Risk Factors for Small-for-Gestational-age and Preterm Births among 19,269 Tanzanian Newborns.

Few studies have differentiated risk factors for term-small for gestational age (SGA), preterm-appropriate for gestational age (AGA), and preterm-SGA, despite evidence of varying risk of child mortality and poor developmental outcomes. We analyzed birth outcome data from singleton infants, who were enrolled in a large randomized, double-blind, placebo-controlled trial of neonatal vitamin A supplementation conducted in Tanzania. SGA was defined as birth weight <10th percentile for gestation age and sex using INTERGROWTH standards and preterm birth as delivery at <37 complete weeks of gestation. Risk factors for term-SGA, preterm-AGA, and preterm-SGA were examined independently using log-binomial regression. Among 19,269 singleton Tanzanian newborns included in this analysis, 68.3 % were term-AGA, 15.8 % term-SGA, 15.5 % preterm-AGA, and 0.3 % preterm-SGA. In multivariate analyses, significant risk factors for term-SGA included maternal age <20 years, starting antenatal care (ANC) in the 3(rd) trimester, short maternal stature, being firstborn, and male sex (all p < 0.05). Independent risk factors for preterm-AGA were maternal age <25 years, short maternal stature, firstborns, and decreased wealth (all p < 0.05). In addition, receiving ANC services in the 1(st) trimester significantly reduced the risk of preterm-AGA (p = 0.01). Significant risk factors for preterm-SGA included maternal age >30 years, being firstborn, and short maternal stature which appeared to carry a particularly strong risk (all p < 0.05). Over 30 % of newborns in this large urban and rural cohort of Tanzanian newborns were born preterm and/or SGA. Interventions to promote early attendance to ANC services, reduce unintended young pregnancies, increased maternal height, and reduce poverty may significantly decrease the burden of SGA and preterm birth in sub-Saharan Africa

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation

Silencing of Aphid Genes by dsRNA Feeding from Plants

RNA interference (RNAi) is a valuable reverse genetics tool to study gene function in various organisms, including hemipteran insects such as aphids. Previous work has shown that RNAi-mediated knockdown of pea aphid (Acyrthosiphon pisum) genes can be achieved through direct injection of double-stranded RNA (dsRNA) or small-interfering RNAs (siRNA) into the pea aphid hemolymph or by feeding these insects on artificial diets containing the small RNAs.In this study, we have developed the plant-mediated RNAi technology for aphids to allow for gene silencing in the aphid natural environment and minimize handling of these insects during experiments. The green peach aphid M. persicae was selected because it has a broad plant host range that includes the model plants Nicotiana benthamiana and Arabidopsis thaliana for which transgenic materials can relatively quickly be generated. We targeted M. persicae Rack1, which is predominantly expressed in the gut, and M. persicae C002 (MpC002), which is predominantly expressed in the salivary glands. The aphids were fed on N. benthamiana leaf disks transiently producing dsRNA corresponding to these genes and on A. thaliana plants stably producing the dsRNAs. MpC002 and Rack-1 expression were knocked down by up to 60% on transgenic N. benthamiana and A. thaliana. Moreover, silenced M. persicae produced less progeny consistent with these genes having essential functions.Similar levels of gene silencing were achieved in our plant-mediated RNAi approach and published silencing methods for aphids. Furthermore, the N. benthamiana leaf disk assay can be developed into a screen to assess which genes are essential for aphid survival on plants. Our results also demonstrate the feasibility of the plant-mediated RNAi approach for aphid control

Simulating the vibrational quantum dynamics of molecules using photonics

Advances in control techniques for vibrational quantum states in molecules present new challenges for modelling such systems, which could be amenable to quantum simulation methods. Here, by exploiting a natural mapping between vibrations in molecules and photons in waveguides, we demonstrate a reprogrammable photonic chip as a versatile simulation platform for a range of quantum dynamic behaviour in different molecules. We begin by simulating the time evolution of vibrational excitations in the harmonic approximation for several four-atom molecules, including H2CS, SO3, HNCO, HFHF, N4 and P4. We then simulate coherent and dephased energy transport in the simplest model of the peptide bond in proteins—N-methylacetamide—and simulate thermal relaxation and the effect of anharmonicities in H2O. Finally, we use multi-photon statistics with a feedback control algorithm to iteratively identify quantum states that increase a particular dissociation pathway of NH3. These methods point to powerful new simulation tools for molecular quantum dynamics and the field of femtochemistry

Nonparametric tests for and against likelihood ratio ordering in the two-sample problem

We derive nonparametric procedures for testing for and against likelihood ratio ordering in the two-population setting with continuous distributions. We account for this ordering by examining the least concave majorant of the ordinal dominance curve formed from the nonparametric maximum likelihood estimators of the continuous distribution functions F and G. In particular, we focus on testing equality of F and G versus likelihood ratio ordering and testing for a violation of likelihood ratio ordering. For both testing problems, we propose area-based and sup-norm-based test statistics, derive appropriate limiting distributions, and provide simulation results that characterise the performance of our procedures. We illustrate our methods using data from a controlled experiment involving the effects of radiation on mice. Copyright 2005, Oxford University Press.

Inherent Fibrin Fiber Tension Propels Mechanisms of Network Clearance During Fibrinolysis

Proper wound healing necessitates both coagulation (the formation of a blood clot) and fibrinolysis (the dissolution of a blood clot). A thrombus resistant to clot dissolution can obstruct blood flow , leading to vascular pathologies. This study seeks to understand the mechanisms by which individual fibrin fibers , the main structural component of blood clots , are cleared from a local volume during fibrinolysis. We observed 2-D fibrin networks during lysis by plasmin , recording the clearance of each individual fiber. We found that , in addition to transverse cleavage of fibers , there were multiple other pathways by which clot dissolution occurred , including fiber bundling , buckling , and collapsing. These processes are all influenced by concentration of plasmin utilized in lysis. The network fiber density influenced the kinetics and distribution of these pathways. Individual cleavage events often resulted in large morphological changes in network structure , suggesting that the inherent tension in fibers played a role in fiber clearance. Using images before and after a cleavage event to measure fiber lengths , we estimated that fibers are strained ˆ¼23% beyond their equilibrium length during polymerization. To understand the role of fiber tension in fibrinolysis we modeled network clearance under differing amounts of fiber polymerized strain (prestrain). The comparison of experimental and model data indicated that fibrin tension enables 35% more network clearance due to network rearrangements after individual cleavage events than would occur if fibers polymerized in a non-tensed state. Our results highlight many characteristics and mechanisms of fibrin breakdown , which have implications on future fibrin studies , our understanding of the fibrinolytic process , and the development of thrombolytic therapies

Linear programmable nanophotonic processors

© 2018 Optical Society of America. Advances in photonic integrated circuits have recently enabled electrically reconfigurable optical systems that can implement universal linear optics transformations on spatial mode sets. This review paper covers progress in such “programmable nanophotonic processors” as well as emerging applications of the technology to problems including classical and quantum information processing and machine learning