dataesr/scientific-paragraphs-categorization huggingface dataset:
2] [13] [14]  We were not able to confirm a sex-associated difference in the presentation or progression of depression, in either the longitudinal data or the Fox Insight dataset. However, female PD patients expressed a depressive mood more frequently than male patients, in response to the related NMSQuest question ('feeling sad, 'low' or 'blue') from the Fox Insight dataset. However, the magnitude of the association was not different between PD and non-PD participants, indicating that the sex difference associated with this outcome may not be PD-specific. Regarding the NMSQ items evaluated, the similar null results except for NMSQ_Smell were reported previously in a cross-sectional analysis of de novo PD patients. 31 Regarding the discrepancy in NMSQ_Smell, it may be possible that the sex-difference in reported loss of smell/taste may be only detectable in the de novo PD stage.
The current study has some limitations. Fox Insight is an online-only cohort, which is inherently different from a clinicbased cohort; however, our analyses were mostly consistent across these two different settings. Additionally, because the study participants were almost all of European descent, the generalizability of these observations across different ancestrally distinct groups should be verified. In this study, we focused on the overall associations between sex and phenotypes and did not separate the biological mechanisms from the environmental mechanisms. For example , the effect of estrogen on PD has been investigated frequently and the conflicting results were reported. 32 but we did not collect necessary data to rigorously evaluate the impact of estrogen on the differences. Similarly, we did not have enough data to investigate environmental factors such as smoking, alcohol, diet, physical activity levels, and socio-economic factors.
The different distribution of these factors by sex may explain the differences we observed in the current study. Well-designed studies are warranted to dissect the overall differences into each underlying pathway. Despite some limitations, the current study has some strengths. First, the total number of participants examined in our longitudinal analysis was one of the largest populations studied. Second, although each study had different cohort characteristics, we controlled for heterogeneity and multiple comparisons to detect robust signals. Most of the associations identified between sex and disease presentation and progression were consistent between the longitudinal cohort and analyses performed using the independent Fox Insight dataset. Thus, our results could be generalized to PD patients across various disease stages in different contexts, given the range of studies incorporated.
The adjusted covariates were linear and square age for non-PD participants. For PD participants, linear and square disease duration, and indicators of levodopa and dopamine agonist usage were further adjusted. SE, standard error; PDAQ-15, the Penn Parkinson's Daily Activities Questionnaire-15 (15-item measure of cognitive instrumental activities of daily living (IADL) for Parkinson's disease patients derived from the original 50-item PDAQ), ranging 0-60 (the lower the worse); Depression, Geriatric Depression Scale score more than 5. Consistency with longitudinal dataset analyses were evaluated for outcomes (Consistency with LT analysis). NMSQ, Non Motor Symptom Questionnaire; NMSQ_awake: difficult to stay awake; NMSQ_Sleep, difficulty getting sleep at night; NMSQ_Feel, feeling sad, 'low' or 'blue'; NMSQ_Smell, loss or change in your ability to taste or smell.
Methods: We tested 40 clinical phenotypes, using longitudinal, clinic-based patient cohorts, consisting of 5,946 patients, with a median follow-up of 3. 1 years. For continuous outcomes, we used linear regressions at baseline to test sexassociated differences in presentation, and linear mixed-effects models to test sex-associated differences in progression. For binomial outcomes, we used logistic regression models at baseline and Cox regression models for survival analyses. We adjusted for age, disease duration, and medication use. In the secondary analyses, data from 17,719 PD patients and 7,588 non-PD participants from an online-only, self-assessment PD cohort were cross-sectionally evaluated to determine whether the sex-associated differences identified in the primary analyses were consistent and unique to PD.
Fox Insight To evaluate the consistency of results from the longitudinal dataset, we explored an independent dataset, Fox Insight. Fox Insight is an online-only, PD research cohort. 18 The details of the study are available online (https://foxinsight. michaeljfox. org/). Individuals, aged 18 or older, with and without PD, were enrolled through in-person referral or online advertisements. The participants provided online informed consent, and self-reported demographic, characteristics, symptoms, medical history, and PD medication data were collected. Although Fox Insight is a longitudinal study, we analyzed the data cross-sectionally for the present study because the follow-up periods were relatively short (e. g. , the median follow-up period was 0. 4 years for Non-Motor Symptoms Questionnaire).
