dataesr/scientific-paragraphs-categorization huggingface dataset:
Plans for assessment and collection of outcomes {18a} Patient demographic data is collected at baseline. In total, participants are asked to ll in ve online questionnaires, each occurring within an interval of 6 months, containing personal questions about sexual behaviour, drug use and clinical data (for more details see Table  2 ). The questionnaires were developed using LimeSurvey software (LimeSurvey GmbH, Hamburg, Germany). Data on the use of the behavioural intervention (i. e. , web-based application), including which of the components were used and how often they were used, are collected from the study website using login-data obtained from a backend log. Data on the number of free HCV home-based self-sampling test(s) used and their test results are collected from either the Castor EDC platform (in the Netherlands) or from a centralized laboratory database (in France).
Additional data on markers and treatment of HCV reinfection (i. e. , HCV RNA test date(s), test results and name of HCV treatment regimens along with their dates of initiation and discontinuation) are collected from laboratory and clinical les of participating sites and for participants from centres in the Netherlands who provided consent for data linkage, from the Dutch HIV monitoring foundation (stichting hiv monitoring). Additional HCV RNA blood testing will retrospectively be performed to test for HCV reinfection if: 1) the participant did not receive an HCV RNA test in the 6 months prior to the end of the study period, 2) there is a blood sample available for HCV RNA testing stored at the laboratory of the participating site, and 3) the participant provided consent for retrospective HCV RNA testing.
Con dentiality {27} To protect the privacy of the participants, all data and body materials are coded. Participant data are stored in two separate databases: one containing the code and directly identi able personal data (i. e. , participant identi cation code list) and the other containing the code and study data (e. g. , sexual risk behaviour data). The participant identi cation code list is safeguarded by a key and is only accessible to the study coordinator and data manager, if necessary. When participants are assigned to the additional testing or combined intervention, the data relating to the home-based tests are also stored separately from the personally identi able information. Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} DBS samples from the home sampling intervention are kept in the laboratory of the Amsterdam UMC, location AMC (for samples collected in the Netherlands) or La Pitié-Salpêtrière Hospital (for samples collected in France) until 1 year after the end of the project.
Compared to the total summer insolation or the integrated insolation above a threshold (ISI), the mean insolation of the astronomical half-year summer (it is referred as mean summer insolation hereafter) considers not only the total amount of energy received during the summer, but also the length of the summer which could also be important. Fig. 2  shows the comparison between TAC and the mean summer insolation. Since we focus here on the orbital-scale variations, a low-pass filter (> 12 ka) has been applied to the TAC data before the comparison in order to eliminate the highfrequency signals. A good resemblance in term of temporal structure and amplitude is observed between the two variables and the two datasets appear well correlated with a R 2 correlation coefficient of 0. 39 (Fig. 2 ).
Acknowledgments The authors would like to warmly thank  Jean Jouzel  and  Paul Duval  for insightful discussions. The modelling part of this work was supported by the  Fonds de la Recherche Scientifique-FNRS  under Grant n°  T. 0246. 23. QY is Research Associate  F. R. S. -FNRS. Computational resources have been provided by the supercomputing facilities of the  Université catholique de Louvain (CISM/UCL)  and the  Consortium des Équipements de Calcul Intensif  en Fédération https://doi. org/10. 5194/egusphere-2023-2360 Preprint. Discussion started: 17 October 2023 c Author(s) 2023. CC BY 4. 0  License.
It uses pre-trained neural networks to make predictions about the different őelds in a coupled system, such as the ŕow, electric and concentration őelds. The framework is designed to be fast and efficient, and can be used to build models with very little data. DeepM and MNet are versatile algorithms for modeling complex, multiphysics and multiscale dynamic systems. DeepM uses a multilayer perceptron architecture, while MNet uses a combination of convolutional and long short-term memory networks. Both algorithms are able to capture intricate patterns and trends in time series data. However, the best approach for using PINN in a particular biological system depends on the available data and knowledge of the physics of the system. We will explore three possible scenarios in which PINN can be applied in biology.
On the other hand, the BFGS algorithm is a Quasi-Newton method for optimization, which approximates the Hessian matrix using a series of updates  [41, 42]. One of the most widely used Quasi-Newton methods is L-BFGS (Limited-memory BFGS  [43, 44] ), which is more memory efőcient than BFGS, as it only stores a few vectors representing the approximation of the Hessian matrix instead of the full matrix. L-BFGS is more computationally demanding than Adam, but can be faster in situations where the second-order moments are known analytically. Instead of storing the full n x n estimation of the inverse Hessian, L-BFGS stores only a few vectors that represent the approximation implicitly which make it more practical for usage in ML settings with small-mid sized datasets. This method is computationally more demanding.
