dataesr/scientific-paragraphs-categorization huggingface dataset:
It consisted in an artemisinin-based combination therapy (artemether and lumefantrine). TBS were also performed every month to detect asymptomatic malaria infections. In addition, mothers were invited to bring their infants to the health center, at any time, in case of fever (suspected by the mother) or clinical signs, whether or not they were related to malaria, and the same protocol was applied. Venous blood samples were collected quarterly for hematological and immunological measurements. Venous blood was centrifuged for plasma isolation, and genomic DNA extraction was performed from buffy coat using the QIAamp ® DNA blood Mini Kit (QIAGEN) according to the manufacturer's instructions. To assess the environmental risk of malaria exposure, environmental (information on house characteristics and its immediate surrounding) and geographical data (satellite images, soil type, watercourse nearby, vegetation index, rainfall) were recorded.
The homozygous or heterozygous carriage of these alleles led to the ten following possible G3m p h e n o t y p e s : G  3 m 5 , 1 0 , 1 1 , 1 3 , 1 4 ; G 3 m 5 , 6 , 1 0 , 1 1 , 1 4 ; G3m5,   6,10,11,13,14; G3m5,6,11,24; G3m5,6,10,11,13,14,24; G3m5,6,10,11,14,24; G3m10,11,13,15; G3m5,10,11,13,14,15; G3m5,6,10,11,13,14,15; and G3m5,6,10,11,13,15,24. FcgRIIA, FcgRIIIA, and FcgRIIIB Genotyping Genomic DNA was available for 369 out of the 407 infants for whom Gm data were retained. Among these 369 infants, 368 Gm data were available, while all were genotyped for FcgRIIA, 365 for FcgRIIIA and 353 for FcgRIIIB. However, multivariate models using the four complete FcgR polymorphisms and G3m phenotypes determinations were done on 350 infants.
Environmental exposure was categorized into low versus high exposure, taking the median as the threshold value. Univariate analyzes on each of the covariates were made and only those with a P value < 0. 20 in the univariate model were included in the final multivariate model. The statistical analysis was carried out using the Stata software version 13. Finally, to evaluate the influence of combinations of G3m allotypes, FcgRIIA, FcgRIIIA, and FcgRIIIB polymorphisms on the number of P. falciparum infections during the follow-up, and their potential interaction with mosquito exposure, we performed multivariate analyses by using the generalized multifactor dimensionality reduction (GMDR) method  (40). This non-parametric and genetic model-free approach overcomes some of the limitations of the traditional statistical methods (i.
e. sample size limitation) to detect and characterize gene-gene and gene-environment interactions. GMDR is very similar to the original MDR method. However, instead of using the counts of individuals, the GMDR method uses a residualbased score in order to classify the individuals, thus allowing adjustment for discrete and continuous covariates. Since the GMDR software requires a complete dataset with no missing values for analysis, we removed individuals with missing genotype data for at least two polymorphisms and those with unknown number of antenatal visits (n = 21). The remaining missing data for FcgR polymorphisms were imputed using the R package MICE (Multivariate Imputation by Chained Equations) (41), a multivariate imputation approach that takes into consideration patterns in the data such as linkage disequilibrium.
Detection of Gene-Gene and Gene-Environment Interactions by Generalized Multifactor Dimensionality Reduction (GMDR) GMDR was used to screen the potential interactions among G3m and FcgR polymorphisms and to evaluate the impact of geneexposure interaction on the risk of malaria infection. Among all possible one-to four-locus models evaluated by the GMDR method, none reached the cut-off significance level of 0. 05 when mosquito exposure was included as a covariate in the GMDR models, along with mother and child age, bednet use, and number of antenatal visits (data not shown). In contrast, when the exposure variable was combined with G3m and FcgR polymorphisms to detect potential gene-environment interactions, several models displayed significant results (Table  9 ). The five-factor interaction model combining G3m, FcgRIIA, FcgRIIIA, FcgRIIIB polymorphisms, and mosquito exposure was the best model identified, with the maximum prediction accuracy of 62.
ACKNOWLEDGMENTS The authors are grateful to all women and infants of  Tori Bossito  who agreed to participate in this project, to field supervisors and community health workers of contributing villages, to midwives, nurses, and health auxiliaries of the three health centers, as well as to laboratory technicians of  Tori Bossito  for their collaboration. The authors would like to thank the  Facultédes Sciences de la Santé(FSS) , the  Institut des Sciences Biomedicales Appliqueés de Cotonou (ISBA) , the  Programme National de Lutte contre le Paludisme (PNLP)  for their institutional support.
The experiment was controlled using EPrime software (Version 2. 0, Psychology Software Tools, Pittsburgh). Humans. Eight university students participated in this study (five females, age range 21-28). All participants were paid for their participation, had normal or corrected-to-normal vision, and were naïve as to the goal of the study. Testing lasted approximately 30 minutes per participant and was divided in two sessions (one for each experiment) separated by a 5 minutes break. Written informed consent was obtained from all participants. Procedure. Task. The general procedure of the task is illustrated in Fig. 2a. Each trial began with the display of a fixation cross (120 × 120 pixels) presented at the bottom of the screen. Touching this stimulus triggered the display of 15 white crosses (60 × 60 pixels each) in addition to one target stimulus (a blue circle of 80 × 80 pixels) which were all displayed on a 16 (4 ×4) matrix.
e. no learning of the novel sequences could take place throughout testing); and (4) the second halves of the sequences were identical in the mirror consistent, mirror inconsistent, repeat consistent and repeat inconsistent conditions, thereby controlling for motor constraints across conditions. Sequences from the different conditions therefore differed from each other only regarding the matching between the two halves of the sequences. Custom-written Python code (www. python. org) was used to generate the sequences. The list of all the sequences used in each phase is available in Supplementary Table  2. Further details concerning the sequence construction procedure used are provided below for each phase: Exposure. Sixty-four length-4 training sequences were generated. Each of the 16 locations of the matrix was presented four times in the first position of the sequence and combined with four different locations presented in second position of the sequence (i.
The former variable was therefore not controlled during that phase. Control. The same set of mirror consistent, mirror inconsistent, repeat consistent and repeat inconsistent sequences as in Test 3 was used. Human experiment. Humans performed the mirror and repeat experiments in a within-participants design (presented in a counterbalanced order across participants). They were first exposed to the grammar during two 64-trial blocks using the same length-4 training sequences as for baboons. They were then presented with Test 3, using the same sequences as used for baboons (i. e. the same 48 length-6 baseline sequences, 16 length-6 consistent and 16 length-6 inconsistent sequences, each presented twice). Data analyses. Incorrect trials were removed from the data set (3. 35%), as well as RTs greater than three standard deviations from the mean (2.
Acknowledgements The authors thank the staff of the  CNRS primate center (Rousset-sur-Arc, France ) as well as  Julie Gullstrand  for technical support. This research was supported by the project  Premilang2  ( ANR-13-BSH2-0002 ), labex  BLRI  ( ANR-11-LABX-0036 ) and the  Convergence ILCB institute  ( ANR-16-CONV-0002 ). R. M. was supported by a PhD scolarship from the ED356 of  Aix-Marseille University  and a post-doctoral Fyssen grant. S. D. was funded by  INSERM ,  CEA ,  Collège de France , and an  ERC  grant « NeuroSyntax ».
The project also consists of a git link (https://github. com/ jyotikab/stn_gpe_ssbn) to the code required to simulate and analyze the model. The β band oscillations are also accompanied by an increase in spike bursting along with the firing rate changes. In MPTP models of non-human primates, the proportion of bursty spikes in STN and GPe is significantly higher in animals with PD than the healthy animals  [8, 49]. Increased spike bursting in GPe and STN is also observed in 6-OHDA treated rodents  [50, 51]. But it remains unclear how increased spike bursting affects the duration and power of β band oscillations. However, it should be noted neurons in the STN-GPe network show spike bursting even in healthy conditions  [8, 49]. Therefore, it is important to understand whether the spike bursting and the pathological oscillations share a causal relationship and if this is the case, then why spike bursts are also observed in healthy states  [8, 49].
