Craighorne7366

Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm-2 in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen.Smart contracts are regarded as one of the most promising and appealing notions in blockchain technology. Their self-enforcing and event-driven features make some online activities possible without a trusted third party. Nevertheless, problems such as miscellaneous attacks, privacy leakage, and low processing rates prevent them from being widely applied. Various schemes and tools have been proposed to facilitate the construction and execution of secure smart contracts. However, a comprehensive survey for these proposals is absent, hindering new researchers and developers from a quick start. This paper surveys the literature and online resources on smart contract construction and execution over the period 2008-2020. We divide the studies into three categories (1) design paradigms that give examples and patterns on contract construction, (2) design tools that facilitate the development of secure smart contracts, and (3) extensions and alternatives that improve the privacy or efficiency of the system. We start by grouping the relevant construction schemes into the first two categories. We then review the execution mechanisms in the last category and further divide the state-of-the-art solutions into three classes private contracts with extra tools, off-chain channels, and extensions on core functionalities. Finally, we summarize several challenges and identify future research directions toward developing secure, privacy-preserving, and efficient smart contracts.The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.The assay for transposase accessible chromatin (ATAC-seq) is a method for mapping genome-wide chromatin accessibility. Coupled with high-throughput sequencing, it enables integrative epigenomics analyses. ATAC-seq requires direct access to cell nuclei, a major challenge in non-model species such as small invertebrates, whose soft tissue is surrounded by a protective exoskeleton. Here, we present modifications of the ATAC-seq protocol for applications in small crustaceans, extending applications to non-model species. For complete information on the use and execution of this protocol, please refer to Buenrostro et al. (2013).Lysosomes are critical for maintaining protein homeostasis and cellular metabolism. Lysosomal dysfunction and disrupted protein trafficking contribute to cell death in neurodegenerative disorders, including Parkinson's disease and dementia. We describe three complementary protocols-the use of protein glycosylation, western blotting, immunofluorescence, and hydrolase activity measurement-to analyze the trafficking and activity of lysosomal proteins in patient-derived neurons differentiated from iPSCs. These methods should help to identify lysosomal phenotypes in patient-derived cultures and aid the discovery of therapeutics that augment lysosomal function. For complete details on the use and execution of this protocol, please refer to Cuddy et al. (2019).Evaluating drug sensitivity is improved by directly quantifying death kinetics, rather than correlates of viability, such as metabolic activity. This is challenging, requiring time-lapse microscopy and genetically encoded labels to distinguish live and dead cells. Here, we describe fluorescence-based and lysis-dependent inference of cell death kinetics (FLICK). This method requires only a standard fluorescence plate reader, retaining the high-throughput nature and broad accessibility of common viability assays. However, FLICK specifically quantifies death, including an accurate inference of death kinetics. For complete details on the use and execution of this protocol, please refer to Richards et al. (2020).Neuropeptides are essential signaling molecules secreted by dense-core vesicles (DCVs). They contribute to information processing in the brain, controlling a variety of physiological conditions. Defective neuropeptide signaling is implicated in several psychiatric disorders. Here, we provide a protocol for the quantitative analysis of DCV fusion events in rodent neurons using pH-sensitive DCV fusion probes and custom-written analysis algorithms. This method can be used to study DCV fusion mechanisms and is easily adapted to investigate fusion principles of other secretory organelles. For complete details on the use and execution of this protocol, please refer to Persoon et al. (2019).The nature of plant tissues has continuously hampered understanding of the spatio-temporal and subcellular distribution of RNA-guided processes. Here, we describe a universal protocol based on Arabidopsis to investigate subcellular RNA distribution from virtually any plant species using flow cytometry sorting. This protocol includes all necessary control steps to assess the quality of the nuclear RNA purification. Moreover, it can be easily applied to different plant developmental stages, tissues, cell cycle phases, experimental growth conditions, and specific cell type(s). For complete information on the use and execution of this protocol, please refer to Bologna et al. (2018) and de Leone et al. (2020).We describe two differentiation protocols to derive sensory spinal interneurons (INs) from human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs). In protocol 1, we use retinoic acid (RA) to induce pain, itch, and heat mediating dI4/dI6 interneurons, and in protocol 2, RA with bone morphogenetic protein 4 (RA+BMP4) is used to induce proprioceptive dI1s and mechanosensory dI3s in hPSC cultures. These protocols provide an important step toward developing therapies for regaining sensation in spinal cord injury patients. For complete details on the use and execution of this protocol, please refer to Gupta et al. (2018).N-glycosylation is a fundamental post-translational protein modification in the endoplasmic reticulum of eukaryotic cells. The biosynthetic and catabolic flux of N-glycans in eukaryotic cells has long been analyzed by metabolic labeling using radiolabeled sugars. Here, we introduce a non-radiolabeling protocol for the isolation, structural determination, and quantification of N-glycan precursors, dolichol-linked oligosaccharides, and the related metabolites, including phosphorylated oligosaccharides and nucleotide sugars. Our protocol allows for capturing of the biosynthesis and degradation of N-glycan precursors at steady state. For complete details on the use and execution of this protocol, please refer to Harada et al. (2013), Harada et al. (2020), and Nakajima et al. (2013).Here, we describe a generic protocol for monitoring protein-RNA interaction using a cleavable GFP fusion of a recombinant RNA-binding protein. We detail each expression and purification step, including high salt and heparin column for contaminant RNA removal. After the assembly of RNA into the ribonucleoprotein complex, the MicroScale Thermophoresis assay enables the binding affinity to be obtained quickly with a small amount of sample. Further Gaussian accelerated molecular dynamics simulations allow us to analyze proteinRNA interactions in detail. For complete details on the use and execution of this protocol, please refer to Gao et al. (2020).Histone deacetylases (HDACs) are ubiquitous enzymes that cleave post-translational ε-N-acyllysine modifications. The continued identification of diverse acyl modifications at lysine residues in proteins has resulted in discovery of new insight into the biological roles of these enzymes. Here, we describe a fluorogenic high-throughput screening protocol to identify deacylase activities. We describe the careful optimization of continuous, coupled enzyme assays, which provide efficient determination of kinetic parameters. These techniques can facilitate inhibitor assay design and provide fundamental understanding of HDAC biochemistry. For complete details on the use and execution of this protocol, please refer to Moreno-Yruela et al. (2018).Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications.