dataesr/scientific-paragraphs-categorization huggingface dataset:
Sighting data deriving from the ACCOBAMS 1 Survey Initiative (ASI), conducted through the CeNoBS 2 project, enabled the investigation of the habitat preferences for three different cetacean subspecies occurring in the Black Sea waters: the bottlenose dolphins (Tursiops truncatus), the common dolphins (Delphinus delphis) and the harbour porpoise (Phocoena phocoena). ASI aerial surveys, aiming at assessing the distribution and abundance of cetacean populations, were conducted during summer of 2019 in waters in front of Romania, Georgia, Bulgaria, Turkey and Ukraine. The surveys allowed recording of 1716 sightings: 117 bottlenose dolphins, 715 common dolphins and 884 harbour porpoises. The aim of this study was twofold: (i) to develop habitat models, using physical characteristics, such as depth and slope, as covariates, in order to estimate the presence probability of the three cetacean species in the Black Sea; (ii) to demonstrate the usefulness of the habitat models in support of environmental status assessments on marine mammals where the stressor is the shipping noise.
Data set and data collection The data used for this study were collected through the EUfunded CeNoBS project, in collaboration with and co-funded by ACCOBAMS under the ACCOBAMS Survey Initiative (ASI). The CeNoBS project  (ACCOBAMS, 2021a; Paiu et al. , 2021)  supported the implementation of the MSFD in the Black Sea through the establishment of a regional cetacean monitoring system and noise monitoring to assess the status of cetaceans in the Black Sea through MSFD descriptors and particularly of Descriptors 1 (D1, Biological Diversity) and Descriptor D11 (Energy including Underwater Noise). CeNoBS data were collected through regional aerial surveys conducted in the Black Sea between June and July 2019, following specific shared protocols. Data were collected, following the distance sampling method  (Buckland et al.
, 2015) , by observers on board small twin-engine aircrafts, equipped with bubble windows, allowing the sight of cetaceans and other marine megafauna below the aircraft. Specific transects were designed and prepared to ensure fair coverage and representation of the study area. The surveys covered the waters in front of Romania, Bulgaria, Turkey, Ukraine and Georgia. The transects were predefined and adapted to the flight limitations in some areas. Six blocks and zigzag traces were drawn for each of them in order to have a minimum coverage of 3% of the study areas  (Paiu et al. , 2021)  (Figure  1 ). Two observers, a team leader and the pilot were on board. Data were collected according to specific protocols prepared by the project's researchers and scientific collaborators. During the surveys, target altitude was 183 m (600 feet) with target speed of 100 knots.
The software used to collect data was SAMMOA dedicated to marine megafauna data collection  (SAMMOA 1. 1. 2, 2017 (SAMMOA 1. 1. 2, -2018 ; Pelagis Observatory-La Rochelle University-CNRS), linked to GPS to collect position data  (Paiu et al. , 2021). Data regarding environmental conditions were collected at the beginning of each transect and whenever a change occurred. Variables considered were sea state (Beaufort scale), glare, cloud cover and sighting conditions. The three cetacean species (common dolphin, bottlenose dolphin and harbour porpoise) were the main target species, and data were collected also on group size and composition [mixed groups and two age classes Adults and Juveniles (calves)], swimming direction and group behaviour (using 8 defined categories)  (SCANS II, 2008; Hammond et al.
Data analysis 2. 3. 1 Data matrix preparation Using QGIS 3. 20. 3, a grid was created for the Black Sea with a total of 2069 cells of size 0. 16°x 0. 16°(10 nm x 10 nm, corresponding to 18. 5 km x 18. 5 km). Bathymetric data were obtained through GEBCO with a 2021 model, providing elevation data in metres on a grid at 15 arc second intervals (https:// download. gebco. net/). Sea bed slope was calculated using QGIS 3. 20. 3 from the depth layer via the GDAL slope command. The six blocks and tracks covered by planes in the waters of Romania, Bulgaria, Turkey, Ukraine and Georgia (map source: ACCOBAMS-CeNoBS project). Bathymetric data were integrated with the grid, enabling the calculation of depth and slope statistics within each cell. Such statistics were later used as potential covariates for the habitat models.
Habitat model development Habitat model development and evaluation was conducted by means of a binary logistic regression analysis, used also in similar ecological applications  (Guisan and Zimmermann, 2000; Davis et al. , 2002; Azzellino et al. , 2008; Anderwald et al. , 2011; Azzellino et al. , 2011). All the analyses were performed using the IBM SPSS Statistics package (version 27). To make the analysis more efficient, the number of absence cells (i. e. pseudo-absence of the target species) in the dataset was balanced with the number of presence cells (presence of 1 or more sightings of the target species). Following  Azzellino et al. (2012) , the total number of presence cells was in fact maintained, while a number of absence cells (absence of sightings) equal to the number of presence cells was randomly extracted from the overall number of absence cells.
As a first step a noise exposure map for the study area was generated covering the period from 01. 07. 2018 to 31. 08. 2018 using a reference grid of 100 m x 100 m mesh size. Three daily random AIS (Automatic Identification System) images were used to build a 60-days summer scenario of ship traffic which was used as basis to simulate noise levels. AIS data were supplied by Spire Group https://spire. com/maritime/. Sinay, who created the noise maps thanks to a partnership with Spire Group, requested AIS data worldwide on-demand through APIcalls for a period/time of interest. For the present work, historical data in the study period were accessed. Row AIS data appear, for a given instant t, as a list of vessels for which an AIS message has been received at that instant t, with associated metadata such as the geographical position of each vessel (coordinates X,Y), their identification code-MMSI, speed, size, destination etc.
The slope is an excellent predictor, as the preferences just described refer to the areas where the continental shelf begins and where the mean slope values per cell are highest. For example, within the central eastern part of the basin and in the Sea of Azov, the presence values are lower as the slope tends to decrease. The harbour porpoise is therefore present in both shallower and pelagic waters, with a depth range between 25 and 2200 m. As far as Black Sea Harbour porpoise is concerned this study habitat models only partially agree with the ones presented by  Sańchez-Cabanes et al. (2017) , which predict a greater presence in shallow waters below 200 m with preferences for shelf waters between 50 and 150 m, and a predicted habitat similar to that of bottlenose dolphins. However this discrepancy could be the effect of the effort bias deriving from the opportunistic surveys which are the source of their dataset.
Standard Protocol Approvals, Registrations, and Patient Consents In the present study, routine healthcare data were assessed. All data were anonymously collected with the patient's agreement in accordance with the general regulations on data protection applicable in France, which is available in eAppendix 1, links. lww. com/NXI/A824. Ethics approval was obtained from the Institutional Review Board of Montpellier University (approval number: IRB-MTP_2021_06_202100806 is available in eAppendix 2, links. lww. com/NXI/A825). Our manuscript is in accordance with guidelines of STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) that is available in eAppendix 3, links. lww. com/NXI/A826.
Data Collection Clinical Assessment The following patient characteristics were prospectively collected: sex, age, personal neurologic history, and hematologic malignancy characteristics (age at onset, Ann Arbor staging, and central or peripheral nervous system involvement). Each patient was evaluated following the American Society for Blood and Marrow Transplantation consensus definition and grading system for CRS that include the presence of fever (≥38°C), hypotension (requiring or not vasopressors), and hypoxia (requiring or not the use of oxygen delivered with a low-flow, ≤6 L/min, nasal cannula or a blow-by method). 11 urologic signs/symptoms of ICANS were monitored and scored using the Chimeric Antigen Receptor Toxicity-10 questionnaire  11  (the list is available in eAppendix 4, links.
EEG As neurotoxicity symptoms usually reach their peak at day 7 after CAR T-cell infusion, EEG was performed between day 6 and day 8 after infusion and on the day of ICANS occurrence, if outside this time window. A new EEG was performed in patients with neurologic worsening and then every 2 days until neurologic normalization. EEG electrodes were placed according to the 10-20 international system using 21 electrodes and a 240 Hz sampling rate. EEG recording lasted up to 30 minutes. Signals were obtained using the Micromed Brain quick amplifier and Micromed Brainspy software (Micromed, Italy). A notch filter (60 Hz) and bandpass filter (0. 5-70 Hz) were applied. The EEG background was graded according to the Synek scale from 0 (i. e. , normal) to 5 (i. e. , severe amplitude suppression). 12 Besides the predominant posterior rhythm, focal slowing and paroxysmal activities were assessed (i.
