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This paper presents the first release of a graph database of derived data of online fiction corpora taken from various sources in five different languages (English, Spanish, Italian, Indonesian, Korean).

Project comprehension questions, such as “which modified artifacts can affect my work” and “how can I identify the developers who should be assigned to a given task” are difficult to answer, require an analysis of the project and its data, are context specific, and cannot always be pre-defined. Current research approaches are restricted to post hoc analyses over software repositories. Very few interactive exploratory tools exist because the large amount of data that need to be analyzed prohibits its exploration at interactive rates. Moreover, such analyses typically require the user to create complex scripts or queries to extract the desired information from data. Here we present Dominoes, a tool for interactive data exploration aimed at end users (i.e., project managers or developers). Dominoes allows users to interact with different types and units of data to investigate project relationships and view intermediate results as charts, tables, and graphs. Additionally, it allows users to save the derived data as well as their exploration paths for later use. In a scenario-based evaluation study, participants achieved a success rate of 86% in their explorations, with a mean time of 7.25 minutes for answering a set of (project) exploration questions.

Recovering information encodings from charts is to extract textual and graphical information from the diverse representations of charts to aid various applications that entail the hidden and directly available information. This paper proposes an end-to-end relationship-aware model for evaginating scientific charts for which an automated encoder-decoder framework is adopted. The framework integrates the CNN-LSTM model to extract direct and derived data by the influence of semantic relationships between the textual and graphical components. Semantic relationship makes the model robust towards diverse chart structures and orientations. Entity relationship-aware module extracts and builds relations amid textual-graphical components of the charts, and decodes the hidden data from the chart images. Our framework is one of its kind to recover data encoding from chart images based upon their inter-object semantic relationships, to the best of our knowledge. Model is tested upon public datasets, obtaining more than 97% accuracy compared with benchmark systems.

We develop a new statistically-robust adaptive regression method (SARM) to extract clear sky true color imagery, approximating the near-surface imagery, derived from multiple satellite daily imagery time series, while avoiding artifacts due to clouds and cloud shadows. We compare the SARM-derived near-surface imagery against simpler approaches for various surface types, and perform a quantitative evaluation. Existing mapped daily imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A and Sentinel-3B satellites is used to produce global clear sky near-surface imagery over various time intervals. We provide several examples of satellite-derived clear sky near-surface imagery over various regions to show potential applications. In addition, we apply this new method to derive clear sky near-surface imagery using higher spatial resolution Landsat-8 data, and discuss characteristics and limitations of our approach. The clear sky near-surface imagery is a useful satellite-derived product, representing the human perception of Earth’s near-surface features, which can be more directly interpreted and easily understood by the general public, and aids visualization and interpretation of various types of satellite derived data.

Internet and social media data provide new sources of information for examining social issues, but their potential for scholars interested in religion remains unclear. Focusing on cross-national religion data, we test the validity of measures drawn from Google and Twitter against well-known existing data. We find that Google Trend (GT) searches for the dominant religions’ major holidays, along with “Buddhism,” can be validated against traditional sources. We also find that GT and traditional measures account for similar amounts of variation, and the GT measures do not differ substantially from established ones for explaining several cross-national outcomes (e.g., fertility, circumcision, and alcohol use), as well as new ones (e.g., interest in religious buildings and sex). The Twitter measures do not perform as well. Our study provides insight into best practices for generating and using these measures, and offers evidence that internet-generated data can replicate existing measures that are less accessible and more expensive.

The quench of high-temperature superconducting (HTS) devices will seriously affect its safety operation. A fast and reliable quench detection method is of great significance for quench protection. In this paper, a quench detection method based on the CNN-LSTM model is proposed. By processing and analyzing the collected voltage signals in real time, the model can realize quench detection instantly and accurately. Furthermore, by adjusting the probability threshold, the sensitivity and the accuracy of the model can be balanced. Based on a series of quench experiments of HTS tapes and derived data, the CNN-LSTM model is trained and tested. The results show that the proposed method is advanced compared with the voltage threshold method.

Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

The selection of foreign-registered ships upon entering ports poses challenges to maritime authorities in ensuring the effectiveness of the port state control (PSC) inspection. The derived data from the ships' arrival notification system has been utilised in this study to identify the preferred vessel type for inspection. A combination of the Grey Relational Analysis (GRA) model and Entropy Weight Method (EWM) has been applied to discern the various types of ships that entered five selected ports in Malaysia and disclose the propensity of each vessel type. Based on 100,623 ship arrival records obtained for a period of five years (2015–2019), the types of ships were identified, analysed, scored, and graded. The result shows that the oil tanker has the highest value of grey relational grade for Bintulu port while the passenger ship is in the pole position for Penang port. Kuching port and Port Kelang share the container type as the first in the ranking, whereas Lahad Datu posted the oil tanker. Other results include the sequence of types at individual ports for providing useful information to help policymakers in establishing an effective inspection design. A similar approach is applicable in PSC inspection records for further analysis.

Introduction Secondhand (SHS) smoke exposure has caused various health problems. Therefore, continuous monitoring of SHS exposure is important to determine the efficacy of various anti-tobacco measure implemented. The study aims to compare the prevalence and factor(s) associated with SHS exposure among secondary school-going adolescents in Malaysia during 2012 and 2017. Methods We derived data from the Global School Health Survey (GSHS) 2012 and GSHS 2017, which was carried out in Malaysia using multistage sampling to select representative samples of secondary school-going adolescents. Both surveys used similar questionnaires to measure SHS exposure. Descriptive and multivariate logistic regression was used to determine the prevalence and factors associated with SHS exposure. Results Approximately four in ten respondents were exposed to SHS in the past week in both surveys (41.5% in GSHS 2012 and 42.0% in GSHS 2017, respectively). Both surveys revealed a significantly higher SHS exposure among respondents who smoked than among non-smokers and higher among males compared to females. The likelihood of SHS exposure in both surveys was also similar, with a higher likelihood of SHS exposure among smoking adolescents and non-smoking adolescents who had at least one smoking parent/guardian, regardless of their own smoking status. Male adolescents had a higher risk of SHS exposure compared to their female counterparts. Meanwhile, SHS risk also increased with age, regardless of smoking status. Conclusions Our findings suggested that there were no changes in the prevalence of SHS exposure and recorded only a slight change in the factors associated with exposure to SHS among school-going adolescents in Malaysia between the years 2012 and 2017. A more pro-active, extensive and comprehensive programme should be implemented to address the problem of SHS exposure. Parents should be advised to stop smoking or abstain from smoking in the presence of their children, and smoking cessation interventions are necessary for smoking adolescents and their parents.

Objective To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. Materials and Methods A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. Results Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P Conclusions For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

Dental study models are essential records for orthodontic diagnosis, planning and treatment evaluations. As plaster models are prone to damage and loss, require storage space and inefficient to retrieve and transfer, digital models are been used to eliminate drawbacks of plaster model regarded as potential replacement of traditional plaster models. Aim: To compare the accuracy and reliability of linear measurements on digital models derived from Cone Beam Computed Tomography (CBCT) and three-dimensional (3D) laser scanned impressions with conventional plaster models. Objectives: i. To compare the accuracy and reliability of measurements made on Plaster and CBCT derived digital models. ii. To compare the accuracy and reliability of measurements made on plaster and 3- dimensional laser scanned impression models. iii. To compare the accuracy and reliability of measurements made on CBCT models and 3D laser scanned impression models. Materials and Methods: CBCT and plaster models will be prepared from 12 patients. Rubber base impressions will be scanned using 3D dental laser scanner. Landmarks will be marked and the linear measurements of all the three models will be compared using 3D software. Results: The derived data was subjected to ANOVA test, Post Hoc Tukey test, Intraclass correlation coefficients (ICCs) and Method Error to evaluate the reliability of repeated measurements. No significant difference was found between plaster models, CBCT models and impression scanned laser models. Conclusion: Plaster models, CBCT models and models obtained from laser scan of impressions are three different diagnostic records. Each have their own advantages and disadvantages. The present study shows that models obtained from laser scanned impressions are comparable to plaster models. The accuracy is adequate for initial diagnosis and treatment planning in clinical orthodontics Keywords: Digital models, Cone beam computed tomography, Laser scan, Meshlab, DIC

