dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptomic Response of A. flavus to CFS4 Exposure To study the impact of CFS4 (1. 5 g/L) on A. flavus at the transcriptional level, a genomewide transcriptomic analysis by microarray was performed. A set of 8544 probes (corresponding to 8544 genes) was identified as significantly differentially expressed (FDR < 5%) between the control and the CFS4 exposure condition. Principal component analysis (PCA) performed on the log2FC values of the dataset revealed a clear separation of the control and CFS4 condition, along with the first component, which explained 83. 6% of the total variance (Figure  6A ). Hierarchical clustering of individual expression values was performed on 5198 significant features for the CFS4-CTRL comparison, based on the FDR method, with a p-value and fold change (FC) threshold respectively set to 0.
Because of recombination, phylodynamic frameworks cannot be applied directly to malaria (phylodynamic methods that accommodate recombination treat it as noise and not signal  [12] ) and an equivalent framework for malaria is lacking. However, efforts to develop simulationbased models are ongoing (e. g. , the R package SIMPLEGEN, https://mrc-ide. github. io/ SIMPLEGEN/). Agent-based models linked to genomic processes have been used to estimate R 0 and changes in transmission intensity  [4, 57] , to investigate the relationship between different descriptive statistics of parasite genetic data and transmission intensity  [55, 58] , to study the effect of heterogeneity on the spatial distribution of multiclonal infections and on the stability of transmission  [38] , and to study the effect of selective pressures on evolution under different transmission  Step 1, genomic data are collected.
Weighting coefficients A weighting coefficient was computed for each subject when data were available. This coefficient took into account both the survey weight and the non-participation correction factor, based on the follow-up of a control cohort of non-participants  [28, 29]. In cases where the medico-administrative data of a participant could not be obtained, the weighting coefficient could not be calculated. However, participants with or without a weight had the same characteristics. Moreover, the probability of having completed the 2017 follow-up questionnaire was estimated for each participant in order to compute a participation weight for this follow-up. The product of this weight with the weight at baseline provided the final weight.
M-P. d'Ortho  has received research grants from  Sunrise Medical, Desitin, ResMed, Löwenstein and Philips , honoraria for lectures from  ResMed, Bioprojet, LVL Medical, VitalAire and Jazz Pharmaceuticals , payment for educational events from  Jazz Pharmaceuticals , and support for attending meetings from  Bioprojet  and  Jazz Pharmaceuticals. A. Renuy ,  J. Matta ,  M. Goldberg ,  M. Zins  and  E. Wiernik  declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Support statement: The CONSTANCES cohort benefits from a grant from the  French National Research Agency. CONSTANCES is supported by the  Caisse Nationale d'Assurance Maladie , the  French Ministry of Health , the  Ministry of Research  and the  Institut National de la Santé et de la Recherche Médicale.
CONSTANCES is also partly funded by  AstraZeneca ,  Lundbeck ,  L'Oréal  and  Merck Sharp & Dohme Corp. This present study was sponsored by  AP-HP (Paris, France ), supported by the  Recherche Hospitalo-Universitaire  iVASC grant (www. ivasc. eu)( ANR-16-RHUS-00010 ) from the  French National Research Agency (ANR)  and funded by  Plan d'Investissement d'Avenir-3 (PIA3-RHU ,  Ministry of Health ).
Participants and procedure The study used a two-group non-randomised controlled design with a pre-test and a post-test. The data were collected in April 2020. The participants were French-speaking volunteers who enrolled in the SPARK Resilience Programme. The intervention was delivered in a group coaching format via synchronous video conferencing using Zoom software over eight 90-minute sessions spanning four weeks. The sessions included a combination of teaching, whole- group interactions, questioning, using voice and chat functionalities, quizzes, small virtual group discussions, peer coaching, guided mindfulness exercises, and non-mandatory homework. The SPARK sessions were conducted by two trainers with postgraduate degrees in psychology (MSc and PhD, respectively), background in positive and organizational psychology, and extensive experience in group facilitation and resilience work.
The participants were recruited from among the individuals who had registered for a free pilot trial of the SPARK Resilience Programme for French-speaking adults scheduled to take place in April 2020. The training was advertised using mailing lists and positive psychologythemed websites in March. Although the advertisements mainly targeted working professionals, students were also allowed to register. Once it became clear that the number of potential participants would exceed the limitations imposed by the Zoom platform (N = 300), a second wave of the training was scheduled to take place in June 2020 in order to accommodate those who registered later. The data were collected in April, with the intervention and the wait-list control group comprised of April and June wave participants, respectively.
The research was conducted in accordance with the Declaration of Helsinki. Informed consent in an electronic form was obtained from all participants. The study protocol was approved by the HSE University Research Ethics Committee. The number of questionnaires collected was 390 for the pre-test (177 in the intervention group and 213 in the control group) and 232 for the post-test (140 in the intervention group and 92 in the control group). For the present study, we used the data of participants who completed both the pre-test and the post-test and whose questionnaires could be matched based on the identifiers they provided. The resulting sample included 101 participants in the intervention group and 86 in the control group (three control group participants provided incomplete questionnaires; we report the effective sample size for each analysis).
The qualitative data extend the quantitative results by shedding light on the specific benefits and areas of improvement resulting from the SPARK intervention in the pandemic context. Some of the emerging themes, such as awareness of cognition-emotion interactions, development of the repertoire of emotion regulation strategies, as well as strengthened positive relationships, are generally consistent with the previous qualitative findings in an adolescent sample  [44]. Other themes, such as growth from adversity, focus on mental health, and action orientation reflecting the need to find motivation and energy to go on, might reflect the specific positive effects of the intervention in the context of an ongoing pandemic. In short, these qualitative results can be seen as a case study showing how the SPARK Resilience Programme can help people to discover the resources needed to face an unprecedented and very challenging situation.
Tumour response by RECIST v1. 1 was used to calculate progression-free survival (PFS, primary endpoint). The PFS by RECIST1. 1 was assessed based on the blinded centrally reviewed tumour assessment data and additionally, median PFS was estimated by RECIST 1. 1 based on central imaging review. Information about the patients' survival status was recorded in the electronic case record form and the respective information was used to determine OS in phase 2.
Once the link was accessed, patients were presented with a data protection notice or e-consent form (depending on local country regulations) before being requested to complete eligibility screening questions. Patients then self-confirmed their eligibility before being granted access to the survey. The survey was developed to capture key information relating to the study objectives and was tested in a pilot phase involv-ing cognitive debriefing telephone interviews of 17 patients with MM in the UK. The purpose of the pilot phase was to understand whether patients interpreted the survey questions and response options in the way that the study team intended; to document any difficulties in completion; and to capture the overall experience of completing the survey, including whether any important aspects were missing.
Statistical Analyses All analyses were descriptive and were performed using the primary analysis set (PAS). This set comprised all enrolled patients, including those who submitted completed or partially completed surveys. Continuous variables are reported using the median, and categorical variables are reported using frequencies and percentages of the study population; the percentages were calculated using the number of observed responses in the denominator (excluding missing data). This paper reports results from the overall dataset across the participating countries. All results are reported for the PAS population, unless stated otherwise.
Although members of patient organizations may be more engaged in seeking information about their condition than the overall MM patient population, the survey results indicate that only a small proportion of respondents referred to MM organizations or support groups as their main source of information, suggesting that this particular patient selection bias was not strong in this study. Furthermore, although these results may not fully reflect the preferences of patients who are less likely to take part in research, this study aims to gain a better understanding of patient information preferences in those who are interested in receiving this information, and for whom it would likely enhance confidence in decision-making. Data were analyzed based on the specialty of the primary physician treating MM rather than on clinic type.