Set S k = α k P k and update X k+1 = X k + S k y k = ∇f (X k+1 ) -∇f (X k ) B k+1 = B k + Y k Y T k s k Y T k + B k s k s T k B T k B k s k s T k end for where X k are the model parameters, f is the loss function, B k is an approximation of the Hessian matrix, and S k and Y k are the differences between the current and previous values of X and ∇f (X), respectively. The L-BFGS and ADAM optimization algorithms are both commonly used in the context of training neural networks, including PINN. Here is a comparison of some key features of these algorithms: • Convergence rate: L-BFGS typically has a faster convergence rate than ADAM. However, ADAM can still be effective in practice and may be preferred in some cases due to its simplicity and ability to adapt to changing data  [40]. • Memory requirements: L-BFGS requires storing a set of past gradients in memory, which can be costly for large datasets or networks.
In our study, we found that the ADAM give best results. Optimization algorithms L-BFGS ADAM x k+1 = x k + α k d k m k = β 1 m k-1 + (1 -β 1 )g k s k = x k+1 -x k v k = β 2 v k-1 + (1 -β 2 )g 2 k y k = g k+1 -g k m ′ k = m k 1-β k 1 α k = s T k y k y T k H k y k v ′ k = v k 1-β k 2 x k+1 = x k + α k d k x k+1 = x k -η √ v ′ k +ϵ m ′ k Table 2: x k represents the current estimate of the solution, d k is the search direction, α k is the step size, s k and y k are the difference vectors, g k is the gradient of the objective function at x k , m k and v k are the őrst and second moments of the gradient estimates, β 1 and β 2 are the decay rates for the moving averages, η is the learning rate, ϵ is a small constant to prevent division by zero, and H k is the approximate Hessian matrix. Note that the formulas for L-BFGS involve the computation of the Hessian matrix and its inverse, which can be computationally expensive for large datasets or networks.
• The estimation of a score of the disease severity with the use of őrst-order ODE for spatial distribution. PINN is used to solve these equations and to estimate the score. • A transfer learning model for the classiőcation of the visual appearance of the different type of lesions. The deep learning model can use an image dataset annotated by gastroenterologists. • Based on the spatial information extracted from images, PINN is used to predict the evolution and the spread of the two diseases. • PINN is used to predict the lesions distributions along the colon. Figure  15:  The experimental framework studied in our paper.
Transfer learning for image transformation Transfer learning is a deep learning technique that allows the use of pre-trained models to perform image transformation tasks. The pre-trained models have already learned general features from a large dataset, making them suitable for transfer learning to related tasks. In the context of spatial distribution extraction, transfer learning can be used to improve the accuracy of feature extraction for speciőc image classes. The residual network ResNet  [53]  has been used for this transfer learning taks. Its architecture is available in many different forms, each with a different number of layers, a variant with 50 layers of neural networks is referred known as Resnet50. The Resnet50 and Resnet34 architectures differ signiőcantly in one key area. In this case, the building block was changed into a bottleneck design due to worries about the amount of time needed to train the layers.
After inpainting the images, we noticed that they still contain some noise in them. So in order to tackle this problem, we tried kmeans  [54]  for clustering. Clustering algorithms are unsupervised algorithms, meaning they don't use labelled data. They are used to assign data points from a population to different groups where data points belonging to the same group have similar traits. In our instance, clustering the image enabled us to blend the several image segments together in order to lessen the noise that persisted even after the inpainting. Figure  26 : In order from top and from left to right, the őgures present: the original and real image extracted directly from the Kvasir database, the image after application of a segmentation, after application of the algorithm of inpainting and őnally after applying segmentation.
2008 ) and even to its local disappearance in some places such as Guam  (Scheibling and Metaxas 2008). In most of the recent scientific publications (e. g. ,  Alvarado et al. 1986 , Bos et al. 2008 , Chim and Tan 2012 , Nakajima et al. 2013 , Marsh and Fromont 2020)  as well as marine life books and the World Asteroidea Database  (Mah 2022 ), this species is uniformly described as common in the Indo-Pacific region from East Africa to New Caledonia and Japan. This description appears to be based generally on the classical Monograph of shallow-water Indo-West Pacific echinoderms  (Clark and Rowe 1971) , which summarizes statements from other papers to describe the distribution as including "Islands of western Indian Ocean" and "East Africa & Madagascar". Linnaeus' original type location was stated as "M[ari] Indico", an expression which then actually fitted with the Indo-Pacific region in general.
Museum specimens We searched for specimens in the world's main museum collections of Indian Ocean sea stars in order to check if there was any specimen of P. nodosus collected in the Indian Ocean or any ambiguous specimen of P. lincki or Pentaceraster spp. (all formerly grouped under the obsolete genus Pentaceros). The list of Museum collections reviewed is the following: Paris MNHN, online collection databases of the London NHM, and Washington Smithsonian.
