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The global response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrated the value of timely and open sharing of genomic data with standardized metadata to facilitate monitoring of the emergence and spread of new variants. Here, we make the case for the value of Salmonella Typhi (S. Typhi) genomic data and demonstrate the utility of freely available platforms and services that support the generation, analysis, and visualization of S. Typhi genomic data on the African continent and more broadly by introducing the Africa Centres for Disease Control and Prevention's Pathogen Genomics Initiative, SEQAFRICA, Typhi Pathogenwatch, TyphiNET, and the Global Typhoid Genomics Consortium. [ABSTRACT FROM AUTHOR]

Sexual dimorphism has been poorly evaluated or investigated in Pleistocene Eurasian Stephanorhinus species, leaving a gap in our knowledge about their morphometric variability. Among the representatives of this genus, S. etruscus is the most abundant species, with several remains collected from Western European localities, allowing us to investigate the presence of sexual dimorphism in the limb bones of this taxon. We considered measurements taken on 45 postcranial variables and three different statistical metrics to identify patterns of bimodality in the dataset. This work represents the first application of sex-combined statistical analysis to a dataset composed of individuals from various European localities. The morphometrical analyses revealed that a relatively weak sexual dimorphism is present in all the considered bones. Larger forelimbs and hindlimbs are interpreted as belonging to adult males of S. etruscus, similarly to what was observed in the modern Sumatran rhino, where males are a little bit larger than females. The recognition of a weak sexual dimorphism in the postcranial bones of S. etruscus increases our understanding of the paleoecology of this extinct taxon. However, only a better study of the morphological and morphometrical variability of the crania of fossil rhinoceroses could deeply contribute to the investigation of the social habits and behavior of these taxa. [ABSTRACT FROM AUTHOR]

Sexual dimorphism has been poorly evaluated or investigated in Pleistocene Eurasian Stephanorhinus species, leaving a gap in our knowledge about their morphometric variability. Among the representatives of this genus, S. etruscus is the most abundant species, with several remains collected from Western European localities, allowing us to investigate the presence of sexual dimorphism in the limb bones of this taxon. We considered measurements taken on 45 postcranial variables and three different statistical metrics to identify patterns of bimodality in the dataset. This work represents the first application of sex-combined statistical analysis to a dataset composed of individuals from various European localities. The morphometrical analyses revealed that a relatively weak sexual dimorphism is present in all the considered bones. Larger forelimbs and hindlimbs are interpreted as belonging to adult males of S. etruscus, similarly to what was observed in the modern Sumatran rhino, where males are a little bit larger than females. The recognition of a weak sexual dimorphism in the postcranial bones of S. etruscus increases our understanding of the paleoecology of this extinct taxon. However, only a better study of the morphological and morphometrical variability of the crania of fossil rhinoceroses could deeply contribute to the investigation of the social habits and behavior of these taxa.

Recently there has been an increasing adoption of electronic health records (EHRs) in different countries. Thanks to these systems, multiple health bodies can now store, manage and process their data effectively. However, the existence of such powerful and meticulous entities raise new challenges and issues for health practitioners. In fact, while the main objective of EHRs is to gain actionable big data insights from the health workflow, very few physicians exploit widely analytic tools, this is mainly due to the fact of having to deal with multiple systems and steps, which completely discourage them from engaging more and more. In this paper, we shed light and explore precisely the proper adaptation of analytical tools to EHRs in order to upgrade their use by health practitioners. For that, we present a case study of the implementation process of an EHR based OpenEHR and investigate health analytics adoption in each step of the methodology. [ABSTRACT FROM AUTHOR]

Over the last few years, big data has emerged as an important topic of discussion in most firms owing to its ability of creation, storage and processing of content at a reasonable price. Big data consists of advanced tools and techniques to process large volumes of data in organisations. Investment in big data analytics has almost become a necessity in large-sized firms, particularly multinational companies, for its unique benefits, particularly in prediction and identification of various trends. Some of the most popular big data analytics software used today are MapReduce, Hive, Tableau and Hive, while the framework Hadoop enables easy processing of such extremely large data sets. The current research attempts to create a comparative assessment of five such applications namely IBM SPSS, IBM Watson Analytics, R, Minitab and SAS. The case taken into effect for the test was that of the factors affecting housing affordability in the US. Based on the statistics obtained from the American Housing Survey (AHS) database, the researcher has identified different factors impacting the affordability in the states. The technique of reducing variables though Principal Component Analysis (PCA) and a model based on partial least square regression/polynomial regression was fitted to check the impact on the affordability. The primary findings suggest that majorly age of the head of the household, income earned were the two most important factors affecting the pricing in the region. Also, a comparison is drawn at the end of study with interpretation of the most and least effective applications. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and objective considerations need to be made before implementing an advanced analytics solution. Critical evaluation before, during, and after its implementation will ensure safe care, good outcomes, and the elimination of waste. In this commentary we offer basic practical considerations for developing, implementing, and evaluating such solutions based on many years of experience. [ABSTRACT FROM AUTHOR]

The United States government funds research and development programs to advance the state of technological innovations across many fields. One targeted field is assistive technology devices and services for persons with disabilities. Although these sponsored programs intend to benefit society, they channel most of their funding to universitybased scholars. This approach leaves a gap between the specific project outputs (academic papers, patent claims), and their transformation into products, services and related outcomes capable of delivering beneficial socio-economic impacts. One participant/ observer recounts one government agency's efforts to bridge this gap over the past twenty-five years, by initiating projects addressing the transformational processes of technology transfer and knowledge translation. [ABSTRACT FROM AUTHOR]

Formal grammatical description has not generally been applied in the human factors area, which traditionally draws on behavioral science for its methodology. This paper illustrates, by means of a detailed example, how formal grammatical description can be used as a predictive tool to compare alternative designs for ease of use and to identify design choices which could cause users to make mistakes. The paper describes the human interface for two versions of an interactive graphics system intended for use by nonprogrammers. It presents the "action languages" for the two versions, then shows how these user languages can be described in terms of a production rule notation. Particular emphasis is given, in the notation, to actions the user has to learn and remember (i.e., to "cognitive" factors). The paper then presents predictions about human performance based on the formal description, and exploratory results of testing some of the predictions. Since the predictions arc based on general properties of the formal description, the technique should also be applicable to other "action languages." [ABSTRACT FROM AUTHOR]

Big data analytics can add value and provide a new perspective by improving predictive analysis and modeling practices. This research is centered on supply-chain management and how big data analytics can help Romanian supply-chain companies assess their experience, strategies, and professional capabilities in successfully implementing big data analytics, as well as assessing the tools needed to achieve these goals, including the results of implementation and performance achievement based on them. The research method used in the quantitative study was a sampling survey, using a questionnaire as a data collection tool. It included closed questions, measured with nominal and ordinal scales. A total of 205 managers provided complete and useful answers for this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. The major contributions of this research highlight the fact that companies are concerned with identifying new statistical methods, tools, and approaches, such as cloud computing and security technologies, that need to be rigorously explored. [ABSTRACT FROM AUTHOR]