Frost forecasting model using graph neural networks with spatio-temporal attention

International audienceFrost forecast is an important issue in climate research because of its economic impact in several industries. In this study, a graph neural network (GNN) with spatio-temporal architecture is proposed to predict minimum temperatures in an experimental site. The model considers spatial and temporal relations and processes multiple time series simultaneously. Performing predictions of 6, 12, and 24 hrs this model outperforms statistical and non-graph deep learning models

A Graph Neural Network with Spatio-temporal Attention for Multi-sources Time Series Data: An application to Frost Forecast

International audienceFrost forecast is an important issue in climate research because of its economic impact on several industries. In this study, we propose GRAST-Frost, a graph neural network (GNN) with spatio-temporal architecture, which is used to predict minimum temperatures and the incidence of frost. We developed an IoT platform capable of acquiring weather data from an experimental site, in addition data was collected from 10 weather stations in close proximity to the aforementioned site. The model considers spatial and temporal relations while processing multiple time series simultaneously. Performing predictions of 6, 12, 24 and 48 hours in advance, this model outperforms classical time series forecasting methods including, linear and non-linear machine learning methods, simple deep learning architectures and non-graph deep learning models. In addition, we show that our model significantly improves on the current state of the art of frost forecasting methods

Effect of Zinc Supplementation on Growth of Low Birth Weight Infants Aged 1–6 Mo in Ardabil, Iran

Objective To assess the effect of zinc supplementation on growth of low birth weight (LBW) infants aged 1–6 mo. Methods LBW infants were enrolled at birth and randomly assigned to receive 5 mg elemental Zn per day (n=45) or placebo (n=45) until 6 mo of age. They were followed monthly for information on compliance; anthropometric measurements were performed monthly. Results After randomization, 5 infants from zinc group and 9 from placebo group were excluded. At 6 mo of age, significantly greater weight gains were observed in the zinc than in the placebo group (4995±741g in zinc group vs. 3896±865 g in placebo group, p = 0.036). Length gain during the study period improved in zinc group (16.9±8.2 cm vs. 15.1±4.1 cm, p = 0.039); after zinc supplementation head circumference were increased (8.7±1.4 cm vs.7.4± 1.5 cm p<0.001). In male infants, total weight gain and height and head circumference gain were higher in the zinc than in the placebo group. However, only head circumference change was statistically significant. A similar trend was observed among female infants, but these differences were not statistically significant. There was no significant relation between breast-feeding status and the main outcome variables. Conclusions Infants in the present study showed improve¬ments in growth rate, but more studies are required in this field to confirm this fact

Costs Associated with Low Birth Weight in a Rural Area of Southern Mozambique

BACKGROUND: Low Birth Weight (LBW) is prevalent in low-income countries. Even though the economic evaluation of interventions to reduce this burden is essential to guide health policies, data on costs associated with LBW are scarce. This study aims to estimate the costs to the health system and to the household and the Disability Adjusted Life Years (DALYs) arising from infant deaths associated with LBW in Southern Mozambique. METHODS AND FINDINGS: Costs incurred by the households were collected through exit surveys. Health system costs were gathered from data obtained onsite and from published information. DALYs due to death of LBW babies were based on local estimates of prevalence of LBW (12%), very low birth weight (VLBW) (1%) and of case fatality rates compared to non-LBW weight babies [for LBW (12%) and VLBW (80%)]. Costs associated with LBW excess morbidity were calculated on the incremental number of hospital admissions in LBW babies compared to non-LBW weight babies. Direct and indirect household costs for routine health care were 24.12 US$(CI 95$ (CI 95% 6.33; 10.72). Of the 3,322 live births that occurred in one year in the study area, health system costs associated to LBW (routine health care and excess morbidity) and DALYs were 169,957.61 US$ (CI 95% 144,900.00; 195,500.00) and 2,746.06, respectively. CONCLUSIONS: This first cost evaluation of LBW in a low-income country shows that reducing the prevalence of LBW would translate into important cost savings to the health system and the household. These results are of relevance for similar settings and should serve to promote interventions aimed at improving maternal care

