Platform-independent Approach Research Articles

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study

In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users’ ideology on the controversy; (iii) the construction of users’ debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump’s presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.

Development of Platform Independent Mobile Learning Tool in Saudi Universities

The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.

Revealing Challenges within the Application of Machine Learning Services – A Delphi Study

Over the past years, Machine Learning has been applied to an increasing number of problems across numerous industries. However, the steady rise in the application of Machine Learning has not come without challenges since companies often lack the expertise or infrastructure to build their own Machine Learning systems. These challenges led to the emergence of a new paradigm, called Machine Learning as a Service. Scientific literature has mainly analyzed this topic in the context of platform solutions that provide ready-to-use environments for companies. We recently have developed a platform-independent approach and labeled it Machine Learning Services. The aim of the present study is to identify and evaluate challenges and opportunities in the application of Machine Learning Services. To do so, we conducted a Delphi Study with a panel of machine learning experts. The study consisted of three rounds and was structured according to the five steps of the Data Science Lifecycle. A variety of challenges from the areas “Communication”, “Environment”, “Approach”, “Data”, “Retraining, Testing, Monitoring and Updating”, “Model Training and Evaluation” were identified. Subsequently, the challenges revealed by the Delphi Study were compared with previous work on Machine Learning as a Service, which resulted from a structured literature review. The identified areas serve as possible future research fields and give further implications for practice. Alleviating communication issues and assessing the business IT infrastructure prior to the machine learning project are among the key findings of our study.

Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python–based workflow

The analysis of environmental microplastic particles using FTIR microscopy is a challenging task, due to the very high number of individual particles within a single sample. Therefore, automatable, fast and robust approaches are highly requested. Micro particles were commonly enriched on filters, and sub- or the whole filter area was investigated, which took more than 20h and produced millions of data, which had to be evaluated. This paper presents a new approach of such filter area analysis using an intelligent algorithm to measure only those spots on a filter that would produce evaluable FTIR data. Empty spaces or IR absorbers like carbon black particles were not measured which successfully reduced the total analysis time from 50h to 7h. The presented method is based on system independent Python workflow and can easily be implemented on other FTIR systems.•Fast and intelligent FTIR microscopy area mapping without FPA detector•Total time reduction from 50 h to 7 h•Platform independent approach based on Python

Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells.

Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.

Platform-independent Specification and Verification of the Standard Mathematical Square Root Function

The project “Platform-independent approach to formal specification and verification of standard mathematical functions” is aimed onto the development of incremental combined approach to specification and verification of standard Mathematical functions like sqrt, cos, sin, etc. Platform-independence means that we attempt to design a relatively simple axiomatization of the computer arithmetics in terms of real arithmetics (i.e. the field \(\mathbb{R}\) of real numbers) but do not specify neither base of the computer arithmetics, nor a format of numbers representation. Incrementality means that we start with the most straightforward specification of the simplest case to verify the algorithm in real numbers and finish with a realistic specification and a verification of the algorithm in computer arithmetics. We call our approach combined because we start with manual (pen-and-paper) verification of the algorithm in real numbers, then use this verification as proof-outlines for a manual verification of the algorithm in computer arithmetics, and finish with a computer-aided validation of the manual proofs with a proof-assistant system (to avoid appeals to “obviousness” that are common in human-carried proofs). In the paper, we apply our platform-independent incremental combined approach to specification and verification of the standard Mathematical square root function. Currently a computer-aided validation was carried for correctness (consistency) of our fix-point arithmetics and for the existence of a look-up table with the initial approximations of the square roots for fix-point numbers.

Delay Estimation of Industrial IoT Applications Based on Messaging Protocols

Information and operational technologies merge into the so-called industrial Internet of Things, which is one of the basic pillars of the Industry 4.0 paradigm. Roughly speaking, yet-to-come services will be offered in the automation scenario by industrial devices having an internet connection for sharing data in the cloud. Currently, most efforts are in the development of protocols able to ensure horizontal interoperability among heterogeneous applications. Consequently, poor attention is devoted to time-related performance. In this paper, a new, full software, platform-independent approach is proposed for experimentally evaluating the delay in transferring information across local and intercontinental routes by applications leveraging on messaging middleware. The application is realized using the node-RED web-based framework, due to its availability on different platforms; the widely accepted message queue telemetry transport protocol has been chosen thanks to its low overhead and complexity. For sake of completeness, five different, private and public, brokers are used. The adopted industrial-grade hardware, complemented by global positioning system time reference, permits an overall synchronization and timestamping accuracy of a few milliseconds. The vast measurement campaign highlighted that, generally, quality of service (QoS) type 1 offers low end-to-end delay (average value less than 0.5 s) with reduced variability (0.1 s). However, the maximum end-to-end one-way delay ranges from 1 s for QoS 0 to less than 1.5 s for fully acknowledged QoS 2.

GEL: A Platform-Independent Reasoner for Parallel Classification with OWL EL Ontologies Using Graph Representation

In the community of Semantic Web, the state-of-the-art reasoners are all implemented on specific platforms. As the Web Ontology Language (OWL) has been widely used in different real-world applications, it is required that the task of reasoning can be flexibly adapted to different platforms. In this paper, we take the first effort to give a platformindependent approach for parallel classification of the description logic OWL EL, which is a tractable fragment of OWL 2. Classification, which is the task of computing a subsumption hierarchy between concepts, plays an important role in many applications of OWL EL. The current classification methods rely on the specific parallel computation platforms and can hardly achieve a tradeoff between efficiency and scalability. To develop a platform-independent approach for performing classification in OWL EL, we first give a novel and well defined graph formalism GEL for representing EL ontologies and present a group of sound and complete rules for ontology classification on a GEL graph. Based on this formalism, we design a parallel classification algorithm, which can be implemented on different parallel computation platforms, and we also show the correctness of the algorithm. We implement a system, which can easily switch between multi-core and a distributed cluster. Finally, we conduct experiments on several real-world OWL EL ontologies. The experimental results show that our system outperforms two state-of-the-art EL reasoning systems, and has a linear scalability on the extensions of GO ontologies.

Robust and tissue-independent gender-specific transcript biomarkers

Context: Correct gender assignment in humans at the molecular level is crucial in many scientific disciplines and applied areas.Materials and methods: Candidate gender markers were identified through supervised statistical analysis of genome wide microarray expression data from human blood samples (N = 123, 58 female, 65 male) as a training set. The potential of the markers to predict undisclosed tissue donor gender was tested on microarray data from 13 healthy and 11 cancerous human tissue collections (internal) and external datasets from samples of varying tissue origin. The abundance of some genes in the marker panel was quantified by RT-PCR as alternative analytical technology.Results: We identified and qualified predictive, gender-specific transcript markers based on a set of five genes (RPS4Y1, EIF1AY, DDX3Y, KDM5D and XIST).Conclusion: Gene expression marker panels can be used as a robust tissue- and platform-independent predictive approach for gender determination.

Platform Independent Analysis of Probabilities on Multithreaded Programs

A concurrent program is intuitively associated with probability: the executions of the program can produce nondeterministic execution program paths due to the interleavings of threads, whereas some paths can always be executed more frequently than the others. An exploration of the probabilities on the execution paths is expected to provide engineers or compilers with support in helping, either at coding phase or at compile time, to optimize some hottest paths. However, it is not easy to take a static analysis of the probabilities on a concurrent program in that the scheduling of threads of a concurrent program usually depends on the operating system and hardware (e.g., processor) on which the program is executed, which may be vary from machine to machine. In this paper the authors propose a platform independent approach, called ProbPP, to analyzing probabilities on the execution paths of the multithreaded programs. The main idea of ProbPP is to calculate the probabilities on the basis of two kinds of probabilities: Primitive Dependent Probabilities (PDPs) representing the control dependent probabilities among the program statements and Thread Execution Probabilities (TEPs) representing the probabilities of threads being scheduled to execute. The authors have also conducted two preliminary experiments to evaluate the effectiveness and performance of ProbPP, and the experimental results show that ProbPP can provide engineers with acceptable accuracy.