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

Objectives Does adapted cardiac rehabilitation (CR) improve the physical behaviours of people with mild-to-moderate stroke in the sub-acute recovery phase using a compositional data analysis (CoDA) approach? Design Before-after. Setting University Hospitals of Leicester, Glenfield Hospital, UK. Participants 24 individuals completed CR and provided valid physical activity (PA) data (mean (SD) 63.1 (14.6) years, 58% male (14/24)). Intervention 6-week adapted CR program within 6-months of stroke. Main outcome measures Physical behaviours were assessed using waist-worn accelerometry. Step count, stationary time (ST), light PA (LPA), and moderate-to-vigorous PA (MVPA) were compared pre post CR using conventional analyses and CoDA. Analysed compositions were: Waking day (ST, LPA, MVPA); ST (1–9-minutes, 10–29-minutes, ≥30-minutes bouts); and MVPA (1–4-minutes, 5–9-minutes, ≥10-minutes bouts). Results Following CR, patients took significantly more steps (mean (SD) 3255 (2864) vs 3908 (3399) steps/day, P = 0.004) and engaged in more bouts of MVPA lasting ≥5 and ≥10-minutes (≥5-minutes: mean (SD) 0.7 (1.4) vs 1.2 (1.8) bouts/day, P = 0.008). Using CoDA, no changes in waking day or ST compositions occurred. For waking day, 42% (10/24) increased their LPA and MVPA at the expense of ST. For ST, 33% (8/24) increased their short bouts at the expense of medium and long bouts. For MVPA, 13% (3/24) increased their medium and long bouts at the expense of short bouts. Conclusion People with stroke in the sub-acute stage of recovery exhibited low levels of PA. CR appears to be an effective intervention to increase step count but did not alter the overall proportion of time individuals spent being sedentary, or engaging in LPA or in MVPA. Registration ISRCTN65957980.

Preceptors are essential to nurse practitioner (NP) students' transition from being a student to competent entry-level NP graduate. The literature is replete with data pertaining to the benefits of and barriers to preceptors engaging in the clinical education of NP students, and little has changed in the last two decades in this regard. Therefore, faculty solicited preceptor input to enhance curriculum revision and clinical training preparation. This qualitative inquiry project derived data from interviews with 13 preceptors in a variety of clinical settings. Interviews were audio-recorded, transcribed verbatim, and analyzed using a content analysis method. Recruiting, training, and retaining qualified, willing preceptors are of paramount importance to NP programs. This article describes preceptor expectations of NP students' knowledge, skills, and attitudes for optimal clinical rotation experiences. The results have important implications for innovative NP educational models, developing trust in NP education programs and promoting competency development of the NP student using entrustable professional activities.

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity. In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco. For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest. Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm. The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.

PurposeService research has highlighted the role of emotional labor in service delivery but has neglected service employees’ actions. This study aims to distinguish between the recurrent in-role and extra-role actions of service employees and to examine the joint effect of service employees’ actions and their emotional labor, which may color these actions on customer buying behavior (number of purchased items and total bill).Design/methodology/approachPhase I comprised two studies: Study 1 examined 70 service interaction videos to identify employees’ service actions, and Study 2 quantitatively validated the most frequent employee actions, used for further study, by examining 40 employee–customer interactions in fashion stores. For Phase II, Study 3 derived data from 60 service employees’ diaries to predict the joint effect of performed emotional labor and service actions on customer buying behavior.FindingsFindings revealed that emotional labor moderated the relationship between service actions and customer buying behavior. The relationship between in-role/extra-role actions and buying behavior was stronger in the lower surface-acting (less emotional faking) condition, whereas the relationship between in-role/extra-role actions and buying behavior was stronger for the higher deep-acting (more emotionally authentic) condition.Practical implicationsService organizations should not limit training to the more easily attained service actions. This possibility may be lacking if it ignores the emotional component that accompanied the action. This may shift the focus from customer satisfaction to customer delight.Originality/valueThis study is a pioneering effort to examine the specific circumstances in which service employees’ actions (regardless of in-role or extra-role status) will not produce the desired customer-related outcome in the presence of emotional labor.

