dataesr/scientific-paragraphs-categorization huggingface dataset:
This is mainly because, in the RC, the power coming from the 16 parallel modules is sum together in a single step, whereas in the SC, this function is accomplished by cascading three different levels of recombination  [26]. On the other hand, thanks to a more compact and integrated structure, the SC outperforms  the RC in terms of size and weight. This is essential because, in the SC, the MMICs are not individually packaged, being the hermeticity achieved by sealing the shell of the structure with a laser. Together with the hermeticity, in a space environment, it is fundamental to guarantee that the adopted structure is multipactor-free and that the junction temperature (T j ) of the GaN MMIC components does not exceed the boundary limit of 160 • C. In this respect, both solutions have been simulated by using Spark3D software for verifying the multipaction compliance, whereas the thermal-mechanical behavior has been evaluated by using dedicated multiphysics tools.
Moreover, to maximize the recombination efficiency in the HPS, the position of each HPM was chosen accounting for their individual phase and amplitude responses, as well as those of each path of the radial splitter/combiner structures reported in Fig. 11. This was done by performing an optimization process in ADS Keysight software, in which the optimized variable was the position of each HPM, and the goal was the minimization of both the phase misalignment and amplitude variation among the 16 paths. Finally, Fig. 14(a)  shows the measured output power in CW of all packaged MMICs as a function of the frequency, whereas Fig. 14(b ) reports their PAE as a function of the output power at center frequency. The performance is well in line with those measured on-wafer  [20]  with the modules achieving an output power larger than 40 dBm all over the bandwidth and a PAE larger than 35%.
, 2022). This methodology has been elaborated based on original datasets of concentration time series that are both high-frequency (23 to 45 minutes) and multielemental as they include all major ions. These datasets were produced by a field laboratory prototype, the so-called 'Riverlab'  (Floury et al. , 2017)  installed in two observatories of the French OZCAR Critical Zone Study Network (https://www. ozcarri. org), contrasted in terms of pedo-climate and of land use and cover. The overall aim of this work is to propose a systematic methodology applicable to the analysis and interpretation of a large chemical dataset covering contrasting hydrological events from a multitude of different catchments.
Data treatment and analysis The focus of this article lies on presenting a methodology to analyse and interpret the variation of the stream chemistry during storm events. We therefore only present data of storm events. The following paragraphs outline the data acquisition period as well as the selection procedure of the storm events. The field laboratories were operational from June 2018 onwards at Kervidy-Naizin and from November 2020 onwards at Strengbach. Here, we present the analysis of data collected until August 2022 in both catchments. Gaps in the dataset were due to variable technical problems, namely failure of the main pump, power cuts due to lightning, chemical alteration of the filtered water likely due to the filtration system, problems of the analytical instrument, periods without technical support and the COVID-19 pandemic.
Acknowledgements The  Critex Programme   ANR-11-EQPX-0011  funded the Riverlabs and most of the costs associated with their running. The people who run the Riverlabs were staff from  ORACLE ,  OHGE  and  AgrHyS Critical Zone Observatories  (i. e. staff from  CNRS and INRAE). The research units  UR HYCAR ,  IPGP ,  LHYGES/ITES ,  UMR SAS  and  UMR Geosciences Rennes  contributed to the current costs, especially vehicle costs associated with regular travels to the site for maintenance, power supply, etc. The post-doctoral position of N. B. was co-founded by  Region Bretagne ,  UMR SAS ,  INRAE (AQUA)  and  OZCAR-RI. We thank technical and analytical staff ( Beatrice Trinkler ,  Laure Cordier ,  Sophie Gangloff ,  Arnaud Blanchouin ,  Alain Hernandez ,  Anthony Julien ,  Pascal Friedmann ) as well as  Laurent Ruiz ,  Rémi Dupas  and  Chantal Gascuel-Odoux  for inspiring discussions.
Improving lifetime lactation efficiency of dairy cows by selection is difficult due to the complexity of this trait and the existence of genotype-by-environment interactions. This study aimed at assessing the relevance of traits derived from body reserves as lifetime efficiency indicators under contrasting nutritional environments. Given the absence of large-scale datasets covering a panel of feeding regimes, phenotypes were simulated for populations of 20 000 dairy cows using a mechanistic bioenergetic model. Ten phenotypes were computed for third-lactation cows. Analysed phenotypes comprised total milk production, lactation efficiency, BW at calving (BWcalv), DM intake (DMI) and interval between first insemination and conception. Five traits described levels and changes of body reserves at different periods during lactation.
To our knowledge, little research has addressed this issue given the scarcity of datasets available with both body reserve and feed intake records undercontrasted nutritional environments. To overcome this issue, we capitalised on the mechanistic bioenergetic model of a dairy cow developed by  Puillet et al. (2021)  to simulate performance datasets of a population of related cows. This mechanistic model is based on the energy allocation framework applied to livestock  (Beilharz et al. , 1993; Rauw, 2009). In the model, acquisition and allocation equations are viewed as ''meta-mechanisms"  (Tardieu et al. , 2020)  that aggregate underlying processes bound to the dynamic interplay between life functions. Therefore, it is possible to represent variation in lifetime trajectories of various phenotypes by defining different combination of acquisition and allocation parameters (AAPs).
Genetic determinism of acquisition and allocation parameters The four AAPs were under genetic control, meaning that part of the phenotypic variance around the mean values was explained by additive genetic effects. The genetic parameters assumed for acquisition and allocation of input traits were determined based on a sensitivity analysis to generate simulated datasets with realistic genetic parameters for milk production and feed efficiency traits  (Bouquet et al. , 2022a). Heritability was set to 0. 35 for the four AAPs and the phenotypic CV to 0. 10. A genetic module enabled the simulation of a population of 20 000 cows with a simple half-sib pedigree structure. It was simulated by mating at random 200 unrelated sires and 20 000 unrelated dams to generate a single generation of 200 half-sib families of 100 cows.
simulated using the conception probability that was estimated based on milk production, energy balance and BCS (Annex 1 in Supplementary Material). IFC spanned from 0d to 63d due to the 10-week mating period. To avoid data censoring for cows that failed to reproduce, a penalty value of 84d was set by default for cows that were not pregnant after the 4th oestrus as in  Puillet et al. (2021). Five traits were derived from body reserve trajectories to evaluate the ability of cows to mobilise and reconstitute their body reserves during lactation. In this simulation study, body reserve traits were estimated as the ratio of labile mass over empty BW and expressed in percentage points. We considered the level of body reserves at calving (BRcalv) and at nadir (BRmin), as well as the time between calving and nadir (TimeNadir, in days).
Variance components were estimated for all four AAPs and for the above-mentioned traits. Initially, univariate analyses were carried out to estimate heritabilities. Then, bivariate analyses were carried out to estimate genetic and phenotypic covariances between traits within a given environment. Within each replicate, the same 200-daughter groups were simulated in each of the environments allowing us to estimate genetic correlations for the same trait between environments. All calculations were performed using the Average Information ReML algorithm implemented in the DMU software  (Madsen and Jensen, 2013).
07). Genetic correlations estimated between HS and LS environments were higher than 0. 80 for all the other traits. Correlations could  Abbreviations: Lact_Eff = Energy conversion ratio in third lactation; Life_Eff = Lifetime energy conversion ratio; BRcalv = Body reserves at calving; BRmin = Body reserves at nadir; TimeNadir = Lactation day when body reserves reach nadir; dBRearly = change in body reserves between calving and nadir; dBRtot = change in body reserves between calving and end of lactation 1 Average SE of genetic correlation estimates ranged from 0. 02 to 0. 10. 2 SEM of genetic correlation estimates ranged from 0. 01 to 0. 04. not be estimated for IFC as the heritability and dataset size were too low to enable proper convergence of the REML algorithm in bivariate analyses.
Tests of significance carried out for estimated genetic parameters may therefore be slightly more permissive than observed with real data. However, we expect that this effect is limited in situations where progenies of sires are randomly distributed over contemporary groups of sufficient and balanced size. Secondly, it was assumed that the four genetically driven acquisition and allocation traits were uncorrelated. It was shown that violation of this assumption can substantially modify the phenotypic response to an environmental challenge  (Doeschl-Wilson et al. , 2008; Descamps et al. , 2016). Real datasets with longitudinal records on several successive lactations for milk production, energy intake, BW and BCS of hundreds of cows are needed to validate these assumptions. Finally, lactation acquisition that describes the innate increase in DMI triggered by lactation, was not associated with higher maintenance requirements.
Relative wall thickness (RWT) was measured to assess LV geometry. 22  End-diastolic volume (EDV), end-systolic volume, EF, and left atrial volume (LAV) were measured from two-dimensional apical views. Global longitudinal strain (GLS) was measured using a Tomtec 2D Cardiac Performance Analysis package (Tomtec, TTA2). End-diastolic volume and LAV were indexed to body surface area (EDVi and LAVi). Tissue Doppler imaging was obtained at the level of the lateral and septal mitral annulus for measurement of the e' wave and s' wave (average of lateral and septal values). The E/e' ratio was used as a surrogate measure of LV filling pressure. Ejection time (ET) was defined as R wave to the end of systole. First-Phase EF and Arterial Stiffness. First-phase EF was defined as the percentage change in LV volume from end diastole to the time of peak aortic flow velocity (TPAFV), a time that approximates the time of peak ventricular contraction in individual myocytes.
The most likely mechanism, therefore, is a reduction in the intrinsic contractile function of the myocardium. A possible explanation for this is that alterations in metabolism, particularly glucose metabolism, that are well recognized in CKD have an impact on the efficiency of myocardial contraction. 40  Our study is subject to limitations. Its cross-sectional design limits inferences on causality. Children with CKD were not characterized according to proteinuria. However, associations of EF1 with CKD defined by eGFR were robust and persisted across all stages of CKD. Office BP was measured using different methods between the 2 studies, but a sensitivity analysis performed separately in the HOT-KID and 4C study cohorts revealed comparable results (data not shown). Ambulatory BP monitoring and central BP measurements would ideally be used in this type of analysis but were not avail-able in most children.
Land productivity shifter Each field is associated to potential yields for all crops. These potential yields are aggregated at the 30-arcminute level from the 5-arcminute information provided by the GAEZ project (IIASA/FAO, 2021). Appendix B. 2 shows that the land productivity shifter, 𝐴 𝑓 𝑘 𝑖 , can be set equal to the information on potential yields. Potential yields are obtained under high inputs and rain-fed assumptions and are calculated based on the average weather observed between 1981 and 2010. Market equilibrium Most of the data characterizing the initial market equilibrium are from FAOSTAT, augmented by the GTAP database version 9. 2  (Aguiar et al. , 2016). GTAP 9. 2 provides information base on a 2011 reference year. In the case of the FAOSTAT data, to remove potential outliers I take the average for 2010-2.
The value of production for all crops, except grass, is from the FAOSTAT Value of Agricultural Production database. There are missing values in this database, when prices are not available. In the case of missing values I impute prices based on the average world price weighted by the quantities produced, and the value of production is calculated as price multiplied by quantity. The value of production of livestock products is from GTAP. The value of production of grass is also from GTAP and is represented by payments to the land factor by the livestock sectors. Land rents, 𝑅 𝑘 𝑖 , are obtained by multiplying the previously obtained value of crop production by the share of land in crop production costs from GTAP. Trade values for crops are from FAOSTAT, and by default use importer declarations where available.
BACKGROUND: Associations between tobacco use and poor TB treatment outcomes are well documented. However, for important outcomes such as TB recurrence or relapse and mortality during treatment, as well as for associations with smokeless tobacco (ST), the evidence is not summarized systematically. RESEARCH QUESTION: Is tobacco use associated with risk of poor treatment outcomes among people with TB? STUDY DESIGN AND METHODS: The MEDLINE, Embase, and Cumulative Index of Nursing and Allied Health Literature databases were searched on November 22, 2021. Epidemiologic studies reporting associations between tobacco use and at least one TB treatment outcome were eligible. Independent double-screening, extractions, and quality assessments were undertaken. Random effects meta-analyses were conducted for the two primary review outcomes (TB recurrence or relapse and mortality during treatment), and heterogeneity was explored using subgroups.
Study Design and Methods This review was registered with PROSPERO (Identifier: CRD42017060821) and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (e-Appendix 1). 16  Search Strategy and Selection Criteria Three electronic databases (MEDLINE, Embase, and Cumulative Index of Nursing and Allied Health Literature) were searched from inception to November 22, 2021. Search terms for tobacco use (smoking, smokeless) were developed from previous reviews, whereas those for TB outcomes were developed from a monograph on TB and tobacco control. 17  Searches were conducted by combining both sets of terms (e-Appendix 2); no language restrictions were applied during searching. We included epidemiologic studies (cohort, case control, and crosssectional) on people with TB (not restricted by age, sex, comorbidities, pulmonary or extrapulmonary presentation, or geographic region) that measured the effect of ever, current, or past tobacco use (with smoke and smokeless) on TB treatment outcomes (Table  1 ).
Data Extraction and Synthesis Groups of two reviewers (A. R. and A. -M. M. or A. J. and A. L. V. ) independently extracted data from included studies using a piloted data extraction form specifically designed for this review. The main sections included: study design and characteristics; sample size and  Delayed conversion rate of positive sputum smear results in patients with pulmonary TB at follow-up (after 2 months of therapy). Nonconversion was defined as persistent positive sputum smear results for patients with TB at the end of the 2-or 3-mo intensive phase of treatment.
Severity of disease b Higher bacillary load (smear grading 3þ and higher), more cavitation (advanced radiologic lesions), hospitalized, symptoms (cough, dyspnea, upper zone involvement) Drug-resistant TB a TB that is resistant to $ 1 first-line antituberculosis drugs a World Health Organization Global Tuberculosis Report, 2013. 18 b TB/tobacco monograph. 17  participant demographics; and exposure and outcome details, including type of tobacco (with smoke or smokeless), type of exposure (ever, current, or past), frequency of outcome among exposed and unexposed participants, and the measures of effect reported. The extraction forms were compared, and disagreements were resolved in the first instance by discussion or with a third reviewer (O. D. ) if consensus could not be reached. Risk of bias was evaluated using the Quality Assessment Tool for Quantitative Studies,  19  and each study was rated as strong, moderate, or weak in the following categories: study design, analysis, withdrawals and dropouts, data collection, selection bias, and confounders.
Based on these, an overall rating was provided. Subsequently, we considered the influence of studies with weak methodologic quality on summary effect sizes. Meta-analysis was carried out using RevMan version 5. 4 software (Cochrane Collaboration). 20  We classified the studies according to tobacco type (with smoke or smokeless) and exposure type (ever vs never use or current use vs current nonuse or past use vs never use) for each treatment outcome and performed meta-analysis for groups that included two or more studies. We limited the meta-analysis to our two primary outcomes (TB recurrence or relapse and mortality during treatment) and narratively synthesized the additional outcomes because they largely were covered in two reviews published in 2020. 13, 14  For the meta-analyses, we used the number of individuals exposed, number of individuals unexposed, and events observed in both those groups as reported in the individual studies to calculate risk ratios (RRs) and 95% CIs.
66  Our analytical strategy also meant that the effect of important confounders such as age, alcohol, and so forth were not accounted for adequately. The way data were reported on covariates did not allow for their use in metaregression, as originally planned. However, where available within primary studies, we reported adjusted estimates (e-Table  3 ). Also, when assessing quality, we considered the extent to which studies adjusted for potential confounders. Another limitation of the meta-analyses is the high heterogeneity: only the ever using tobacco and mortality during treatment analysis showed no heterogeneity. Further, we included studies with combined drugsusceptible TB and DRTB, as well as new and retreatment TB samples. However, we did our best to explain our findings using subgroup and sensitivity analyses.
Acknowledgments Author contributions: O. D. , K. S. , and J. E. G. contributed to conceptualization; O. D. contributed to design of literature search strategy; O. D. and A. L. V. conducted literature searches; O. D. , K. S. , and A. L. V. contributed to study design; A. L. V. , A. R. , M. B. , A. J. , F. S. , A. -M. M. , J. A. , and O. D. contributed to data extraction and quality appraisal; A. L. V. and O. D. contributed to data analysis; A. L. V. , O. D. , and A. J. contributed to data interpretation; A. L. V. and O. D. contributed to manuscript writing; A. L. V. , A. R. , M. B. , A. J. , F. S. , J. E. G. , K. S. , and O. D. contributed to revision of manuscript and editing. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
It should be noted that, due to the constraints of our photographic methods, our analysis was limited to the planar area of each colony, which represents a two-dimensional, topdown view of the coral colony surface. Bleaching does not occur uniformly across a coral colony. Typically, areas not exposed to direct sunlight exhibit less bleaching compared to the colony's uppermost parts that receive more sunlight. Therefore, to keep estimates consistent, all photographs were taken from the same perspective, ensuring that we compared equivalent portions of each colony (the planar area). All images were processed using the ImageJ software  (Schneider et al. , 2012)  with the 1 m 2 quadrat serving as a scale.
Again, exposure level and colony size were treated as interacting fixed effects. In both models, transect identity was also included as a random effect to account for any lack of spatial independence in the sampling design. Each model was based on 3 MCMC chains with 5000 iterations, including 1000 iterations to warm-up and a thinning interval of 3, with weakly informative priors. Model fits and assumptions were assessed using residual and autocorrelation plots, supplemented by metrics of effective sample size (neff) and sampling efficiency (rhat) scores, all of which were satisfactory and showed that the MCMC chains were well mixed and converged. Both Bayesian models were performed in Stan (Stan Development  Team, 2021)  via the brms package  (Bürkner, 2017) in the software R (R Core Team, 2020).
Acknowledgements We thank  R. P. Streit ,  J. A Schlaefer ,  M. Mihalitsis ,  C. Bowden ,  C. R. Hemingson ,  S. Swan ,  J. Grimm ,  P. O'Brien ,  C. H. R. Goatley  and the  Lizard Island Research Station  staff for field support. We also thank the  Australian Research Council  (DRB:  FL190100062 ), the  Ian Potter Foundation  (JM, SBT), the  Australian Museum's Lizard Island Research Station  (JM, SBT), and the  International Coral Reef Society (SBT)  for financial support.
To achieve this goal, we created and evaluated a 3D U-Net deep learning model to predict deep-disconnectomes for lesioned brains. Detailed descriptions of the used datasets are summarized in the supplementary material in Table  1. We created a 3D U-Net model  (Ronnenberger, Fischer & Brox, 2015)  that consists of 4 encoding-and 4 decoding layers and trained the model on 1333 synthetically created lesion-disconnectome dataset pairs (dataset 1). After training and validating the model, we tested it on an out-of-sample dataset of 1333 natural stroke lesions (dataset 2). Additionally, we tested the predictive power of deepdisconnectomes to predict clinical scores following a recently published approach on datasets 3 and 4  (Talozzi et al. , 2023). The accuracy of the neuropsychological predictions derived from the deep-disconnectomes was better than conventional disconnectomes (t(85) = -1.
Deep-disconnectome predictivity power for neuropsychological scores Based on the work of  Talozzi et al. (2023) , we used the same N=1333 stroke lesions (dataset 2) to derive deep-disconnectomes. The same dataset was imported into the Uniform Manifold Approximation and Projection (UMAP) embedding  (McInnes, Healy & Melville, 2018) , obtaining a novel morphospace for deep-disconnectomes (Figure  2a ). To test the deep-disconnectome predictions of neuropsychological scores and compare these to the predictions of the conventional disconnectomes, we embedded a dataset of new patients (n = 119, dataset 3) into the novel morphospace and followed the analysis steps described in  Talozzi et al. (2023). With this method, we were able to predict N=86 different behavioral and cognitive scores. An average accuracy of 85.