dataesr/scientific-paragraphs-categorization huggingface dataset:
Education is particularly interesting since it is the only industry that is positively correlated with both Civic and Inspiration, which might suggest that the Education sector may be also a "moral market," just like renewable energies, sustainable forestry, and recycling  (Georgallis & Lee, 2020). In the case of Fame, although non-significant correlations were found (probably due to its low frequency, only 2% of the dataset), the analysis revealed some associations with the Culture industry, where start-ups tended to valorize their reputation (the first travel community), impact (nationwide) and outreach (with more than 150,000 users in Latino America). These semantic patterns would not be suitable to exploit in other industries.
Patients with a final diagnostic of cardiopathy and those who fulfilled the Amylo-AFFECT-QOL questionnaire were included in the study between January 2017 and December 2020. Populations analyses were distributed in three groups as follow (Figure  1 ): The first random population of patients referred for "Suspicion of CA" who had filled the Amylo-AFFECT-QOL was used as the base for HR-QoL evaluation (QoL population). From this first random group, the Learning prognostic population for prognostic model determination was selected after excluding patients without amyloidosis diagnosis and those without biological data available. A second random population was identified as the Validation prognostic population after exclusion of patients without amyloidosis and without available biological data and was used to validate the prognostic models.
Assessment of Amylo-AFFECT-QOL prognostic value using the learning prognostic population and the validation prognostic population As described below, the Learning prognostic population, including patients with amyloidosis diagnosis and the completed Amylo-AFFECT-QOL questionnaire, was used to construct the prognostic model. A hot-deck multiple imputation (N = 5 imputations) was performed to impute missing data for the prognostic study [all results were pooled using the Rubin formula  (19) ]. Three logistic regression models were developed using the Learning prognostic population according to the following inputs: Amylo-AFFECT-QOL dimension scores alone, biological markers (troponin and NT-proBNP) alone, or both. The output variable was defined as death or heart transplant after 1 year of followup.
Statistical analyses All statistical analyses were performed using Statistical Analysis Software version 9. 4 (SAS Institute, Inc. , Créteil, France). The study was approved by the ethics committee (authorization number #1431858), and informed consent for participation in this research was obtained from all patients. Data were recorded electronically in the Henri Mondor Amyloidosis Network registry, as authorized by the French CNIL (Commission National de l'Informatique et des Libertés). All data were centralized in a secure database.
The ongoing phase II/III EA2174 study is evaluating whether the addition of nivolumab to chemoradiotherapy in the preoperative setting and ipilimumab to adjuvant nivolumab (NCT03604991). The place of immunotherapy is also evaluated in patients treated with perioperative chemotherapy. The preliminary results of the randomised phase II DANTE trial evaluating FLOT ± atezolizumab reported promising results on the complete pathological response rate, 23% versus 14% with an improvement according to CPS-PD-L1 status  [85]. Other randomised trials are ongoing, including the phase III KEYNOTE-585 trial (NCT03221426) evaluating cisplatin-5FU ± pembrolizumab (study amended to include data with the FLOT protocol) with a recent press release mentioning negative results for primary endpoint  [86] , the phase III MATTERHORN trial exploring FLOT ± durvalumab (NCT04592913)  [87] , and phase II IMAGINE trial studying FLOT ± nivolumab with or without relatlimab (an anti-LAG3 monoclonal antibody) (NCT04062656) (Fig.
Regarding patients with gastroesophageal adenocarcinoma and MSI, recent published data are encouraging with immunotherapy and the best approach is under investigation. Indeed, if MSI status is associated with a favourable prognosis in patients with locally advanced disease treated by surgery alone, they have no or limited benefit from perioperative chemotherapy. In the exploratory analysis of patients with MSI tumours enroled in the perioperative MAGIC trial, patients with MSI tumours had a better prognosis when treated with surgery alone and potentially experienced deleterious effects from chemotherapy  [91]. The prospective phase II GERCOR-NEONIPIGA study that enroled 32 patients with MSI gastroesophageal cancers evaluated preoperative treatment with nivolumab and ipilimumab followed by adjuvant nivolumab alone, without combined chemotherapy.