Brain MRI MRI was performed with a 1. 5T or a 3T scanner between day 6 and day 8 after CAR T-cell infusion (33/38 patients). Standard pre-and post-gadolinium T1w, T2w, fluid-attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI), and susceptibility-weighted images were available. All neuroimaging data were reviewed by an expert neurologist (X. A. ). A control MRI was systematically performed in patients with neurologic worsening.
EEG findings (i. e. , Synek scale grade and FIRDA presence) were not correlated with ICANS severity and neurologic signs or symptoms (data are available in eTable 2, links. lww. com/NXI/A831). However, FIRDA was observed in all patients with expressive aphasia (N = 13/13). Abnormal EEG findings (e. g. , FIRDA) and ICANS disappeared concomitantly after steroid therapy (N = 13, 87%) or spontaneously (N = 2, 13%). MRI was normal in 15 of 17 patients with ICANS. The 2 patients with abnormal MRI findings (FLAIR hyperintensities in the left hippocampal body in one and in the periventricular white matter around the right occipital horn in the other) had ICANS grade 3. These MRI lesions matched with the DWI and ADC map hyperintensities, suggestive of vasogenic edema (eFigure 1, links. lww. com/NXI/A829).
Parameters Correlated With Neurotoxicity Occurring During ICANS Between day 6 and day 8 after CAR T-cell infusion, 15 of 17 patients with ICANS had FIRDA (88%), whereas EEG was normal in all patients without ICANS (p < 0. 001). FIRDA preceded ICANS in only 3 patients (18%). FIRDA disappeared after steroid therapy (N = 13, 87%) or spontaneously (N = 2, 13%), when patients no longer had signs of neurotoxicity. Abnormal MRI findings (N = 2 patients with ICANS) were not associated with neurotoxicity (p = 0. 2). Patients with FIRDA (N = 15, all had ICANS) had higher CRP levels (p = 0. 02) and lower sodium levels (p = 0. 003). Univariate regression analysis confirmed these results. Data are available in eTable5 and eTable6, links. lww. com/NXI/ A834 and links. lww. com/NXI/A835.
Most impact evaluations of IPV prevention interventions use binary measures of "any" versus "no" physical and/or sexual IPV as their primary outcome measure, missing opportunities to capture nuance. In this study, we reanalysed secondary data from six randomised controlled trials conducted in low and middle-income countries-Bandebereho (Rwanda), Becoming One (Uganda), Indashyikirwa (Rwanda), MAISHA CRT01, MAISHA CRT02 (Tanzania), Stepping Stones Creating Futures (South Africa), and Unite for a Better Life (Ethiopia), to assess how different conceptualisations and coding of IPV variables can influence interpretations of the impact of an intervention. We compared standard outcome measures to new measures that reflect the severity and intensity of violence and whether interventions prevent new cases of IPV or reduce or stop ongoing violence.
Description of Studies Appendix Table  3  provides an overview of the six different trials included in this paper. The Bandebereho trial in Rwanda was a two-arm multi-site randomised controlled trial. The intervention uses the transition to parenthood as an entry point to work with men and their partners to transform harmful masculine attitudes and support more equitable and non-violent couple and family relationships. A 15-session curriculum covers topics such as gender and power, fatherhood; couple communication and decision-making; IPV; child development; and men's engagement in prenatal and infant care. Men participated in all sessions, and women in up to 8 sessions. At 21 months, 94% of men (1123) and 97% of women (1162) were retained  (Doyle et al. , 2018). Data from female participants is used for the secondary analyses presented in this study.
