dataesr/scientific-paragraphs-categorization huggingface dataset:
Training the neural networks We used Matlab to study the performance on NNs on the superelastic behavior of SMA (Figure  1 ). Treatment of data set, neurons' weights, algorithm, and network type are the critical factors of training NNs to perform excellently. The tested and trained NNs are feedforward NN, cascade forward, fitting NN, and LSTM NN. The data set (learning base) is prepared with 64 values of stress vs. 64 of strain, with a step variation of 0. 01 for strain and 25 MPa for stress. We studied the feedforward NN, cascade forward, fitting NN, and LSTM NN training on the whole data set as an input to see if they could succeed in testing the whole data one at a time. Afterwards, we found the need to study the data set of the lower and upper parts separately. Moreover, we have studied the data set by splitting the continuous parts of each cycle.
Results and analysis In the beginning, we use the whole data set for the training (the two cycles together). Then, we split the cycles and separate the continuous parts. Finally, we standardize the data set for the training of the successful NNs. In each interval, we vary the number of hidden layers between 2 and 10, and the percentage of data used for training is 70% and 30% for testing for feedforward NN, cascade forward NN, and fitting NN. For LSTM NN, we vary the epoch number (complete iterations) between 1000 and 300 and the hidden units between 200 and 800. The previously specified parameters and ways of treating the training data depend on the linearity of the continuous part and the step of discretization in the data. They are chosen empirically; the lowest RMSE value (root mean square error) proves the best parameters and data treatment.
1 and 0. 5. Therefore, LSTM has comparable results to that of the fitting NN. To enhance the results after comparing the different NNs, we use the standardization of the data set to train again the LSTM and fitting NNs (see Figures  4  and 5 ). Thus, LSTM NN, by standardizing the training data (and with splitting the continuous parts) achieves the lowest RMSE of around 0. 06 and 0. 08, while that of fitting NN are around 0. 2 and 3. The highlight here is that the results of fitting NN with standardizing data are higher than those without standardizing the data. As we can see in Figure  5 , this is because of the lower ability to follow the non-constant continuous parts of the unloading cycle. Finally, LSTM can model the nonlinear SMA in both cycles, in which standardization has better results than non-standardization.
Heating SMA 1 stretches SMA 2, leading to the martensitic transformation. Then, heating the SMA 2 leads to return to its initial position and to stretch the SMA 1. By that, the cyclic movement of coupling the two agonistic wires of SMA is produced, measured by a force sensor and an LVDT displacement sensor. This part will be considered in Section 6. The strain-stress data set is prepared by regulating the rate of variation and separating the continuous lines of each test.
Experimental background During this experiment, we focus on the mid-point of the two SMA wires to model their behavior. For that, we consider they are heated alternatively to create a displacement of this mid-point. The initial state of the experiment is at the austenitic phase of the two SMA wires, and the prestretching of the two wires transforms them partially into oriented martensite. Then, the four steps that perform one cycle are applied and repeated cyclically: (1) heat SMA 1, (2) cool down SMA 1 to the room temperature, (3) heat SMA 2, (4) cool down SMA 2 to room temperature. The superposition of the stress-strain curves for a few cycles gives a "butterfly-like" diagram  [25]. The data recorded are the mid-point displacement, voltage and current intensity at the wires' terminals, and the force passing through the wire.
Methodology In the previous systems, the LSTM NN with a regression layer proved its ability to model the hysteresis behavior using the standardized input data. Therefore, we check its training for this curvy and complex butterfly shape. We test two cycles, repeating the four steps twice, using the regularization and splitting input data into two parts: left part to zero and right part to zero (negative deformation and positive deformation) for each cycle. On the Matlab platform, the LSTM with a regression layer is trained by this input data to obtain the experimental output data with low RMSE values and low computational time. For that, we decrease the hidden layer's size to 500 and the epoch value to 500 as suitable values.
