Alternative Predictors Research Articles

Information technology competencies as antecedents for absorptive capacity and innovation capabilities in a high-tech industry

PurposePrevious studies equated information technology (IT) with the notion of effective resources. ITs improved firms’ competitive advantage and innovativeness. Yet, far fewer studies investigated types of IT competencies that corresponded to innovation capabilities, particularly in developing countries. The aim of this paper is to provide an investigation concerning the types of IT competencies and examine their influence on the innovation capabilities across high-tech firms.Design/methodology/approachA survey was randomly distributed to 274 respondents across four main sectors of Malaysian high-tech firms. The main sectors were electric and electronic, aerospace, computers and office machinery and pharmaceuticals. A structural equation model, Amos, was used to analyse data.FindingsThree findings were surmised. First, IT competencies driven by IT infrastructure, alignment, management affected high-tech firms’ innovation capabilities. Second, absorptive capacity (AC) partially determined the relationship between IT infrastructure and IT alignment and innovation capabilities. AC also determined the relationship between IT management and innovation capabilities. The significance of IT competencies in the improvement of innovation capabilities was presented as a key predictor in bolstering high-tech manufacturing firms’ competitive advantage.Originality/valueTwo points on novelty were presented. First, by conceptualising IT competencies from resource-based theory (RBV), a shift in understanding RBV was presented. Second, alternative key predictors concerning how IT competencies could improve aspects of AC and innovation capabilities were presented.

Albumin-Corrected Fructosamine Predicts All-Cause and Non-CVD Mortality Among the Very Elderly Aged 80 Years or Older Without Diabetes.

Several guidelines have suggested alternative glycemic markers for hemoglobin A1c among older adults with limited life expectancy or multiple coexisting chronic illnesses. We evaluated associations between fructosamine, albumin-corrected fructosamine (AlbF), fasting plasma glucose (FPG), and mortality in the diabetic and nondiabetic subpopulations, and compared which marker better predicts mortality among participants aged 80 and older. Included were 2 238 subjects from the Healthy Ageing and Biomarkers Cohort Study (2012-2018) and 207 participants had diabetes at baseline. Multivariable Cox proportional hazards regression models investigated the associations of fructosamine, AlbF, FPG, and all-cause, cardiovascular disease (CVD), and non-CVD mortality in the diabetic and nondiabetic subpopulations. Restricted cubic splines explored potential nonlinear relations. C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) evaluated the additive value of different glycemic markers to predict mortality. Overall, 1 191 deaths were documented during 6 793 person-years of follow-up. In the linear model, per unit increases of fructosamine, AlbF, and FPG were associated with a higher risk of mortality in nondiabetic participants, with hazard ratios of 1.02 (1.00, 1.05), 1.27 (1.14, 1.42), and 1.04 (0.98, 1.11) for all-cause mortality, and 1.04 (1.00, 1.07), 1.38 (1.19, 1.59), and 1.10 (1.01, 1.19) for non-CVD mortality, respectively. Comparisons indicated that AlbF better predicts all-cause and non-CVD mortality in nondiabetic participants with significant improvement in IDI and NRI. Higher concentrations of fructosamine, AlbF, and FPG were associated with a higher risk of all-cause or non-CVD mortality among the very elderly where AlbF may constitute an alternative prospective glycemic predictor of mortality.

Laboratory Predictors of Esophageal Varices in Children with Chronic Liver Disease

Variceal bleeding results as a consequence of portal hypertension and it is a leading cause of morbidity and mortality of children with chronic liver disease (CLD). Upper gastrointestinal endoscopy is the only confirmatory tool for detecting esophageal varices but due to its invasive nature, high cost and lack of available facilities for pediatric endoscopy, alternative laboratory predictors are essential. In this study, we aimed at identifying laboratory predictors that may predict the presence of esophageal varices in children with CLD. This cross-sectional study was done at the department of Pediatric Gastroenterology and Nutrition, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh, from July 2008 to June 2010. Fifty consecutive children with CLD, aged 3-15 years of both sexes, who had no history of active/recent variceal bleeding, taking beta blockers or surgery for esophageal varices were included in the study. All patients underwent history and physical examination. Venous blood of the patients was taken for laboratory analysis of serum bilirubin, serum alanine aminotransferase, serum albumin, platelet count and International Normalization Ratio (INR). Later, upper gastrointestinal endoscopy of the patients were done. Based on endoscopic findings children were divided into two groups. Group-I: CLD with esophageal varices included 29 children and Group-II: CLD without esophageal varices included 21 children. A univariate analysis was initially done on laboratory variables followed by a logistic regression analysis to identify the independent variables associated with presence of esophageal varices. Then performance of these independent variables were analyzed using upper gastrointestinal endoscopy as the gold standard test. Out of 50 patients 30 were male. Male-female ratio was 1.5:1. Fifty eight percent (29 out of 50) had esophageal varices. Amongst all the laboratory variables, thrombocytopenia (platelet count <150000/mm3) was an independent predictor of esophageal varices (p=0.018). Thrombocytopenia showed good sensitivity and specificity (82.7% and 80.9% respectively) to be used as a screening test for predicting esophageal varices in children with chronic liver disease. Thrombocytopenia can be used as an independent predictor for esophageal varices in children with chronic liver disease. Faridpur Med. Coll. J. 2021;16(1):3-7

