Visualization Multi-Pipeline for Communicating Biology.

Big data analytics in health is an emerging area due to the urgent need to derive actionable intelligence from the large volumes of healthcare data to efficiently manage the healthcare system and to improve health outcomes. In this paper we present a ‘big’ healthcare data analytics platform—termed as Healthcare Analytics for Intelligence and Learning (HAIL)—that is an end-to-end healthcare data analytics solution to derive data-driven actionable intelligence and situational awareness to inform and transform health decision-making, systems management and policy development. The innovative aspects of HAIL are: (a) the integration of data-driven and knowledge-driven analytics approaches, (b) a sand-box environment for healthcare analysts to develop and test health policy/process models by exploiting a range of data preparation, analytical and visualization methods, (c) the incorporation of specialized healthcare data standards, terminologies and concept maps to support data analytics, and (d) text analytics to analyze unstructured healthcare data. The architecture of HAIL comprises the following four main modules (fig 1): (A) Health Data Integration module that entails a semantics-based metadata manager to synthesize health data originating from a range of healthcare institutions to formulate a rich contextualized data resource. The data integration is achieved through ETL workflows designed by health analysts and researchers, (B) Health Analytics Module provides a range of healthcare analytics capabilities including, (i) Exploratory Analytics using data mining to perform data clustering, classification and association tasks, (ii) Predictive Analytics to predict future trends/outcomes derived from past observations of the healthcare processes, (iii) Text Analytics to analyze unstructured texts (such as clinical notes, discharge summaries, referral notes, clinical guidelines, etc.), (iv) Simulation-based Analytics to simulate what-if questions based on simulation models, (v) Workflow analytics to interact with modeled clinical workflows to understand the affects of various confounding factors, (vi) Semantic Analytics to infer contextualized relationships, anomalies and deviations through reasoning over a semantic health data model, and (vii) Informational Analytics to present summaries, aggregations, charts and reports, (C) Data Visualization Module offers a range of interactive data visualizations, such as geospatial visualizations, causal networks, 2D and 3D graphs, pattern clusters and interactive visualizations to explore high dimensional data, (D) Data Analytics Workbench is an interactive workspace to enable data health analysts to specify and set-up their analytics process in terms of data preparation, selection and set-up of analytical methods and the selection of visualization methods. Using the workbench analysts can design sophisticated data analytics workflows/models using a range of data integration, analytical and visualization methods. The HAIL platform is available via a web portal and the desktop application, and it is deployed on a cloud infrastructure. HAIL can be connected with existing health data sources to provide front-end data analytics. Fig 2 shows the technical architecture. The HAIL platform has been applied to analyze a real-life clinical healthcare situations using actual data from the provincial clinical data warehouse. We will present two case studies of the use of HAIL. In conclusion, the HAIL platform addresses a critical need for healthcare analytics to impact health decision-making, systems management and policy development.

The amount of available data has experienced significant growth as the result of technology advances in this era of Big Data. The biomedical domain, in particular, is one exemplar field where the number and scale of data sources have increased exponentially in the last decade. They are expected to keep growing even more rapidly to reach the level of Zetta bytes per year in the following year very soon. While more data obtained from advanced biotechnologies, such as high-throughput sequencing that encodes valuable information, are becoming overwhelmed, to discover knowledge from which for biology and medical research is still facing challenging problems with existing approaches. We study in this dissertation how to effectively and efficiently utilize these large-scare data from different numbers and types of sources for biomedical knowledge discovery. Raw data from biological organism such as microbiome usually have intrinsic high dimensionality of the feature space, which inevitably and exponentially raises the computational complexity of existing algorithms. We proposed a new approach using spectral interpolation technique to represent the high-dimensional data in low dimension space that not only greatly improves the efficiency of computing large-scale data but also preserves as much information as possible from original data. The resulting preferable outcomes for clustering and visualization tasks better facilitate the knowledge revealing of patterns and insights for microbial communities. We further studied how to enhance knowledge discovery while more than one data sources are available. Large-scale relational data such as protein-protein interactions (PPI) can be constructed in the form of network to invoke a system-wide perspective than traditional mechanistic approaches to interpret complex biological processes and functionalities. While bio-experiments are exhausted and costly, with two or more networks from different data sources we can apply computational comparative analysis such as Network Alignment to bridge the knowledge between well-studied species and under-examined species. We proposed new methods to globally align multiple large-scale biological networks from different species at the same time. We utilize both topological features and biological features of PPI networks and search heuristically for the best results. Representation learning for network is also integrated into our proposed framework to provide a new way to quantify the structural features of a node with its surrounding topology for the node embedding. The real data experiments showed promising results in finding homologous proteins as well as conserved protein complexes in poor-studied species for knowledge transferring from well-studied species. Besides utilizing homogeneous data from one and more data sources of one type, we keep exploring the possibility of harnessing sources of different types to take advantage of their underlying relational knowledge across heterogeneous data and capture the complex biomedical associations. The heterogeneous disease information networks we formulated in one research include types of sources from disease, pathway, and chemicals. They are filtered and calculated using Dynamic Time Warping (DTW) algorithm and meta path method for topological and semantics scores which lead to effective measurement of the similarity of diseases. In another study, we proposed a novel framework with Graph Convolutional Network to identify and predict disease-RNAs associations to better support the discovery of relational knowledge at the molecular level for medical applications such as disease diagnosis, therapy, and monitoring.