Results Modeling the butterfly shape (Figure  9 ) is done with RMSE values, between 0. 5 and 0. 9, using a low computational time of 1 min to 2 min. The tests are decomposed into two series: cycle one and cycle two. Then each series has two parts: the positive deformation and the negative deformation. Therefore, because of the high non-linear behavior of the middle point of the antagonistic actuator, standardizing the data enhances the training of LSTM NN more than for a single SMA wire. Moreover, the RMSE values of cycle one and cycle two again prove the effect of non-linearity on the NN training, which are 0. 85 for cycle one and 0. 6 for cycle two.
The RMSE values and the computational time of the training do not exceed 2% and 3 min on an average, respectively. The prediction times after the training are of the order of several ms. The best way to train the NNs is to prepare regular input data sets with constant variation rates to provide the best-predicted output. These data regulation heuristics can be easily applied to more extensive databases if needed. At the same time, the computational time for the training can be considered reasonable compared to the construction of other approximation methods or problems. Also, the approximated predicted output data is close to the theoretical and experimental ones. As a perspective, the proposed procedure can be used as a module predicting the inner load without a sensor in real-time control schemes.
Weak branch union (binary) Presence or absence of an epicormic branch or branch attachment with included bark. z If more than one defect were present, assessors judged which was the most likely to cause failure. Forests 2022, 13, 978 4 of 11 A pair of International Society of Arboriculture (ISA) Certified Arborists holding the ISA Tree Risk Assessment Qualification (TRAQ) conducted the inventory. Table  1  includes data they collected, grouped by the following tree attributes: location, size, defects, and likelihood of failure rating. The likelihood of failure rating and assessment of defects were made in accordance with Pokorny  [29]. If more than one defect were present, assessors recorded them and judged which was the most likely to cause failure. In September 2017, Hurricane Irma made landfall in Southwest Florida as a category 3 hurricane.
Analysis of Sheltering We used previously-generated land cover raster data of the City of Tampa  [35]  to assess the potential wind protection offered by neighboring trees and structures. The land cover used six classes derived from the National Land Cover Database (NLCD) classification system: bare earth, buildings, grass/shrub, impervious, water, and tree canopy. The raster data was at a 6-inch cell resolution and covered the entire Tampa city limits. For computational tractability, we buffered the three target neighborhood polygons by 500 m and cropped the raster to these borders. To assess the possible sheltering effect of nearby aboveground structures, we classified land cover types of "buildings" and "tree canopy" as "windbreak". The percentages of windbreak cover in buffers surrounding individual trees were calculated within buffers from 10-100 m, in 10-m intervals.
The windbreak term, although insignificant, was retained in the model because it was part of our third hypothesis. We used the Moran's I statistic to test for spatial autocorrelation in the model residuals. Moran's I is used to characterize whether nearby quantities are more similar than expected by chance, thereby violating the key model assumption of independence of observations. For point data, Moran's I may consider nearby points based on a fixed distance, or a list of the k-nearest points. To detect spatial autocorrelation, we created neighborhoods based on both distance-based and K-nearest-neighbor (KNN) methods using the knearneigh() and dnearneigh() functions in package spdep  [39]. KNN neighborhoods were defined as the 5 nearest neighbors, while distance neighborhoods consisted of all trees within 150 m of a central tree.
We used the R function moran. test in package spdep  [39]  to perform the Moran's I tests. Due to significant spatial autocorrelation in the logistic model residuals, a spatial autoregressive logistic model, hereafter referred to as the spatial GLS, with a Matérn correlation structure was fit using the fitme() function in packages spaMM  [40]. A Matérn process models clustering of like values in a point pattern; inclusion of an explicit Matérn process model in a regression can remove spatial autocorrelation in the residuals. Significant residual autocorrelation violates the independence of observations model assumption and can result in unreliable estimates of the significance in model terms  [41]. There was no significant spatial autcorrelation detected in the residuals of the spatial GLS.
Animals Following the method developed by  Palhière et al. (2018) , a genetic evaluation for functional longevity of 8,787 Alpine AI bucks was carried out. Briefly, length of productive life was computed for 84,454 Alpine goats as the time interval (in days) between first kidding (first milk recording) and the last milk recording registered in the national performance-recording database. Estimated breeding values for length of functional life were then estimated for AI bucks using BLUP based on phenotypic information, pedigree information, and variance component estimates. Sires of the 2 lines were also required to show similar and favorable EBV for milk production traits to avoid confounding effects from an indirect response to selection for production traits. In 2020, the EBV of the bucks used were +85.
1 d of functional life for the bucks of high longevity (high_LGV) lines and -108. 7 d for the bucks of low longevity (low_LGV) lines (Table  1 ). From 2017 and until 2021, we selected 35 AI bucks in total as follows: the 16 bucks who had the highest EBV among the whole AI bucks population founded the high_LGV line and the 19 bucks who had the lowest EBV founded the low_LGV line (Table  1 ). The selection was mainly made on the males; the very strong selection that was made on the buck (the most extreme EBV of the national database) was relevant to create a model to produce the most divergent animals possible using functional longevity-based selection (high selection pressure on males: 36 out of 8,787 bucks with breeding values). In the later years of the  a-c Letters correspond to significant differences within a row (P < 0.
Data Used to Decipher Survival Each reason for culling was indicated by the farm staff. For processing, these data were grouped into the following 6 classes of culling reason: birth-associated, infection, accident, metabolic, skinny, and other. The passive immune transfer from the goat to the kid can be evaluated measuring serum IgG concentration at birth  (Weaver et al. , 2000). It was done by a radial immunodiffusion technique between 1 and 14 d of age. Logarithmic transformation was applied to values before statistical analysis to normalize the distribution of the data. The date of each kidding was recorded, as well as any difficulties during kidding, the number of kids, and the weight of the litter. All health treatments were recorded in the same way and then grouped into the following 5 groups: infection, accident, metabolic, mechanical, and other.
Integration of Weight Data During the First Year of Life We used the functional data analysis smoothing method described by  Ramsay and Silverman (2005). The application of this method to the BW data made it possible to estimate a corrected weight of individuals for every month from birth to 1 yr old as well as the first derivative. We used B-spline basis functions, which can construct piece-wise polynomial functions joined at the knots. The roughness penalty is adjusted by the scalar λ (as λ increases, the penalty term becomes more decisive, and thus the second derivative converges to a straight line). The B-spline smoothing was run over  6,747 BW records for the 364 goats that were weighed at least 5 times (from 5-27 times). The age of the 75 goats for which less than 5 weight measures were recorded ranged from 1 to 30 d old.
We used the lme4 package in R  (Bates et al. , 2015). Only months when at least 20 goats were recorded were included in the analysis for weight and body morphology. We used raw data (no Spline transformed) to compare weights after the first year of life. We included the fixed effect of gestation during the 3 last months before kidding (0 vs. 1) for weight analysis. Supplementary analyses were run where line effect was nested into month for weight and body morphology analysis.
Survival Analysis The R package 'survival' was used for the survival analysis  (Zhang et al. , 2018). Survival analysis was performed using a Cox model  (Cox, 1972)  as follows: log l og , h t x h t x | ' ( ) ( )= ( ) ( )+ 0 β where h(t) is the expected hazard at time t, h0(t) is the baseline hazard, and β is the coefficient associated with the covariate x′. The model included both fixed effects (i. e. , those that were the same through all the animal's life including line, year of birth) and time-dependent covariates using the monthly records for data that changed with time  (Zhang et al. , 2018). The physiological status of the goats (kidding or not), their weight, and any treatments received could change every month. Before 1 yr old, the weight variable affecting survival at month j was assumed to be the weight deviation value at month j -1 (WDj -1).
