Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks

Cross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts risk of re-admission for a particular set of patients will be more generalizable if developed with data from multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each participating site contributes to model parameter estimation without revealing any patient health information (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-preserving online machine learning with a private Blockchain network, apply transaction metadata to disseminate partial models, and design a new proof-of-information algorithm to determine the order of the online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities such as Patient-Centered Outcomes Research (PCOR)

Finding Related Publications: Extending the Set of Terms Used to Assess Article Similarity.

Recommendation of related articles is an important feature of the PubMed. The PubMed Related Citations (PRC) algorithm is the engine that enables this feature, and it leverages information on 22 million citations. We analyzed the performance of the PRC algorithm on 4584 annotated articles from the 2005 Text REtrieval Conference (TREC) Genomics Track data. Our analysis indicated that the PRC highest weighted term was not always consistent with the critical term that was most directly related to the topic of the article. We implemented term expansion and found that it was a promising and easy-to-implement approach to improve the performance of the PRC algorithm for the TREC 2005 Genomics data and for the TREC 2014 Clinical Decision Support Track data. For term expansion, we trained a Skip-gram model using the Word2Vec package. This extended PRC algorithm resulted in higher average precision for a large subset of articles. A combination of both algorithms may lead to improved performance in related article recommendations

An Improved Vector Quantizer Design Method: the Codebook Reorganization Algorithm

Abstract Generalized Lloyd Algorithm(GLA) is important in vector quantizer design. It runs fast, but it is sensitive to initial conditions and it may find a local optimum. We propose an improved approach based on GLA, named vector quantized codebook reorganization algorithm (VQCRA). VQCRA finds better codebooks in less time, and is insensitive to initial conditions as compared with GLA.. The experiments are performed on the 10 images extracted from USC-SIPI Image Database. The performance improvement ranges from 7.3% (the size of the codebook is 32) to 46.4% (the size of the codebook is 512)

Inhibitory effects of armepavine against hepatic fibrosis in rats

Activation of hepatic stellate cells (HSCs) plays a crucial role in liver fibrogenesis. armepavine (Arm, C19H23O3N), an active compound from Nelumbo nucifera, has been shown to exert immunosuppressive effects on T lymphocytes and on lupus nephritic mice. The aim of this study was to investigate whether Arm could exert anti-hepatic fibrogenic effects in vitro and in vivo. A cell line of rat HSCs (HSC-T6) was stimulated with tumor necrosis factor-α (TNF-α) or lipopolysaccharide (LPS) to evaluate the inhibitory effects of Arm. An in vivo therapeutic study was conducted in bile duct-ligated (BDL) rats. BDL rats were given Arm (3 or 10 mg/kg) by gavage twice daily for 3 weeks starting from the onset of BDL. Liver sections were taken for fibrosis scoring, immuno-fluorescence staining and quantitative real-time mRNA measurements. In vitro, Arm (1-10 μM) concentration-dependently attenuated TNF-α- and LPS-stimulated α-SMA protein expression and AP-1 activation by HSC-T6 cells without adverse cytotoxicity. Arm also suppressed TNF-α-induced collagen collagen deposition, NFκB activation and MAPK (p38, ERK1/2, and JNK) phosphorylations. In vivo, Arm treatment significantly reduced plasma AST and ALT levels, hepatic α-SMA expression and collagen contents, and fibrosis scores of BDL rats as compared with vehicle treatment. Moreover, Arm attenuated the mRNA expression levels of col 1α2, TGF-β1, TIMP-1, ICAM-1, iNOS, and IL-6 genes, but up-regulated metallothionein genes. Our study results showed that Arm exerted both in vitro and in vivo antifibrotic effects in rats, possibly through anti-NF-κB activation pathways

Impact of emotional and motivational regulation on putting performance: a frontal alpha asymmetry study

Background The efficacy of emotional and motivational regulation can determine athletic performance. Giving the short duration and fast changing nature of emotions experienced by athletes in competition, it is important to examine the temporal dynamics of emotional and motivational regulation. The aim of this study was to investigate emotional and motivational regulation as measured by frontal alpha asymmetry in skilled golfers during putting performance after a performance failure. Methods Twenty skilled university golfers were recruited and requested to perform 40 putts at an individualized difficulty level of 40–60% successful putting rate. Trials immediately after a failed putt were selected for analysis. Successful performances were those trials where a hole was and unsuccessful performances were those that failed. The frontal alpha asymmetry index of LnF4-LnF3 was derived for statistical analysis. Results (1) Successful performance was preceded by a larger frontal alpha asymmetry index at T2 than that of T1, and (2) a larger frontal alpha asymmetry index was observed for unsuccessful performance than for successful performance at T1. Discussion The results suggest that successful emotional and motivational regulation was characterized by a progressive increase of frontal alpha asymmetry, which led to subsequent putting success when facing an emotionally provocative putting failure. These findings shed light on the application of frontal alpha asymmetry for the understanding and enhancement of emotional and motivational regulation during sport performance

Impact of emotional and motivational regulation on putting performance: a frontal alpha asymmetry study

Chen T-T, Wang K-P, Cheng M-Y, Chang Y-T, Huang C-J, Hung T-M. Impact of emotional and motivational regulation on putting performance: a frontal alpha asymmetry study. PeerJ. 2019;7: e6777.**Background** The efficacy of emotional and motivational regulation can determine athletic performance. Giving the short duration and fast changing nature of emotions experienced by athletes in competition, it is important to examine the temporal dynamics of emotional and motivational regulation. The aim of this study was to investigate emotional and motivational regulation as measured by frontal alpha asymmetry in skilled golfers during putting performance after a performance failure. **Methods** Twenty skilled university golfers were recruited and requested to perform 40 putts at an individualized difficulty level of 40–60% successful putting rate. Trials immediately after a failed putt were selected for analysis. Successful performances were those trials where a hole was and unsuccessful performances were those that failed. The frontal alpha asymmetry index of LnF4-LnF3 was derived for statistical analysis. **Results** (1) Successful performance was preceded by a larger frontal alpha asymmetry index at T2 than that of T1, and (2) a larger frontal alpha asymmetry index was observed for unsuccessful performance than for successful performance at T1. **Discussion** The results suggest that successful emotional and motivational regulation was characterized by a progressive increase of frontal alpha asymmetry, which led to subsequent putting success when facing an emotionally provocative putting failure. These findings shed light on the application of frontal alpha asymmetry for the understanding and enhancement of emotional and motivational regulation during sport performance