The importance of the impact created by different uncertainties in the policies on the overall economy of a country or a region cannot be denied. These uncertainties might be because of political reasons but sometimes the overall economic environment brings such uncertainties as well. Being a crucial part of economy of the country, German firms might face different issues because of these uncertainties. In this regard, the researcher conducted this study with the motive to find the influence that EPU has on the investments made by various firms of Germany and for this purpose, the researcher derived data from the non-financial companies regarding their finance activities on quarterly basis for the time span consisting of 20 years. SYS GMM estimation was applied on the gathered data to fulfil the purpose of the study. The major result showed that EPU has important but negative impact on the investments made by the German firms. The next result that involves the heterogeneity impact of the firms show that although EFU alone has negative or declining impact on investments but when it is incorporated with the heterogeneity variables of the firm, the impact of these interactions become positive on the firms’ investments. The last result including marketization is that when the marketization index interacts with EPU, the impact of EPU is more sensitive in case of highly marketed companies as compared to lower ones. The study has suggested that the policymakers must make transparent and favorable policies for the firms in regard of investments.

Hedonic house price models are frequently used to improve our understanding of local housing markets. In recent years, rich registers containing details about home‐qualities and neighbourhood characteristics have successfully been coupled with spatial qualities such as job‐accessibility or distances to transport. Additional data sources provided by Open data communities, NGOs, data created by governmental agencies on regional national and international level has the potential of being very useful for analysing housing prices. However, the recent methodological advances in GIS and spatial analysis have not been extensively applied. We expand the hedonic price modelling toolbox with geo‐coded free data on environmental amenities. We specifically include local measures describing the view‐shed, and more varied specifications of access or dominance of green and blue amenities, in addition to urban public‐type service and sport facilities. The GIS‐derived data is used to study how the variables should be specified and to study their ability to improve even well specified hedonic price models. To our knowledge, this paper is the first to combine all the listed environmental properties in a hedonic model, and at the same time controlling for a large number of other important local neighbourhood characteristics. This paper is based on research funded by the Norwegian Research Council, Grant 217210/H2.

Machine learning (ML) models have been increasingly adopted in drug development for faster identification of potential targets. Cross-validation techniques are commonly used to evaluate these models. However, the reliability of such validation methods can be affected by the presence of data doppelgängers. Data doppelgängers occur when independently derived data are very similar to each other, causing models to perform well regardless of how they are trained (i.e., the doppelgänger effect). Despite the abundance of data doppelgängers in biomedical data and their inflationary effects, they remain uncharacterized. We show their prevalence in biomedical data, demonstrate how doppelgängers arise, and provide proof of their confounding effects. To mitigate the doppelgänger effect, we recommend identifying data doppelgängers before the training-validation split. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research/project is supported by the National Research Foundation, Singapore under its Industry Alignment Fund – Prepositioning (IAF-PP) Funding Initiative. W.W.B.G. also acknowledges support from a Ministry of Education (MOE), Singapore Tier 1 grant (Grant No. RG35/20).

BackgroundSurgical site infection (SSI) is the most common complication of surgery, increasing healthcare costs and hospital stay. Topical biocides such as chlorhexidine (CHX) and povidone-iodine (PVI) are used for skin antisepsis to minimise SSIs. There is an increasing concern of developing resistance to topical biocides, however the clinical implications of this remains unclear.OutcomesThe objective of this review was to determine whether the Minimum Bactericidal Concentration (MBC) for topical preparations of CHX or PVI have changed over time, in microbes relevant to SSI.MethodsWe searched for studies which reported the mean bactericidal concentration (MBC) of laboratory and clinical isolates of common SSI causing microbes to CHX and PVI. We excluded samples derived from non-humans and studies using antimicrobial solvents or mixtures of biocides with other active substances. MBC was pooled in random effects meta-analyses and change in MBC over time was explored using meta-regression.Results79 studies were including, analysing 6218 microbes between 1976 and 2021. Most studies used CHX (93%) and there was insufficient data for meta-analysis of PVI. Enterobacteriales had the highest MBC for CHX (20 mg/L [95% CI 14, 25]; I2 95%) whilst MRSA had the lowest (3 mg/L [95% CI 1, 2]; I2 93%). There was no change in MBC of CHX to Staphylococci (β 0.12 [-1.13, 1.37]; I2 99%) or Streptococci (β 0.13 [-0.35, 0.62]; I2 97%).ConclusionsThere is no evidence of change in susceptibility of common SSI-causing microbes to CHX over time. This study provides reassurance that the worldwide guidance that CHX should remain the first-choice agent for skin asepsis prior to surgery.

