dataesr/scientific-paragraphs-categorization huggingface dataset:
With respect to sampling duration, the annual exposure levels were estimated on the basis of the short-term mea surement data (ie, daily or hourly mean con centrations) in different seasons across different years. These data were not equal to the annual levels based on the actual long-term field measurements, which were unlikely to be available for decades since it is time-consuming, expensive, and usually refused by residents. We should cautiously show the estimated data but even so, they provide us valuable references. Future studies can use newly emerging methods to measure exposure (eg, low-cost sensors) for large-scale, long-term exposure monitoring to improve reliability of the estimates. Second, there are some limitations on the exposureresponse relationships. Except for PM 2•5 , exposureresponse relationships for most IAPs were based on case-control or cross-sectional studies due to a scarcity of cohort studies, so evidence for causal relationships is scarce.
Besides, due to a scarcity of data, the exposure-response relationships in the present study relied on non-Chinese global epidemiological studies and cannot evaluate possible ethnic differences. Future studies should include more databases, such as Embase, to enrich related evidence. Third, current epidemiological studies have not considered IAP coexposures from common sources, which might lead to overestimation of DALYs attributable to a single IAP. Fourth, as the total DALYs of various health outcomes for different age and gender groups at the province level are not available, the attributable burden of IAPs for different age and gender groups cannot be estimated, nor can susceptible subpopulations be identified. Tackling these problems would help improve and deepen the understanding of the burden of disease for IAPs in China in the future.
Acknowledgments This research was funded by the  National Key Research and Development Program of China  (grant number  2017YFC0702700 ) and the  National Natural Science Foundation of China  (grant numbers  51976106  and  92043301 ). We are also grateful to  Chen Wang  and  Zaiying Zhou  from the  Department of Industrial Engineering, Tsinghua University, Beijing, China , for their advice in data analysis. LBW is an independent researcher. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
Data collection, DCI de nition, and outcome assessment Data corresponding to clinical characteristics such as age, sex, Glasgow Coma Scale  21  , and World Federation of Neurosurgical Societies (WFNS) scale  22  were recorded at admission. The Simpli ed Acute Physiology Score 2 (SAPS II)  23  was calculated within 24 hours after NCCU admission. Speci c neurologic events (intracerebral hematoma, rebleeding, neurogenic pulmonary edema, seizure and cerebral salt wasting syndrome) were collected. Based on the latest recommendations  24, 25  , DCI was de ned as follows: development of focal neurologic signs; reduction by at least 2 points on the Glasgow Coma Scale that lasts for at least one hour and is associated with angiographic cerebral vasospasm, detected either with CT angiography or digital subtraction angiography; or a new cerebral infarction detected on a CT scan, either within 6 weeks after an aSAH or before discharge, after excluding a procedure-related infarction.
The results were expressed as odds ratios with 95% con dence intervals. All statistics were carried out using JMP (version 14. 0, SAS, Cary, NC, USA) except for the ROC analyses and kappa coe cient calculation, which were performed using MedCalc (version 9. 2, MedCalc Software, Ostend, Belgium). Statistical signi cance was assumed at p values of 0. 05 and below.
Acknowledgements We would like to thank to our ICU and neuroradiologist colleagues for supporting our study. We con rm that manuscript complies with all instructions to authors. We con rm that authorship requirements have been met and the nal manuscript was approved by all authors. We con rm that this manuscript has not been published elsewhere and is not under consideration by another journal. We con rm adherence to ethical guidelines and indicate ethical approvals ( Comité d'éthique pour la recherche en Anesthésie -Réanimation - IRB 000102542019081 ). Pr Couret  has nothing to disclose. Dr Boussen  has nothing to disclose. Dr Cardoso  has nothing to disclose. Dr Alonzo  has nothing to disclose. Dr Madec  has nothing to disclose. Dr Reyre  has nothing to disclose. Dr Brunel  has nothing to disclose.