Acknowledgments This study was sponsored by  Amgen. Myeloma Patients Europe  recruited for the pilot study and the survey and reviewed the study documents in partnership with  Amgen. The authors are thankful to the Myeloma Patients Europe organizations who participated in the dissemination of the study. The study design was conducted by IQVIA, who received funding from  Amgen. The authors are also thankful to  Stephanie Philpott  ( IQVIA, UK ) for her contributions to the survey development and qualitative pilot study leading into this work, and to  Montse Pedros (IQVIA)  for managing the project, including administration, resourcing and recruitment. Medical writing support was provided by  Angela Kwan , MSc,  PharmaGenesis London, London, UK , with funding from  Amgen. Carine Thual of Amgen (Europe) GmbH  provided medical writing and editorial support.
ACKNOWLEDGMENTS We would like to thank  Pasi Janne  ( Harvard Medical School ) for the PFR3, GR4, and WZR12 cell lines. We also thank  Aaron Hata  ( Harvard Medical School ) for the patientderived MGH134 cell line. We thank  Bert O'Malley  ( Baylor College of Medicine ) for MCB-613, and for his continued support and feedback on this project. We thank  Ross Tomaino  ( Taplin Mass Spectrometry Facility, Harvard Medical School ) and  Erik Soderblom  ( Proteomics and Metabolics Core Facility, Duke University School of Medicine ) for their input and assistance on proteomic studies. Finally, we would like to thank  Kevin H. Lin  and  Justine C. Rutter  for their invaluable advice and support along the way. This work was supported by the  NIH  ( R01CA263593  to K. C. W. ,  K00CA245732-04  to J. H.
| Methods of analysis The primary efficacy analysis compared the relapse rates in the 2 treatment groups using a continuity-corrected χ 2 test conducted at the 5% level of statistical significance, with a missing relapse outcome imputed as no relapse. Estimated relapse rates in each treatment group and the difference in relapse rates between the treatment groups were determined, along with the 95% confidence intervals (CIs). 24, 25 Several additional prespecified sensitivity analyses of the primary endpoint were also performed to assess the impact of imputing missing relapse data as "no relapse" and the requirement for a confirmatory INCAT assessment. Prespecified sensitivity analyses included comparisons of the relapse rates in: (1) the modified intent-to-treat (MITT) analysis set with missing or incomplete relapse data imputed as relapse; (2) a MITT observed cases analysis with missing relapse outcomes excluded; (3) the per protocol set with missing relapse outcomes imputed as no relapse; and (4) the MITT analysis set with missing relapse data imputed as no relapse, where relapse was alternatively defined as an increase in adjusted INCAT disability score of ≥1 point relative to the presubcutaneous treatment baseline score, but on a single INCAT assessment only.
[n = 2], fSCIG 10% [n = 6]; Table The two additional ad hoc sensitivity analyses also supported the primary analysis. When missing relapse outcomes were imputed as relapse for patients who had a missing confirmatory INCAT assessment in a setting of clinical deterioration in the MITT population, the treatment difference between fSCIG 10% and placebo was statistically significant (À16. 9% [95% CI: À30. 30%, À2. 40%; p =. 0373]). The multiple imputation analysis with missing outcomes imputed   Note: Data presented for the MITT analysis set. The MRC assessment included 6 muscles from each side of the body, and the sum score ranged from 0 (paralysis) to 60 (healthy strength). Adjusted INCAT disability scores (used for primary endpoint assessment) were identical to INCAT disability scores (scored 0-10, higher values indicating increasing inability to make purposeful movements), with the exception that upper extremity score changes from 0 to 1 (normal to minor symptoms) or from 1 to 0 were excluded.
MATERIALS AND METHODS 2. 1 | Study design ADVANCE-CIDP 1 was a phase 3, prospective, randomized, doubleblind, multicenter, placebo-controlled study (NCT02549170) enrolling adults with CIDP from 54 sites in 21 countries conducted from December 15, 2015 to February 23, 2022 (details of sites and investigators are provided in Table.
ACKNOWLEDGMENTS The authors thank the patients who participated in this trial, their caregivers, study-site personnel, and the investigators. The authors would also like to thank  Dr Mary L. Vo, M. D. , of  Weill Cornell Medicine, New York, New York, USA  for her analysis of electrodiagnostic studies as part of the study inclusion criteria. This study was funded by  Takeda Development Center Americas, Inc. Medical writing services were provided by  Alexandra Kisbey-Ascott of Oxford Pharma-Genesis Ltd, Oxford, UK  and funded by  Takeda Development Center Americas, Inc.
