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Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity. [ABSTRACT FROM AUTHOR]

Scientific research is often thought of as being conducted by individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex and integrated system of people, papers, and ideas. Studies of co-authorship and citation networks have revealed important structural properties of researchers and articles, but currently the structure of scientific ideas themselves is not well understood. In this study, we posit that topic networks may be a useful framework for revealing the nature of conceptual relationships. Using this framework, we map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000, constructing networks in which nodes represent topics of study and edges give the extent to which topics occur in the same papers. The network displays small-world architecture, characterized by regions of dense local connectivity with sparse connectivity between them. In this network, dense local connectivity additionally gives rise to distinct clusters of related topics. Yet notably, these clusters tend not to align with assigned article classifications, and instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define two measures of interdisciplinarity, one of which is found to be positively associated with PNAS’s impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of energy management of mixed criticality applications in a multi-core partitioned architecture. Instead of focusing on new scheduling algorithms to adjust frequency in order to save energy, we propose a partition to CPU allocation that takes into account not only the different frequencies at which the CPU can operate but the level of criticality of the partitions. The goal is to provide a set of pre-calculated allocations, called profiles, so at run time the system can switch to different modes depending on the battery level. These profiles achieve different levels of energy saving and performance applying different strategies. We also present a comparison in terms of energy saving of the most used bin-packing algorithms for partition allocation. As this is an heuristic, it is not possible to ensure that our results involve the minimum energy consumption. For this reason, we also provide a comparison with a exact method, such as constraint programming. [ABSTRACT FROM AUTHOR]

The research about unmanned aerial vehicle (UAV) swarm has developed rapidly in recent years, especially the UAV swarm with sensors which is becoming common means of achieving situational awareness. Due to inadequate researches of the UAV swarm with complex control structure currently, we propose a patrolling task planning algorithm for the UAV swarm with double-layer centralized control structure under the uncertain and dynamic environment. The main objective of the UAV swarm is to collect environment information as much as possible. To summarized, the primary contributions of this paper are as follows. We first define the patrolling problem. After that, the patrolling problem is modeled as the Partially Observable Markov Decision Process (POMDP) problem. Building upon this, we put forward a myopic and scalable online task planning algorithm. The algorithm contains online heuristic function, sequential allocation method, and the mechanism of bottom-up information flow and top-down command flow, reducing the computation complexity effectively. Moreover, as the number of control layers increases, this algorithm guarantees the performance without increasing the computation complexity for the swarm leader. Finally, we empirically evaluate our algorithm in the specific scenarios. [ABSTRACT FROM AUTHOR]

Background: As statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader. Methods: Statistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles. Results: Of 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal’s website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal’s website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32). Conclusion: These analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation. [ABSTRACT FROM AUTHOR]

This paper presents a method for classifying the ancestry of Brazilian surnames based on historical sources. The information obtained forms the basis for applying fuzzy matching and machine learning classification algorithms to more than 46 million workers in 5 categories: Iberian, Italian, Japanese, German and East European. The vast majority (96.7%) of the single surnames were identified using a fuzzy matching and the rest using a method proposed by Cavnar and Trenkle (1994). A comparison of the results of the procedures with data on foreigners in the 1920 Census and with the geographic distribution of non-Iberian surnames underscores the accuracy of the procedure. The study shows that surname ancestry is associated with significant differences in wages and schooling. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach. [ABSTRACT FROM AUTHOR]

Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. [ABSTRACT FROM AUTHOR]

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results. [ABSTRACT FROM AUTHOR]

This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger’s causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike’s power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems. [ABSTRACT FROM AUTHOR]

The integration of ubiquitous technologies in the field of education has considerably enhanced our way of learning. Such technologies enable students to get a gradual feedback about their performance and to provide adapted learning materials. It is particularly important in the domain of foreign language learning which requires intense daily practice. One of the main inputs of adaptive learning systems is the user’s understanding of a reading material. The reader’s understanding can be divided into two parts: the objective understanding and the subjective understanding. The objective understanding can be measured by comprehension questions about the content of the text. The subjective understanding is the reader’s perception of his own understanding. The subjective understanding plays an important role in the reader’s motivation, self-esteem and confidence. However, its automatic estimation remains a challenging task. This paper is one of the first to propose a method to estimate the subjective understanding. We show that using the eye gaze to predict the subjective understanding improves the estimation by 13% as compared to using comprehension questions. [ABSTRACT FROM AUTHOR]

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society. [ABSTRACT FROM AUTHOR]