Accessing the SEED Genome Databases via Web Services API: Tools for Programmers

BackgroundThe SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups.ResultsThe currently exposed Web services encompass over forty different methods for accessing data related to microbial genome annotations. The Web services provide comprehensive access to the database back end, allowing any programmer access to the most consistent and accurate genome annotations available. The Web services are deployed using a platform independent service-oriented approach that allows the user to choose the most suitable programming platform for their application. Example code demonstrate that Web services can be used to access the SEED using common bioinformatics programming languages such as Perl, Python, and Java.ConclusionsWe present a novel approach to access the SEED database. Using Web services, a robust API for access to genomics data is provided, without requiring large volume downloads all at once. The API ensures timely access to the most current datasets available, including the new genomes as soon as they come online.

EMod: platform independent modelling, description and enactment of parameterisable EAI patterns

As enterprises merge, split up, or grow, existing legacy systems from different vendors implemented in different programming languages and running in different environments as well as roll-your-own applications have to be integrated. This fact is denoted as the Enterprise Application Integration (EAI) problem and is one of the major challenges IT departments are facing today. As a result of the need for operational flexibility and reuse, Service-Oriented Architectures (SOA) gain more and more importance as they allow for a flexible and standardised way to describe, reuse and integrate services in heterogeneous application landscapes. Enterprise Application Integration Patterns (EAI Patterns) are a means to describe integration problems using a set of patterns. The patterns therefore present nuggets of advice on how to solve complex recurring integration problems. In this paper we introduce a platform independent approach (EMod) to model, describe and enact EAI patterns in service-oriented architectures. We describe how EMod can be used to build stand-alone EAI solutions that integrate different applications in a Software as a Service (SaaS) environment. Furthermore we show how EMod can be used as a part of an application to integrate services into composite service-oriented applications. This is done by integrating EMod into the Service Component Architecture (SCA).

Extraction of manufacturing information from design-by-feature solid model through feature recognition

Feature recognition is the key to the computer-aided design (CAD) and computer-aided manufacturing (CAM) integration to build a computer-integrated manufacturing system. There are two approaches to CAD feature recognition: platform-dependent and platform-independent. In the platform-independent approach, the part’s geometrical data are extracted from a neutral file such as DXF, IGES, or STEP. In contrast, the platform-dependent approach extracts the information of the design features directly from a design-by-feature solid model through the object-oriented model of a part. This paper explains a platform-dependent approach which is implemented to translate design features into manufacturing information. This approach begins with simplification using the suppression of fillets, and clustering non-intersecting design features is done. Then, the rule-based method is employed in order to recognize machining features. Finally, the needed manufacturing information such as tool accessing direction, dimensions, material removal regions, and geometrical and topological data is recognized. The application of the proposed system would be exhibited in generating machine path code for rapid prototyping and CNC machines and providing a database for computer-aided process planning. The proposed system was implemented on Autodesk Inventor and successfully tested for many complex 3D models.