BACKGROUND: Up to 50% of patients with uveal melanoma develop metastases (MUM) with a poor prognosis and median overall survival of approximately 1 year. METHODS: This phase I study evaluated the safety, tolerability, pharmacokinetics, pharmacodynamics and efficacy of the oral protein kinase C inhibitor LXS196 in 68 patients with MUM (NCT02601378). Patients received LXS196 doses ranging from 100-1000 mg once daily (QD; n = 38) and 200-400 mg twice daily (BID; n = 30). RESULTS: First cycle dose-limiting toxicities (DLTs) were observed in 7/38 (18. 4%) QD and 2/17 (11. 8%) BID patients. Hypotension was the most common DLT, occurring at doses ≥500 mg/day, and manageable with LXS196 interruption and dose reduction. Median duration of exposure to LXS196 was 3. 71 months (range: 1. 81-15. 28) for QD and 4.
MATERIALS AND METHODS This was a phase I, first-in human, multicentre, open-label study (NCT02601378), designed and sponsored by Novartis Pharmaceuticals Corporation and initiated on 1 February 2016. The study protocol and amendments were approved by the Independent Ethics Committee or Institutional Review Board for each centre, and all patients provided written informed consent. The study was conducted according to the principles of the Declaration of Helsinki and performed in compliance with Good Clinical Practice. All patients were aged ≥18 years old and had biopsy-proven MUM with progressive and measurable disease. Patients were either treatment naïve or their disease had progressed (radiologically or clinically) on their most recent therapy. There was no eligibility limit to the number of prior lines of therapy, including PKC inhibitors other than LXS196.
Exploratory objectives included assessment of mutations in cancer driver genes by transcriptome and targeted DNA sequencing of baseline tumour biopsies. During dose escalation, patients received oral LXS196 either once daily (QD; 100-1000 mg) or twice daily (BID; 200-400 mg) in 28-day cycles until disease progression, intolerable toxicity or withdrawal of consent (Supplementary Fig. 1 ). Dosing was omitted on the second day of the first cycle (C1D2) to allow for longer post-dose sampling in order to better characterise PK. LXS196 was given on an empty stomach, at least 1 h before or 2 h after a meal. Dose escalation was guided by safety, PK data and a two-parameter Bayesian Logistic Regression Model (BLRM)  [21]  employing escalation with overdose control (EWOC) criteria  [22]. The DLT period lasted 28 days after the first dose of LXS196.
All samples were run in duplicate wells. Total and phosphorylated PKCdelta (PKCd; pPKCd, phosphorylated at S299) levels were determined using microcapillary electrophoresis on the Sally Sue platform (Protein Simple, San Jose, CA, USA). Area under the peaks corresponding to the target was measured and used for further calculation. All results were normalised for the amount of protein used in each assay, and the ratio of phosphorylated protein to the corresponding total protein was reported. The calculation of data for analysis are described in the supplementary appendix.
Patient characteristics A total of 68 patients with MUM were treated with LXS196 in this first-in-human study: 56 in the dose escalation part and 12 in dose expansion (Supplementary Fig. 1 ). Patients enroled in dose escalation received doses ranging from 100-1000 mg QD (n = 38) and 200-400 mg BID (n = 18) until the RDE was established. Patients enroled in the expansion part (n = 12) received LXS196 at the RDE. Supplementary Table  1  shows patient disposition. At the final data cut-off date (7 January 2022), all patients in the QD schedule had discontinued treatment due to disease progression. Patient demographics and baseline characteristics are shown in Table  1. The median age was 56. 0 years, 36 (52. 9%) were male, and most (n = 62; 91. 2%) had a performance status of 0. The majority of patients (n = 60; 88.
DISCUSSION In this study, we explored the possibility of targeting GNAQ/GNA11 and other G-protein pathway-associated mutations by inhibiting the downstream effector PKC using LXS196. The patients enroled in this first-in-human phase I study displayed characteristics representative of the MUM patient population. Liver metastases were present in 88% of the patients, and baseline LDH was >ULN in 56%. The majority of patients (86. 8%) had received prior systemic therapy, including immunotherapy in 53%, and had not achieved a response to prior therapy. Overall, LXS196 was generally well tolerated as a single agent, with the majority of patients not requiring interruption of LXS196 or a dose reduction from their assigned dose level. Non-clinical data suggested patients may experience a decrease in systolic blood pressure, and indeed hypotension was observed in a number of patients on study at doses ≥500 mg QD.
