dataesr/scientific-paragraphs-categorization huggingface dataset:
MAPER's design ensures relative robustness towards pathological change (Heckemann et al. , 2010) ; nevertheless, pathology such as mesial temporal sclerosis usually implies suboptimal representation of the target image in the atlas data. When pathology alters a brain's anatomy, expert human raters are more flexible to adapt their visual interpretation of the target image. Accordingly, automatic segmentation labels tend to disagree more with the manual reference, the more the target image diverges from the 'normal' represented by the reference. This is reflected in the Bland-Altman plots: those generated on the Hammers Atlas Database group do not show proportional bias, whereas those for the TLE and ADNI groups do, that is, the volume loss tends to be underestimated compared with manual segmentation.
17 One method to overcome this bias is by using a population PK analysis, which is a powerful approach to estimate PK parameters even when sampling time-points are sparse. This method can also handle data below the limit of quantification and can consider different quantification methods and their analytical performance. 17 ata from clinical trials are robust; however, the controlled nature of trial conditions and the limited selection of patients can mean the results cannot be generalizable to the wider patient population. 9] [20]  However, the PK parameters calculated in a real-world clinical setting and the pivotal clinical trials do not always align due to the differences in the methodology in the protocols and the complexity of the data. 17 he Observational register of Patients with haEmophilia A tReatedwithAFSTYLA® (OPERA;clinicaltrials.
gov:NCT04675541) is a national, noninterventional, longitudinal, multicenter, cohort study of hemophilia A patients who have been treated with rVIII-SingleChain in France. The objective of this study was to collect real-life data from patients with hemophilia A treated with rVIII-SingleChain to confirm the efficacy and safety findings established in the AFFINITY clinical trial program. This interim analysis also aimed to conduct a population PK analysis on the real-world usage of rVIII-SingleChain.
Study Population Enrolled patients had hemophilia A and were being treated or had been treated with rVIII-SingleChain at French Hemophilia Treatment centers (HTCs). This interim analysis includes patients who were recruited between January 2018 -September 2021 and had been treated in prophylaxis with rVIII-SingleChain. All patients had received previous treat-ment with FVIII prior to enrolment. Data on patients' previous treatment regimen were collected retrospectively for the year preceding the switch to rVIII-SingleChain. A prophylactic regimen was defined as at least one injection per week for at least 3 months. Patients had no active FVIII inhibitors and/or were not undergoing treatment for immune tolerance induction at the time of inclusion in the study. All patients gave written informed consent prior to participation.
Population Pharmacokinetic Analysis FVIII trough levels following the switch to rVIII-SingleChain were also collected during the study visits and were used to carry out a population PK analysis. FVIII levels were determined with either chromogenic or chronometric assays. According to the Summary of Product Characteristics of rVIII-SingleChain and French recommendations, FVIII activity measured by chronometric assay are underestimated by approximately 45%, compared with the chromogenic assay. To account for this underestimation, chronometric assay results were adjusted by multiplying by a conversion factor of 2 to determine the patient's FVIII activity level. 21, 22 opulation PK analysis was used to estimate individual PK parameters from time-concentration data. Due to data sparseness issues, the dataset did not allow the estimation of the parameters of the suitable model.
25 odel evaluation was based on visual inspection of the goodness-of-fit plots and distribution of individual parameters. From the variance-covariance matrix of the estimated PK parameters, Monte Carlo simulations were performed using Simulx® software. A total of 1,000 PK profiles were generated according to the patient's characteristic (bodyweight) of the study of Zhang. 23 In the simulation, all patients received steady state dose of 50 IU/kg or 20 IU/kg of rVIII-SingleChain every 3. 5 days (similarly to Zhang et al  23  ). For the simulations, 90% of the prediction interval of the model were superimposed to patients <12 or !12 years of age.
Statistical Analysis The descriptive statistics using percentages, mean, standard deviation (SD), median, and range were performed when appropriate. Differences in number of injections per week, factor consumption, and ABR/AsBR were evaluated for a population of <30 patients with available datapoints both pre-and postswitch, using Wilcoxon signed-rank tests for matched pairs. A p-value <0. 05 was considered statistically significant. When considering the full population (!30 patients), the same analyses were performed using paired Student's t-tests. Patients with incomplete datasets were excluded from analyses requiring the respective missing variable; however, they were still included in analyses for which they had applicable data. Statistical analyses were performed using GraphPad Prism 7.
These three cases show that eyes presenting with partial severe LSCD can exhibit residual limbal epithelial cells. Despite intense ocular surface optimization, allogeneic LSCs transplant in the first case and SLET in the second case, promoting residual LSC function was unsuccessful. These three patients were offered an autologous ex-vivo LSC transplantation, as part of an ongoing clinical trial looking at the safety and efficacy of ex-vivo LSCs transplantation in the management of unilateral LSCD (NCT03957954)  7. Alternative emerging techniques include the use of non-epithelial cell transplants (oral mucosal graft, embryonic stem cells, induced pluripotent stem cells, MSCs). Growth factors revitalizing the limbal niche are also explored, such as the use of plasma and platelet-rich growth factors  32, 33.
Supplemental Fig. 2  provide the immunostainings in the control group. Immunofluorescence staining analysis. Images were taken with a Keyence BZ-X710 inverted microscope (Osaka, Japan), and quantitation of expression of markers was performed by using the BZ-X analyzer software (version 1. 3. 0. 3) with the macro hybrid cell-count function. The percentage of positive cells was automatically analyzed using the Keyence BZ-X710 inverted microscope with the BZ-X analyzer software. By specifying the mask area, the software tracked information about multiple parameters, such as the intensity of fluorescence signals in different channels, cell counts, and target area measurements  57. For p63, the threshold defining a bright cell was manually chosen based on the appearance of typical bright cells in the control group.
All these components are heuristic analyzers of the roles of actors, changes in decision-making, and shifts in governance that SWM digitalization brings about. In India, many cities have begun to digitalize solid waste. Bengaluru, for example, employs a basket of ICT-based interventions that include geotagging of routes, geofencing, monthly data analysis through algorithms, and a smartphone app  (Biswas et al. 2021). Kakinada, a municipal corporation in Andhra Pradesh, collects waste collectors' attendance using facial recognition software (ibid. ). Delhi and Nagpur have implemented QR code based solid waste tracking  (Chakraborty 2021 ; Express News Service 2022). However, the case of Mangaluru is of particular interest for a set of reasons. Despite being a thriving small town since the 15 th century, 4 changes in its urban management remain understudied.
Therefore, the introduction of digital instruments by the SPVs created under the Smart Cities Mission (SCM) is bound to have some governance and sectoral impacts. As figure 1 points out, the digitalization of SWM is one of the most common projects adopted by Smart Cities under the Mission. A database of digital projects under SCM prepared by our team found that 72 out of 100 cities have at least one project relating to SWM. 8 It shows that 147 out of 1,867 digital projects across all Smart Cities are related to SWM, making it one of the highest sectors in terms of the number of Smart City projects. Therefore, lessons from Mangaluru can help with drawing lessons for the SWM sector as a whole. Second, SWM is a sector that is extremely dependent on workers who collect (and recycle) the waste.
Antony Waste has also installed GPS in collection vehicles to track their movements and ensure that all locations are covered. The SWM digitalization project seeks to introduce a more efficient technology-based system. 12 It entails a set of the following interventions: (i) installation of unique QR codes at each property, which waste workers should scan while collecting the waste; (ii) real-time updating of scanned data on the ICCC dashboard, where monitoring of vehicle location can also be done; (iii) GPS-based devices installed on the waste collection vehicles to track their movement and the geofencing feature to show on the dashboard if the vehicles have moved out of their pre-designated area; and (iv) OneTouch Mangaluru app to enable the residents to file complaints about any issues with the waste collection.
As with many other Smart Cities in India, the ICCC is the nerve center of all the ICTbased interventions  (Praharaj 2020). The ICCC dashboard is supposed to provide a bird's-eye view of the city's SWM by displaying daily information on the number of waste collectors working, the number of properties covered, the number of vehicles plying their routes, and the amount of waste collected. The ICCC personnel can check these data on their monitors and alert the Antony Waste supervisors if they find any issues. The project has been decided, designed, and introduced by the Smart City rather than by MCC. 13 The Smart City company is governed by a board of directors constituted of state government officials and municipal corporators, and is directly responsible for implementing all the projects funded by SCM.
19 The second change relates to the arrival of two new types of private stakeholders. The first is the local system integrator 18 who was awarded the contract to deliver the ICCC project. In Mangaluru, the local system integrator is a Chennai-based company, Madras Security Printers Private Limited (Madras Security). 19 This integration of all digital interventions into the ICCC is done through multiple vendors. For SWM, the services are provided by a Bengaluru-based company, Trinity Mobility Pvt Limited. (Trinity). 20  As Figure  3  illustrates, the digitalization of SWM involves various components. For example, the GPS component requires fitting GPS machines to all the vehicles collecting solid waste and transferring the GPS data to the ICCC. Trinity undertakes some of these tasks using its in-house resources or through other vendors.
For example, for the QR code installation, Trinity relied upon Datatronics, a small Mangaluru-based company. Datatronics, in turn got a local company to manufacture the QR code sheets and hired the gig-workers to affix the sheets to the houses and vehicles and collect household information to create the database. Unlike Antony Waste's role as a contractor for MCC, which has been a standard in Indian cities for the last two decades, the presence of companies such as Trinity, Datatronics, and Madras Printers brings in a new category of private actors to urban governance. Local system integration through a chain of vendors introduces a major shift. These vendors provide very specific ICT-based tools to the cities, calling them "solutions. " Not only this turn in governance has rarely been paid attention to, the difference that technology vendors bring in urban management is also hardly understood.
Consequently, the chain of nested companies formed by StepnStones, and the Smart City technology vendors involved in designing digital interventions ended up replicating the GPS infrastructure of Antony Waste, incurring additional costs to collect data that was already being collected by the contractor before digitalization. Such incidents highlight a lack of coordination among various actors that neither the municipal corporation nor the Smart City acknowledges or considers as an issue since they understand them as two different projects and contracts. 24 Further, aside from redundant infrastructure, some of the features that were planned have not been implemented, revealing the disconnect between design and execution. Trinity had intended to implement a ward-level geofence. The goal of this feature was to compare the effectiveness of a ward's waste-collection operation to that of other wards.
The paradoxes of digitalizing a labor-intensive basic service Globally, Solid Waste Management is an area of work where the workers are likely to come from marginalized communities  (Beall 2022; Blincow 1986  In Mangaluru, waste workers are made up of people from scheduled caste and scheduled tribe communities. Though the digitalization of SWM is an IT project, the data fed into the system is generated through a labor-intensive process of scanning every premise from where waste is collected. That makes it highly dependent on the workers and the constraints they face on the ground since they are the ones who have to scan the QR codes at each household. Waste collectors employed by Antony Waste follow a designated route to collect waste in their allotted neighborhoods. The collection is done by groups of two (sometimes three) workers: one gathers the waste in buckets and puts it in the truck, while the other drives the vehicle.
We understand the idea of accountability as the ability for residents to understand the shifts in the delivery of basic services and their capacity to put forward their grievances. The digitalization of SWM in Mangaluru promises to set up a one-stop grievance redressal mechanism through the OneTouch Mangaluru app in order to make the system more accountable and enable everyone to file their grievances. However, a close inquiry into how this mechanism works indicates otherwise. A low-level awareness of a partially digitalized collection system Most of the people we met during our fieldwork were unaware about the use of QR codes installed on their properties. For example, a resident (who happens to be a retired state-level bureaucrat and ex-member of Karnataka's legislative assembly) thought that the QR codes were installed as a part of data collection undertaken by the state education department.
Third, the supposed promises of digitalization with regard to accountability are far from being fulfilled. What the Mangaluru case shows us is the opposite: a digital veneer that does not solve the problems faced by residents, who still prefer to resort to other means to make their voices heard. But beyond that, fears about the nature of the data collected, even within the municipal administrations, reinforce the mistrust that is already known to be a major feature of relations between citizens and the state in India. In this sense, forced digitalization does not seem to be going in the direction of improving the local social contract. cautious about their harmful effects. In this light, we contend that while Smart Cities in India have not been game-changers, their effects cannot be completely dismissed.
FFGM biomass estimates We compiled an exhaustive dataset of in situ pelagic tunicate (i. e. , thaliacean) concentrations from large-scale planktonmonitoring programs and previous plankton data compilations to derive monthly fields of pelagic tunicates biomass (in mg C m -3 ). This product can be used as a standard dataset to evaluate the FFGM biomass estimated by PISCES-FFGM. First, three main data sources were retrieved: NOAA's Coastal and Oceanic Plankton Ecology, Production, and Observation Database (COPEPOD; O'Brien, 2014); the Jellyfish Database Initiative (JeDI;  Lucas et al. , 2014) ; and KRILLBASE  (Atkinson et al. , 2017). The Australian Continuous Plankton Recorder (CPR) survey (AusCPR; IMOS, 2021) and the Southern Ocean CPR survey (SO-CPR;  Hosie, 2021)  were excluded because they were found to not quantitatively sample thaliaceans (see Appendix Text A1).
This compilation gathered planetary-scale plankton concentration measurements collected through a broad variety of sampling devices over the last 100 years, with taxonomic identification of varying precision and scientific names, some of which changed through time. Therefore, we curated the scientific names and the taxonomic classification of each observation to harmonize names across all datasets and to correct deprecated names and synonyms based on the backbone classification of the World Register of Marine Species (WoRMS; WoRMS Editorial Board, 2023), using the "worms" R package version 0. 2. 2  (Holstein, 2018). Then, only those observations corresponding to an organism belonging to the class Thaliacea were kept. Observations without a precise sampling date and at least one sampling depth indicator (usually maximum sampling depth, in meters) were discarded.
All datasets provided concentrations in ind m -3 , except for KRILLBASE, which provided salp (mostly Salpa thompsoni) densities in ind m -2 , which we converted to ind m -3 based on the maximum sampling depth of the corresponding net tows. In KRILLBASE, 5186 observations of thaliaceans with missing density values were discarded (35. 6 % of the original 14 543 observations). In COPEPOD, concentrations are standardized as if they were all taken from a plankton net equipped with a 330 µ m mesh  (Moriarty and O'Brien, 2013). A total of 862 point observations with missing concentration values were discarded (3. 5 % of the original 24 316 observations). We examined the composition of the original data sources compiled within JeDI and COPEPOD by assessing the recorded institution codes and their corresponding spatiotemporal distributions to evaluate the observations overlapping between these two previous data syntheses.
We logically observed a very high overlap between COPEPOD and JeDI, as the former dataset was the main data contributor to the latter. Therefore, overlapping records were identified based on their sampling metadata, scientific names, concentration values, the recorded institution codes and recorded data sources, and they were removed from JeDI. This removed 14 198 (74. 1 %) of the JeDI's original thaliacean observations. This synthesis of thaliacean concentrations gathered globally distributed 34 566 point observations (Fig. A1 ), collected at a mean (± SD) maximum sampling depth of 193 (± 198) m over the 1926-2009 time period (mean ± SD of the sampling year is 1975. 9 ± 19. 3). The range of observed thaliacean concentrations ranged from 0. 0 ind m -3 to 10 90 ind m -3 , with an average of 4.
4 d -1 1. 4 d -1 - - FFGM non-predatory quadratic mortality 4 × 10 3 L mol -1 d -1 - - 10 5 L mol -1 d -1 - - Carcasses and Fecal pellets sinking velocities 100-1000 m d -1 - - - 30 m d -1 - Clogging threshold ∞ - - - - 0. 5 mg Chl m -3 sured biomasses and sample numbers are low below 300 m (see Fig. A2 ). The biomass levels of this subset ranged between 0. 0 and 488 mg C m -3 (4. 9 ± 25. 7 mg C m -3 ). Thaliacean concentrations issued from single-net samples were summed when necessary (e. g. , when species and/or genera counts were sorted within one plankton sample) to be representative of a Thaliacea-level point measurement. At this point, the dataset contains 18 875 single observations of thaliacean biomass. Hereafter, we will refer to this dataset as "AtlantECO dataset". Ultimately, monthly thaliacean biomass fields were computed for validating the monthly FFGM biomass fields of PISCES-FFGM.