Arabic script is highly sensitive to changes in meaning with respect to the accurate arrangement of diacritics and other related symbols. The most sensitive Arabic text available online is the Digital Qur’an, the sacred book of Revelation in Islam that all Muslims including non-Arabs recite as part of their worship. Due to the different characteristics of the Arabic letters like diacritics (punctuation symbols), kashida (extended letters) and other symbols, it is written and available in different styles like Kufi, Naskh, Thuluth, Uthmani, etc. As social media has become part of our daily life, posting downloaded Qur’anic verses from the web is common. This leads to the problem of authenticating the selected Qur’anic passages available in different styles. This paper presents a residual approach for authenticating Uthmani and plain Qur’an verses using one common database. Residual (difference) is obtained by analyzing the differences between Uthmani and plain Quranic styles using XOR operation. Based on predefined data, the proposed approach converts Uthmani text into plain text. Furthermore, we propose to use the Tuned BM algorithm (BMT) exact pattern matching algorithm to verify the substituted Uthmani verse with a given database of plain Qur’anic style. Experimental results show that the proposed approach is useful and effective in authenticating multi-style texts of the Qur’an with 87.1% accuracy. [ABSTRACT FROM AUTHOR]

Background: Depression is currently the second largest contributor to non-fatal disease burden globally. For that reason, economic evaluations are increasingly being conducted using data from depression prevalence estimates to analyze return on investments for services that target mental health. Psychiatric epidemiology studies have reported large cross-national differences in the prevalence of depression. These differences may impact the cost-effectiveness assessments of mental health interventions, thereby affecting decisions regarding government and multi-lateral investment in mental health services. Some portion of the differences in prevalence estimates across countries may be due to true discrepancies in depression prevalence, resulting from differential levels of risk in environmental and demographic factors. However, some portion of those differences may reflect non-invariance in the way standard tools measure depression across countries. This paper attempts to discern the extent to which measurement differences are responsible for reported differences in the prevalence of depression across countries. Methods and findings: This analysis uses data from the World Mental Health Surveys, a coordinated series of psychiatric epidemiology studies in 27 countries using multistage household probability samples to assess prevalence and correlates of mental disorders. Data in the current study include responses to the depression module of the World Mental Health Composite International Diagnostic Interview (CIDI) in four countries: Two high-income, western countries—the United States (n = 20, 015) and New Zealand (n = 12,992)—an upper-middle income sub-Saharan African country, South Africa (n = 4,351), and a lower-middle income sub-Saharan African country, Nigeria (n = 6,752). Latent class analysis, a type of finite mixture modeling, was used to categorize respondents into underlying categories based on the variation in their responses to questions in each of three sequential parts of the CIDI depression module: 1) The initial screening items, 2) Additional duration and severity exclusion criteria, and 3) The core symptom questions. After each of these parts, exclusion criteria expel respondents from the remainder of the diagnostic interview, rendering a diagnosis of “not depressed”. Latent class models were fit to each of the three parts in each of the four countries, and model fit was assessed using overall chi-square values and Pearson standardized residuals. Latent transition analysis was then applied in order to model participants’ progression through the CIDI depression module. Proportion of individuals falling into each latent class and probabilities of transitioning into subsequent classes were used to estimate the percentage in each country that ultimately fell into the more symptomatic class, i.e. classified as “depressed”. This latent variable design allows for a non-zero probability that individuals were incorrectly excluded from or retained in the diagnostic interview at any of the three exclusion points and therefore incorrectly diagnosed. Prevalence estimates based on the latent transition model reversed the order of depression prevalence across countries. Based on the latent transition model in this analysis, Nigeria has the highest prevalence (21.6%), followed by New Zealand (17.4%), then South Africa (15.0%), and finally the US (12.5%). That is compared to the estimates in the World Mental Health Surveys that do not allow for measurement differences, in which Nigeria had by far the lowest prevalence (3.1%), followed by South Africa (9.8%), then the United States (13.5%) and finally New Zealand (17.8%). Individuals endorsing the screening questions in Nigeria and South Africa were more likely to endorse more severe depression symptomology later in the module (i.e. they had higher transition probabilities), suggesting that individuals in the two Western countries may be more likely to endorse screening questions even when they don’t have as severe symptoms. These differences narrow the range of depression prevalence between countries 14 percentage points in the original estimates to 6 percentage points in the estimate taking account of measurement differences. Conclusions: These data suggest fewer differences in cross-national prevalence of depression than previous estimates. Given that prevalence data are used to support key decisions regarding resource-allocation for mental health services, more critical attention should be paid to differences in the functioning of measurement across contexts and the impact these differences have on prevalence estimates. Future research should include qualitative methods as well as external measures of disease severity, such as impairment, to assess how the latent classes predict these external variables, to better understand the way that standard tools estimate depression prevalence across contexts. Adjustments could then be made to prevalence estimates used in cost-effectiveness analyses. [ABSTRACT FROM AUTHOR]

