dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012). The absence of any direct connection between ATM and the rendering companies was highlighted by dissatisfied owners as a major drawback. Indeed, this connection is crucial for facilitating removal procedures for owners and saving their time. It could avoid the difficulties of making an appointment with the rendering company and the need to provide individual equine information twice. Besides, this connection could improve dead equine traceability in two ways. Firstly, it could increase the number of ATM users and thus provide high-quality information on the dead equine population in real time (numbers of live/dead equines and dates of death directly updated in the SIRE database through an existing link from ATM database). Secondly, this new connection would allow the animal's correct identification number to be sent by ATM to the rendering companies.
Subsequently, the identification number could be recorded in the fallen stock data interchange database and thus allow the traceability of the dead equine population through the cross-referencing of data between SIRE, ATM and FSDI databases. Regarding the return of the animal's passport, surprisingly, most owners returned the passport to the IFCE either through the rendering company (65%) or directly (2%). This result is much higher than the IFCE's estimations of 30-40% of the overall owner population, and suggests that owners using ATM services are more aware than others of the current legislation. In addition, ATM users are probably more involved in the equine sector and concerned by health issues. In comparison, in the UK, 61% of owners agreed that they should be legally bound to report any changes during the life of their equine, but only 27% of owners who lost an equine complied with the legal process of returning the passport directly or indirectly by the disposal representative to the issuing authorities  (nfsco, 2013; Defra, 2018).
Conclusions Equine owners are legally responsible for equine traceability, but in the field ATM and the rendering companies are also key players in dead equine traceability and need to be supported in this process. ATM facilitates the recording and verification of equine identification thanks to its connection with the SIRE database, and provides high-quality information on the dead equine population. ATM services should therefore be developed through improvements to the website and the implementation of a direct link with rendering companies to allow a direct appointment scheduling for cadaver removal, so as to reach the greatest number of owners. This direct connection is important because it could also allow the cross-referencing of data between SIRE, ATM and FSDI databases and guarantee dead equine traceability.
Acknowledgements The authors are grateful to the  French rendering companies ATEMAX (F. Bellanger)  and  SARIA (H. Fumery ,  A. Angel ) for participating in the survey design, and to the equine owners for answering the questionnaire. The authors are also grateful to  Delphine Libby-Claybrough  for her review of the manuscript. This study is part of the  ValDonEqui  project funded by the  IFCE , the " Fonds Eperon " and the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES).
Material and methods This prospective cohort study was performed within the French Hernia-Club registry. The French Hernia-Club registry is approved by the French 'Commission Nationale de l'Informatique et des Libertés' (CNIL registration number: 1993959v0) and complies to the General Data Protection Regulation. Because this study is registry-based and guarantees completely anonymized data, additional participant consent and approval were not required according to the French and Dutch national ethical standards. This study was conducted following the STROBE (Strengthening the Reporting of Observational studies in Epidemiology), STROCSS (Strengthening the Reporting of Cohort Studies in Surgery) statements, and the European Registry of Abdominal Wall Hernias (EuraHS) recommendations  [15] [16] [17].
Study design A registry-based, prospective cohort study was performed. Adult patients undergoing incisional hernia repair registered in the French Hernia-Club registry, between September 1, 2011 and May 22, 2019 were eligible for inclusion. For this study, patients were selected with a minimum follow-up of 3-6 months with available data on pre-and postoperative pain and discomfort. Two groups were defined: • Patients without pre-operative discomfort or pain: asymptomatic patients. • Patients with pre-operative discomfort or pain: symptomatic patients. Patients were considered symptomatic if they experienced either pain, sensitive complaints (dysesthesia, hypoesthesia), incarceration of the bowel, or discomfort not otherwise specified. At the end of followup, the incidence of postoperative pain or discomfort was compared in these groups.
