dataesr/scientific-paragraphs-categorization huggingface dataset:
These incentives may have equity implications that warrant the formal assessment of the distributional characteristics of drug development. Therefore, the main purpose of this study is to assess the extent to which R&D for oncological medicines is equitable, i. e. distributed across disease areas according to their relative need. Need is proxied here by either prevalence, incidence, or 1-year and 5-year survival rates. To do so, we leverage a unique global database of clinical trials activity and costs between 1996 and 2016, constructed for 227 different cancer types. Using the different need proxies, we measure for rare and nonrare cancers: i) inequalities and inequity of clinical trial activity, considering all trials as well as split by R&D stage; ii) inequalities and inequity in R&D investment proxied by trial enrollment and duration; iii) evolution of inequity over time.
Data sources We build a unique dataset that links clinical trials targeting rare and non-rare cancers with epidemiological data per type of cancer and over time. We do so by merging data from three different sources: i) the Cl inicaltrial. gov database that provides registry data on the number of clinical trials targeting each type of rare and non-rare cancer between 1996 and 2016; ii) Orpha. net that provides data on clinical trials targeting rare cancers between 1996 and 2016; iii) Rarecarenet. eu that provides epidemiological data on prevalence, incidence, 1-and 5-year survival rates for the year 2007, per cancer type. Clinicaltrials. gov is a publicly available database provided by the U. S. National Library of Medicine at the National Institute of Health (NIH), containing privately and publicly funded clinical trials globally since 1970.
The data provides the number of clinical trials for 20 non-rare cancer types. We use medical subject headings (MeSH) (MeSH is a comprehensive and controlled vocabulary for the purpose of indexing journal articles and books in the life sciences; it serves as a thesaurus that facilitates searching) to characterize clinical trials by types of cancer in terms of their site of origin. Commonly, one MeSH term corresponds to more than one type of cancer, given that one clinical trial might target several cancer types simultaneously. We consider all multiple cancer types targeted by each clinical trial. Using this information, we construct two datasets for non-rare cancers with a total of 31,081 observations: one dataset that contains yearly counts of the number of clinical trials targeting each non-rare cancer type assigning equal weights to all observations-henceforth labelled as non-rare clinical trial dataset (NRD) -and a second dataset where observations are weighted according to the number of cancer types targeted by each trial -henceforth labelled the non-rare weighted clinical trial dataset (NRWD)-with weights inversely proportional to the number of cancer types in each trial.
This enables accounting for trials targeting a large number of cancers being less effective in developing cancer-specific treatments. The NRWD allows us to perform the analysis correcting for possible over-representation of trials with many cancer types associated. Fig. 1  shows each step of the dataset construction. For rare cancers, we also collect the number of clinical trials from clinicaltrials. gov. As in  Bell and Smith (2014) , we identify clinical trials targeting rare cancers if at least one submitted MeSH annotation in the conditions was found in the disaggregated data. Trials primarily targeting non-rare cancer types, with a possible application to a rare cancer, will tend to report all possible applications, even though research is dominantly non-rare cancer targeted. This may lead to an overestimation of the number of rare cancer trials.
Therefore, to identify clinical trials truly targeting rare cancers, we use Orpha. net database which provides information on clinical trials between 1996 and 2016, classified into rare types of cancer by clinical experts. Orphanet classification of rare diseases is based on the definition European Medicines Agency (EMA) as life-threatening or chronically debilitating conditions that affect no more than 5 in 10,000 people in the EU (European Medicines Agency, 2013). We consider only those clinical trials registered on clinicaltrials. gov that target rare cancers according to Orpha. net. Naturally, the question arises as to whether we lose information by using only a subset of the Orphanet's database. While merging the two registers would enable a more comprehensive characterization of trials targeting rare disease, most of the clinical trials included in the Orpha.
net dataset do not have a trial number (NCT) which would enable us to identify them easily in the clinicaltrial. gov dataset. This makes it very difficult to match the trials on the basis of year and pathology as rare cancers could be named differently (differences in the precision of the reported clinical subtype, for example) in Orphanet versus clinicaltrials. gov. Therefore and to avoid biases that affect differently the measurement of trials for rare and non-rare diseases, we limit our data to clinicaltrials. gov, as having only one source in the case of the non-rare cancers but several in the case of rare cancers might bias our analyses. Furthermore, as already mentioned, we assume that cancer types are uncorrelated with the proportion of clinical trials registered. We call one observation a trial addressing one cancer type; therefore, we construct two rare cancer clinical trials datasets according to the way we assign weights to each observation.
We refer to rare cancer clinical trials data (RD) as the dataset of those observations with equal weights, and to rare weighted clinical trials data (RWD) the dataset with weights that are inversely proportional to the number of entries of the trial in the dataset. The process of dataset construction for rare cancers is presented in Fig. 2. Besides trial activity measured by the number of entries, we assess inequalities and inequity in R&D investment proxied by the number of enrolled patients per year and the duration of clinical trials. Albeit being imperfect measures of total R&D efforts  (Martinez, 2016) , trials' duration and costs are a significant part of overall expenditure in R&D. According to  Sertkaya et al. (2016) , around 87% of the required investment to cover identifiable costs is related to trial enrolment (46%) and duration (41%).
, 2002; Agrawal and Emanuel, 2003). Duration of the trial is measured as the number of days that occur between the date the trial was firstly posted and its completion date. Finally, to measure need, we use estimates of European 15-year prevalence, age-specific incidence, as well as 1-year and 5-year ageadjusted relative survival rates for cancers, the latter being obtained from the rarecancernet. eu. Relative survival was estimated by the EUROCARE-5 study according to the Hakulinen method  (Rossi et al. , 2015). Their data is drawn from the EUROCARE-5 study, the largest, European collaboration of research efforts to provide information on the epidemiology of rare cancers. They provide estimates of the prevalence, incidence and survival rate based on data obtained from 92 Cancer Registries, in 22 European countries, including more than 21 million cancer diagnoses until 2007.
The list of rare tumors is hierarchically structured in three layers based on the ICD-O morphology and topography. The final dataset contains each cancer's epidemiological data matched with the number of clinical trials/weighted clinical trials targeting the given type of cancer, divided by year/period/phase. The question arises whether using epidemiological data estimated at a given point in time while clinical trials, are collected for the whole period, could bias our analysis. While this is a hypothetical limitation of the analyses due to data unavailability, it is unlikely to bias our analyses given that the ranking of cancers in terms of epidemiological variables is likely to remain constant over time. Based on IHME Global Burden of Disease data, when comparing prevalence of non-rare cancers in 1996 and 2016, we find a very small difference in rankings (Institute for Health Metrics and Evaluation, 2022).
As the concentration curves and indices measure relative inequality and inequity, they are invariant to equi-proportional changes in the variables. Given that the classification of rare cancers clinical trials differs between Orphanet and EUROCARE-5 study (which uses ICD-O morphology and defines 194 rare cancers), we harmonize by matching manually the Orphanet classification onto the EUROCARE classification. The accuracy of harmonization of the two classifications is then verified by an expert specialized in genetics. The final dataset consists of 32,535 observations, with 31,081 observations for non-rare cancers and 1454 for rare cancers. One trial may target several cancers and thus count as several observations. Considering a trial only once, we obtain 26,948 clinical trials observations: 801 trials targeting rare cancers and 26,147 targeting non-rare cancers.
Comparing rare and non-rare cancers' characteristics Table  1a  summarizes the main differences between rare and non-rare cancers in our database. The average prevalence among non-rare cancers is 93. 52 per 100,000, while for rare cancers it is 4. 64; the incidence is 30. 22 for non-rare cancers and 0. 58 for rare cancers (p < 0. 01). Although non-significant, the survival rate is slightly higher for rare cancers (51. 45%) compared to non-rare cancers (48. 46%). The average number of clinical trials is 1554 for each non-rare cancer and 7. 02 for the rare ones (p < 0. 01). The reason for such a difference is that, during the period of the study, only 102 rare cancers have at least one clinical trial, which represents 49% of all the rare cancers identified in Orphanet. Average enrollment and duration were calculated by only considering cancers with at least one trial.
Robustness checks As described in the data section, a trial might target multiple cancers and therefore our analyses are at cancer-trial level, with each trial targeting on average 1. 8 and 1. 18 cancer types respectively for rare and non-rare cancers. To ensure that duplicate entries with equal weights do not distort our analysis, as a robustness check, we recalculate Tables 2-4 using the weighted dataset. Results (Tables 5, 6 and 7 in the supplementary material) show very similar patterns after estimating the CIs and HIIs with the weighted data and confirm our findings in the previous section.
Our results should be interpreted in the light of data-related limitations. While we contribute to the literature by considering activity in different R&D phases, the use of clinical trials as a proxy for innovation has also limitations. Clinical trial activity data is an imperfect proxy for investment allocation across the different disease areas. We do not observe R&D expenditure across all trial phases and therefore imperfectly capture trials costs. Additionally, our data sources do not offer a complete mapping all R&D activity. For rare diseases Orphanet data may provide a more comprehensive account of clinical trial activity for rare diseases. We have opted to use clinicaltrials. gov data instead to ensure comparability across rare and non-rare diseases, but also to mitigate any biases that could arise from different levels of reporting across the two registries.
While it is highly plausible that trial underreporting in clinicaltrials. gov is unrelated with cancer type, we cannot rule out bias arising from a potential association of the level of reporting with the development of orphan drugs. Also, while we attempt to use the most disaggregated level of data to characterize cancer types by relying on trial data, these data may still fail to capture the full characterization of each disease type. The extent to which this biases our findings is unclear because in the same way may be allocating more trials to a group of cancer types, we may also attach to that group a higher disease need (i. e. need across all different genetic types of the same cancer). In addition, while we consider prevalence, incidence, and survival rates as proxies for need, these are still imperfect measures of unmet cancer need since they do not capture the overall burden associated with disease, nor the fact that some diseases may be treated with non-drug treatments.
, 2017). In the present study, our objective was to assess the impact of temperature and thermal acclimation on short-term interaction strength which represent the effect of a predator on the prey population density. We thus considered the total impact of crayfish on prey population by summing the numbers of dead, killed, and eaten prey (but see Figure  S3  for results based on eaten prey only). The number of eaten prey was obtained by subtracting the number of dead and killed (but not eaten) prey from the initial prey density. Prey mortality in controls (i. e. , treatment without consumers) was negligible (proportion dead: mean ± SD = 1. 45 ± 2. 44%) and data were thus not corrected for background prey mortality. In addition, prey killed but not eaten reach 35. 82 ± 35. 20% across treatments and densities.
Purpose: Serum lipids are modifiable, routinely collected blood test features associated with cardiovascular health. We examined the association of commonly collected serum lipid measures (total cholesterol [TC], highdensity lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], and triglycerides) with intraocular pressure (IOP). Design: Cross-sectional study in the UK Biobank and European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohorts. Participants: We included 94 323 participants from the UK Biobank (mean age, 57 years) and 6230 participants from the EPIC-Norfolk (mean age, 68 years) cohorts with data on TC, HDL-C, LDL-C, and triglycerides collected between 2006 and 2009. Methods: Multivariate linear regression adjusting for demographic, lifestyle, anthropometric, medical, and ophthalmic covariables was used to examine the associations of serum lipids with corneal-compensated IOP (IOPcc).
14 Serum lipid concentrations, including TC, HDL-C, LDL-C, and triglycerides, were measured by biochemical assays from the blood samples collected at baseline using the Beckman Coulter AU5800 Platform and were reported in millimoles per liter. Details of the blood measurements and processing are available on the UK Biobank online showcase and protocol. 15 cular Measurements. Ocular assessment was introduced as an enhancement in 2009. Ophthalmic data were collected for 128 180 UK Biobank participants at 6 assessment centers across the United Kingdom. 16 Refractive error was measured using an autorefractor (Tomey RC 5000), and spherical equivalent was calculated as sphere power plus half cylinder power. 17 The IOP was measured once for each eye using the Ocular Response Analyzer (ORA) noncontact tonometer (Reichert Corp).
All participants gave written informed consent. The study protocol is available online at https://www. epic-norfolk. org. uk/. Participants provided demographic information, completed general health questionnaires, which collected information on lifestyle factors and diet, and attended a baseline health check, during which blood samples were obtained for future analysis. The Townsend deprivation index was determined in the same fashion as described for the UK Biobank cohort. Smoking and alcohol use were determined by self-report. Participants were asked to bring their medications and related medical documents to the health examinations, and these were recorded by a research nurse using an electronic form. Diabetes status was determined mainly from a lifestyle questionnaire sent to participants at the baseline assessment (and related questions were repeated in follow-up health and lifestyle questionnaires), and additional data from sources, including deaths, hospital admissions, questionnaires, and general practitioner registers up to 2006, were also used to identify patients with diabetes at the time of the EPIC Eye Study.
Assessment of Ocular Measurements. Refractive error was measured once in each eye using an autorefractor (Humphrey model 500; Humphrey Instruments), and spherical equivalent was calculated as sphere power plus half cylinder power. Intraocular pressure was measured with the ORA, and IOPcc and Goldmann applanation tonometryecorrelated IOP were obtained as described previously. Three ORA readings were obtained per eye, and the best signal value for each eye was used (based on the best-quality pressure waveform as assessed by the ORA software). The mean IOPs of the right and left eyes of each participant were used for analyses. If data were available only for 1 eye, then the IOP of that eye was considered for the participant. Participants who had a history of glaucoma laser therapy or surgery were excluded, and we imputed pretreatment IOP for participants reporting topical glaucoma medication using the same method as described previously.
Multivariate models were adjusted for age, sex, ethnicity, Townsend deprivation index, BMI, systolic blood pressure, height, smoking, alcohol use, diabetes status, statin use, oral b-blocker use, and spherical equivalent. These data were plotted using the multivariate regression and marginsplot commands using the STATA software package version 16 (StataCorp LP). We conducted various secondary analyses: (1) sex stratification and interaction for each of the aforementioned analyses, (2) additional analyses of the association of IOPcc with apolipoprotein A and B and with lipid ratios commonly used in clinical practice (TC to HDL-C, LDL to HDL-C, and TG to HDL-C),  6  (3) sensitivity analyses adjusting for multiple lipid components in a single model, (4) sensitivity analyses excluding statin users, (5) sensitivity analyses excluding BMI as a covariable in multivariate regression models (to determine if upstream factors were driving potential associations), (  6 ) sensitivity analyses restricted to participants reporting White ethnicity, and (7) sensitivity analyses additionally adjusting for age squared (to model potential nonlinear associations more accurately).
Results A total of 94 323 participants in the UK Biobank were included in this analysis after excluding 29 766 participants with missing data and 4091 participants who had undergone glaucoma laser therapy or surgery. A total of 6230 participants from the EPIC-Norfolk cohort were included in this analysis after excluding 1028 with missing data and 62 who had undergone glaucoma laser therapy or surgery (Fig  1). Table  1  presents the baseline characteristics of both EPIC-Norfolk and UK Biobank participants included in the study. Compared with UK Biobank participants, EPIC-Norfolk participants were on average 12 years older, more likely to be White, women, shorter, and live in a less deprived area, and less likely ever to have smoked or currently to consume alcohol. They were more likely to use statin medication or oral b-blockers and had modest but significantly lower BMI, systolic blood pressure, and TC, LDL-C, and triglyceride levels.
Our study is limited in its use of cross-sectional analysis, preventing determination of potentially causal associations. Data collected using questionnaires are potentially subject to recall, social desirability, and misclassification biases. For example, participants may not recall accurately (or may underreport) the amount of alcohol they have consumed. Although our study is limited in its use of selfreported medications, self-reported statin and other lipid medication use specifically were validated in large epidemiologic studies with an accuracy of between 91% and 97%. 48 One further limitation is that serum samples were obtained from nonfasting patients, which could be subject to recent meal ingestions. Serum samples from those who have not fasted generally would result in higher values than samples from those who have fasted, and this could mask real associations, although this likely would have reduced the statistical power of the analyses because of the reduced sample size, rather than affecting estimates of association.
This potential bias additionally is offset by consistent findings across 2 independent cohorts. Furthermore, the identified associations could have been the result of unmeasured exposures that link serum lipids and IOP, and UK Biobank and EPIC-Norfolk participants may not necessarily be representative of the general population. Our study additionally is limited by missing data for 26% of the participants in the UK Biobank cohort and 15% of the participants in the EPIC-Norfolk cohorts for whom ophthalmic data were available. This is largely because of the exclusion of participants with missing data for variables included in the multivariate analyses. Excluded participants were more likely to be non-White and slightly shorter and were more likely to consume alcohol than included participants (Table  S7 , available at www.
L. W. ]); and  UK Research and Innovation Future Leaders Fellowship (Medical Research Council  grant no. :  MR/T040912/1  [A. P. K. ]). The authors acknowledge a proportion of their financial support from the  United Kingdom Department of Health  through an award made by the  National Institute for Health Research to Moorfields Eye Hospital NHS Foundation Trust  and  UCL Institute of Ophthalmology for a Biomedical Research Centre for Ophthalmology. This research used data from the  UK Biobank Resource  under data access request nos. 2112 and  36741. The  UK Biobank Eye and Vision Consortium  is supported by grants from  Moorfields Eye Charity , The  NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust  and  UCL Institute of Ophthalmology , the  Alcon Research Institute , and the  International Glaucoma Association (United Kingdom).
These unsupervised deep learning techniques are today affordable thanks to the increasing availability of SAR temporal profiles, in particular via the open source Sentinel-1 data of the Copernicus Program. Hence, in this paper, we consider using the autoencoding method presented in (Di  Martino et al. , 2021) , which is a method for reducing the dimension of temporal profiles by autoencoding. We will call this method CAE for Convolutional AutoEncoder. In a first case, we consider the Kyagar glacier, for which a surge event occurred in 2016  (Round et al. , 2017). In order to consider the contribution of the visualization method, we apply it to two image stacks, one constituted during the surge event, and the other after. The comparison of the behavior of the PCA and CAE methods carried out in this case study will be the subject of section 3.
Dataset preparation Our analysis was performed on Sentinel-1 time stacks acquired between late 2014 and 2020. The Sentinel-1 images were coregistered with each other by  (Round et al. , 2017). The glacier surge which occurs in 2015 opens crevasses which are characterized by a strong backscattering signal  (Leclercq et al. , 2021). Therefore, we have split the time stacks in two: one during and one after the surge. The number of images inside each time stack are roughly the same to make sure that the seasonal dynamics are comparable. Thus, the surge period concerns the year 2015, with the ten dates from January to May: 01-06, 01-18, 01-30, 02-23, 03-07, 03-19, 03-31, 04-12, 04-24, 05-06, while the post-surge period concerns the year 2018: 01-02, 01-14, 01-26, 02-07, 02-19, 03-03, 03-15, 03-27, 04-08, 04-20.