Anti-community detection in networks can discover negative relations among objects. However, a few researches pay attention to detecting anti-community structure and they do not consider the node degree and most of them require high computational cost. Block models are promising methods for exploring modular regularities, but their results are highly dependent on the observed structure. In this paper, we first propose a Degree-based Block Model (DBM) for anti-community structure. DBM takes the node degree into consideration and evolves a new objective function Q(C) for evaluation. And then, a Local Expansion Optimization Algorithm (LEOA), which preferentially considers the nodes with high degree, is proposed for anti-community detection. LEOA consists of three stages: structural center detection, local anti-community expansion and group membership adjustment. Based on the formulation of DBM, we develop a synthetic benchmark DBM-Net for evaluating comparison algorithms in detecting known anti-community structures. Experiments on DBM-Net with up to 100000 nodes and 17 real-world networks demonstrate the effectiveness and efficiency of LEOA for anti-community detection in networks. [ABSTRACT FROM AUTHOR]

Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations. [ABSTRACT FROM AUTHOR]

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology based on non-negative matrix factorization to detect communities and to identify central nodes in a network as well as within communities. The method is specifically designed for directed weighted networks and, consequently, it has been applied to the interbank network derived from the e-MID interbank market. In an interbank network indeed links are directed, representing flows of funds between lenders and borrowers. Besides distinguishing between Systemically Important Borrowers and Lenders, the technique complements the detection of systemically important banks, revealing the community structure of the network, that proxies the most plausible areas of contagion of institutions’ distress. [ABSTRACT FROM AUTHOR]

Various systems have been proposed to support biological image analysis, with the intent of decreasing false annotations and reducing the heavy burden on biologists. These systems generally comprise a feature extraction method and a classification method. Task-oriented methods for feature extraction leverage characteristic images for each problem, and they are very effective at improving the classification accuracy. However, it is difficult to utilize such feature extraction methods for versatile task in practice, because few biologists specialize in Computer Vision and/or Pattern Recognition to design the task-oriented methods. Thus, in order to improve the usability of these supporting systems, it will be useful to develop a method that can automatically transform the image features of general propose into the effective form toward the task of their interest. In this paper, we propose a semi-supervised feature transformation method, which is formulated as a natural coupling of principal component analysis (PCA) and linear discriminant analysis (LDA) in the framework of graph-embedding. Compared with other feature transformation methods, our method showed favorable classification performance in biological image analysis. [ABSTRACT FROM AUTHOR]

Early childhood anxiety disorders are common, impairing, and predictive of anxiety and mood disorders later in childhood. Epidemiological studies over the last decade find that the prevalence of impairing anxiety disorders in preschool children ranges from 0.3% to 6.5%. Yet, less than 15% of young children with an impairing anxiety disorder receive a mental health evaluation or treatment. One possible reason for the low rate of care for anxious preschoolers is the lack of affordable, timely, reliable and valid tools for identifying young children with clinically significant anxiety. Diagnostic interviews assessing psychopathology in young children require intensive training, take hours to administer and code, and are not available for use outside of research settings. The Preschool Age Psychiatric Assessment (PAPA) is a reliable and valid structured diagnostic parent-report interview for assessing psychopathology, including anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s risk for an anxiety disorder. Using machine learning, we were able to decrease by an order of magnitude the number of items needed to identify a child who is at risk for an anxiety disorder with an accuracy of over 96% for both generalized anxiety disorder (GAD) and separation anxiety disorder (SAD). Additionally, rather than considering GAD or SAD as discrete/binary entities, we present a continuous risk score representing the child’s risk of meeting criteria for GAD or SAD. Identification of a short question-set that assesses risk for an anxiety disorder could be a first step toward development and validation of a relatively short screening tool feasible for use in pediatric clinics and daycare/preschool settings. [ABSTRACT FROM AUTHOR]

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods. [ABSTRACT FROM AUTHOR]

The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA′) in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA′. The Efficacy Ratio adjusts the AMA′ to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market) in range trading and by entering early into new trends in trend trading. The efficacy of AMA′ is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA′ are superior to the passive buy-and-hold strategy. Specifically, AMA′ outperforms the other models for the United States Dollar against PHP (USD/PHP) and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets. [ABSTRACT FROM AUTHOR]

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. [ABSTRACT FROM AUTHOR]

Regional co-location scoping intends to identify local regions where spatial features of interest are frequently located together. Most of the previous researches in this domain are conducted on a global scale and they assume that spatial objects are embedded in a 2-D space, but the movement in urban space is actually constrained by the street network. In this paper we refine the scope of co-location patterns to 1-D paths consisting of nodes and segments. Furthermore, since the relations between spatial events are usually inversely proportional to their separation distance, the proposed method introduces the “Distance Decay Effects” to improve the result. Specifically, our approach first subdivides the street edges into continuous small linear segments. Then a value representing the local distribution intensity of events is estimated for each linear segment using the distance-decay function. Each kind of geographic feature can lead to a tessellated network with density attribute, and the generated multiple networks for the pattern of interest will be finally combined into a composite network by calculating the co-location prevalence measure values, which are based on the density variation between different features. Our experiments verify that the proposed approach is effective in urban analysis. [ABSTRACT FROM AUTHOR]

Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations. [ABSTRACT FROM AUTHOR]

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results. [ABSTRACT FROM AUTHOR]

Forecasting extremely rare events is a pressing problem, but efforts to model such outcomes are often limited by the presence of multiple causes within classes of events, insufficient observations of the outcome to assess fit, and biased estimates due to insufficient observations of the outcome. We introduce a novel approach for analyzing rare event data that addresses these challenges by turning attention to the conditions under which rare outcomes do not occur. We detail how configurational methods can be used to identify conditions or sets of conditions that would preclude the occurrence of a rare outcome. Results from Monte Carlo experiments show that our approach can be used to systematically preclude up to 78.6% of observations, and application to ground-truth data coupled with a bootstrap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses. [ABSTRACT FROM AUTHOR]

Background and objective: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods: The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results: We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion: The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR). [ABSTRACT FROM AUTHOR]

Objective: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap. Materials and methods: Using data gathered from a single surveillance site, we applied 8 supervised learning algorithms to predict whether children meet the case definition for ASD based solely on the words in their evaluations. We compared the algorithms’ performance across 10 random train-test splits of the data, using classification accuracy, F1 score, and number of positive calls to evaluate their potential use for surveillance. Results: Across the 10 train-test cycles, the random forest and support vector machine with Naive Bayes features (NB-SVM) each achieved slightly more than 87% mean accuracy. The NB-SVM produced significantly more false negatives than false positives (P = 0.027), but the random forest did not, making its prevalence estimates very close to the true prevalence in the data. The best-performing neural network performed similarly to the random forest on both measures. Discussion: The random forest performed as well as more recently available models like the NB-SVM and the neural network, and it also produced good prevalence estimates. NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives. More sophisticated algorithms, like hierarchical convolutional neural networks, may not be feasible to train due to characteristics of the data. Current algorithms might perform better if the data are abstracted and processed differently and if they take into account information about the children in addition to their evaluations. Conclusion: Deep learning models performed similarly to traditional machine learning methods at predicting the clinician-assigned case status for CDC’s autism surveillance system. While deep learning methods had limited benefit in this task, they may have applications in other surveillance systems. [ABSTRACT FROM AUTHOR]

Background: Mobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited. Objective: The aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population. Methods: Twenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms. Results: Particularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s). Conclusion: With the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds. [ABSTRACT FROM AUTHOR]

Traditionally, social scientists perceived causality as regularity. As a consequence, qualitative comparative case study research was regarded as unsuitable for drawing causal inferences since a few cases cannot establish regularity. The dominant perception of causality has changed, however. Nowadays, social scientists define and identify causality through the counterfactual effect of a treatment. This brings causal inference in qualitative comparative research back on the agenda since comparative case studies can identify counterfactual treatment effects. We argue that the validity of causal inferences from the comparative study of cases depends on the employed case-selection algorithm. We employ Monte Carlo techniques to demonstrate that different case-selection rules strongly differ in their ex ante reliability for making valid causal inferences and identify the most and the least reliable case selection rules. [ABSTRACT FROM AUTHOR]

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory. [ABSTRACT FROM AUTHOR]

The article offers information on the challenging scientific problems faced by humanity. Topics discussed include information on the efforts to empower Africans to join the global neuroscience community; discussions on the BRAIN initiative in the U.S. and the Human Brain Project in Europe; and the information on the limiting factor for computational neuroscience development in Africa.

Objectives: Value of information (VOI) analysis provides information on opportunity cost of a decision in healthcare by estimating the cost of reducing parametric uncertainty and quantifying the value of generating additional evidence. This study is an application of the VOI methodology to the problem of choosing between home telemonitoring and nurse telephone support over usual care in chronic heart failure management in the Netherlands. Methods: The expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) analyses were based on an informal threshold of €20K per quality-adjusted life-year. These VOI-analyses were applied to a probabilistic Markov model comparing the 20-year costs and effects in three interventions. The EVPPI explored the value of decision uncertainty caused by the following group of parameters: treatment-specific transition probabilities between New York Heart Association (NYHA) defined disease states, utilities associated with the disease states, number of hospitalizations and ER visits, health state specific costs, and the distribution of patients per NYHA group. We performed the analysis for two population sizes in the Netherlands—patients in all NYHA classes of severity, and patients in NYHA IV class only. Results: The population EVPI for an effective population of 2,841,567 CHF patients in All NYHA classes of severity over the next 20 years is more than €4.5B, implying that further research is highly cost-effective. In the NYHA IV only analysis, for the effective population of 208,003 patients over next 20 years, the population EVPI at the same informal threshold is approx. €590M. The EVPPI analysis showed that the only relevant group of parameters that contribute to the overall decision uncertainty are transition probabilities, in both All NYHA and NYHA IV analyses. Conclusions: Results of our VOI exercise show that the cost of uncertainty regarding the decision on reimbursement of telehealth interventions for chronic heart failure patients is high in the Netherlands, and that future research is needed, mainly on the transition probabilities. [ABSTRACT FROM AUTHOR]