The range of STN and GPe inputs was same as used to generate the results shown in Fig 2. Each model was simulated five times with different random number seeds. The spectral entropy for the five trials was averaged to obtain the state space (e. g. Fig 2C ) for each parameter set. Next, we identified the parameter set that results in a state space which had linearly separable oscillatory (spectral entropy � 0. 45) and non-oscillatory (spectral entropy � 0. 55) regions. This was done using a Support Vector Classifier (SVC) from python library sklearn to classify our networks into two classes (class label 0: non-oscillatory, spectral entropy � 0. 55 and class label 1: oscillatory, spectral entropy � 0. 45). Using this analysis we retained the models that resulted in a classification score of 1.
Data analysis Spectrum of the population activity. To estimate the spectrum of the network activity we binned (bin width = 5 ms) the spiking activity of all the STN or GPe neurons to obtain the population activity (S). We subtracted the mean and estimated the spectrum (P) using the fast Fourier transform (frequency resolution = 5 Hz). To estimate spectral entropy (see below) we measured the P for the whole duration of simulations (7500 ms). To estimate the time-resolved spectrum we measured P for sliding windows (window size = 200ms; overlap = 50ms). Spectral entropy. To quantify how oscillatory the network activity was, we computed the spectral entropy H S , which is a measure of dispersion of spectral energy of a signal  [52, 56]. H S ¼ À P k P k logP k logN where P k is the spectral power at frequency k and N is the total number of frequency bins considered.
Simulation and data analysis tools The dynamics of STN-GPe network was simulated using NEST (version 2. 12. 0)  [53]  with a simulation resolution of 0. 1ms. The SSB neuron model was added to NEST and the code as well as instructions on recompilation are described in https://github. com/jyotikab/stn_gpe_ ssbn. Spiking activity of the network was analyzed using custom code written using SciPy and NumPy libraries. Visualizations were done using Matplotlib  [61].
Such non-Poissonian inputs might underlie resonance of STN-GPe network at certain frequencies  [34]. In future models effect of non-Poissonian inputs should explored in more detail. As is typical for computational models, necessary parameters are rarely available from a single animal model and single experimental conditions. To counter this limitation, we varied the parameters by 10-20% to ensure the robustness of our results. Even though the model was constrained by data from rodents some of the model results are consistent with experimental observations made in non-human primates and human patients. This similarity underscores the generality of the model and the experimental phenomena (i. e. properties of beta band oscillations).
Genetic diversity has been assessed for thousands of species, and meta-analyses of hundreds of datasets show that genetic diversity is decreasing, especially since the industrial revolution, because of habitat degradation and population loss, unsustainable harvest, invasive species and increasing extreme climatic events  (Aguilar et al. , 2008; Leigh et al. , 2019; Miraldo et al. , 2016; Pinsky and Palumbi, 2014). Genetic diversity is recognized as one of three basic elements of biodiversity under the Convention on Biological Diversity (CBD; www. cbd. int), an international treaty under the United Nations Environmental Programme (UNEP) opened at the Earth Summit in Rio de Janeiro in 1992 and currently ratified by 195 states, plus the European Union. In 2010, the CBD adopted a Strategic Plan (Convention on Biological Diversity, 2010a) for the period 2011-2020 that included 20 targets to be achieved by 2020 (i.
New genetic indicators to assess progress towards global conservation targets The Biodiversity Indicators Partnership, which supports CBD indicator development, recently stated, "it is still possible to identify datasets and indicators of which we were previously unaware, and there is often ongoing work that, with minimal support, could provide a new and innovative indicator for use" (http://www. bipindicators. net). We explain here three genetic diversity indicators (elaborating from  Laikre et al. , 2020) , all of which are essentially counts of populations or species meeting a criteria and can be applied to wild or domesticated species, in situ or ex situ (Fig. 1 ). (1) Number of populations within species with effective population size  (Ne)  above 500 versus those with Ne below 500: A principal component of genetic diversity change is the genetic effective size (Ne) of populations, which determines rates of inbreeding, loss of genetic variation, and loss of adaptive potential.
, 2017). The loss of distinct populations could be calculated from IUCN Red List data, historic Global Biodiversity Information Facility (GBIF) occurrences, or data underlying the Living Planet Index. The question arises, what is a population? While populations are often geographically distinct (e. g. a wetland, a forest patch), in many cases they are not (see caveat above for Ne). Genetic data can reveal population boundaries  (Waples and Gaggiotti 2006) , though observed genetic differences should be interpreted carefully to avoid misinterpretation. For example, recent population decline and/or cessation of gene flow can lead to observed population differentiation that is only very recent. Moreover, high resolution genomic datasets may detect very small population divergence with little conservation significance.
Such knowledge is often used to directly guide management for safeguarding genetic diversity, such as clarifying taxonomic distinctiveness of rare species, assessing current vs. historic genetic connectivity to inform translocations and restoration, optimal protected area design, informing captive breeding, and identifying genetic consequences of stocking or harvest  (Bowman et al. , 2016; Holliday et al. , 2017; Koskela et al. , 2013). We thus propose an indicator as the number of populations of extant species whose within-species genetic diversity (more than one individual and ideally multiple populations) has been assessed in a publication or published in online databases and/ or where this data is used to inform conservation. An improvement upon this, as genetic data accumulates (see Conclusion), will be to focus on the number of species with routine monitoring of genetic diversity to facilitate conservation action.
We consider this to be an informative and practical indicator but recognise that it is a proxy for conservation genetic data observations and knowledge rather than conservation genetic action (e. g. the research-implementation gap,  Taylor et al. 2017). This indicator could also reflect increased laboratory capacity, or an increase in imperiled species needing conservation efforts, and therefore cannot be relied on in isolation. Data sources for this indicator could include journal articles, reports (cf. Bowman et al. , 2016)  and the number of species' populations with genetic datasets submitted to major repositories including GenBank, EMBL-EBI, GEOME, Sequence Read Archive, Dryad, FishBank, and CartograTree. The submission of genetic information in such repositories in a coordinated, standardized fashion is still largely lacking, a clear task for the groups in Table  1.
Within five to ten years, additional indicators of genetic erosion based on large genetic datasets for large numbers of species, with data or inferences reaching back hundreds of years, are likely. In addition, genetic erosion can in some cases be inferred by monitoring anthropogenic causative factors (e. g. , deterioration of habitat, climate change), though the relationship between such factors and genetic erosion is unknown for most species, is sometimes non-linear, multifaceted, and time-lagged  (Carvalho et al. , 2019). However, if the links between specific causal drivers and genetic erosion are established  (Wasserman et al. , 2012) , monitoring might focus on the drivers directly without the need for genetic analysis; this is a critical area of research. Similarly, linking critical thresholds for Ne to minimum area requirements of suitable habitat could allow determining if a population confined to a certain patch size is large enough to maintain genetic diversity over time.
The indicators we propose should be supplemented as data accumulates and improves. Indicators could also build on the concept of Essential Biodiversity Variables (EBVs), which are summaries of complex biodiversity data  (Navarro et al. , 2017) genetic composition EBVs include allelic diversity, genetic differentiation, and Ne. Progress towards any indicator, including those noted above will require and contribute to capacity-building (Convention on Biological Diversity, 2020; Target 15) for documenting, curating, sharing, and accessing regional and global databases on population demography and genetic diversity information, especially in biodiverse regions. Currently, most collected genetic data (like most biodiversity data) is not easily accessible or lacks sufficient meta-data (Convention on Biological Diversity, 2010b;  Pope et al.
Acknowledgements We would like to acknowledge funding from the  EU Framework Horizon 2020  through the  COST Action 18134 'Genomic Biodiversity Knowledge for Resilient Ecosystems'-GBIKE. This work has benefited from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ), funding via  The Morton Arboretum Center for Tree Science, Swedish Research Council Formas , Grant Number:  FR-2016/0005 ,  Swedish Research Council , Grant Number:  2019-05503. We also thank  Jerker Lokrantz/Azote  for preparing Fig. 1. The open access was financed through the SwEPA and BIBSAM.
Background People with chronic diseases in France frequently incur out-of-pocket expenses (OOPE) related to their medical care. Objective The objective of this study was to evaluate OOPE incurred by people with multiple sclerosis (MS) with respect to direct non-medical and medical expenditure. Methods Data were collected through a web-based survey using an online patient community platform (Carenity). The survey questionnaire contained 87 questions (numerical response or Likert scale) and took less than 30 min to complete. Participants rated their disability on a ten-point scale. Results In total, 376 patients, with a mean age of 48. 3 years (95% confidence interval [CI] 47. 2-49. 5), participated in the survey. Participants estimated that they spent an average of €127 each month on OOPE for their MS, principally on physician consultations (mean annual expenditure of €75 by 183 participants), non-physician consultations (€358 by 135 participants) and non-prescription medication (mean expense of €67 per pharmacy visit by 234 patients).
Key Points for Decision Makers Two-thirds of patients with multiple sclerosis (MS) incur out-of-pocket expenses (OOPE) associated with their pathology in spite of benefiting from ALD (Affection de Longue Durée) full reimbursement status. The OOPE estimated by patients with MS represents 7% of the per capita disposable household income in France For most patients with MS, the OOPE for medications was for over-the-counter medications, whereas OOPE for consultations related to expenses related to specialists who had tariffs above ALD reimbursement levels. data are shared anonymously and in aggregate form, which means participants cannot be identified. The MS patient community on Carenity currently has around 7000 members, of whom 78. 9% are patients. The present survey was available to any patient who was a member of the Carenity MS community, was aged ≥ 18 years and was resident in France.
Data Collection Data collected from participants included their age, sex and information on their level of disability. As patients may not know their Expanded Disability Status Scale score  [14] , the reference disability measure in MS, patients self-reported their disability based on a single questionnaire used previously in surveys carried out by the LFSEP  [15]. This scale assigns a grade of 1-10 according to the patient's level of disability (see Table  1 ). Reimbursement status for healthcare consumption was documented. In France, the public health service does not provide comprehensive coverage of expenditure, and patients usually must pay the residual cost of care at the point of access. In total, 95% of patients subscribe to complementary health insurance, either private (subscribed to or provided by their employer) or public (so-called Universal Complementary Health Coverage [CMUC]), which may reimburse at least part of this residual cost.
Existing studies on supply location decisions and network design, especially those with the simulation and modelling approaches that have been the tools for many studies on complex and multi-tier supply chains  [5] , pay little attention to third-tier suppliers and TBL sustainability. Most relevant existing studies consider only first-tier suppliers who produce final products, while recent studies have started to consider second-tier suppliers who supply materials to the first-tier suppliers, as shown in Table  1. The nine articles in Table  1  were selected by reading abstracts of all retrieved articles from the Scopus database with search terms relating to design or model and manufacturing or supplier locations. Re-Cycling  [10]  Minimize total costs and maximize effectiveness of suppliers' equipment Automotive x x x x  [11]  Minimize risks (time, reliability, inventory, cost) N/A x  [12]  Order frequency and lead-time impacts on costs and supply chain design Automotive, France x x x x  [13]  Reduce vehicle fuel consumption and emissions Natural honey x x x  [14]  Optimize supply chain total costs Cartridge, India x x x x x x  [15]  Minimize operational and fixed costs with return and demand uncertainty Electric appliance, China x x x  [16]  Minimize costs, and transportation and investment trade-offs Biomass x x x  [17]  Optimize carbon emissions and costs (cap and trade policy) Generated dataset x x x  [18]  Minimize outsourcing costs with allocation and services to customers Automobile, Iran x x x.
Users can use logic models and ABC to reveal these factors. Amounts of manufacturing inputs and outputs, as well as distance and time for sample, product, and manager transportations, are gathered in Step 3. Data for these factors are stored in manufacturing-, logistics-, firm-, reverse logistics-, and recycling-data matrices, whose cells are multiplied with coefficient values from manufacturing-, logistics-, and firm-coefficient matrices. Every data matrix in Step 3 to Step 5 has its own coefficient matrix. The coefficients are beneficial to sensitivity and scenario analyses in Step 10 as they allow for easy changing of factor values and comparison, and are initially set to values of 1.