Noninvasive inference methods for interaction and noise intensities of coupled oscillators using only spike time data.

Measurements of interaction intensity are generally achieved by observing responses to perturbations. In biological and chemical systems, external stimuli tend to deteriorate their inherent nature, and thus, it is necessary to develop noninvasive inference methods. In this paper, we propose theoretical methods to infer coupling strength and noise intensity simultaneously in two well-synchronized noisy oscillators through observations of spontaneously fluctuating events such as neural spikes. A phase oscillator model is applied to derive formulae relating each of the parameters to spike time statistics. Using these formulae, each parameter is inferred from a specific set of statistics. We verify these methods using the FitzHugh-Nagumo model as well as the phase model. Our methods do not require external perturbations and thus can be applied to various experimental systems.

Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction.

BACKGROUND: microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments. RESULTS: In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method. CONCLUSIONS: This research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.

Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data.

BACKGROUND: Antidepressants exert an anticholinergic effect in varying degrees, and various classes of antidepressants can produce a different effect on immune function. While the early use of antidepressants has a notional effect on COVID-19 outcomes, the relationship between the risk of COVID-19 severity and the use of antidepressants has not been properly investigated previously owing to the high costs involved with clinical trials. Large-scale observational data and recent advancements in statistical analysis provide ample opportunity to virtualize a clinical trial to discover the detrimental effects of the early use of antidepressants. OBJECTIVE: We primarily aimed to investigate electronic health records for causal effect estimation and use the data for discovering the causal effects of early antidepressant use on COVID-19 outcomes. As a secondary aim, we developed methods for validating our causal effect estimation pipeline. METHODS: We used the National COVID Cohort Collaborative (N3C), a database aggregating health history for over 12 million people in the United States, including over 5 million with a positive COVID-19 test. We selected 241,952 COVID-19-positive patients (age >13 years) with at least 1 year of medical history. The study included a 18,584-dimensional covariate vector for each person and 16 different antidepressants. We used propensity score weighting based on the logistic regression method to estimate causal effects on the entire data. Then, we used the Node2Vec embedding method to encode SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) medical codes and applied random forest regression to estimate causal effects. We used both methods to estimate causal effects of antidepressants on COVID-19 outcomes. We also selected few negatively effective conditions for COVID-19 outcomes and estimated their effects using our proposed methods to validate their efficacy. RESULTS: The average treatment effect (ATE) of using any one of the antidepressants was -0.076 (95% CI -0.082 to -0.069; P<.001) with the propensity score weighting method. For the method using SNOMED-CT medical embedding, the ATE of using any one of the antidepressants was -0.423 (95% CI -0.382 to -0.463; P<.001). CONCLUSIONS: We applied multiple causal inference methods with novel application of health embeddings to investigate the effects of antidepressants on COVID-19 outcomes. Additionally, we proposed a novel drug effect analysis-based evaluation technique to justify the efficacy of the proposed method. This study offers causal inference methods on large-scale electronic health record data to discover the effects of common antidepressants on COVID-19 hospitalization or a worse outcome. We found that common antidepressants may increase the risk of COVID-19 complications and uncovered a pattern where certain antidepressants were associated with a lower risk of hospitalization. While discovering the detrimental effects of these drugs on outcomes could guide preventive care, identification of beneficial effects would allow us to propose drug repurposing for COVID-19 treatment.

Different Responses in Root Water Uptake of Summer Maize to Planting Density and Nitrogen Fertilization.

Modifying farming practices combined with breeding has the potential to improve water and nutrient use efficiency by regulating root growth, but achieving this goal requires phenotyping the roots, including their architecture and ability to take up water and nutrients from different soil layers. This is challenging due to the difficulty of in situ root measurement and opaqueness of the soil. Using stable isotopes and soil coring, we calculated the change in root water uptake of summer maize in response to planting density and nitrogen fertilization in a 2-year field experiment. We periodically measured root-length density, soil moisture content, and stable isotopes δ18O and δD in the plant stem, soil water, and precipitation concurrently and calculated the root water uptake based on the mass balance of the isotopes and the Bayesian inference method coupled with the Markov Chain Monte Carlo simulation. The results show that the root water uptake increased asymptotically with root-length density and that nitrogen application affected the locations in soil from which the roots acquired water more significantly than planting density. In particular, we find that reducing nitrogen application promoted root penetration to access subsoil nutrients and consequently enhanced their water uptake from the subsoil, while increasing planting density benefited water uptake of the roots in the topsoil. These findings reveal that it is possible to manipulate plant density and fertilization to improve water and nutrient use efficiency of the summer maize and the results thus have imperative implications for agricultural production.

Factors dominating the horizontal and vertical variability of soil water vary with climate and plant type in loess deposits.

Identifying the variability and predominant factors affecting soil water (SW) is essential in regions with thick vadose zones and deep-rooted plants. This information is needed to clarify the balance between water availability and plant water demand. We collected 9263 soil samples from 128 profiles of 7-25 m deep soil under different climates (arid, semiarid and subhumid), soil textures and plant types (shallow or deep roots) in China's Loess Plateau. The factors dominating the horizontal and vertical variability of SW were identified using a multimodel inference approach and stepwise regression analysis. Horizontally, the mean water content and storage increased while the water deficits decreased from the northwest to the southeast. Vertically, mean water content and storage are highest in the relatively stable layer, followed by rapidly changing layers and active layers. Plant age and soil clay content dominate the horizontally varied SW, while plant age and normalized difference vegetation index (NDVI) dominate the vertical variability of SW. However, the dominant factors appeared to differ with climate and plant type. It was determined that for climate, soil clay content and plant age in arid regions, precipitation and plant age in semiarid regions, NDVI and plant age in subhumid regions were important factors. For plants, the dominant factors are NDVI and precipitation under shallow-rooted plants; however, NDVI and plant age were dominant under deep-rooted plants. The dominance of plant age highlighted the impact of vegetation patterns on SW, especially for deep-rooted plants, which should be taken into account when managing water resources and ecosystem rehabilitation in degraded regions.

Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression.

Numerous statistical models have established the relationship between ambient fine particulate matter (PM2.5, with an aerodynamic diameter of less than 2.5 µm) and satellite aerosol optical depth (AOD) along with other meteorological/land-related covariates. However, all the models assumed that all covariates affect the PM2.5 concentration at the same scale, and none could provide a posterior uncertainty analysis at each regression point. Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate. However, the lack of a method for predicting values at unsampled points in the MGTWR model greatly restricts its corresponding application. Thus, this study developed a method for inferring unsampled points and used the posterior uncertainty assessment value to improve the model accuracy. With the aid of the high-resolution satellite multi-angle implementation of atmospheric correction (MAIAC) AOD product, daily PM2.5 concentrations with a 1 km × 1 km resolution were generated over the Beijing-Tianjin-Hebei region between 2013 and 2019. The coefficient of determination (R2) and root mean square error (RMSE) of the fitted MGTWR results vary from 0.90 to 0.94 and from 10.66 to 25.11 µg/m3, respectively. The sample-based and site-based cross-validation R2 and RMSE vary from 0.81 to 0.89 and from 14.40 to 34.43 µg/m3 respectively, demonstrating the effectiveness of the proposed inference method at unsampled points. With the uncertainty constraint, the sample-based and site-based validated MGTWR R2 results for all years are further improved by approximately 0.02-0.04, demonstrating the effectiveness of the posterior uncertainty assessment constraint method. These results suggest that the inference method proposed in this study is promising to overcome the defects of the MGTWR model in inferring the prediction values at unsampled points and could consequently enhance the wide applications of MGTWR modeling.

Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis.

OBJECTIVE: The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). MATERIALS AND METHODS: To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. RESULTS: A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. CONCLUSION: In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO.

Effects of short-term fine particulate matter exposure on acute respiratory infection in children.

BACKGROUND: Previous studies on the association between fine particulate matter (PM2.5) exposure and acute respiratory infection in children are scarce and present inconsistent results. We estimated the association between short-term PM2.5 exposure and acute respiratory infection among children aged 0-4 years using a difference-in-differences approach. METHODS: We used data on the daily PM2.5 concentrations, hospital admissions for acute respiratory infection, and meteorological factors of the 15 regions in the Republic of Korea (2013-2015). To estimate the cumulative effects, we used a difference-in-differences approach generalized to multiple spatial units (regions) and time periods (day) with distributed lag non-linear models. RESULTS: With PM2.5 levels of 20.0 µg/m3 as a reference, PM2.5 levels of 30.0 µg/m3 were positively associated with the risk of acute upper respiratory infection (relative risk (RR) = 1.048, 95% confidence interval (CI): 1.028, 1.069) and bronchitis or bronchiolitis (RR = 1.060, 95% CI: 1.038, 1.082) but not with the risk of acute lower respiratory infection and pneumonia. PM2.5 levels of 40.0 µg/m3 were also positively associated with the risk of acute upper respiratory infection (RR = 1.083, 95% CI: 1.046, 1.122) and bronchitis or bronchiolitis (RR = 1.094, 95% CI: 1.054, 1.136). CONCLUSIONS: We found the associations of short-term PM2.5 exposure with acute upper respiratory infection and bronchitis or bronchiolitis among children aged 0-4 years. As causal inference methods can provide more convincing evidence of the effects of PM2.5 levels on respiratory infections, public health policies and guidelines regarding PM2.5 need to be strengthened accordingly.

Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes.

In the present work, we postulate that a critical value of the stored plastic strain energy density (SPSED) is associated with fatigue failure in metals and is independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress-strain hysteresis loops, we use crystal plasticity finite element simulations to compute the (local) SPSED at each material point within polycrystalline aggregates of a nickel-based superalloy. A Bayesian inference method is used to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and -1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a lognormal distribution. Moreover, for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude; this is indicative of the scatter observed in the fatigue experiments. Finally, the lognormal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.

Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine.

ETHNOPHARMACOLOGICAL RELEVANCE: Herbal medicine is a concoction of numerous chemical ingredients, and it exhibits polypharmacological effects to act on multiple pharmacological targets, regulating different biological mechanisms and treating a variety of diseases. Thus, this complexity is impossible to deconvolute by the reductionist method of extracting one active ingredient acting on one biological target. AIM OF THE STUDY: To dissect the polypharmacological effects of herbal medicines and their underling pharmacological targets as well as their corresponding active ingredients. MATERIALS AND METHODS: We propose a system-biology strategy that combines omics and bioinformatical methodologies for exploring the polypharmacology of herbal mixtures. The myocardial ischemia model was induced by Ameroid constriction of the left anterior descending coronary in Ba-Ma miniature pigs. RNA-seq analysis was utilized to find the differential genes induced by myocardial ischemia in pigs treated with formula QSKL. A transcriptome-based inference method was used to find the landmark drugs with similar mechanisms to QSKL. RESULTS: Gene-level analysis of RNA-seq data in QSKL-treated cases versus control animals yields 279 differential genes. Transcriptome-based inference methods identified 80 landmark drugs that covered nearly all drug classes. Then, based on the landmark drugs, 155 potential pharmacological targets and 57 indications were identified for QSKL. CONCLUSION: Our results demonstrate the power of a combined approach for exploring the pharmacological target and chemical space of herbal medicines. We hope that our method could enhance our understanding of the molecular mechanisms of herbal systems and further accelerate the exploration of the value of traditional herbal medicine systems.

The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.