The study shows the feasibility of predicting firms’ expenditures in innovation, as reported in the Community Innovation Survey, applying a supervised machine-learning approach on a sample of Italian firms. Using an integrated dataset of administrative records and balance sheet data, designed to include all informative variables related to innovation but also easily accessible for most of the cohort, random forest algorithm is implemented to obtain a classification model aimed to identify firms that are potential innovation performers. The performance of the classifier, estimated in terms of AUC, is 0.794. Although innovation investments do not always result in patenting, the model is able to identify 71.92% of firms with patents. More encouraging results emerge from the analysis of the inner working of the model: predictors identified as most important—such as firm size, sector belonging and investment in intangible assets—confirm previous findings of literature, but in a completely different framework. The outcomes of this study are considered relevant for both economic analysts, because it demonstrates the potential of data-driven models for understanding the nature of innovation behaviour, and practitioners, such as policymakers or venture capitalists, who can benefit by evidence-based tools in the decision-making process. [ABSTRACT FROM AUTHOR]

Academic prestige is difficult to quantify in objective terms. Network theory offers the opportunity to use a mathematical formalism to model both the prestige associated with an academic and the relationships between academic colleagues. Early attempts using this line of reasoning have focused on intellectual genealogy as constituted by supervisor student networks. The process of examination is critical in many areas of study but has not played a part in existing models. A network theoretical “social” model is proposed as a tool to explore and understand the dynamics of prestige in the academic hierarchy. It is observed that such a model naturally gives rise to the idea that the prestige associated with a node in the graph (the prestige of an individual academic) can be viewed as a dynamic quantity that evolves with time based on both local and non-local changes in the properties in the network. The toy model studied here includes both supervisor-student and examiner-student relationships. This gives an insight into some of the key features of academic genealogies and naturally leads to a proposed model for “prestige propagation” on academic networks. This propagation is not solely directed forward in time (from teacher to progeny) but sometimes also flows in the other direction. As collaborators do well, this reflects well on those with whom they choose to collaborate and those that taught them. Furthermore, prestige as a quantity continues to be dynamic even after the end of a relationship or career. Given that time ordering of relationships on the network are implicit but that measures such as betweenness are independent of this implicit time dependence: the success of a PhD student later in their career can improve the prestige of their doctoral supervisor. Thus, prestige can be interpreted to have dynamics that flow both forward and backward in time. [ABSTRACT FROM AUTHOR]

Introduction: Insulin infusion is recommended during management of diabetic patients in critical care units to rapidly achieve glycaemic stability and reduce the mortality. The application of an easy-to-use standardized protocol, compatible with the workload is preferred. Glycaemic target must quickly be reached, therefore static algorithms should be replaced by dynamic ones. The dynamic algorithm seems closer to the physiological situation and appreciates insulin sensitivity. However, the protocol must meet both safety and efficiency requirements. Indeed, apprehension from hypoglycaemia is the main deadlock with the dynamic algorithms, thus their application remains limited. In contrary to the critical care units, to date, no prospective study evaluated a dynamic algorithm of insulin infusion in non-critically ill patients. Aim: This study primarily aimed to evaluate the efficacy of a dynamic algorithm of intravenous insulin therapy in non-critically-ill patients, and addressed its safety and feasibility in different departments of our university hospital. Methods: A "before-after" study was conducted in five hospital departments (endocrinology and four “non-expert” units) comparing a dynamic algorithm (during the "after" period-P2) to the static protocol (the “before” period-P1). Static protocol is based on determining insulin infusion according to an instant blood glycaemia (BG) level at a given time. In the dynamic algorithm, insulin infusion rate is determined according to the rate of change of the BG (the previous and actual BG under a specific insulin infusion rate). Additionally, two distinct glycaemic targets were defined according to the patients’ profile: 100–180 mg/dl (5.5–10 mmol/l) for vigorous patients and 140–220 mg/dl (7.8–12.2 mmol/l) for frail ones. Different BG measurements for each patient were collected and recorded in a specific database (e-CRF) in order to analyse the rates of hypo- and hyperglycaemia. A satisfaction survey was also performed. A study approval was obtained from the institutional revision board before starting the study. Results: Over 8 months, 72 and 66 patients during P1 and P2 were respectively included. The dynamic algorithm was more efficient, with reduced time to control hyperglycaemia (P1 vs P2:8.3 vs 5.3 hours; HR: 2.02 [1.27; 3.21]; p<0.01), increased the number of in-target BG measurements (P1 vs P2: 37.0% vs 41.8%; p<0.05), and reduced the glycaemic variability related to each patient (P1 vs P2, %CV: 40.9 vs 38.2;p<0.05, Index Correlation Class:0.30 vs 0.14; p<0.05). In patients after the first event of hypoglycemia after having started the infusion, new events were lower (P1 vs P2: 19.4 vs 11.4; p<0.001) thanks to an earlier reaction to hypoglycaemia (8.3% during P1 vs 44.3% during P2; p = 0.004). With the dynamic algorithm, the percentage of recurrence of mild hypoglycaemia was significantly lower in frail patients (20.5% vs 10.2%; p<0.001), and in patients managed in the non-expert units (18 vs 7.1%, p<0.001). The %CV was significantly improved in frail patients (36.9%). Mean BG measurements for each patient/day were 5.5±1.1 during P1 and 6.0±1.6 during P2 (p = 0.6). The threat from hypoglycaemia and the difficulty in using dynamic algorithm are barriers for nurses’ adherence. Conclusions: This dynamic algorithm for non-critically-ill patients is more efficient and safe than the static protocol, and adapted for frail patients and non-expert units. [ABSTRACT FROM AUTHOR]

