dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment For the purpose of open access, the author has applied a CC BY public copyright licence to all Author Accepted Manuscripts arising from this submission. This research was supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. The  UCL Movement Disorders Centre  is supported by the  Edmond J. Safra Philanthropic Foundation. Data used in the preparation of this article were obtained from the AMP-PD Knowledge Platform (amp-pd. org). AMP-PD is a public-private partnership managed by the  FNIH  and funded by  Celgene ,  GSK ,  Michael J. Fox Foundation for Parkinson's Research , the  National Institute of Neurological Disorders and Stroke ,  Pfizer , and  Verily. Clinical data and biosamples used in preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) and the Parkinson's  Disease Biomarkers Program  (PDBP).
PPMI-a public-private partnership-is funded by the  Michael J. Fox Foundation for Parkinson's Research  and funding partners, including [list the full names of all of the PPMI funding partners found at ppmiinfo. org/about-ppmi/who-we-are/study-sponsors]. The PPMI Investigators have not participated in reviewing the data analysis or content of the manuscript. For up-to-date information on the study, visit ppmi-info. org. The Parkinson's Disease Biomarker Program (PDBP) consortium is supported by the  National Institute of Neurological Disorders and Stroke (NINDS)  at the  National Institutes of Health. A full list of PDBP investigators can be found at pdbp. ninds. nih. gov/policy. The PDBP Investigators have not participated in reviewing the data analysis or content of the manuscript. The DIGPD cohort (ClinicalTrials.
gov, NCT01564992) is a multicenter longitudinal cohort conducted in four Universities and four General Hospitals in France between 2009 and 2019, sponsored by  Assistance Publique Hôpitaux de Paris , and funded by a grant from the  French Ministry of Health  ( PHRC 2008 ,  AOR0810 ) and a grant from the  Agence Nationale de Sécurité et des Médicaments  ( ANSM-2013 ). The authors thank the  DIGPD Study group  which collected the data made available for this work. Both TPD and OPDC cohorts are primarily funded and supported by  Parkinson's UK  (parkinsons. org. uk/) and supported by the  National Institute for Health and Care Research (NIHR) Clinical Research Network (CRN). The TPD study is also supported by  NHS Greater Glasgow and Clyde. The OPDC cohort is also supported by the  NIHR Oxford Biomedical Research Centre , based at the  Oxford University Hospitals NHS Trust , and the  University of Oxford.
Safra Foundation, National Institute of Health and Care Research, and Cure Parkinson's. J. C. C. has served on advisory boards for Biogen, Denali, Idorsia, Prevail Therapeutic, Servier, Theranexus, and UCB and received grants from Sanofi and the Michael J. Fox Foundation outside of this work. A. E. received funding/grant support by Agence Nationale de la Recherche, France Parkinson, and the Michael J. Fox foundation. J. H. is supported by the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's.
Data Availability We have made our summary statistics available (doi. org/10. 5281/ zenodo. 7257484). TPD data are available on access request from trackingparkinsons. org. uk/about-1/data/. The PDBP and PPMI data were accessed from Accelerating Medicines Partnership: Parkinson's Disease (AMP-PD), and data are available on registration at amp-pd. org/. OPDC data are available on request from the Dementias Platform UK (portal. dementiasplatform. uk/Apply). DIGPD data are available on request to the principal investigator (JC Corvol, Assistance Publique Hôpitaux de Paris; clinicaltrials. gov/ct2/show/NCT01564992). PD STAT is available on request to the principal investigator (C Carroll, Plymouth University, penctu. psmd. plymouth. ac. uk/pdstat/#:;:text=PD% 20STAT%20%2D%20Simvastatin%20as%20a,brain%20from% 20injury%20or%20loss).
HapMap phase 3 data (HapMap3) is available for download at ftp://ftp. ncbi. nlm. nih. gov/hapmap/. Cis-QTL data were obtained from eQTLGen (eqtlgen. org/ciseqtls. html) and PsychENCODE (resource. psychencode. org). MetaBrain cis-eQTL data can be accessed on access request form (metabrain. nl/cis-eqtls. html). eQTL data from eQTL catalog can be ftp-accessed (ebi. ac. uk/eqtl/Data_access/). FANTOM5 CAGE-seq and Nott brain cell type-specific enhancer-promoter interactome data were accessed through echolocatoR (github. com/RajLabMSSM/echolocatoR).