CONCLUSION These results confirm the tolerable safety profile of LXS196. The encouraging clinical activity demonstrated provides evidence that targeting the PKC pathway in MUM should be explored and supports the continued evaluation of LXS196 in combination with other targeted therapies. LXS196, now known as darovasertib (IDE196), is currently being explored by Ideaya biosciences in doublet combinations with the MEK inhibitor binimetinib and the cMET inhibitor crizotinib (NCT03947385).
ACKNOWLEDGEMENTS The authors thank the patients participating in this clinical trial, and their families. The authors thank the staff at each participating institution that assisted with the study. The authors also acknowledge  Michael Visser ,  Andrew Wylie ,  Vesselina Cooke ,  Alexandre Vautrain ,  Sandra Bekker ,  Rakesh Kumar , and  Somesh Choudhary  for all their work on LXS196 and this study. We thank  Dr Riddhi Sharma BDS, MSc ,  Novartis Healthcare Pvt. Ltd. , Hyderabad, India  for medical editorial assistance with the manuscript.
Data The data employed here are from Kantar Worldpanel surveys conducted in 2014, 2015, 2016, and 2017. These surveys were administered to over 20,000 households, which reported their weekly food acquisition. All participating households registered grocery purchases through the use of barcode scanning. Then, a group of households (half of the participating households) was asked to also specifically report the purchases of fresh products (meat, fish, fruit, and vegetables). These purchasing data provide an inventory (in quantity and expenditure) of the French population's consumption of foodat-home. However, these data do not provide information on food-away-from-home or self-produced food consumption (for example, from a home garden), thus they represent around 80% of total consumption. For our study, we selected the group of households with complete information on purchases, i.
e. , who reported both their grocery purchases and their fresh produce acquisition (over 12,000 households). It is worth noting that the Kantar data distinguish 13 periods of 4 weeks each throughout the year. Therefore, we retained households that reported their food acquisition for at least 1 week. We excluded households that did not communicate information about sociodemographic and economic variables (such as income or education level). Consequently, our sample includes 10,914 households in 2014, 11,074 households in 2015, 11,031 households in 2016, and 10,764 households in 2017. Each database is considered as a cross-section. We selected a set of products including Food-away-from-home was estimated to constitute % of all food consumed in ( ) and over % of household food expenditure in ( ).
Kantar data make it possible to employ panel data and to follow the same households over multiple years. In our sample, fewer than % of households present in recorded their purchases in ; therefore, we did not work with panel data. We considered our databases as cross-sectional and constructed a pseudo-panel in section Estimation of changes in pulse purchasing over the awareness campaign period using a two-way fixed-e ects model, which has the advantage of avoiding selection bias associated with attrition e ects (which increase with the number of periods) and bias associated with learning e ects. pulses in different forms, including raw and subject to various preparations: dried (lentils, beans, flageolet beans, split peas, and chickpeas), canned (flageolet beans, white and red beans, lentils, and chickpeas), frozen (flageolet beans), preparations/recipes (falafel, tofu, sausages with lentils, soy steaks, and other meat substitutes), desserts made of soy, soy beverages, and soy ingredients.
The dependent variable used in our analysis was the annual purchase of pulse-based products at the individual level. We obtained this by cumulating the period quantities registered in the Kantar database at the household level for each year of study. We then divided these quantities by the number of persons in the household. Therefore, the purchase of pulse-based products in kg/year/pers is expressed as follows: q Kg Year /pers = quantity kg * 52 Nw N , where Nw is the number of weeks of food purchases in a given year and N is the number of persons in the household. The purchasing of pulse products increased by 6. 9% between 2014 and 2017 (Figure  1 ; 2. 68 kg/year per capita in 2014 and 2. 86 kg/year in 2017, Appendix A). A large majority of consumers (84. 9-88. 4%) bought pulse-based products (Figure  2 ; Appendix A).
