Pairwise Correlation Analysis Research Articles

VDAC genes down-regulation in brain samples of individuals with schizophrenia is revealed by a systematic meta-analysis

Mitochondrial dysfunction was shown to be involved in schizophrenia pathophysiology. Abnormal energy states can lead to alterations in neural function and thereby to the cognitive and behavioral aberrations characteristics of schizophrenia. Voltage-dependent anion-selective channels (VDAC) are located in the outer mitochondrial membrane and are involved in mitochondrial energy production. Only few studies explored VDAC genes’ expression in schizophrenia, and their results were not consistent. We conducted a systematic meta-analysis of ten brain samples gene expression datasets (overall 368 samples, 179 schizophrenia, 189 controls). In addition, we conducted a meta-analysis of three blood samples datasets (overall 300 samples, 167 schizophrenia, 133 controls). Pairwise correlation analysis was conducted between the VDAC and proteasome subunit genes’ expression patterns. VDAC1, VDAC2 and VDAC3 showed significant down-regulation in brain samples of patients with schizophrenia. They also showed significant positive correlations with the proteasome subunit genes’ expression levels. Our findings suggest that VDAC genes might play a role in mitochondrial dysfunction in schizophrenia. VDAC1 was down-regulated also in blood samples, which suggests its potential role as a biomarker for schizophrenia. The correlation with proteasome subunits, which were previously shown to be down-regulated in a subgroup of the patients, suggests that our findings might characterize a subgroup of the patients. This direction has the potential to lead to patients’ stratification and more precisely-targeted therapy and necessitates further study.

Integration of patient experience factors improves readmission prediction.

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.

Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.

Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI. We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis). Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR≥90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered. This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.

Homogeneous-multiset-CCA-based brain covariation and contravariance connectivity network modeling.

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

Pair-Wise Correlation Analysis of Accounting Standards and Quality of Financial Reports of Nigerian Deposit Money Banks

Pair-Wise Correlation Analysis of Accounting Standards and Quality of Financial Reports of Nigerian Deposit Money Banks

Association Between Physical Performance Tests and External Load During Scrimmages in Highly Trained Youth Ice Hockey Players.

To investigate the relationship between physical performance tests and on-ice external load from simulated games (scrimmages) in ice hockey. A total of 14 players completed a physical performance test battery consisting of 30-m sprint test-run and 30-m sprint test-skate (including 10-m split times and maximum speed), countermovement jump, standing long jump, bench press, pull-ups, and trap bar deadlift and participated in 4 scrimmages. External load variables from scrimmages included total distance; peak speed; slow (< 11.0km/h), moderate (11.0-16.9km/h), high (17.0-23.9km/h), and sprint (> 24.0km/h) speed skating distance; number of sprints; PlayerLoad™; number of high-intensity events (> 2.5m/s); accelerations; decelerations; and changes of direction. Bayesian pairwise correlation analyses were performed to assess the relationship between physical performance tests and external load performance variables. The results showed strong evidence (Bayes factor > 10) for associations between pull-ups and high-intensity events (τ = .61) and between maximum speed skate and peak speed (τ = .55). There was moderate evidence (Bayes factor >3 to <10) for 6 associations: both maximum speed skate (τ = .44) and countermovement jump (τ = .44) with sprint speed skating distance, countermovement jump with number of sprints (τ = .46), pull-ups with changes of direction (τ = .50), trap bar with peak speed (τ = .45), and body mass with total distance (τ = .49). This study found physical performance tests to be associated with some of the external load variables from scrimmages. Nevertheless, the majority of correlations did not display meaningful associations, possibly being influenced by the selection of physical performance tests.

AlertInsight: Mining Multiple Correlation For Alert Reduction

Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools, which generate massive numbers of alerts and events that are not actionable. These alerts usually carry isolated messages that are missing service contexts. Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems. Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages. One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations. Based on these guidelines, AlertInsight, a framework for alert event reduction, is proposed in this paper. In AlertInsight, the correlations among event sources are found by mining a sequence of historical events. Then, event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream. Finally, the correlated events are aggregated into a single high-level event for alert reduction. Because of the weaknesses of the commonly used pairwise correlation analysis methods in complex environments, an innovative approach for multiple correlation mining, which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism, is proposed in this paper. In addition, a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed. Experiments are conducted to test the effectiveness of AlertInsight. The experimental results (precision = 0.92, recall = 0.93, and F1-score = 0.93) demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction.