Therefore, and as developed in localised forms of gastric cancer with the FLOT regimen, some phase 2 studies have explored oxaliplatin-based triplet first-line chemotherapy in advanced disease. Based on these promising results, the GASTFOX randomised phase III trial was conducted in this setting (NCT03006432)  [97]  Preferred second-line treatments include taxanes, irinotecan (or FOLFIRI), or ramucirumab as monotherapy or in combination with paclitaxel  [6, 8]. The only third-line chemotherapy currently approved is trifluridine tipiracil validated by the large international phase III TAGS trial  [98]. Fig. 3  (right panel) summarises the algorithm for advanced/metastatic GC/GEJC treatment. GC is associated with immune system evasion and overexpression of immune checkpoint proteins, providing the rationale for immunotherapy with PD-1/PD-L1 therapy  [17].
Immunotherapy We have seen above that the combination of nivolumab with firstline chemotherapy improved the survival of patients with a tumour PDL1 CPS ≥5. Nevertheless, other biomarkers may help to better select the most sensitive patients to immunotherapy, even if some degree of overlap exists between them, such as dMMR/MSI, EBV or high TMB  [116]. Furthermore, the combination of ICI with other drugs could also make it possible to restore anti-tumour immunity. In particular, the synergetic effect of the combination with anti-angiogenics agents could be promising, especially with multikinase inhibitor compounds, such as apatinib, lenvatinib, or regorafenib (Fig. 3 ). Based on the positive results of the phase III INTEGRATE-IIa trial  [117] , the phase III INTEGRATE-IIb trial, which is currently ongoing, evaluates the combination of regorafenib and nivolumab versus chemotherapy left to the discretion of investigators in third and later line treatment of patients with advanced GC (NCT04879368).
TDX-d has been tested in 2 phase II trials dedicated to patients with HER2 advanced GC from Asian (DESTINY-GASTRIC-01)  [120]  and non-Asian (DESTINY-GASTRIC-02)  [121]  populations, in second-line or beyond, showing convincing results in terms of tumour response and survival. TDX-d is currently being compared with paclitaxel-ramucirumab in second-line treatment of advanced GC patients with HER2 positive status confirmed by a new biopsy at progression disease (DESTINY-GASTRIC-04) (NCT04704934). Other HER2 agents are currently evaluated in ongoing clinical trials, such as Margetuximab, an anti-HER2 antibody with a high affinity, Tucatinib, which is a small-molecule drug that reversibly binds to the adenosine triphosphate (ATP) binding pocket of the internal domain of the HER2 receptor or Zanidatamab, a bispecific antibody that inhibits extracellular domains 2 and 4 of HER2 (Fig.
Interestingly, a significant positive correlation was found between the presence of homogeneous FGFR2b-positive staining (IHC score 2+ or 3+ in ≥10% of tumour cells) and the survival benefit in those receiving bemarituzumab  [125]. Based on these promising results, bemarituzumab is currently evaluated in first-line treatment in two phase III clinical trials in patients FGFR2-overexpressing GC, in combination with chemotherapy alone (FORTITUDE-101; NCT05052801) or with nivolumab (FORTI-TUDE-102; NCT05111626) if tumours overexpressed PDL1 (CPS≥5). There are also other agents targeting FGFR2 in development for the treatment of GC. Derazantinib is an orally bioavailable FGFR1, FGFR2, and FGFR3 multikinase inhibitors. This compound is currently tested alone or in combination with chemotherapy or ICI in GC patients with FGFR2 genetic aberrations (FGFR2 translocations, FGFR2 amplifications or mutations in FGFR1, FGFR2 and FGFR3).
R-curve study SEVNB specimens were tested in a three-point fixture (support span 21 mm) using an Instron 8500 machine (Norwood, USA). The support and upper cylinders were made of alumina and had a diameter of 8 mm. During the test, the specimen deflection was recorded using a Linear Variable Displacement Transducer (LVDT) placed near the sample notch. The R-Curves of the materials were built from the loaddisplacement (P-δ) data obtained from the three-point bending tests according to two different methods. In most cases, R-Curves were obtained using the indirect compliance method  [26]. In addition, and in order to validate this indirect method, the R-Curve of the composite with the highest crack resistance behavior (2. 5rGO┴ composite) was also estimated from the direct measurement of crack growth by optical microscopy (load-unload three-point bending test).