Background Wavefront-optimized laser-assisted in situ keratomileusis (LASIK) ablation is the most commonly performed procedure in refractive surgery, but new technologies have become available. Our goal was to compare topography-guided (Contoura) and asphericity-guided (Custom-Q) customized ablation treatments for the correction of myopia with or without astigmatism. Methods This prospective, randomized, double-blind, contralateral eye study included 60 eyes of 30 patients with myopia or myopic astigmatism requiring femtosecond LASIK (FemtoLASIK) treatment. For each patient, one eye was randomized to undergo Contoura treatment, and the other underwent Custom-Q abaltion. Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), manifest refractive spherical equivalent (MRSE), sphere (SPH), cylinder (CYL), 6.0-mm total corneal aberration root mean square (RMS), coma (COMA), trefoil (TREF), and spherical aberration (SA) were measured and analysed after a 1-year follow-up. Results The UDVA was − 0.08 ± 0.06 logMAR in Contoura eyes and − 0.08 ± 0.05 logMAR in Custom-Q eyes (p = 0.309) after 12 months. Twenty-five eyes (83%) in the Contoura group and twenty-six eyes (87%) in the Custom-Q group had a UDVA of 20/16 at the end of 12 months, and 100% of eyes in both groups reached a UDVA of 20/25 or better. Ninety and 100% of eyes in the Contoura and Custom-Q groups, respectively, achieved a residual CYL ≤0.50 D (p = 0.237). No statistically significant difference was observed between the surgical techniques in the preoperative to 1-year postoperative changes for any of the parameters evaluated (MRSE, CYL, RMS, DEF, COMA, TREF, and SA). Conclusions The Contoura and Custom-Q techniques yielded excellent visual and refractive results, but the evidence did not reveal any clear differences between these two methods after 1 year of follow-up. Trial registration ReBEC - Registro Brasileiro de Ensaios Clínicos [Internet]: Rio de Janeiro (RJ): Instituto de Informação Científica e Tecnológica em Saúde (Brazil); 2010 -. Identifier RBR-8rs5kt Myopia and Astigmatism Topography-guided Refractive Surgery by Contoura Method Versus Customized by Asphericity in Contralateral Eyes: A prospective Double blind Randomized Study. Available from https://ensaiosclinicos.gov.br/rg/RBR-8rs5kt Date of registration: 02/03/2020 (dd/mm/yyyy). CAAE:96778718.9.0000.5192. Issuing authority: Plataforma Brasil. CEP:2.979.279. Issuing authority: HUOC.

The measurement and treatment of acute pain in animals is essential from a welfare perspective. Valid pain-related outcome measures are also crucial for ensuring reliable and translatable findings in veterinary clinical trials. The short form of the Glasgow Composite Measure Pain Scale (CMPS-SF) is a multi-item behavioral pain assessment tool, developed and validated using a psychometric approach, to measure acute pain in the dog. Here we conduct a scoping review to identify prospective research studies that have used the CMPS-SF. We aim to describe the contexts in which it has been used, verify the correct use of the scale, and examine whether these studies are well-designed and adequately powered. We identify 114 eligible studies, indicating widespread use of the scale. We also document a limited number of modifications to the scale and intervention level, which would alter its validity. A variety of methods, with no consensus, were used to analyse data derived from the scale. However, we also find many deficiencies in reporting of experimental design in terms of the observers used, the underlying hypothesis of the research, the statement of primary outcome, and the use of a priori sample size calculations. These deficiencies may predispose to both type I and type II statistical errors in the small animal pain literature. We recommend more robust use of the scale and derived data to ensure success of future studies using the tool ensuring reliable and translatable outcomes.

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens and Culex restuans mosquito populations. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like the West Nile virus.