Флуктуирующая асимметрия листьев рябины обыкновенной (Sorbus aucuparia L.) как биоиндикатор аэротехногенного загрязнения города Оренбурга

An assessment of environmental pollution in the city of Orenburg was conducted using the fluctuating asymmetry method. The common mountain ash was chosen as an environ­mental indicator. Leaf collection took place at seven points in the city with varying anthropo­genic loads. The research aimed to determine the impact of aerotechnogenic pollution on the magnitude of the fluctuating asymmetry of the leaf blade of the populations of mountain ash within the city of Orenburg. The study revealed that the leaf blades of mountain ash in urban conditions are affected by anthropogenic factors, experiencing air pollution and accumulating harmful substances. The fluctuating asymmetry method can be used as an indicator of plant development instability in urban ecosystems. Elevated environmental stress, based on the integral indicator of fluctuating asymmetry, was noted at all points except the Ural River Embankment. In this area, the growing conditions for plants were characterized as satisfacto­ry. Through pairwise correlation analysis of the examined characteristics, the closest connec­tions were found between two pairs: the width of the leaf half — the distance between the ba­ses of the first and second order veins; the distance from the base of the leaf plate to the end of the second-order vein — the angle between the main vein and the second from the base of the leaf.

Relationship between Complex Integration Indices and Inflation Indicators and Their Impact on the Development of Regional Cooperation between Countries to Reduce the Level of Inflationary Risks: Case of the SCO Member Countries

In this study, we focused on the development of cooperation between partner countries, which may affect the reduction of inflationary risks for partnership participants in the context of global and urgent changes in the world. This article aims to identify the relationship between inflation indicators and various types of globalization (complex integration indices) of each of the member countries of the Shanghai Cooperation Organization (SCO) in order to develop measures to contain inflation risks in these countries. The authors used the methods of pairwise linear regression, correlation analysis, and multiple linear regression. As variables, the authors used complex indicators that characterize six types of globalization: Economic, financial, demographic, industrial, information, and political indices. The authors concluded that China and India more effectively curb inflation and are less prone to inflation risks. The inflation rate and the independent variables have a close negative correlation, which indicates a strong degree of mutual influence and has a downward effect on the consumer price index. The most significant variables that have a strong influence on the inflation rate are the factors of financial and information integration. The impact of other types of integration considered in this study is not significant. In order to reduce the level of inflationary risks, the SCO member countries most vulnerable to the price volatility of raw materials (Uzbekistan, Tajikistan, and Kyrgyzstan) are encouraged to develop trade cooperation more actively, for example, by reducing or eliminating import duties on raw materials from the SCO countries.

Study of the influence of indicators of the development of the information society in terms of indicators characterizing electronic business on costs aimed at acquiring information, computer and equipment

In the context of the implementation of the main strategic goal of national development — digital transformation, the tasks of intensive implementation of information technologies in the business processes of enterprises come to the fore. To optimize this process, it is important to correctly determine the strategic direction of the enterprise development and choose a business model for digital transformation. The article deals with issues related to the assessment of the digital maturity of an enterprise. The possibility of using the indicators of the development of the information society of the Russian Federation to create a model of an integral indicator for assessing digital maturity is determined. The question of the interdependence of development indicators in the sections on the availability of financial resources and e-business is considered in detail. The studies are carried out using the method of pairwise correlation analysis based on the data of federal statistical observations on the issues of monitoring the development of the information society in the Russian Federation. In general, the impact of 29 out of 32 indicators from the "Electronic Business" section, which have a sufficient amount of statistical data, on the indicator "Volume of investments in fixed assets directed to information, computer and telecommunications equipment" was studied. Only two of them (The share of organizations that had special software for managing the purchase of goods (works, services), in the total number of surveyed organizations and the share of organizations that had special software for managing the sale of goods (works, services), in the total number of surveyed organizations) show little or no correlation. The rest demonstrate the tightness of the correlation from noticeable to very high. The identified indicators from the e-business section, which have stable relationships with the indicator "Volume of investments in fixed assets aimed at the acquisition of information, computer and telecommunications equipment", will be further included in the integral indicator for assessing the digital maturity of an enterprise, which allows for a quantitative assessment based on the model multiple regression.

