dataesr/scientific-paragraphs-categorization huggingface dataset:
No viable subgroup for statistical analyses was observed in young adults (43 and 4 individuals in the subgroups) or when using MEG metrics (41 and 6 individuals in the subgroups). This replicates our findings with our own data set that older individuals with lower structural network clustering coefficient show less stable functional network clustering. The Cam-CAN dataset also showed that lower structural clustering was associated with less stable functional network efficiency. However, unlike in our DTI/EEG study, no behavioral difference was observed between these structural network subgroups.
Corroborating findings in an independent dataset Additional analyses were performed using the Cam-CAN dataset to determine the degree of generalization of results and the subgroup identification method. Similar to our EEG dataset, differences in structural network clustering were observed between young and older participants, and were then used to identify two subgroups within the older adults that differed in MEG resting state dynamic networks. Consistent with our EEG dataset, older adults in the Cam-CAN dataset with lower structural network clustering showed lower high-gamma network efficiency and larger variability of both alpha clustering and high-gamma clustering relative to the other subgroup. Larger resting-state alpha variability in older adults is in line with previous work  ( Ariza et al.
, 2015 )  and was interpreted as reflecting a decreased ability to engage and maintain functional networks. Interestingly, unlike in our EEG dataset, no difference in cognitive performance was observed between subgroups. This could reflect the lower sensitivity or specificity of the cognitive tests used, as none of the tests in the Cam-CAN dataset specifically targets working memory updating or inhibitory processes. The results confirm the findings from our EEG dataset in highlighting the influence of structural network properties on dynamic network activities.
Acknowledgments We would like to thank  Eda Incekara ,  Claire Narang ,  Daniella Needleman ,  Pranit Singh , and  Phillip Sumardi , for their help with data collection and preprocessing. We also thank  Carrie Speck  for her help with participant recruitment and screening. This work was supported by the  Albstein Research Foundation , the  Fahs-Beck fund for Research and Experimentation , and the  William and Ella Owens Medical Research Foundation. Data collection and sharing for this project were partly provided by the  Cambridge center for Ageing and Neuroscience (Cam-CAN ). Cam-CAN funding was provided by the  UK Biotechnology and Biological Sciences Research Council  (grant number  BB/H008217/1 ), together with support from the  UK Medical Research Council and University of Cambridge, UK.
S1. ). Our results indicate that discrete α bursts may thus contribute to episodic timing. To test whether this relation was selective to retrospective timing, we collected additional data using a prospective time estimation task in a subsample of participants: all recording parameters were identical to the retrospective task, but participants were now told beforehand that they would have to estimate how much time elapses during their quiet wakefulness. In prospective timing, the same participants now overtly paid attention to the passage of time. As before, we computed the ratio between verbal time estimations and clock duration as relative prospective time estimates (pTE). The collected pTE showed a significant overestimation of duration spent in quiet wakefulness (M = 1. 16 +/-0. 31 a. u.
Acknowledgments: We thank the members of  UNIACT at NeuroSpin  for their help in recruiting volunteers and of the  Cognition & Brain Dynamics lab  for their feedback on the work. In particular, we thank  Dragana Manasova ,  Izem Mangione , and  Dr Laetitia Grabot  for their help with data collection; Dr Sophie Herbst ,  Dr Tadeusz Kononowicz ,  Dr Baptiste Gauthier  and  Raphaël Bordas  for their helpful feedback on the work. Last, but not least, we are grateful for the dazzling encouragements of an anonymous examiner on the pilot data of this study in I. P. master's thesis: "The most remarkable thing about the work reported in this thesis is that anyone could have thought that it could ever have yielded positive results![. ]Even though I'd estimate that the.
Dataset All data used to perform this study were collected during the OPTIMISTIC trial. Trial specific information, including ethical committee approval and methodological details have been published elsewhere  [10]. In short, 255 genetically proven, adult, ambulant, DM1 patients that were severely fatigued (CIS-fatigue score ≥ 35) from four clinical sites [Paris, France (n = 71, 28%); Munich, Germany (n = 66, 26%); Newcastle, UK (n = 52, 20%) and Nijmegen, Netherlands (n = 66, 26%)], were included after providing written informed consent. Participants in the OPTIMISTIC study were on average 45. 6 years old, with an estimated age at onset of 25. 4 years; 117 participants (46%) were female. Participants were randomized 1:1 to either standard care (n = 127) or standard care with a behavioural intervention (n = 128).
