Detection of source inhomogeneity through event-by-event two-pion Bose-Einstein correlations

We develop a method for detecting the inhomogeneity of the pion-emitting sources produced in ultra-relativistic heavy ion collisions, through event-by-event two-pion Bose-Einstein correlations. The root-mean-square of the error-inverse-weighted fluctuations between the two-pion correlation functions of single and mixed events are useful observables for the detection. By investigating the root-mean-square of the weighted fluctuations for different impact parameter regions people may hopefully determine the inhomogeneity of the particle-emitting in the coming Large Hadron Collider (LHC) heavy ion experiments.Comment: 10 pages, 6 figure

Patterning in time and space: HoxB cluster gene expression in the developing chick embryo

The developing embryo is a paradigmatic model to study molecular mechanisms of time control in Biology. Hox genes are key players in the specification of tissue identity during embryo development and their expression is under strict temporal regulation. However, the molecular mechanisms underlying timely Hox activation in the early embryo remain unknown. This is hindered by the lack of a rigorous temporal framework of sequential Hox expression within a single cluster. Herein, a thorough characterization of HoxB cluster gene expression was performed over time and space in the early chick embryo. Clear temporal collinearity of HoxB cluster gene expression activation was observed. Spatial collinearity of HoxB expression was evidenced in different stages of development and in multiple tissues. Using embryo explant cultures we showed that HoxB2 is cyclically expressed in the rostral presomitic mesoderm with the same periodicity as somite formation, suggesting a link between timely tissue specification and somite formation. We foresee that the molecular framework herein provided will facilitate experimental approaches aimed at identifying the regulatory mechanisms underlying Hox expression in Time and Space.Fundacao para a Ciencia e a Tecnologia (FCT), Portugal [PTDC/SAU-OBD/105111/2008, UMINHO/BI/7/2014, SFRH/BPD/65652/2009]; Ciencia Program Contract; Programa Operacional Regional do Norte (ON. 2) [NORTE-07-0124-FEDER-000017]; FCT (National and FEDER COMPETE Program) [PTDC/SAU-BID/121459/2010, PTDC/SAU-OBD/099758/2008]; [PEst-OE/EQB/LA0023/2011]info:eu-repo/semantics/publishedVersio

Development of a Decision Support System for the Management of Mummy Berry Disease in Northwestern Washington

Mummy berry, caused by Monilinia vaccinii-corymbosi, is the most important disease of the northern highbush blueberry (Vaccinium corymbosum L.) in North America and can cause up to 70% yield losses in affected fields. A key event in the mummy berry disease cycle is the primary infection phase where ascospores are released by apothecia that infect emerging floral and vegetative tissues. Current management of mummy berry disease in northwestern Washington is predominantly reliant on the prevention of primary infections through prophylactic, calendar-based fungicide spray applications early in the growing season. To improve the understanding of risk during these periods and to help tailor management strategies, we developed a decision support system (DSS) based on field records spanning over five seasons and four locations in northwestern Washington. Environmental conditions across the region were highly uniform but different dynamics of apothecial development were observed under high- and low-management regimes. Based on our analysis, we suggest basing the initial iteration of the DSS on two sub-models. The first sub-model predicts the onset of apothecia based on chill-unit accumulation under high- and low-management regimes, and the second predicts primary infection risk, which provides opportunities to improve the timing of fungicide applications. The synoptic DSS proposed here is based on the current biological knowledge of the pathosystem and available data for the northwestern Washington region. We provide the analysis and the DSS implementation and evaluation as an open-source repository, providing opportunities for further improvements. Finally, we provide suggestions for future research and the operational efforts needed for improving the utility and accuracy of the mummy berry DSS.publishedVersio

Pediatric minor head trauma: do cranial CT scans change the therapeutic approach?

OBJECTIVES: 1) To verify clinical signs correlated with appropriate cranial computed tomography scan indications and changes in the therapeutic approach in pediatric minor head trauma scenarios. 2) To estimate the radiation exposure of computed tomography scans with low dose protocols in the context of trauma and the additional associated risk. METHODS: Investigators reviewed the medical records of all children with minor head trauma, which was defined as a Glasgow coma scale ≥13 at the time of admission to the emergency room, who underwent computed tomography scans during the years of 2013 and 2014. A change in the therapeutic approach was defined as a neurosurgical intervention performed within 30 days, hospitalization, >;12 hours of observation, or neuro-specialist evaluation. RESULTS: Of the 1006 children evaluated, 101 showed some abnormality on head computed tomography scans, including 49 who were hospitalized, 16 who remained under observation and 36 who were dismissed. No patient underwent neurosurgery. No statistically significant relationship was observed between patient age, time between trauma and admission, or signs/symptoms related to trauma and abnormal imaging results. A statistically significant relationship between abnormal image results and a fall higher than 1.0 meter was observed (p=0.044). The mean effective dose was 2.0 mSv (0.1 to 6.8 mSv), corresponding to an estimated additional cancer risk of 0.05%. CONCLUSION: A computed tomography scan after minor head injury in pediatric patients did not show clinically relevant abnormalities that could lead to neurosurgical indications. Patients who fell more than 1.0 m were more likely to have changes in imaging tests, although these changes did not require neurosurgical intervention; therefore, the use of computed tomography scans may be questioned in this group. The results support the trend of more careful indications for cranial computed tomography scans for children with minor head trauma

Analysis of maternal polymorphisms in arsenic (+3 oxidation state)-methyltransferase AS3MT and fetal sex in relation to arsenic metabolism and infant birth outcomes: Implications for risk analysis

Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in the metabolism of inorganic arsenic (iAs). Polymorphisms of AS3MT influence adverse health effects in adults, but little is known about their role in iAs metabolism in pregnant women and infants. The relationships between seven single nucleotide polymorphisms (SNPs) in AS3MT and urinary concentrations of iAs and its methylated metabolites were assessed in mother-infant pairs of the Biomarkers of Exposure to ARsenic (BEAR) cohort. Maternal alleles for five of the seven SNPs (rs7085104, rs3740400, rs3740393, rs3740390, and rs1046778) were associated with urinary concentrations of iAs metabolites, and alleles for one SNP (rs3740393) were associated with birth outcomes/measures. These associations were strongly dependent upon the male sex of the fetus but independent of fetal genotype for AS3MT. These data highlight a potential sex-dependence of the relationships among maternal genotype, iAs metabolism and infant health outcomes

Neonatal Metabolomic Profiles Related to Prenatal Arsenic Exposure

Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in GoÌmez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and %dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package's modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models