dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotypic characterization For genetic analysis, 28 SSRs that had been optimized previously to work in pooled samples of 15 individuals were selected (S2 Table ). These SSRs gave good coverage of the maize genome with all linkage groups represented. Fragments (alleles) of each SSR were generated via PCR according to CIMMYT standard protocols  [34]. Electrophoresis was conducted using an automatic capillary sequencer ABI 3100 (Applied Biosystems, Foster City, CA) to separate and size the fragments. Data were analyzed using the programs GeneScan 1 3. 1 (PerkinElmer / Applied Biosystems, Foster City, CA) and Genotyper 1 2. 1 (PerkinElmer / Applied Biosystems, Foster City, CA) to generate a data set of all fragments including size in base pairs, peak height (corresponding to intensity of the amplified fragment), and quality score.
Data analysis Frequency of each allele in the bulk was calculated from peak height (intensity) in R, an opensource computer program and language for data analysis (http://cran. r-project. org), using the program Freqs-R  [35]. This program removes false peaks caused by PCR stuttering or preferential amplification. For each pooled sample, the R program FtoL-R  [36]  was used to simulate the alleles (calculated as length in base pairs) of 15 individuals to meet allele frequencies and expected heterozygosity of each sample, for analyses requiring genotypic data for individuals, rather than population allelic frequencies. Number of alleles, genetic diversity, and genetic distance between populations (from proportion of shared alleles) were calculated using the program PowerMarker  [37] , for each accession or defined subgroup.
The program Darwin 5. 0  [38]  was used for cluster analysis using the Neighbor-Joining method, and Principal Coordinate Analyses (PCoA) based on the genetic distances matrix obtained from PowerMarker. The model-based clustering method, Structure 2. 2  [39]  was used to analyze population structure and identify sub-groups within the overall set of populations. Assumptions were set to an admixture model in which K populations/groups were characterized by a set of allele frequencies at each locus. Populations were not assigned to any group a priori, and individual simulations of each population were allowed to vary. After the first analysis including all populations, additional Structure analyses were performed in order to reveal possible sub-structure within each of the main clusters detected.
Although mutation and selection over such long time periods as those suggested for domesticated maize evolution  [7]  make it difficult to recognize the links between ancient landraces, Structure and NJ cluster analyses that find Central American populations frequently interspersed with other groups may reflect patterns of independent migrations through Central America at different times. In this sense, two distinct maize expansions from Mesoamerica may be proposed and cross validated using maize genetic data  [47]. The first, from Central America through the highlands into the Andes and the western coast of South America, faces geographical barriers including extremely steep mountains in Central America, and the jungles and mountains of Colombia. This route is somewhat unlikely, especially since maize cannot quickly adapt to large differences in elevation  [55] , and indeed, our results do not offer strong support for this maize migration route.
The aging of people living with HIV (PLHIV) raises the problem of bone health in this population. The number of PLHIV aged 50 years and over is increasing, and presently, there are more than 4 million patients, with half of them living in sub-Saharan Africa  [17]. However, limited data exists regarding BMD levels in HIV-infected patients living in sub-Saharan Africa. The ANRS 12169 2LADY randomized trial (NCT00928187), conducted in three African cities, which aimed to compare efficacy and safety of three second-line regimens on TDF-naïve patients, provided an opportunity to assess the impact of TDF-based regimen on bone loss  [18]. In the trial sub-study ANRS 12250 METABODY, bone quality was assessed via the use of calcaneal quantitative ultrasound (QUS) as an alternative to dual-energy X-ray absorptiometry (DXA), which is not available in most African settings  [7, 19, 20].
Clinical and biological data collection Clinical and biological data (e. g. , general condition, weight, blood pressure, creatinine, glycemia, cholesterolemia, insulinemia, blood count, 25-hydroxy vitamin D (25-OH vitamin D), parathyroid hormone (PTH), CD4 count, viral load, triglyceridemia, SI, physical activity, hormonal contraception use, glucocorticoid use, dysmenorrhea and diagnosis of menopause) were collected at baseline and every 24 weeks. Participant physical activity was assessed with a short frequency questionnaire, in which "regular physical activity" was defined as moderate activity occurring each day or strenuous activity occurring at least twice a week. Some laboratory tests related to bone metabolism that were not available at the study sites were centralized in the laboratory of Montpellier University Hospital (in France).
Statistical analysis Analyses included available data from all participants enrolled in METABODY sub-study and those who completed a baseline measure of bone quality. For women who became pregnant during the study, bone quality measures from the date of pregnancy were not included in the longitudinal analyses. The main dependent variable was mean SI. The effects of TDF were assessed by comparing TDF-containing treatment arms (TDF/FTC + LPVr or DRVr) to a TDF-sparing treatment arm (ABC + ddI + LPVr). Demographic factors (i. e. , study site, sex, age, year of inclusion); HIV related factors (i. e. , WHO clinical stage, CD4 count, viral load, first-line ART duration, Efavirenz (EFV)-based first line treatment); and other factors involved or possibly involved in bone metabolism (i. e. , regular physical activity, metabolic syndrome, body mass index, menopause, contraception with Depo-Provera1 (Pfizer, New York, NY, USA), hepatitis B antigen-positive, 25-OH vitamin D, PTH) were also assessed for association with bone quality outcomes.
In a baseline characteristics comparison, Student's t-test was used for continuous variables, and Pearson chi-square or Fisher's exact test was used for categorical ones. Specific factors associated with baseline SI were identified using linear regression models, and mixed linear models with random effects were used to determine associated factors during participant follow-up  [22]. Furthermore, to account for women-specific characteristics (i. e. , menopause, amenorrhea and/or use of hormonal contraception), additional analyses were performed in the women sub-group only. Ultimately, our study demonstrated enough power (80%) to show a difference of 4. 1 SI units (representing about 0. 2 SD) between treatments given with and without TDF (which was calculated using the R package " Longpower " for linear mixed models based on the formula due to Diggle)  [23].
7%  [46]. Limited data on vitamin D levels in adults infected with HIV in Africa exists, however. A study in Uganda reported 35% of 25-OH vitamin D deficiency in patients with HIV infection  [51]. The average 25-OH vitamin D was 34. 1 (SD = 13. 9) ng/ml in the general population in Guinea Bissau, with 37% of men and 41% of women having a 25-OH vitamin D deficiency  [52]. The prevalence of 25-OH vitamin D deficiency in our study (34. 6%) is similar to those found in other studies in sub-Saharan Africa, but lower than those observed in European and North American cohorts  [51] [52] [53]. Our study has several limitations. First, we recorded a great number of participants lost to follow-up, especially after week 48. Second, bone changes were assessed over only 96 weeks of follow-up. Hence, further investigation with a longer duration of follow-up is needed to assess whether the rapid bone deterioration observed in the two first years is sustained over time.
Baseline cues were not presented during this TNT phase. Next, participants performed a speeded associative recognition task followed by an SAM valence rating task on all picture categories (Think, No-Think, and Baseline) to evaluate how suppression affected memory and emotional perceptions, respectively. On these final tests, baseline items provided an estimate of memory or affect, given that neither retrieval nor suppression has been performed in the interim. The pictures displayed here are not issued from IAPS database but are free-use pictures taken from the internet for illustrative purpose. B, The dynamics predicted to bring about parallel inhibition of memory and emotion for intrusive negative scenes. Cue input to the hippocampus is predicted to drive pattern completion, followed by recurrent reactivation of scene and emotional features in parahippocampal cortex and amygdala, respectively; intrusionrelated reactivation in these regions is predicted to trigger parallel inhibition by the right MFG.
, 2008)  database. Half of the critical scenes were normed as negative  (IAPS number: 1301 (IAPS number: , 2053 (IAPS number: , 2141 (IAPS number: , 2700 (IAPS number: , 2710 (IAPS number: , 2900, 3280, 6020, 6244, 6571, 6831, 9041, 9042, 9102, 9180, 9181, 9320, 9420, 9470, 9520, 9561, 9800, 9830, 9911, 3280, 6020, 6244, 6571, 6831, 9041, 9042, 9102, 9180, 9181, 9320, 9420, 9470, 9520, 9561, 9800, 9830, 9911 ; mean valence ϭ 2. 8; SD valence ϭ 1. 7; mean arousal ϭ 5. 3; SD arousal ϭ 2. 3), whereas the other half were neutral  (IAPS number: 1121 (IAPS number: , 1313 (IAPS number: , 1640 (IAPS number: , 1810 (IAPS number: , 2250 (IAPS number: , 2487 (IAPS number: , 2616, 4100, 4535, 5395, 5455, 5628, 7289, 7351, 7402, 7480, 7495, 7503, 7510, 7560, 7640, 8060, 8117, 8250, 4100, 4535, 5395, 5455, 5628, 7289, 7351, 7402, 7480, 7495, 7503, 7510, 7560, 7640, 8060, 8117, 8250 ; mean valence ϭ 5.