Regression framework validation To validate Bootstrap enhanced Elastic-Net (BeEN) regression, it was first used to reproduce the published regression model of baseline DM1-Activ-c We generated an exact reproduction of this model using the stepAIC function of the R package 'MASS' (v 7. 3. 53. 1) on 246 patients of the OPTIMISTIC datasets  [20]. Next, we implemented BeEN regression using the R packages 'glmnet' (v 4. 1. 1) and 'boot' (v 1. 3. 27) by using the cv. glmnet function with alpha = 0. 5 and 5 fold cross validation on 5000 different bootstrap distributions  [21, 22]. Per predictor, its variable inclusion probability (VIP) was calculated based on the percentage of its non-zero coefficients among the 5000 bootstrap distributions. We selected VIPs greater or equal to 60% and subsequently used them to fit a linear regression model on the same dataset.
If an interaction term has been selected, the respective main effects were selected as well. As a measure of linear regression model performance, we report the adjusted R-squared (a. Rsq) as well as the Root Mean Squared Error (RMSE) for both models. To study the external validity and the impact of overfitting, we split the dataset in training (n = 184, 75% cases) and testing (n = 62, 25% cases) subsets. We used the training set to both select variables using either BSR or BeEN regression and subsequently build ordinary least squares models. We calculated model specific out-of-sample (OS) statistical performance measures (a. Rsq and RMSE) based on the prediction results of the testing set. To account for randomness based on the training/testing splits this was repeated 10 times. Through selection of identical cohort splits for both regression approaches, we assured comparability.
Variable selection for delta-DM1-Activ-c prediction CBT treatment response was defined as changes in the primary OPTIMISTIC research outcome DM1-Activ-c between 10 months and baseline, as expressed in delta values (dDM1-Activ-c). All variables listed in Table  1  and 2 were considered as independent variables, resulting in an initial set of 40 distinct potential predictors. This analysis was limited to the patients of the intervention cohort (n = 128). Data from 22 patients were excluded from the analysis for the following reasons: missing essential treatment related information (n = 7), missing DM1-Activ-c scores at baseline or at t = 10 months (n = 11), maximum baseline DM1-Activ-c scores (n = 4). Patients with maximum DM1-Activ-c baseline values were removed as those cases cannot be used to identify variables associated with positive treatment response.
We identified and removed 2 outliers by combining the IQR rule (Q1 -2 * IQR and Q3 + 2 * IQR as respective lower and upper boundaries) with visual inspection. Baseline variables with more than 25% missing values among all cases were excluded (CSI, AESI, CTGDiagnostic). We imputed three missing values regarding CBT therapy session time of two patients by averaging the session times of their respective other sessions. The remaining missing values were imputed using the 'Multiple Imputation by Chained Equations' (MICE) algorithm (v. 3. 13. 0) [23]. A total of 10 different imputed datasets were generated, each using 50 iterations for predictive mean matching. The impact of imputation was mainly studied by its influence on variable inclusion probabilities (VIPs). Each imputed dataset was used to fit Elastic-Net regression on 5000 bootstrap distributions using the R functions 'boot' and 'cv.
glmnet' with alpha = 0. 5 and 5 fold cross validation  [21, 22]. Subsequently, we calculated VIPs for each variable based on the percentage of their non-zero coefficients among the 5000 bootstrap distributions. Imputation independent predictors were obtained by calculating mean VIP's among all imputed datasets. Predictors with a mean VIP exceeding 60% were selected and used to fit a linear model on 80 unimputed intervention cases without missing relevant values. After removal of one outlier (n = 79), all assumptions of linear regression as assessed by the R package 'gvlma' (v 1. 0. 0. 3) were met  [24]. Unstandardized regression coefficients as well as their error terms are reported. Supplementary to the main analysis, we repeated the same prediction analysis for absolute changes of 6MWT scores in the intervention group, expressed as delta 6MWT.