Thermal niche dimensionality could limit species’ responses to temperature changes: Insights from dung beetles

AbstractAimAdequate responses of species to climate changes require that thermal changes remain compatible across different key biological aspects (e.g. reproduction, feeding and development). However, limits of thermal compatibility to such biological aspects are largely unknown in extant ectotherm groups. To fill this gap, we studied the intraspecific congruence of thermal responses across biological aspects.LocationIberian Peninsula.TaxonScarabaeidae Dung beetles.MethodsWe studied the role of temperature in determining the diel, seasonal and geographical occurrences of 16 dung beetle species. We fitted polynomial GLMs of the abundance/occurrence of each species as a function of temperature and alternative predictors for each spatiotemporal scale, using deviance partitioning to explore the relative contribution of temperature. We used the fitted models to estimate realized thermal niche attributes at these three spatiotemporal scales, and assessed their intraspecific congruence through the correlation of niche attributes from different scales.ResultsWe found that temperature has relatively low—but not negligible—explanatory capacity at the three spatiotemporal scales, once alternative predictors are taken into account. More importantly, the estimated thermal responses were largely incongruent across scales, indicating that these species have multidimensional thermal niches.Main conclusionsThe multidimensionality of thermal niches entails that species’ adjustments to fulfil temperature requirements for one biological aspect (such as seasonal ontogenetic cycles) may result in detrimental effects on other elements (e.g. diel activity). These trade‐offs could expose individuals to inadequate temperatures at certain moments, reducing populations’ performance. Paradoxically, the weak effects of temperature we found may have severe consequences for species responses to warming if temperature regulates essential aspects of their biology in divergent ways.

Does high COVID-19 spread impact neighbouring countries? Quasi-experimental evidence from the first year of the pandemic in Ireland.

Background : Coronavirus disease 2019 (COVID-19) has necessitated public health responses on an unprecedented scale. Controlling infectious diseases requires understanding of the conditions that increase spread. Prior studies have identified sociodemographic, epidemiological and geographic associations. Ireland offers an unusual opportunity to quantify how high infection rates in one country impacted cases in a neighbouring country. Methods : We analysed official statistics on confirmed COVID-19 cases on the island of Ireland for 52 weeks from March 2020. Our main research question was: Did higher cases in Northern Ireland (NI) impact the number of cases in the Republic of Ireland (ROI)? We used least squares regression to compare confirmed cases in ROI counties that border NI with the rest of the state. We included in our model sociodemographic, epidemiological and geographic factors. We employed the latitude of each county town as an instrumental variable to isolate a quasi-experimental estimate of the cross-border spread. Results : In the quasi-experimental framework, and controlling for population density, age distribution and circulatory disease prevalence, border counties had an extra 21.0 (95%CI: 8.4-33.6) confirmed COVID-19 cases per 1000 people. This equates to an estimated 9,611 additional cases in ROI, or 4% of the national total in the first year of the pandemic. Our results were substantively similar in non-experimental frameworks, with alternative additional predictors, and in sensitivity analyses. Additionally, population density in ROI counties was positively associated with confirmed cases and higher proportions of residents in the professional classes was negatively associated. Conclusion : On the island of Ireland during the first year of the COVID-19 pandemic, high infection rates in NI increased cases in the neighbouring ROI. Maximising co-ordination of pandemic responses among neighbouring countries is essential to minimising disease spread, and its associated disruptions to society and the economy. Socioeconomic disadvantage appeared to confer significant additional risk of spread.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Predicting outcomes of psychotherapy for depression with electronic health record data.