What zinc supplementation does and does not achieve in diarrhea prevention: a systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Prevention of diarrhea has presented indomitable challenges. A preventive strategy that has received significant interest is zinc supplementation. Existing literature including quantitative meta-analyses and systematic reviews tend to show that zinc supplementation is beneficial however evidence to the contrary is augmenting. We therefore conducted an updated and comprehensive meta-analytical synthesis of the existing literature on the effect of zinc supplementation in prevention of diarrhea.</p> <p>Methods</p> <p>EMBASE^®, MEDLINE ^®and CINAHL^®databases were searched for published reviews and meta-analyses on the use of zinc supplementation for the prevention childhood diarrhea. Additional RCTs published following the meta-analyses were also sought. Effect of zinc supplementation on the following five outcomes was studied: incidence of diarrhea, prevalence of diarrhea, incidence of persistent diarrhea, incidence of dysentery and incidence of mortality. The published RCTs were combined using random-effects meta-analyses, subgroup meta-analyses, meta-regression, cumulative meta-analyses and restricted meta-analyses to quantify and characterize the role of zinc supplementation with the afore stated outcomes.</p> <p>Results</p> <p>We found that zinc supplementation has a modest beneficial association (9% reduction) with incidence of diarrhea, a stronger beneficial association (19% reduction) with prevalence of diarrhea and occurrence of multiple diarrheal episodes (28% reduction) but there was significant unexplained heterogeneity across the studies for these associations. Age, continent of study origin, zinc salt and risk of bias contributed significantly to between studies heterogeneity. Zinc supplementation did not show statistically significant benefit in reducing the incidence of persistent diarrhea, dysentery or mortality. In most instances, the 95% prediction intervals for summary relative risk estimates straddled unity.</p> <p>Conclusions</p> <p>Demonstrable benefit of preventive zinc supplementation was observed against two of the five diarrhea-related outcomes but the prediction intervals straddled unity. Thus the evidence for a preventive benefit of zinc against diarrhea is inconclusive. Continued efforts are needed to better understand the sources of heterogeneity. The outcomes of zinc supplementation may be improved by identifying subgroups that need zinc supplementation.</p

Cardiac Biomarker Release after Endurance Exercise in Male and Female Adults and Adolescents.

OBJECTIVES: To compare the responses of high-sensitivity cardiac troponin T (hs-cTnT) and NH2-terminal probrain natriuretic peptide (NT-proBNP) after 60 minutes of swimming in male and female adults and adolescents with different pubertal status. STUDY DESIGN: Adolescent swimmers (25 male and 25 female) and adult swimmers (7 male and 9 female) participated in a 60-minute maximal swimming test with serial assessment of hs-cTnT and NT-proBNP at rest, immediately postexercise, and at 1, 3, 6, 12, and 24 hours postexercise. Adolescents were classified according to pubertal status: Tanner stages 3 (n = 14), 4 (n = 22), and 5 (n = 14). RESULTS: Exercise resulted in an increase in both biomarkers. hs-cTnT responses to exercise were similar in adolescents with different pubertal status and adults, although there was substantial individual variability in peak hs-cTnT, with the upper reference limit exceeding in 62% of the participants. Postexercise kinetics for hs-cTnT were largely consistent across all groups with a return to near baseline levels 24 hours postexercise. The male participants showed higher values of hs-cTnT at baseline and postexercise. All groups had similar NT-proBNP responses to acute exercise and recovery. One swimmer exceeded the upper reference limit for NT-proBNP. CONCLUSIONS: An exercise-associated increase in hs-cTnT and NT-proBNP occurred in response to a 60-minute maximal swimming test that was independent of pubertal status/adolescent vs adults. The present data also suggests that baseline and postexercise hs-cTnT values are higher in male compared with female, with no sex differences in NT-proBNP values

Perinatal asphyxia: current status and approaches towards neuroprotective strategies, with focus on sentinel proteins

