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In most countries, buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use. To reduce this massive consumption and decrease carbon emission, thermal insulation of buildings can play an important role. The estimation of energy savings following the improvement of a building's insulation remains a key area of research in order to calculate the cost savings and the payback period. In this paper, a case study has been presented where deep retrofitting has been introduced to an existing building to bring it closer to a Passivhaus standard with the introduction of insulation and solar photovoltaic panels. The thermal performance of the building with its improved insulation has been evaluated using infrared thermography. Artificial intelligence using deep learning neural networks is implemented to predict the thermal performance of the building and the expected energy savings. The prediction of neural networks is compared with the actual savings calculated using historical weather data. The results of the neural network show high accuracy of predicting the actual energy savings with success rate of about 82% when compared with the calculated values. The results show that this suggested approach can be used to rapidly predict energy savings from retrofitting of buildings with reasonable accuracy, hence providing a practical rapid tool for the building industry and communities to estimate energy savings. A mathematical model has been also developed which has indicated a life-long monitoring will be needed to precisely estimate the benefits of energy savings in retrofitting due to the change in weather conditions and people's behaviour.

Summary: The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

The current state of much of the Wuhan pneumonia virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies and requires unimpeded access to data, analysis tools, and computational infrastructure. Here, we show that community efforts in developing open analytical software tools over the past 10 years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all SARS-CoV-2 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.

Study region: pan-Europe, from Western Europe to Central Asia. Study focus: Knowledge of transboundary aquifers (TBAs) in the pan-European region has evolved through an inventory under the Convention on the Protection and Use of Transboundary Watercourses and International Lakes (Water Convention) completed in 1999, and the First and Second Assessments of transboundary waters undertaken in 2007 and 2011. These “snapshots”, together with intergovernmental review processes indicate significant advances in the recognition of TBAs. Reference is made to where collaboration on transboundary groundwaters has developed, demonstrated by cooperation arrangements and joint projects. New hydrological insights for the region: The findings highlight a number of factors; inventories, participation in the work of the Water Convention and application of guidelines, joint projects, the legal and institutional framework for management and the role of EU legislation. There is a need for sound conceptual hydrogeological models of TBAs, but also for broadening joint assessment to better foster cooperation. Bringing together in transboundary dialogues technical information with an understanding of the demands for groundwater and related drivers as well as better understanding of the human, social and institutional dimensions of managing TBAs can add to their effectiveness. Such approaches can better provide for defining and discussing policy responses and for mobilising resources and the necessary political support. Keywords: Water resources, Groundwater, Convention, Governance, Assessment, Monitoring, Transboundary cooperation

We identified a homozygous missense alteration (c.75C>A, p.D25E) in CLCC1, encoding a presumptive intracellular chloride channel highly expressed in the retina, associated with autosomal recessive retinitis pigmentosa (arRP) in eight consanguineous families of Pakistani descent. The p.D25E alteration decreased CLCC1 channel function accompanied by accumulation of mutant protein in granules within the ER lumen, while siRNA knockdown of CLCC1 mRNA induced apoptosis in cultured ARPE-19 cells. TALEN KO in zebrafish was lethal 11 days post fertilization. The depressed electroretinogram (ERG) cone response and cone spectral sensitivity of 5 dpf KO zebrafish and reduced eye size, retinal thickness, and expression of rod and cone opsins could be rescued by injection of wild type CLCC1 mRNA. Clcc1+/- KO mice showed decreased ERGs and photoreceptor number. Together these results strongly suggest that intracellular chloride transport by CLCC1 is a critical process in maintaining retinal integrity, and CLCC1 is crucial for survival and function of retinal cells.

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.

Este artículo analiza la aplicación de formas básicas estructurales de Marco Recíproco y su uso en diferentes tipologías arquitectónicas. El principio morfológico de este tipo de estructuras se resume como una grilla (sistema) de vigas tridimensionales, el que ha sido patentado como "Marco Recíproco" (Reciprocal Frame, RF).

Galaxy provides a web-based platform for interactive, large-scale data analyses, which integrates bioinformatics tools written in a variety of languages. A substantial number of these tools are written in the R programming language, which enables powerful analysis and visualization of complex data. The Bioconductor Project provides access to these open source R tools and currently contains over 1200 R packages. While some R/Bioconductor tools are currently available in Galaxy, scientific research communities would benefit greatly if they were integrated on a larger scale. Tool development in Galaxy is an early entry point for Galaxy developers, biologists, and bioinformaticians, who want to make their work more accessible to a larger community of scientists. Here, we present a guide and best practices for R/Bioconductor tool integration into Galaxy. In addition, we introduce new functionalities to existing software that resolve dependency issues and semi-automate generation of tool integration components. With these improvements, novice and experienced developers can easily integrate R/Bioconductor tools into Galaxy to make their work more accessible to the scientific community.