The aim of the current study was to analyse performance differences of football players 2-years prior and the year after signing a new contract (the following year) while taking playing position, nationality, player’s role, team ability, and age into account. The sample was comprised of 249 players (n = 109 defenders, n = 113 midfielders; and n = 27 forwards) from four of the major European Leagues (Bundesliga, English FA Premier League, Ligue 1, and La Liga) during the seasons 2008 to 2015. The dependent variables studied were: shooting accuracy, defense (the sum of defensive actions, tackles, blocks, and interceptions), yellow cards, red cards, passing accuracy, tackle success, and minutes played per match. Two-step cluster analysis allowed classifying the sample into three groups of defenders (national important, foreign important, and less important players) and four groups of midfielders and forwards (national important, foreign important, national less important, and foreign less important players). Magnitude Based Inference (MBI) was used to test the differences between player’s performances during the years of analyses. The main results (very likely and most likely effects) showed better performance in the year prior to signing a new contract than the previous year for foreign important defenders (decreased number of red cards), national important midfielders (increased number of minutes played), foreign important forwards (increased minutes played and defense), and national important forwards (increased minutes played). In addition, performance was lower the year after signing the contract compared to the previous one for less important defenders (decreasing defense), national less important midfielders (decreased minutes played), and foreign less important forwards (decreased defense). On the other hand, the players showed better performance in defense and more minutes played the year after signing the contract for less important defenders, national less important midfielders, and foreign less important forwards. These results may assist coaches to decide on when a new contract should be signed or the duration of the contract. [ABSTRACT FROM AUTHOR]

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners. [ABSTRACT FROM AUTHOR]

Chromosomal aberrations, such as micronuclei (MN), have served as biomarkers of genotoxic exposure and cancer risk. Guidelines for the process of scoring MN have been presented by the HUman MicroNucleus (HUMN) project. However, these guidelines were developed for assay performance but do not address how to statistically analyze the data generated by the assay. This has led to the application of various statistical methods that may render different interpretations and conclusions. By combining MN with data from other high-throughput genomic technologies such as gene expression microarray data, we may elucidate molecular features involved in micronucleation. Traditional methods that can model discrete (synonymously, count) data, such as MN frequency, require that the number of explanatory variables (P) is less than the number of samples (N). Due to this limitation, penalized models have been developed to enable model fitting for such over-parameterized datasets. Because penalized models in the discrete response setting are lacking, particularly when the count outcome is over-dispersed, herein we present our penalized negative binomial regression model that can be fit when P > N. Using simulation studies we demonstrate the performance of our method in comparison to commonly used penalized Poisson models when the outcome is over-dispersed and applied it to MN frequency and gene expression data collected as part of the Norwegian Mother and Child Cohort Study. Our R package is available for download from the Comprehensive R Archive Network and can be applied to datasets having a discrete outcome that is either Poisson or negative binomial distributed and a high-dimensional covariate space. [ABSTRACT FROM AUTHOR]

The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results. [ABSTRACT FROM AUTHOR]

