Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain

OBJECTIVE: overweight/obese weight status during pregnancy increases risk of a range of adverse health outcomes for mother and child. Whereas identification of those who are overweight/obese pre-pregnancy and in early pregnancy is straightforward, prediction of who will experience excessive gestational weight gain (EGWG), and thus be at greater risk of becoming overweight or obese during pregnancy is more challenging. The present study sought to better identify those at risk of EGWG by exploring pre-pregnancy BMI as well as a range of psychosocial risk factors identified as risk factors in prior research. METHODS: 225 pregnant women completed self-reported via postal survey measures of height, weight, and psychosocial variables at 16-18 weeks gestation, and reported their weight again at 32-34 weeks to calculate GWG. Classification and regression tree analysis (CART) was used to find subgroups in the data with increased risk of EGWG based on their pre-pregnancy BMI and psychosocial risk factor scores at Time 1. FINDINGS: CART confirmed that self-reported BMI status was a strong predictor of EGWG risk for women who were overweight/obese pre-pregnancy. Normal weight women with low motivation to maintain a healthy diet and who reported lower levels of partner support were also at considerable risk of EGWG. IMPLICATIONS FOR PRACTICE: present findings offer support for inclusion of psychosocial measures (in addition to BMI) in early antenatal visits to detect risk of EGWG. However, these findings also underscore the need for further consideration of effect modifiers that place women at increased or decreased risk of EGWG. Proposed additional constructs are discussed to direct further theory-driven research.

Specific mood symptoms confer risk for subsequent suicidal ideation in bipolar disorder with and without suicide attempt history: multi-wave data from STEP-BD

BACKGROUND: Little is known about specific mood symptoms that may confer risk for suicidal ideation (SI) among patients with bipolar disorder (BD). We evaluated prospectively whether particular symptoms of depression and mania precede the onset or worsening of SI, among adults with or without a history of a suicide attempt. METHODS: We examined prospective data from a large (N = 2,741) cohort of patients participating in the Systematic Treatment Enhancement Program for BD (STEP-BD). We evaluated history of suicide attempts at baseline, and symptoms of depression and mania at baseline and follow-up visits. Hierarchical linear modeling tested whether specific mood symptoms predicted subsequent levels of SI, and whether the strength of the associations differed based on suicide attempt history, after accounting for the influence of other mood symptoms and current SI. RESULTS: Beyond overall current depression and mania symptom severity, baseline SI, and illness characteristics, several mood symptoms, including guilt, reduced self-esteem, psychomotor retardation and agitation, increases in appetite, and distractibility predicted more severe levels of subsequent SI. Problems with concentration, distraction, sleep loss and decreased need for sleep predicted subsequent SI more strongly among individuals with a suicide attempt history. CONCLUSIONS: Several specific mood symptoms may confer risk for the onset or worsening of SI among treatment-seeking patients with BD. Individuals with a previous suicide attempt may be at greater risk in part due to greater reactivity to certain mood symptoms in the form of SI. However, overall, effect sizes were small, suggesting the need to identify additional proximal predictors of SI.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

A damage accumulation model for complex strain paths: prediction of ductile failure in metals

The characterisation of strain path with respect to the directionality of defect formation is discussed. The criterion of non-monotonic strain path is used in the scalar and tensor models for damage accumulation and recovery. Comparable analysis of models and their verification has been obtained by simulation of crack initiation in a two-stage metal forming operation consisting of wire drawing followed by constrained upsetting.

Psychosocial working conditions and suicide ideation: evidence from a cross-sectional survey of working Australians

OBJECTIVES: This study examined the relationship between psychosocial working factors such as job control, job demands, job insecurity, supervisor support, and workplace bullying as risk factors for suicide ideation. METHODS: We used a logistic analytic approach to assess risk factors for thoughts of suicide in a cross-sectional sample of working Australians. Potential predictors included psychosocial job stressors (described above); we also controlled for age, gender, occupational skill level, and psychological distress. RESULTS: We found that workplace bullying or harassment was associated with 1.54 greater odds of suicide ideation (95% confidence interval 1.64 to 2.05) in the model including psychological distress. Results also suggest that higher job control and security were associated with lower odds of suicide ideation. CONCLUSIONS: These results suggest the need for organizational level intervention to address psychosocial job stressors, including bullying.