Predictive analytics with electronic health record (EHR) data holds promise for improving outcomes of psychiatric care. This study evaluated models for predicting outcomes of psychotherapy for depression in a clinical practice setting. EHR data from two large integrated health systems (Kaiser Permanente Colorado and Washington) included 5,554 new psychotherapy episodes with a baseline Patient Health Questionnaire (PHQ-9) score ≥ 10 and a follow-up PHQ-9 14–180 days after treatment initiation. Baseline predictors included demographics and diagnostic, medication, and encounter history. Prediction models for two outcomes—follow-up PHQ-9 score and treatment response (≥ 50% PHQ-9 reduction)—were trained in a random sample of 70% of episodes and validated in the remaining 30%. Two methods were used for modeling: generalized linear regression models with variable selection and random forests. Sensitivity analyses considered alternate predictor, outcome, and model specifications. Predictions of follow-up PHQ-9 scores poorly estimated observed outcomes (mean squared error = 31 for linear regression, 40 for random forest). Predictions of treatment response had low discrimination (AUC = 0.57 for logistic regression, 0.61 for random forest), low classification accuracy, and poor calibration. Sensitivity analyses showed similar results. We note that prediction model performance may vary for settings with different care or EHR documentation practices. In conclusion, prediction models did not accurately predict depression treatment outcomes despite using rich EHR data and advanced analytic techniques. Health systems should proceed cautiously when considering prediction models for psychiatric outcomes using baseline intake information. Transparent research should be conducted to evaluate performance of any model intended for clinical use.

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723–0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865–0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899–0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.

No associations in preregistered study of youth depression and functional connectivity of fronto-parietal and default mode networks

Adolescence is characterized by vulnerability to the onset of major depressive disorder (MDD). The goal of this preregistered study was to assess neural correlates of depression symptoms in young adolescents, both cross-sectionally and longitudinally. The default mode network (DMN) is believed to support internal attention towards self-referential thoughts, while the fronto-parietal network (FPN) is theorized to support cognitive control and regulation of attention. As MDD diagnosis has been associated with heightened connectivity within DMN regions and diminished connectivity within FPN regions relative to healthy controls, our study builds upon group-difference analyses by using dimensional measures of depression severity. Our preregistered hypotheses were that within-DMN functional connectivity would be positively associated with concurrent depression severity, while within-FPN functional connectivity would be negatively associated with concurrent depression severity. Preregistered analyses also examined between DMN-FPN connectivity as an alternative predictor variable, and assessed the longitudinal associations between all three functional connectivity measures and change in depression severity over three subsequent waves. Multiple regression models tested cross-sectional analyses and hierarchical linear models tested longitudinal analyses. One hundred and twenty-four youth completed a resting state functional MRI. Their depression severity was assessed at the time of the scan and at three follow-up sessions. None of the predictor variables were associated with concurrent depression severity, nor with the slope of depression symptom trajectories in longitudinal analyses. These negative results add to extant cross-sectional studies, and may inform future investigations of brain correlates of depression psychopathology in youth.

Does high COVID-19 spread impact neighbouring countries? Quasi-experimental evidence from the first year of the pandemic in Ireland

Background : Coronavirus disease 2019 (COVID-19) has necessitated public health responses on an unprecedented scale. Controlling infectious diseases requires understanding of the conditions that increase spread. Prior studies have identified sociodemographic, epidemiological and geographic associations. Ireland offers an unusual opportunity to quantify how high infection rates in one country impacted cases in a neighbouring country.Methods : We analysed official statistics on confirmed COVID-19 cases on the island of Ireland for 52 weeks from March 2020. Our main research question was: Did higher cases in Northern Ireland (NI) impact the number of cases in the Republic of Ireland (ROI)? We used least squares regression to compare confirmed cases in ROI counties that border NI with the rest of the state. We included in our model sociodemographic, epidemiological and geographic factors. We employed the latitude of each county town as an instrumental variable to isolate a quasi-experimental estimate of the cross-border spread.Results : In the quasi-experimental framework, and controlling for population density, age distribution and circulatory disease prevalence, border counties had an extra 21.0 (95%CI: 8.4-33.6) confirmed COVID-19 cases per 1000 people. This equates to an estimated 9,611 additional cases in ROI, or 4% of the national total in the first year of the pandemic. Our results were substantively similar in non-experimental frameworks, with alternative additional predictors, and in sensitivity analyses. Additionally, population density in ROI counties was positively associated with confirmed cases and higher proportions of residents in the professional classes was negatively associated.Conclusion : On the island of Ireland during the first year of the COVID-19 pandemic, high infection rates in NI increased cases in the neighbouring ROI. Maximising co-ordination of pandemic responses among neighbouring countries is essential to minimising disease spread, and its associated disruptions to society and the economy. Socioeconomic disadvantage appeared to confer significant additional risk of spread.