Fog computing can extend cloud computing to the edge of the network so as to reduce latency and network congestion. However, existing encryption schemes were rarely used in fog environment, resulting in high computational and storage overhead. Aiming at the demands of local information for terminal device and the shortcomings of cloud computing framework in supporting mobile applications, by taking the hospital scene as an example, a searchable personal health records framework with fine-grained access control in cloud-fog computing is proposed. The proposed framework combines the attribute-based encryption (ABE) technology and search encryption (SE) technology to implement keyword search function and fine-grained access control ability. When keyword index and trapdoor match are successful, the cloud server provider only returns relevant search results to the user, thus achieving a more accurate search. At the same time, the scheme is multi-authority, and the key leakage problem is solved by dividing the user secret key distribution task. Moreover, in the proposed scheme, we securely outsource part of the encryption and decryption operations to the fog node. It is effective both in local resources and in resource-constrained mobile devices. Based on the decisional q-parallel bilinear Diffie-Hellman exponent (q-DBDHE) assumption and decisional bilinear Diffie-Hellman (DBDH) assumption, our scheme is proven to be secure. Simulation experiments show that our scheme is efficient in the cloud-fog environment. [ABSTRACT FROM AUTHOR]

Allergic rhinitis (AR) is a chronic disease affecting a large amount of the population. To optimize treatment and disease management, it is crucial to detect patients suffering from severe forms. Several tools have been used to classify patients according to severity: standardized questionnaires, visual analogue scales (VAS) and cluster analysis. The aim of this study was to evaluate the best method to stratify patients suffering from seasonal AR and to propose cut-offs to identify severe forms of the disease. In a multicenter French study (PollinAir), patients suffering from seasonal AR were assessed by a physician that completed a 17 items questionnaire and answered a self-assessment VAS. Five methods were evaluated to stratify patients according to AR severity: k-means clustering, agglomerative hierarchical clustering, Allergic Rhinitis Physician Score (ARPhyS), total symptoms score (TSS-17), and VAS. Fisher linear, quadratic discriminant analysis, non-parametric kernel density estimation methods were used to evaluate miss-classification of the patients and cross-validation was used to assess the validity of each scale. 28,109 patients were categorized into “mild”, “moderate”, and “severe”, through the 5 different methods. The best discrimination was offered by the ARPhyS scale. With the ARPhyS scale, cut-offs at a score of 8–9 for mild to moderate and of 11–12 for moderate to severe symptoms were found. Score reliability was also acceptable (Cronbach’s α coefficient: 0.626) for the ARPhyS scale, and excellent for the TSS-17 (0.864). The ARPhyS scale seems the best method to target patients with severe seasonal AR. In the present study, we highlighted optimal discrimination cut-offs. This tool could be implemented in daily practice to identify severe patients that need a specialized intervention. [ABSTRACT FROM AUTHOR]

With the high cost of the research assessment exercises in the UK, many have called for simpler and less time-consuming alternatives. In this work, we gathered publicly available REF data, combined them with library-subscribed data, and used machine learning to examine whether the overall result of the Research Excellence Framework 2014 could be replicated. A Bayesian additive regression tree model predicting university grade point average (GPA) from an initial set of 18 candidate explanatory variables was developed. One hundred and nine universities were randomly divided into a training set (n = 79) and test set (n = 30). The model “learned” associations between GPA and the other variables in the training set and was made to predict the GPA of universities in the test set. GPA could be predicted from just three variables: the number of Web of Science documents, entry tariff, and percentage of students coming from state schools (r-squared = .88). Implications of this finding are discussed and proposals are given. [ABSTRACT FROM AUTHOR]

Background: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.Methods and Findings: We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.Conclusions: In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics. [ABSTRACT FROM AUTHOR]

Sexual desire discrepancy is one of the most frequently reported sexual concerns for individuals and couples and has been shown to be negatively associated with sexual and relationship satisfaction. Sexual desire has increasingly been examined as a state-like construct that ebbs and flows, but little is known about whether there are patterns in the fluctuation of sexual desire. Utilizing spectral and cross-spectral analysis, we transformed 30 days of dyadic daily diary data for perceived levels of sexual desire for a non-clinical sample of 133 couples (266 individuals) into the frequency domain to identify shared periodic state fluctuations in sexual desire. Spectral analysis is a technique commonly used in physics and engineering that allows time series data to be analyzed for the presence of regular cycles of fluctuation. Cross-spectral analysis allows for dyadic data to be analyzed for shared rates of fluctuation between partners as well as the degree of (a)synchrony (or phase shift) between these fluctuations. Men and women were found to exhibit fluctuations in sexual desire at various frequencies including rates of once and twice per month, and to have sexual desire that was unlikely to fluctuate over periods of three days or less and therefore exhibited persistence. Similar patterns of fluctuation were exhibited within couples and these patterns were found to be largely synchronous. While instances of desire discrepancy may arise due to differences in rates of sexual desire fluctuation and random fluctuations, such instances may be normal for romantic relationships. The results have important implications for researchers, clinicians, and educators in that they corroborate the supposition that sexual desire ebbs and flows and suggest that it does so with predictable regularity. [ABSTRACT FROM AUTHOR]