Econometric methodology Estimation of the relationship between sociodemographic characteristics and pulse purchases using a Box-Cox double-hurdle model Our aim is to identify the sociodemographic factors associated with the purchase of pulse products. Linear regression models are generally inappropriate for analyzing such data since some households may not purchase pulses in a given year, i. e. , the presence of zeros. The Tobit model (  45 ) is often used when a dependent variable is zero for a part of the population but positive (and with different outcomes) for the rest of the population. Here, we specify the Tobit model as follows: y * h = x h β + ε h ∀ h = 1,. N y h = y * h if y * h > 0 0 if y * h ≤ 0, (1) where y * h is the latent variable and y h is the quantity of pulse products purchased.
Reducing the number of cohorts can also increase the heterogeneity of individuals in a single unit and, therefore, increase the variance of estimators, making them less effective. A compromise needs to be found between the cohorts large enough to limit measurement errors, sufficiently homogeneous cohorts, and sufficient observations Here, we deal with statistical cohorts, in contrast to cohorts identified from health surveys. In this paper, a cohort is a group of households with the same characteristics (e. g. , "year of birth" of the reference person in the household, or "county size"). Frontiers in Nutrition frontiersin. org to obtain adequately precise estimators. We have four crosssectional databases spanning 2 years (2014 and 2015) before the awareness campaign and 2 years (2016 and 2017) after.
Each "pre-campaign" and "post-campaign" period includes 8 quarters. Each dataset contains more than 10,000 households for 13 periods of 4 weeks (52 weeks of the calendar year). We used the sum of those 13 purchasing periods in order to have quarterly purchasing information for each household. We defined our cohorts using two variables: the "year of birth" of the reference person in the household and "county size, " composed of three levels (rural area with fewer than 2,000 inhabitants, urban area with 2,000 to 199,999 inhabitants, urban area of 200,000 inhabitants or more). Thereby, we formed 165 annual cohorts observed at minimum over 8 quarters and at maximum over 16 quarters (4 years * 4 quarters). The first three cohorts include the heads of household born in 1936 and living, respectively, in the three types of counties mentioned above.
Independent variables Price is a key factor; however, the database does not provide a price variable. We computed the unit value of pulse-based products as the ratio between expenditure and the quantity purchased and adjusted the unit value for quality by following the procedure of Crawford et al. (60) , who attributed variation in actual price to spatial and time differences only. Other independent variables included were the socioeconomic and demographic characteristics of the household. These include the characteristics of the participant in the survey, namely, age, level of education, and socioprofessional category, which are considered major individual drivers of dietary patterns in protein intake  (24). Our age variable has 3 levels, distinguishing 2 potentially active phases (18-44 and 45-64 years) from the retirement period (from 65 years).
, 2018). The advent of advanced remote sensing technology has helped pave an e cient way to map surface changes, particularly changes in the landscapes accruing to extreme climatological events, anthropogenic factors, and ground shaking. Remote sensing is very bene cial as it allows for large-scale mapping of slope failures  (Fernández et al. , 2016). Remote sensing provides mostly updated data with relatively low resolution. However, "unmanned aerial vehicles (UAVs)" have become a very suitable tool for preparing low-cost, up-to-date, and precise eld datasets  (Ghorbanzadeh et al. , 2019; Zhang et al. , 2019). The use of "UAVs" in natural events and urgent situations is developing  (Watson et al. , 2019; Windrim et al. , 2019) , and compared to conventional tools, it has the potential to prepare data with better resolution  (Brovkina et al.
By applying the "spectral information from the UAV-derived imagery", in conjunction with the slope dataset, we have illustrated the e ciency of DL-based automated approaches and the bene ts and shortcomings of applying topographic dataset, especially slope dataset, for sinkhole and landslide detection. We contrasted the output maps prepared by applying automated approaches with manually extracted sinkhole and landslide inventory datasets created from a variety of sources. The detected sinkholes and landslides were then validated applying "conventional computer vision validation techniques".
Dataset creation The "UAV-based photogrammetric" capture yielded accurate landslide and sinkhole location identi cation. In the investigation, landslides and sinkholes were found in areas with optical and morphological erosion and deposition evidence. A stationary "UAV" ("Sense y eBee x") with the camera type "Sense y Aeria X" and a focal length of 18. 50 mm was used. Acquisitions were scheduled to ensure a "Ground Sampling Distance (GSD)" of 5. 0 cm on average. The orthomosaic's resulting pixel size is 5. 0 cm/pixel. An experienced UAV operator planned the ight using the "senseFly eMotion" ight control program. A "GPS RTK" adjustment has also been included. The UAV trajectories were constructed with a 70% lateral overlap and an 85% fore-and-aft overlap. The ight's mean altitude was 220 meters above the ground.
The Pix4Dmapper photogrammetry program was used to process aerial pictures captured during the study. For each composite image (see Table  1 ) and the corresponding mask, we extracted a large tile from the whole research region to construct the corresponding dataset. The tile's dimensions were 512x512 pixels, and 3, 4, or 7 bands, depending on the band arrangement. In the case of the landslide's dataset, the number of pixels in the studied region reveals that there are 2064288 pixels inside landslides and 487868622 pixels in the background class, for a ratio of 236. The tile and mask were separated into 512 x 512-pixel patches without overlapping, resulting, in this case, in 6508 patches. As for the sinkhole's dataset, instead, we nd 2096241 pixels belonging to the sinkholes class and 1864415983 pixels to the background class, for a ratio of 899.
In both cases, we only saved images that had at least one pixel from the researched target (132 for landslides and 203 for sinkholes). This sampling approach was chosen to reduce the disparity between classes while also supplying a constant quantity of target pixels. We used 80% of all patches for training and 20% for testing  (Agrawal et al. , 2017). A further validation dataset is created using 20% of the training dataset. The patches were randomly shu ed before being divided into three datasets including, test, validation, and training. In both cases, "patches" were sampled without overlap, to avoid biases in the calibration process. We nally standardized the highest and lowest pixel values between 0 and 1 for each patch. structure and consists of succeeding "convolution blocks". Every convolutional block has two convolutional layers with a 3 × 3 kernel size and a 2 x 2 max-pooling layer.
Sinkholes For all band combinations, the performances of "ADSMS U-Net" are superior to the ones of the "conventional U-Net" architecture. The best overall scores are yielded by the "ADSMS U-Net" when trained on the ALL dataset. We can notice, as in the previous case, the higher recall of the "ADSMS U-Net", with respect to the conventional architecture. As for the effectiveness of the different composites evaluated in this paper, we can notice contradictory behavior of the models. The conventional "U-Net" achieves the best performance when using just the optical RGB combination, while "ADSMS U-Net" can exploit the information of both topographical derivatives and optical data, achieving the best results with the ALL composite. Moreover, it is noticeable that the DSHC dataset alone achieves the worst results for both models (Table  3 ).
By a visual evaluation of the results in Fig. 6 , we can notice a tendency of the models to underpredict the results when predicting using just topographical derivatives. On the other hand, when using the RGB and ALL datasets, we see that almost all objects are predicted correctly. However, we can notice some imprecise predictions of the sinkhole boundaries' delineation. Overall, training with the RGB data tends to outperform the data curated simply of the "UAV-derived topographic" data. However, as seen in the true color images, quite often trees are present in the sinkholes and therefore contribute to errors, while using the DSMs, in detecting the sinkholes. In fact, the measurements of the depth of the sinkholes are hampered by vegetation and the resulting DSM often does not represent the actual sinkhole structure.
However, it is possible to notice that the U-Net yields false positive classi cation of landslides where no real ones are present. This misclassi cation tendency is not found when using the "ADSMS U-Net" and this promotes a good predictive capability of the latter model. This behavior is due to the multiscale inputs as well as the Soft Attention Gates as described above. As for sinkholes, it is evident how some sinkholes are more easily detectable with RGB data while others are more clearly mapped using topographical derivatives. This difference can be due to the presence of trees in most of the collapsed pipes, which are captured by both the optical data and DSM. When using the ALL dataset, however, the optical and topographic information combined allowed "ADSMS U-Net" to reach the highest mapping performances.