Revisiting International Public Sector Accounting Standards Adoption in Developing Countries

ABSTRACT Based on a comprehensive review of recent studies on IPSAS adoption around the globe, we develop in this article a conceptual model to examine alternative predictors of adoption for developing countries. Drawing from this framework, we develop a rigorous econometric modelling on the impact of legal, political and accounting environments in the developing countries’ drive for IPSAS adoption. Contrary to what existing literature projects, our study reveals that a country’s IFRS and ISA experience is more important and significant drivers of IPSAS adoption compared to IFRS adoption. Likewise, political system, regulatory enforcement, lenders and borrowers’ rights and the level of corruption in a country also influence IPSAS adoption.

Does high COVID-19 spread impact neighbouring countries? Evidence from Ireland.

Background : Coronavirus disease 2019 (COVID-19) has necessitated public health responses on an unprecedented scale. Controlling infectious diseases requires understanding of the conditions that increase spread. Prior studies have identified sociodemographic, epidemiological and geographic associations. Ireland offers an unusual opportunity to quantify how high infection rates in one country impacted cases in a neighbouring country. Methods : We analysed official statistics on confirmed COVID-19 cases on the island of Ireland for 52 weeks from March 2020. Our main research question was: Did higher cases in Northern Ireland (NI) impact the number of cases in the Republic of Ireland (ROI)? We used least squares regression to compare confirmed cases in ROI counties that border NI with the rest of the state. We included in our model sociodemographic, epidemiological and geographic factors. We employed the latitude of each county town as an instrumental variable to isolate a quasi-experimental estimate of the cross-border spread. Results : In the quasi-experimental framework, and controlling for population density, age distribution and circulatory disease prevalence, border counties had an extra 21.0 (95%CI: 8.4-33.6) confirmed COVID-19 cases per 1000 people. This equates to an estimated 9,611 additional cases in ROI, or 4% of the national total in the first year of the pandemic. Our results were substantively similar in non-experimental frameworks, with alternative additional predictors, and in sensitivity analyses. Additionally, population density in ROI counties was positively associated with confirmed cases and higher proportions of residents in the professional classes was negatively associated. Conclusion : On the island of Ireland during the first year of the COVID-19 pandemic, high infection rates in NI increased cases in the neighbouring ROI. Maximising co-ordination of pandemic responses among neighbouring countries is essential to minimising disease spread, and its associated disruptions to society and the economy. Socioeconomic disadvantage appeared to confer significant additional risk of spread.

Euclid preparation: IX. EuclidEmulator2 – power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

ABSTRACT We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. 2 per cent level accurate emulation is now supported in the eight-dimensional parameter space of w0waCDM+∑mν models between redshift z = 0 and z = 3 for spatial scales within the range $0.01 \, h\, {\rm Mpc}^{-1}\le k \le 10\, h\, {\rm Mpc}^{-1}$. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (i) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy, and the metric field, (ii) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (iii) we run over 250 high-resolution simulations with 30003 particles in boxes of 1(h−1 Gpc)3 volumes based on paired-and-fixed initial conditions, and (iv) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter wa significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors such as HALOFIT, HMCode, and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of $1{{\ \rm per\ cent}}$ or better for $0.01 \, h\, {\rm Mpc}^{-1}\le k \le 10\, h\, {\rm Mpc}^{-1}$ and z ≤ 3 compared to high-resolution dark-matter-only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2.

Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients.

The present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using real world demographic and clinical data from 1,802 heart failure patients enrolled in the UK-HEART2 cohort, the study found that univariable GLMs using LCA-generated subgroup/class membership as the sole candidate predictor of survival were inferior to standard multivariable GLMs using the same four covariates as those used in the LCA. The inclusion of the LCA subgroup/class membership together with these four covariates as candidate predictors in a multivariable GLM showed no improvement in predictive acuity. In contrast, LCR modelling resulted in a 18–22% improvement in predictive acuity and provided a range of alternative models from which it would be possible to balance predictive acuity against entropy to select models that were optimally suited to improve the efficient allocation of clinical resources to address the differential risk of the outcome (in this instance, survival). These findings provide proof-of-principle that LCR modelling can improve the predictive acuity of GLMs and enhance the clinical utility of their predictions. These improvements warrant further attention and exploration, including the use of alternative techniques (including machine learning algorithms) that are also capable of generating latent class structure while determining outcome predictions, particularly for use with large and routinely collected clinical datasets, and with binary, count and continuous variables.

