dataesr/scientific-paragraphs-categorization huggingface dataset:
The influence of the number of templates used is relatively limited and could indicate that the number of surface-sliders is too high. We believe that with more individuals implemented in the dataset, the differences would be more significant. Curvesliders could also be placed on the edges of the tracks. Nevertheless, the edges are dependent of the depth of the track, making them relatively variable between tracks. In the same way, as 3D models are polygonal meshes, the manual removal of tracks can induce additional noise from the irregularities of the remaining mesh faces. We believe that using curve-sliders would have led to more statistical noise than usable information and made the method more tedious. The graph of PCs 1 and 2 for the front right tracks (Figure  5B ) shows that some individuals are more clearly segregated from others.
This separation seems to correspond to the general size of the individuals. For example, AM3 is the largest cheetah in this dataset. His tracks appear as a cluster distinct from the others. It would be interesting to test the relationship between the form of the track and the body condition of the animal in further studies. Links between track form and morphological features such as body mass are being investigated for different species and for estimation of features of extinct animals  [27]. This algorithm might allow a more precise estimation of the morphological parameters of cheetahs such as their overall size or weight. Such information would be valuable for scientists in charge of setting up conservation strategies specifically for elusive carnivores  [14, 15]. Besides, AM4 is a smaller male, and PC1 and PC2 hardly distinguish it from females.
Nevertheless, the differences between our 2D and 3D algorithms show better predictions with 3D data. The principal limitations of the 3D approaches lie in the processing time for model reconstruction and manual segmentation. These steps are time-consuming, especially for large datasets. However, the possibility of using cloud computing would overcome the first limitation by providing access to computers powerful enough to make 3D reconstruction virtually instantaneous. The recent development of LiDAR sensors integrated in smartphones is also very promising as they allow instantaneous 3D perception. For segmentation, an approach using machine learning should be able to separate the tracks from the substrate. It would need to first recognize tracks based on their geometry and depth information, and segment them from the model.
AFigure 6. 6 Figure 6. (A) Total accuracy for sex prediction. (B) Total accuracy for identity prediction. Numbers inside bars correspond to number of identification "steps" in the combination: 1 = all tracks used directly for prediction, 2 = foot position prediction for entire track set combined to identity prediction with datasets already divided by foot position (i. e. , two steps: identify foot position and identify identity in dataset of previously found foot position).
The laser pulse duration FWHM was tunable from ∆t = 55 fs to ∆t = 15 ps by adjusting the final pulse compressor in the chirped pulse amplifier. Individual single-shot laser pulses could be obtained from our laser system. A MOKE microscope was used for imaging the sample magnetization after each single laser pulse shot and check for the reversal at various pulse energies. The system also allows one to obtain time-resolved MOKE data in a pump/probe fashion. However, when stretching the pulse duration for the pump, the probe stretches equally, reducing the experimental time-resolution. The probe was focused through a 50x objective down to a  The coercivity diverges as the Gdconcentration approach the compensation point. b) The experimentally measured coercive field Hc of subfigure a) as function of the Gd concentration.
ACKNOWLEDGEMENT At the FU Berlin support by the Deutsche Forschungsgemeinschaft through SFB/TRR 227 "Ultrafast Spin Dynamics", Project A08 is gratefully acknowledged. Portland Technology Development Department, Intel Corp. Hillsboro, OR, 97006, USA ‡ To whom correspondence should be addressed: jon. gorchon@univ-lorraine. fr, unai. atxitia@fu-berlin. de. ; New address: Universite de Lorraine, CNRS, IJL, F-54000 Nancy, France § unai. atxitia@fu-berlin. de.
Data sources During the ICBP SURVMARK-2 project, data for patients diagnosed with oesophageal and gastric cancer were collected from 21 population-based cancer registries in seven countries. Data submitted included information on histology, morphology, basis of diagnosis, stage at diagnosis and treatment. Quality checks were conducted on each dataset using a standard data protocol, which is described in more detail elsewhere. [3]  This included screening the data for specific anomalies such as instances of negative survival duration, out-of-range dates of diagnosis and/or dates of death, availability of stage at diagnosis information and invalid vital status codes. In the current analyses, we included oesophageal and gastric cancer patients diagnosed during 2012-2014 and followed-up until 31 Dec 2015 from the 14 registries that were able to provide information on stage at diagnosis for at least 50% of the registered cases : Australia (New South inconsistencies in stage information (e.
