dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus ICNIRP has now organized a project group that is charged with drafting a research agenda that highlights the gaps in knowledge that have been identified during the development of its guidelines for each frequency range of the NIR spectrum. This Appendix describes the structured approach that was developed by the project group in order to encourage both transparency in methodology and consistency across NIR domains. This methodology provides a means to select the issues considered relevant to guidelines when the results are considered weak or not strong enough or when there are no data with which to characterize a particular phenomenon.
Acknowledgments-Collaborators:  Carmela Marino ,  ICNIRP and Agency for New Technologies, Energy and Sustainable Economic Development, Italy ;  Rodney Croft ,  ICNIRP  and  Australian Centre for Electromagnetic Bioeffects Research ,  Illawarra Health & Medical Research Institute, University of Wollongong, Australia ;  Maria Feychting , ICNIRP and  Karolinska Institutet , Sweden;  Adèle C Green , ICNIRP and  QIMR Berghofer Medical Research Institute, Brisbane, Australia  and  CRUK Manchester Institute, University of Manchester , Manchester, UK;  Akimasa Hirata , ICNIRP and  Nagoya Institute of Technology, Japan ;  Guglielmo d'Inzeo, ICNIRP and La Sapienza University Rome, Italy ;  Sharon Miller , ICNIRP; Gunnhild Oftedal, ICNIRP and  Norwegian University of Science and Technology ;  Tsutomu Okuno , ICNIRP;  Martin Röösli , ICNIRP and  Swiss Tropical  and  Public Health Institute, Basel, Switzerland ;  Zenon Sienkiewicz , ICNIRP;  Eric van Rongen, ICNIRP and Health Council , The Netherlands;  Soichi Watanabe ,  ICNIRP  and  National Institute of Information and Communications Technology, Japan ;  Isabelle Lagroye ,  ICNIRP SEG  and  IMS Laboratory, University of Bordeaux I, France ;  Ken Karipidis ,  ICNIRP SEG and Australian Radiation Protection and Nuclear Safety Agency ;  Mats-Olof Mattsson , ICNIRP SEG (until 2017) and  SciProof International AB, Sweden ;  Andrew Wood , ICNIRP SEG and  Swinburne University of Technology, Melbourne, Australia.
Acknowledgements This work is part of the doctoral research of the lead author at  Paris 1 Pantheon Sorbonne University  focusing on the fuel economy of late prehistoric coastal occupations in northwestern Alaska, supported by a Ph. D. fellowship from  Paris 1 Pantheon Sorbonne University in France. An earlier draft of this research was presented at the 45 th annual meeting of the  Alaska Anthropological Association (AkAA ) and was awarded a Travel Grant from the AkAA in 2018. This research is also part of a collaborative Project supported by the  Office of Polar Programs (OPP)  at the  National Science Foundation (NSF)  awarded to  C. Alix  and  N. H. Bigelow  at  UAF  ( ARC-1523160 ),  O. K. Mason  at  INSTAAR -University of Colorado Boulder  ( ARC-1523205 ),  D. H. O'Rourke  at the  University of Kansas  ( ARC-1523059 ) and  S.
This paper is focused on mental health among hospital doctors. This is a review of the literature dated January 1, 2005-December 31, 2019, from the MedLine and Scopus databases. The prevalence of post-traumatic stress disorder and anxiety disorders ranged 2. 2-14. 6% and 10. 5-19. 3%, respectively. Several risk factors were significant, such as having had blood exposure accidents, or the interaction between family and work life. The prevalence of mood disorders ranged 7. 8-48%. Occupational constraints, such as night work or psychological demand, were related to the presence of mood disorders. This literature review showed the prevalence of disorders that can be reactive at work in hospital doctors. The risk factors studied can guide prevention policies within hospitals.
