dataesr/scientific-paragraphs-categorization huggingface dataset:
a) Hospitalization data All RSV-associated hospital stays in general medicine, surgery and obstetric medical facilities (PMSI-MSO) as well as all-cause hospitalization from 2010 through 2018 in children aged < 5 years were selected for this analysis. RSV-associated hospitalization were identified using the following International Classification of Diseases 10th revision (ICD-10) codes as the primary diagnosis (PD): J210 (acute bronchiolitis due to RSV), J219 (acute bronchiolitis, unspecified), J121 (pneumonia due to RSV), J205 (bronchitis due to RSV), J45 (asthma) and R062 (wheezing). Hospital admissions with an ICD-10 code of bronchiolitis (J210 or J219 ICD-10 codes) recorded as an associated diagnosis (AD) were also selected when the primary diagnosis was a "disorder of the respiratory system" (J00-J99 ICD-10 codes) or "certain conditions originating in the perinatal period" (P00-P96 ICD-10 codes), assuming that RSV-associated bronchiolitis contributed to the hospitalization despite not being listed as primary diagnosis.
Statistical analyses A descriptive analysis of RSV-associated hospitalizations during RSV seasons (defined from October to March the following year) was carried out over the period 2010-2018  [17]. Analyses were stratified by age group, defined by chronological ages (< 1 year old vs. ≥1 year old) and further stratified by month categories and by gestational weeks at birth (weeks' gestational age, wGA). wGA was determined based on mother's information on the number of weeks of amenorrhea, directly available in PMSI database. Data on mother's amenorrhea was not consistently available, especially for records of children born before 2009. Full term birth was defined as birth after 36 weeks of amenorrhea, otherwise it was considered as preterm birth, for which subcategories were defined: extremely preterm birth (< 29 weeks), very preterm birth (29-32 weeks) and moderate preterm birth (33-35 weeks).
d) Economic analysis Direct cost of RSV burden was calculated based on the resources used during RSV-associated hospitalizations recorded in PMSI database. Total cost and average cost per stay per patient were calculated by RSV season from a collective perspective, considering the French national health insurance reimbursement, medical professionals costs and running costs of the medical unit (e. g. cleaning and laundry), public complementary and private insurance and out-of-pocket charges. These costs were calculated for all RSV seasons according to the DRG (Diagnosis Related Group) based payment system for reimbursement of acute care (MSO). The costs were adjusted to 2018 euro value by applying the price index of health products in mainland France  [16].
In our study, we observed a negative correlation between RSV-associated hospitalization incidence and age groups, most of the burden being observed among children < 1 year old. This trend was also described by other studies  [14, 18, 19]. Our findings, from 37 to 53 RSV hospitalizations per 1000 personyears in infants < 1 year of age between 2010 and 2018, is in accordance with the French study conducted using the PMSI database in 2009 with a bronchiolitis-associated hospitalization rate of 35. 8 per 1000 infants  [10]. RSV-associated hospitalization rates were the highest for children born between September and November and early exposed to their first RSV season. This result has been reported in previous studies such as the study from Kramer et al. [11] , where the highest risk of RSV-associated hospitalization was observed for children born in November.
Highly integrated, low noise, low power consumption and ultrafast electronics The QSD array will be readout by an application specific integrated circuit (ASIC). The small cell size and the target time resolution impose also for this part of the system an improvement with respect to the state of the art. Today, advanced photon TOF systems based on multi-tier approach, feature, as an example, a pixel size of 19. 8µm x 19. 8µm, a time resolution of 60ps. and a fill factor of 31% (see for instance  [47]  and references therein). The functionality of the readout electronics can be partitioned into four main tasks: signal amplification and discrimination, timeto-digital conversion (TDC), online data processing and data transmission. The signal amplification and discrimination must be performed at the level of the individual QSD cell.
An on-chip, real-time data reduction becomes thus feasible. This can be obtained by grouping the pixels into regions or macro-pixels. In each macro-pixel, a digital processing unit selects the time-stamps of only the first few photons which define the time-resolution, while the overall number of photons is counted to measure the total energy deposited by the event. Such an approach can reduce the amount of transmitted data by at least one or two orders of magnitude, depending on the total number of photons that are associated to a given event. In the optimal case, the number of microcells forming a macro-pixel is configurable, so that the same readout can serve different applications, each requiring its optimal space resolution. The need of a flexible, high performance and low power logic together with ultra-fast serialisers are other compelling factors that call for the use of a technology node with a feature size as small as possible.
