Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk.

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

BLOOM: BLoom filter based oblivious outsourced matchings.

BACKGROUND: Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. METHODS: We propose FHE-BLOOM and PHE-BLOOM, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-BLOOM is fully secure in the semi-honest model while PHE-BLOOM slightly relaxes security guarantees in a trade-off for highly improved performance. RESULTS: We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-BLOOM is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. CONCLUSIONS: Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-BLOOM, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-BLOOM, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.

Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening.

BACKGROUND: Language reflects the state of one's mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one's natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis. OBJECTIVE: The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation. METHODS: The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs. RESULTS: The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like "sad", "unworthy", or "tired" which are almost always accompanied by personal pronouns, such as "I", "I'm" or "my" and in Filipino, "ako" or "ko". For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version. CONCLUSIONS: The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms.

Psychologist in a pocket: towards depression screening on mobile phones.

Depression is the most prevalent clinical disorder and one of the main causes of disability. This makes early detection of depressive symptoms critical in its prevention and management. This paper presents and discusses the development of Psychologist in a Pocket (PiaP), a mental mHealth application for Android which screens and monitors for these symptoms, and-given the explicit permission of the user-alerts a trusted contact such as the mental health professional or a close friend, if it detects symptoms. All text inputted electronically-such as short message services, emails, social network posts-is analyzed based on keywords related to depression based on DSM-5 and ICD criteria as well as Beck's Cognitive Theory of Depression and the Self-Focus Model. Data evaluation and collection happen in the background, on-device, without requiring any user involvement. Currently, the application is in an early prototype phase entering initial clinical validation.

Towards Bringing EEG Research and Diagnostics out of the Lab.

Bringing brain research tools like EEG devices out of the lab into the pockets of practitioners and researchers may fundamentally change the way we perform diagnostics and research. While most of the current techniques are limited to research clinics and require excessive set-up, new consumer EEG devices connected to standard, off-the-shelf mobile devices allow us to lift these limitations. This allows neuropsychological assessment and research in mobile settings, possibly even in remote areas with limited accessibility and infrastructure, thus bringing the equipment to the patient, instead of bringing the patient to the equipment. We are developing an Android based mobile framework to perform EEG studies. By connecting a mobile consumer EEG headset directly to an unmodified mobile device, presenting auditory and visual stimuli, as well as user interaction, we create a self-contained experimental platform. We complement this platform by a toolkit for immediate evaluation of the recorded data directly on the device, even without Internet connectivity. Initial results from the replication of two Event Related Potentials studies indicate the feasibility of the approach.

Ratpack: wearable sensor networks for animal observation.

The goal of our project is to describe the behavior of rats. For this purpose we are using wireless sensor networks, monitoring various quantities that yield important information to complement current knowledge on the behavioral repertoire of rats. So far, on the sensing and processing side we have developed innovative, minimalist approaches pointing in two directions: vocalization analysis and movement tracking. On the data collection and routing side we have adapted to the known burrowing habits of rats by developing new methods for synchronization and data aggregation under the paradigm of sporadic connectivity in a sparse, dynamic network.