The introduction of screening programs and prophylactic gastrectomies targeting high-risk individuals may have led to an increased identification at early stage and therefore better survival e. g. , in New Zealand. More biological factors have also been reported, for example, germline CDH1 mutations have been found to contribute to the high frequency of early-onset diffuse gastric cancer cases in the Māori population of New Zealand, who carry a disproportionate burden from this cancer. [41, 42]  Finally, previous studies have documented survival advantages in women when compared with men, pointing towards sex as an independent prognostic factor. [43, 44]  We confirmed this observation for oesophageal cancer, but only marginal differences in gastric cancer survival by sex. The data used for this study were provided by high-quality cancer registries from countries with similar access to healthcare.
Acknowledgements The authors would like to thank  Lucie Hooper ,  Samantha Harrison ,  Charles Norell ,  Shanta Keshwala  and  Charlotte Lynch of Cancer Research UK  for managing the programme. The  ICBP SurvMark-2 Local Leads  for their contributions to the study protocol and advice to help understand the data. The  ICBP Clinical Committees  for their interpretation of the results and the ICBP SurvMark-2 Academic Reference Group for.
1) Background: Some medications may be dangerous for older patients. Potentially inappropriate medication prescribing (PIP) among older patients represents a significant cause of morbidity. The aim of this study was to create an algorithm to detect PIP in a geriatric database (Multidomain Alzheimer Preventive Trial (MAPT) study), and then to assess the algorithm construct validity by comparing the prevalence of PIP and associated factors with literature data. (2) Methods: An algorithm was constructed to detect PIP and was based on different explicit criteria among which the European list of potentially inappropriate medications (EU(7)-PIM), the STOPP and START version 2 tools. For construct validity assessment, logistic mixed-effects model repeated measures analyses were used to identify factors associated with PIP.
(3) Results: Prevalence of PIP was 59. 0% with the EU(7)-PIM list criteria, 43. 2% with the STOPP criteria and 51. 3% with the START criteria. Age, polypharmacy, and higher Charlson comorbidity index were associated with PIP. (4) Conclusions: Prevalence of PIP and associated factors are consistent with literature data, supporting the construct validity of our algorithm. This algorithm opens up interesting perspectives both in terms of analysis of very large databases and integration into e-prescribing or pharmaceutical validation software.
This made them more sensitive but less practical to use  [4]. PIP identification tools are used both in routine practice to improve individual patient safety and in research to measure PIPs frequency and give insight into the risk factors associated with PIPs. In this second case, PIPs are detected in large databases, using either lists of potentially inappropriate medications  [7]  or algorithms that translate multiple criteria rules  [8]. The algorithms integrating the clinical, biological and prescription data were first designed to detect potentially inappropriate medications [9,10] and then permitted to detect both prescriptions omissions and potentially inappropriate medications, based on STOPP/START v1 criteria  [11]. An algorithm based on several rules (START and STOPP v2 criteria and the Beers Criteria) is announced in the COME-ON study protocol (Collaborative approach to Optimize MEdication use for Older people in Nursing homes)  [8, 12].
Database We used the data from the Multidomain Alzheimer Preventive Trial (MAPT) to conduct a longitudinal secondary analysis. The Multidomain Alzheimer Preventive Trial (MAPT) was a 3-year, multicenter (13 memory centers in France and Monaco), randomized trial aimed to test the preventive effect of omega-3 supplementation or multidomain intervention (nutritional and exercise counseling and cognitive training) or both on dementia and follow-up data every 6 months. Participants were aged 70 years or older, and community-dwelling without dementia or any difficulty in basic activities of daily living (ADL)  [13]  at baseline. The multidomain intervention was not focused on medication review  [14]. The trial protocol was approved by the French Ethical Committee located in Toulouse (CPP SOOM II).
This specific secondary analysis was approved for ethics and feasibility by the Multidomain Alzheimer Preventive Trial/Data Sharing Alzheimer (MAPT/DSA) Group (Appendix A). The database included prescription and over-the-counter (OTC) medications, with the medication name, International Non-proprietary Name, Anatomical Therapeutic Chemical (ATC) fifth level class (classification system of active substances according to their target organs and their chemical, pharmacological or therapeutic properties, 5th level is the chemical substance), dosage, and start and end dates. Comorbidities (free text coded) and their date of onset were available in the database. We calculated the Charlson comorbidity index for each patient at baseline  [15]. The database also contained other clinical parameters (Fried frailty phenotype  [16] , systolic and diastolic blood pressure in supine and standing positions, heart rate, geriatric depression scale (GDS)  [17] , etc.
• The As there are many explicit criteria tools available, we chose EU(7)-PIM list, the STOPP and START version 2 criteria because they were the most recent tools in Europe. In addition, they were validated for both inpatients and outpatients and the combination of the two allowed us to detect situations of overuse, misuse and underuse  [5]. We added three other tools to comply with the recommendations of French and European health agencies related to medication prescription. We identified market withdrawals manually from the websites of the agencies concerned and identified contraindications by querying a medication database approved by the French National Authority for Health. During this development, we identified contradictions or redundancies between the various explicit criteria-based tools.
The choice of the combination of several criteria was guided by the objective of relating as much as possible to the pharmaceutical analysis carried out by a clinical pharmacist who uses all available sources. A clinical pharmacist with expertise in geriatrics coded explicit criteria. Another pharmacist then checked the entire coding. In case of disagreement on a part of the code, a conciliation was performed and a decision was made by consensus between the two pharmacists. The programming of the computer algorithm was performed with SAS© 9. 3 software (SAS Institute Inc. , Cary, NC, USA). Some explicit criteria required methodological choices for coding. For the STOPP A1 criterion "Any medication prescribed without an evidence-based clinical indication", we only coded two sub-criteria proposed by a panel of Belgian, Canadian, French and Swiss experts (1: aspirin and statin in primary cardiovascular prevention, 2: proton pump inhibitor without recent oesogastric damage)  [19].
g. , STOPP D3, D5, or G4 criteria). The START C3 criterion "acetylcholinesterase inhibitor for mild/moderate Alzheimer's dementia or Lewy Body dementia" was not taken into account since these medications are no longer recommended in France. Six clinical practice indicators AMI were considered to be prescription monitoring criteria more than PIP detection criteria, and therefore, were not coded. The other uncoded criteria were related to the unavailability of certain variables in the database. Then, the computer algorithm was used on the database combining the different datasets (medication prescription, comorbidities, clinical data (e. g. , blood pressure, as-sessment of frailty)) of the MAPT study. The computer algorithm generated a number of medication-related potential noncompliances (MRNC) by prescription at each follow-up time.
Statistical Analysis As we made the hypothesis that data were missing at random (MAR), the missing data were imputed by Multiple Imputation by Chained Equations (MICE)  [20]  if it exceeded 1%. The baseline numbers and percentages for the categorical variables were presented; comparisons were made using the χ 2 test (or Fisher's exact test (for expected values < 5)). To verify the construct validity of the computer algorithm, we performed a multivariate analysis to assess the association between patient characteristics (age, gender, number of medications prescribed, Charlson comorbidity index, education level, frailty) and PIP. These patient characteristics were chosen because they were frequently associated with PIP in other studies  [21, 22]. We also introduced MAPT intervention groups and follow-up time (categorical variable) in the model.
Discussion and Conclusions We incorporated in the computer algorithm the entire EU(7)-PIM list, 59% (47/80) of STOPP criteria and 70% (24/34) of START criteria. Prevalence of PIP was 59. 0% with EU(7)-PIM list criteria, 43. 2% with STOPP criteria and 51. 3% with START criteria. In the multivariate analysis, we found older age, polypharmacy, and a high comorbidity index to be associated factors with PIPs. Conversely, neither the intervention group of the MAPT study nor the level of education was associated with PIPs. It should be emphasized that PIP detection tools were first designed for use by clinicians, who have complete clinical data as well as the patient's care history. Coding of these tools within algorithms, followed by application of algorithms to research databases, both lead to several limits.
Firstly, the main encoding difficulties arise from the fact that some criteria are not precisely defined and leave a margin of interpretation; others need clarification to avoid false positives  [8]. Secondly, PIP detection on large research databases may be limited by the unavailability of clinical or biological data. In fact, 85% of the STOPP criteria and 97% of the START criteria require other information in addition to medication prescription data (laboratory and clinical parameters, or medication histories)  [8]. In our study, the combination of our coding and data available in the database allowed us to take into account 59% of STOPP criteria and 70% of START criteria, which is literature data: respectively 40% and 59% of STOPP and START v1 criteria were applied on a database of 3454 Irish adults aged ≥65 years  [25] ; Nauta et al.
were able to apply 63% of the STOPP v1 criteria and 69% of the START v1 criteria on a Dutch primary care database, including 1187 patients aged ≥65 years  [11]. Regarding validation of computer algorithms applied to research databases, Nauta et al. stress that there is no gold standard for evaluating the appropriateness of a prescription, and choose to estimate the validity of how its algorithm is built by comparing the characteristics of the PIPs detected with those found in published studies  [11]. Two characteristics can be compared: prevalence of PIP, and factors associated with PIP. First, it must be emphasized that PIP prevalence assessed on a large database using algorithms should not be considered as a reliable indicator of the clinical relevance of prescriptions: in our study, potential inappropriateness is considered as a dichotomized variable, and results are only intended to validate the construction of the algorithm.
This is linked to the fact that age and comorbidity score increase polypharmacy, and therefore, the likelihood of having an inappropriate medication. Likewise, the number of comorbidities also increases with age. Regarding other factors identified in our study, Gallagher et al. , also show a correlation between potential prescribing omissions and the Charlson comorbidity index  [28]. Muhlack et al. show an association between PIPs and frailty, but only for Beers Criteria in patients with dementia  [22]. Our study has strengths. First, we designed a computer algorithm to detect PIPs, based on four explicit criteria-based tools and also major drugs interactions, whereas previous studies only integrated one or two explicit criteria-based tools. As the database included over-the-counter (OTC) medications, we can evaluated the appropriateness of using OTC because the EU(7)-PIM list contains OTC medications.
Clinical and biological data available in our database allowed us to encode the majority of START and STOPP v2 criteria. Then, we tested the applicability of this computer algorithm on a large longitudinal geriatric database. Several factors support the construct validity of our algorithm. First, the coding of criteria was defined by consensus between pharmacists and double-checked. Then, we found that factors associated with PIPs in our study were consistent with literature data. The main limitation of this study is that the PIP detection computer algorithm was not validated by comparison with human pharmaceutical analysis. Yet such a manual screening of the prescriptions raises two difficulties: firstly, it should only be based on the explicit criteria selected in the study, and secondly, it may concern only a sample of the 9643 prescriptions database.
Composition and diversity of the soil bacterial community Soil DNA was extracted using the Power soil DNA isolation kit (Qiagen, Germany) and quantified with the Quant-iT™ PicoGreen® dsDNAassay kit (Invitrogen, France) according to the manufacturer's recommendations. Bacterial community composition and diversity were determined by Mi-Seq sequencing of 16S rRNA amplicons generated with a two-step PCR as described in  (Gallego et al. , 2020). Sequences were deposited in the GenBank and submitted to the sequence read archive (SRA) under the accession numbers PRJNA630861: SRR11727342-SRR11723789. Bacterial α-diversity indices describing richness (Chao1), evenness (Simpson reciprocal) and relatedness (PD whole tree) were calculated based on rarefied tables (37,000 sequences per sample) (Suppl. Table  S1  and Figure  S1 ).