METHODS This article is a review of the literature dated January 1, 2005-December 31, 2019. Searches were conducted using the MedLine, Propero and Scopus databases. The key words were as follows: "medical staff, hospital" (Mesh) and "anxiety" (Mesh) or "performance anxiety" (Mesh) or "anxiety disorders" (Mesh); and "medical staff, hospital" (Mesh) and "depression" (Mesh) or "depressive disorder" (Mesh) or "mood disorder" (Mesh). For Scopus, the following keywords used were: "hospital doctors" or "doctor" and "hospital, " and "anxiety, " "depression. " Articles in English, French, Spanish, Italian and German were selected. To search for gray literature, the open archive site and the following website: http://www. these. fr were used. Two occupational physicians selected articles based on pre-established criteria.
This is particularly the case with the study by Koren et al. [46]. In addition, this literature review focused on the emergence of anxiety and mood disorders. However, some articles have shown that exposure to serious events is a source of maturity for caregivers  [47]. These articles were, therefore, not taken into account because of their nosographic inaccuracy. Studies on residents were not included, although this population is affected by these disorders, too  [48, 49]. This review also exhibits some limitations as regards its methodology. Querying the databases and verifying the protocol depot sites seemed to allow for some completeness. However, the authors did not interview the teams that published papers on the subject; some unpublished data during their studies may have enriched this article.
Similarly, a multilingual exploration of the gray literature was not carried out. The bases used mainly concerned the French medical world. In addition, it was not possible to use certain study databases, often on therapeutic trials. However, as this article had a descriptive Other abbreviations as in Table  1. and analytical epidemiological purpose to better understand the state of health of doctors in hospitals, it did not seem necessary to ensure the quality of the analysis for this topic. The main limitation of this study was the possible variability of nosographic feature definitions. For example, some entities have only been defined recently. Additionally, mood disorder or depression were terms understood as generic. The articles did not establish finely whether they were disruptive disorders, with bouts of aggression >3 times/week, or characterized a depressive disorder lasting for >2 weeks but <2 years, with fewer than 3 factors of depreciation.
12 he EMR system used throughout Zambia is the SmartCare system, a Windows-based platform developed by the Zambian Ministry of Health (MoH) in collaboration with the Centers for Disease Control and Prevention (CDC). The SmartCare EMR was first introduced in 2004 to capture individual-level HIV patient data and programme indicators, but was later expanded to include other health facility services (e. g. , outpatient care, laboratory testing, etc. ). The system has become an integral part of the Zambian ART programme, serving as a clinical information management system to promote care continuity, and is vital to national and PEPFAR (President's Emergency Fund for AIDS Relief) monitoring and evaluation. The SmartCare system has evolved over time. A paper-based patient file was part of the initial implementation of SmartCare, with data clerks entering all information from the paper-based file into the EMR at the end of the clinic day: this was called Electronic-Last.
Public Health Action upon the completeness and quality of data entered in the SmartCare EMR. The aim of the present study was to understand the effects of SmartCare transition to Electronic-First by comparing data completeness and accuracy for key HIV-related variables following change in data entry methodology from Electronic-Last to Electronic-First in selected high-volume facilities in Lusaka, Zambia, over the period of SmartCare transition (2017 and 2019).
Data collection, variables and analysis Data entered retrospectively into the SmartCare system was defined as Electronic-Last, while real-time data entry by healthcare workers was defined as Electronic-First. We selected key programme variables for our analysis based on 1) their utility for providing direct clinical care; and 2) their high frequency of entry to ensure a large sample size of observations representative of data in the SmartCare EMR (Table  1 ). We extracted de-identified patient data for these variables from the last 6 months of data entry for Electronic-Last and the first 6 months of entry for Electronic-First using an SQL script from the main SmartCare EMR server, and assessed these for com-pleteness and accuracy. Completeness was determined by confirming whether data for each of the required variable fields had been checked or entered into the electronic patient file.
Data accuracy was evaluated through logic checks and assessment of whether there were outliers from normal accepted ranges. Frequencies and proportions of completeness and errors were calculated for each variable during the periods of interest for Electronic-Last and Electronic-First implementation. Variables for each model and between facilities and service delivery areas were compared by calculating proportions. All analyses were conducted in Microsoft Excel (Microsoft; Redmond, WA, USA) and STATA v15 (Stata Corp, College Station, TX, USA).
DISCUSSION We assessed the completeness and accuracy of data for key HIV programme variables needed for monitoring the health of PLHIV when starting and sustaining ART in Lusaka, Zambia. We observed important differences in data completeness and accuracy for these variables when transitioning from Electronic-Last to Electronic-First data capture at all health facilities. This information may be useful to clinicians, health facilities, implementing partners and the MoH for improving patient management, the ART programme, and planning the scale-up of Electronic-First across all health facilities in Zambia. There was no difference in data completeness at registration service points at all health facilities when compared before and after the transition. Completeness of data at these registration service points has been of consistently high standards during both Electronic-First and Electronic-Last implementation.
19 It is therefore vital that TB screening is performed and recorded at every clinic interaction. Overall completeness of ART regimen data at pharmacies slightly decreased following the transition; this was in line with reports from in Ethiopia. 15 This incompleteness, while involving small margins, can nonetheless lead to critical misreporting, which might impact programme implementation, leading to potential stock outs of commodities. The impact of incomplete data at facility level cascades upward to the national level, and affects the procurement, planning and budgeting of drugs for health services. 20 Data from SmartCare EMR is used by the MoH and other implementing partners to report to donors, and this can also affect overall planning for health services across Zambia. Anthropometric measurement and vital sign data did not improve in completeness, and in fact became more inaccurate following transition in data entry methodology from Electronic-Last to Electronic-First.
Similar findings of missing and incorrect anthropometric and vital sign data in an EMR were reported in Mozambique. 21 Although anthropometric and vital sign data may not influence programme management, it is still critical that these data are accurate for monitoring a patient's clinical status. Changes in a patient's clinical indicators may be an early sign of ART failure or the development of new opportunistic infections or malignancy. Monitoring a patient's blood pressure is important for the early detection of hypertension. 3] [24]  As access to ART increases and virologic suppression is achieved, HIV-infected patients will live longer,  25  and experience a longer life expectancy with increasing risk of CVD such as coronary heart disease. 23 The estimated prevalence of hypertension in Zambia is 25.
9%. 26, 27 It is therefore vital that blood pressure be monitored accurately and routinely as part of a comprehensive hypertension management in PLHIV. Growth monitoring (height and weight) is an essential part of providing care for children living with HIV. Poor growth could indicate ART treatment failure, or the presence of TB, opportunistic infections, malnutrition, or malignancy. Weight is also critical for drug-dose calculations in children and for monitoring progress of adults on ART. In Mozambique, height and weight data were the most commonly missed and inaccurate variables. 21 nthropometric and vital sign data may be missing or inaccurate due to lack or limited availability of equipment, such as a sphygmomanometer or weight scale. Inadequate lay health worker training and supervision on taking anthropometric and vital sign measurements, and entering these data into SmartCare, may also have contributed to poor data quality due to the limited understanding of normal anthropometric ranges among this cadre.
Public Health Action First, at the time of analysis, only 10 health facilities were eligible to participate in the Lusaka Urban District. Restricting our analysis to Lusaka hindered our ability to detect important differences in data accuracy and completeness in other districts, limiting the generalisability of our findings. Second, because of our pragmatic, before-after descriptive approach, we could not account for factors in our analysis that may have influenced data accuracy and completeness during the transition from Electronic-Last to Electronic-First, such as health worker training on data management. Based on our study findings, we make the following recommendations for improved SmartCare EMR implementation under the Electronic-First strategy: 1) improve comprehensive training and mentorship of lay health workers in task-shifted anthropometric and vital sign data capture and entry; 2) remind clinicians and nurses about the importance of conducting and documenting routine TB screening; 3) incorporate automated data validation checks into the SmartCare EMR to only accept inputs within logical numerical data ranges; 4) regularly supervise and mentor staff on complete and accurate data entry; and 5) conduct routine data audits to assess the completeness and accuracy of SmartCare EMR data.
CONCLUSION With the notable exception of clinical consultation data, there was no discernable improvement in the completeness or accuracy of data following the transition of data entry method from Electronic-Last to Electronic-First at the health facilities studied. Quality improvement interventions should be undertaken to ensure that the data entered into the SmartCare EMR is complete and accurate across all health facility levels and departments. These findings may be useful for other health actors looking to implement similar EMR systems in Zambia and other low and middle-income country settings.
Insect assemblage A total of 88 different beetle taxa were found in the samples from the Nunalleq peat profile. This includes 39 taxa identified to species plus 49 other taxa identified to lower taxonomic levels (e. g. tribe, family, genus, subgenus or group). Rove beetles (Staphylinidae) and ground beetles (Carabidae) dominate the assemblage, which also contains Dytiscidae, Ptiliidae, Latridiidae, Byrrhidae, Chrysomelidae, Brachyceridae and Curculionidae. Two lice (order Phthiraptera) species were also recovered: the human louse, Pediculus humanus L. , and the dog-biting louse, Trichodectes canis (De Geer), which are parasitic on humans and canids, respectively. The majority of the beetle taxa (72%) are Holarctic, the remaining being of worldwide or Nearctic distribution. Notably, this dataset includes the first record of the pill beetle Simplocaria elongata J.
Palaeoenvironmental reconstruction Our dataset encompasses the mid-15th to the early 20th century, and ends just prior to the beginning of instrumental records in the Y-K delta from the weather station at Bethel Airport (Fig. 2 ). Palynological analysis of the Nunalleq peat profile identifies evidence for human activity and occupation of the archaeological site from a depth of 33 cm, dating to c. AD 1490-1660. The end of occupation is difficult to ascertain from the palynological record, however, it likely occurs at 21 cm, between AD 1725 and 1815  (Ledger, 2018). These age ranges are in agreement with the findings of radiocarbon dating from the archaeological site  (Ledger et al. , 2018). The sedimentary profile therefore encapsulates the entirety of the Nunalleq occupation and provides both pre-and post-occupational palaeoenvironmental and climatic baselines to human activity at the site.
9 ). The clearest trend from the dataset is that the summer climate of the Y-K delta from the mid-15th to late 19th century was, in all likelihood, cooler than that of the 20th century. There are no instances where the minimum values of the reconstructed T MAX exceed either the early 20th century or modern means. Indeed, in all instances (with exception of the late 19th/early 20th century), the majority of the range of T MAX is lower than both 20th century reference periods (Fig. 9a, c ). There are two points when mean summer temperatures in the Y-K delta were definitively cooler than in the early 20th century. The first of these occurs at the onset of the occupation of the Nunalleq site (c. cal. AD 1570), when T MAX was ≥0. 3ᵒC below the early 20th century mean. Similarly, summer temperatures for the early 19th century (cal.
, 2005). This is a simplified picture however, as palaeoclimate records present regional and local responses to global changes in atmospheric and oceanic conditions. The Nunalleq palaeoclimate reconstruction  (cal. AD 1460 (cal. AD -1910) )  covers the latter of these two periods: LIA cooling and recent anthropogenic warming, while the occupation of Nunalleq (cal. AD 1570-1670) was centred on the LIA. Despite an abundance of palaeoclimatic records available from across Alaska, many focus on earlier periods such as the Pleistocene and early Holocene (e. g. Kuzmina et al. , 2008; Elias et al. , 1999b) , or are poorly resolved for the modern period (e. g. Brubaker et al. , 2001). Few studies of the past millennium present palaeo-temperature reconstructionsinstead the majority make palaeoclimate inferences on the basis of departures from long-term trends within datasets.
Conclusions Climatic change associated with the LIA is frequently implicated in interpretative paradigms of the archaeological record of Arctic and sub-Arctic regions. Yet despite evidence showing that the intensity and occurrence of the LIA is both temporally and spatially transgressive, site-based palaeoclimate data are rare. Typically, palaeoclimatic patterns established at a geographically proximate location are extrapolated to the archaeological site in question to form an interpretative canvas. In instances where the geography (i. e. elevation, latitude, ecology) of an archaeological site is comparable with the source of the extrapolated data, or the site occurs within a region with a dense network of internally coherent palaeoclimate data, this approach is defensible. However, where local, or geographically similar studies are absent, and notwithstanding the chronological uncertainties of transposing datasets, such an approach is problematic.
In the case of Nunalleq, the nearest available palaeoclimate data are from a series of interior montane lakes, making local palaeoclimatic data essential. To this end we undertook a high (contiguous 2 cm) resolution study of the Coleoptera fauna of a peat profile within the immediate vicinity of the archaeological site of Nunalleq. Through the MCR method, we reconstructed palaeo-temperature change at approximately 20-50 year intervals, spanning the period of prehistoric Yup'ik occupation. Precise MCR climate reconstructions rely on the preservation and recovery of the diversity of past beetle faunas in a geographic location. Typically, this is achieved by means of large sample volumes collected using a coarse vertical sampling resolution of c. 4-5 cm, an approach which results in low (> 50 yr) chronological resolution and smooths variation within a dataset.
Acknowledgements This research was funded through an  Arts and Humanities Research Council  grant ( AH/K006029/1 ) awarded to  Drs. Rick Knecht ,  Charlotta Hillerdal  and  Kate Britton , the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  703322  awarded to  Dr. Véronique Forbes , and two  NERC Radiocarbon Facility grants  ( NF/2015/1/6  and  NF/2015/2/3 ) awarded to  Drs. Rick Knecht  and  Paul Ledger. Our  work at Nunalleq also benefitted from the support of the local community who made us all feel at home in Quinhagak and provided consistently warm hospitality. Logistical support from  Warren Jones  and  Qanirtuuq Incorporated  was also invaluable. VF would like to thank  Anthony Davies  for his invaluable help with the identification of Stenus species and for verifying her identification of other staphylinid taxa.
Data acquisition was performed on a 4-laser LSRFortessa (BD Bioscience). All samples were kept as individuals and not pooled. Immune cell phenotypic characterization was performed using FlowJo software (version 10. 1, Tree Star). Fluorescence minus one (FMO) staining controls were used for all panels where necessary (Supplementary Figure  1 ). Flow cytometry data quality was based on primary time gates to ensure appropriate laser delay (pre-determined by automated CS&T) during sample acquisition.
Flow Cytometric Statistical Analyses Statistical analysis and graphing was performed using GraphPad Prism (GraphPad software; version 7. 0a). Statistical significance of p<0. 05 was considered significant. Unpaired, two-tailed Student's t-test or Mann Whitney U test were used based on distribution of the data as determined by D'Agostino-Pearson omnibus normality test. Internal correlation within the untreated and OM-85 treated groups was assessed by controlling for family clustering using Generalized Estimation Equation  (20)  or Wilcoxon Rank-Based Test for Clustered Data (arXiv:1706. 03409) as based on distribution of the data. Significance of the findings were not influenced by family clustering. Part of the cDC flow cytometry data presented in Figure  1  and MDP data presented in Figure  2  has been published in a forerunner manuscript  (15).
RNA-Seq Data Analysis Pre-Processing and Exploratory Data Analysis RNA-seq data was analysed in the R environment for statistical computing. Sequencing data quality control (QC) was performed with the Bioconductor package Rqc  (21). Sequencing reads were aligned to the reference murine genome (mm10) using Subread and summarized at the gene level using featureCounts  (22). Genes with <500 total counts across the data were removed from the analysis. Sample QC was performed by analysing the distribution of the raw read counts to check for sample outliers using boxplots, relative log-transformed expression (RLE) plots and principal component analysis (PCA), before and after global-scale median normalization. Differential expression analysis: Differentially expressed genes (DEG) were identified employing the DESeq2 package  (23).
DESeq2 utilizes a negative binomial distribution model, with a False Discovery Rate adjusted P-value for multiple comparisons. Genes were deemed significant with an adjusted P-value < 0. 1. Pathways analysis: Pathways enrichment analysis was performed using the InnateDB database  (24)  with Benjamini & Hochberg adjusted P-value ≤ 0. 05 deemed significant. Upstream regulator analysis: Ingenuity Systems Upstream Regulator Analysis (25) was employed to identify putative molecular drives of the DEG patterns. Significance was determined by activation Z-score ≥ 2 and P-value of overlap ≤ 0. 05.