In this paper, we describe an approach to distributed enforcement of sticky policies in heterogeneous hardware and software environments. These heterogeneous environments might have differing mechanisms for attesting to their security capabilities and data sources might specify different levels of trust for different data items. Such an environment requires highly flexible policy specification and enforcement mechanisms. We employ sticky policies that travel with data wherever it travels, and we separate them into two components, a hosting policy and a usage policy. Hosting policies are used to ensure that data are transferred only to entities that are provably capable of providing local enforcement and only further transferring data under the same policies. Usage policies confer access, viewing, and update capabilities on users based on their attributes. The approach is supported by attribute-based certificates and policies, which include what authorities are trusted to certify attributes. In addition to presenting a full description of the approach, we report on a prototype implementation that includes all of the aforementioned components and also makes use of a modified version of Excel we developed to track security labels as data move through spreadsheets that are being shared by multiple users across different systems.

Virtualization technology has been widely applied in data centers and IT infrastructures, with advantages of server consolidation and live migration. Through live migration, data centers could flexibly move virtual machines among different physical machines to balance workloads, reduce energy consumption and enhance service availability.Today's data centers can grow to a huge scale. This implies that frequent live migration would be desirable for the economic use of hardware resources. Then, the performance of the live migration strategy will be an issue. So, we need a reliant evaluation method to choose the software and hardware environments that will produce the best live migration performance.However, there is not a complete live migration benchmark available currently. In addition, the existing evaluation methodologies select different metrics, different workloads and different test means. Thus, it is difficult to compare their results.In this paper we first survey the current research and their evaluation methods on live migration. We then summarize the critical issues for the live migration evaluation and also raise other unreported potential problems.We propose our solutions and present an implementation in our live migration benchmark -- Virt-LM. This is a benchmark for comparing live migration performance among different software and hardware environments in a data center scenario. We detail its design and provide some experimental results to validate its effectiveness.

The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.Contact:peter@biomatters.com

BioJS is an open-source project whose main objective is the visualization of biological data in JavaScript. BioJS provides an easy-to-use consistent framework for bioinformatics application programmers. It follows a community-driven standard specification that includes a collection of components purposely designed to require a very simple configuration and installation. In addition to the programming framework, BioJS provides a centralized repository of components available for reutilization by the bioinformatics community. http://code.google.com/p/biojs/. Supplementary data are available at Bioinformatics online.

This chapter discusses the methodological underpinnings of a doctoral study that examined boys’ performances of gender in physical education (PE) at a single-sex secondary school in Auckland, New Zealand. Initial findings are also presented; however, they only serve to demonstrate the potential of such an approach and not as an exhaustive report of findings. Using a participatory visual research approach involving video recordings of boys participating in PE, the boys’ representations and interpretations of the visual data were explored during both focus groups and individual interviews. The boys’ visual representations and interpretations highlight how their performances of gender are embedded in the design and structure of the physical spaces and places associated with PE. Through a Foucauldian (poststructural) lens the boys’ responses also illuminate how the gendered self is performed in multiple, contradictory and fluid ways involving particular technologies of the self. Visual research methods that focus on young people’s visual representations and interpretations might help identify (gendered) identity issues that are seen as important to the students themselves. It creates a space for young people to critically think about, reflect, articulate, and reason their lived experiences, their relationships with their peers and more importantly themselves. The use of such research approaches has the potential of realizing one of the key aims of symbolic interactionism by opening up new analytical possibilities for understanding young people’s lived experiences in both formal and informal pedagogical contexts.