Adhesive bond quality classification using machine learning algorithms based on ultrasonic pulse-echo immersion data

In this study, we explored the detection of weak bonds (WBs) due to contamination and faulty curing (FC) using linear ultrasound and machine learning. For this purpose, aluminium single-lap adhesive joints containing three variants of bonding quality were investigated: perfect bond, WB due to release agent (RA) contamination, and WB due to FC. The data, according to the deviation of the bonding protocol, were arranged in two groups, creating two datasets: distinct and complete. Each dataset included all bonding conditions (perfect, RA, and FC), although the distinct dataset contained only marginal cases, which were expected to be well separable, whereas the complete dataset included data with minor deviations from the bonding protocol. Pulse-echo C-scan images were acquired for all prepared samples in the immersion tank, and 45 features were initially extracted from the time traces representing each bonding group. The initial data were analysed via a t-test and pairwise correlation analysis to reveal statistically significant features. Then, we performed dimensionality reduction using tree-based, recursive, sequential, and linear discriminant analysis (LDA) feature selectors to explore feature importance and classification accuracy with different feature subsets. Finally, the important features identified with the different feature selectors were fed to support vector machine (SVM) classifiers, and the classification accuracies were compared amongst the different feature subsets. The classification accuracy using a distinct dataset in some cases demonstrated nearly 99% accuracy, indicating that significant bonding protocol deviations could be easily detected. It was demonstrated that classification accuracy increased with the number of features. However, even in the case of the 2D feature space obtained using linear discriminant analysis, the bonding quality classification accuracy remained higher than 84%. The feature subspace reduction with LDA demonstrated sufficient classification accuracy and an improvement of nearly 40% in training time compared with that for the initial feature set. Thus, the classical ultrasonic pulse-echo C-scan with an LDA feature transformation and SVM classifier could be used to identify the deviations in the bonding protocol in aluminium single-lap adhesive joints.

Baseline Clinical Characteristics and Complement Biomarkers of Patients with C3 Glomerulopathy Enrolled in Two Phase 2 Studies Investigating the Factor D Inhibitor Danicopan

Introduction: C3 glomerulopathy (C3G) is a rare, progressive kidney disease resulting from dysregulation of the alternative pathway (AP) of complement. Biomarkers at baseline were investigated in patients with C3G who participated in two phase 2 studies with the factor D (FD) inhibitor, danicopan. Methods: Patients with biopsy-confirmed C3G, proteinuria ≥500 mg/day, and estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m2 were enrolled into two studies (NCT03369236 and NCT03459443). Biomarker analysis was performed for patients with C3G confirmed by central pathology laboratory re-evaluation. Complement and clinical biomarkers, biopsy composite score, and activity and chronicity indices were assessed at baseline and analyzed by pairwise Spearman correlation analysis. Results: Twenty-nine patients were included in the analysis (median [interquartile range] age: 24.0 [10.0] years). Systemic complement AP activation was evident by reduced median concentrations of C3 and C5, elevated sC5b-9, and normal C4, relative to reference ranges. C3 showed strong pairwise correlations with C5 and sC5b-9 (r = 0.80 and −0.73, respectively; p < 0.0001). Baseline Ba and FD concentrations were inversely correlated with eGFR (r = −0.83 and −0.87, respectively; p < 0.0001). Urinary concentrations of sC5b-9 were correlated with both plasma sC5b-9 and proteinuria (r = 0.69 and r = 0.83, respectively; p < 0.0001). Biopsy activity indices correlated strongly with biomarkers of systemic AP activation, including C3 (r = −0.76, p < 0.0001), whereas chronicity indices aligned more closely with eGFR (r = −0.57, p = 0.0021). Conclusion: Associations among complement biomarkers, kidney function, and kidney histology may add to the current understanding of C3G and assist with the characterization of patients with this heterogenous disease.

