The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments ...implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency.
An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted ...learning (OWL) framework which transforms the optimal decision rule problem into a weighted classification problem. We further employ an ensemble of deep neural networks (DNNs) to learn the optimal decision rule. Utilizing the flexibility of DNNs and the stability of bootstrap aggregation, the proposed method achieves a considerable improvement over existing methods. An R package “ITRlearn” is developed to implement the proposed method. Numerical performance is demonstrated via simulation studies and a real data analysis of the Cancer Cell Line Encyclopedia data.
Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by respiratory distress, multiorgan dysfunction, and, in some cases, ...death. The pathological mechanisms underlying COVID-19 respiratory distress and the interplay with aggravating risk factors have not been fully defined. Lung autopsy samples from 18 patients with fatal COVID-19, with symptom onset-to-death times ranging from 3 to 47 days, and antemortem plasma samples from 6 of these cases were evaluated using deep sequencing of SARS-CoV-2 RNA, multiplex plasma protein measurements, and pulmonary gene expression and imaging analyses. Prominent histopathological features in this case series included progressive diffuse alveolar damage with excessive thrombosis and late-onset pulmonary tissue and vascular remodeling. Acute damage at the alveolar-capillary barrier was characterized by the loss of surfactant protein expression with injury to alveolar epithelial cells, endothelial cells, respiratory epithelial basal cells, and defective tissue repair processes. Other key findings included impaired clot fibrinolysis with increased concentrations of plasma and lung plasminogen activator inhibitor-1 and modulation of cellular senescence markers, including p21 and sirtuin-1, in both lung epithelial and endothelial cells. Together, these findings further define the molecular pathological features underlying the pulmonary response to SARS-CoV-2 infection and provide important insights into signaling pathways that may be amenable to therapeutic intervention.
Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune ...responses to Mycobacterium tuberculosis can provide new diagnostic insights into not only the status of LTBI infection, but also the risk of reactivation. After the initial proof-of-concept study, we developed a 13-plex immunoassay panel to profile cytokine release from peripheral blood mononuclear cells stimulated separately with Mtb-relevant and non-specific antigens to identify putative biomarker signatures. We sequentially enrolled 65 subjects with various risk of TB exposure, including 32 subjects with diagnosis of LTBI. Random Forest feature selection and statistical data reduction methods were applied to determine cytokine levels across different normalized stimulation conditions. Receiver Operator Characteristic (ROC) analysis for full and reduced feature sets revealed differences in biomarkers signatures for LTBI status and reactivation risk designations. The reduced set for increased risk included IP-10, IL-2, IFN-γ, TNF-α, IL-15, IL-17, CCL3, and CCL8 under varying normalized stimulation conditions. ROC curves determined predictive accuracies of > 80% for both LTBI diagnosis and increased risk designations. Our study findings suggest that a multiparameter diagnostic approach to detect normalized cytokine biomarker signatures might improve risk stratification in LTBI.
Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the ...available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.
Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic ...armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.
Variable selection for multivariate nonparametric regression models usually involves parameterized approximation for nonparametric functions in the objective function. However, this parameterized ...approximation often increases the number of parameters significantly, leading to the “curse of dimensionality” and inaccurate estimation. In this paper, we propose a novel and easily implemented approach to do variable selection in nonparametric models without parameterized approximation, enabling selection consistency to be achieved. The proposed method is applied to do variable selection for additive models. A two-stage procedure with selection and adaptive estimation is proposed, and the properties of this method are investigated. This two-stage algorithm is adaptive to the smoothness of the underlying components, and the estimation consistency can reach a parametric rate if the underlying model is really parametric. Simulation studies are conducted to examine the performance of the proposed method. Furthermore, a real data example is analyzed for illustration.
Many studies have utilized physical activity for predicting mortality risk, using measures such as participant walk tests and self-reported walking pace. The rise of passive monitors to measure ...participant activity without requiring specific actions opens the possibility for population level analysis. We have developed novel technology for this predictive health monitoring, using limited sensor inputs. In previous studies, we validated these models in clinical experiments with carried smartphones, using only their embedded accelerometers as motion sensors. Using smartphones as passive monitors for population measurement is critically important for health equity, since they are already ubiquitous in high-income countries and increasingly common in low-income countries. Our current study simulates smartphone data by extracting walking window inputs from wrist worn sensors. To analyze a population at national scale, we studied 100,000 participants in the UK Biobank who wore activity monitors with motion sensors for 1 week. This national cohort is demographically representative of the UK population, and this dataset represents the largest such available sensor record. We characterized participant motion during normal activities, including daily living equivalent of timed walk tests. We then compute walking intensity from sensor data, as input to survival analysis. Simulating passive smartphone monitoring, we validated predictive models using only sensors and demographics. This resulted in C-index of 0.76 for 1-year risk decreasing to 0.73 for 5-year. A minimum set of sensor features achieves C-index of 0.72 for 5-year risk, which is similar accuracy to other studies using methods not achievable with smartphone sensors. The smallest minimum model uses average acceleration, which has predictive value independent of demographics of age and sex, similar to physical measures of gait speed. Our results show passive measures with motion sensors can achieve similar accuracy to active measures of gait speed and walk pace, which utilize physical walk tests and self-reported questionnaires.
Background
Health coaching is an emerging intervention that has been shown to improve clinical and patient-relevant outcomes for type 2 diabetes. Advances in artificial intelligence may provide an ...avenue for developing a more personalized, adaptive, and cost-effective approach to diabetes health coaching.
Objective
We aim to apply Q-learning, a widely used reinforcement learning algorithm, to a diabetes health-coaching data set to develop a model for recommending an optimal coaching intervention at each decision point that is tailored to a patient’s accumulated history.
Methods
In this pilot study, we fit a two-stage reinforcement learning model on 177 patients from the intervention arm of a community-based randomized controlled trial conducted in Canada. The policy produced by the reinforcement learning model can recommend a coaching intervention at each decision point that is tailored to a patient’s accumulated history and is expected to maximize the composite clinical outcome of hemoglobin A1c reduction and quality of life improvement (normalized to 0, 1 , with a higher score being better). Our data, models, and source code are publicly available.
Results
Among the 177 patients, the coaching intervention recommended by our policy mirrored the observed diabetes health coach’s interventions in 17.5% (n=31) of the patients in stage 1 and 14.1% (n=25) of the patients in stage 2. Where there was agreement in both stages, the average cumulative composite outcome (0.839, 95% CI 0.460-1.220) was better than those for whom the optimal policy agreed with the diabetes health coach in only one stage (0.791, 95% CI 0.747-0.836) or differed in both stages (0.755, 95% CI 0.728-0.781). Additionally, the average cumulative composite outcome predicted for the policy’s recommendations was significantly better than that of the observed diabetes health coach’s recommendations (tn-1=10.040; P<.001).
Conclusions
Applying reinforcement learning to diabetes health coaching could allow for both the automation of health coaching and an improvement in health outcomes produced by this type of intervention.
Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the ...heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at
https://github.com/boyiguo1/MOTE.RF
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