Techniques for Text Generation: Automatic text generation involves the use of various techniques, including rule-based systems, template-based approaches, statistical methods, and more recently, deep learning models such as recurrent neural networks (RNNs) and transformers. These models leverage vast amounts of training data to learn patterns and generate coherent and contextually relevant text. Rule-based and template-based approaches rely on predefined rules and templates to generate text. They are often used for specific tasks such as form filling or generating structured reports. Statistical methods, on the other hand, employ probabilistic models based on observed patterns in data to generate text. In recent years, deep learning models, especially transformer models like the GPT (Generative Pre-trained Transformer) series, have gained significant attention for their ability to generate highly realistic and contextually rich text.
Existing Approaches for Text Generation Detection Several approaches have been employed for the detection of generated texts. These approaches encompass a range of techniques, including linguistic analysis, statistical methods, and machine learning algorithms. Linguistic analysis involves examining various linguistic features of the text, such as vocabulary, grammar, and syntax, to identify patterns that distinguish generated content from human-generated content. Statistical methods utilize quantitative metrics, such as word frequency or n-gram analysis, to identify anomalies in the generated text. Machine learning algorithms, particularly supervised learning approaches, have gained prominence in detecting generated texts. These algorithms are trained on labeled datasets, where the distinction between generated and human-generated content is known.
Advancements in Machine Learning and NLP for Detection Advancements in machine learning and natural language processing have significantly improved the accuracy and effectiveness of automated detection systems. Techniques such as deep learning, specifically using neural networks, have shown promise in detecting generated texts. State-of-the-art models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can capture complex patterns and dependencies in textual data, enabling more accurate identification of generated content. Additionally, transformer-based models, such as BERT (Bidirectional Encoder Representations from Transformers), have demonstrated strong performance in language understanding tasks, which can be leveraged for detection purposes. Moreover, the availability of large labeled datasets, specifically curated for detecting generated content, has facilitated the training and evaluation of detection models.
Collaborative Efforts and Partnerships Addressing the challenges posed by generated texts in the context of the environment requires collaborative efforts and partnerships among stakeholders. Collaboration between technology companies, research institutions, environmental organizations, and media outlets can lead to the development of shared standards, best practices, and tools for detecting and countering generated content. Collaborative initiatives can involve the establishment of shared databases of generated texts for training and evaluating detection models, joint research projects to enhance detection algorithms, and the creation of platforms for knowledge exchange and information sharing. Moreover, partnerships with social media platforms and online content providers are vital. These platforms can implement measures to flag or limit the dissemination of generated texts that violate ethical standards or perpetuate misinformation.
Advancements in Detection Technologies Continued research and development efforts are essential to enhance the effectiveness and robustness of automated detection systems. Future directions in detection technologies may include the exploration of advanced machine learning algorithms, such as deep neural networks and reinforcement learning, to improve the accuracy of distinguishing generated texts from human-generated content. Additionally, integrating multimodal information, such as images and audio, into detection systems can further enhance their ability to identify generated content that extends beyond textual information. This interdisciplinary approach can contribute to more comprehensive and reliable detection mechanisms. Furthermore, collaboration between researchers, industry experts, and technology companies is crucial for sharing insights, datasets, and benchmarking techniques.
Interdisciplinary Research and Collaboration The relationship between automatic detection of generated texts and the environment necessitates interdisciplinary research and collaboration. Environmental scientists, AI researchers, linguists, and communication experts should collaborate to better understand the specific challenges and implications of generated texts in the context of environmental preservation. Interdisciplinary research efforts can focus on refining detection algorithms tailored to environmental domains, developing domain-specific datasets for training and evaluation, and exploring novel approaches that consider the unique characteristics of environmental discourse. Moreover, partnerships between academia, industry, and non-governmental organizations can foster collaborations that facilitate the development and deployment of effective detection systems.
g. Davi et al. 2006; Delpierre et al. 2012; Davi & Cailleret 2017). Both models, in their standard version (called here after expert calibration), are parameterized using various sources of information. Some parameters are directly measured or found in the literature such as leaf life span, specific leaf area, LT50 (freezing temperature causing 50% mortality) of leaves, leaf reflectance, and so on. Other parameters cannot be measured at all, or their measurement require an enormous effort that cannot be deployed for a large number of species. These parameters are thus inferred by inverse modelling using either Bayesian methods or optimizing methods and data on the specific process they are describing. This is for example the case for phenology models for which all parameters cannot be measured, especially those describing bud dormancy break regulation, since no method allow to measure dormancy break precisely so far (except for a few fruit tree species).
Data for the calibration 1. 2. 1. Climate and soil data Raw climatic variables were extracted from ERA5-Land hourly dataset (Muñoz Sabater 2019, 2021) from 1970 to 2000, at a spatial resolution of 0. 1 degree in latitude and longitude. We calculated the daily mean values of the following variables used by PHENOFIT and CASTANEA: minimum, mean and maximum daily temperatures, mean dewpoint temperature, daily precipitation, daily global radiation and daily mean wind speed. We computed the daily relative humidity with the ratio of vapor pressure and saturation vapor pressure (both calculated with Clausius-Clapeyron equation) using humidity R package  (Cai 2019 ). Daily potential evapotranspiration was calculated with Penman-Monteith equation (FAO standard of hypothetical grass reference surface) using a slightly modified version of the ET() function in Evapotranspiration R package  (Guo et al.
Tree occurrence data used for the calibration Sources of occurrence data are known to differ even for common European trees  (Duputié et al. 2014)  and this makes it quite challenging to gather comprehensive data at a sufficient spatial resolution all over Europe. The occurrence data we used essentially rely on the EU-Forest dataset  (Mauri et al. 2017 ) which benefits from inventory and monitoring programmes implemented in most European countries. As EU-Forest is limited to forest ecosystems, we combined it with presence records extracted from the Global Biodiversity Information Facility (GBIF 2022, see Appendix B for all download links) but removing observations outside natural species ranges as defined by Atlas Flora Europeae (AFE,  Jalas & Suominen 1972 -2005)  and EuroVegMap  (Bohn et al.
2003). By doing so, we also included occurrences of isolated native trees living outside forests, excluding records from arboreta or gardens where the species would have been planted as an exotic. For holm oak, we also added occurrence records in the Mediterranean Basin from the WOODIV database  (Monnet et al. 2021) , leaving out EU-Forest and GBIF records we had already gathered. We upscaled all species records at the ERA5-Land resolution (0. 1°, see 1. 2. 1. Climate data), i. e. the species is considered to be present in the cell if there is at least one record. We finally obtained 21458 occurrence cells for beech, 6653 for holm oak and 5385 for silver fir (see Appendix B for details). All the datasets described above are presence-only data. Therefore, we generated cells where species are supposed to be absent, i.
e. pseudo-absence cells. In order to avoid as far as possible creating false absence data, we used EU-Forest cells where the species is not reported present as pseudo-absence cells. We assumed that national forest inventories were exhaustive (which is not true since only specific forest plots in a 0. 1°cell are monitored). We obtained 25423 absence cells for beech, 37931 for holm oak and 38365 for silver fir (see Appendix C). We selected subsets of 2000 points (1000 presences and 1000 pseudo-absences) in order to reduce computational costs. For each species, we generated ten presence clusters (k-means algorithm) of similar bioclimatic conditions based on annual climate normals computed with R package dismo  (Hijmans et al. 2021 ) and ERA5-Land variables. In each cluster, we randomly sampled a number of cells where the species is present proportional to the total number of a number of cells where the species is present in the cluster.
Our stopping criterion for the optimization procedure was the budget, i. e. the number of model runs. For an easier use and the sake of reproducibility, we chose to use a pure R implementation of CMA-ES available in the R package cmaes  (Trautmann et al. 2011). The function cma_es() enables us to do λ function evaluations in parallel so as to substantially reduce computation time. It also allows us to define lower and upper bound constraints, by penalising the objective function value of the candidate solution if it violates the boundaries. We customized the cma_es() function to add an option to define death penalty constraints (rejection of the infeasible candidate solution who is sampled again), in order to define a range of ecologically possible solutions in terms of inequality constraints between parameters (see Appendix D for details about boundaries and constraints handling).
Acknowledgements The authors would like to thank  Hendrik Davi  for helping us in using the CASTANEA model. We are also deeply grateful for many helpful comments from Florence Tauc. We would also like to thank  François de Coligny , manager of the  CAPSIS platform ,  Gilles Le Moguedec , and the  GenOuest  and TGCC teams for their support. Finally, we would like to thank  Florian Hartig  and another anonymous reviewer whose comments and suggestions helped us improve and clarify this manuscript. V. V. was supported by a  GAIA doctoral school PhD Fellowship.
Data availability ERA5-Land dataset is available on the Copernicus Climate Change Service website. EU-SoilHydroGrids is available on the European Soil Data Centre website. SoilGrids250m is available on the International Soil Reference and Information Centre website. EU-Forest database is available on FigShare. The R code associated with this work is available on this GitHub repository, as well as on Zenodo (10. 5281/zenodo. 7774981).
Video Recording and Flight Path Analysis The bees' trajectories were filmed at 100 frames per second with a 640 × 480 pixels resolution monochrome CMOS camera (Teledyne Dalsa Genie HM640, sensor size: 1/3 ) fitted with a manually adjustable optical lens (Ref. H2Z0414C-MP, Computar, NY, USA) adjusted to a focal length of 4. 9 mm. The camera was located at 1. 52 m from the left wall (at 1. 65 m from the tunnel midline), and its field of view (160 cm in width and 71 cm in height corresponding to 52°× 25°) covered the whole height of the tunnel, from abscissa x = 0 cm to abscissa x = 160 cm in all experiments. Image sequences were recorded with StreamPix 7 software (NorPix, Inc. , Montreal, QC, Canada). They were then calibrated and corrected using a 3 × 3 cm checkerboard pattern of calibration (undistortImage, Matlab); then, they were cropped (size: 620 × 210 pixels) and analyzed using a custom-made Matlab program (The MathWorks, Inc.
, Netick, MA, USA), URL: https://github. com/rm1720/ bees-applications, accessed on 6 February 2023). This program automatically determined the bees' position (x, z), forming an ellipsis of 5 × 2 pixels in each frame, as a function of the time t. Next, by considering the ellipsis center in each frame, the bee's trajectory in the vertical plane can be conveniently plotted. The aperture of our optical lens is adjustable from F1. 4 to F16. The depth of field is therefore sufficient to capture a sharp image of the bees' lateral positions in our tunnel (see online Depth of Field Calculator, URL: https://www. dofmaster. com/dofjs. html, accessed on 6 February 2023). Our calibration procedure therefore generates a maximal error of 1 pixel, which vertically represents a maximum error of 3. 4 mm. This latter error should be compared with the vertical parallax error, which can be estimated at 2 pixels by assuming the maximal oscillations of bees' flight is ±3 cm around the recorded sagittal plane (see URL: https://www.
youtube. com/watch?v=8WDcC-LmM9k, accessed on 6 February 2023), which, in total, represents a maximal error of 6. 7 mm vertically. Two-dimensional coordinates of bees were then sampled every 10 ms. Then, they were discretized through binning (binMed, Matlab). Each bin represents the median of 15 coordinates, meaning 150 ms.
Statistical Analysis The data did not follow a normal distribution (Shapiro-Wilk tests, p < 0. 05) and did not respect homoscedasticity (Fligner-Killeen tests, p < 0. 01). In experiment 1, Friedman tests ( f riedman. test R function) have been performed to test the influence of SARC manipulations on flight altitude. When main effects were found significant (p < 0. 01), exact all-pairs comparisons tests of Friedman-type ranked data comparisons  [37]  (PMCMRplus package) were used to compare altitude binning distribution by pairs: bins before perturbation vs. bins after perturbation in the same condition (parallel condition before vs. after perturbation, converging condition before vs. after perturbation, etc. ). In experiment 1, three bins before perturbation were compared to three bins after the perturbation.