During the analysis step, we adjusted for age and disease duration. To limit the impacts of the extreme data points, we included participants from the middle 80% of the age distribution and the disease duration distribution (only among PD participants), which excluded any participants younger than the lower 10th percentile (< 46. 8 years old) or older than the 90th percentile (> 77. 4 years old) and PD patients with a disease duration shorter than one year (10th percentile) and longer than 13. 5 years (90th percentile).
14. 8E-5} than male PD patients, and an even stronger association was observed when we adjusted for years of education (HR 0. 59 [0. 48, 0. 73], P = 4. 6E-7, 0. 63 [0. 53, 0. 76], P = 4. 3E-7, Supplemental Material). This association remain significant when we further adjusted for the baseline MoCA score (HR 0. 56 [0. 37, 0. 86], P = 0. 007) or the baseline MMSE score (HR 0. 67 [0. 51, 0. 90], P = 0. 007, Supplemental Material) at the significance level of 0. 05. Additionally, the baseline MoCA scores were higher in female patients (0. 63 [0. 27, 1. 00]) than in male patients, whereas the baseline MMSE score was not significantly different between sexes (P = 0. 97, Supplemental Materials). Female patients presented with a higher rate of developing dyskinesia (HR 1. 29 [1. 16, 1. 44]). To assess the impacts of weight, body mass index (BMI) and medication on this association, we conducted ad hoc analyses on a subset of data (PDBP, PPMI, and NET_PD_LS1: 2,281 participants) for which height at baseline, weight at baseline, and medication at visits were recorded.
When analyzing similar phenotypes within the Fox Insight dataset, we generally confirmed the results of the longitudinal dataset analyses (Table  3 ). In the Fox Insight dataset analysis, the interaction terms between sex and disease duration indicated the average sex-associated differences in the longitudinal trajectories for the outcomes. For example, a positive association for the interaction between disease duration and PDAQ-15 indicated that the PDAQ-15 scores for female patients were higher than those in male patients (i. e. , better cognition-related instrumental functional abilities) among patients with longer disease durations in the Fox Insight dataset. To illustrate this, we visualized the sex differences, stratified by disease duration (Supplemental Materials). The results are consistent with those for the longitudinal dataset analysis, indicating that female patients had a lower risk of developing cognitive impairments during the disease course.
Several associations that were previously reported were not observed in the current analysis. RBD was reported to be more prevalent in males with PD than in females with PD, 9,10 although some studies have disagreed. 29, 30 We were unable to confirm this association in the current longitudinal dataset. Although the prevalence of possible RBD, as detected by single-question screening was higher in male patients among the Fox Insight cohort, a similarly increased prevalence in possible RBD for non-PD male participants makes the PD-specific nature of this association questionable. 2] [13] [14]  We were not able to confirm a sex-associated difference in the presentation or progression of depression, in either the longitudinal data or the Fox Insight dataset. However, female PD patients expressed a depressive mood more frequently than male patients, in response to the related NMSQuest question ('feeling sad, 'low' or 'blue') from the Fox Insight dataset.
Despite some limitations, the current study has some strengths. First, the total number of participants examined in our longitudinal analysis was one of the largest populations studied. Second, although each study had different cohort characteristics, we controlled for heterogeneity and multiple comparisons to detect robust signals. Most of the associations identified between sex and disease presentation and progression were consistent between the longitudinal cohort and analyses performed using the independent Fox Insight dataset. Thus, our results could be generalized to PD patients across various disease stages in different contexts, given the range of studies incorporated. Third, by comparing PD patients with non-PD individuals, we obtained insight into whether sex-associated phenotypes in PD were disease-specific or reflected more general sex differences.