Analyzed pictures were selected based on two criteria: the credibility of the geographical origin provided and the lack of identification issues. Observations in Singapore, Philippines, and Indonesia were used as "control" or proof of the validity of the method, as this species is known to be abundant there. A refined search was undertaken using GPS-located pictures on the specialized websites FlickR and iNaturalist (see  Michonneau & Paulay, 2014  for more information about this collaborative project). Both are important photographic databases; the latter includes many photographs taken or identified by professional scientists. These identifications were checked again within the scope of the present study.
One obvious bias in our method could be due to the use of data from non-professional recreative photographers. But the consistency in results between a peer-reviewed naturalist website such as iNaturalist and results from FlickR and Google prove that such method appears quite reliable, at least for such conspicuous and photographic species. Of course, less beautiful, less common, or smaller species would not be able to be surveyed this way, which works mostly for conspicuous and charismatic species  (Ducarme et al. 2012). The recent disappearance of the species could also be a hypothesis to explain its absence, as most ambiguous observations recorded in the literature date back from 35 to 140 years. This shallow shore species is heavily fished in the Pacific and is already known to have disappeared in some isolated places such as Guam  (Scheibling and Metaxas 2008)  -this extinction being confirmed by our results.
Lessons learned from using a partially citizenscience, web-based methodology This study used a mixed method in order to cover the required geographic scale, a database big enough to span nearly a whole ocean, and the precision of field observations. As artifacts or errors were observed in both scientific literature and non-professional webbased documents, crossing these two sources along with field experience and observations proved of great interest. Tourists constitute an unprecedented population of marine observers and data collectors and can hence provide a high quality geolocated database that no scientific project could ever hope to produce. The development of hybrid tools such as iNaturalist. org and semi-professional networks can also come closer to reaching the aim of unifying a huge number of citizen-based observations with professional identification  (Michonneau and Paulay 2014).
Acknowledgements This study has been supported by the  Muséum National d'Histoire Naturelle of Paris. The author would like to warmly thank w  Chantal Conand ,  François Michonneau ,  Timothy McClanahan , and  Gustav Paulay  who helped with bibliography and field data, and  Christopher Mah  for his insights and kind help at the Smithsonian collection. Marc Eléaume , curator of echinoderms at the MNHN, kindly allowed the lab work and collection browsing. Many thanks must also go to all the divers who participated in the survey, especially  Sylvain le Bris. Field work in Mayotte was supported by the  European Union  through the  BEST RUP program  (project  N°2486 ), in association with IUCN, and New Caledonia one was part of the  Koumac 2019 MNHN expedition ("La Planète revisitée ") organized by  Philippe Bouchet.
Data collection Medication adherence assessment. Medication adherence was evaluated using the patient-reported MARS questionnaire validated in schizophrenia  [13, 18]. The sum of the 10 items yields a final score ranking from 0 (poorest adherence to treatment) to 10 (best adherence to treatment). The initial principal-components analysis revealed three underlying factors  [12]. The first factor included the four first items and was related to "medication adherence behavior. " The second factor included the subsequent four items and represented the "subjects' attitudes toward taking medication. " The remaining two items composed the third factor and represented "subjective negative side effects. " The Brief Adherence Rating Scale (BARS) which is a clinician-rated tool used to evaluate patient medication adherence during the last month was added to compare clinician assessment from the MARS self-rated adherence.
Ethical considerations The study was carried out in accordance with ethical principles for medical research involving humans (WMA, Declaration of Helsinki). The assessment protocol was approved by the relevant ethical review board (CPP-Ile de France IX; January 18, 2010). The details of the cohort design and rationale have been presented in a previous publication  [17]. A web-based application, e-Schizo © , was developed to collect evaluation data for clinical monitoring and research purposes. Access to this system is carefully regulated and approval was obtained from the ethical committee as well as the national committee in charge of the safety of computerized databases (CNIL). A non-opposition form was signed by participants according to French law.
Sample characteristics Analyses were performed on the 485 patients who completed a MARS evaluation at 1 year after inclusion. They were 376 (77. 5%) men, mean aged 32. 1 years (SD = 10. 1) with a mean age at illness onset of 21. 7 years (SD = 6. 7) and mean illness duration of 10. 3 years (SD = 8. 2). The sociodemographic and clinical characteristics of the sample are presented in Table  1. Compared to individuals with follow-up data at 1 year, the "lost to follow-up" participants differed only in that they were more frequently administered long-acting antipsychotics (Table  2 ).
