detail. The SR branch has consistent and excellent fusion performances in the changing and no-changing areas, but the image details are not rich enough compared with the CP branch due to the large amplification factor. Next, a weighted network was designed to combine the advantages of the two branches to produce improved fusion results. We evaluated the performance of the WDBSTF in three representative scenarios, and both visual and quantitative evaluations demonstrate the state-of-the-art performance of our algorithm. (On the LGC dataset, our method outperforms the suboptimal method by 2.577% on SSIM. On the AHB dataset, our method outperforms the suboptimal method by 1.684% on SSIM. On the CIA dataset, our method outperforms the suboptimal method by 5.55% on SAM).

Forecasting the Energy Capacity of Petrochemical Productions Under Conditions of Technological Transformations

The purpose of the study is to identify patterns of energy-saving development of the petrochemical industry in the context of the introduction of innovations and to predict the energy intensity of the industry based on economic and mathematical modeling. As part of achieving the goal, an analysis of the series of dynamics was carried out and prognostic methods based on variant correlation analysis and regression modeling were applied. At the first stage of the study, the dynamics of energy consumption and costs of innovative activities in the development of petrochemical industries in Russia was assessed; at the second stage, a predictive model of changes in the energy intensity of petrochemical industries was built. As a result of the study, the regularities of the development of petrochemical industries in Russia were revealed (stable reduction in energy consumption, unstable dynamics of unit costs for innovative activities of petrochemical industries); the nature of the influence of technological modernization of production on the level of energy intensity is determined, which is manifested in the difference in sectoral accents of innovative development; regression equations are proposed, reflecting the dependence of the energy intensity of petrochemical industries on the volume of sales of industry products; calculated the predicted values of energy intensity, allowing to develop a set of management measures to support the energy efficient development of industry in Russia. The formulated conclusions and results can serve as a basis for improving the strategic directions of development of the petrochemical industry in Russia, taking into account the prioritization of the implementation of energy-saving innovative solutions.

Dynamics of change monitoring of sanitary and chemical indicators of water quality in springs in the subarid region by the model of Saratov region

Springs on the right Bank of the Saratov region can be considered as a predictive model of changes in the chemical composition of spring water in a warming climate. The 14 sources most demanded by the population were identified. The probable causes of the detected deviations in the investigated sanitaryhygienic indicators are also described. The research period is 2016–2019. The sources are located on the territory of the right Bank of the Saratov region, including the city of Saratov. We determined the flow rate and type of sources, the water temperature in them, the presence of captage and sanitary protection zones, the chemical composition of water in terms of smell, taste, color, turbidity, pH, total hardness, total mineralization, Cl, SO4 2-, NO3 , NO2 , NH3 , Pb2+, Cd2+, Fecom, Mncom, Crcom, Ascom. We found that 35 % of samples do not meet the requirements of sanitary standards. Deviations were detected in terms of color, turbidity, total hardness, manganese, chlorides, nitrates, and cadmium. The dynamics of changes in chemical indicators of water quality in all surveyed sources was studied. In 6 of them, from 2016 to 2019, an increase in the level of hardness and total mineralization was revealed. The highest concentrations of the studied substances were recorded in the spring period (flood). An increase in the concentration of manganese was observed in 6 sources. In one of them, in March 2017, the critical value of this indicator was recorded (22 MPC). The necessity of using springs by the population as alternative sources of water supply for drinking purposes is analyzed. To identify the demand for springs in the city of Saratov, a survey of 535 people was conducted directly at the sampling sites. The conducted survey of the population indicates the confidence of the population in the quality of spring water. The probable causes of the detected excess are described. A comparison of our results with available data from 2009–2016 indicates a deterioration in the water quality of the surveyed sources.

Strategic Planning For The Development Of Ukrainian Agriculture In The Face Of Climate Change

The essence of the “strategic planning” is explored and the peculiarities of strategic planning in the agricultural sector are defined. Global climate change as a factor stimulating the formation of new approaches to strategic planning of agricultural development is considered, and the impact of climate change on the agricultural sector of Ukraine is evaluated. A predictive model of changes in the parameters of agricultural production under expected climate change conditions is constructed, and organizational, socio-economical and environmental components of the strategy of agricultural development are outlined and the conceptual bases for improvement of strategic planning for the development of the agricultural sector in the face of climate change are substantiated.

Establishment of a predictive model of serum glucose changes under different exercise intensities and durations among patients with type 2 diabetes mellitus.

Regular exercise is regarded as one of necessary elements in treating diabetes mellitus (DM). The purpose of this study was to investigate the influence of different exercise intensities and durations on serum glucose changes after exercise in type 2 DM patients and to establish a predictive model of changes in serum glucose under different exercise intensities and durations. Thirty-seven type 2 DM patients were recruited from four teaching hospitals. A total of 12 exercise sessions were scheduled according to the results of a graded treadmill exercise test. The 12 exercise sessions were designed on the basis of different exercise intensities (40%, 60%, and 80% maximal workload) and exercise durations (10, 20, 30, and 40 min). Serum glucose level was measured before and after exercise. The findings indicate that the main effect of exercise intensity and duration was significant, but there was no interaction effect. All four variables, including exercise intensity, exercise duration, pre-exercise serum glucose levels, and gender, explained 37% of the variance in serum glucose changes after exercise. In conclusion, a dose-response relationship between exercise amount and serum glucose changes was demonstrated. This is helpful for health professionals to teach type 2 DM patients how to predict serum glucose response in different exercise situations.

Effects of Arm Exercise on Serum Glucose Response in Type 2 DM Patients

The aims of the study were to examine the influence of upper extremity exercise on glucose response and to establish a predictive model of changes in serum glucose under different exercise intensities and durations. Thirty-three type 2 DM patients who met the selection criteria were selected. An arm cycle ergometer exercise test was conducted, and then 12 arm exercise sessions were arranged based on different exercise intensities (40%, 60%, and 80% of maximal workload) and exercise durations (10, 20, 30, and 40 min). Serum glucose levels were measured before and after each exercise session. Serum glucose levels significantly decreased after arm exercise regardless of different intensities or durations. However, no interaction effect (intensity x duration) or main effect in exercise intensity was observed, but a significant main effect in exercise duration was observed (F = 11.756, p <.0001). Also exercise duration was a significant predictor of serum glucose changes after arm exercise. These results suggest that arm exercise can play a useful role in glycemic control for type 2 DM patients and exercise duration is a key factor in determining serum glucose response under upper-extremity exercise.