Introduction: Online surveys are a valuable tool for social science research, but the perceived anonymity provided by online administration may lead to problematic behaviors from study participants. Particularly, if a study offers incentives, some participants may attempt to enroll multiple times. We propose a method to identify clusters of non-independent enrollments in a web-based study, motivated by an analysis of survey data which tests the effectiveness of an online skin-cancer risk reduction program. Methods: To identify groups of enrollments, we used a hierarchical clustering algorithm based on the Euclidean distance matrix formed by participant responses to a series of Likert-type eligibility questions. We then systematically identified clusters that are unusual in terms of both size and similarity, by repeatedly simulating datasets from the empirical distribution of responses under the assumption of independent enrollments. By performing the clustering algorithm on the simulated datasets, we determined the distribution of cluster size and similarity under independence, which is then used to identify groups of outliers in the observed data. Next, we assessed 12 other quality indicators, including previously proposed and study-specific measures. We summarized the quality measures by cluster membership, and compared the cluster groupings to those found when using the quality indicators with latent class modeling. Results and conclusions: When we excluded the clustered enrollments and/or lower-quality latent classes from the analysis of study outcomes, the estimates of the intervention effect were larger. This demonstrates how including repeat or low quality participants can introduce bias into a web-based study. As much as is possible, web-based surveys should be designed to verify participant quality. Our method can be used to verify survey quality and identify problematic groups of enrollments when necessary. [ABSTRACT FROM AUTHOR]

Romani people are a significant minority in Europe counting about 10 million individuals scattered throughout the continent. They are a migratory group originating from Northwestern India. Their exodus from India occurred approximately 1000–1500 years ago. The migration route of the Romani people was reconstructed with the help of cultural anthropology, linguistics and historical records. Their migration made them through Central Asia, Middle East and the Caucasus region, prior to the arriving into Europe. Yet the significance of these regions, especially of the Caucasus, in Roma ancestry was a rather neglected topic. Contribution of the Caucasus and further affected regions to the ancestry of Roma was investigated based on genome-wide autosomal marker data. 158 European Roma samples and 41 populations from the Caucasus region, from Middle East, Central Asia and from South Asia were considered in our tests. Population structure and ancestry analysis algorithms were applied to investigate the relationship of Roma with these populations. Identical by descent DNA segment analyses and admixture linkage disequilibrium based tests were also applied. Our results suggest that the Caucasus region plays also a significant role in the genetic legacy of Romani people besides the main sources, Europe and South Asia, previously investigated by other population genetic studies. The Middle East and Central Asia seems slightly less important but far from negligible in connection with the sources of Roma ancestry. Our results point out that the Caucasus region and altogether the area of the Caspian and Black Seas had a significant role in the migration of Romani people towards Europe and contributed significantly to the genetic legacy of Roma rival to the European and Indian main sources. [ABSTRACT FROM AUTHOR]

Multi-atlas brain segmentation of human brain MR images allows quantification research in structural neuroimaging. To achieve high accuracy and computational efficiency of segmentation relies on a custom subset of atlases for each target subject. However, the criterion for atlas pre-selection remains an open question. In this study, two atlas pre-selection approaches based on location-based feature matching were proposed and compared to random and mutual information-based methods using a database of 47 atlases. A varying number of atlases ranked top with hierarchical structural granularity were compared using Dice overlap. The results indicated that the proposed 4L approach consistently led to the highest level of accuracy at a given number of employed atlases in both adult and geriatric populations. In addition, the proposed two methods (4L and LV) can reduce 20 times computational time compared with the stereotypical mutual information-based method. Our pre-selection strategy would provide better segmentation performance in terms of both accuracy and efficiency. The proposed atlas pre-selection will be further implemented into our online automatic brain image segmentation system (). [ABSTRACT FROM AUTHOR]

Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. The focus of these algorithms is to achieve time efficiency according to applications but not memory consumption. In this work, we propose a novel idea to achieve both time efficiency and memory consumption by splitting query string for searching in Corpus. For a given text, the proposed algorithm split the query pattern into two equal halves and considers the second (right) half as a query string for searching in Corpus. Once the match is found with second halves, the proposed algorithm applies brute force procedure to find remaining match by referring the location of right half. Experimental results on different S1 Dataset, namely Arabic, English, Chinese, Italian and French text databases show that the proposed algorithm outperforms the existing S1 Algorithm in terms of time efficiency and memory consumption as the length of the query pattern increases. [ABSTRACT FROM AUTHOR]