Research Notes : Frequency of spectrum of visible mutations induced by gamma rays in soybean

Although mutation studies are very common in most of the crop plants, soybean (Glycine max [L.] Merrill.) has received comparatively little attention by the mutation breeders. In view of this, systematic mutation studies were started at Ranchi Agriculture College, Kanke. The present study reports the effect of ganma rays on the frequency and spectrum of visible mutations in soybean. Materials and methods: Seeds of a soybean variety Sepaya Black, brought to uniform moisture content, were irradiated with ganma rays at Fertilizer Corporation of India, Sindri, Dhanbad (Bihar) at a dose of 10 kr, 20 kr, 30 kr, and 40 kr

Road Safety in Great Britain: An Exploratory Data Analysis

Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. For the past 30 years, the UK has had a good record in reducing fatalities over the past 30 years, there is still a considerable number of road deaths. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe. This study represents an exploratory analysis with deep insights which could provide policy makers with invaluable insights into how accidents happen and how they can be mitigated. We use STATS19 data published by the UK government. Since we need more information about locations which is not provided in STATA19, we first expand the features of the dataset using OpenStreetMap and Visual Crossing. This paper also provides a discussion regarding new road safety methods

Antiglycation Activity of Otostegia persica (Burm.) Boiss

Diabetes mellitus is a common endocrine disorder characterized by hyperglycemia and long-term complications affecting the eyes, nerves, blood vessels, skin and kidneys. Increased glycation of proteins and accumulation of advanced glycation endproducts (AGEPs) have been implicated in the pathogenesis of diabetic complications. Glycation and AGEP formation are also accompanied by the formation of free radicals via autoxidation of glucose and glycated proteins. Compounds with combined antiglycation and antioxidant properties may offer therapeutic potential. Since the antioxidant activity of different extracts and fractions of aerial parts of Otostegia persica has been evaluated and this plant is used as an antidiabetic agent in Iranian traditional medicine, we evaluated the antiglycation activity of this species. Here, we report the isolation of known compound 3´, 7-dihydroxy-4´,6,8-trimethoxy-flavone for the antiglycation properties of this plant

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

We develop a computational database, web-apps and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Anderson rule, which is based on the band-alignment with respect to the vacuum potential of non-interacting monolayers.We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts.We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3) to compare the band-alignment description with the predictions from Anderson rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we develop ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://www.ctcms.nist.gov/jarvish).Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function 2D-metal contacts

Generic effective source for scalar self-force calculations

A leading approach to the modelling of extreme mass ratio inspirals involves the treatment of the smaller mass as a point particle and the computation of a regularized self-force acting on that particle. In turn, this computation requires knowledge of the regularized retarded field generated by the particle. A direct calculation of this regularized field may be achieved by replacing the point particle with an effective source and solving directly a wave equation for the regularized field. This has the advantage that all quantities are finite and require no further regularization. In this work, we present a method for computing an effective source which is finite and continuous everywhere, and which is valid for a scalar point particle in arbitrary geodesic motion in an arbitrary background spacetime. We explain in detail various technical and practical considerations that underlie its use in several numerical self-force calculations. We consider as examples the cases of a particle in a circular orbit about Schwarzschild and Kerr black holes, and also the case of a particle following a generic time-like geodesic about a highly spinning Kerr black hole. We provide numerical C code for computing an effective source for various orbital configurations about Schwarzschild and Kerr black holes.Comment: 24 pages, 7 figures, final published versio

Reducing the burden of hypoglycaemia in people with diabetes through increased understanding:design of the Hypoglycaemia Redefining Solutions for Better Lives (Hypo-RESOLVE) project

Background Hypoglycaemia is the most frequent complication of treatment with insulin or insulin secretagogues in people with diabetes. Severe hypoglycaemia, i.e. an event requiring external help because of cognitive dysfunction, is associated with a higher risk of adverse cardiovascular outcomes and all‐cause mortality, but underlying mechanism(s) are poorly understood. There is also a gap in the understanding of the clinical, psychological and health economic impact of ‘non‐severe’ hypoglycaemia and the glucose level below which hypoglycaemia causes harm. Aim To increase understanding of hypoglycaemia by addressing the above issues over a 4‐year period. Methods Hypo‐RESOLVE is structured across eight work packages, each with a distinct focus. We will construct a large, sustainable database including hypoglycaemia data from >100 clinical trials to examine predictors of hypoglycaemia and establish glucose threshold(s) below which hypoglycaemia constitutes a risk for adverse biomedical and psychological outcomes, and increases healthcare costs. We will also investigate the mechanism(s) underlying the antecedents and consequences of hypoglycaemia, the significance of glucose sensor‐detected hypoglycaemia, the impact of hypoglycaemia in families, and the costs of hypoglycaemia for healthcare systems. Results The outcomes of Hypo‐RESOLVE will inform evidence‐based definitions regarding the classification of hypoglycaemia in diabetes for use in daily clinical practice, future clinical trials and as a benchmark for comparing glucose‐lowering interventions and strategies across trials. Stakeholders will be engaged to achieve broadly adopted agreement. Conclusion Hypo‐RESOLVE will advance our understanding and refine the classification of hypoglycaemia, with the ultimate aim being to alleviate the burden and consequences of hypoglycaemia in people with diabetes

Neural networks embrace learned diversity

Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Such learned diversity provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.Comment: 6 pages, 6 figure

Cell‐type specific visualization and biochemical isolation of endogenous synaptic proteins in mice

In recent years, the remarkable molecular complexity of synapses has been revealed, with over 1000 proteins identified in the synapse proteome. Although it is known that different receptors and other synaptic proteins are present in different types of neurons, the extent of synapse diversity across the brain is largely unknown. This is mainly due to the limitations of current techniques. Here we report an efficient method for the purification of synaptic protein‐complexes, fusing a high‐affinity tag to endogenous PSD95 in specific cell types. We also developed a strategy which enables the visualization of endogenous PSD95 with fluorescent‐proteins tag in Cre‐recombinase expressing cells. We demonstrate the feasibility of proteomic analysis of synaptic protein‐complexes and visualization of these in specific cell types. We find that the composition of PSD95‐complexes purified from specific cell types differs from those extracted from tissues with diverse cellular composition. The results suggest that there might be differential interactions in the PSD95‐complexes in different brain regions. We have detected differentially interacting proteins by comparing datasets from the whole hippocampus and the CA3 subfield of the hippocampus. Therefore, these novel conditional PSD95 tagging lines will not only serve as powerful tools for precisely dissecting synapse diversity in specific brain regions and subsets of neuronal cells, but also provide an opportunity to better understand brain region‐ and cell type‐specific alterations associated with various psychiatric/neurological diseases. These newly developed conditional gene‐tagging methods can be applied to many different synaptic proteins and will facilitate research on the molecular complexity of synapses