In order to be able to distinguish variables that mostly predict therapy response versus natural history, we also implemented the same regression framework for both the delta DM1-Activ-c and delta 6MWT prediction in the control group, excluding treatment related variables. All statistical analyses were done using the programming language R (version 4. 0. 5). All R scripts used to generate the results are available on github. com/cmbi/OPTIMISTIC Post-hoc. Furthermore, non-reported results of the supplementary analyses, including Variable Inclusion Probabilities, were also added to Git Hub.
A much lower OS than IS performance is a sign of overfitting, which is a common phenomenon when working with relatively small sample sizes. To account for random sampling effects, the split between training and testing subsets was repeated 10 times. Among the 10 training datasets, BeEN regression robustly selected the variables Log(ePAL), Age and the interaction term Age * Log(ePAL). Only Variant Repeats was less frequently selected (70%). On the contrary, BSR yielded more variable results in this down sampling experiment, with now also selecting the interaction terms Log(ePAL) * SI, Age * SI, and Age * Log(ePAL) * SI in 50% of the subsets, while SI (70%) and the interaction term Age * Log(ePAL) (80%) were not always selected (Table  3 ). Irrespective of the differences in variable selection, the average in-sample (IS) performance measures were similar between the two approaches and similar to the results obtained by utilizing the full dataset (Table  4 ).
Delta-DM1-Activ-c prediction After BeEN regression framework validation, we subsequently used BeEN regression to select variables associated with response to the behavioural intervention. As penalized regression models necessitate complete datasets, missing values were imputed using Multiple Imputation by Chained Equations (MICE)  [23, 25]. To account for the effects of imputation, 10 different imputed datasets were generated. Variable inclusion probabilities (VIP) for the potential predictors of delta-DM1-Activ-c are shown in Table  5  for each imputed dataset. For the majority of the predictors stable, imputation independent, VIPs were observed. The variables that have been selected in more than 60% of the imputed datasets are most likely the best and most robust prediction variables. These were DM1-Activ-c, SSLI, MIRS, Sex, nIndications, TMT, Stroop, SSLD and BDIFS.
The largest variability in VIPs was observed for the variables with the most missing values (MeanENMO, L5ENMO, M5ENMO, ASBQ, SSLD). In order to obtain coefficient estimates, the most frequently selected variables were used to fit a linear regression model on the unimputed intervention cohort dataset. These results hinted towards positive CBT response if patients are female, have high baseline scores of BDIFS, TMT, SSLI and nIndications, and lower baseline scores of DM1-Activ-c, Stroop, MIRS and SSLD (Table  6 ). As a supplementary analysis, the same regression framework was used to predict delta-6MWT in the intervention group. Here, the predictors with VIPs higher than 60% were Stroop, Sessiontime, IMQ and ASBQ. MeanENMO and PreBORG barely missed the inclusion threshold with VIPs of respectively 59% and 58%.
Both regression frameworks were also used to predict delta DM1-Activ-c and delta 6MWT in the control groups. In neither of these analyses VIPs higher than 60% were identified, suggesting that the predictors identified in the intervention group are more likely modelling interven- Per baseline predictor and imputed dataset the percentage of its non-zero regression coefficients for delta-DM1-Activ-c prediction among 5000 bootstrap samples is presented in % (Variable Inclusion Probability (VIP)). Predictors are sorted (descending) based on the mean VIP among the 10 imputed datasets. tion response and not natural disease progression (Supplementary results are available on github. com/ cmbi/OPTIMISTIC Post-hoc).
DISCUSSION In this study, we evaluated (1) correlations between cross-sectional data of reported outcome measures, clinical tests and accelerometry data derived from the European OPTIMISTIC trial in myotonic dystrophy type 1 (DM1);  [2]  correlations between longitudinal changes of these outcome measures in the same study  [10]. Next  [3] , we validated the Bootstrap enhanced Elastic-Net (BeEN) regression methodology as a valuable tool to select predictors of a clinical intervention with robust outcomes in small cohorts. Last  [4] , we used the validated BeEN regression methodology to identify promising predictors of a positive effect of cognitive behavioural therapy (CBT) for DM1 patients. In the following paragraphs, we will discuss our results in this order. In the cross-sectional analysis, OPTIMISTIC's primary outcome variable, the DM1-Activ-c, demonstrated moderate to strong correlations with measures for exercise capacity (6MWT), objectively assessed activity (MeanENMO, M5ENMO), disease impact (MDHI), quality of life (INQoL) and disease severity (MIRS).