Associations Between Environmental and Sociodemographic Data and Hepatitis-A Transmission in Pará State (Brazil).

Hepatitis‐A is a waterborne infectious disease transmitted by the eponymous hepatitis‐A virus (HAV). Due to the disease's sociodemographic and environmental characteristics, this study applied public census and remote sensing data to assess risk factors for hepatitis‐A transmission. Municipality‐level data were obtained for the state of Pará, Brazil. Generalized linear and nonlinear models were evaluated as alternative predictors for hepatitis‐A transmission in Pará. The Histogram Gradient Boost (HGB) regression model was deemed the best choice (RMSE= 2.36, and higher R2 = 0.95) among the tested models. Partial dependence analysis and permutation feature importance analysis were used to investigate the partial dependence and the relative importance values of the independent variables in the disease transmission prediction model. Results indicated a complex relationship between the disease transmission and the sociodemographic and environmental characteristics of the study area. Population size, lack of sanitation, urban clustering, year of notification, insufficient public vaccination programs, household proximity to open‐air dumpsites and storm‐drains, and lack of access to healthcare facilities and hospitals were sociodemographic parameters related to HAV transmission. Turbidity and precipitation were the environmental parameters closest related to disease transmission. Based on HGB model, a hepatitis‐A risk map was built for Pará state. The obtained risk map can be thought of as an auxiliary tool for public health strategies. This study reinforces the need to incorporate remote sensing data in epidemiological modelling and surveillance plans for the development of early prevention strategies for hepatitis‐A.

Effectiveness of P-wave ECG index and left atrial appendage volume in predicting atrial fibrillation recurrence after first radiofrequency catheter ablation

BackgroundThe primary aim was to observe the predictive value of P-wave ECG index and left atrial appendage volume (LLAV) for atrial fibrillation recurrence after first radiofrequency catheter ablation.MethodsA total of 196 patients with paroxysmal atrial fibrillation were enrolled. The preoperative LLAV was measured by cardiac enhanced CT. The P-wave ECG index including minimum P-wave duration (P-min), maximum P-wave duration (P-max), mean P-wave duration (mPWD), P-wave dispersion (PWD), P-wave terminal force in lead V1 (PtfV1), PR interval prolongation, and interatrial block (IAB) were analyzed and recorded in 12-lead ECG of sinus rhythm.ResultsAccording to the follow-up results, the patients were divided into two groups: the non-recurrence group and the recurrence group. P-min, PWD, P-max, PtfV1 ≥ 0.04 mV·s, PR interval prolongation, and the ratio of first and third-degree IAB in the recurrence group were higher than those in the non-recurrence group, with significant statistical differences (P < 0.05). Kaplan–Meier curve analysis was performed on time to atrial fibrillation recurrence after catheter ablation when PtfV1 ≥ 0.04 mv s by comparison between groups (Log Rank test: 2 = 4.739, P < 0.001). Kaplan–Meier curve analysis showed that the survival rate without recurrence of atrial fibrillation after catheter ablation was lower when the LLAV exceeded 8.0 mL (log-rank test P < 0.001).ConclusionPWD, P-max, PtfV1, PR interval prolongation, first and third-degree IAB, and LLAV can effectively predict atrial fibrillation recurrence after radiofrequency catheter ablation. The combination might be a valid and alternative independent predictor of recurrence.

Predictive validity of the Seasonal Beliefs Questionnaire for discriminating between seasonal and nonseasonal major depressive disorder.