Hierarchical stratification of the factors related to exertional dyspnoea and exercise intolerance in male COPD patients

Background The order and extent of interactions across the factors affecting exertional dyspnoea (ED) and exercise intolerance (EI) in patients with chronic obstructive pulmonary disease (COPD) are not clear. We hypothesized that lung and non-lung variables were the primary variables, ED was the secondary variable and EI was the tertiary variable. Methods Data on demographics, blood tests, cardiac imaging, lung function tests and invasive dead space fractions (VD/VT) during incremental exercise test of 46 male COPD subjects were obtained. These variables were categorized by factor analysis and pair-wise correlation analysis was conducted. The best factor of each category was selected and then multivariate regression was conducted. Results Peak tidal inspiratory flow (VT/TIpeak), VD/VTpeak and tidal lung expansion capability, and resting diffusing capacity of the lungs (DLCO)% predicted were the primary pulmonary factors most related to ED, whereas body mass index (BMI), haemoglobin and cholesterol levels were the primary non-pulmonary factors. In multivariate regression analysis, VT/TIpeak, VD/VTpeak and DLCO% were the primary factors most related to ED (r 2 = 0.69); ED was most related to EI (r = −0.74 to −0.83). Conclusion Using hierarchical stratification and statistical methods may improve understanding of the pathophysiology of ED and EI in patients with COPD. KEY MESSAGES The pathophysiology of exertional dyspnoea (ED) and exercise intolerance (EI) in chronic obstructive pulmonary disease (COPD) is complex. The order and extent of interactions across factors are not clear. In multivariate regression analysis, we found that tidal inspiratory flow, dead space fraction and resting diffusing capacity of the lungs % but not the non-pulmonary factors affected ED. Using correlation coefficients, we further found that ED was the secondary variable and EI was the tertiary variable. Hierarchical stratification of the important factors associated with ED and EI in patients with COPD clarifies their relationships and could be incorporated into management programmes and outcome studies for these patients.

Risk factors for late presentation among glaucoma patients attending three referral hospitals in South-East Nigeria: case–control study

Late presentation in glaucoma has been associated with rapid deterioration of visual fields and blindness even with good and effective treatment options. Glaucoma prevalence and risk of blindness is higher among the Igbo tribe, who dominate the Eastern part of Nigeria, hence the focus of this study in this area to determine modifiable risks for late presentation in glaucoma. This was an observational, hospital-based, comparative study at three tertiary hospitals. All eligible, consenting participants were examined and researcher-recorded questionnaire administered. Data was entered, validated and imported into Stata version 15.0; appropriate descriptive measures of central tendencies were presented with tables and maps. Univariate, multiple logistic regression and pairwise correlation analysis identified statistically significant risks at p-value of <0.05. 303 participants were enrolled with a mean (SD) age of 56.58 (15.63) years. The adjusted odds of presenting late were found to be 57% lower among females (CI 0.27 to 0.69), 1.73 times higher with each year increase (CI 1.42 to 2.11) and 2.31 times among hypertensives (CI 1.39 to 3.85). There were reduced adjusted odds ratio; 0.55 for participants living in urban areas compared to rural (p 0.02) and 0.52 for those who have heard about glaucoma prior to diagnosis (p 0.01). Increasing age, male sex, rural and non-awareness of glaucoma are identified risk for late presentation. Community-based health promotion activities can be explored to bridge this gap through targeted and cost-effective approaches involving existing health-care delivery systems.

MicroRNA and Hemostasis Profile of Carotid Atherosclerosis.

