Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Host Plant Induced Variation in Gut Bacteria of Helicoverpa armigera

Helicoverpa are important polyphagous agricultural insect pests and they have a worldwide distribution. In this study, we report the bacterial community structure in the midgut of fifth instar larvae of Helicoverpa armigera, a species prevalent in the India, China, South Asia, South East Asia, Southern & Eastern Africa and Australia. Using culturable techniques, we isolated and identified members of Bacillus firmus, Bacillus niabense, Paenibacillus jamilae, Cellulomonas variformis, Acinetobacter schindleri, Micrococcus yunnanesis, Enterobacter sp., and Enterococcus cassiliflavus in insect samples collected from host plants grown in different parts of India. Besides these the presence of Sphingomonas, Ralstonia, Delftia, Paracoccus and Bacteriodetes was determined by culture independent molecular analysis. We found that Enterobacter and Enterococcus were universally present in all our Helicoverpa samples collected from different crops and in different parts of India. The bacterial diversity varied greatly among insects that were from different host plants than those from the same host plant of different locations. This result suggested that the type of host plant greatly influences the midgut bacterial diversity of H. armigera, more than the location of the host plant. On further analyzing the leaf from which the larva was collected, it was found that the H. armigera midgut bacterial community was similar to that of the leaf phyllosphere. This finding indicates that the bacterial flora of the larval midgut is influenced by the leaf surface bacterial community of the crop on which it feeds. Additionally, we found that laboratory made media or the artificial diet is a poor bacterial source for these insects compared to a natural diet of crop plant

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

<p>Abstract</p> <p>Background</p> <p>Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.</p> <p>Methods</p> <p>A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and <it>de novo </it>molecular design.</p> <p>Results</p> <p>Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified <it>in silico </it>and tested <it>in vitro</it>; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.</p> <p>Conclusions</p> <p>Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for <it>de novo </it>design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.</p

Factors Associated With Small Size at Birth in Nepal: Further Analysis of Nepal Demographic and Health Survey 2011

Background: The global Low Birth Weight (LBW) rate is reported to be 15.5% with more than 95% of these LBW infants being from developing countries. LBW is a major factor associated with neonatal deaths in developing countries. The determinants of low birth weight in Nepal have rarely been studied. This study aimed to identify the factors associated with small size at birth among under-five children. Methods: Data from the 2011 Nepal Demographic and Health Survey (NDHS) were used. The association between small size at birth and explanatory variables were analysed using Chi-square tests (χ2) followed by logistic regression. Complex Sample Analysis was used to adjust for study design and sampling.Results: A total of 5240 mother- singleton under five child pairs were included in the analysis, of which 936 (16.0%) children were reported as small size at birth. Of 1922 infants whose birth weight was recorded, 235 (11.5%) infants had low birth weight (<2500 grams). The mean birth weight was 3030 grams (standard deviation: 648.249 grams). The mothers who had no antenatal visits were more likely (odds ratio (OR) 1.315; 95% confidence interval (CI) (1.042-1.661)) to have small size infants than those who had attended four or more antenatal visits. Mothers who lived in the Far-western development region were more likely to have (OR 1.698; 95% CI (1.228-2.349)) small size infants as compared to mothers from the Eastern development region. Female infants were more likely (OR 1.530; 95% CI (1.245-1.880)) to be at risk of being small than males. Conclusion: One in every six infants was reported to be small at birth. Attendance of antenatal care programs appeared to have a significant impact on birth size. Adequate antenatal care visits combined with counselling and nutritional supplementation should be a focus to reduce adverse birth outcomes such as small size at birth, especially in the geographically and economically disadvantaged areas such as Far-western region of Nepal

Heme Oxygenase-1 Accelerates Cutaneous Wound Healing in Mice

Heme oxygenase-1 (HO-1), a cytoprotective, pro-angiogenic and anti-inflammatory enzyme, is strongly induced in injured tissues. Our aim was to clarify its role in cutaneous wound healing. In wild type mice, maximal expression of HO-1 in the skin was observed on the 2nd and 3rd days after wounding. Inhibition of HO-1 by tin protoporphyrin-IX resulted in retardation of wound closure. Healing was also delayed in HO-1 deficient mice, where lack of HO-1 could lead to complete suppression of reepithelialization and to formation of extensive skin lesions, accompanied by impaired neovascularization. Experiments performed in transgenic mice bearing HO-1 under control of keratin 14 promoter showed that increased level of HO-1 in keratinocytes is enough to improve the neovascularization and hasten the closure of wounds. Importantly, induction of HO-1 in wounded skin was relatively weak and delayed in diabetic (db/db) mice, in which also angiogenesis and wound closure were impaired. In such animals local delivery of HO-1 transgene using adenoviral vectors accelerated the wound healing and increased the vascularization. In summary, induction of HO-1 is necessary for efficient wound closure and neovascularization. Impaired wound healing in diabetic mice may be associated with delayed HO-1 upregulation and can be improved by HO-1 gene transfer

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke