dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Data description. Romania covers a total area of 238,439 km  2  and is divided into 42 counties and 2939 communes with a very wide range of areas from 1. 1 to 797 km  2. The smallest commune is the town of Victoria in Brasov County and is surrounded by the commune of Ucea de Jos, while the largest is in the Danube Delta Nature Reserve in Tulcea county (Murighiol). Geographic, hydrologic, and transportation infrastructure data were analyzed at the commune level and are therefore discussed as densities. According to EuroglobalMap specifications, 40,000 km of roads, forming a logical transport network at a map scale of 1:1,000,000 are present across the country, with densities ranging between 0 and 1. 7 km/km 2 at the communal scale. The regions Danube Delta and Carpathian mountains show the lowest road densities.
Railways are relatively sparsely representing 8000 km of line length, mainly located in the south and converging towards Bucharest, and in the northwestern part of the country. Wetlands and lakes are quasi-exclusively located in the Danube Delta Nature Reserve, covering between 55 and 95% of the communes in this area. The Carpathian Mountains represent the major forestry region, although wooded areas are also present at lower altitudes, especially in the county of Braila where the Danube flows. Waterways mostly originate in the Carpathian Mountains and flow into the Danube then to the Black Sea, forming natural borders with Bulgaria to the south, and Ukraine to the north along the Danube Delta. These data are illustrated in Fig. 1  (interactive view in html version). The average population density was evaluated at 94 inhabitants/km 2 , with high density values in cities (7700 inhabitants/km 2 in Bucharest), and some areas with densities close to 0 (Danube Delta Nature Reserve, or highaltitude mountains).
In contrast with the first viral incursion, these epidemics grew dramatically with up to 50 notifications within 1 week. Based on these considerations, we limited our analysis to epidemic data from the first case notification in Tulcea county, ignoring the first sporadic cases that occurred in Satu Mare. The period considered for the analysis therefore ran from June 10, 2018 to August 29, 2019. A total number of 2973 confirmed ASF outbreaks were recorded in domestic pigs and wild boars throughout this period. The temporal course of the epidemic  revealed different patterns in domestic pigs and wild boars. As shown in Fig. 3a , the number of new cases among the domestic pig population exhibited two distinct peaks, in summer 2018 and summer 2019 respectively, while the cumulative number of infected wild boars increased quasi-linearly throughout the study period (Fig.
Discussion and conclusion African swine fever is one of the most important diseases affecting pigs and was introduced into the European continent 12 years ago. It has spread rapidly to many countries that were ASF-free for decades. Romania has been very strongly affected in the last 2 years (2018 and 2019). The first cases were reported from July 2017, and since then, about 3000 cases have been confirmed up to mid-2019 among domestic pigs and wild boars. During this 2-year period, two epidemic phases can easily be identified, with two peaks of case notifications separated by in-between low-noise spread. The Animal Diseases Notification System database provides temporal and geolocalization data for each case, enabling spatiotemporal analysis. Three large clusters, gathering 80% of cases, could be identified.
Altogether, this suggests that related outbreaks (i. e. belonging to the same cluster) were driven by a triad, including (i) human activity (population density, household density, roads), (ii) backyard holding population, and (iii) environmental characteristics (forests, rivers and wetlands). The second step of the study focused on the characteristics of ASF outbreaks at the local scale, taking the commune as the epidemiologic unit. Owing to their specificities, the two epidemic waves were analyzed separately to assess the relevant and specific demographic and environmental determinants of the outbreaks. For both outbreaks, environmental data were found to be the main predictors of the outbreak size at the commune level, with wetland and forest cover for the years 2018 and 2019, respectively.
Materials and methods Data. Four complementary data sources were used to analyze the spatiotemporal patterns of ASF spread, and to identify the risk factors associated with the spread of the disease in Romania: geographic, demographic, agricultural, and epidemiologic data. Geographic data. Geospatial data were extracted from a publicly available European-level database called EuroGlobalMap (EGM), administered by Eurogeographics. With a resolution of 1:1,000,000, this database covers 46 European countries and provides hydrologic, transportation, and landscape data. Data for Romania were extracted and analyzed at the administrative scales corresponding to communes and counties. Layers corresponding to hydrologic data (waterways) and landscape (wetlands, forests and urban areas) were considered.
Human activity was also taken into account through transportation infrastructures (roads and railways). Demographic data. The National Institute of Statistics of Romania (http://www. insse. ro) provides a wide range of datasets regarding demographic characteristics and urbanization. From these datasets, we selected those thought to be relevant concerning the spread of ASF. Importantly, the distribution of domestic pigs is considered to be strongly related to human occupancy, especially in low-urbanized areas. Therefore, the population density, the predominance of urban areas, and the number of households were accounted for in the analysis. Agricultural data. Data on agricultural activity were focused exclusively on domestic pigs and were provided by the Romanian authorities (ANSVSA) or extracted from the county Sanitary Veterinary and Food Safety Direction websites (DSVSA).
These data consist in a census of live pigs at the commune level, as well as the number of industrial holdings, and their capacities. We were therefore able to determine the number of backyard animals in each locality. The total number of domestic pig holdings per county was obtained from the 2010 general agricultural census carried out by the National Institute of Statistics of Romania (http://www. insse. ro/cms/files /RGA20 10/Rezul tate%20defi niti ve%20RGA %20201 0/rezul tate%20defi niti ve%20RGA %20201 0. htm). Epidemiologic data. The Animal Diseases Notification System (ADNS) registers and documents changes in the epidemiologic context in Europe, providing accurate data on the location and characteristics of infected sites and species-wild boars and domestic pigs for ASF-using GPS coordinates.
e. core/border/outlier)  23, 24. The stdbscan R function was used (https ://githu b. com/Kersa uson/ST-DBSCA N). Cluster interpretation. The aim was to highlight explanatory variables that best differentiate the spatiotemporal cluster memberships. A classification method was applied where the categorical dependent variable was the cluster membership, and the explanatory variables the, geographic, demographic, agricultural, and epidemiologic data (described in "Data description" section). It is important to mention that these explanatory variables are likely to have strong correlations. Therefore, a non-parametric classification method, such as a classification tree 25 , was selected. At the time of the development of machine learning, the random forest algorithm  26  was preferred to a classification tree because it has the advantage of correcting overfitting, and thus delivers robust results with exceptional empirical accuracy.
A decision tree is a non-parametric statistical analysis that can be applied to classification (i. e. a categorical dependent variable) or to regression (i. e. a quantitative dependent variable). A decision tree is built on the whole dataset, whereas a random forest randomly selects observations and variables to build a large number of decision trees and then averages the results. More precisely, the set of decision trees in a random forest (500 trees) results from two sources of randomness: (i) initial observations are modified by BAGGING (= Bootstrap and AGGregatING), and (ii) initial variables are modified by a random selection of P' variables among P (usually set to P ′ = √ P for classification and P' = P/3 for regression). The final random forest model is selected from this ensemble of decision trees by a majority vote, i.
However, real-world evidence is limited to specific regions, countries or small patient populations  [13] [14] [15] [16] [17]. Hence, the LUMINOUS (NCT01318941) study is, to our knowledge, the largest, prospective, observational, global trial in the field of medical retina designed to evaluate the long-term effectiveness, safety and treatment patterns associated with ranibizumab 0. 5 mg in routine clinical practice across five approved indications: (i) neovascular age-related macular degeneration, (ii) diabetic macular oedema, (iii) branch retinal vein occlusion, (iv) CRVO and (v) myopic choroidal neovascularisation. The effectiveness of ranibizumab at one year and safety over five years for treatment-naïve patients with CRVO enroled in this study are reported here.
MATERIALS AND METHODS Study design LUMINOUS was a 5-year, open-label, single-arm, global, observational study conducted from March 2011 to April 2016 at 488 clinical sites across 42 countries. Patients with any of the approved indications as per the ranibizumab label at the time of the study were enroled and treated at outpatient or private ophthalmology clinics with intravitreal ranibizumab 0. 5 mg according to the local ranibizumab label  [18]. As patients were recruited over time and the date of study completion was pre-set, the follow-up time varied between patients based on their study entry date. The minimum potential follow-up for each patient was defined as one year in the protocol. The frequency of patient visits was determined by the investigator. It was recommended to capture data at every visit or at a minimum of every three months.
Investigators were encouraged to follow-up with patients who did not visit the clinic for at least six months since their last visit. Patients who were not seen at least once per year or those who switched to another anti-VEGF therapy were discontinued from the study. The study protocol was reviewed and approved by an Independent Ethics Committee or Institutional Review Board for each centre; a complete list of these by study centre is provided in the Supplementary Table  1. The study was conducted in accordance with the Guidelines for Good Pharmacoepidemiology Practices issued by the International Society for Pharmacoepidemiology  [19]  and any applicable national guidelines and ethical principles laid down in the Declaration of Helsinki. Patients provided written informed consent. The study is registered with ClinicalTrials.
Assessments Effectiveness assessments included visual acuity (preferably best-corrected visual acuity [VA]) evaluation by each participating physician as a part of routine care practice using Early Treatment Diabetic Retinopathy Study (ETDRS) letters, Snellen charts or equivalent. To facilitate data analysis, Snellen fractions and decimals were converted to the ETDRS equivalent letter scores. It was recommended that the same method of visual acuity assessment be used throughout the study wherever possible. Other assessments, such as optical coherence tomography (i. e. for central retinal thickness evaluation) and ocular examinations (pre-injection intraocular pressure measurements), were optional but included if the data were available. The number of ranibizumab injections administered overall and over time; the average time interval (in weeks) between consecutive injections; visit frequency; and treatment patterns (unilateral [involving single eye]/bilateral [involving both eyes]) were recorded.
Statistical analysis All effectiveness and safety data were summarised descriptively. Owing to the design of the study, one year data were potentially available for all patients, while the availability of data for subsequent years depended on the patient's study entry date. The effectiveness data are therefore presented here for up to one year. Safety data are presented over the entire 5-year period to provide a comprehensive safety profile. The enroled set included all patients who signed the informed consent and had at least a baseline assessment. The safety set comprised patients in the enroled set, who, in case of treatment naïve patients, were treated with at least one dose of ranibizumab during the study and had at least one safety assessment after the first injection. The primary treated eye set included all primary treated eyes (i.
e. the first eye treated during the study) in patients from the safety set. Treatment exposure up to one year was analysed for patients in the safety set who stayed in the study for at least 365 days. For year 1 effectiveness, patients from the primary treated eye set who had both baseline and year 1 data and remained in the study for at least 365 days are presented. The primary effectiveness end point was the mean change in VA ETDRS letter score from baseline to year 1. Additional effectiveness analyses not prespecified in the protocol but included in the statistical analysis plan are: (1) the mean change in VA from baseline at year 1 by (a) injection frequency during year 1 (1, 2-3, 4-5, 6-8 and >8 injections); (b) patients who received a loading dose (the initial three ranibizumab injections administered up to day 100) versus those who did not; (c) baseline VA category (<23, 23 to <39, 39 to <60, 60 to <74 and ≥74 letters); (d) baseline VA < 73 letters or ≥73 letters (good starting vision or Snellen equivalent 20/40) at year 1; (2) proportion of patients with VA loss (defined as ≤0-letter change from baseline) or gain (defined as >0-letter change from baseline) of >0 to <5 letters, 5 to <10 letters, 10 to <15 letters and ≥15 letters at year 1.
RESULTS A total of 30,138 patients were enroled across all five approved indications in the overall LUMINOUS trial. Of the total population, 1048 (3. 5%) patients had CRVO, of whom 327 (31. 2%) were treatment naïve. The following countries enroled the majority of treatment-naïve patients with CRVO: the United Kingdom (24. 5%), Canada (13. 1%), Russia (11. 0%), Germany (9. 5%) and Poland (9. 4%). Of the 327 treatment-naïve patients, 249 (76. 14%) remained in the study until the end of year 1, and 171 (52. 3%) completed the study. The most common reason for study discontinuation was 'loss to follow-up' at year 1 (n = 27, 8. 3%) and year 5 (n = 64, 19. 6%; Fig. 1 ). The year 1 effectiveness analysis in the primary treated eye set included 144 patients for whom both baseline and 1-year VA data were available (owing to the flexible scheduling of visits, not all patients who completed year 1 in the study had a year 1 visit).
2  Frequency of ranibizumab injections over 1 year (year 1 primary treated eye set). The year 1 primary treated eye set included patients from the primary treated eye set who had both baseline and year 1 data and remained in the study for at least 365 days. observed. One ocular AE (0. 3%, retinal injury) and six non-ocular AEs (all SAEs) led to treatment discontinuation. Ocular SAEs were reported in four (1. 2%) patients: glaucoma, n = 2 (0. 6%) and amaurosis fugax, blindness and cataract trauma, n = 1 each (0. 3%) (Supplementary Table  3 ). No cases of endophthalmitis or retinal break/detachment were reported. The incidence of nonocular SAEs was 6. 7% (n = 22); pneumonia and confusional state (0. 6%; n = 2 each) were the most frequently reported non-ocular SAEs (Supplementary Table  3 ). In total, there were six deaths, with reasons including breast cancer, pneumonia, sepsis and unknown cause.
B By injection frequency. C By baseline VA category. The year 1 primary treated eye set included patients from the primary treated eye set who had both baseline and year 1 data and had remained in the study for at least 365 days. ETDRS early treatment diabetic retinopathy study, n number of patients, VA visual acuity. significant limitations when considering the differences in study designs. Gains in VA with treatment were dependent on the patient's baseline VA as well as on the early initiation of treatment after diagnosis of the disease in studies  [10, 16, [30] [31] [32] [33] [34] [35] [36] [37] [38]. In the LUMINOUS study, results by baseline VA categories support the observations made in phase 3 studies of ranibizumab in patients with CRVO (CRUISE and CRYSTAL)  [9] [10] [11] , suggesting that patients with a poor baseline VA achieve higher VA gains.
Other limitations include variable treatment schedules across regions, host country healthcare systems, physician's discretion of inclusion, access to treatment, cost and reimbursement criteria, all of which can lead to under-treatment, suboptimal therapeutic effectiveness and poor patient follow-up potentially affecting outcomes. In addition, the imaging data were not collected uniformly and analysed. The study also lacked a comparator arm; options for comparator arms were very limited at the start of the study. Other intravitreal VEGF-inhibitors were not yet available, and design options for comparing the treatment patterns of ranibizumab 0. 5 mg were not in scope as the newly emerging treatment regimens like 'treat-and-extend' were not very widespread at the time of the study. To conclude, the results from the LUMINOUS study support the robust real-world effectiveness of ranibizumab over a 1-year treatment period and safety over a 5-year period in treatmentnaïve patients with CRVO.
ACKNOWLEDGEMENTS The authors thank all the investigators (Supplementary Table 4) for their valuable contribution towards this study and also thank  Jafar Hyder Ali Shaik ,  Lakshmi Venkatraman  and  Swapna Ganduri  ( Medical and Clinical Solutions/NBS CONEXTS, Novartis Healthcare Pvt. Ltd. , Hyderabad, India ) for medical writing and editorial assistance in the development of this article.
FUNDING This study was funded and sponsored by  Novartis Pharma AG, Basel, Switzerland  and is registered with https://clinicaltrials. gov/( NCT01318941 ). The sponsor had a role in the study design, study conduction, data collection, data analysis, data interpretation and manuscript preparation. In addition, Novartis Pharma AG was responsible for the conduct of the study and oversight of the collection and management of data. Open Access funding enabled and organized by Projekt DEAL.
1d, e ). Because of their relative scarcity in blood, we could not monitor real-time ILC2 oxygen consumption or glycolytic rates. Instead, we measured ATP and ADP by mass spectrometry. We found that circulating ILC2s have high cytosolic ATP/ADP ratios compared to that observed in NK dim cells (Extended Data Fig. 1f  and Supplementary Data 1), consistent with enhanced mitochondrial function and inhibition of glycolysis. Memory T cells have highly polarized mitochondria and enhanced OXPHOS compared to naive T cells  [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]. When comparing TMRM and MitoTracker patterns in ILC2s versus in naive and central/effector memory CD4 + T cell subsets (Extended Data Fig. 2a ), ILC2s showed a distinct mitochondrial signature, suggesting exposure to environmental signals.
We next assessed the bioenergetic profile of tissue ILC2s. Despite their scarcity (Extended Data Fig. 2b ), tonsil ILC2s appeared to have similar phenotypic and functional profiles as their blood counterparts  27. Tonsil ILC2s (detected as Lin -CD127 + CD161 + GATA-3 + ST2 + cells; Extended Data Fig. 2c-e ) showed similar mitochondria polarization and mass as blood ILC2s (Extended Data Fig. 2f ), suggesting a close metabolic relationship. One explanation is that some tonsil ILC2s may be blood derived and not (yet) affected metabolically by local environmental signals, which could include IL-2, IL-7, IL-33 and/or other cytokines. Individuals with MDs (impaired OXPHOS) have a twofold reduction in frequencies of blood NK dim cells and a preferential loss of long-lived 'memory-like' NK dim cells  39.
1e, f ) and impaired membrane potential, but not mitochondrial mass, in both ILC2s and NK dim cells (Fig. 1g  and Extended Data Fig. 3e ). We observed that rotenone and antimycin A, but not complex II inhibition (TTFA), strongly impaired ILC2 survival, whereas NK dim cells were largely unaffected (Fig. 1h ). These results further support the essential role for active ETC complex I and complex III for survival of blood ILC2s. We next purified ILC2s and analyzed the cellular metabolome using high-resolution mass spectrometry (Supplementary Data 1). Pathway analysis revealed an enrichment in metabolites from arginine, BCAAs (valine, leucine/isoleucine) and glutamine metabolism (Extended Data Fig. 4a ). Valine and isoleucine were the most abundant amino acids in circulating ILC2s, followed by arginine and glutamate (Fig.
Preheated (70 °C) 70% ethanol (99. 9% purity) extraction solvent was added, and samples were incubated for 3 min. After centrifugation (8,000g for 10 min at 0 °C), the supernatants were collected and stored at -80 °C until analysis. The analysis was performed on a platform consisting of an Agilent Series 1100 LC pump coupled to a Gerstel MPS2 autosampler and an Agilent 6520 Series Quadrupole time-of-flight mass spectrometer equipped with an electrospray source operated in negative and positive mode as previously reported  59. All steps of data processing and analysis were performed with Matlab R2010b (MathWorks) using functions embedded in the bioinformatics, statistics, database and parallel computing toolboxes. For each run, a matrix list was produced with the intensity of each mass peak in each analyzed sample.
An accurate m/z was recalculated with a weighted average of the values, and a list of putative metabolites was compiled from the KEGG database. Raw intensities for each metabolite are included in Supplementary Data 1. For each ion, the best metabolite match was chosen among all candidates based on the deviation from the theoretical m/z and a heuristic probability associated with the theoretical ion fragment, which was set lower for, for example, rare adducts, fragments or molecules containing several heteroatoms. Significant changes in metabolite levels compared to the entire dataset were determined by calculating z scores for each sample and ion individually, as previously reported  56. Results were run in iPath3 (https://pathways. embl. de), an online tool for data mapping. A Principal-component analysis was applied to the samples coming from two different screenings using the stats R package.
The screenings were simultaneously analyzed considering only metabolites present in both screenings (total = 122 metabolites). The first principal component indicated that most of the variance of the data corresponded to the screening effect, as expected from the batch effect that each screening represented. The second most variable effect corresponded to the donors. Pathway analysis was done with Qlucore Omic Explorer v3. RNA isolation, library construction, sequencing and analysis. ILC2s (10 3 cells) were sorted by FACS directly into 50 ml of lysis/binding buffer (Life Technologies). mRNA was captured with 15 ml of Dynabeads oligo(dT) (Life Technologies), washed according to manufacturer's instructions and eluted at 85 °C with 6 ml of 10 mM Tris-HCl (pH 7. 5). We used a derivation of MARS-seq as described in ref.