A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

Plasma supports collective modes and particle–wave interactions that lead to complex behaviour in, for example, inertial fusion energy applications. While plasma can sometimes be modelled as a charged fluid, a kinetic description is often crucial for studying nonlinear effects in the higher-dimensional momentum–position phase space that describes the full complexity of the plasma dynamics. We create a differentiable solver for the three-dimensional partial-differential equation describing the plasma kinetics and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion-relevant configuration and find that the optimization process exploits a novel physical effect.

With type 2 diabetes presenting at younger ages, there is a growing need to identify biomarkers of future glucose intolerance. A high (20%) prevalence of glucose intolerance at 18 years was seen in women from the Pune Maternal Nutrition Study (PMNS) birth cohort. We investigated the potential of circulating microRNAs in risk stratification for future pre-diabetes in these women. Here, we provide preliminary longitudinal analyses of circulating microRNAs in normal glucose tolerant (NGT@18y, N = 10) and glucose intolerant (N = 8) women (ADA criteria) at 6, 12 and 17 years of their age using discovery analysis (OpenArray™ platform). Machine-learning workflows involving Lasso with bootstrapping/leave-one-out cross-validation identified microRNAs associated with glucose intolerance at 18 years of age. Several microRNAs, including miR-212-3p, miR-30e-3p and miR-638, stratified glucose-intolerant women from NGT at childhood. Our results suggest that circulating microRNAs, longitudinally assessed over 17 years of life, are dynamic biomarkers associated with and predictive of pre-diabetes at 18 years of age. Validation of these findings in males and remaining participants from the PMNS birth cohort will provide a unique opportunity to study novel epigenetic mechanisms in the life-course progression of glucose intolerance and enhance current clinical risk prediction of pre-diabetes and progression to type 2 diabetes.

Our objective was to investigate associations of body size (birth weight and body mass index (BMI)) and growth in height, body fat (adiposity) and lean mass during childhood and adolescence, with risk markers for diabetes in young South Asian adults. We studied 357 men and women aged 21 years from the Pune Children’s Study birth cohort. Exposures were 1) birth weight, 21-year BMI, both of these mutually adjusted, and their interaction, and 2) uncorrelated conditional measures of growth in height and proxies for gain in adiposity and lean mass from birth to 8 years (childhood) and 8 to 21 years (adolescence) constructed from birth weight, and weight, height, and skinfolds at 8 and 21 years. Outcomes were plasma glucose and insulin concentrations during an oral glucose tolerance test and derived indices of insulin resistance and secretion. Higher 21-year BMI was associated with higher glucose and insulin concentrations and insulin resistance, and lower disposition index. After adjusting for 21-year BMI, higher birth weight was associated with lower 120-min glucose and insulin resistance, and higher disposition index. In the growth analysis, greater adiposity gain during childhood and adolescence was associated with higher glucose, insulin and insulin resistance, and lower disposition index, with stronger effects from adolescent gain. Greater childhood lean gain and adolescent height gain were associated with lower 120-min glucose and insulin. Consistent with other studies, lower birth weight and higher childhood weight gain increases diabetes risk. Disaggregation of weight gain showed that greater child/adolescent adiposity gain and lower lean and height gain may increase risk.

A novel Fluorescence Resonance Energy Transfer (FRET) based ‘Turn-ON’ biosensor has been developed using fluorescent ZnO/APTMS-FITC (ZFA) nanoflakes as sensing probe. In this biosensor, Lactate Dehydrogenase (LDH) is used for the detection of L-lactate, a diagnostic marker for abnormal physiological conditions like muscular dystrophy, myocardial infraction, abnormal tissue formation and tissue damage. Lactate Dehydrogeanse (LDH) catalyses the conversion of L-Lactate to L-Pyruvate, in presence of β-NAD reducing to β-NADH. We tried to explore this mechanism with FRET based system for highly sensitive detection of L-Lactate. The fluorescence of these nanoflakes can be reversibly quenched in the presence of β-NAD.

Cuminum cyminum, a commonly used spice, is known to have anti-diabetic action. The present study aims towards the isolation of bioactive components from C. cyminum and the evaluation of their insulin secretagogue potential with the probable mechanism and β-cell protective action. The anti-diabetic activity was detected in the petroleum ether (pet ether) fraction of the C. cyminum distillate and studied through in vivo and in vitro experiments. Bioactive components were identified through GC–MS, Fourier transform infrared spectroscopy and NMR analysis. The isolated components were evaluated for their insulin secretagogue action using rat pancreatic islets. Further, the probable mechanism of stimulation of islets was evaluated through in vitro studies using diazoxide, nifedipine and 3-isobutyl-1-methylxanthine. β-Cell protection was evaluated using the (1-(4,5-dimethylthiazol-2-yl)-3,5-diphenylformazan) (MTT) assay, the alkaline comet assay and nitrite production. The administration of the pet ether fraction for 45 d to streptozotocin-induced diabetic rats revealed an improved lipid profile. Cuminaldehyde and cuminol were identified as potent insulinotrophic components. Cuminaldehyde and cuminol (25 μg/ml) showed 3·34- and 3·85-fold increased insulin secretion, respectively, than the 11·8 mm-glucose control. The insulinotrophic action of both components was glucose-dependent and due to the closure of the ATP-sensitive K (K+-ATP) channel and the increase in intracellular Ca2+ concentration. An inhibitor of insulin secretion with potent β-cell protective action was also isolated from the same pet ether fraction. In conclusion, C. cyminum was able to lower blood glucose without causing hypoglycaemia or β-cell burn out. Hence, the commonly used spice, C. cyminum, has the potential to be used as a novel insulinotrophic therapy for prolonged treatment of diabetes.

This article presents the results of 4 excavation seasons in which botanical and animal remains were collected at the Harappan site of Kanmer in the Kachchh District of Gujarat, India. The findings revealed a subsistence economy consisting of food production with domesticated plants and animals, hunting, fishing, and wild plant gathering. Cultural relics and radiocarbon dating support our identification of different cultural periods at the site. This study provides new insights into the subsistence strategies during different phases of occupation and offers the potential for new subsistence models to be applied at nearby sites, particularly in this peripheral zone of the Indus civilization.

We present a case study of “Khagol Mandal,” a voluntary organization primarily based in Mumbai, India. In the 20 years since its inception, Khagol Mandal has given more than 1000 public outreach programmes. The volunteers strive to go beyond amateur level by means of various study tours, astronomical experiments and workshops. These activities have inspired a number of students to take professional astronomy careers. With a volunteer force, probably largest in India or even south Asia, Khagol Mandal is well poised to take advantage of the archival data of large telescopes. With a little guidance from senior researchers, organizations like Khagol Mandal can provide a solution to the ever-increasing need for manpower for ancillary science from these large-scale facilities.