The Seasonal Beliefs Questionnaire (SBQ) is a 26-item self-report measure of a winter seasonal affective disorder (SAD)-specific cognitive vulnerability consisting of maladaptive thoughts about the seasons, light availability, and weather conditions. In a known groups comparison, currently depressed adults with SAD had significantly higher SBQ scores than currently depressed adults with nonseasonal major depressive disorder (MDD) and healthy controls, and the MDD group had significantly higher SBQ scores than controls. Using that database, this study explored the predictive validity of using an SBQ cutoff score to differentiate SAD from MDD. Receiver operator characteristic curve analyses used SBQ total score to predict SAD versus MDD, SAD versus control, and MDD versus control status. The SBQ subscale combined score, derived from multivariable logistic regression with SBQ subscales, was examined as an alternative predictor. SBQ total score with a cutpoint of 132 had good predictive ability for distinguishing SAD from MDD (C-statistic = .792, sensitivity = .798, specificity = .794). The SBQ subscale combination score slightly improved predictive ability for the SAD/MDD distinction (C-statistic = .813), with better sensitivity (.930) but worse specificity (.571). In contrast, the score on a generic measure of depressogenic cognitive vulnerability, the Dysfunctional Attitudes Scale, was poor for differentiating SAD from MDD. SBQ total score was excellent in discriminating SAD cases from controls with a cutpoint of 121 (C-statistic = .962, sensitivity = .939, specificity .873), but had poor sensitivity for discriminating MDD cases from controls. Results support using the SBQ to screen for probable SAD in practice settings. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using metabolic markers to identify insulin resistance in premenopausal women with and without polycystic ovary syndrome.

Insulin resistance (IR) is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Quantifying IR is invasive and time-consuming, and thus not routinely used in clinical practice. Simple metabolic markers to predict IR exist, but have not been validated in premenopausal women or women with polycystic ovary syndrome (PCOS). To evaluate the ability of metabolic markers to identify premenopausal women with/without PCOS who are insulin resistant. Cross-sectional analysis. One hundred and seventy-one non-diabetic premenopausal overweight/obese women without PCOS and 71 women with PCOS. IR was quantified by the steady-state plasma glucose during the modified insulin-suppression test. Metabolic markers (BMI, lipid/lipoprotein concentrations, and fasting glucose) were evaluated for their discriminative ability to identify IR, using area under the receiver-operating-characteristic curve (AUROC) analysis. Optimal cut-points were evaluated for predictive power. In the non-PCOS group, the triglyceride/HDL cholesterol ratio (TG/HDL-C) was the best marker (AUROC 0.73). Optimal diagnostic cut-point was 1.9. In the PCOS group, the TG/HDL-C ratio, cholesterol/HDL-C ratio (TC/HDL-C), and HDL-C performed well (AUROC > 0.80), with optimal cut-points for TG/HDL-C 1.3, TC/HDL-C 3.4, and HDL-C 52mg/dL: TG/HDL-C was more sensitive, but HDL-Chad a higher PPV for IR. TG/HDL-C can identify IR in premenopausal women withand/without PCOS; diagnostic cut-points differ from those of men and postmenopausal women. HDL-C is an alternative predictor in women with PCOS. These simple metabolic markers, which are standardized between labs, inexpensive, and routinely measured, can be used to tailor lifestyle and medical interventions to improve health outcomes in insulin-resistant premenopausal women.

Development of alternative predictor equations for blast-induced ground vibrations considering stiffness ratio, blast dimension and free face conditions

ABSTRACT This study tries to create alternative vibration prediction equations considering some important and neglected blast parameters. The target research parameters are stiffness ratio, blast dimensions and free face conditions. Alternative equations were created by classifying the blasts based on operational parameters. Also, cluster analysis and discriminant analysis methods were used to classify blasts into homogenous subgroups. Nine different predictor equations were developed based on scaled distance concept. The developed predictor models are quite promising. The research shows that before the application of complex modelling techniques, all the alternative predictive models should be investigated for mining sites.

Degree day-based model predicts pink bollworm phenology across geographical locations of subtropics and semi-arid tropics of India

There is a global concern about the effects of climate change driven shifts in species phenology on crop pests. Using geographically and temporally extensive data set of moth trap catches and temperatures across the cotton growing states of India, we predicted the phenology of cotton pink bollworm Pectinophora gossypiella (Saunders). Our approach was centered on growing degree days (GDD), a measure of thermal accumulation that provides a mechanistic link between climate change and species’ phenology. The phenology change was predicted by calculating absolute error associated with DD and ordinal date, an alternative predictor of phenology, for peak moth abundance. Our results show that GDD outperformed the ordinal dates in predicting peak moth abundance in 6 out of 10 selected locations. Using established thresholds of 13.0/34.0 °C, mean DD accumulated between the consecutive moth peaks across different years were estimated at 504.05 ± 4.84. Seven generations were determined for pink bollworm in a cropping season, the length of which varied between 35 and 73 days in response to temperature. Pink bollworm population reached its peak during third generation which can be the target for management actions. The study provides essential information for developing pink bollworm management strategies under climate change.