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Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. [ABSTRACT FROM AUTHOR]

Background: There is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way. Methods: A checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument. Results: The scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%. Conclusions: A reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review. [ABSTRACT FROM AUTHOR]

Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews. [ABSTRACT FROM AUTHOR]

University scientific research ability is an important indicator to express the strength of universities. In this paper, the evaluation of university scientific research ability is investigated based on the output of sci-tech papers. Four university alliances from North America, UK, Australia, and China, are selected as the case study of the university scientific research evaluation. Data coming from Thomson Reuters InCites are collected to support the evaluation. The work has contributed new framework to the issue of university scientific research ability evaluation. At first, we have established a hierarchical structure to show the factors that impact the evaluation of university scientific research ability. Then, a new MCDM method called D-AHP model is used to implement the evaluation and ranking of different university alliances, in which a data-driven approach is proposed to automatically generate the D numbers preference relations. Next, a sensitivity analysis has been given to show the impact of weights of factors and sub-factors on the evaluation result. At last, the results obtained by using different methods are compared and discussed to verify the effectiveness and reasonability of this study, and some suggestions are given to promote China’s scientific research ability. [ABSTRACT FROM AUTHOR]

Background: Sponsors that seek to commercialize new drugs apply to the Food and Drug Administration (FDA) which independently analyzes the raw data and reports the results on its website. Objectives: This study sought to determine if there are differences between the FDA assessments and journal reports on biologic agents developed for the treatment of rheumatoid arthritis. Methods: Available data on FDA-approved drugs were extracted from the website, and a systematic literature search was conducted to identify matching studies in peer-reviewed medical journals. Outcome measures were the American College of Rheumatology response criteria ACR20 (efficacy) and withdrawal due to adverse events (safety). As effect size odds ratios were estimated for each active trial arm vs. control arm (i.e. for both sources: FDA and journal report), followed by calculation of the ratios of the FDA and journal report odds ratios. A ratio of odds ratios not equal to 1 was categorized as a discrepancy. Results: FDA reports were available for 8 of 9 FDA-approved biologic agents for rheumatoid arthritis; all identified trials (34) except one were published in peer-reviewed journals. Overall, discrepancies were noted for 20 of the 33 evaluated trials. Differences in the apparent benefit reporting were found in 39% (24/61) pairwise comparisons and in 11 cases these were statistically significant; the FDA report showed greater benefit than the journal publication in 15 comparisons and lesser benefit in 9. Differences in the reported harms were found in 51% (28/55) pairwise comparisons and were statistically significant in 5. The “signal” in FDA reports showed a less harmful effect than the journal publication in 17 comparisons whereas a more harmful effect in 11. The differences were attributed to differences in analytic approach, patient inclusion, rounding effect, and counting discrepancies. However, no differences were categorized as critical. Conclusion: There was no empirical evidence to suggest biased estimates between the two sources. Increased and detailed transparency in publications would improve the understanding and credibility of published results. Further, the FDA report was found to be a useful source when data are missing in the published report (i.e. reporting bias). [ABSTRACT FROM AUTHOR]

Basic color terms used in Mandarin Chinese have been controversial since first discussed by Berlin and Kay in 1969. Previous studies showed much inconsistency on what should be considered as basic color terms in Mandarin Chinese. In the present study, we investigated categories of color rather than merely the color terms used by Taiwanese native Mandarin speakers. Using samples conforming to the Berlin and Kay survey, various colors were chosen from a collection of Natural Color System (NCS) colored papers and mounted on a piece of neutral gray card. The card was then mounted on a touch-screen, under D65 illumination. Thirty-two single-character color related Mandarin terms were selected from a Chinese character database according to frequency of use. Participants were required to select the color sample that matched the term by pressing a virtual button on the touch screen. The results show that certain terms can be directly correlated to basic color terms in English, comparable with the results of Berlin and Kay’s original study and those that followed. However, some terms, such as Mo (墨 ink), Tie (鐵 iron), and Cai (菜vegetable), show a wide spread of term maps and inconsistent use among subjects. Principle component analysis (PCA) procedures were used to analysis the commodity of data among subjects. The findings suggest that the basic color categories among Mandarin Chinese speakers are similar to those found in the World Color Survey (WCS), but are represented by wide-spread and inconsistent color terms among speakers. [ABSTRACT FROM AUTHOR]

Objective: Detection of vascular endothelial growth factor (VEGF) levels in ocular tissue may perhaps provide insight into the role of VEGF in the pathogenesis and progression of diabetic retinopathy (DR). The aim of this study was to evaluate the levels of VEGF in tears and serum amongst type 2 diabetes mellitus (DM) patients. Methods: A comparative cross-sectional study was conducted between August 2016 and May 2018 involving type 2 DM patients with no DR, non-proliferative DR (NPDR), and proliferative DR (PDR). Tear samples were collected using no.41 Whatman filter paper (Schirmer strips) and 5 mL blood samples were drawn by venous puncture. VEGF levels in tears and serum were measured by enzyme-linked immunosorbent assay. Results: A total of 88 type 2 DM patients (no DR: 30 patients, NPDR: 28 patients, PDR: 30 patients) were included in the study. Mean tear VEGF levels were significantly higher in the NPDR and PDR groups (114.4 SD 52.5 pg/mL and 150.8 SD 49.7 pg/mL, respectively) compared to the no DR group (40.4 SD 26.5 pg/mL, p < 0.001). There was no significant difference in the mean serum VEGF levels between the three groups. There was a fair correlation between serum and tear VEGF levels (p = 0.015, r = 0.263). Conclusion: VEGF levels in tears were significantly higher amongst diabetic patients with DR compared to those without DR and were significantly associated with the severity of DR. There was a fair correlation between serum and tear VEGF levels. Detection of VEGF in tears is a good non-invasive predictor test for the severity of DR. A large cohort study is needed for further evaluation. [ABSTRACT FROM AUTHOR]