Acknowledgments This work was supported by the projects  PGC 2018-101377-B-100  funded by  MCIN/AEI/ 10. 13039/501100011033  ( Ministerio de Ciencia e Innovación, Spanish Government, Agencia Estatal de Investigación)  and  ERDF (European Regional Development Funding)  "A  way of making Europe ", by the  European Union Union , and  P20_01024  ( Junta de Andalucía/FEDER ,  UE 2014-2020 ). C. López -Pernía acknowledges the financial support of her stay at the  INSA  through the FPI contract ref:  BES-2016-078711. C. Muñoz-Ferreiro  acknowledges the financial support of a  VI PPIT-US fellowship  through the contract  USE-18740-H. J. Prada-Rodrigo  and  P. Moreno  acknowledge support from  Junta de Castilla y León  (Project  SA136P20 ) and  Ministerio de Ciencia e Innovación  ( PID2020-119003GB-I00 ).
Results Of the 200 biopsies, 100 were expected to be negative for H. pylori. In all, 98 biopsies were negative on the ELITe InGenius using RIDA®GENE H. pylori reagents (Table  1 ) (Suppl Table  1 ). Two biopsies were positive for H. pylori (biopsies 159 and 189, with Ct values of 35 and 38. 4, respectively) (Suppl Table  1 ). The amplification curves of these two samples were similar to those obtained on the other H. pylori positive samples. These two cases were detected as positive again on a second passage on the ELITe InGenius. The 2 DNA samples extracted at the NRCCH for these 2 cases tested negative using RIDA®GENE H. pylori reagents on a CFX96 system (data not shown). No history or suspicion of H. pylori infection was available for these other discordant cases, and thus, they were considered false-positives (Table  1 ) (Table  2 ).
However, it would be interesting to verify these results in a larger number of biopsies of this type. The A2142G or A2143G mutations are the most frequent in France, and in Europe, the A2142C mutation remains anecdotal  [9, 7]. Finally, A2142T and A2143C mutations are rarely found in France (<0. 1% of all macrolide-resistant strains in France (2017-2021 personal data))  [10]. Samples positive for these mutations (one sample each) were tested on the ELITe InGenius; as expected, H. pylori-only was detected (data not shown). In conclusion, the performance of RIDA®GENE H. pylori PCR on the ELITe InGenius was excellent. The performance was similar to that published previously by our team for these same reagents but with a different DNA extraction technique and an independent real-time PCR device, the CFX96  [2]  or the BD Max system  [6].
Interventions Participating hospitals were included in DiVa with respect to the use of common procedures after a specific training on stroke/TIA and MI management. Nurses and pharmacists were trained in the recommended secondary prevention guidelines, the study protocol, the use of electronic Case Report Form (eCRF) and the electronic recording of data collected at each hospital, practice, or home visit. Patients with stroke/TIA or MI are recruited during their hospital stay. After a complete diagnostic work-up and the introduction of secondary prevention therapy according to the current guidelines, they are invited to participate in the trial. Once they have given their consent, the randomisation procedure takes place. For all included patients, a letter summarising the hospital stay, and providing information about the DiVa protocol and patient's personalised pathway, is sent to the GP and other specialists contributing to the DiVa care network.
Ethics and dissemination The study is approved by French ethic committee (CCP; approval 2019-A02299-48) and the Commission Nationale Informatique et Liberté (CNIL, French data protection authority). Patients receive information about the study and give their consent to participate according to the French legislation (online supplemental material). Results of the main trial and each of the secondary analyses will be submitted for publication in a peer-reviewed journal following the CONSORT statement for reporting of pragmatic trials (online supplemental material).
