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We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments. [ABSTRACT FROM AUTHOR]

Novel binary gene expression tools like the LexA-LexAop system could powerfully enhance studies of metabolism, development, and neurobiology in Drosophila. However, specific LexA drivers for neuroendocrine cells and many other developmentally relevant systems remain limited. In a unique high school biology course, we generated a LexA-based enhancer trap collection by transposon mobilization. The initial collection provides a source of novel LexA-based elements that permit targeted gene expression in the corpora cardiaca, cells central for metabolic homeostasis, and other neuroendocrine cell types. The collection further contains specific LexA drivers for stem cells and other enteric cells in the gut, and other developmentally relevant tissue types. We provide detailed analysis of nearly 100 new LexA lines, including molecular mapping of insertions, description of enhancer-driven reporter expression in larval tissues, and adult neuroendocrine cells, comparison with established enhancer trap collections and tissue specific RNAseq. Generation of this open-resource LexA collection facilitates neuroendocrine and developmental biology investigations, and shows how empowering secondary school science can achieve research and educational goals.

Computer models of intense, laser-driven ion acceleration require expensive particle-in-cell simulations that may struggle to capture all the multi-scale, multi-dimensional physics involved at reasonable costs. Explored is an approach to ameliorate this deficiency using a multi-fidelity framework that can incorporate physical trends and phenomena at different levels. As the basis for this study, an ensemble of approximately 8000 1D simulations was generated to buttress separate ensembles of hundreds of higher fidelity 1D and 2D simulations. Using transfer learning with deep neural networks, one can reproduce the results of more complex physics at a much lower cost. The networks trained in this fashion can, in turn, act as surrogate models for the simulations themselves, allowing for quick and efficient exploration of the parameter space of interest. Standard figures-of-merit were used as benchmarks such as the hot electron temperature, peak ion energy, conversion efficiency, and so on. We can rapidly identify and explore under what conditions differing fidelities become an important effect and search for outliers in feature space. [ABSTRACT FROM AUTHOR]

Insulin is a major regulator of metabolism in metazoans, including the fruit fly Drosophila melanogaster. Genome-wide association studies (GWAS) suggest a genetic basis for reductions of both insulin sensitivity and insulin secretion, phenotypes commonly observed in humans with type 2 diabetes mellitus (T2DM). To identify molecular functions of genes linked to T2DM risk, we developed a genetic tool to measure insulin-like peptide 2 (Ilp2) levels in Drosophila, a model organism with superb experimental genetics. Our system permitted sensitive quantification of circulating Ilp2, including measures of Ilp2 dynamics during fasting and re-feeding, and demonstration of adaptive Ilp2 secretion in response to insulin receptor haploinsufficiency. Tissue specific dissection of this reduced insulin signaling phenotype revealed a critical role for insulin signaling in specific peripheral tissues. Knockdown of the Drosophila orthologues of human T2DM risk genes, including GLIS3 and BCL11A, revealed roles of these Drosophila genes in Ilp2 production or secretion. Discovery of Drosophila mechanisms and regulators controlling in vivo insulin dynamics should accelerate functional dissection of diabetes genetics.

Insulin is a major regulator of metabolism in metazoans, including the fruit fly Drosophila melanogaster. Genome-wide association studies (GWAS) suggest a genetic basis for reductions of both insulin sensitivity and insulin secretion, phenotypes commonly observed in humans with type 2 diabetes mellitus (T2DM). To identify molecular functions of genes linked to T2DM risk, we developed a genetic tool to measure insulin-like peptide 2 (Ilp2) levels in Drosophila, a model organism with superb experimental genetics. Our system permitted sensitive quantification of circulating Ilp2, including measures of Ilp2 dynamics during fasting and re-feeding, and demonstration of adaptive Ilp2 secretion in response to insulin receptor haploinsufficiency. Tissue specific dissection of this reduced insulin signaling phenotype revealed a critical role for insulin signaling in specific peripheral tissues. Knockdown of the Drosophila orthologues of human T2DM risk genes, including GLIS3 and BCL11A, revealed roles of these Drosophila genes in Ilp2 production or secretion. Discovery of Drosophila mechanisms and regulators controlling in vivo insulin dynamics should accelerate functional dissection of diabetes genetics. [ABSTRACT FROM AUTHOR]

1. A ribonuclease isolated from porcine thyroid cytosol using phenol: sodium dodecylsulfate treatment was associated with RNA and identical to latent alkaline ribonuclease. 2. Distribution of activity between aqueous and phenolic phases depended on pH, RNA, and ribonuclease inhibitor. 3. The ribonuclease was totally resistant to urea, guanidinium: HCl, chloroform:isoamyl alcohol, ethanol, heating at 100 degrees C for 10 min or at 80 degrees C plus 100 mM NaCl. It was highly resistant to hydrolysis by proteinase K except in the presence of detergent. 4. The extreme stability and other properties of latent alkaline ribonuclease could be the result of its association with RNA.