Topics included knowledge and awareness of traditional gender norms and IPV, communication and conflict resolution skills, peer support and social capital. At 24 months, 89% (485) of the intervention and 86% (434) of the control group women provided data for the impact evaluation  (Kapiga et al. , 2019). The MAISHA CRT02 trial evaluated the impact of the same intervention on the IPV experiences of women residing in the same neighbourhoods which were not part of any microfinance groups. At the 24-month follow-up,. 88% (551) of intervention and 90% (575) of the control group women provided data  (Harvey et al. , 2021). The Stepping Stones and Creating Futures (SS-CF) trial in South Africa was a twoarm cluster randomised controlled trial with a wait-list control condition. SS-CF is a behavioural intervention to reduce IPV by transforming gender attitudes and relationships and strengthening livelihoods.
Measures To explore how the choice of coding affects the measured impact of each intervention, we constructed a range of new outcome measures for the reanalysis of data from the trials above. All trials used a version of the WHO instrument for assessing IPV (García-Moreno, Jansen, Ellsberg,  Heise, & Watts, 2005). The original WHO study included measures of physical IPV (5 acts), sexual IPV (3 acts), and emotional IPV (4 acts). All scales. used behaviourally specific questions to inquire about women's victimisation and men's perpetration of IPV over the past 12 months (e. g. , in the past 12 months, how many times has a current husband or boyfriend ever slapped you or thrown something at you which could hurt you?). Responses typically were: '0=never', '1=once', '2=a few times', or '3=many times.
Discussion In this study, we reanalysed data from six trials to assess how different conceptualisations and coding of IPV variables can influence interpretations of the impact of an intervention. We compared standard outcome measures to new measures that reflect the severity of violence and whether interventions prevent new cases of IPV or reduce or stop ongoing violence. While we did not observe any clear trends across studies, we see important differences in intervention impact when comparing the standard outcome measures to the new ones. Importantly, in many trials, the traditional binary indicators masked some of the more subtle intervention effects, and the use of the new indicators allowed for a better understanding of the impacts of the interventions. At the same time, differences in results within studies between standard and new outcomes also differ across the six trials.
Background and objective: Epileptic seizures are associated with a higher incidence of Developmental Disabilities and Cerebral Palsy. Early evaluation and management of epilepsy is strongly recommended. We propose and discuss an application to predict epilespy (PredictMed-Epilepsy) and seizures via a deep-learning module (PredictMed-Seizures) encompassed within a multi-agent based healthcare system (PredictMed-MHS); this system is meant, in perspective, to be integrated into a clinical decision support system (PredictMed-CDSS). PredictMed-Epilespy, in particular, aims to identify factors associated with epilepsy in children with Developmental Disabilities and Cerebral Palsy by using a prediction-learning model named PredictMed. PredictMed-epilespy methods: We performed a longitudinal, multicenter, double-blinded, descriptive study of one hundred and two children with Developmental Disabilities and Cerebral Palsy (58 males, 44 females; 65 inpatients, 37 outpatients; 72 had epilepsy -22 of intractable epilepsy, age: 16.
002 )] were associated with having epilepsy. The prediction model scored an average of 82% of accuracy, sensitivity, and specificity. Thus, PredictMed defined the computational phenotype of children with Developmental Disabilities/Cerebral Palsy at risk of epilepsy. Novel contribution of the work: We have been developing and we have prototypically implemented a Multi-Agent Systems (MAS) that encapsulates the PredictMed-Epilepsy module. More specifically, we have implemented the Patient Observing MAS (PoMAS), which, as a novelty w. r. t. the existing literature, includes a complex event processing module that provides real-time detention of short-and long-term events related to the patient's condition.
Introduction Epileptic seizures are frequent in children with developmental disabilities (DDs) and cerebral palsy (CP). In fact, DDs happen as a result of neurological abnormalities that have their onset before birth or during childhood, and they are associated with significant long-term difficulties. CP accounts for a large proportion of children with DDs. Specifically, CP is a group of non-progressive disorders of motor and postural control that occur secondarily to brain damage during early stages of development  [1]. In order to cope with these issues, we have been developing and we have prototypically implemented a Multi-Agent Systems (MAS) that encapsulates the PredictMed-Epilepsy module. This is done in order to improve the exploitation of existing data gather-ing capabilities of non-invasive/wearable detection sensors, and to contribute, in perspective, to the specification and implementation of an enhanced (with respect to existing ones) CDSS.