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

Objective: Studies suggest that parenteral MTX may be more efficacious than the oral form at equivalent doses for the treatment of rheumatoid arthritis. We carried out a meta-analysis to compare the efficacy of oral versus parenteral MTX in RA. Methods: PubMed, Web of Science and Embase were systematically searched from inception to June 8th 2017 and reviewed following PRISMA 2009 guidelines, by two independent reviewers. To be included, trials had to study adults with RA randomized to the same dose of either oral or parenteral MTX. The primary endpoint was ACR20 at 6 months. Intention-to-treat analysis results were used when possible. Data from direct comparisons between oral and parenteral methotrexate quantitatively analyzed using maximum likelihood random effects meta-analysis. Relative treatment effects were generated as an odds ratio [OR] (OR>1 indicated a benefit for parenteral therapy). Results: The search yielded 357 papers or abstracts. After review of titles or abstracts and full text papers, we found 4 that met inclusion criteria with 703 patients randomized. Dose of MTX started at 15mg/week and increased up to 25mg/week. The summary OR for achieving ACR20 using parenteral vs. oral MTX was 3.02 (95% CI 1.41, 6.46), with no significant difference in the risk for all adverse events. Conclusion: Parenteral MTX therapy had significantly higher odds than oral MTX of achieving reduction in disease activity. We propose that parenteral MTX is more effective than weekly oral MTX; its widespread use may lead to better control of disease and a decrease in demand for biologic agents. [ABSTRACT FROM AUTHOR]

Complex network theory has been successful at unveiling the topology of the brain and showing alterations to the network structure due to brain disease, cognitive function and behavior. Functional connectivity networks (FCNs) represent different brain regions as the nodes and the connectivity between them as the edges of a graph. Graph theoretic measures provide a way to extract features from these networks enabling subsequent characterization and discrimination of networks across conditions. However, these measures are constrained mostly to binary networks and highly dependent on the network size. In this paper, we propose a novel graph-to-signal transform that overcomes these shortcomings to extract features from functional connectivity networks. The proposed transformation is based on classical multidimensional scaling (CMDS) theory and transforms a graph into signals such that the Euclidean distance between the nodes of the network is preserved. In this paper, we propose to use the resistance distance matrix for transforming weighted functional connectivity networks into signals. Our results illustrate how well-known network structures transform into distinct signals using the proposed graph-to-signal transformation. We then compute well-known signal features on the extracted graph signals to discriminate between FCNs constructed across different experimental conditions. Based on our results, the signals obtained from the graph-to-signal transformation allow for the characterization of functional connectivity networks, and the corresponding features are more discriminative compared to graph theoretic measures. [ABSTRACT FROM AUTHOR]

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period−cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005–2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies. [ABSTRACT FROM AUTHOR]

The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development. [ABSTRACT FROM AUTHOR]

This paper proposes a mixture regression model-based method for drug sensitivity prediction. The proposed method explicitly addresses two fundamental issues in drug sensitivity prediction, namely, population heterogeneity and feature selection pertaining to each of the subpopulations. The mixture regression model is estimated using the imputation-conditional consistency algorithm, and the resulting estimator is consistent. This paper also proposes an average-BIC criterion for determining the number of components for the mixture regression model. The proposed method is applied to the CCLE dataset, and the numerical results indicate that the proposed method can make a drastic improvement over the existing ones, such as random forest, support vector regression, and regularized linear regression, in both drug sensitivity prediction and feature selection. The p-values for the comparisons in drug sensitivity prediction can reach the order O(10−8) or lower for the drugs with heterogeneous populations. [ABSTRACT FROM AUTHOR]

Co-diagnosis of HIV and tuberculosis presents a treatment dilemma. Starting both treatments at the same time can cause a flood of immune response called immune reconstitution inflammatory syndrome (IRIS) which can be lethal. But, how long to delay HIV treatment is less understood. In 2011, based on the conclusions of three separate studies, WHO recommended starting HIV treatment earlier for those with later HIV disease progression. This paper conducts a replication study of one of the three studies, by Havlir and colleagues. Using their publicly available data, we were able to replicate most of the results presented in the original paper. In our measurement and estimation analyses we use different estimation techniques to assess the robustness of the results. We find that adjusting for loss to follow-up does not affect the main results of the paper. However, an ANCOVA estimation and an instrumental variable model weaken the main result of the paper of better outcomes with early HIV treatment only for those who are sicker, reducing significance from the 5% to the 10% level. A change-point analysis also detects no changes in effect by timing of HIV treatment initiation or different thresholds of CD4 count for the primary outcome. This result suggests that the choice of start time for HIV treatment initiation should be based on other factors including potential drug interactions, overlapping side effects, a high pill burden and severity of illness rather than CD4 threshold and preset timeframes. While we caution against overgeneralizing, the result of this replication is aligned with more recent studies that show no evidence that early initiation of HIV treatment reduces mortality for any patients. [ABSTRACT FROM AUTHOR]

Introduction: In this paper we perform a replication analysis of “Effect of a cash transfer programme for schooling on prevalence of HIV and herpes simplex type 2 in Malawi: a cluster randomised trial” by Sarah Baird and others published in “The Lancet” in 2012. The original study was a two-year cluster randomized intervention trial of never married girls aged 13–22 in Malawi. Enumeration areas were randomized to either an intervention involving cash transfer (conditional or unconditional of school enrollment) or control. The study included 1708 Malawian girls, who were enrolled at baseline and had biological testing for HIV and herpes simplex virus type 2 (HSV-2) at 18 months. The original findings showed that in the cohort of girls enrolled in school at baseline, the intervention had an effect on school enrollment, sexual outcomes, and HIV and HSV-2 prevalence. However, in the baseline school dropout cohort, the original study showed no intervention effect on HIV and HSV-2 prevalence. Methods: We performed a replication of the study to investigate the consistency and robustness of key results reported. A pre-specified replication plan was approved and published online. Cleaned data was obtained from the original authors. A pure replication was conducted by reading the methods section and reproducing the results and tables found in the original paper. Robustness of the results were examined with alternative analysis methods in a measurement and estimation analysis (MEA) approach. A theory of change analysis was performed testing a causal pathway, the effect of intervention on HIV awareness, and whether the intervention effect depended on the wealth of the individual. Results: The pure replication found that other than a few minor discrepancies, the original study was well replicated. However, the randomization and sampling weights could not be verified due to the lack of access to raw data and a detailed sample selection plan. Therefore, we are unable to determine how sampling influenced the results, which could be highly dependent on the sample. In MEA it was found that the intervention effect on HIV prevalence in the baseline schoolgirls cohort was somewhat sensitive to model choice, with a non-significant intervention effect for HIV depending on the statistical model used. The intervention effect on HSV-2 prevalence was more robust in terms of statistical significance, however, the odds ratios and confidence intervals differed from the original result by more than 10%. A theory of change analysis showed no effect of intervention on HIV awareness. In a causal pathway analysis, several variables were partial mediators, or potential mediators, indicating that the intervention could be working through its effect on school enrollment or selected sexual behaviors. Conclusions: The effect of intervention on HIV prevalence in the baseline schoolgirls was sensitive to the model choice; however, HSV-2 prevalence results were confirmed. We recommend that the results from the original published analysis indicating the impact of cash transfers on HIV prevalence be treated with caution. [ABSTRACT FROM AUTHOR]

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers. [ABSTRACT FROM AUTHOR]

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks. [ABSTRACT FROM AUTHOR]

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a disease which is widely diagnosed using (colour fundus) images. Efficiency and accuracy are critical in diagnosing DR as lack of timely intervention can lead to irreversible visual impairment. In this paper, we examine strategies for scrutinizing images which affect diagnostic performance of medical practitioners via an eye-tracking study. A total of 56 subjects with 0 to 18 years of experience participated in the study. Every subject was asked to detect DR from 40 images. The findings indicate that practitioners use mainly two types of strategies characterized by either higher dwell duration or longer track length. The main findings of the study are that higher dwell-based strategy led to higher average accuracy (> 85%) in diagnosis, irrespective of the expertise of practitioner; whereas, the average obtained accuracy with a long-track length-based strategy was dependent on the expertise of the practitioner. In the second part of the paper, we use the experimental findings to recommend a scanning strategy for fast and accurate diagnosis of DR that can be potentially used by image readers. This is derived by combining the eye-tracking gaze maps of medical experts in a novel manner based on a set of rules. This strategy requires scrutiny of images in a manner which is consistent with spatial preferences found in human perception in general and in the domain of fundus images in particular. The Levenshtein distance-based assessment of gaze patterns also establish the effectiveness of the derived scanning pattern and is thus recommended for image readers. [ABSTRACT FROM AUTHOR]

In this paper we investigate the changes in the functional connectivity intensity, and some related properties, in healthy people, across the life span and at resting state. For the explicit computation of the functional connectivity we exploit a recently proposed model, that bases not only on the correlations data provided by the acquisition equipment, but also on different parameters, such as the anatomical distances between nodes and their degrees. The leading purpose of the paper is to show that the proposed approach is able to recover the main aspects of resting state condition known from the available literature, as well as to suggest new insights, perspectives and speculations from a neurobiological point of view. Our study involves 133 subjects, both males and females of different ages, with no evidence of neurological diseases or systemic disorders. First, we show how the model applies to the sample, where the subjects are grouped into 28 different groups (14 of males and 14 of females), according to their age. This leads to the construction of two graphs (one for males and one for females), that can be realistically interpreted as representative of the neural network during the resting state. Second, following the idea that the brain network is better understood by focusing on specific nodes having a kind of centrality, we refine the two output graphs by introducing a new metric that favours the selection of nodes having higher degrees. As a third step, we extensively comment and discuss the obtained results. In particular, it is remarkable that, despite a great overlapping exists between the outcomes concerning males and females, some intriguing differences appear. This motivates a deeper local investigation, which represents the fourth part of the paper, carried out through a thorough statistical analysis. As a result, we are enabled to support that, for two special age groups, a few links contribute in differentiating the behaviour of males and females. In addition, we performed an average-based comparison between the proposed model and the traditional statistical correlation-based approach, then discussing and commenting the main outlined discrepancies. [ABSTRACT FROM AUTHOR]

In clinical neuropsychology the cognitive abilities of neurological patients are commonly estimated using well-established paper-based tests. Typically, scores on some tests remain relatively well preserved, whilst others exhibit a significant and disproportionate decline. Scores on those tests that measure preserved cognitive functions (so-called ‘hold’ tests) may be used to estimate premorbid abilities, including scores in non-hold tests that would have been expected prior to the onset of cognitive impairment. Many hold tests entail word reading, with each word being graded as correctly or incorrectly pronounced. Inevitably, such tests are likely to contain words that provide little or no diagnostic power (i.e., can be eliminated without negatively affecting prediction accuracy). In this paper, a genetic algorithm is developed and demonstrated, using n = 92 neurologically healthy participants, to identify optimal word subsets from the National Adult Reading Test that minimize the mean error in predicting the most widely used clinical measure of IQ and cognitive ability, the Wechsler Adult Intelligence Scale Fourth Edition IQ. In addition to requiring only 17–20 of the original 50 words (suggesting that this test could be revised to be up to 66% shorter) and minimizing mean prediction error, the algorithm increases the proportion of the variance in the predicted variable explained in comparison to using all words (from r2 = 0.46 to r2 = 0.61). In a clinical setting this would improve estimates of premorbid cognitive function and, if an abbreviated revision to this test were to be adopted, reduce the arduousness of the test for patients. The proposed method is evaluated with jackknifing and leave one out cross validation. The general approach may be used to optimize the relationship between any two psychological tests by finding the question subset in one test that minimizes the prediction error in a second test by training the genetic algorithm using data collected from participants upon whom both tests have been administered. This approach may also be used to develop new predictive tests, since it provides a method to identify an optimal subset of a set of candidate questions (for which empirical data have been collected) that maximizes prediction accuracy and the proportion of variance in the predicted variable that can be explained. [ABSTRACT FROM AUTHOR]

This paper describes and presents a stable and reliable set of stereo-photogrammetric normative data for global and spino-pelvic sagittal alignment, as a proven reference system for evaluating/measuring a fully unconstrained natural upright neutral standing attitude in a young healthy adult population. The methodological features described in this article will enable future studies to replicate and/or directly compare a wide range of different postural tests and/or sagittal alignment assessment procedures including the study of sagittal spine shape variations occurring during gait performance. To date, the quantitative evaluation of adult spinal deformity (ASD) has been mainly confined to the X-ray imaging approach and, more recently, to 3D X-ray reconstruction. Within the existing evaluation framework an opportunity exists for an additional approach: a quantitative evaluation procedure which is easy, accurate, relatively speedy and non-ionising, in order to monitor and track the progress of patients in the areas of both surgical and non-surgical treatment. The resources and methodology described in this paper have been proven to meet all these criteria. They have enabled full 3D posture (including 3D spine shape and sagittal alignment of the skeleton) to be consistently and successfully measured in adult volunteers. All the measurement/evaluation procedures and outcomes carried out were based entirely on the new non-ionising 3D opto-electronic stereo-photogrammetric approach described in this article. The protocol for this methodology was based on a standard set of 27 pre-selected anatomical “landmarks” on the human body, providing standard reference points for observation and measurement. A total of 124 healthy subjects were successfully assessed and, for each subject, 27 individual markers were applied to the corresponding locations on his/her body. Statistical tests to investigate gender differences were also carried out. Descriptive statistics are provided for all 15 of the spino-pelvic parameters under consideration. Results indicated significant differences between genders in five sets of parameters: Kyphosis tilt, Head tilt, Pelvic tilt, Spino-pelvic angle and T1-pelvic angle. The data also demonstrate a high degree of congruity with results obtained using the X-ray method, as evidenced by the existing literature in the field. In summary, the current study presents a new stereo-photogrammetric opto-electronic technology which can be used successfully for ASD evaluation and introduces a comprehensive set of normative data analogous to those proposed in X-ray analysis for sagittal spino-pelvic and total body alignment. [ABSTRACT FROM AUTHOR]

Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. ℓ1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by ℓ1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People’s Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method. [ABSTRACT FROM AUTHOR]

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed. [ABSTRACT FROM AUTHOR]

The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative. [ABSTRACT FROM AUTHOR]

Purpose: The 2014 outbreak of Ebola virus disease (EVD) in West Africa was the largest in history. Starting in September 2014, International Medical Corps (IMC) operated five Ebola treatment units (ETUs) in Sierra Leone and Liberia. This paper explores how future infectious disease outbreak facilities in resource-limited settings can be planned, organized, and managed by analyzing data collected on water, sanitation, and hygiene (WASH) and infection prevention control (IPC) protocols. Design/Methodology/Approach: We conducted a retrospective cohort study by analyzing WASH/IPC activity data routinely recorded on paper forms or white boards at ETUs during the outbreak and later merged into a database from two IMC-run ETUs in Sierra Leone between December 2014 and December 2015. Findings: The IMC WASH/IPC database contains data from over 369 days. Our results highlight parameters key to designing and maintaining an ETU. High concentration chlorine solution usage was highly correlated with both daily patient occupancy and high-risk zone staff entries; low concentration chlorine usage was less well explained by these measures. There is high demand for laundering and disinfecting of personal protective equipment (PPE) on a daily basis and approximately 1 (0–4) piece of PPE is damaged each day. Research limitations/Implications: Lack of standardization in the type and format of data collected at ETUs made constructing the WASH/IPC database difficult. However, the data presented here may help inform humanitarian response operations in future epidemics. [ABSTRACT FROM AUTHOR]

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. [ABSTRACT FROM AUTHOR]

Background: In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. Methodology: In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. Results: In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. Conclusions: This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions. [ABSTRACT FROM AUTHOR]

Background: Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. Methods: RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. Results: 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33). Conclusions: This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings. [ABSTRACT FROM AUTHOR]

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands. [ABSTRACT FROM AUTHOR]

Background: The aim of the present systematic review was to evaluate the hypothesis of an association between periodontitis and the development of cancer. Methods: Two reviewers independently screened electronic and manual sources for pertinent articles. Primary outcome measures were the occurrence of neoplasm diagnosis in exposed and non-exposed groups, reported to evaluate association between cancer and periodontitis. Results: Of the 490 initially retrieved papers 10 were included in the qualitative synthesis and eight in the quantitative synthesis; the eight papers covered six studies. Considering hazard ratios, a statistically significant association was found for all cancers studied (1.14; CI 95%: 1.04, 1.24), digestive tract cancer (1.34; CI 95%: 1.05, 1.72), pancreatic cancer (1.74; CI 95%: 1.21, 2.52), prostate cancer (1.25; CI 95%: 1.04, 1.51), breast cancer (1.11; CI 95%: 1.00, 1.23), corpus uteri cancer (2.20; CI 95%: 1.16, 4.18), lung cancer (1.24; CI 95%: 1.06, 1.45), hematological cancer (1.30; CI 95%: 1.11, 1.53), esophagus / oropharyngeal cancer pooled together (2.25; CI 95%: 1.30, 3.90) and Non-Hodgkin lymphoma (1.30; CI 95%: 1.11, 1.52). Conclusions: Despite the sparse scientific evidence and considering the low statistical power of the results, this systematic review revealed a substantial lack of studies with standardized and comparable methods to speculate about the association between periodontitis and cancer; more studies are need in order to explore further the scientific evidence of such correlation. [ABSTRACT FROM AUTHOR]

The “publish or perish” incentive drives many researchers to increase the quantity of their papers at the cost of quality. Lowering quality increases the number of false positive errors which is a key cause of the reproducibility crisis. We adapted a previously published simulation of the research world where labs that produce many papers are more likely to have “child” labs that inherit their characteristics. This selection creates a competitive spiral that favours quantity over quality. To try to halt the competitive spiral we added random audits that could detect and remove labs with a high proportion of false positives, and also improved the behaviour of “child” and “parent” labs who increased their effort and so lowered their probability of making a false positive error. Without auditing, only 0.2% of simulations did not experience the competitive spiral, defined by a convergence to the highest possible false positive probability. Auditing 1.35% of papers avoided the competitive spiral in 71% of simulations, and auditing 1.94% of papers in 95% of simulations. Audits worked best when they were only applied to established labs with 50 or more papers compared with labs with 25 or more papers. Adding a ±20% random error to the number of false positives to simulate peer reviewer error did not reduce the audits’ efficacy. The main benefit of the audits was via the increase in effort in “child” and “parent” labs. Audits improved the literature by reducing the number of false positives from 30.2 per 100 papers to 12.3 per 100 papers. Auditing 1.94% of papers would cost an estimated $15.9 million per year if applied to papers produced by National Institutes of Health funding. Our simulation greatly simplifies the research world and there are many unanswered questions about if and how audits would work that can only be addressed by a trial of an audit. [ABSTRACT FROM AUTHOR]

Using convenient stratification criteria such as geographical regions or other natural conditions like age, gender, etc., is not beneficial in order to maximize the precision of the estimates of variables of interest. Thus, one has to look for an efficient stratification design to divide the whole population into homogeneous strata that achieves higher precision in the estimation. In this paper, a procedure for determining Optimum Stratum Boundaries (OSB) and Optimum Sample Sizes (OSS) for each stratum of a variable of interest in health surveys is developed. The determination of OSB and OSS based on the study variable is not feasible in practice since the study variable is not available prior to the survey. Since many variables in health surveys are generally skewed, the proposed technique considers the readily-available auxiliary variables to determine the OSB and OSS. This stratification problem is formulated into a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. It is then solved for the OSB by using a dynamic programming (DP) technique. A numerical example with a real data set of a population, aiming to estimate the Haemoglobin content in women in a national Iron Deficiency Anaemia survey, is presented to illustrate the procedure developed in this paper. Upon comparisons with other methods available in literature, results reveal that the proposed approach yields a substantial gain in efficiency over the other methods. A simulation study also reveals similar results. [ABSTRACT FROM AUTHOR]

Pruning is applied in order to combat over-fitting problem where the tree is pruned back with the goal of identifying decision tree with the lowest error rate on previously unobserved instances, breaking ties in favour of smaller trees with high accuracy. In this paper, pruning with Bayes minimum risk is introduced for estimating the risk-rate. This method proceeds in a bottom-up fashion converting a parent node of a subtree to a leaf node if the estimated risk-rate of the parent node for that subtree is less than the risk-rates of its leaf. This paper proposes a post-pruning method that considers various evaluation standards such as attribute selection, accuracy, tree complexity, and time taken to prune the tree, precision/recall scores, TP/FN rates and area under ROC. The experimental results show that the proposed method produces better classification accuracy and its complexity is not much different than the complexities of reduced-error pruning and minimum-error pruning approaches. The experiments also demonstrate that the proposed method shows satisfactory performance in terms of precision score, recall score, TP rate, FP rate and area under ROC. [ABSTRACT FROM AUTHOR]

A considerable body of research has documented the negative effects of job burnout on employees and their organizations, emphasizing the importance of the identification of early signs of the phenomenon for the purposes of prevention and intervention. However, such timely identification is difficult due to the time and cost of assessing the burnout levels of all employees in an organization using established scales. In this paper, we propose an innovative way to identify employees at risk of job burnout by analyzing their e-mail communication patterns. Building on the Job Demands–Resources model, we theorize about the relationship between e-mail communication patterns and levels of employee exhaustion and disengagement (two dimensions of burnout). We analyzed 52,190 e-mails exchanged between 57 employees of a medium sized R&D company over a five-month period. We then related these employees’ communication patterns to their levels of burnout, collected using an established scale (the OLBI–Oldenburg Burnout Inventory). Our results provide support for the overall proposition of the paper, that e-mail communications can be used to identify individuals at risk of job burnout. Our models explain up to 34% of the variance of burnout and up to 37% and 19% respectively of the variance of exhaustion and disengagement. They also successfully distinguish between employees with a higher risk of burnout and those with lower levels of risk (F1 score of 84% with recall of 100% and 73% precision). We discuss the implications of our results and present suggestions for future research. [ABSTRACT FROM AUTHOR]

This paper addresses patient-reported outcomes (PROs) and telemonitoring in congestive heart failure (CHF), both increasingly important topics. The interest in CHF trials is shifting from hard end-points such as hospitalization and mortality, to softer end-points such health-related quality of life. However, the relation of these softer end-points to objective parameters is not well studied. Telemonitoring is suitable for collecting both patient-reported outcomes and objective parameters. Most telemonitoring studies, however, do not take full advantage of the available sensor technology and intelligent data analysis. The Chiron clinical observational study was performed among 24 CHF patients (17 men and 7 women, age 62.9 ± 9.4 years, 15 NYHA class II and 9 class III, 10 of ishaemic, aetiology, 6 dilated, 2 valvular, and 6 of multiple aetiologies or cardiomyopathy) in Italy and UK. A large number of physiological and ambient parameters were collected by wearable and other devices, together with PROs describing how well the patients felt, over 1,086 days of observation. The resulting data were mined for relations between the objective parameters and the PROs. The objective parameters (humidity, ambient temperature, blood pressure, SpO2, and sweeting intensity) could predict the PROs with accuracies up to 86% and AUC up to 0.83, making this the first report providing evidence for ambient and physiological parameters to be objectively related to PROs in CHF patients. We also analyzed the relations in the predictive models, gaining some insights into what affects the feeling of health, which was also generally not attempted in previous investigations. The paper strongly points to the possibility of using PROs as primary end-points in future trials. [ABSTRACT FROM AUTHOR]

In his 2016 paper, Myhrvold criticized ours from 2014 on maximum growth rates (Gmax, maximum gain in body mass observed within a time unit throughout an individual’s ontogeny) and thermoregulation strategies (ectothermy, endothermy) of 17 dinosaurs. In our paper, we showed that Gmax values of similar-sized extant ectothermic and endothermic vertebrates overlap. This strongly questions a correct assignment of a thermoregulation strategy to a dinosaur only based on its Gmax and (adult) body mass (M). Contrary, Gmax separated similar-sized extant reptiles and birds (Sauropsida) and Gmax values of our studied dinosaurs were similar to those seen in extant similar-sized (if necessary scaled-up) fast growing ectothermic reptiles. Myhrvold examined two hypotheses (H1 and H2) regarding our study. However, we did neither infer dinosaurian thermoregulation strategies from group-wide averages (H1) nor were our results based on that Gmax and metabolic rate (MR) are related (H2). In order to assess whether single dinosaurian Gmax values fit to those of extant endotherms (birds) or of ectotherms (reptiles), we already used a method suggested by Myhrvold to avoid H1, and we only discussed pros and cons of a relation between Gmax and MR and did not apply it (H2). We appreciate Myhrvold’s efforts in eliminating the correlation between Gmax and M in order to statistically improve vertebrate scaling regressions on maximum gain in body mass. However, we show here that his mass-specific maximum growth rate (kC) replacing Gmax (= MkC) does not model the expected higher mass gain in larger than in smaller species for any set of species. We also comment on, why we considered extant reptiles and birds as reference models for extinct dinosaurs and why we used phylogenetically-informed regression analysis throughout our study. Finally, we question several arguments given in Myhrvold in order to support his results. [ABSTRACT FROM AUTHOR]

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. [ABSTRACT FROM AUTHOR]

Background: Previous epidemiologic studies have reported famine exposure during early life association with overweight or obesity in adulthood, but a consistent perspective has not been established to date. Purpose: To determine, by conducting a systematic review and meta-analysis, whether exposure to famine could increase body mass index (BMI) in adult or not, and assess the association between famine exposure and the risk of overweight or obesity. Methods: Published articles were systematically searched (until August, 2017) from PubMed, ScienceDirect, Cochrane, and China National Knowledge Infrastructure. Initially, comparing differences in BMI between exposed and non-exposed groups that weight mean difference (WMD) were used. Subsequently, the effect of famine exposure on overweight or obesity risk, which pooled relative risks (RRs), odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Result: Twenty studies were included in this systematic review and meta-analysis. Compared with non-exposed group, famine exposure group significantly increased the risk of overweight (OR = 1.10, 95% CI: 1.04–1.16) and obesity (OR = 1.15, 95% CI: 1.05–1.24). Sensitivity analyses revealed no significant change in the famine exposure and BMI, the risk of overweight and obesity study when any one study was excluded. Subgroup analyses showed that age, gender, exposure type, study type, continent, famine cause and paper publication date were associated with BMI, the risk of overweight and obesity. Meta-regression analyses suggested that continent, famine cause could partially explain heterogeneity for famine exposure and BMI studies. Conclusion: The systematic review and meta-analysis indicates that famine exposure during early life may increase BMI, the risk of overweight and obesity, especially for female, fetal famine exposure or subject age less than 50. Furthermore, famine exposure group the risk of overweight and obesity in cross-sectional studies, Asian studies, famine cause by natural disaster or paper published from 2015 to the present studies are higher than that of non-exposed group. [ABSTRACT FROM AUTHOR]

Although racism has been posited as driver of racial/ethnic inequities in healthcare, the relationship between racism and health service use and experience has yet to be systematically reviewed or meta-analysed. This paper presents a systematic review and meta-analysis of quantitative empirical studies that report associations between self-reported racism and various measures of healthcare service utilisation. Data were reviewed and extracted from 83 papers reporting 70 studies. Studies included 250,850 participants and were conducted predominately in the U.S. The meta-analysis included 59 papers reporting 52 studies, which were analysed using random effects models and mean weighted effect sizes. Racism was associated with more negative patient experiences of health services (HSU-E) (OR = 0.351 (95% CI [0.236,0.521], k = 19), including lower levels of healthcare-related trust, satisfaction, and communication. Racism was not associated with health service use (HSU-U) as an outcome group, and was not associated with most individual HSU-U outcomes, including having had examinations, health service visits and admissions to health professionals and services. Racism was associated with health service use outcomes such as delaying/not getting healthcare, and lack of adherence to treatment uptake, although these effects may be influenced by a small sample of studies, and publication bias, respectively. Limitations to the literature reviewed in terms of study designs, sampling methods and measurements are discussed along with suggested future directions in the field. [ABSTRACT FROM AUTHOR]

Publication metadata help deliver rich analyses of scholarly communication. However, research concepts and ideas are more effectively expressed through unstructured fields such as full texts. Thus, the goals of this paper are to employ a full-text enabled method to extract terms relevant to disciplinary vocabularies, and through them, to understand the relationships between disciplines. This paper uses an efficient, domain-independent term extraction method to extract disciplinary vocabularies from a large multidisciplinary corpus of PLoS ONE publications. It finds a power-law pattern in the frequency distributions of terms present in each discipline, indicating a semantic richness potentially sufficient for further study and advanced analysis. The salient relationships amongst these vocabularies become apparent in application of a principal component analysis. For example, Mathematics and Computer and Information Sciences were found to have similar vocabulary use patterns along with Engineering and Physics; while Chemistry and the Social Sciences were found to exhibit contrasting vocabulary use patterns along with the Earth Sciences and Chemistry. These results have implications to studies of scholarly communication as scholars attempt to identify the epistemological cultures of disciplines, and as a full text-based methodology could lead to machine learning applications in the automated classification of scholarly work according to disciplinary vocabularies. [ABSTRACT FROM AUTHOR]

Background and aims: Persistent intestinal damage is associated with higher complication rates in celiac disease. We aimed to assess the potential modifiers of mucosal recovery. Materials and methods: We screened databases (PubMed, Embase, Cochrane Trials, and Web of Science) for papers on celiac disease. Papers discussing (1) celiac patients (2) follow-up biopsy and (3) mucosal recovery after commencement of a gluten-free diet were included. The primary outcome was to produce a comprehensive analysis of complete mucosal recovery (i.e., Marsh 0 on follow-up). We compared children’s recovery ratios to those of adults. Patients following a strict gluten-free dietary regimen were included in a subgroup. Summary point estimates, 95% confidence intervals (CIs), and 95% predictive intervals (PIs) were calculated. Heterogeneity was tested with I2-statistic. The PROSPERO registration number is CRD42016053482. Results: The overall complete mucosal recovery ratio, calculated from 37 observational studies, was 0.36 (CI: 0.28–0.44, PI: -0.12–0.84; I2: 98.4%, p<0.01). Children showed higher complete mucosal recovery ratio than adults (p<0.01): 0.65 (CI: 0.44–0.85, PI: -0.10–1.39; I2: 96.5%, p<0.01) as opposed to 0.24 (CI: 0.15–0.33, PI: -0.19–1.08; I2: 96.3%, p<0.01). In the strict dietary adherence subgroup, complete mucosal recovery ratio was 0.47 (CI: 0.24–0.70, PI: -0.47–1.41; I2: 98.8%, p<0.001). On meta-regression, diagnostic villous atrophy (Marsh 3) ratio (-8.97, p<0.01) and male ratio (+6.04, p<0.01) proved to be a significant determinant of complete mucosal recovery, unlike duration of gluten-free diet (+0.01, p = 0.62). The correlation between complete mucosal recovery ratio and age on diagnosis is of borderline significance (-0.03, p = 0.05). Conclusions: There is considerable heterogeneity across studies concerning complete mucosal recovery ratios achieved by a gluten-free diet in celiac disease. Several celiac patients fail to achieve complete mucosal recovery even if a strict dietary regimen is followed. Younger age on diagnosis, less severe initial histologic damage and male gender predispose for achieving mucosal recovery. [ABSTRACT FROM AUTHOR]

The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify “core documents”. First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers. [ABSTRACT FROM AUTHOR]

Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator. [ABSTRACT FROM AUTHOR]

Many countries have some kind of energy-system transformation either planned or ongoing for various reasons, such as to curb carbon emissions or to compensate for the phasing out of nuclear energy. One important component of these transformations is the overall reduction in energy demand. It is generally acknowledged that the domestic sector represents a large share of total energy consumption in many countries. Increased energy efficiency is one factor that reduces energy demand, but behavioral approaches (known as “sufficiency”) and their respective interventions also play important roles. In this paper, we address citizens’ heterogeneity regarding both their current behaviors and their willingness to realize their sufficiency potentials—that is, to reduce their energy consumption through behavioral change. We collaborated with three Swiss cities for this study. A survey conducted in the three cities yielded thematic sets of energy-consumption behavior that various groups of participants rated differently. Using this data, we identified four groups of participants with different patterns of both current behaviors and sufficiency potentials. The paper discusses intervention types and addresses citizens’ heterogeneity and behaviors from a city-based perspective. [ABSTRACT FROM AUTHOR]

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. [ABSTRACT FROM AUTHOR]

Background: Daodi-herb is a part of Chinese culture, which has been naturally selected by traditional Chinese medicine clinical practice for many years. Sweet wormwood herb is a kind of Daodi-herb, and comes from Artemisia annua L. Artemisinin is a kind of effective antimalarial drug being extracted from A. annua. Because of artemisinin, Sweet wormwood herb earns a reputation. Based on the Pharmacopoeia of the People's Republic of China (PPRC), Sweet wormwood herb can be used to resolve summerheat-heat, and prevent malaria. Besides, it also has other medical efficacies. A. annua, a medicinal plant that is widely distributed in the world contains many kinds of chemical composition. Research has shown that compatibility of artemisinin, scopoletin, arteannuin B and arteannuic acid has antimalarial effect. Compatibility of scopoletin, arteannuin B and arteannuic acid is conducive to resolving summerheat-heat. Chemical constituents in A. annua vary significantly according to geographical locations. So, distribution of A. annua may play a key role in the characteristics of efficacy and chemical constituents of Sweet wormwood herb. It is of great significance to study this relationship. Objectives: We mainly analyzed the relationship between the chemical constituents (arteannuin B, artemisinin, artemisinic acid, and scopoletin) with special efficacy in A. annua that come from different provinces in china, and analyzed the relationship between chemical constituents and spatial distribution, in order to find out the relationship between efficacy, chemical constituents and distribution. Methods: A field survey was carried out to collect A. annua plant samples. A global positioning system (GPS) was used for obtaining geographical coordinates of sampling sites. Chemical constituents in A. annua were determined by liquid chromatography tandem an atmospheric pressure ionization-electrospray mass spectrometry. Relationship between chemical constituents including proportions, correlation analysis (CoA), principal component analysis (PCA) and cluster analysis (ClA) was displayed through Excel and R software version2.3.2(R), while the one between efficacy, chemical constituents and spatial distribution was presented through ArcGIS10.0, Excel and R software. Results: According to the results of CoA, arteannuin B content presented a strong positive correlation with artemisinic acid content (p = 0), and a strong negative correlation with artemisinin content (p = 0). Scopoletin content presented a strong positive correlation with artemisinin content (p = 0), and a strong negative correlation with artemisinic acid content (p = 0). According to the results of PCA, the first two principal components accounted for 81.57% of the total accumulation contribution rate. The contribution of the first principal component is about 45.12%, manly including arteannuin B and artemisinic acid. The contribution of the second principal component is 36.45% of the total, manly including artemisinin and scopoletin. According to the ClA by using the principal component scores, 19 provinces could be divided into two groups. In terms of provinces in group one, the proportions of artemisinin are all higher than 80%. Based on the results of PCA, ClA, percentages and scatter plot analysis, chemical types are defined as "QHYS type", "INT type" and "QHS type." Conclusion: As a conclusion, this paper shows the relationship between efficacy, chemical constituents and distribution. Sweet wormwood herb with high arteannuin B and artemisinic acid content, mainly distributes in northern China. Sweet wormwood herb with high artemisinin and scopoletin content has the medical function of preventing malaria, which mainly distributes in southern China. In this paper, it is proved that Sweet wormwood Daodi herb growing in particular geographic regions, has more significant therapeutical effect and higher chemical constituents compared with other same kind of CMM. And also, it has proved the old saying in China that Sweet wormwood Daodi herb which has been used to resolve summerheat-heat and prevent malaria, which distributed in central China. But in modern time, Daodi Sweet wormwood herb mainly has been used to extract artemisinin and prevent malaria, so the Daod-region has transferred to the southern China. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to elucidate the effects of cholecystectomy on the risk of colorectal cancer (CRC) by conducting a meta-analysis of 10 cohort studies. Methods: The eligible cohort studies were selected by searching the PubMed and EMBASE databases from their origination to June 30, 2016, as well as by consulting the reference lists of the selected articles. Two authors individually collected the data from the 10 papers. When the data showed marked heterogeneity, we used a random-effects model to estimate the overall pooled risk; otherwise, a fixed effects model was employed. Results: The final analysis included ten cohort studies. According to the Newcastle-Ottawa Scale (NOS), nine papers were considered high quality. After the data of these 9 studies were combined, an increased risk of CRC was found among the individuals who had undergone cholecystectomy (risk ratio (RR) 1.22; 95% confidence interval (CI) 1.08–1.38). In addition, we also found a promising increased risk for colon cancer (CC) (RR 1.30, 95% CI 1.07–1.58), but no relationship between cholecystectomy and rectum cancer (RC) (RR 1.09; 95% CI 0.89–1.34) was observed. Additionally, in the sub-group analysis of the tumor location in the colon, a positive risk for ascending colon cancer (ACC) was found (RR 1.18, 95% CI 1.11–1.26). After combining the ACC, transverse colon cancer (TCC), sigmoid colon cancer (SCC) and descending colon cancer (DCC) patients, we found a positive relationship with cholecystectomy (RR 1.18, 95% CI 1.11–1.26). Furthermore, after combining the ACC and DCC patients, we also found a positive relationship with cholecystectomy (RR 1.28; 95% CI 1.11–1.26) in the sub-group analysis. In an additional sub-group analysis of patients from Western countries, there was a positive relationship between cholecystectomy and the risk of CRC (RR 1.20; 95% CI 1.05–1.36). Furthermore, a positive relationship between female gender and CRC was also found (RR 1.17; 95% CI 1.03–1.34). However, there was no relationship between gender and CC or RC. Furthermore, no publication bias was observed, and the sensitivity analysis indicated stable results. Conclusions: This meta-analysis of 10 cohort studies revealed that cholecystectomy is associated with an increased risk for CRC, CC and ACC, particularly in Western countries. No relationship between cholecystectomy and RC was observed. There was no relationship between gender and either CC or RC, but a positive relationship between female gender and CRC was observed. [ABSTRACT FROM AUTHOR]

The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [1946, Journal of the American Statistical Association, Vol 41(236), Page 517- 529]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate(s) whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate(s) for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response. The provided method is more economical than the available estimators under callback methods as we are working sub-sampling and also increase response rate as a stronger mode of interview is employed for data collection. A numerical study using Monte Carlo is presented to illustrate the behavior of the proposed and the efficiency comparison. [ABSTRACT FROM AUTHOR]