Hernia-Club registry The Hernia-Club registry is a collaborative, prospective, anonymized online database of all surgical procedures for primary and incisional hernias. French surgeons specialized in abdominal wall surgery performed all surgical procedures. Each participating surgeon must accept and sign the Charter of Quality, which states that: 'all input must be registered in a consecutive, unselected and exhaustive manner and in real time. ' A total of 191 parameters were collected by the operating surgeon and the blinded, independent, clinical research associates, using online forms. Parameters comprise data from screening, pre-, peri-, and postoperative periods. Participants consent to random peer review of original medical charts to ensure high-quality data. The medical records were also checked in the case of any discrepancies.
Data collection Baseline patient characteristics extracted from the registry comprised age, sex, body mass index (BMI), smoking, diabetes mellitus, recent corticosteroid use, recent radiotherapy, recent chemotherapy, history of abdominal aortic aneurysm (AAA), connective tissue disorder, anticoagulant use or coagulopathy, history of ventral hernia, family history of hernia, American Society of Anesthesiologists (ASA) classification, presence of ascites, chronic cough, constipation and heavy lifting. With reference to pre-operative symptoms, the presence of pain, sensitive complaints (dysesthesia, hyperesthesia, hyperpathia, hypoesthesia) or other (non-specified) discomfort were extracted. Baseline hernia characteristics comprised presence of recurrent hernia, previous surgery with mesh, location of previous mesh (onlay, inlay, retromuscular sublay, preperitoneal sublay, intraperitoneal onlay), defect location in the midline (subxiphoid, epigastric, periumbilical, infra-umbilical, suprapubic), if applicable lateral defect location lateral (subcostal, flank, iliac, lumbar) and width of the defect according to EHS width classification  [18].
Statistical analysis Statistical analysis was performed with SPSS version 25 (IBM SPSS Statistics for Windows, version 25. 0. 0. 1, IBM Corp, Armonk, New York). Continuous variables are presented as mean and standard deviation (SD). Discrete variables are presented as absolute numbers and percentages. Continuous variables were compared with a Student's T-test or Mann Whitney U test, as appropriate. Discrete variables were compared with a chi-quare test. The primary outcome, the incidence of pain and discomfort postoperatively, was compared between the pre-operatively asymptomatic and symptomatic patients, was reported as absolute numbers and percentages. Additionally, these proportions were compared with a X 2 test. Secondarily, to assess factors potentially associated with long-term postoperative pain and discomfort, uni-and multivariable logistic regression was used.
Limitations This cohort study has several limitations. Pain and discomfort, although collected by standardized scores and questionnaires, remain subjective measurements and probably differ over time. Additionally, data on functional limitations as a result of pain and discomfort were only available in a subset of patients. Although data was collected in a prospective manner, this study remains observational, therefore causality of found associations cannot be confirmed. Therefore, current data  should be interpreted with caution. Nevertheless, the sample at hand may represent the general patient population undergoing surgical incisional hernia repair, as seen in every day clinical practice. Although this introduces some heterogeneity, this sample is not limited to one technique or a certain subset of complex patients.
Ethical approval This prospective cohort study was performed within the French Hernia-Club registry. The French Hernia-Club registry is approved by the French 'Commission Nationale de l'Informatique et des Libertés' (CNIL registration number: 1993959v0) and complies to the General Data Protection Regulation. Because this study is registry-based and guarantees completely anonymized data, additional participant consent and approval were not required according to the French and Dutch national ethical standards.
LESSONS FROM THE PAST: LOCK-IN AND ESCAPE FROM THE PESTICIDE TREADMILL We hypothesize that the pesticide treadmill involves a complexity of interacting enabling and disabling factors, which can be found in all four domains described above. To test this hypothesis, we selected historic case studies that met the following criteria: (i) availability of data on pesticide use in a cropping system of 30 years or more, (ii) availability of information on drivers of pesticide use, and (iii) documentation of lock-in and escape from the pesticide treadmill. We found five case studies that met these criteria: cotton production in Sudan, cotton production in Nicaragua, cotton production in Texas, USA, banana production in Costa Rica, and rice production in Indonesia. To identify which drivers governed the lock-in and escape of the pesticide treadmill, we further divided the pesticide treadmill into three phases  (Doutt and Smith 1971) : subsistence and exploitation, crisis and disaster, and escape & reorganization (Table  1 ).