-Individual time course of altitude changes in two experimental conditions of experiment 1: A. i and B. i are control conditions, A. ii is the dynamic converging condition and B. ii is the dynamic diverging condition above white ground. While the altitude remains unchanged in both the control and diverging conditions, an increase in altitude can be observed after the perturbation (green dotted line) in converging condition. 13 randomly selected ights are plotted in each condition, each one respecting a ground-following criteria (see section 5. A). Median altitude is represented in bold. The data was acquired at 100Hz, then low-pass ltered by a second-order Butterworth lter with a cut-o frequency of 3Hz to smooth trajectories. Corresponding boxpots are visible in Fig. 6-A,B. -Individual time course of altitude changes in four experimental conditions of experiment 2: A is the static converging condition above white ground, B is the static diverging condition above white ground, C is the static converging condition above striped ground and D is the static diverging condition above striped ground.
Acknowledgments: The authors would like to thank  Marc Boyron ,  Julien Diperi ,  Frank Buloup , and  Jean-Marc Ingargiola  for their technical assistance in designing the flight tunnel,  Philippe Cinzia-Castaldo  for the installation of the beehives by the association Apis AMU,  Romain Miot  for taking part in the programming of the custom-made  Matlab -based software program used to analyze the bees' trajectories.
01). Above striped ground, bees kept the same altitude in each condition and their respective control conditions (Figure6C,D; for all comparisons p > 0. 01). In all graphs, asterisks show significant differences between groups (p < 0. 01) and outliers are represented by solid circles. Randomly selected flights were analyzed in each condition, respecting a ground-following criterion (see Section 2. 2. 4). Raw trajectories from the trials are visible in FiguresS1A,Band S2C,D. Data in (A,B) were already used in [32].
We further checked for overlap between the 351 DMPs in Berardinelli-Seip patients and differentially methylated CpG sites in progeroid laminopathies (GSE182991) and Werner syndrome (GSE131752)  (33, 39). This analysis revealed one common CpG site (cg06216080) with similar changes in blood DNA methylation of typical WS patients but not in progeroid laminopathy patients (Figure  2  a and b). Solo CpGs, which are located in a WCGW context in partially methylated domains, lose methylation in response to cell proliferation  (40). We performed an analysis of solo-WCGW sites across highly methylated domains (HMDs) and partially methylated domains (PMDs) that revealed no methylation difference between patients and controls (p-value =0. 60 and 0. 22, respectively) (Figure  2 c ).
We next checked the effect of BSCL2 mutations on DUSP22 and PM20D1 in the heterozygous healthy parents (carriers) of the Berardinelli-Seip Congenital U N C O R R E C T E D M A N U S C R I P T 9 3 a, Table Lipodystrophy patients. This revealed that all carriers apart from one individual had similar methylation differences to that observed in the CGL2 patients at the DUSP22 promoter (Figure  3a ). The PM20D1 region was described as a methylation quantitative trait loci (mQTL) where genetic variants could affect the DNA methylation profile of the CpG sites (41, 42). For this reason, we mainly focused on the DUSP22 promoter for further analysis, which has been reported as a negative mQTL region. Next, we checked two publicly available RNA sequencing (RNA-seq) datasets to investigate the effect of SEIPIN deficiency on DUSP22 expression.
In GSE159337, RNA-seq was performed on peripheral blood mononuclear cells of CGL2 patients and their age and gender-matched controls. However, this analysis revealed no significant change in the expression of DUSP22 between the CGL2 patients and controls (adj. p-value= 0. 71, log2FC= 0. 12). Similarly, GSE145070 showed that adipocyte-specific conditional deletion of seipin in mouse mature brown adipocytes had no effect on Dusp22 expression (adj. p-value= 0. 99, log2FC=-0. 02). Next, we looked at DUSP22 and BSCL2 gene expression changes during adipocyte differentiation. We observed a significant upregulation of BSCL2 during early adipocyte differentiation (p < 0. 01), but not during late differentiation (p = 0. 123) (Supplementary Figure  6 ). For DUSP22, we did not detect significant differences following differentiation, nevertheless we could observe a tendency for transcriptional We did not detect a significant association between DUSP22 methylation levels and any of the investigated metabolic traits (Supplementary Table  8 ).