An epidemic model based temporal violation prediction strategy for large batch of parallel business cloud workflows

Temporal violations often take place during the running of large batch of parallel business cloud workflow, which have a serious impact on the on-time completion of massive concurrent user requests. Existing studies have shown that local temporal violations (namely the delays of workflow activities) occurring during cloud workflow execution are the fundamental causes for failed on-time completion. Therefore, accurate prediction of temporal violations is a very important yet challenging task for business cloud workflows. In this paper, based on an epidemic model, a novel temporal violation prediction strategy is proposed to estimate the number of local temporal violations and the number of violations that must be handled so as to achieve a certain on-time completion rate before the execution of workflows. The prediction result can be served as an important reference for temporal violation prevention and handling strategies such as static resource reservation and dynamic provision. Specifically, we first analyze the queuing process of the parallel workflow activities, then we predict the number of potential temporal violations based on a novel temporal violation transmission model inspired by an epidemic model. Comprehensive experimental results demonstrate that our strategy can achieve very high prediction accuracy under different situations.

Chemical structure based prediction of PAN and oxidized PAN fiber density through a non-linear mathematical model

The production of carbon fiber, particularly the oxidation/stabilization step, is a complex process. In the present study, a non-linear mathematical model has been developed for the prediction of density of polyacrylonitrile (PAN) and oxidized PAN fiber (OPF), as a key physical property for various applications, such as energy and material optimization, modeling, and design of the stabilization process. The model is based on the available functional groups in PAN and OPF. Expected functional groups, including [Formula presented], [Formula presented], –CH2, [Formula presented], and [Formula presented], were identified and quantified through the full deconvolution analysis of Fourier transform infrared attenuated total reflectance (FT-IR ATR) spectra obtained from fibers. These functional groups form the basis of three stabilization rendering parameters, representing the cyclization, dehydrogenation and oxidation reactions that occur during PAN stabilization, and are used as the independent variables of the non-linear predictive model. The k-fold cross validation approach, with k = 10, has been employed to find the coefficients of the model. This model estimates the density of PAN and OPF independent of operational parameters and can be expanded to all operational parameters. Statistical analysis revealed good agreement between the governing model and experiments. The maximum relative error was less than 1% for the present model.

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

BACKGROUND: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests.

METHODS: De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke's R(2), c statistic.

RESULTS: Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596-0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction.

CONCLUSIONS: For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.

Low control and high demands at work as risk factors for suicide: An Australian national population-level case-control study

OBJECTIVES: Previous research suggests that psychosocial job stressors may be plausible risk factors for suicide. This study assessed the relationship between psychosocial job stressors and suicide mortality across the Australian population. METHODS: We developed a job exposure matrix to objectively measure job stressors across the working population. Suicide data came from a nationwide coronial register. Living controls were selected from a nationally representative cohort study. Incidence density sampling was used to ensure that controls were sampled at the time of death of each case. The period of observation for both cases and controls was 2001 to 2012. We used multilevel logistic regression to assess the odds of suicide in relation to 2 psychosocial job stressors (job control and job demands), after matching for age, sex, and year of death/survey and adjusting for socioeconomic status. RESULTS: Across 9,010 cases and 14,007 matched controls, our results suggest that low job control (odds ratio [OR], 1.35; 95% confidence interval [CI], 1.26-1.44; p < .001) and high job demands (OR, 1.36; 95% CI, 1.26-1.46; p < .001) were associated with increased odds of male suicide after adjusting for socioeconomic status. High demands were associated with lower odds of female suicide (OR, 0.81; 95% CI, 0.72-0.92; p = .002). CONCLUSIONS: It seems that adverse experiences at work are a risk factor for male suicide while not being associated with an elevated risk among females. Future studies on job stressors and suicide are needed, both to further understand the biobehavioral mechanisms explaining the link between job stress and suicide, and to inform targeted prevention initiatives.