Satellites May Underestimate Rice Residue and Associated Burning Emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg/sq m (+/-0.033), 0.61 kg/sq m (+/-0.076), and 0.88 kg/sq m (+/-0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning

KATANA - a charge-sensitive triggering system for the Sπ\piRIT experiment

KATANA - the Krakow Array for Triggering with Amplitude discrimiNAtion - has been built and used as a trigger and veto detector for the S$\pi$RIT TPC at RIKEN. Its construction allows operating in magnetic field and providing fast response for ionizing particles, giving the approximate forward multiplicity and charge information. Depending on this information, trigger and veto signals are generated. The article presents performance of the detector and details of its construction. A simple phenomenological parametrization of the number of emitted scintillation photons in plastic scintillator is proposed. The effect of the light output deterioration in the plastic scintillator due to the in-beam irradiation is discussed.Comment: 14 pages, 11 figure

Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

A predictive model for secondary RNA structure using graph theory and a neural network

Background: Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.Results: The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.Conclusions: There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes

Pilot study on developing a decision support tool for guiding re-administration of chemotherapeutic agent after a serious adverse drug reaction

<p>Abstract</p> <p>Background</p> <p>Currently, there are no standard guidelines for recommending re-administration of a chemotherapeutic drug to a patient after a serious adverse drug reaction (ADR) incident. The decision on whether to rechallenge the patient is based on the experience of the clinician and is highly subjective. Thus the aim of this study is to develop a decision support tool to assist clinicians in this decision making process.</p> <p>Methods</p> <p>The inclusion criteria for patients in this study are: (1) had chemotherapy at National Cancer Centre Singapore between 2004 to 2009, (2) suffered from serious ADRs, and (3) were rechallenged. A total of 46 patients fulfilled the inclusion criteria. A genetic algorithm attribute selection method was used to identify clinical predictors for patients' rechallenge status. A Naïve Bayes model was then developed using 35 patients and externally validated using 11 patients.</p> <p>Results</p> <p>Eight patient attributes (age, chemotherapeutic drug, albumin level, red blood cell level, platelet level, abnormal white blood cell level, abnormal alkaline phosphatase level and abnormal alanine aminotransferase level) were identified as clinical predictors for rechallenge status of patients. The Naïve Bayes model had an AUC of 0.767 and was found to be useful for assisting clinical decision making after clinicians had identified a group of patients for rechallenge. A platform independent version and an online version of the model is available to facilitate independent validation of the model.</p> <p>Conclusion</p> <p>Due to the limited size of the validation set, a more extensive validation of the model is necessary before it can be adopted for routine clinical use. Once validated, the model can be used to assist clinicians in deciding whether to rechallenge patients by determining if their initial assessment of rechallenge status of patients is accurate.</p

DNA damage signalling prevents deleterious telomere addition at DNA breaks

The response to DNA damage involves regulation of multiple essential processes to maximize the accuracy of DNA damage repair and cell survival 1. Telomerase has the potential to interfere with repair by inappropriately adding telomeres to DNA breaks. It was unknown whether cells modulate telomerase in response to DNA damage, to increase the accuracy of repair. Here we report that telomerase action is regulated as a part of the cellular response to a DNA double-strand break (DSB). Using yeast, we show that the major ATR/Mec1 DNA damage signalling pathway regulates telomerase action at DSBs. Upon DNA damage, MEC1-RAD53-DUN1-dependent phosphorylation of the telomerase inhibitor Pif1 occurs. Utilizing a separation of function PIF1 mutation, we show that this phosphorylation is required for the Pif1-mediated telomerase inhibition that takes place specifically at DNA breaks, but not telomeres. Hence DNA damage signalling down-modulates telomerase action at a DNA break via Pif1 phosphorylation, thus preventing aberrant healing of broken DNA ends by telomerase. These findings uncover a novel regulatory mechanism that coordinates competing DNA end-processing activities and thereby promotes DNA repair accuracy and genome integrity

The ASY-EOS experiment at GSI: investigating the symmetry energy at supra-saturation densities

The elliptic-flow ratio of neutrons with respect to protons in reactions of neutron rich heavy-ions systems at intermediate energies has been proposed as an observable sensitive to the strength of the symmetry term in the nuclear Equation Of State (EOS) at supra-saturation densities. The recent results obtained from the existing FOPI/LAND data for $^{197}$Au+$^{197}$Au collisions at 400 MeV/nucleon in comparison with the UrQMD model allowed a first estimate of the symmetry term of the EOS but suffer from a considerable statistical uncertainty. In order to obtain an improved data set for Au+Au collisions and to extend the study to other systems, a new experiment was carried out at the GSI laboratory by the ASY-EOS collaboration in May 2011.Comment: Talk given by P. Russotto at the 11th International Conference on Nucleus-Nucleus Collisions (NN2012), San Antonio, Texas, USA, May 27-June 1, 2012. To appear in the NN2012 Proceedings in Journal of Physics: Conference Series (JPCS

Identification of dietary alanine toxicity and trafficking dysfunction in a Drosophila model of hereditary sensory and autonomic neuropathy type 1

Hereditary sensory and autonomic neuropathy type 1 (HSAN1) is characterized by a loss of distal peripheral sensory and motorneuronal function, neuropathic pain and tissue necrosis. The most common cause of HSAN1 is due to dominant mutations in serine palmitoyl-transferase subunit 1 (SPT1). SPT catalyses the condensation of serine with palmitoyl-CoA, the initial step in sphingolipid biogenesis. Identified mutations in SPT1 are known to both reduce sphingolipid synthesis and generate catalytic promiscuity, incorporating alanine or glycine into the precursor sphingolipid to generate a deoxysphingoid base (DSB). Why either loss of function in SPT1, or generation of DSBs should generate deficits in distal sensory function remains unclear. To address these questions, we generated a Drosophila model of HSAN1. Expression of dSpt1 bearing a disease-related mutation induced morphological deficits in synapse growth at the larval neuromuscular junction consistent with a dominant-negative action. Expression of mutant dSpt1 globally was found to be mildly toxic, but was completely toxic when the diet was supplemented with alanine, when DSBs were observed in abundance. Expression of mutant dSpt1 in sensory neurons generated developmental deficits in dendritic arborization with concomitant sensory deficits. A membrane trafficking defect was observed in soma of sensory neurons expressing mutant dSpt1, consistent with endoplasmic reticulum (ER) to Golgi block. We found that we could rescue sensory function in neurons expressing mutant dSpt1 by co-expressing an effector of ER-Golgi function, Rab1 suggesting compromised ER function in HSAN1 affected dendritic neurons. Our Drosophila model identifies a novel strategy to explore the pathological mechanisms of HSAN1

Circular DNA Intermediate in the Duplication of Nile Tilapia vasa Genes

vasa is a highly conserved RNA helicase involved in animal germ cell development. Among vertebrate species, it is typically present as a single copy per genome. Here we report the isolation and sequencing of BAC clones for Nile tilapia vasa genes. Contrary to a previous report that Nile tilapia have a single copy of the vasa gene, we find evidence for at least three vasa gene loci. The vasa gene locus was duplicated from the original site and integrated into two distant novel sites. For one of these insertions we find evidence that the duplication was mediated by a circular DNA intermediate. This mechanism of gene duplication may explain the origin of isolated gene duplicates during the evolution of fish genomes. These data provide a foundation for studying the role of multiple vasa genes in the development of tilapia gonads, and will contribute to investigations of the molecular mechanisms of sex determination and evolution in cichlid fishes