7% of high_LGV kiddings). There were 247 goats with at least 1 kidding recorded. The median number of body morphology records per goats was 6. Mixed models were applied to height and chest size data over the whole life of goats. A line effect was found for chest size (P = 0. 03), with high_LGV goats having a greater chest size than low_LGV goats (83. 7 cm vs 83. 0 cm). The weight at birth was similar for low_LGV and high_LGV (mean = 3. 94 kg, min = 1. 6, max = 5. 9, n = 440), as well as mean weight deviation (mean_WD) and mean speed of growth deviation (mean_dWD; Table  3 ). No line effect was found. However, when the line effect was nested into the month effect, high_LGV goat were significantly heavier than low_LGV line between 12 and 16 mo (F test; P < 0. 05), with a difference of 1. 5 kg between lines at 13 mo of age (t-test; P = 0.
Such a selection for functional longevity was run on rabbits and led to significant difference in longevity  (Garreau et al. , 2010). To our knowledge, selection on longevity has never been done in dairy ruminants. Our finding that hyperselection for functional longevity resulted in increased survival was observed in a single breed (Alpine) and in a single experimental herd over 5 yr with a management representative to that found in many commercial dairy goat farms (i. e. , indoors, feeding forage and concentrate, using AI and twice day milking). According to the national performance-recording database, the average milk yield for first lactation Alpine goats over 250 d was 707 kg in 2019  (Thomas and Bourrigan, 2019). In the present study, milk yield (± SD) was 619. 6 kg (± 121. 2) on a 250-d basis (Fleischmann's method).
In the national database, the average LSCS in Alpine goats was 5. 34 (± 1. 59) compared with 4. 74 (± 2. 05) in the present study. The survival advantage of the high_LGV line was consistent between years, even though mortality rates and environment conditions (i. e. , forage quality, infectious pressure) varied. This study showed that it is possible to get a selection response for functional longevity in goats despite a low heritability. The high_LGV median survival time was 830 d compared with 787 d for low_LGV line (difference = 43 d), showing that there was a strong delay in involuntary culling achieved by selection. This difference is, however, lower than what we estimated based on the length of productive life of the AI bucks' daughters (1,071 d for the high_LGV and 909 for the low_LGV line, difference = 162 d).
ACKNOWLEDGMENTS The authors thank the staff of the  INRAE experimental unit at Bourges, France , for technical support in producing, raising, and monitoring the animals. This work was performed in the framework of the  EU   H2020   SMARTER  project (grant no. 772787 ,  Castanet Tolosan, France ) and the national  APIS-GENE (Paris, France ) funded projects  ActiveGoat  and  Resilait. M. Ithurbide  acknowledges  EU H2020  (SMARTER) and  APIS-GENE (Resilait)  for her PhD grant. The authors have not stated any conflicts of interest.
Molecular Docking The approach starts by retrieving the PDB structure of the candidate kinases from RSCB PDB database. Discovery Studio software (V 21. 0. 20298) was used to prepare the downloaded molecules by removing water molecules, already existent ligands and adding polar hydrogens. In the next step we used AutoDockTools-1. 5. 6 to convert PDB structure of the prepared macromolecule to PDBQT format after adding Kollman, Gasteiger charges and AD4 type atoms. The ligand 4f was energy minimized using open babel binaries (2. 4. 0 release) and converted to PDBQT format using AutoDockTools-1. 5. 6. Vina executable was run after specifying X, Y and Z coordinates of the macromolecule, as well as the radius value in the configuration file. The resulting conformations were separated using vina split executable and visualized in the Discovery Studio software.
However, these findings may not be applicable to other settings, especially since hydroxychloroquine was given to only 64. 7% of cases and severe SLE patients were excluded from the PROMISSE study  [9]. In 2014, we set up a French prospective study of pregnancies in women affected with rare diseases including SLE (the GR2 study, clinicaltrial. gov NCT02450396). Here, we aim to report pregnancy outcomes (maternal flares and APOs) in this large cohort of pregnant women with SLE. We tested remission definitions and LLDAS as well as cumulative damage (Systemic Lupus International Collaborating Clinics-SLICC-Damage Index) in the first trimester as predictors of poor outcome (flares and APOs) later in pregnancy.
PATIENTS AND METHODS We report data from the GR2 ("Groupe de recherche sur la Grossesse et les Maladies Rares") study, a French multicentre prospective observational study of pregnant women with rare and/or rheumatological diseases, including SLE and antiphospholipid syndrome (APS), conducted since October 2014 in 63 active centres (not all recruiting patients with SLE as the cohort is intended to study several rare and rheumatological diseases). Pregnant women are included by their clinicians (internists, rheumatologists, and nephrologists) and are followed up to 12 months postpartum. The treating physicians made all treatment decisions. The GR2 study is part of the European network of pregnancy registers in Rheumatology (EuNeP) supported by FOREUM (Foundation for Research in Rheumatology)  [10]  and follow EULAR recommendations regarding core data sets for pregnancy registers in rheumatology  [11].
Inclusion criteria. Criteria for the current analysis required inclusion in the GR2 before 13 weeks, SLE classified according to the SLICC 2012 criteria  [12] , and conception before July 15, 2019 (to have complete data at delivery), with an ongoing singleton pregnancy that reached 12 weeks. Only the first singleton pregnancy per woman was analysed. Data collected. At first-trimester consultations, we assessed demographic, clinical, serological, and treatment features. Anti-phospholipid (aPL) status included anticardiolipin (aCL), anti-Beta2 glycoprotein type I antibodies (anti-β2GPI), and LAC. In France, all laboratories are regularly audited and certified by a central agency. More details on the variety and types of assays are reported in Supplementary Data S1, available at Rheumatology online.
Triple positive aPL status was defined by positive aCL, anti-β2GPI, and LAC. All data were prospectively collected in electronic case report forms at each consultation. Because all women received standard treatment, written informed consent was not required by French law. The women were, however, informed of their right to oppose the use of their data for the study and orally stated their lack of objection. This project adheres to the principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (CPP Ile de France VI, Groupe Hospitalier Pitié-Salpétrière, 29/08/2012). Definitions of remission, LLDAS, disease activity, and damage. Disease activity was scored by the SLE Disease Activity Index-2000 (SLEDAI-2K)  [13]  adapted to pregnancy (SLEPDAI)  [14]  and we considered the first SLEPDAI available during the first trimester.
Acknowledgements: We thank all the investigators of the GR2 group. We also thank  Miss Ada Clarke  for her assistance, the patients for agreeing to participate, and the patient associations for their strong support. Finally, we acknowledge the  French Society of Internal Medicine (SNFMI) , the  French Society of Rheumatology (SFR) , and the  FAI2R  (filière de santé des maladies auto-immunes et autoinflammatoires rares) for their scientific, technical and financial support.
Funding: This work was supported by grants from patient associations ( Lupus France ;  association des Sclérodermiques de France , association  Gougerot Sjögren ,  AFPCA -Association Francophone contre la Polychondrite chronique atrophiante ), from the  AFM-Telethon , the  French Society of Internal Medicine (SNFMI) , the  French Society of Rheumatology (SFR) , the  CMEL commission for Research and Innovation of Cochin Hospital , the  Ministère de la Santé  (the Clinical REsearch Contract -Database  CRCBDD17003 ),  FOREUM (Foundation for Research in Rheumatology), ORRICK society (Price Véronique ROUALET) , and an unrestricted grant from  UCB  (the company had no role in the initiation, planning, conduct, data assembly, analysis or interpretation of the study). The ' Direction de la Recherche Clinique et du Développement' provided logistic  and administrative support.
Search strategy A systematic review was conducted to identify eligible studies on MDR/RR-TB treatment outcomes in Embase, Web of Science, Scopus, and PubMed/MEDLINE databases. The search was performed on March 13, 2022. The databases were searched with a combination of the following keywords: "tuberculosis", "TB", "multidrug-resistant", "MDR" "extensively drug-resistant", "XDR" "drug-resistant tuberculosis", "Africa" and "treatment outcome", among others (search string available in Appendix 2). The search string was combined with the name of the countries in Central and West Africa. Country members of the Economic Community of Central African States and Central Africa of the Economic Community of West African States were included in studies from Central and West Africa, respectively ( ECCAS, 2022 ; ECOWAS, 2022 ).