Given the comparable ISperformance estimates among the regression models based on either variable selection approach, these variables likely have the strongest predictive effect on baseline DM1-Activ-c among the subset of predictors considered. However, irrespective of the different down sampling based cohorts used, substantial, methodology independent, variance among the out-of-sample (OS) performance measures has been found. These results hint towards an insufficiently large cohort size to allow for training/testing splits in which both accurately represent the study population. Based on these findings it was concluded that the intervention dataset was sufficiently large to identify variables associated with treatment response when using BeEN regression, but too small to also create a linear model and test its OS statistical performance.
ACKNOWLEDGMENTS This study was partially funded by the  European Union   Seventh Framework Program , under grant agreement no. 305697  (the  Observational Prolonged Trial In Myotonic dystrophy type 1 to Improve Quality of Life Standards, a Target Identification Collaboration [OPTIMISTIC]  project). This study also received funding from the  European Union 's  Horizon 2020 research and innovation programme  " ERA-NET rare disease research implementing IRDiRC objectives -N • 643578 " via the Dutch research funding agency ZON-MW, through the  E-Rare Joint Transnational Call JTC 2018 "Translational Research Projects on Rare Diseases " ( ReCognitION  project: Recognition and validation druggable targets from the response to Cognitive Behaviour Therapy in Myotonic Dystrophy type 1 patients from integrated -omics networks).
, 2017; Mucci et al. , 2017). Hence, there is a need to adopt a research approach that facilitates the identification of primary or idiopathic negative symptoms by using objective measures. Expert consensus  (Kirkpatrick et al. , 2006)  and recent empirical work on large datasets  (Strauss et al. , 2019; Strauss et al. , 2018)  confirm that negative symptoms are best represented by a constellation of five symptoms consisting of blunted affect, alogia, anhedonia, avolition, and asociality. The study of negative symptoms requires a systematic and replicable operational definition of a group of patients presenting with such psychopathology. To that end,  Buchanan (2007)  proposed the construct of persistent negative symptoms (PNS) as a way to operationally define a subgroup of patients with negative symptoms of at least moderate severity but with a low level of potential confounds.
Study design and data collection Following admission to PEPP-Montréal between 2003 and 2018, participants took part in a systematic assessment protocol with multiple timepoints across a twoyear span (See  Jordan et al. , 2014  for more information of the assessment protocol). For the purpose of the current study, symptom assessment performed at the time of the neurocognitive testing (Month 3 following admission) were examined in addition to month 6 and 12 following admission. The two latter assessments were used to categorize patients into PNS, sPNS, or non-PNS groups. Symptom rating data during that period was available from 515 participants. From those, 90 did not complete the neurocognitive assessment for various reasons including clinical instability, lack of interest, or disengagement with services.
Neurocognitive assessment Neurocognitive assessments were administered in pen and paper format with the Wechsler Memory Scale-Third Edition WMS-III  (Wechsler, 1997b)  to participants recruited between 2003 and 2010 (FEP: n=246; non-clinical controls: n=69) and in computerized format with the CogState Research Battery (CSRB;  Pietrzak et al. , 2009)  to participants recruited thereafter (FEP: n=179; non-clinical controls: n=69). Table  1  provides a complete list of neurocognitive tests included in each battery. Neurocognitive assessments took place on a single occasion, when patients were in a stable but not necessarily asymptomatic condition (approximately 3 months following admission to PEPP), and were not repeated on subsequent timepoints. Average z-scores from non-clinical controls were used as normative data to transform individual patient data into battery-specific z-scores.