Cluster computing software for GATE simulations

Geometry and tracking (GEANT4) is a Monte Carlo package designed for high energy physics experiments. It is used as the basis layer for Monte Carlo simulations of nuclear medicine acquisition systems in GEANT4 Application for Tomographic Emission (GATE). GATE allows the user to realistically model experiments using accurate physics models and time synchronization for detector movement through a script language contained in a macro file. The downside of this high accuracy is long computation time. This paper describes a platform independent computing approach for running GATE simulations on a cluster of computers in order to reduce the overall simulation time. Our software automatically creates fully resolved, nonparametrized macros accompanied with an on-the-fly generated cluster specific submit file used to launch the simulations. The scalability of GATE simulations on a cluster is investigated for two imaging modalities, positron emission tomography (PET) and single photon emission computed tomography (SPECT). Due to a higher sensitivity, PET simulations are characterized by relatively high data output rates that create rather large output files. SPECT simulations, on the other hand, have lower data output rates but require a long collimator setup time. Both of these characteristics hamper scalability as a function of the number of CPUs. The scalability of PET simulations is improved here by the development of a fast output merger. The scalability of SPECT simulations is improved by greatly reducing the collimator setup time. Accordingly, these two new developments result in higher scalability for both PET and SPECT simulations and reduce the computation time to more practical values.

Computational network federations: a middleware architecture for network-based computing

Computational Network Federations (CNFs) enable an arbitrary set of heterogeneous hosts which are connected via any type of network to form dynamic virtual distributed systems that cooperate to execute an application, or serve as generalized application service platforms to end users. CNFs motivate a view of the Internet as a vast unified host: a repository of information, application services, and an omnipresent supercomputing resource regardless of the type of access device or access methodology. CNFs provide a powerful way of virtualizing generalized enterprise networks (or even the Internet), and an economic and resilient model for deploying enterprise applications, (such as CRM) and peer-2-peer services (e.g., chatrooms). This paper describes a middleware architecture that enables network-based computing, communications, and services through a unified, access, and platform-independent approach. CNFs borrow from the capabilities of grid computing and aim toward intelligent computational service networks that are ubiquitous, secure, and adaptive to user and access-method idiosyncrasies. CNFs encompass a set of abstractions and interfaces that provide: 1) a unified service-oriented view of the network to the user; 2) a homogeneous host abstraction to applications; and 3) a shared-memory abstraction to software developers. This paper outlines the architecture of CNFs and describes in more detail i-DVM, a distributed multithreaded meta-OS that forms the core of a CNF and implements the virtual machine abstraction and location transparency.

Towards a unifying approach to mobile computing

In this paper we address the diversity of mobile devices and the related problematic issues with a largely platform-independent approach to development. Our work is motivated by the amount and variety of mobile devices foreseen to reside in future ubiquitous computing environments. To reduce the development effort and to augment data exchange facilities, we present an application framework that abstractly defines common interaction objects in user interfaces on mobile devices.

Java-Based Framework for Remote Access to Laboratory Experiments

This paper presents a platform independent approach to remote experimentation. It addresses a wide area of problems occurring in conjunction with remote laboratory experiments, starting with access management procedures via remote control to network- based analysis of measured data. It presents the architecture of the remote laboratory at the University of Hagen. Students have access to the experiments via Internet from anywhere at any time. They control the experiments exclusively with their standard Web browser, no additional software is needed. The remote laboratory is based on a client/server architecture, which is mainly implemented in the Java programming language. The methods and software modules developed for the lab are generic and frequently used in several remote experiments at some German universities. Furthermore the paper discusses the motivation of remote experimentation.

Integrating information appliances into an interactive workspace

The authors present a robust, infrastructure-centric, and platform-independent approach to integrating information appliances into the iRoom, an interactive workspace. The Interactive Workspaces Project at Stanford explores new possibilities for people to work together in technology-rich spaces with computing and interaction devices on many different scales. It includes faculty and students from the areas of graphics, human-computer interaction (HCI), networking, ubiquitous computing, and databases, and draws on previous work in all those areas. We design and experiment with multidevice, multiuser environments based on a new architecture that makes it easy to create and add new display and input devices, to move work of all kinds from one computing device to another, and to support and facilitate group interactions. In the same way that today's standard operating systems make it feasible to write single-workstation software that uses multiple devices and networked resources, we are constructing a higher level operating system for the world of ubiquitous computing. We combine research on infrastructure (ways of flexibly configuring and connecting devices, processes, and communication links) with research on HCI (ways of interacting with heterogeneous changing collections of devices with multiple modalities).