Design of nanophotonic circuits for autonomous subsystem quantum error correction

We reapply our approach to designing nanophotonic quantum memories to formulate an optical network that autonomously protects a single logical qubit against arbitrary single-qubit errors. Emulating the 9 qubit Bacon-Shor subsystem code, the network replaces the traditionally discrete syndrome measurement and correction steps by continuous, time-independent optical interactions and coherent feedback of unitarily processed optical fields.Comment: 12 pages, 4 figure

Remote Sensing Analysis of Recent Carbonate Platforms, East of Sabah: Potential Analogues for Miocene Carbonate Platforms of the South China Sea

DOI: 10.17014/ijog.v7i3.141Recent carbonate platforms may provide useful analogues for fossil platforms and reefs, and could allow the generation of quantitative tools for the prediction of facies distribution, reservoir volumes, and reservoir quality. Data from these modern analogues would greatly enhance our ability to construct more reliable and calibrated reservoir models for carbonate fossil fields, such as the Miocene carbonate platforms of Central Luconia Province, Sarawak. In this study, carbonate facies maps and quantitative reservoir data will be generated by using remote sensing techniques. Satellite imageries over the carbonate platforms east of Sabah have been acquired and processed for generating facies maps. In order to produce a reliable facies map, the composition and grain size distribution of the sediments that make up individual facies must be known in order to determine the classes that have been identified on the initial facies maps. Samples collected from the Gaya and Selekan platforms were analyzed and the results of grain size analysis are illustrated

Intuitive Staging Correlates With King's Clinical Stage.

BACKGROUND: Clinical stage in amyotrophic lateral sclerosis (ALS) can be assigned using King's staging with a simple protocol based on the number of CNS regions involved and the presence of significant nutritional or respiratory failure. It is important that the assigned clinical stage matches expectations, and generally corresponds with how a health care professional would intuitively stage the patient. We therefore investigated the relationship between King's clinical ALS stage and ALS stage as intuitively assigned by health care professionals. METHODS: We wrote 17 case vignettes describing people with ALS at different disease stages from very early limited disease involvement through to severe, multi-domain disease. During two workshops, we asked health care professionals to intuitively stage the vignettes and compared the answers with the actual King's clinical ALS stage. RESULTS: There was a good correlation between King's clinical ALS stage and intuitively assigned stage, with a Spearman's Rank correlation coefficient of 0.64 (p < 0.001). There was no difference in the intuitive stages assigned by practitioners of different types or at different levels of experience. CONCLUSIONS: Across a spectrum of ALS scenarios, King's clinical ALS stage corresponds to intuitive ALS stage as assigned by a range of health care professionals

National human rights action plans: a roadmap to development

Applying the “human rights-based approach to development” (HRBAD) in practice is the biggest challenge facing advocates of human rights and development. This article seeks to bridge between. HRBAD at a theoretical level and the National Human Rights Action Plans (NHRAP) at a practical level in order to not merely provide a tool for putting HRBAD into practice but also to prepare a theoretical foundation for NHRAPs. To integrate HRBAD into NHRAPs, the whole conceptual space of development is mapped into a matrix called a “Substantive-Procedural Matrix of Development” (SPMD). This matrix helps states develop their plans within the road to development. The utility of this matrix as a heuristic will be demonstrated in the case of the right to health

DNAscan2: a versatile, scalable, and user-friendly analysis pipeline for human next-generation sequencing data

SUMMARY: The current widespread adoption of next-generation sequencing (NGS) in all branches of basic research and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis software. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats, and other large structural variants; (ii) covers all standard steps of NGS analysis, from quality control of raw data and genome alignment to variant calling, annotation, and generation of reports for the interpretation and prioritization of results; (iii) is highly adaptable as it can be deployed and run via either a graphic user interface for non-bioinformaticians and a command line tool for personal computer usage; (iv) is scalable as it can be executed in parallel as a Snakemake workflow, and; (v) is computationally efficient by minimizing RAM and CPU time requirements. AVAILABILITY AND IMPLEMENTATION: DNAscan2 is implemented in Python3 and is available at https://github.com/KHP-Informatics/DNAscanv2

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration