Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases

OBJECTIVE Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. DESIGN The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. MEASUREMENTS The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. RESULTS Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. CONCLUSION A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.
A short review and primer on cardiovascular signals in human computer interaction applications

The use of psychophysiologic signals in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of different physiological signals and analysis techniques. Cardiovascular signals such as heart rate variability and blood pressure variability are commonly used in psychophysiology in order to investigate phenomena such as mental workload. In this paper we present a short review of different cardiovascular metrics useful in the context of human-computer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We emphasise everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, with 12 similar chapters, plus application guidelines and systematic review. Any citation to this paper should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016. this http URL
A Bandit Framework for Strategic Regression

We consider a learner's problem of acquiring data dynamically for training a regression model, where the training data are collected from strategic data sources. A fundamental challenge is to incentivize data holders to exert effort to improve the quality of their reported data, despite that the quality is not directly verifiable by the learner. In this work, we study a dynamic data acquisition process where data holders can contribute multiple times. Using a bandit framework, we leverage on the long-term incentive of future job opportunities to incentivize high-quality contributions. We propose a Strategic Regression-Upper Confidence Bound (SR-UCB) framework, an UCB-style index combined with a simple payment rule, where the index of a worker approximates the quality of his past contributions and is used by the learner to determine whether the worker receives future work. For linear regression and certain family of non-linear regression problems, we show that SR-UCB enables a $O(\sqrt{\log T/T})$-Bayesian Nash Equilibrium (BNE) where each worker exerting a target effort level that the learner has chosen, with $T$ being the number of data acquisition stages. The SR-UCB framework also has some other desirable properties: (1) The indexes can be updated in an online fashion (hence computationally light). (2) A slight variant, namely Private SR-UCB (PSR-UCB), is able to preserve $(O(\log^{-1} T), O(\log^{-1} T))$-differential privacy for workers' data, with only a small compromise on incentives (achieving $O(\log^{6} T/\sqrt{T})$-BNE).
Comparison of fecal samples collected per rectum and off the ground for estimation of environmental contamination attributable to beef cattle.

OBJECTIVES To determine whether sampling feces off the ground replicates prevalence estimates for specific pathogens obtained from fecal samples collected per rectum of adult cows, and to determine characteristics of feces on the ground (fecal pats) that are associated with subsequent identification of Campylobacter spp, Cryptosporidium parvum, and Giardia duodenalis. ANIMALS A random sample of adult beef cattle from 25 herds located throughout California. PROCEDURE 1,115 rectal and ground fecal samples were obtained. Samples were submitted for culture of Campylobacter spp and examined, using a direct fluorescent antibody assay, to detect C parvum oocysts and G duodenalis cysts. Characteristics of fecal pats, such as volume and consistency, were recorded. RESULTS Prevalence of Campylobacter spp was 5.0% (20/401) for rectal fecal samples, which was significantly greater than prevalence determined for ground fecal samples (2/402; 0.5%). Most isolates were C jejuni subsp jejuni. Prevalence of C parvum was higher in rectal fecal samples (6/557; 1.1%) than in ground fecal samples (1/558; 0.2%), but this difference was not significant. Prevalence of G duodenalis did not differ for rectal (36/557; 6.5%) versus ground (26/558; 4.7%) fecal samples. CONCLUSIONS AND CLINICAL RELEVANCE Evaluation of ground fecal samples may not accurately indicate the prevalence of Campylobacter spp or C parvum in cattle but may reflect prevalence of G duodenalis. Differences in prevalence estimates between the 2 methods suggest inactivation of pathogens in feces after cattle have defecated. Prevalence estimates generated by evaluation of ground fecal samples, however, may more accurately estimate environmental pathogen burden.
Associations between UCP1 -3826A/G, UCP2 -866G/A, Ala55Val and Ins/Del, and UCP3 -55C/T Polymorphisms and Susceptibility to Type 2 Diabetes Mellitus: Case-Control Study and Meta-Analysis

Background Some studies have reported associations between five uncoupling protein (UCP) 1–3 polymorphisms and type 2 diabetes mellitus (T2DM). However, other studies have failed to confirm the associations. This paper describes a case-control study and a meta-analysis conducted to attempt to determine whether the following polymorphisms are associated with T2DM: -3826A/G (UCP1); -866G/A, Ala55Val and Ins/Del (UCP2) and -55C/T (UCP3). Methods The case-control study enrolled 981 T2DM patients and 534 nondiabetic subjects, all of European ancestry. A literature search was run to identify all studies that investigated associations between UCP1–3 polymorphisms and T2DM. Pooled odds ratios (OR) were calculated for allele contrast, additive, recessive, dominant and co-dominant inheritance models. Sensitivity analyses were performed after stratification by ethnicity. Results In the case-control study the frequencies of the UCP polymorphisms did not differ significantly between T2DM and nondiabetic groups (P>0.05). Twenty-three studies were eligible for the meta-analysis. Meta-analysis results showed that the Ala55Val polymorphism was associated with T2DM under a dominant model (OR = 1.27, 95% CI 1.03–1.57); while the -55C/T polymorphism was associated with this disease in almost all genetic models: allele contrast (OR = 1.17, 95% CI 1.02–1.34), additive (OR = 1.32, 95% CI 1.01–1.72) and dominant (OR = 1.18, 95% CI 1.02–1.37). However, after stratification by ethnicity, the UCP2 55Val and UCP3 -55C/T alleles remained associated with T2DM only in Asians (OR = 1.25, 95% CI 1.02–1.51 and OR = 1.22, 95% CI 1.04–1.44, respectively; allele contrast model). No significant association of the -3826A/G, -866G/A and Ins/Del polymorphisms with T2DM was observed. Conclusions In our case-control study of people with European ancestry we were not able to demonstrate any association between the UCP polymorphisms and T2DM; however, our meta-analysis detected a significant association between the UCP2 Ala55Val and UCP3 -55C/T polymorphisms and increased susceptibility for T2DM in Asians.
Achieving Optimal Performance by Using the IEEE 802.11 MAC Protocol With Service Differentiation Enhancements

Wireless local area networks are currently evolving to support adequate degrees of service differentiation. Work is in progress to define an enhanced version of the IEEE 802.11 distributed coordination function, which is capable of supporting quality of service for multimedia traffic at the medium access control layer. In this paper, we aim at gaining insight into one mechanism to differentiate among traffic categories, i.e., differentiating the minimum contention window sizes according to the priority of different traffic categories. The contribution of this paper is the analysis of the optimal operation point where maximum throughput can be achieved. Through the analysis, we answer some fundamental questions about the existence and uniqueness of the optimal operation point, about the maximum system throughput, about the existence of simple rules to decide if the system operates under the optimal state or not, and about procedures to lead the system to the optimal operation point. The other contribution is the proposal of a simple adaptive scheme that makes the system operate under the optimal operation point and, at the same time, achieve target service differentiation between different traffic flows. The results that are obtained in this paper are relevant to both theoretical research and implementations of real systems
Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model

We examine the statistics of natural monochromatic images decomposed using a multi-scale wavelet basis. Although the coefficients of this representation are nearly decorrelated, they exhibit important higher-order statistical dependencies that cannot be eliminated with purely linear procssing. In particular, rectified coefficients corresponding to basis functions at neighboring spatial positions, orientations and scales are highly correlated. A method of removing these dependencies is to divide each coefficient by a weighted combination of its rectified neighbors. Several successful models of the steady -state behavior of neurons in primary visual cortex are based on such "divisive normalization" computations, and thus our analysis provides a theoretical justification for these models. Perhaps more importantly, the statistical measurements explicitly specify the weights that should be used in computing the normalization signal. We demonstrate that this weighting is qualitatively consistent with recent physiological experiments that characterize the suppressive effect of stimuli presented outside of the classical receptive field. Our observations thus provide evidence for the hypothesis that early visual neural processing is well matched to these statistical properties of images.
Optimal External Memory Interval Management

In this paper we present the external interval tree, an optimal external memory data structure for answering stabbing queries on a set of dynamically maintained intervals. The external interval tree can be used in an optimal solution to the dynamic interval management problem, which is a central problem for object-oriented and temporal databases and for constraint logic programming. Part of the structure uses a weight-balancing technique for efficient worst-case manipulation of balanced trees, which is of independent interest. The external interval tree, as well as our new balancing technique, have recently been used to develop several efficient external data structures.
Linguistic Typology

An elicitation task was conducted with speakers of Japonic varieties to investigate whether stimuli of varying sensory modalities (e.g. audio, visual, tactile etc.) were more or less likely to elicit ideophones or iconic words. Stimuli representing sounds, movements, shapes and textures were most likely to elicit ideophones, and this is posited to reflect the relative ease or naturalness with which these domains can be mapped iconically to speech. The results mirror macro-level patterns of linguistic diversity, as these are also domains in which ideophones are most widely attested cross-linguistically. The findings call for the revision of a previously constructed implicational hierarchy for the semantic development of ideophone systems, adding to it the categories of FORM and TEXTURE as domains inwhich ideophones aremost likely to develop, after SOUND and MOVEMENT. Independent evidence for the revised hierarchy comes from a semantic analysis of the elicited ideophones, where it was found that domains early in the hierarchy were more likely to be sources for semantic extension, while later domains were more likely to be targets. These findings are expected to be replicable for other languages, and offer exciting new directions for research into the semantic typology of ideophones.
Phase II Trial of Temozolomide and Sorafenib in Advanced Melanoma Patients with or without Brain Metastases

Purpose: The combination of the oral alkylating agent temozolomide and the oral multikinase inhibitor sorafenib was evaluated in advanced melanoma patients. Experimental Design: Patients with metastatic melanoma (n = 167) were treated on four arms. All patients received sorafenib at 400 mg p.o. twice daily without interruption. Patients without brain metastases or prior temozolomide were randomized between arm A: extended dosing of temozolomide (75 mg/m2 temozolomide daily for 6 of every 8 weeks) and arm B: standard dosing (150 mg/m2 temozolomide daily for 5 of every 28 days). Patients previously treated with temozolomide were enrolled on arm C: extended dosing of temozolomide. Patients with brain metastases and no prior temozolomide were assigned to arm D: standard dosing. The primary end point was 6-month progression-free survival (PFS) rate. Secondary end points included response rate, toxicity rates, and the rates of BRAF or NRAS mutations. Results: The 6-month PFS rate for arms A, B, C, and D were 50%, 40%, 11%, and 23%. The median PFS for patients on arm A, B, C, and D was 5.9, 4.2, 2.2, and 3.5 months, respectively. No significant differences were observed between arms A and B in 6-month PFS rate, median PFS, or response rates. Treatment was well tolerated in all arms. No significant differences in toxicity were observed between arms A and B except for more grade 3 to 4 lymphopenia in arm A. Conclusion: Temozolomide plus sorafenib was well tolerated and showed activity in melanoma patients without prior history of temozolomide. The activity of this combination regimen warrants further investigation. (Clin Cancer Res 2009;15(24):7711–8)
Real-time running and jumping pattern generation for bipedal robots based on ZMP and Euler's equations

This paper is presenting a method to generate real-time running and jumping trajectories that can be applied to bipedal humanoid robots. The proposed method is based on maintaining the overall dynamic balance by using the ZMP stability criterion throughout support phases. To be able to reach this goal, we utilize ZMP equations in spherical coordinates, so that the rate change of angular momentum terms in ZMP equations are included naturally by using Eulerian equations of motion for unsymmetrical bodies. Thus, undesired torso angle fluctuation is expected to be more restrainable comparing to other methods in which angular momentum information is ignored or zero-referenced. Applying the aforementioned technique, we primarily simulated running motion on a dynamic 3-D simulator. Secondarily, one-legged jumping experiments were conducted on the actual bipedal robot. In conclusion, we obtained repetitive and successful running and jumping cycles which satisfactorily verify the proposed method.
Implicit Processing of the Eyes and Mouth: Evidence from Human Electrophysiology

The current study examined the time course of implicit processing of distinct facial features and the associate event-related potential (ERP) components. To this end, we used a masked priming paradigm to investigate implicit processing of the eyes and mouth in upright and inverted faces, using a prime duration of 33 ms. Two types of prime-target pairs were used: 1. congruent (e.g., open eyes only in both prime and target or open mouth only in both prime and target); 2. incongruent (e.g., open mouth only in prime and open eyes only in target or open eyes only in prime and open mouth only in target). The identity of the faces changed between prime and target. Participants pressed a button when the target face had the eyes open and another button when the target face had the mouth open. The behavioral results showed faster RTs for the eyes in upright faces than the eyes in inverted faces, the mouth in upright and inverted faces. Moreover they also revealed a congruent priming effect for the mouth in upright faces. The ERP findings showed a face orientation effect across all ERP components studied (P1, N1, N170, P2, N2, P3) starting at about 80 ms, and a congruency/priming effect on late components (P2, N2, P3), starting at about 150 ms. Crucially, the results showed that the orientation effect was driven by the eye region (N170, P2) and that the congruency effect started earlier (P2) for the eyes than for the mouth (N2). These findings mark the time course of the processing of internal facial features and provide further evidence that the eyes are automatically processed and that they are very salient facial features that strongly affect the amplitude, latency, and distribution of neural responses to faces.
A Validation of Object-Oriented Design Metrics as Quality Indicators

This paper presents the results of a study in which we empirically investigated the suite of object-oriented (OO) design metrics introduced in (Chidamber and Kemerer, 1994). More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described in (Li and Henry, 1993) where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known OO analysis/design method and the C++ programming language. Based on empirical and quantitative analysis, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber and Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.
Cross-species queries of large gene expression databases

MOTIVATION Expression databases, including the Gene Expression Omnibus and ArrayExpress, have experienced significant growth over the past decade and now hold hundreds of thousands of arrays from multiple species. Since most drugs are initially tested on model organisms, the ability to compare expression experiments across species may help identify pathways that are activated in a similar way in humans and other organisms. However, while several methods exist for finding co-expressed genes in the same species as a query gene, looking at co-expression of homologs or arbitrary genes in other species is challenging. Unlike sequence, which is static, expression is dynamic and changes between tissues, conditions and time. Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species. RESULTS To facilitate queries in large databases, we developed a new method for comparing expression experiments from different species. We define a distance metric between the ranking of orthologous genes in the two species. We show how to solve an optimization problem for learning the parameters of this function using a training dataset of known similar expression experiments pairs. The function we learn outperforms previous methods and simpler rank comparison methods that have been used in the past for single species analysis. We used our method to compare millions of array pairs from mouse and human expression experiments. The resulting matches can be used to find functionally related genes, to hypothesize about biological response mechanisms and to highlight conditions and diseases that are activating similar pathways in both species. AVAILABILITY Supporting methods, results and a Matlab implementation are available from http://sb.cs. cmu.edu/ExpQ/.
Deep features for breast cancer histopathological image classification

Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives. Recent results on BC recognition show that Convolution Neural Networks (CNN) can achieve higher recognition rates than hand-crafted feature descriptors, but the price to pay is an increase in complexity to develop the system, requiring longer training time and specific expertise to fine-tune the architecture of the CNN. DeCAF (or deep) features consist of an in-between solution it is based on reusing a previously trained CNN only as feature vectors, which is then used as input for a classifier trained only for the new classification task. In the light of this, we present an evaluation of DeCaf features for BC recognition, in order to better understand how they compare to the other approaches. The experimental evaluation shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.