Comparison of chlorophyll data of three sets of CMIP5 models for RCP 4.5 (MPI-ESM-MR, HadGEM2-ES and GFDL-ESM2M) and RCP 6.0 (IPSL-CM5A-LR, HadGEM2-ES and GFDL-ESM2M) were done with satellite derived data (OC-CCI) for the period of 1998–2017 along four Indian coastal regions. The monthly, yearly and zone-wise seasonal comparison between model and satellite data were carried out. Analysis of monthly variations of chlorophyll during 1998–2017 reveals that the satellite data show maximum value of 0.53 mg/m3 in September, whereas all other models show maximum in August. Yearly analysis indicates maximum satellite data in the year 2004, while minimum was observed in 2015. HadGEM2-ES exhibited maximum model value and the lowest was found for IPSL-CM5A-LR. It was observed that the maximum chlorophyll value of 2.56 mg/m3 for satellite data was in the monsoon season and the lowest value of 0.14 mg/m3 was in the pre-monsoon. Seasonal analysis reveals no clear match among model and satellite values in any of the coastal regions. In northwest and northeast regions, the satellite values were found higher than the model values in most of the years, whereas in other regions, the model values were found fluctuating with the satellite values. Owing to the mismatch of the model and the satellite values, the work cautions to apply biases or corrections on usage of RCP model data for regional marine climate change research.

For both society and organizations, intellectual capital is critical. It could be a source of competitive business advantage that leads to the generation of wealth. This study aims to conduct a bibliometric review on current trend and development of ‘Intellectual Capital’ and ‘Performance’ over 63 years (1956-2020) on topics related to business, economics and accounting. By adopting bibliometric analysis, we derived data from Scopus online database as of December 12, 2020. Based on the ‘keywords’ search results, our study finalized 1,621 valid documents for further assessments. We utilised Harzing’s Publish and Perish to analyse basic evaluations before proceeding with VOSviewer for data visualization purposes. The findings highlight the trend of literature on ‘Intellectual Capital’ and ‘Performance’ since its inception in 1956. The number of publications achieved 100 publications since 2013 and keep increasing each year. Most previous publications were in English, with more than 178 authors from more than 97 different countries. Using specific keywords of ‘Intellectual Capital’ and ‘Performance’, the results were derived based on the titles and keywords of the documents. Thus, the results of the search query for other fields such as abstracts, and different keywords are excluded. This paper offers an overview and better understanding of the current trends and development of the topic since its inception in 1956, and also serve as a valuable reference and direction for future research.

BackgroundTranscriptomic and proteomic profiling of human brain tissue is hindered by availability of fresh samples from living patients. Postmortem samples usually represent the advanced disease stage of the patient. Furthermore, the postmortem interval affects the observed transcriptomic and proteomic profiles. Therefore, access to fresh brain tissue samples from living patients is valuable resource to obtain information on metabolically intact tissue. The implantation of deep brain stimulation (DBS) electrodes into the human brain is a neurosurgical treatment for, e.g., movement disorders. Here, we describe an improved approach to collect brain tissue from surgical instruments used in implantation of DBS device for transcriptomics and proteomics analyses.MethodsSamples were extracted from guide tubes and recording electrodes used in routine DBS implantation procedure that was carried out to treat patients with Parkinson’s disease, genetic dystonia and tremor. RNA sequencing was carried out to tissue extracted from the recording microelectrodes and liquid chromatography-mass spectrometry was carried out to analyze tissue from guide tubes. To assess the performance of the current approach, obtained datasets were compared with previously published datasets representing brain tissue.ResultsIn RNA sequencing, altogether 32,034 transcripts representing unique Ensembl gene identifiers were detected from eight samples representing both hemispheres of four patients. By using liquid chromatography-mass spectrometry, we identified 734 unique proteins from 31 samples collected from 14 patients. Comparison with previously published brain derived data indicated that both of our datasets reflected the expected brain tissue specific features. The datasets are available via BioStudies database (accession number S-BSST667).ConclusionsSurgical instruments used in DBS installation retain enough brain material for protein and gene expression studies. Analysis of the datasets indicated that hemisphere-specific expression data can be obtained from individual patients without any sample pooling and without any modifications to the standard surgical protocol. Comparison with previously published datasets obtained with similar approach proved the robustness and reproducibility of the current improved protocol. This approach overcomes the issues that arise from using postmortem tissue, such as effect of postmortem interval, on proteomic and transcriptomic landscape of the brain and can be used for studying molecular aspects of DBS-treatable diseases.