Data Availability Statement: The data that were derived from resources available in  the public domain are available at: Great Britain Pesticide Usage Surveys: https://secure. fera. defra. gov. uk/pusstats/; Statistics Netherlands database: https://opendata. cbs. nl/statline/#/CBS/en/; USA: USGS Pesticide National Synthesis Project: https://water. usgs. gov/ nawqa/pnsp/usage/maps/county-level/ LITERATURE CITED Alburaki, M. , S. Boutin, P. -L. Mercier, Y. Loublier, M. Chagnon, and N. Derome. 2015. Neonicotinoid-coated Zea mays seeds indirectly affect honeybee performance and pathogen susceptibility in field trials. PLoS ONE 10(5):e0125790. https://.
F ollowing genetics and other biological domains, imaging neuroscience recently started to become a big data science. The brain sciences have been proposed to be one of the most data-rich medical specialties 1 due to amassing high-resolution imaging data. Several data collection initiatives stand out in the brainimaging landscape  2, 3  , including the Human Connectome Project (HCP), the UKBiobank (UKBB) imaging study, and the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. The UKBB is today perhaps the most compelling, as this resource includes genetic profiling and an extensive variety of phenotyping descriptors. The data aggregation set out around 2006 to gather genetic and environmental data of ~500,000 volunteers and is currently the world's largest biomedical dataset.
In 2014, UKBB launched its brain-imaging extension, aiming to gather several magnetic resonance imaging (MRI) modalities of ~100,000 subjects by 2022  4. UKBB is specifically designed for prospective population epidemiology. Instead, the ambition of HCP lies in functional and anatomical connectivity in healthy subjects, whereas ENIGMA places a premium on genetic profiling in combination with brain scanning in psychiatric and neurological disease. The creation, curation, and collaboration of largescale brain-imaging datasets with thousands of subjects promises to enable more advanced quantitative analytics than are currently the norm. An important benefit of such large-scale data collections is that they may allow for more expressive models that could more powerfully isolate and describe phenomena in the brain-models that can capture complicated nonlinear interactions dormant in commonly analyzed, now abundant brain scans.
In short, from a historical perspective, kernel-based models have outperformed linear models in many applications. Deep learning models have again refined pattern extraction and thus further boosted prediction accuracy in structured data such as natural images. Our study systematically delineates the scaling behaviors of these three modeling regimes on brain-imaging data and compared to the performance profiles on standard machine learning datasets. Our quantitative findings provide some careful skepticism as to the question: do emerging large-scale brainimaging datasets contain nonlinear information to better predict common phenotypes that can be exploited by currently available kernel and deep models?.
Results Rationale and summary of workflow. The aim of our study was to assess how much the analysis of brain-imaging data can benefit from using nonlinear methods or even deep learning for predicting important demographic or lifestyle phenotypes. For results to be generalizable, we wished to shed light on the general types of information in brain-imaging data and whether those properties demand more complicated models making the most out of increasing sample size. Gaining such important intuitions would allow us to not only observe that a specific analytical approach works well on brain-imaging data, but provide indicators why that might be the case to allow for conclusions about broader classes of methods. To this end, we evaluated how the prediction performance scales with increasing sample sizes for model classes with increasing prediction capacity and datasets of increasing prediction difficulty.