Our aim is to provide support to: on the one hand, patients, who are constantly monitored by the system, which can provide them (or their caregivers) with support and advice; on the other hand, medical doctors, who are constantly informed about their patients' conditions and are provided with all the necessary elements to take their clinical decisions. We have implemented in particular the Patient Observing MAS (PoMAS), which, as a novelty w. r. t. the existing literature, includes a complex event processing module that provides real-time detention of short-and long-term events related to the patient's condition. Via this system, the doctor is able to identify children who show clinical signs related to seizures, to evaluate the actual likelihood via PredictMed-Seizures, and to properly manage young patients.
Furthermore, we present its potential clinical use through the implementation of a Prediction Application to Detect and Predict Seizures (PredictMed-Seizures) encapsulated within a multi-agent based healthcare system (PredictMed-MHS) to be integrated into a clinical decision support system (PredictMed-CDSS). The currently existing prototype implementation of a subset of such a system, namely a multi-agent system (MAS) for Patient Observing and Monitoring (POMAS), which also provides support to physicians, has been developed in the agent-oriented DALI logic programming language  [10] [11] [12]  and will be discussed in Section 3. POMAS also encompasses a Complex Event Processing (CEP) module that provides real-time detention of short-term and long-term events related to the patient health condition, that require countermeasures to be taken (immediate, in case of short-term events) and doctors to be informed.
The resulting system then supports clinicians, through complex event detection via the CEP module, in the task of identifying children who show clinical signs related to epilepsy. The clinical parameters for these children are then evaluated through PredictMed-Seizures to estimate the actual probability that a child actually has seizures. Finally, the system supports medical doctors also in the management of high-risk or ill young patients via constant monitoring, again performed by POMAS via the CEP module, so as to early detect and properly manage dangerous situations. The proposed POMAS is, to the best of our knowledge, an absolute novelty, as it integrates within the MAS operation complex event processing to evaluate a patient's condition and a deep learning module to quantitatively estimate the possibility of a patient having a given disease.
Epileptic were selected if having at least one seizure in the previous three months. All participants will be monitored by non-invasive/wearable detection devices 24h/day, every day, for six months. The devices collected physiological as well as movement cues. However, seizures are to be made known and validated (to exclude false alarms) whenever possible, via the MAS, by medical staff and families. Data on seizures (types and duration) for health care use, communicated by doctors and families, will be collected monthly and cross-validated using data from the devices. Data from wearable devices (accelerometric or electrodermal data, ECG data, etc. ) will be processed in order to extract from them the most relevant features. To this purpose, we leverage on previous studies  [ 17, 18 ]  in order to obtain features useful for the learning modules by processing wearable devices data.
These features will be part of a Time Series data stream to be given in input to tailored Machine Learning (ML) algorithms for anomaly detection (as for example Principal Component Analysis (PCA), Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN). The basic idea is that seizures on CP patients are analogue to a machine malfunction (e. g. , an anomaly) and can be predicted in advance using well established ML techniques used for machine anomaly prediction by sensor data processing. PCA, LSTM, RNN are well established ML algorithms for anomaly detection: by learning on a Training Set of time series data the normal behavior of a machine (or a patient) they are able to detect anomalies in the pattern of data when a fixed threshold is exceeded (thus generating a warning in advance to the seizure to occur).
After their development, the predictive power of the algorithms will be tested on a Test Set of data in order to select the model with the highest accuracy, specifity, sensitivity. After testing and validating of the best prediction model, it will be used to implement a real-time automated seizure detection system for CP patients. Therefore, for each patient we will obtain a matrix with all the physiological variations preceding an epileptic seizure. We plan to use this data to implement a clinical decision support system (CDSS) based on a multi-agent based healthcare system (PredictMed-MHS). The proposed PredictMed-MHS architecture has been designed, in the line of existing literature, into a Body Sensor Network, a Smart Healthcare Server and a Caretaker System  [ 14 ]. • Body Sensor Network (BSN) sensors are connected to body territory with the end goal to catch bio-signals, including body temperature, heartbeat, breathing, blood pressure, and so on; • Smart Healthcare Server (SHS) gets data from the Body Sensor Network.