Additional datasets We also used the monthly fields derived from observations as a standard dataset to evaluate some of the other PISCES-FFGM compartments: total macrozooplankton, mesozooplankton, total chlorophyll, nutrients and oxygen. As with FFGM, for total macrozooplankton observations, a low-resolution grid has been used. We use monthly macrozooplankton abundances binned on a 72 × 36 grid (ind m -3 , vertically integrated between 0 and 300 m to ensure that most of the organisms present in the epipelagic zone are included) from MARine Ecosystem DATa (MAREDAT;  Moriarty et al. , 2013)  and then convert abundances to carbonbased concentrations to evaluate our modeled distribution of total macrozooplankton biomass (i. e. , FFGM and GM). To convert to carbon concentration, an average individual weight of 588 µg was chosen by considering an individual with a mean size of 6.
Model evaluation The model evaluation is based on monthly fields averaged over the last 20 years of the PISCES-FFGM reference simulation. For each unique observation in the AtlantECO dataset, we sampled the modeled FFGM biomass from the PISCES-FFGM climatology at the corresponding coordinates (latitude, longitude), month and depth range (minimal depth and maximal depth) so that each observed biomass can be compared to a "model-sampled" biomass. When compared to the AtlantECO climatology, the annual mean FFGM biomass fields and the statistics (Table  4 ) are calculated from these "model-sampled" biomasses to avoid bias due to sampling. For other variables, model outputs (NO - 3 , PO 3- 4 , Chl, mesozooplankton and GM+FFGM) were regridded horizontally and vertically on the same grid as the corresponding observations (see previous section).
96 and 26. 9 mg C m -3 respectively. The standard and log10 biases are closer to 0 than those calculated for macrozooplankton (Table  4 ). We also compared the observed and modeled relationships between FFGM biomass distributions and chlorophyll levels. The dotted black lines and points in Fig. 5  show the FFGM biomass from the AtlantECO database plotted against the corresponding chlorophyll concentrations from OC-CCI (see Sect. 2. 4. 2). Despite considerable scatter, this databased analysis suggests a modest decrease of FFGM biomass for chlorophyll concentrations above about 0. 3 mg Chl m -3. Yet, this decrease is far from systematic, since even at high chlorophyll concentrations, FFGM biomass can be very high (> 10 mg C m -3 ). In our reference PISCES-FFGM simulation (dotted blue line and points in Fig.
5  and Sect. 3. 1). Given the paucity of data, it is currently difficult to evaluate these model insights from macrozooplankton databases alone. Heneghan et al. (2020)  showed that salps dominate other macrozooplankton groups in low-productivity regions, but, contrary to our model results, these authors also showed that these organisms are more abundant in absolute values in these lowproductivity regions than elsewhere in the ocean. Yet, they did not explore the processes that could drive this distribution. As evidenced by our PISCES-CLG experiment, clogging may be a potential explanatory mechanism, but the evidence for this process is weak. Future studies are needed to determine the processes involved in limiting FFGM biomass at high chlorophyll concentrations.
Data-based climatology To evaluate the modeled FFGM biomasses, we compiled data from different sources (Sect. 2. 4) to produce a gridded climatology of large pelagic tunicates. Our AtlantECO dataset is based on similar observations as the previously compiled dataset  (Luo et al. , 2020 (Luo et al. , , 2022)) , but we used a different approach to convert abundances to biomasses by taking into account the taxonomic information available on the samples, even when the species is not indicated. Our model predicts a median biomass of FFGM that is similar to our dataset (0. 80 vs. 1. 11 mg C m -3 ) and reproduces 91 % of the areas where biomass is high (> 0. 5; Table 4). The introduction of a clogging mechanism, which would represent a saturation of the salp-filtering apparatus for high prey concentrations, improves the representation of low-biomass areas (Sect.