We considered the achieved prediction performance as a function of the available sample size to obtain a principled empirical assessment of sample complexity (cf. "Methods"). Analyzing the empirical sample complexity allows for insight into the information content of data as perceived through the assumptions of a given model class. For example, linear models are blind to nonlinear patterns in data by construction. Nonlinear kernel methods assume that a certain type of nonlinear interaction (i. e. , the kernel) is best suited to identify decision boundaries for accurate classification. Going another step up, DNNs also expect nonlinear interactions in the data, but, intuitively, this class of models learns the kernel rather than pre-assuming this input variable expansion. We employed three classes of learning algorithms to evaluate cross-validated phenotype prediction performance on reference datasets (Fig.
For deepnonlinear models, we used fully connected and convolutional DNNs with and without global average pooling (GAP). We thus juxtaposed several analysis methods that recapitulate important periods in the recent data science evolution. Model performance on machine learning reference datasets. To verify that we can obtain empirical estimates of sample complexity differences between linear, kernel, and deep models, we initially examined two reference datasets that have been pervasively used in the machine learning community. This setup did not only validate our approach to chart the scaling behavior of predictive performance with increasing sample size, but also established a point of comparison for performance differences for the three model classes when applied to brain-imaging data. The MNIST dataset (Modified National Institute of Standards and Technology dataset  15  ) is an important classical benchmark datasets in the machine learning community.
This resource consists of greyscale images of handwritten digits with the digit value (0-9) to be classified from the raw pixel information. To quantitatively characterize the effects of a more challenging prediction goal, we also analyzed Zalando's Fashion dataset  16. The sometimes-called Fashion-MNIST dataset consists of grayscale images of fashion products, designed for classification of ten categories of clothing. This resource was created to pose a more difficult prediction problem than MNIST while retaining the same number of targets (i. e. , 10 categories), dimensionality (i. e. , 28 × 28 = 768 variables), and sample size (i. e. , 70,000 images). To make sure that our model implementations work as would be expected from the technical literature, we replicated several current-best prediction performances on the MNIST dataset.
On the full dataset comprising 60,000 training observations, our logistic regression model achieved 91. 32 ± 0. 90% classification performance (out-of-sample prediction accuracy mean ± SD across 20 resampling iterations), which is very similar to earlier machine-learning studies (91. 7% reported in ref. 16  ). The more modern SVMs with a RBF kernel (RBF-SVM) achieved 96. 79 ± 0. 65% accuracy (98. 6% in ref. 17  ). Finally, standard convolutional DNN model achieved 99. 03 ± 0. 39% prediction accuracy-in line with the 98. 9% reported by LeCun et al. 15  based on a similar deep learning architecture. Our model performances thus replicated state-of-the-art classification accuracies that were reported in previous research in the technical communities. To carefully quantify the degree to which nonlinear structure can be exploited in MNIST and Fashion datasets, we compared the performance of linear models and nonlinear kernel SVMs with gradually increasing number of training images.
Systematic improvements in prediction accuracy when elevating from linear models to nonlinear models consistently indicated the existence of exploitable nonlinear information that was predictive of the digit category in the dataset. On the MNIST dataset (Fig. 2a ), linear and kernel models performed indistinguishably for low sample sizes. Yet, we observed that linear models and kernel models have diverged in prediction accuracy starting at around 1000 example images. Exceeding this sample size, the worst among the three kernel models (RBF-SVM) outperformed the Fig. 1  Workflow and experimental design. We directly compared the properties of machine learning datasets to brain-imaging datasets based on performance curves of three classes of predictive models. a Wellunderstood benchmark datasets from the machine learning community were elected to replicate previous results and serves as a reference point for the scaling behavior in relation to imaging neuroscience: the MNIST images were to be classified into different handwritten digits and Zalando's Fashion images were to be classified into different types of clothing.