Single-molecule molecular inversion probes (smMIPs) provides a modular and cost-effective platform for high-multiplex targeted next-generation sequencing (NGS). Nevertheless, translating the raw smMIP-derived sequencing data into accurate and meaningful information currently requires proficient computational skills and a large amount of computational work, prohibiting wide-scale adoption of smMIP-based technologies. To enable easy, efficient, and accurate interrogation of smMIP-derived data, we developed SmMIP-tools, a computational toolset that combines the critical analytic steps for smMIP data interpretation into a single computational pipeline. Here, we describe in detail two of the software’s major components. The first is a read processing tool that performs quality control steps, generates read-smMIP linkages and retrieves molecular tags. The second is an error-aware variant caller capable of detecting single nucleotide variants (SNVs) and short insertions and deletions (indels). Using a cell-line DNA dilution series and a cohort of blood cancer patients, we benchmarked SmMIP-tools and evaluated its performance against clinical sequencing reports. We anticipate that SmMIP-tools will increase accessibility to smMIP-technology, enabling cost-effective genetic research to push personalized medicine forward.

A substantial fraction of biomedical research depends on the reliability of human biospecimens but variations in sample manipulation during collection, processing, and storage can differentially alter molecular integrity and influence interpretation of the resulting derived data. Details of biobanking processes are rarely adequately described in research publications, preventing reviewers, readers, and scientists seeking to replicate the findings, from appreciating and adequately considering preanalytical variations contributing to results. To address these shortcomings, a set of reporting guidelines, the Biospecimen Reporting for Improved Study Quality (BRISQ) criteria, were developed in 2011. In this study we evaluated the uptake and reporting of BRISQ criteria in 324 articles across four leading biomedical journals using human biospecimens and published before (161; in 2010) and after (163; in 2014) the delineation of the BRISQ guidelines. We found that even within journals recommending use of BRISQ, manuscript-level uptake. and reporting of the relevant biospecimen information is not widespread or uniform. In the future, an enhanced biospecimen reporting strategy to better serve the needs of researchers, reviewers, and journals may be considered to strengthen research reproducibility for the benefit of the research community at large.

Background In this study, the authors analyzed patterns, trends, and correlates of hospital inpatient admissions for nontraumatic dental conditions (NTDCs) in Florida for the calendar years 2006 through 2016. Methods The study was a retrospective analysis of hospital inpatient admissions for NTDCs throughout Florida. The authors derived data from a database maintained by the Florida Agency for Health Care Administration, the state’s primary health policy and planning entity. Results There were 26,659 hospital inpatient admissions for NTDCs during the 11-year period. The number of admissions increased from 1,808 in 2006 to 3,542 in 2016, and total charges increased from $46.1 million to $166.5 million. The most common diagnosis was periapical abscess without sinus. Emergency departments were the most frequent source of admission. Conclusions Hospital inpatient admissions for NTDCs are a growing challenge in Florida. If trends continue, hospital inpatient admissions for NTDCs will place an increasing burden on the state’s health care system. Practical Implications Increasing numbers of hospital inpatient admissions for NTDCs are largely a consequence of using emergency departments to address dental-related conditions. This growing dental public health concern has major implications for health care resources and outcomes.

Highlights • Isosteviol partitions extensively into plasma compartments of the blood in man and rat, with an estimated 97% bound to plasma proteins in vitro., Background Isosteviol is a synthetic derivative of steviol glycosides with promising pharmacological properties and might find future use as a cardioprotective agent. Objective A simple LC-MS/MS technique was developed and validated for the bioanalysis of isosteviol in plasma and erythrocytes. This method was subsequently utilized for the in vitro assessment of isosteviol's partitioning into blood compartments of humans and rats. Methods Fresh blood samples from healthy humans and Wistar rats were equilibrated with 1, 10, and 30 µM isosteviol at 37 °C in a shaking dry-bath. The levels of isosteviol in plasma and erythrocytes partitions were determined in these samples, after separation, at intervals over a 60-minute period. The data derived was used to estimate erythrocyte-to-plasma and blood-to-plasma coefficients. Results Mean erythrocyte-to-plasma partition coefficients (SD) after 60 minutes of equilibration were observed to be 0.039 (0.002) and 0.040 (0.003) in humans and rats, respectively. Derived values for the blood-to-plasma ratio (SD) were 0.576 (0.001) in humans and 0.543 (0.007) in rats, whereas plasma component binding was estimated to be more than 96%. Conclusions The findings suggest that isosteviol preferentially partitions into plasma compartments in humans and rats. The significance of this profile for the efficacy, tissue uptake, and retention of isosteviol will have to be further studied.