Acknowledgements We thank  Marilyne Pouchain  for study coordination and the  Clinical Research Unit (USMR)  for randomisation. Contributors Coprincipal study investigators: YB, YC,  MG. Design  and redaction of the study protocol:  YB, AL-S, MarcB, YC, LV, LV, MathieuB, MG. Statistical analysis plan: AL-S,  MarcB. Drafting  of the manuscript: YB,  AL-S, MarcB, MG. Critical review of the manuscript for scientific content: YB, AL-S, MarcB, GD, TP, GL, YC, LV, HA, MathieuB, MG. All authors and approved the final manuscript. Funding DiVa study is funded by the  Caisse Nationale d'Assurance Maladie (Fonds pour l'Innovation du Système de Santé (FISS)  (Grant number N/A), the  Agence Régionale de Santé Bourgogne Franche Comté (Fonds d'Intervention Régional and eTICSS conception)  (Grant number N/A), and the  University Hospital of Dijon  (Grant number N/A).
Using data on the movements of car-sharing vehicles in several Italian cities, we infer a model using the Maximum Entropy (MaxEnt) principle. The model allows for an accurate spatio-temporal prediction of car-sharing vehicles' presence in different city areas and, thanks to its simple yet general formulation, to precisely perform anomaly detection (e. g. , detect strikes and bad weather conditions from car-sharing data only). We compare the forecasting capabilities of our model with different state-of-the-art models explicitly made for time-series forecasting: SARIMA models and Deep Learning Models. We find that MaxEnt models are highly predictive, outperforming SARIMAs while having similar performances of deep Neural Networks -but with advantages of being more interpretable, more flexibile-i.
e. , they can be applied to different tasks-and being computationally efficient. Our results show that statistical inference might play a fundamental role in building robust and general models describing urban systems phenomena. In recent years, pressing societal and environmental problems, such as population growth, migration, and climate change, have boosted research on the Science of Cities and the related study of mobility. The parallel growth in the availability of extensive and detailed datasets covering the mobility of individuals at different granular levels has contributed to an increase in the interest of researchers in this field 1,2. Being multi-disciplinary, Science of Cities studies embrace diverse areas of research. For example, scientists have applied statistical methods to city growth 3 , multi-layer networks to urban resilience 4 , and spatial networks to describe and characterize the structure and the evolution of the phenomena that arise from them 5.
Therefore, to build a powerful, general, and robust statistical model, we need to understand the critical variables that play a central role in the phenomenon we want to model. To this end, we study urban mobility patterns exploiting the Maximum Entropy (MaxEnt) principle. The latter builds on information-theoretical grounds to state that the model generating an empirical dataset is the one featuring the most general probability distribution that reproduces the statistically significant observables (e. g. , the average value or the standard deviation of a variable). In other words, the MaxEnt modelling framework is a Statistical Inference technique that infers the model's parameters in a data-driven fashion by maximizing the entropy of its probability distribution, setting a few constraints driven by data observations.
The MaxEnt model has already proved to be successful in a wide array of interdisciplinary applications  26  , from biology  27  to melodic styles  28. Here, we propose a Statistical Inference (Maximum Entropy) approach to study and predict urban mobility patterns. To solve it, we need to analyze the dataset we want to study, identify the essential dynamic properties and then model it solving the problem of optimizing the resulting entropy. The data used in this work represent the 30minutebinned count of cars parked in each district of a city. The cars we focus on are those offered by the major car-sharing service in Italy. The data consist of a multivariate time series of the activity (i. e. , the number of parked cars seen in a given area during a given time bin) in different zones inside the city.
Subsequently, the "Discussion" Section will follow. In the Methods section, we describe the data and the variables in use. To determine the essential variables we represent, we carry out a historical analysis and observe the dynamics of contraction and dilation of the time-shifted correlations between different zones. With this, we identify relevant observables, and finally, we define and derive the formulae for the ME model describing our data. In Supplementary Information we add similar results found for the cities of Rome, Florence, and Turin.
Data description. The dataset contains the position of the vehicles of a major Italian car-sharing service in four Italian cities in 2017 (Turin, Milan, Florence, and Rome). Data were obtained by constantly querying the web APIs of the service provider and record the parking location of each car. This information allows us to establish the origin and destination of each trip. In the "Methods" section, we present the preprocessing procedure we used to wrangle the data and to obtain the time series, which are the input of our models. The preprocessing output is a series of parking events with a location (latitude and longitude) and a starting and ending timestamp. We aggregated these events based on the census tessellation, i. e. , the municipality zones inside a city. In the following, we show the case of Milan, which will be our reference city, while we present the remaining cities in the Supplementary Information section.