2016 ). However, the synergistic effect of EW and VST on the soil bacterial community may not only be the result of the simple addition of the effects of the active ingredient and of the changes of soil habitat resulting from EW activity. Part of the synergistic effect might be mediated by the gut microbiota of the EW which is known to modify the abundance, diversity and activity of soil microorganisms at least in their biostructures  (Kersanté et al. , 2006). Nonetheless this hypothesis cannot be tested with our dataset because we only sampled the soil and not the biostructures formed by EW. CAP ordination analysis based on weighted Unifrac distance matrix further confirmed αdiversity observations by showing that the soil bacterial community composition changed in response to VST, to earthworms, or to their combination as compared to the control.
Acknowledgements This study was financially supported by the  EU  through the WaterJPI-2015  AWARE  project ( PCIN-2017-067 ), and by the  Spanish Ministry of Science and Innovation  (Project  CEX2018-000794-S ). The authors thank the  Water Challenges  for a Changing World Joint Programming Initiative. They also thank  SCIEX  for loaning the instrument  LC/HRMS QTOF X500R  and  Bekolut GmbH & Co. KG  for contributing to QuEChERS kit extraction.
References MAP analysis of BTSO reactivity with sera from patients with invasive candidiasis, ICU colonized patients and controls. Sera were diluted 1:4800 in PBS and incubated for 30 min at 37 °C. After three washes in PBST, the microspheres were incubated with goat anti-human IgG coupled with phycoerythrin (1 µg/mL) (Southern Biotech, USA). For both procedures, after three washes in PBST the microspheres were re-suspended in sheath fluid in a test tube and the reaction was monitored on a Luminex Lab MAP system 100 (Luminex USA) at 532 nm. The results are expressed as mean fluorescence intensity determined for 100 microspheres of each BSTO identified by its microsphere spectral signature. For human sera, the results were then treated with the "ETALONNAGE" software (University of Lille) and converted into arbitrary units (AU) from calibration curves.
Pooled human serum was used as the control standard. SPR analysis of BSTO reactivity with anti-carbohydrate MAbs. BIAcore 3000 instrument, BIAevaluation software 3. 0, and sensor chip SA (streptavidin) were obtained from BIAcore (GE Healthcare). 5 nM of BSTOs were fixed onto the sensorchip in HBS (HEPES buffered saline) buffer according to the manufacturer's instructions using NHS/EDC. The immobilization rate was approximately 25-30 response units (RU). 5B2, EB-CA1, B6. 1 and EB-A2 MAbs were injected at 250 nM in HBS to 30 μL/min over a 2 min period. The regeneration of sensorship was performed with a 250 mM NaCl/10 mM NaOH buffer. A reference flow cell (i. e. flow cell without BSTOs) was used for each used BSTOs. Quantification of specific binding was obtained from the difference between the ligand and reference response as previously described  10.
In FSIVGTT, glucose is intravenously injected and stimulate directly the β-cells. Because it does not have to pass through the digestive system, it is free from incretin effect and various gastric emptying time compared with OGTT. FSIVGTT is influenced by insulin and glucose clearance. Minimal model analysis was developed by Bergman et al. [36]. Data obtained from FSIVGTT are calculated using the computer program MINMOD to generate an index of β-cell function, insulin sensitivity and glucose effectiveness. C-peptide can be used instead of insulin in minimal model analysis with FSIVGTT. Minimal model analysis is defined by two couple differential equations, describing glucose dynamics in a mono-compartmental "glucose space" and insulin dynamics in a "remote compartment. " Minimal model uniquely identifies model parameters that determine a best fit to glucose disappearance during the modified FSIVGTT.
Model species and sampling sites The gudgeon (Gobio occitaniae) is a widely distributed fish species in the Garonne watershed (South-West France). Within this area, gudgeons populations are exposed to various levels of contaminants (mostly trace metal elements and, to a lesser extent, pesticides)  (Petitjean et al. , 2020b ; Shinn et al. , 2015)  and parasitism (mostly monogenean ectoparasites such as gyrodactylids and to a lesser extent copepods and nematodes)  (Loot et al. , 2007) , which can contribute to phenotypic variability  (Knapen et al. , 2004; Shinn et al. , 2015). We used the database from Adour-Garonne Water Agency (SIE, 2016) to calculate a Toxic Unit based on metals concentrations measured between 2012 and 2016 in a set of 16 sampling sites previously characterized  (Petitjean, 2019; Petitjean et al.