This work introduces a new technique that provides real-time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non-invasive approach for automatic prediction of personal thermal comfort and mean time to warm discomfort using machine learning. The prediction framework described uses temperature information extracted from multiple local body parts to model an individual's thermal preference, with sensing measurements that capture local body part variance as well as differences between body parts. We compared the efficacy of using machine learning with classical measurements such as skin temperature along with our approach of using multi-part measurements and derived data. An analysis of the performance of machine learning shows that our method improved the accuracy of personal thermal comfort prediction by an average of 60%, and the accuracy of mean time to warm discomfort prediction by an average of 40%. The proposed thermal models were tested on subjects’ data extracted from an office setup with room temperature varying from low (21.11 °C) to high (27.78 °C). When all proposed features were used, personal thermal comfort was predicted with an accuracy higher than 80% and mean time to warm discomfort with more than 85% accuracy. Further analysis of the machine learning efficacy showed that temperature differences had the highest impact on performance of individual thermal preference prediction, while the proposed approach was found not sensitive to the actual machine learning algorithm.

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high-resolution observation-based gridded precipitation products, the Climatic Research Unit (CRU) time-series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU-FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter-annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time-varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system. (Less)

Background Whether Candida interacts with Gram-positive bacteria, such as Staphylococcus aureus, coagulase negative Staphylococci (CNS) and Enterococci, to enhance their invasive potential from the microbiome of ICU patients remains unclear. Several effective anti-septic, antibiotic, anti-fungal, and non-decontamination based interventions studied for prevention of ventilator associated pneumonia (VAP) and other ICU acquired infections among patients receiving prolonged mechanical ventilation (MV) are known to variably impact Candida colonization. The collective observations within control and intervention groups from numerous ICU infection prevention studies enables tests of these postulated microbial interactions in the clinical context. Methods Four candidate generalized structural equation models (GSEM), each with Staphylococcus aureus, CNS and Enterococci colonization, defined as latent variables, were confronted with blood culture and respiratory tract isolate data derived from 460 groups of ICU patients receiving prolonged MV from 283 infection prevention studies. Results Introducing interaction terms between Candida colonization and each of S aureus (coefficient + 0.40; 95% confidence interval + 0.24 to + 0.55), CNS (+ 0.68; + 0.34 to + 1.0) and Enterococcal (+ 0.56; + 0.33 to + 0.79) colonization (all as latent variables) improved the fit for each model. The magnitude and significance level of the interaction terms were similar to the positive associations between exposure to topical antibiotic prophylaxis (TAP) on Enterococcal (+ 0.51; + 0.12 to + 0.89) and Candida colonization (+ 0.98; + 0.35 to + 1.61) versus the negative association of TAP with S aureus (− 0.45; − 0.70 to − 0.20) colonization and the negative association of anti-fungal exposure and Candida colonization (− 1.41; − 1.6 to − 0.72). Conclusions GSEM modelling of published ICU infection prevention data enables the postulated interactions between Candida and Gram-positive bacteria to be tested using clinically derived data. The optimal model implies interactions occurring in the human microbiome facilitating bacterial invasion and infection. This interaction might also account for the paradoxically high bacteremia incidences among studies of TAP in ICU patients.

Innovative tools for modeling infectious agents are essential for better understanding disease spread given the inherent complexity of changing and interacting ecological, environmental, and demographic factors. We leveraged fine-scale satellite data on urban areas to build a road-connected geospatial network upon which to model disease spread. This model was tested by simulating the spread of the 2009 pandemic influenza in Rwanda and also used to determine the effects of vaccination regimens on outbreak spread and impact. Our results were comparable to data collected during the actual pandemic in Rwanda, determining the initial places affected after outbreak introduction in Kigali. They also highlighted the effectiveness of preventing outbreaks by targeting mitigation efforts at points of outbreak origin. This modeling approach can be valuable for planning and control purposes in real-time disease situations, providing helpful baseline scenarios during initial phases of outbreaks, and can be applied to other infectious diseases where high population mobility promotes rapid disease propagation.