DATA AVAILABILITY STATEMENT The study is registered with the ClincialTrials. gov registry at https:// clinicaltrials. gov/ct2/show/NCT02549170 (ClinicalTrials. gov identifier: NCT02549170). Information pertaining to this study and the associated study protocol may be found at ClinicalTrials. gov. The data sets, including the redacted study protocol, redacted statistical analysis plan, and individual participant data supporting the results reported in this article will be made available within 3 months from initial request to researchers who provide a methodologically sound proposal. The data will be provided after its de-identification, in compliance with applicable privacy laws, data protection, and requirements for consent and anonymization.
2 | 2 MATERIALS AND METHODS 2. 1 | Study design ADVANCE-CIDP 1 was a phase 3, prospective, randomized, doubleblind, multicenter, placebo-controlled study (NCT02549170) enrolling adults with CIDP from 54 sites in 21 countries conducted from December 15, 2015 to February 23, 2022 (details of sites and investigators are provided in Table.
Introduction To better understand neuronal correlates of human behaviour, studies typically investigate the association between brain structural measurements in regions-of-interest (ROI) and behavioural traits. Recent efforts to collect large-scale neuroimaging data enable unprecedented opportunities for powerful statistical analyses. One of the most used software for structural brain analysis is FreeSurfer  (Fischl et al. , 2002)  which is used to determine regional brain measures from a participant's brain image. Briefly summarised, FreeSurfer models the cortex in a two-dimensional mesh, with an arbitrary resolution of ~150,000 vertices per hemisphere (known as "fsaverage standard space"). Measurements are extracted at each vertex, and include cortical thickness, surface area and volume.
Vertex-wise morphometricity has been shown to be robust across samples, and it explained considerably more variance in traits such as fluid intelligence (R 2 ~13. 2%) compared with recent predictions models using different methods to maximise brain-based predictive ability (R 2 ~7%) of behavioural Fig. 1  e The surface-based cortical representations considered in this Registered Report (right hemisphere view). The first six commonly-used atlases were visualised with the ggseg package in R  (Mowinckel & Vidal-Piñeiro, 2020). Note that the Yeo atlas annotation files dictate 17 networks in each hemisphere, resulting in 34 ROIs. Vertices were visualised based on 150,000 randomly simulated points. Some colours were generated with the circulize package in R  (Gu, Gu, Eils, Schlesner, & Brors, 2014).
The Julich-Brain atlas and the random parcellations were visualised from 3D coordinates using the rgl package in R (https://dmurdoch. github. io/rgl/). In random atlases, vertices with larger radius indicate the seeds from which random ROIs were grown. We also analysed a random atlas with 50,000 ROIs (i. e. , 5 times the number of regions as displayed for the 10,000 ROIs), which is not shown in the figure. traits  (Gong, Beckmann, & Smith, 2021). This illustrates that morphometricity estimates could be promising indicators for how well different representations of the brain (vertices or different atlases) can account for individual differences. This could help formulate a recommendation for which brain representation is most promising to capture structural brain associations with behavioural traits.
, 2020). Most fsaverage atlas annotation files were obtained from MultiAtlasTT (https://github. com/faskowit/multiAtlasTT. Click or tap if you trust this link. ">https://github. com/faskowit/multiAtlasTT), which were in. gcs format and were easily mapped using FreeSurfer 6. 0 commands (mris_ca_label & mris_anatomical_stats). From this resource, Schaefer atlas annotation files were only available for a maximum of 500 ROIs. Annotation files for  Yeo et al. (2011)  were downloaded as part of the FreeSurfer 6. 0 software. Ju ¨lich-Brain atlas  (Amunts et al. , 2020)  annotation files (v2. 9) were obtained from  Mangin, Rivi ere, and Amunts (2021). Mapping atlases from. annot files required a purpose-built pipeline, which we share on GitHub (https://annafurtjes. github. io/Comparing_atlases/). Surface area and volume ROIs are summarised as the sum of vertices, and cortical thickness is an average measure.