Several representations of brain structure and function were obtained from the UKBiobank resource: region volumes and whole-brain slices from structural MRI, as well as functional connectivity strengths from resting-state functional MRI. Brain-image data were used to predict the subjects' age and sex in 10 subgroups to match the ten target categories of MNIST and Fashion. b Prediction performance for each dataset was profiled using predictive models that have capacity for successively increasing predictive power: linear models (red tone), shallow-nonlinear kernel SVMs (green tone), and deep-nonlinear neural network algorithms (blue tones). c For each combination of dataset and model, we systematically varied the number of data points available for the training the model. The resulting empirical estimates of sample complexity allow to extrapolate conclusions to always larger sample sizes.
best among the three linear models (logistic regression). In the Fashion dataset (Fig. 2c ), the worst kernel model (sigmoid-SVM) began to outperform the best linear model (logistic regression) starting from 4000 observations. This difference in performance scaling grew to 4. 03 percentage points (p. p. ) and 1. 76 p. p. at 8000 observations for MNIST and Fashion, respectively. That is, we show a less prominent gap between linear and shallow nonlinear methods in one of two comparable datasets with a more complicated prediction goal (i. e. , detect clothing rather than handwritten digits). For sufficiently large sample sizes available for model training, kernel methods outperformed linear models in both the MNIST and Fashion datasets. Hence, kernel SVMs could readily exploit nonlinear structure in the examined datasets that is inaccessible to simpler linear models by principle.
Next, we examined more closely how the difference in the tenclass prediction problem scales with steadily growing sample sizes in the MNIST and Fashion datasets. The sample size necessary to saturate the prediction performance of a classifier relates to the complexity of patterns that can be reliably derived from the amount and dimensionality of observations available for model training. The out-of-sample prediction performance of both linear and kernel models saturated with increasing sample size in MNIST. Performance of linear models (logistic regression) improved by 14. 67 p. p. from 71. 64 ± 2. 62% to 86. 31 ± 1. 06% when the data availability increased from 100 to 1000 observations, whereas the performance for shallow nonlinear models (i. e. , RBF-SVM) improved by 15. 60 p. p. from 72.
58 ± 2. 34% to 88. 23 ± 1. 11%. Improvements reduced to 3. 50 p. p. (5. 62 p. p. for RBF-SVM) from 1000 to 8000 image observations. This saturation of prediction accuracy was less pronounced when applying the same learning algorithms to images of the Fashion dataset: linear models (logistic regression) improved by 10. 22 p. p. from 69. 34 ± 2. 95% to 79. 55 ± 1. 67%, from 100 to 1000 observations, but only 4. 31 p. p. from 1000 to 8000 observations. Shallow nonlinear models (SVM-RBF).
Logistic regression Linear discriminant analysis Fig. 2  Classification performance gains with more powerful algorithms in two machine learning datasets. Shows performance scaling of prediction accuracy (y axis) as a function of increasing sample size (x axis) for generic linear models (red tones), kernel models (green tones), and deep neural network models (blue tones). All model performances are evaluated on the same independent test set. a In handwritten digit classification on the MNIST dataset, the classes of three linear, three kernel, and three deep models show distinct scaling behavior: linear models are outperformed by kernel models, which are, in turn, outperformed by deep models. The prediction accuracies of most models start to exhibit saturation at high sample sizes, with convolutional neural network models approaching near perfect classification of ten digits.
c As a more difficult successor of MNIST, the Fashion dataset is about classifying ten categories of clothing in photos. Similar to MNIST, linear models are outperformed by kernel models, which are outperformed by deep models. In contrast to MNIST, the performances of the model classes are harder to distinguish for low sample sizes and begin to fan out with growing sample size. In the Fashion dataset, more images are necessary for kernel and deep models to effectively exploit nonlinear structure to supersede linear models. b, d Image-derived brain phenotypes (IDPs) provided by the UKBiobank were used to classify subjects into ten subject groups divided by sex and age. The number of categories is equivalent to and the feature number p is similar to MNIST and Fashion. In both commonly acquired structural (sMRI) and functional (fMRI) brain images, linear, kernel, and deep models are virtually indistinguishable across all examined training image sets and prediction accuracies do not visibly saturate.