Delivery is a stressful and risky event menacing the newborn. The mother-dependent respiration has to be replaced by autonomous pulmonary breathing immediately after delivery. If delayed, it may lead to deficient oxygen supply compromising survival and development of the central nervous system. Lack of oxygen availability gives rise to depletion of NAD+ tissue stores, decrease of ATP formation, weakening of the electron transport pump and anaerobic metabolism and acidosis, leading necessarily to death if oxygenation is not promptly re-established. Re-oxygenation triggers a cascade of compensatory biochemical events to restore function, which may be accompanied by improper homeostasis and oxidative stress. Consequences may be incomplete recovery, or excess reactions that worsen the biological outcome by disturbed metabolism and/or imbalance produced by over-expression of alternative metabolic pathways. Perinatal asphyxia has been associated with severe neurological and psychiatric sequelae with delayed clinical onset. No specific treatments have yet been established. In the clinical setting, after resuscitation of an infant with birth asphyxia, the emphasis is on supportive therapy. Several interventions have been proposed to attenuate secondary neuronal injuries elicited by asphyxia, including hypothermia. Although promising, the clinical efficacy of hypothermia has not been fully demonstrated. It is evident that new approaches are warranted. The purpose of this review is to discuss the concept of sentinel proteins as targets for neuroprotection. Several sentinel proteins have been described to protect the integrity of the genome (e.g. PARP-1; XRCC1; DNA ligase IIIα; DNA polymerase β, ERCC2, DNA-dependent protein kinases). They act by eliciting metabolic cascades leading to (i) activation of cell survival and neurotrophic pathways; (ii) early and delayed programmed cell death, and (iii) promotion of cell proliferation, differentiation, neuritogenesis and synaptogenesis. It is proposed that sentinel proteins can be used as markers for characterising long-term effects of perinatal asphyxia, and as targets for novel therapeutic development and innovative strategies for neonatal care

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes

Abstract Background Building prognostic models of clinical outcomes is an increasingly important research task and will remain a vital area in genomic medicine. Prognostic models of clinical outcomes are usually built and validated utilizing variable selection methods and machine learning tools. The challenges, however, in ultra-high dimensional space are not only to reduce the dimensionality of the data, but also to retain the important variables which predict the outcome. Screening approaches, such as the sure independence screening (SIS), iterative SIS (ISIS), and principled SIS (PSIS), have been developed to overcome the challenge of high dimensionality. We are interested in identifying important single-nucleotide polymorphisms (SNPs) and integrating them into a validated prognostic model of overall survival in patients with metastatic prostate cancer. While the abovementioned variable selection approaches have theoretical justification in selecting SNPs, the comparison and the performance of these combined methods in predicting time-to-event outcomes have not been previously studied in ultra-high dimensional space with hundreds of thousands of variables. Methods We conducted a series of simulations to compare the performance of different combinations of variable selection approaches and classification trees, such as the least absolute shrinkage and selection operator (LASSO), adaptive least absolute shrinkage and selection operator (ALASSO), and random survival forest (RSF), in ultra-high dimensional setting data for the purpose of developing prognostic models for a time-to-event outcome that is subject to censoring. The variable selection methods were evaluated for discrimination (Harrell’s concordance statistic), calibration, and overall performance. In addition, we applied these approaches to 498,081 SNPs from 623 Caucasian patients with prostate cancer. Results When n = 300, ISIS-LASSO and ISIS-ALASSO chose all the informative variables which resulted in the highest Harrell’s c-index (> 0.80). On the other hand, with a small sample size (n = 150), ALASSO performed better than any other combinations as demonstrated by the highest c-index and/or overall performance, although there was evidence of overfitting. In analyzing the prostate cancer data, ISIS-ALASSO, SIS-LASSO, and SIS-ALASSO combinations achieved the highest discrimination with c-index of 0.67. Conclusions Choosing the appropriate variable selection method for training a model is a critical step in developing a robust prognostic model. Based on the simulation studies, the effective use of ALASSO or a combination of methods, such as ISIS-LASSO and ISIS-ALASSO, allows both for the development of prognostic models with high predictive accuracy and a low risk of overfitting assuming moderate sample sizes