PPMI -a public-private partnership -is funded by The Michael J. Fox Foundation for Parkinson's Research and funding partners, including AbbVie, Allergan, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali Incorporated, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck & Co. , Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier Laboratories, Takeda, Study (FI) is funded by The Michael J. Fox Foundation for Parkinson's Research. We would like to thank the Parkinson's community for participating in this study to make this research possible. We also thank the following grants and financial supporters of above studies; DATATOP was supported by a Public Health Service grant (NS24778) from the National Institute of Neurological Disorders and Stroke (NINDS); by grants from the General Clinical Research Centers Program of the National Institutes of Health at Columbia University (RR00645), the University of Virginia (RR00847), the University of Pennsylvania (RR00040), the University of Iowa (RR00059), Ohio State University (RR00034), Massachusetts General Hospital (RR01066), the University of Rochester (RR00044), Brown University (RR02038), Oregon Health Sciences University (RR00334), Baylor College of Medicine (RR00350), the University of California (RR00827), Johns Hopkins University (RR00035), the University of Michigan (RR00042), and Washington University (RR00036), the Parkinson's Disease Foundation at Columbia-Presbyterian Medical Center, the National Parkinson Foundation, the Parkinson Foundation of Canada, the United Parkinson.
, the American Parkinson's Disease Association, New York, and the University of Rochester; DIGPD is supported by Assistance Publique Hôpitaux de Paris, funded by a grant from the French Ministry of Health (PHRC 2008, AOM08010) and a grant from the Agence Nationale pour la Sécurité des Médicaments (ANSM 2013); HBS is supported by the Harvard NeuroDiscovery Center, Michael J Fox Foundation, NINDS U01NS082157, U01NS100603, and the Massachusetts Alzheimer's Disease Research Center NIA P50AG005134; NET-PD_LS1 was supported by NINDS grants U01NS043128; ParkFit is supported by ZonMw (the Netherlands Organization for Health Research and Development (75020012)) and the Michael J Fox Foundation for Parkinson's research, VGZ (health insurance company), GlaxoSmithKline, and the National Parkinson Foundation; ParkWest is supported by the Research Council of Norway, the Western Norway Regional Health Authority, Stavanger University Hospital Research Funds, and the Norwegian Parkinson's Disease Association; PDBP is a consortium with NINDS initiative; PICNICS has received funding from the Cure Parkinson's Trust, the Van Geest Foundation and is supported by the National Institute of Health Research Cambridge Biomedical Research Centre; PPMI is supported by the Michael J Fox Foundation for Parkinson's research; PreCEPT and PostCEPT were funded by NINDS 5U01NS050095-05, Department of Defense Neurotoxin Exposure Treatment Parkinson's Research Program.
Grant Number: W23RRYX7022N606, the Michael J Fox Foundation for Parkinson's research, Parkinson's Disease Foundation, Lundbeck Pharmaceuticals. Cephalon Inc, Lundbeck Inc, John Blume Foundation, Smart Family Foundation, RJG Foundation, Kinetics Foundation, National Parkinson Foundation, Amarin Neuroscience LTD, CHDI Foundation Inc, National Institutes of Health (NHGRI, NINDS), Columbia Parkinson's Disease Research Center; ProPARK is funded by the Alkemade-Keuls Foundation, Stichting Parkinson Fonds, Parkinson Vereniging, The Netherlands Organisation for Health Research and Development; Udall is supported by NINDS. Authors roles 1. Research project: A. Conception, B. Organization, C. Execution; 2. Statistical Analysis: A. Design, B. Execution, C. Review and Critique; 3. Manuscript Preparation: A.
Fox Foundation for Parkinson's Research and funding partners, including AbbVie, Allergan, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali Incorporated, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck & Co. , Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier Laboratories, Takeda, Study (FI) is funded by The Michael J. Fox Foundation for Parkinson's Research. We would like to thank the Parkinson's community for participating in this study to make this research possible. We also thank the following grants and financial supporters of above studies; DATATOP was supported by a Public Health Service grant (NS24778) from the National Institute of Neurological Disorders and Stroke (NINDS); by grants from the General Clinical Research Centers Program of the National Institutes of Health at Columbia University (RR00645), the University of Virginia (RR00847), the University of Pennsylvania (RR00040), the University of Iowa (RR00059), Ohio State University (RR00034), Massachusetts General Hospital (RR01066), the University of Rochester.
