A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis.

Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules.

Kinases on Double Duty: A Review of UniProtKB Annotated Bifunctionality within the Kinome.

Phosphorylation facilitates the regulation of all fundamental biological processes, which has triggered extensive research of protein kinases and their roles in human health and disease. In addition to their phosphotransferase activity, certain kinases have evolved to adopt additional catalytic functions, while others have completely lost all catalytic activity. We searched the Universal Protein Resource Knowledgebase (UniProtKB) database for bifunctional protein kinases and focused on kinases that are critical for bacterial and human cellular homeostasis. These kinases engage in diverse functional roles, ranging from environmental sensing and metabolic regulation to immune-host defense and cell cycle control. Herein, we describe their dual catalytic activities and how they contribute to disease pathogenesis.

FLEXGRID - A novel smart grid architecture that facilitates high-RES penetration through innovative flexibility markets towards efficient stakeholder interaction.

The FLEXGRID 2 project develops a digital platform designed to offer Digital Energy Services (DESs) that facilitate energy sector stakeholders (i.e. DSOs, TSOs, market operators, RES producers, retailers, flexibility aggregators) towards: i) automating and optimizing their investments and operation/management of their systems/assets, and ii) interacting in a dynamic and efficient way with their environment (electricity system) and the rest of the stakeholders. In this way, FLEXGRID envisages secure, sustainable, competitive, and affordable smart grids. A key objective is the incentivization of large-scale bottom-up investments in Distributed Energy Resources (DERs) through innovative smart grid management. Towards this goal, FLEXGRID develops innovative data models and energy market architectures (with high liquidity and efficiency) that effectively manage smart grids through an advanced TSO-DSO interaction as well as interaction between Transmission Network and Distribution Network level energy markets. Consequently, and through intelligence that exploits the innovation of the proposed market architecture, FLEXGRID develops investment tools able to examine in depth the emerging energy ecosystem and allow in this way: i) the financial sustainability of DER investors, and ii) the market liquidity/efficiency through advanced exploitation of DERs and intelligent network upgrades.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

BACKGROUND: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. OBJECTIVE: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research. METHODS: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. RESULTS: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. CONCLUSION: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Annotation of gene product function from high-throughput studies using the Gene Ontology.

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

What Makes a Kinase Promiscuous for Inhibitors?

ATP-competitive kinase inhibitors often bind several kinases due to the high conservation of the ATP binding pocket. Through clustering analysis of a large kinome profiling dataset, we found a cluster of eight promiscuous kinases that on average bind more than five times more kinase inhibitors than the other 398 kinases in the dataset. To understand the structural basis of promiscuous inhibitor binding, we determined the co-crystal structure of the receptor tyrosine kinase DDR1 with the type I inhibitors dasatinib and VX-680. Surprisingly, we find that DDR1 binds these type I inhibitors in an inactive conformation typically reserved for type II inhibitors. Our computational and biochemical studies show that DDR1 is unusually stable in this inactive conformation, giving a mechanistic explanation for inhibitor promiscuity. This phenotypic clustering analysis provides a strategy to obtain functional insights not available by sequence comparison alone.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia.

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

Structural and Biochemical Basis for Intracellular Kinase Inhibition by Src-specific Peptidic Macrocycles.

Protein kinases are attractive therapeutic targets because their dysregulation underlies many diseases, including cancer. The high conservation of the kinase domain and the evolution of drug resistance, however, pose major challenges to the development of specific kinase inhibitors. We recently discovered selective Src kinase inhibitors from a DNA-templated macrocycle library. Here, we reveal the structural basis for how these inhibitors retain activity against a disease-relevant, drug-resistant kinase mutant, while maintaining Src specificity. We find that these macrocycles display a degree of modularity: two of their three variable groups interact with sites on the kinase that confer selectivity, while the third group interacts with the universally conserved catalytic lysine and thereby retains the ability to inhibit the "gatekeeper" kinase mutant. We also show that these macrocycles inhibit migration of MDA-MB-231 breast tumor cells. Our findings establish intracellular kinase inhibition by peptidic macrocycles, and inform the development of potent and specific kinase inhibitors.

Highly specific, bisubstrate-competitive Src inhibitors from DNA-templated macrocycles.

Protein kinases are attractive therapeutic targets, but their high sequence and structural conservation complicates the development of specific inhibitors. We recently identified, in a DNA-templated macrocycle library, inhibitors with unusually high selectivity among Src-family kinases. Starting from these compounds, we developed and characterized in molecular detail potent macrocyclic inhibitors of Src kinase and its cancer-associated 'gatekeeper' mutant. We solved two cocrystal structures of macrocycles bound to Src kinase. These structures reveal the molecular basis of the combined ATP- and substrate peptide-competitive inhibitory mechanism and the remarkable kinase specificity of the compounds. The most potent compounds inhibit Src activity in cultured mammalian cells. Our work establishes that macrocycles can inhibit protein kinases through a bisubstrate-competitive mechanism with high potency and exceptional specificity, reveals the precise molecular basis for their desirable properties and provides new insights into the development of Src-specific inhibitors with potential therapeutic relevance.

Improved field localization in transcranial magnetic stimulation of the brain with the utilization of a conductive shield plate in the stimulator.

In this paper, a carefully designed conductive shield plate is presented, which helps to improve localization of the electric field distribution induced by transcranial magnetic stimulation for neuron stimulation. The shield plate is introduced between a figure-of-eight coil and the head. In order to accurately predict the field distribution inside the brain and to examine the effects of the shield plate, a realistic head model is constructed from magnetic resonance image data with the help of image processing tools and the finite-element method in three dimensions is employed. Finally, to show the improvements obtained, the results are compared with two conventional coil designs. It is found that an incorporation of the shield plate into the coil, effectively improves the induced field localization by more than 50%, and prevents other parts of the brain from exposure to high pulsed magnetic fields.

Numerical simulation of DNA sample preconcentration in microdevice electrophoresis.

A numerical model is presented for the accurate and efficient prediction of preconcentration and transport of DNA during sample introduction and injection in microcapillary electrophoresis. The model incorporates conservation laws for the different buffer ions, salt ions, and DNA sample, coupled through a Gaussian electric field to account for the field modifications that cause electromigration. The accuracy and efficiency required to capture the physics associated with such a complex transient problem are realized by the use of the finite element-flux corrected transport (FE-FCT) algorithm in two dimensions. The model has been employed for the prediction of DNA sample preconcentration and transport during electrophoresis in a double-T injector microdevice. To test its validity, the numerical results have been compared with the corresponding experimental data under similar conditions, and excellent agreement has been found. Finally, detailed results from a simulation of DNA sample preconcentration in electrophoretic microdevices are presented using as parameters the electric field strength and the other species concentrations. The effect of the Tris concentration on sample stacking is also investigated. These results demonstrate the great potential offered by the model for future optimization of such microchip devices with respect to significantly enhanced speed and resolution of sample separation.