Bonferroni correction was used for post-hoc tests. All statistical tests were performed on SPSS version 27 (SPSS, Chicago, IL, USA) and were two-tailed with an alpha level of 0. 05. As can be seen in Table  2 , non-clinical controls significantly differed from all patient groups on full-scale IQ, years of education and age. When examining FEP as a whole group, there was no significant difference on the sex or handedness proportions relative to non-clinical controls. When FEP patients were categorized into PNS groups (PNS, sPNS and non-PNS), significant differences were noted for age at entry (F(2,422)=3. 22, p=. 041; PNS < non-PNS (p=. 042)), sex (χ 2 (2)=14. 76, p=. 001; PNS<non-PNS), years of education (F(2,405)=10. 99, p<. 001; non-PNS > PNS/sPNS (p<. 001 and p=. 047 respectively)), and IQ (F(2,417)=6.
Study design and metadata We determined the metabolome of serum samples from 204 cancer patients enrolled in the ONCOVID trial (https://clinicaltrials. gov/ct2/show/NCT04341207). Patients were divided into "controls" (no diagnosis of Covid-19, no signs of respiratory infection) and two categories of PCR-confirmed Covid-19 patients with "mild disease" (with ambulatory treatment) or "moderate/severe disease" (requiring hospitalization and respiratory assistance). Indeed, the number of patients with severe Covid-19 (requiring intubation and mechanical ventilation) was too low to be analyzed as a separate group. Importantly, the duration of viral shedding (short-term shedding: <40 days, long-term shedding: >40 days) was variable among Covid-19 infected cancer patients, yet tended to be shorter in mild than in moderate/severe cases (Figure  1 ) as already observed by Goubet et al.
Study ONCOVID design and participants Gustave Roussy Cancer Center mentored the "ONCOVID" trial and collaborated with the academic authors. All patients provided written informed consent. This clinical trial was conducted in accordance with the principles of the Declaration of Helsinki. Protocol approval was obtained from an independent ethics committee (ethics protocol number EudraCT No: 2020-001250-21). The protocol is available with the full text of this article at https://clinicaltrials. gov/ct2/show/ NCT04341207. Blood samples were drawn from patients enrolled in ONCOVID at Gustave Roussy Cancer Campus (Villejuif, France). Whole human peripheral blood was collected into sterile vacutainer tubes. Serum were collected after centrifugation at 600 × g for 10 min at room temperature and transferred to -80° C freezer to await analysis.
2% of formic acid (A) and acetonitrile/isopropanol (1/1; v/v) (B) freshly made. Flow rate was set to 0. 3 mL/min, and gradient as follow: initial condition was 70% phase A and 30% phase B, maintained during 1. 5 min. Molecules were then eluted using a gradient from 30% to 60% phase B over 9 min. Column was washed using 98% mobile phase B for 2 minutes and equilibrated using 30% mobile phase B for 2 min. After each injection, needle was washed twice with isopropanol and thrice with water. The autosampler was kept at 4° C. Collision gas was nitrogen. Scan mode used was the MRM for biological samples. Peak detection and integration of the analytes were performed using the Sciex MultiQuant quantitative software (Version 3. 0. 3), exported as tables and processed with R software (version 4. 0. 3) and the GRMeta package (Github/ kroemerlab).
1 % of HFBA (B) freshly made. The flow rate was set to 0. 2 ml/min, and gradient as follow: initial condition was 95% phase A and 5% phase B. Molecules were then eluted using a gradient from 5% to 40% phase B over 10 min. The column was washed using 90% mobile phase B for 2. 5 minutes and equilibrated using 5% mobile phase B for 4 min. The autosampler was kept at 4° C. The collision gas was nitrogen. The scan mode used was the MRM for biological samples. Peak detection and integration of analytes were performed using the Agilent Mass Hunter quantitative software (B. 07. 01), exported as tables and processed with R software (version 4. 0. 3) and the GRMeta package (Github/ kroemerlab).
01% of formic acid (B). Flow rate was set to 0. 4 mL/min, and gradient as follow: initial condition was 80% phase A and 20% phase B, maintained during 6 min. Molecules were then eluted using a gradient from 20% to 45% phase B over 7 min. Column was washed using 95% mobile phase B for 5 minutes and equilibrated using 20% mobile phase B for 4 min. Autosampler was kept at 4° C. The collision gas was nitrogen. The scan mode used was the MRM for biological samples. Peak detection and integration of analytes were performed using the Agilent Mass Hunter quantitative software (B. 07. 01), exported as tables and processed with R software (version 4. 0. 3) and the GRMeta package (Github/ kroemerlab).