UKB traits of interest Morphometricity was estimated for non-brain traits to quantify the proportion of variance accounted for when using cortical measures represented by ROIs from a given atlas. We selected the following dependent variables, because they are highly morphometric, and well-phenotyped in the UKB sample. We considered age (field ID 21003), sex (field ID 31) and body mass index (field ID 21001). We used measures of cognitive ability to construct a "g" factor of general cognitive ability using the lavaan package in R  (Rosseel, 2012). We used a confirmatory factor analysis with one factor, based   (Hu & Bentler, 1998) , the factor demonstrated good model fit (CFI ¼. 97, RMSEA ¼. 05; SFig. 1). Additionally, we estimated morphometricity for educational qualification (field ID 6138), number of cigarettes smoked daily (field ID 2887) and frequency of drinking alcohol (field ID 20414; excluding former drinkers field ID 20406).
Outliers that fall beyond 4 standard deviations from the mean were removed from the sample. This criterion was only applied to non-brain traits, as this criterion applied to brain measures created too many missing data entries for LMMs to reliably converge. Covariates included in all models consisted of UKB acquisition site (field ID 54), and head positioning in the MRI scanner (X, Y, Z coordinates, field IDs 25756, 25757, 25758).
Statistical analyses Morphometricity. Morphometricity quantifies the proportion of inter-individual differences in a non-brain trait (dependent variable) accounted for by cortical measurements (independent variables). The dependent variables in this study are listed above and include age, sex, body mass index, and cognitive ability. Independent variables include the following ROI measures within cortical atlases: either ~300,000 vertex-wise measures, or atlas-wide brain ROIs, which include different amounts of ROIs depending on the atlas. Additionally, we considered four covariates (outlined above in UKB traits of interest). To estimate morphometricity, we fitted linear mixed models (LMMs, Fig. 2 ). This approach recognises the correlation structure between many independent variables, and was presented and validated elsewhere, using vertexwise data  (Couvy-Duchesne et al.
, 2020). Briefly, the LMM fits all cortical measurements as a vector of random effects that is constrained to a normal distribution and a structure of variance-covariance derived from the brain relatedness matrix (BRM, B). The BRM quantifies how similar participants are to one another based on cortical measures. We excluded 4 participants with outlying covariance (±8SD from mean brainrelatedness) from the analyses as this indicated oddly similar or dissimilar brains and could bias the LMM results. More detailed model definitions are outlined in the Supplementary Material. All covariates held constant, we used Restricted Maximum Likelihood (REML) implemented in the OSCA software (OmicS-data-based Complex trait Analysis;  Zhang et al. , 2019)  to estimate the variance of the brain random effect (s b 2 ).
Sensitivity analyses Penalised regression. To test the robustness of the main results, we estimated how well grey-matter structure measures predicted non-brain traits by using LASSO models. LASSO is a "least absolute shrinkage and selection operator"  (Tibshirani, 1996) , that maximises predictive ability and can handle more predictors than observations. We estimated LASSO parameters on a training subset (random 80% sample subset, ~30,400 participants depending on number of total participants surviving data cleaning) with the big_spLinReg function in the bigstatsr package (Priv e, Aschard, Ziyatdinov, & Blum, 2018). Using ten-fold cross-validation, the function separates the training set in ten folds, employs a Cross-Model Selection and Averaging procedure and outputs an averaged vector of coefficients.
Descriptive statistics Overall, we processed structural neuroimaging data for 42,957 participants (4 exclusions based on ±8SD BRM criterion). Participants who withdrew consent and those without T2-FLAIR measurements were removed from the phenotype data (n removed ¼ 1,377). Depending on data availability, differing numbers of participants were included in the morphometricity analysis of the seven non-brain traits. Final descriptive statistics are displayed by non-brain trait in Fig. 3.