e. , a "briefly" or "frequent" response) or not (a "never" response) in binary fashion. For each repetition of a given condition (e. g. , the No-Think, negative-valence condition), we averaged these binary intrusion reports across all 8 items in that condition to compute an intrusion proportion for that repetition. We then averaged these intrusion proportions across the 10 repetitions of TNT instruction (i. e. , across the 2 repetitions in each of the 5 sessions) to derive the overall intrusion rate for a given participant. At the end of the 5 TNT sessions, participants also performed a spatial cueing task for 8 min (data not reported here). Outside the scanner, aftereffects of memory suppression were examined via an associative recognition memory test on Think and No-Think items. In addition, a third group of items (Baseline items) was tested during this task.
Imaging acquisition parameters Scanning was performed on a 3-T Siemens Allegra MRI system at the Lewis Center for Neuroimaging at the University of Oregon using a 32-channel whole-head coil. High-resolution (0. 5 ϫ 0. 5 ϫ 1 mm) T1-weighted image was collected for anatomical visualization and normalization. Functional data were acquired using a gradient-echo, echoplanar pulse sequence (repetition time ϭ 2000 ms, echo time ϭ 30 ms, 31 interleaved slice acquisition, 3 ϫ 3 ϫ 3 mm voxel size). The first eight volumes of each session were discarded to allow for magnetic field stabilization.
fMRI preprocessing Data were analyzed using Statistical Parametric Mapping software (SPM12, Wellcome Department of Imaging Neuroscience, London; RRID:SCR_007037). During preprocessing, images were first corrected for slice acquisition temporal delay before being spatially realigned to correct for motion. Images were then normalized using the parameters derived from the nonlinear normalization of individual gray-matter T1 images to the T1 template of the MNI and spatially smoothed using a 10 mm FWHM Gaussian kernel for second-level univariate analyses. However, native space 4 mm smoothed images were used for medial temporal lobe (MTL) region of interest (ROI) analyses to ensure maximum accuracy and demarcation between MTL hand-drawn ROIs (i. e. , parahippocampal cortex, hippocampus, and amygdala).
Error bars indicate bootstrapped 95% CI. Brain scores reflect the contribution of each participant to a given LV. The correlation between brain scores and behaviors thus reveals the meaning of the LV. B, Scatter plots observed in the right MFG illustrating the relationship captured by PLS analysis between the upregulation (Intrusion Ϫ Non-Intrusion) and behavioral scores for Negative and Neutral scenes. These findings reveal voxels whose upregulation is associated with reduced intrusion frequency for both Negative and Neutral scenes and also with increased affect suppression score only in the case of Negative scenes (reduced negative affect for suppressed images). BSR maps were rendered on the top of the PALS human surface using Caret software  (Van Essen et al. , 2001)  (RRID:SCR_006260).
Error bars indicate bootstrapped 95% CI. Voxels were identified using a BSR higher or lower than 1. 96/Ϫ1. 96, respectively (i. e. , p Ͻ 0. 05). Correlations between participants' brain scores and behaviors for the first significant LV are also reported in A. Clusters of BSR exceeding threshold were rendered onto a 3D reconstruction of a standard MTL template. The 3D representation of the MTL was obtained by transforming MTL binary masks into 3D meshes using "Anatomist/BrainVISA" software (http://www. brainvisa. info/; RRID:SCR_007354). B, Scatter plots illustrating the relationship captured by PLS analysis between the downregulation observed in the MTL cluster (A) and behavioral scores for both Negative and Neutral scenes. parallel regulation models (EP ϭ 0. 99, and EPP ϭ 0. 63) over either emotion regulation (EP ϭ 0.
The correlation between brain scores and behaviors thus reveals the meaning of the LV. B, Scatter plots observed in the right MFG illustrating the relationship captured by PLS analysis between the upregulation (Intrusion Ϫ Non-Intrusion) and behavioral scores for Negative and Neutral scenes. These findings reveal voxels whose upregulation is associated with reduced intrusion frequency for both Negative and Neutral scenes and also with increased affect suppression score only in the case of Negative scenes (reduced negative affect for suppressed images). BSR maps were rendered on the top of the PALS human surface using Caret software (Van Essen et al. , 2001)  (RRID:SCR_006260).