Carotid atherosclerosis (CA) is an important risk factor for ischemic stroke. We described the miRNA and hemostasis profile of patients with moderate and advanced stages of carotid atherosclerosis and elucidated potential correlations with hemostatic activation. A prospective case-control study included 61 patients with evidence of carotid atherosclerosis (via ultrasound). The study population was divided into groups depending on the degree of carotid artery stenosis: 60% or more (advanced) and <60% (moderate). All patients underwent the following blood tests: general blood test, hemostatic parameters and microRNA. Extraction of microRNA was performed using Leukocyte RNA Purification Kit (NORGEN Biotec Corp., Thorold, ON, Canada); miRNA quantification was performed via RT-PCR. Statistical analysis was performed in R programming language (v. 4.1.0) using RSudio. MicroRNA expression profile was different depending on CA degree. MiR-33a-5p/3p levels were higher in patients with ≥60% carotid stenosis (42.70 and 42.45 versus 38.50 and 38.50, respectively, p < 0.05). Almost complete separation can be visualized with the levels of miR-126-5p: 9.50 in the moderate CA group versus 5.25 in the advanced CA (p < 0.001). MiR-29-5p was higher in the moderate CA group: 28.60 [25.50;33.05] than in advanced CA group: 25.75 [24.38;29.50] (p = 0.086); miR-29-3p was also higher in the moderate CA group: 10.36 [8.60;14.99] than in advanced CA group: 8.46 [7.47;10.3] (p = 0.001). By-group pairwise correlation analyses revealed at least three clusters with significant positive correlations in the moderate CA group: miR-29-3p with factors V and XII (r = 0.53 and r = 0.37, respectively, p < 0.05); miR-21-5p with ADAMTS13, erythrocyte sedimentation rate and D-dimer (r = 0.42, r = 0.36 and r = 0.44, respectively, p < 0.05); stenosis degree with miR-33a-5p/3p and factor VIII levels (r = 0.43 (both) and r = 0.62, respectively, p < 0.05). Hemostasis parameters did not reveal significant changes in CA patients: the only statistically significant differences concerned factor VIII, plasminogen and (marginally significant) ADAMTS-13 and protein C. Down-regulation of miR-126-5p expression has been identified as a promising biomarker of advanced carotid atherosclerosis with high specificity and sensitivity. Correlation cluster analysis showed potential interplay between miRNAs and hemostatic activation in the setting of carotid atherosclerosis.

Gender disparities in rural education attainments and agricultural landownership from the perspective of Sustainable Development Goals (SDGs): evidence from 16 Sub-Sahara African countries

Purpose The purpose of this study is to explore the relationship between gender disparities in rural education attainments and agricultural landownership (ALO) in Sub-Sahara Africa with Sustainable Development Goal (SDG) perspective. Design/methodology/approach This study uses SDG indicators interactions and pairwise correlation analysis. Findings There is a significant negative association between gender disparities in rural education attainments and ALO in Sub-Sahara Africa. Such negative relationship is not influenced by national economic development and living standards. Research limitations/implications The data is limited with 16 Sub-Sahara African countries, and as this is an early output of a number of follow-up studies in the author’s plan, the methodology is relatively simple. Practical implications Reducing gender disparity in rural Sub-Sahara Africa especially in ALO requires more integrated approaches which also address other aspects of sustainable development. This is particularly the situation because of the strong male-favored customary practices in rural Sub-Sahara Africa. The prioritization of different dimensions of sustainable development is also important in Sub-Sahara Africa. Social implications Strong awareness of SDGs is important. Further efforts in collecting data for and use data of sustainable development, especially the SDGs, are essential. Emerging trend of studying the interactions across SDGs reflects the future direction of relevant fields. Originality/value This paper has high originality because it is an early-stage research in the SDG interactions in Sub-Sahara African countries with the perspective of gender, gender disparity, Sub-Sahara Africa, SDGs, ALO and rural education attainments. This paper has both academic and practical values because of its innovative research thoughts and policy-oriented implications.

Acute Magnetic Resonance Imaging Predictors of Chronic Motor Function and Tissue Sparing in Rat Cervical Spinal Cord Injury.

Predicting functional outcomes from spinal cord injury (SCI) at the acute setting is important for patient management. This work investigated the relationship of early magnetic resonance imaging (MRI) biomarkers in a rat model of cervical contusion SCI with long-term functional outcome and tissue sparing. Forty rats with contusion injury at C5 at either the spinal cord midline (bilateral) or over the lateral cord (unilateral) were examined using in vivo multi-modal quantitative MRI at 1 day post-injury. The extent of T2-weighted hyperintensity reflecting edema was greater in the bilateral model compared with the unilateral injury. Diffusion tensor imaging (DTI) exhibited microscopic damage in similar regions of the cord as reductions in fractional anisotropy (FA) and mean diffusivity (MD), but DTI parameter maps were also confounded by the presence of vasogenic edema that locally increased FA and MD. In comparison, filtered diffusion-weighted imaging (fDWI) more clearly delineated the location of acute axonal damage without effects of vasogenic edema. Pairwise correlation analysis revealed that 28-day motor functional outcomes were most strongly associated with the extent of edema (R = -0.69). Principal component analysis identified close associations of motor functional score with tissue sparing, the extent of edema, lesion area, and injury type (unilateral or bilateral). Among the diffusion MRI parameters, lesion areas measured with fDWI had the strongest association with functional outcome (R = -0.41). Voxelwise correlation analysis identified a locus of white matter damage associated with function in the dorsal white matter, although this was likely driven by variance across the two injury patterns (unilateral and bilateral injury). Nonetheless, correlation with motor function within the damaged region found in the voxelwise analysis outperformed morphological lesion area measurement as a predictor of chronic function. Collectively, this study characterized anatomical and diffusion MRI signatures of acute SCI at cervical spine and their association with chronic functional outcomes and histological results.

Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.

Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity,and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%,and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.

Consistency and uncertainty of gridded terrestrial evapotranspiration estimations over China

Terrestrial evapotranspiration (ET) is a critical process in water, energy and carbon cycles but challenging to estimate. Several gridded terrestrial ET products have been developed for regional or global applications based on different algorithms and forcing inputs and with different temporal-spatial resolutions and accuracy. This study systematically investigates the consistency and uncertainty of eight ET products over China for the period from 2003 to 2014. Temporal and spatial pairwise correlation analysis indicates that the ET products are largely consistent with each other, among which the ET estimates from GLEAM and EB-ET presenting highest spatial consistency with each other (R2 = 0.91). Compared to the ground-based observations from the flux network, the centered root-mean-squared-deviation (RMSD) for each of the eight products is found lower than 30 mm/month, according to which CR and PMLv2 outperform the others. The ET products however show considerable difference in the estimated mean annual ET averaged over China (from 378 mm/a to 528 mm/a). Uncertainty assessment based on the extended collocation method (ECM) suggests the uncertainties in ET estimates are more evident in the arid region than in the humid region. It also indicates that model averaging (like that in generating the GLASS ET product) could substantially reduce the uncertainties. The findings of this research could facilitate the application of the ET products to assess water balance dynamics over China while considering their uncertainties.

Epigallocatechin-3-Gallate (EGCG) as a potential therapeutic against cardiovascular disease risk in mice

Objective: Research is focused on cardiovascular disease (CVD). CVD is the leading cause of death in the world and remains one of the major diseases strongly affected by diet and sedentary lifestyle. CVD is a class of diseases relates to the heart and or blood vessels that include, stroke, heart failure, hypertension, coronary artery disease, peripheral artery disease, and atherosclerosis caused partly due to dyslipidemia. The aim of this study was to determine the potential effect of epigallacatechin-3-gallate (EGCG) on blood lipid and glucose profiles in mice exposed to conditions that lead to CVD. Groups of male mice were subjected to different diet (standard fat diet, high fat diet and low-fat diet), exercise (voluntary, chronic and sedentary lifestyle) and EGCG supplementation. EGCG powder was dissolved in 4 ml of drinking water to deliver a daily dose of 30 mg/kg. Mice were anesthetized with isoflurane (Abbott, Cham, Switzerland) 2% (v/v) in a 20% O2 and 80% air mixture by inhalation in a closed container, then euthanized by manual cervical dislocation and were put on the animal's bed for a blood sample from the cardiac puncture. Blood was collected for lipid profiles and glucose analysis. The data was analyzed using a multi variate approach including one-way analysis of variance test (one-way ANOVA), Tukey –Kramer multiple comparison test and pairwise correlation analysis was used to determine the significance between control and treated groups. Results: The mice subjected to high-fat diet (Anova p-value = 0.00) and sedentary lifestyle (Anova p-value = 0.00) showed elevated levels for total cholesterol, low density lipoprotein’s cholesterol, triglycerides and glucose. EGCG supplementation in mice undergoing high fat diet and sedentary lifestyle resulted in a significant reduction in the lipid profiles and blood glucose (Anova p-value = 0.00). Our study showed that a high fat diet and a sedentary lifestyle predispose to hyperlipidemia and high blood glucose levels in mice and also proved that EGCG fed to hyperlipidemia mice reduces lipid levels in blood.