1741 patients from 50 sites in the US and Canada participated. 4 They were within 5 years from diagnosis. The plan was for them to be followed for at least 5 years, but the study ended early for futility based on an interim analysis at which point the median follow-up time was 4 years. Financial support for the LS-1 study was provided by National Institute of Neurological Disorders and Stroke (NINDS) grant U01NS43128. Oslo PD study[Citation error] (Oslo) is an ongoing study since 2007, with 317 patients diagnosed with ULPDSBB criteria with modification of allowing family history. The participants are being followed up to 6 years in prospective (30 years in retrospective) at Oslo University Hospital in Norway. 5 Oslo PD is supported by the Research Council of Norway andSouth-Eastern Norway Regional Health Authority.
,  Michael J. Fox Foundation. Ole A. Andreassen Consultant HealthLytix Supplemental Figures. 2 Forest plots for the sex differences in rate of change in UPDRS part III. 2 Visualization of the sex differences in FI dataset -1. 3 Visualization of the sex differences in FI dataset -2. 4 Sex differences in developing cognitive impairment further adjusted for years of education and baseline cognitive test results. 5 Supplemental Tables. 6 Cohort specific definitions of binomial outcomes. 7 All meta-analysis results. 8 Associations between sex and dyskinesia in survival models with further adjustment. 9 R model specifications. 10 Supplemental documents about the longitudinal cohorts. 20 Descriptions. 20 Study investigators. 23 References. 33 N. Y. ;  Matthew Galloway, Ph. D. , Detroit;  Ian Irwin, Ph.
RCM can be driven by various driving models or datasets such as meteorological reanalyzes, GCMs and other RCMs. RCM generally provides improved climate simulations, especially with respect to statistical properties of extremes, such as cyclones, intense precipitation and strong winds  (Giorgi and Mearns, 1991). Improvements can also come from regionally specific empirical adjustments of the model parameterizations. Meanwhile, RCM is far from being a perfect solution for all needs of climate downscaling. RCM brings added-values with respect to GCM or reanalysis, but it can also present drawbacks. Many challenges still require attention and efforts. First of all, RCM is under constraints, with nudging applied at the lateral boundaries, inconsistent behaviors can occur. Nudging is a simple operation that can be realized by adding a "Newtonian relaxation" in the dynamical equations governing the evolution of wind, temperature and humidity, noted as.
Difference between "Big Brother" and "Little Brother" obviously reveals the upmost theoretical performance of RCM. This protocol is particularly interesting for cases where there is no reliable high-resolution dataset to evaluate the performance of RCM. The original geographic domain of interest in  Denis et al. (2002)  is on the east coastal area of North America, and their model used is CRCM (Canadian Regional Climate Model)  (Caya and Laprise, 1999). Although their simulations cover only a month  (February 1993) , they were able to conclude that the one-way nesting as designed in  Davies and Turner (1977)  for application to RCM did a good job in climate downscaling from large scales to regional scales. The common point of BBE with our "Master versus Slave" protocol is the concept of perfect model which makes it possible to assess the downscaling approach and the operational procedure by getting rid of physical imperfections of the climate model used.
Such modes or regimes are then used to stratify the correlation coefficients. The analysis is again applied to daily data of geopotential height at 500 hPa retaining the only synoptic variability of the atmospheric circulation, after eliminating the mean seasonal cycle. The EOF analysis gives, in descending order of explained variance, the dominant spatiotemporal patterns, and leaves the noise in the EOF structures of higher order. In order to compare the time series (PC: principal component) characterizing the synoptic sequence, it is necessary to have a series of common spatial structures for RCM and GCM. To do so, we just joined the two datasets together to perform the analysis in a combined way. We obtained very close results (not shown) if we perform the analysis to one dataset and project the spatial structures to another.