Brain scores reflect the contribution of each participant to a given LV. The correlation between brain scores and behaviors thus reveals the meaning of the LV. B, Scatter plots observed in the right MFG illustrating the relationship captured by PLS analysis between the upregulation (Intrusion Ϫ Non-Intrusion) and behavioral scores for Negative and Neutral scenes. These findings reveal voxels whose upregulation is associated with reduced intrusion frequency for both Negative and Neutral scenes and also with increased affect suppression score only in the case of Negative scenes (reduced negative affect for suppressed images). BSR maps were rendered on the top of the PALS human surface using Caret software (Van Essen et al. , 2001)  (RRID:SCR_006260).
Clinical characteristics and dyspnea assessment Demographic data (age, sex), BMI, waist and hip measurements, comorbidities, treatments, smoking status and type of bariatric surgery (sleeve gastrectomy or gastric bypass) were systematically recorded. Dyspnea in daily living was evaluated using the mMRC scale (Table  1 ). The mMRC scale consists of five statements that almost entirely describe the range of dyspnea from none (grade 0) to almost complete incapacity (grade 4). The mMRC scale is the most commonly used validated scale to assess dyspnea in daily living in chronic respiratory diseases. A systematic review of measures used for the sensation of breathlessness in obesity had shown that the mMRC scale can be recommended in obese people based on its reliability and concurrent validity  [15].
Comparisons between patients with mMRC improvement and those without mMRC improvement were performed by univariate analysis, using the Wilcoxon tests and multivariate analysis. For multivariate analysis, colinearity between repiratory variables were studied. A logistic regression will be performed using backward stepwise selection with an exit threshold of 0. 10. Age, variation of BMI, variation of CRP, variation of TLC and variation of ERV were included in the multivariate model. Association between improvement of the mMRC score and evolution of lung function tests or laboratory parameters were studied using the Spearman's correlation coefficient. For all analyses, a p value <0. 05 was considered statistically significant. All analyses were performed using SAS version 9. 3 (SAS Inc. , Cary, NC, USA).
Clinical and demographic characteristics Fifty-seven patients were included in the study. Nine patients did not attend the postoperative evaluation, two patients were excluded because of asthma and one patient died just after surgery due to acute respiratory failure. The data for the remaining 45 patients were analyzed (Fig  1 ). Clinical and demographic characteristics of the 45 patients analyzed are presented in Table  2. Mean age was 43 ± 11 years. Five men (11%) and forty women (89%) underwent sleeve gastrectomy (31 patients, 69%) or gastric bypass (14 patients, 31%). Mean BMI was 46. 2 ± 6. 8 kg/m 2. No significant difference was observed between the 12 excluded patients and the 45 analyzed patients in terms of demographic data, weight, BMI, waist measurement, hip measurement, smoking status, comorbidities, dyspnea (according to mMRC and Borg before and after 6MWT), 6MWT, arterial blood gases, PFTs and laboratory parameters.
2. 28) on NCBI and BioEdit (version 7. 1. 11) (Hall, 1999)  were used to edit and trim the vector sequence from the contigs. VecScreen on NCBI was used to search the sequences for vector contamination. Open reading frames (ORFs) were determined using the NCBI ORF finder program  (Wheeler et al. , 2003)  and all ORFs with homology to antimicrobial genes and/or peptides were collated (Table  1 ). Based on expression levels and final protein yield, an ORF composed of 71 amino acids and named buwchitin was further investigated. Here, we report the activity of buwchitin.
Amplification of Antimicrobial Genes Extracted fosmid DNA (1 µl) from a metagenomic clone containing the buwchitin insert was used as template for PCR amplification. The buwchitin sequence was deposited in the GenBank database with accession number KY823515 and predicted to contain a signal peptide, when analyzed on the SignalP 4. 1 server  (Petersen et al. , 2011). Primers were designed to start and stop at the first predicted methionine and at the last stop codon, respectively in order to conserve the reading frame and take account of the entire gene of interest. The primers used for the amplification of buwchitin gene were 5 ′ -ATGAGGCTGTCACACGTTTG-3 ′ (forward primer) and 5 ′ -TCACCAATCTGTATGGCACCG-3 ′ (reverse primer). Primers were diluted to a stock concentration of 100 µM and a total volume of 50 µl PCR reaction was set up as follows: 2 µl DNA template, 1 µl each of the forward and reverse primers (2 µM final concentration), 39.
Molecular Modeling of Peptide 3D Structures Structural modeling of buwchitin was completed using the PHYRE2 web portal  (Kelley et al. , 2015). Results were visualized using the PyMOL v1. 7. 6 program  (Schrödinger, 2010). The biophysical properties of buwchitin were predicted on the antimicrobial peptide database (APD2)  (Wang et al. , 2009).
Sequencing, Cloning, Expression and Purification Using In vivo Expression Systems The buwchitin gene was successfully PCR amplified using DNA from the fosmid clone, SAB PL27 L10/66. Bands of the correct size (expected size of 216 bp) were excised from the gel before proceeding to cloning. Electrophoresis results confirmed that the transformants carried the gene of the correct size, which was also confirmed by Sanger sequencing. The antimicrobial protein was expressed with an N-terminal 6xHis-Tag in E. coli to facilitate purification and investigation of its biochemical properties. Preliminary protein expression assay indicated that protein expression was optimal 4 h after induction (data not shown). SDS PAGE analysis of negative expression control (E. coli Top10 cells without plasmid) showed no protein expression bands while positive expression control (E.
Structural Modeling of Buwchitin Modeling and visualization of the 3D conformation of buwchitin using PHYRE2  (Kelley et al. , 2015)  and PyMOL v1. 7. 6  (Schrödinger, 2010) , respectively, suggested that buwchitin is composed of a compact, all-helical, structure with major amphipathic helix connecting two smaller helices (Figure  4 ). The amphipathic helix agrees with a common structural feature of AMPs as the dual hydrophilic/hydrophobic nature allows the interaction and embedding of cellular membranes  (Hancock and Sahl, 2006). As predicted by the APD2 database, buwchitin (71AA) is positively charged (+9), has a total hydrophobicity ratio of 29% and total Arginine and Lysine ratio of 19%.
5 standard deviations. Given the known association of episodic future thinking with temporal discounting behavior, i. e. the preference for smaller-sooner over larger-later rewards, we also ran models predicting temporal discounting directly from linguistic features of AI narratives. Here, prediction accuracy was much lower, but involved the same text feature components as prediction of internal (episodic) details. Our findings highlight the potential feasibility of using tools from quantitative text analysis to analyze AI datasets, and we discuss potential future applications of this approach. Autobiographical memory (AM) is central to our personal identity, and changes in this process characterize developmental phases as well as effects of neurological and psychiatric disorders. Recent findings have illustrated striking similarities in the neural systems supporting episodic memory and the capacity to mentally project oneself into the future (episodic future thinking, EFT) 1.
Text features were computed both manually by cross-referencing words with publicly available linguistic data bases, and using commercially-available quantitative text analysis software, the Linguistic Inquiry and Word Count (LIWC) package  24. Note that one previous study applied the LIWC to autobiographical memory narratives  25  , but that study focused on differences in emotional content between younger and older adults. In addition, two earlier studies examine the use of past-tense verbs in AI tasks in neurodegenerative disorders  26  and temporal lobe epilepsy  27  , but did not explore additional text features or comprehensively analyzed detail sum score prediction. The first aim of this report is an initial proof-of-concept. We aimed to assess how well AI based internal and semantic detail sum scores can be predicted from low-level linguistic features using statistical methods with a combination of dimensionality reduction and regression techniques with out-of-sample prediction (cross-validation).
Methods Participants. We re-analyzed autobiographical interview (AI) data from two datasets. The first dataset (dataset 1) comprised interview data from n = 46 adolescents (age range: 12-16, 23 male). The second data set (dataset 2) comprised data from n = 20 pathological gamblers (mean age [range]: 32. 9 [19-59], 19 male), and from n = 20 healthy control participants (mean age [range]: 32. 55 [18-58], 19 male). All subjects provided informed written consent prior to participation. For minor participants, the parent or legal guardian provided written consent. All procedures were approved by the local ethics committee (Hamburg Board of Physicians) and all methods were conducted in accordance with the guidelines and regulations of this committee. Autobiographical Interview. Data were acquired using a modified version of the Autobiographical Interview (AI) Interviews were conducted by U.