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher-level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources. By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content. The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous. An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations. Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

A survey on machine learning based analysis of heterogeneous data in industrial automation

ABSTRACTBiomedical research data visualization faces several challenges, including insufficient expertise and fragmented methodologies, which severely limit research efficiency and result quality. FigureYa is a standardized visualization framework composed of 317 modular R/python scripts, rather than a standalone software or desktop application. It covers key domains such as expression profiling, immune analysis, survival analysis, and single‐cell data visualization. Based on the concept of “replace data and use,” FigureYa significantly lowers the technical threshold, allowing researchers to generate high‐quality charts without requiring an extensive programming background. Compared to generic online R code snippets, FigureYa offers rigorously developed, thoroughly validated, and biologically contextualized visualization modules originally written by the author team. Each script includes version‐matched environments, example datasets, and detailed annotations, providing clear advantages in automation, reproducibility, and scientific professionalism, thereby providing a standardized visualization solution for complex biomedical data. This innovative tool optimizes research time allocation, promotes interdisciplinary collaboration, accelerates scientific discovery and clinical translation, and provides robust data visualization support for biomedical research.

TUIO is an open framework that defines a common protocol and API for tangible- and multitouch-surfaces. The protocol is based on Open Sound Control and allows the platform-independent encoding and transmission of an abstract description of interactive surfaces, including touch events and tangible object states. While the original TUIO specification has been implemented for various hardware and software environments, there are not as many feature complete reference implementations of the next TUIO generation, although its specification [2] has been finalized and partially implemented by community members. The TUIO Hackathon at the International Conference for Interactive Tabletops and Surfaces is addressing expert users and developers of hardware and software environments for surface-based tangible user interfaces that are interested in experimenting with this new framework, with the goal of initiating the development and integration of new TUIO implementations.

As an efficient technique to accelerate system-on-chip (SoC) design, software/hardware co-simulation gives a flexible method to simulate hardware and software simultaneously and save the time-to-market. In this paper, a platform of software/hardware co-simulation is implemented, which utilize a debugger to interconnect the server and the design under test (DUT) on remote FPGA board via Internet. The software environment and hardware environment are run on the server and remote FPGA board respectively. The debugger establishes a real-time bidirectional data path between them and performs the data conversion. The embedded interface module can be synthesized with user's DUT smoothly. This platform takes full advantage of software's flexibility and hardware's high speed, supports remote operation and various types of co-simulation modes, while consumes few hardware resource.

Since the Keccak algorithm was selected by the US National Institute of Standards and Technology (NIST) as the standard SHA-3 hash algorithm for replacing the currently used SHA-2 algorithm in 2015, various optimization methods have been studied in parallel and hardware environments. However, in a software environment, the SHA-3 algorithm is much slower than the existing SHA-2 family; therefore, the use of the SHA-3 algorithm is low in a limited environment using embedded devices such as a Wireless Sensor Networks (WSN) enviornment. In this article, we propose a software optimization method that can be used generally to break through the speed limit of SHA-3. We combine the θ, π, and ρ processes into one, reducing memory access to the internal state more efficiently than conventional software methods. In addition, we present a new SHA-3 implementation for the proposed method in the most constrained environment, the 8-bit AVR microcontroller. This new implementation method, which we call the chaining optimization methodology, implicitly performs the π process of the f-function while minimizing memory access to the internal state of SHA-3. Through this, it achieves up to 26.1% performance improvement compared to the previous implementation in an AVR microcontroller and reduces the performance gap with the SHA-2 family to the maximum. Finally, we apply our SHA-3 implementation in Hash_Deterministic Random Bit Generator (Hash_DRBG), one of the upper algorithms of a hash function, to prove the applicability of our chaining optimization methodology on 8-bit AVR MCUs.