For each day of the 30-year simulations, we can calculate the spatial correlation coefficients and the root-mean square errors for the synoptic variability. Figure  11  displays the statistical properties of these two datasets in the form of box-and-whisker plots. It is easy to see that the soft-boundary configuration diminishes the resemblance between RCM and GCM, since there is a robust decrease of the spatial correlation coefficients (with median value from 0. 79 to 0. 69, mean value from 0. 75 to 0. 65) and an increase of the RMS errors (with median value from 0. 95 to 1. 18°C, mean value from 1. 00 to 1. 26°C). Such a situation is a little surprising for us, since we intuitively thought that the soft-boundary configuration would increase the control from GCM to RCM. In fact, it reduces only spurious effects near boundaries, but deteriorates the synoptic-scale control of GCM exerting on RCM.
We also want to remind that our RCM used in this work is of very low resolution, compared to more recent practices. A higher-resolution RCM would increase its autonomy and show more deviations from the driving GCM. Figure  8. Box-and-whisker plots showing the statistical properties (convention as usual or as in Fig. 2 ) of all spatial correlation coefficients between GCM and RCM for synoptic-scale surface air temperature anomalies, in different sensitivity experiments (as described in Fig. 7 ). Facilities with Linux command "make/Makefile" are generally used in compiling the code. One needs to carefully check and adjust different options in these Makefiles. The interface "IOIPSL" and the land surface module "ORCHIDEE" are managed by "make/Makefile". But the code "LMDZ4" is managed by FCM (Flexible Configuration Management: https://metomi.
github. io/fcm/doc/) which needs to be preinstalled before the compilation. Compiling options for FCM are stored in "machine/arch. path" and "machine/arch. fcm". They need to be coherent with what stored in Makefiles for "IOIPSL" and "ORCHIDEE". The shell script served as a manager of compilation for "LMDZ4" is "makegcm_fcm". To compile the code, one needs to use the compilation instructions written in the file "compile_instructions" recorded in the directory "config/IPSLCM4_v2". That is, in this directory, one can sequentially enter the commands: "gmake libioipsl" to compile "IOIPSL", "gmake liborchidee" to compile "ORCHIDEE", and "gmake lmdz9671twoway" to compile "LMDZ4". Objet files are stored in "lib" for "IOIPSL", "ORCHIDEE"or "modeles/LMDZ4/libo" for "LMDZ4". Executable file "gcm. e" is stored in the directory "bin" when the compilation is successfully accomplished.
How to run simulations? Configuration files, initial data, boundary conditions and job launching shell scripts for LMDZ4 are stored in an archived file which will be available through a permanent server. It is currently available with the link: "http://www. lmd. jussieu. fr/~li/LMDZ4_data. tar. gz" (volume size 111 Mb). The unpacking command is "tar -zxvf LMDZ4_data. tar. gz" which retrieves all files, as illustrated in the left-lower corner in Figure  A1. The root directory is "running". Three sub-directories are created: "master", "slave" and "launch". The first two sub-directories, for each of them, contain three sub-directories: "divers", "redem" and "result". The directory "divers" contains all configuration files and boundary conditions. The above-mentioned executable file "gcm. e" corresponding to the right configuration should be moved in the sub-directory "divers".
nc"). The sub-directory "result" is to receive model outputs. "master" and "slave" are paired to run together with an identical LMDZ4 model for both of them. "master_hi" and "slave_hi" are the same as the precedent configuration, but the "slave" model is of higher-resolution. The directory "launch" contains shell scripts under the job managing system LoadLeveler and POE (Parallel Operating Environment) to run the model with the mode MPMD (Multiple-Program Multiple-Data). "oneway_type" is to run the pair "master/slave", and "oneway_hi_type" is to run a simulation with the high-resolution slave model. Both scripts use the multi-step management option: the first step is to prepare the model configuration, the second step is to perform the actual calculation in a parallel way, and the third step is a post-processing step for model outputs.
Acknowledgements. This paper is about a part of  Shan Li 's thesis prepared in  Sorbonne Université and LMD (Laboratoire de Mé té orologie Dynamique) in Paris. Thanks to Sorbonne Université  ,  LMD and Institute Pierre Simon Laplace (IPSL). The Institute for Development and Resources in Intensive Scientific Computing (IDRIS)  and the Mesocentre of IPSL provided computing resources for simulations and data processing. Shan Li  would also like to thank  Patricia Cadule  for her comments on the manuscript, for her suggestions, discussions and encouragements during the thesis preparation.