The loglikelihood of a single day is just We can now assign a label L(d i ) = 0 if d i as a standard day and L(d i ) = 1 if it is a day where a specific external event occurred. We considered two types of external events: 1. Weather conditions: on that day, there was fog, a storm, or it was raining. 2. Strikes: that day, there was a strike in the city that could affect car traffic. We considered taxis, public transport, and general strikes involving all working categories. We had 50% of days labelled as 1 among the days in the test dataset. In Fig. 5 , we show the ROC curve for the classification of such days, using the log-likelihood trained with the parameters { = 48, = 0. 006}. The area  under the ROC curve (AuROC) is 0. 81, indicating that L is a good indicator of whether an external event occurred on a specific day.
Discussion In this work, we have addressed the problem of building statistical and machine learning models to predict mobility patterns within the Milan Metropolitan Area. We focused on predicting how many car-sharing vehicles are parked within a specific area at different times. To do this, we analyzed a longitudinal dataset of parking events collected from the APIs of the Italian car-sharing company Enjoy. The processed data consisted of a collection of time series representing the number of parked vehicles in a given area at a given time. To predict the evolution of these time series, we leveraged the Maximum Entropy (ME) modelling framework, which requires the identification of relevant observables from the data and the use of these observables to define probability density functions depending on some parameters.
Maximum Entropy modelling has proven to be very effective at studying and determining the activity patterns inside the city (Fig. 3 ). We compared our model with other models built explicitly for time series forecasting. In particular, we used a SARIMA model, a Feed-Forward Neural Network, and a Long Short-Term Memory Neural Network. Maximum Entropy models outperformed SARIMA models with reference to all the metrics used in the evaluation. Our model is as predictive as LSTMs Neural Networks but uses two orders of magnitude fewer parameters, and it is possible to use our model to perform different kinds of predictions. Finally, we also used the statistical model to identify extreme events affecting urban traffic, such as bad weather conditions or strikes. In conclusion, we contribute to the literature on aggregated data human mobility studies in different ways.
Season-dependent models could be built by taking into account larger datasets. • Evaluate how the prediction ability of the Maximum Entropy models is dependent upon the city's structure or the resolution of the time series. • Entangling mobility patterns with other city indicators, such as socio-political disparities and economic indicators, can lead to a better model that depicts human distribution. • The inclusion of non-linear interaction in the ME models could be difficult if made by defining standard observables. Instead, hidden variables approaches could be taken into account, e. g. Restricted Boltzmann Machines  48. • The ME models could be adapted to perform other anomaly detection tasks, i. e. identifying parts of the time series which are not standard fluctuations of the system  49, 50.
Methods Dataset. The dataset used in this work is a log of the locations of cars belonging to Enjoy, a famous Italian car-sharing service. We report all the details about collecting and handling data in the section Data Availability. We collected the data recording all the movements of Enjoy cars within the cities of Milan, Florence, Turin, and Rome in 2017. In the rest of this article, we will focus mainly on parking events, but our methods and analyses could also be applied to study the volume of travel events. One aim of the work was to predict the number of vehicles parked at a given time in each city district. To this end, we divided the service area into the different municipalities (we will call them zones). We considered different tessellation strategies (e. g. , using hexagons with a 900m side), but the municipality division provides more statistically stable training data, and thus more precise models.
To keep the notation formalism, we will indicate z i (t) with x i (t) , keeping in mind that x now refers to a normalized activity. From now on, we will work on the normalized x i (t) : these indicate how much a given area is more "active"- i. e. , has more cars parked -concerning the average volume of that time bin t (typical day activity) by weighting this fluctuation with the standard deviation observed for that zone-hour volume. In this way, we can compare the signal of areas with high fluctuations and high activities with less frequented areas around the city. The final step when processing data for inference and Machine Learning is to divide the data into a train set and a test set. Taking the time series representing all the Zones' activity in a working year, we split the dataset, 80% of it for the train and 20% for the test set.