Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km² ). LAGOS includes two modules: LAGOS[subscript]GEO , with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOS[subscript]LIMNO , with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.Keywords: LAGOS, Integrated database, Data harmonization, Database Ecoinformatics, Macrosystems ecology, Landscape limnology, Water qualityKeywords: LAGOS, Integrated database, Ecoinformatics, Data harmonization, Water quality, Data sharing, Landscape limnology, Macrosystems ecology, Database documentation, Data reus

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

A neural model for predicting the time performance of traditional general contract (TGC) project

Several studies had shown that many project managers are facing difficulties in predicting the time performance of Traditional General Contract (TGC) projects because there are many factors that affect TGC project success. This study presents the development of a model that can be used to predict the time performance of TGC project. Through literature research, fortyfour success factors affecting TGC project have been established. The degree of importance for these factors was determined through questionnaire survey. The outcome of the survey formed a basis for the development of the time performance prediction model using Artificial Neural Network technique. The best model was found to be a multi-layer back-propagation neural network consists of eight input nodes, five hidden nodes and three output nodes. The model was tested by using data from nine new projects. The results show that the mean error for this prediction model is relatively low. The developed model enables all parties involved in TGC projects to predict and ensure that their project is on tim

Predicting the performance of design-bid-build projects: a neural-network based approach

Several studies had shown that many project managers are facing difficulties in predicting the performance of Design- bid-build (DBB) projects. This is due to the fact that there are many factors that affect DBB project success. This research is carried out to identify these factors. In addition, a model to predict the performance of DBB project was developed based on time. Through literature research, a total of forty-four factors that affect DBB project success had been established. The degree of importance for these factors had been determined through questionnaire survey. Eight out of forty-four factors that affecting project performance were found to be the most important factors ITom the viewpoint of project managers and contractors in the Malaysia construction industry. The outcome of the survey formed a basis for the model development. Artificial neural network (ANN) technique is used to construct the models to predict construction project performance based on time. The best performance model was the multiplayer back-propagation neural network model, which consisted of eight input nodes, five hidden nodes and three output nodes. These models were tested by using data ITom nine new projects. The results indicated that the developed model can give a good prediction. In this study, it was concluded that the ANN prediction model can be an efficient tool for predicting the performance ofDBB project from the time aspect

Predicting the performance of traditional general contract projects: A neural network based approach

Several studies had shown that many project managers are facing difficulties in predicting the performance of Traditional General Contract (TGC) projects. This is due to the fact that there are many factors that affect TGC project success. This paper presents the TGS project success factors that have been identified. In addition, a model to predict the performance of TGC project based on time is also described. Through literature research, a total of forty-four factors affecting TGC project success had been established. The degree of importance for these factors was determined through questionnaire survey. The outcome of the survey formed a basis for the development of the project performance prediction model. The best model was found to be a multi-layer back-propagation neural network consists of eight input nodes, five hidden nodes and three output nodes. The model was tested by using data from nine new projects. The results showed that the mean error for this prediction model is relatively low. The model enables all parties involved in TGC projects to predict and ensure that their project performance is within the time constraints

Short-term, daily intake of yogurt containing Bifidobacterium animalis ssp. lactis Bf-6 (LMG 24384) does not affect colonic transit time in women

The present study investigated the effect of Bifidobacterium animalis ssp. lactis Bf-6 (LMG 24384) (Bf-6)-supplemented yogurt on colonic transit time (CTT). A triple-blinded, randomised, placebo-controlled, two-period cross-over trial was conducted with sixty-eight women with a self-reported history of straining during bowel movements or hard or lumpy stools in the past 2 years. As per regulatory requirements for probiotic studies, eligible women were generally healthy and not actively constipated at the time of enrolment. Participants consumed both Bf-6 and placebo yogurts for 14d each in a randomised order, with a 6-week washout period between the treatments. The primary outcome, CTT, was assessed via Sitz marker X-rays. The average CTT was 42·1h for the active period and 43·3h for the control period (mean difference 1·2h, 95% CI-4·9, 7·4). Since the statistical tests for the cross-over study implied that the mean CTT for the active and control periods in period 2 were biased, the standard protocol suggests examining the results of only period 1 as a traditional randomised controlled trial. This showed that the mean CTT was 35·2h for the active period v. 52·9h for the control period (P=0·015). Bootstrapping demonstrated that both the mean and median differences remained significant (P=0·016 and P=0·045, respectively). Few adverse events were noted, with no differences among the active and control periods. The paired analysis showed no differences between the active and control periods during the cross-over trial. Further trials should be conducted in populations with underlying problems associated with disordered transit to determine the potential value of probiotic supplementation more accurately. Copyright © The Authors 2013

Cognitive diversity in context: US-China developmental trajectories on 4 tasks in 3-12yos

Early abstract reasoning follows qualitatively different developmental trajectories in the US and China (Carstensen et al., 2019), but the causal mechanisms for these differences are unknown. Existing accounts implicate several potential factors that differ between the US and China, including language (Hoyos et al., 2016), executive function (Richland et al., 2010), visual attention (Christie et al., 2020), and social reasoning (Jurkat et al., 2022). While there is extensive work documenting both language and executive function in US and Chinese children, much less is known about the development of cross-cultural differences in visual attention and social reasoning. We document abstract reasoning about relations (Ambiguous cRMTS, Carstensen et al., 2019) alongside the potential moderating factors of visual attention (Free Description; Imada et al., 2013), and social reasoning (Causal Attribution, Seiver et al., 2013; Uniqueness Preference, Kim & Markus, 1999) in a cross-sectional sample of 240 3-12-year-olds, and observe both similarities and differences

Cognitive diversity in context: US-China differences in children’s reasoning, visual attention, and social cognition

Outward differences between cultures are very salient, with Western and East Asian cultures as a prominent comparison pair. A large literature describes cross-cultural variation in cognition, but relatively less research has explored the developmental origins of this variation. This study helps to fill the empirical gap by replicating four prominent findings documenting cross-cultural differences in children’s reasoning, visual attention, and social cognition in a cross-sectional sample of 240 3-12-year-olds from the US and China. We observe cross-cultural differences in three of the four tasks and describe the distinct developmental trajectory that each task follows throughout early and middle childhood