ACKNOWLEDGEMENTS We would like to thank the patients who accepted to participate in the present study. We would also like to thank the team of the  FondaMental foundation ,  Hakim Laouamri , and his team ( Seif Ben Salem ,  Karmène Souyris ,  Victor Barteau , and  Mohamed Laaidi ) for data management, quality control, and regulatory aspects. 1  Fondation FondaMental ,  F-94010 Créteil ,  France. 2  University of Bordeaux, Aquitaine Institute for Cognitive and Integrative Neuroscience (CNRS UMR 5287-INCIA) ,  Bordeaux, France. 3  Department of Adult Psychiatry, Charles Perrens Hospital, Bordeaux, France. 4  Université Paris-Est-Créteil (UPEC) ,  AP-HP ,  Hôpitaux Universitaires "H. Mondor" ,  DMU IMPACT ,  INSERM ,  IMRB ,  Translational Neuropsychiatry, Bordeaux, France. 5 AP-HM,  Aix-Marseille Univ, School of medicine -La Timone Medical Campus , EA 3279:  CEReSS -Health Service Research and Quality of Life Center ,  27 Boulevard Jean Moulin ,  13005   Marseille ,  France.
FUNDING This work was supported by the  Fondation FondaMental (RTRS Santé Mentale)  and by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02  and  ANR-10-COHO-10-01. The funding source had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.
Background Lassa fever (LF), a haemorrhagic illness caused by the Lassa fever virus (LASV), is endemic in West Africa and causes 5000 fatalities every year. The true prevalence and incidence rates of LF are unknown as infections are often asymptomatic, clinical presentations are varied, and surveillance systems are not robust. The aim of the Enable Lassa research programme is to estimate the incidences of LASV infection and LF disease in five West African countries. The core protocol described here harmonises key study components, such as eligibility criteria, case definitions, outcome measures, and laboratory tests, which will maximise the comparability of data for between-country analyses.
Liberia We will purposively select three documented Lassa fever hotspot communities in Bong County. We will calculate the number of participants required in each community in proportion to the size of the community and convert this into the number of households using available demographic data (average household size estimated at five members). We will record the boundaries of each community using GPS. We will use satellite imagery and a random selection algorithm to select the desired number of dwellings in each village, plus 50% in reserve. We will visit these dwellings consecutively using a smartphone equipped with a GPS application until the target number of participants is included, using the dwellings from the reserve list if necessary.
Nigeria-Owo We will purposively select ten documented Lassa fever hotspot communities. We will calculate the number of participants required in each community in proportion to the size of the community and convert this into number of households using available demographic data (average household size estimated at five members). We will conduct systematic random sampling from geographical data, including locations (streets), as well as demographic information on those geographical locations (number of households). We will continue recruitment until the target number of participants is reached. Sierra Leone We will randomly select 18 villages from the 3782 villages in four districts surrounding Kenema General Hospital, from where the number of Lassa cases have been previously admitted to the Lassa fever ward.
Baseline data collection. Data on the Global Positioning System (GPS) position and description of the household will be collected using REDCap (Research Electronic Data Capture) on 4G tablets. The research team will complete a questionnaire for each household through a combination of observed and household head-reported information. Data collected will include household composition; socioeconomic status of the household as indicated by dwelling structure and amenities, occupation of resident adults, and assets owned; and exposure to potential LF risk factors. Researchers will record participants' age, sex, education level, ethnicity, occupation, and LF history. Additional questions about LF knowledge and risk factors will be asked as deemed useful by the country teams. Baseline blood sample collection.
Researchers will also test for the detection of Plasmodium falciparum-specific HRP2 antigens and common Plasmodium lactate dehydrogenase (pLDH) of Plasmodium species using the First Response 1 Malaria Antigen Card Test (Premier Medical Corporation Limited, India) or CareStart™ malaria HRP2/pLDH (Pf/pan) combo test (Access Bio, USA). The participant will be informed of the results. Follow-up of LF confirmed cases. Once participants have been classified as confirmed LF cases, infection control and personal protection measures will be appropriately taken by the researchers who interact with them. The study team will inform the local authorities, who will then be responsible for their medical care and infection control measures. Researchers will extract data on clinical signs and symptoms, blood chemistry results, co-morbidities and medications and treatments given from the medical records of confirmed cases.
Scenario B: the LASV infection cohort study component is implemented by itself. In this scenario, study participants will be selected using a stratified sampling method at the household level described in Table  5. Informed consent (or assent) from the participant or their parent/guardian will be sought before enrolment. Baseline data and blood specimen collection. For sites implementing scenario B, baseline data and blood specimen collection and tests will be performed as described for the disease cohort. Serological testing will be done by ELISA for IgG at baseline. Follow-up of study participants. At 6, 12, 18, and 24 months after baseline, researchers will visit participants at home to draw another 5-ml blood sample. The sample will again be divided into two cryovials: one sample will be used to evaluate LASV serostatus by ELISA, this time testing for both IgG and IgM, and again the second will be stored for future assessments.