30 In the COSMIC database reporting 130 cases of primary mucinous ovarian tumors, KRAS was mutated in 44% of cases, and the two most frequent mutations were G12D (41. 5%) and G12 V (37. 7%). 17,30 KRAS gene alterations, mainly located at codons 12 or 13, are present in up to 85% of PA. 17, [31] [32] [33]  They include G12D (49%), G12 V (31%), and G12R (13%) mutations. 17 ecause of the very low intra-tumoral heterogeneity for KRAS gene status, the mutations should be similar in both primary and metastatic sites. 31 t also does not vary with tumoral stage, unlike DPC4 and TP53 gene alterations which usually occur in late stages of disease. The most common KRAS mutation is G12D in both PA and primary ovarian tumors. Because of the narrow spectrum of KRAS mutations, it is impossible to exclude that both of these tumors harbor the same mutation by chance.
ACKNOWLEDGMENTS We would like to thank  Feng-Kun Guo  and  Eulogio Oset  for a careful reading of the manuscript. M. P. V. would also like to thank the  IJCLab of Orsay , where part of this work has been done, for its long-term hospitality. This work is partly supported by the  National Natural Science Foundation of China  under Grants No. 11735003  and  11975041 , the  Fundamental Research Funds for the Central Universities  and the  Thousand Talents Plan for Young Professionals.
• Authors' contributions Rachida Souidi: analysis, interpretation of data and a major contributor in writing the manuscript. Yasmina Khane: interpretation of data and help for writing the manuscript. Lahcen Belarbi: have drafted the work Smain Bousalem: revised the work     Calibration curve of absorbance against concentration of methylene blue. Figure 4 Schematic illustration of the preparation procedure for vine wood sawdust waste by H2SO4. Figure  5  FTIR spectra of the vine wood sawdust before and after chemical activation. The point of zero charge (pH pzc) of VWS. Figure 12 Effect of particle size on kinetics of MB dye adsorption. Figure 13 Effect of adsorbent dose on the adsorption of MB onto VWS. Figure 14 Effect of contact time on the adsorption of the BM onto VWS with various concentrations.
Results and discussion The reproducibility of the obtained results is illustrated in Fig. 5. Here the measured mixture compositions are given along the central axis of the jet as a function of the distance z from the capillary nozzle. In the sub-figure a) the data points represent macro-scale compositions x CO2,macro measured at three different days when the pressure and temperature were set to 10 MPa and 313 K, respectively. Each data point is the mean of 75 single-shot x CO2,macro -values. The error bars represent the standard deviation (one-sigma interval) of the 75 single-shot x CO2,macro -values. In the sub-figure b) the data points and their error bars represent the mean microscale composition x CO2,micro and the respective standard deviation, also computed from 75 single-shot x CO2,micro -values.
DFCI 95-096 The DFCI 95-096 trial enrolled 206 men with clinically localized T1 to T2 disease who had a PSA level of 10 to 40 ng/mL (to convert to μg/L, multiply by 1. 0), a Gleason score of at least 7, or endorectal magnetic resonance imaging evidence of extracapsular extension or seminal vesicle invasion at Harvard University-affiliated hospitals from December 1, 1995, to April 15, 2001. 9 All men received EBRT, in which 70. 2 Gy was delivered in 39 fractions. Men were randomized to 0 vs 6 months of androgen deprivation therapy (ADT) and followed up prospectively for PSA failure (nadir plus 2 definition)  2  and death. The final cutoff date for data collection was October 9, 2016. 7 All 206 men were included in this analysis.
0 (2. 9-8. 9) NA specific antigen. a Unless specified otherwise, data are expressed as Follow-up time, median (IQR), y 6. 3 (6. 2-6. 5) 6. 7 (6. 5-7. 2) <. 001 No. (%) of patients. Follow-up status Alive 540 (79. 8) 197 (67. 9) Cause of death Prostate cancer 7 (1. 0) 68 (23. 4) NA Cardiovascular disease 46 (6. 8) 10 (3. 4) Other 84 (12. 4) 15 (5. 2) b The Wilcoxon rank sum test was used for age and PSA level; the Fisher exact test was used for T stage, Gleason score, performance status, and treatment arm; and the log-rank test was used for followup time. cSI conversion factor: To convert PSA to μg/L, multiply by 1. 0. JAMA Network Open | OncologyIncreased PSA Levels After Treatment in Locally Advanced vs Localized Prostate Cancer JAMA Network Open. 2021;4(5):e2111092. doi:10. 1001/jamanetworkopen.
Participants and data collection Nearly 350 teachers of grades 1-4 (i. e. , all of the teachers employed in 10 schools in the lead by the minister of education of the Canton, selected as pilot institutions for the CPD program) participated in the CPD program and the evaluations reported in this study. At the end of each training session the teachers were administered a survey to assess their perception of the CPD program and their adoption of the content. Details about the response rates we obtained are provided in Table  3.
Acknowledgements We would like to thank the  NCCR Robotics, Switzerland  as well as the members of the different institutions ( Department of Education -DFJC , the  University of Teacher Education -HEP Vaud , the teams from the two universities -EPFL and Unil, and the pilot schools) that participated in setting up the project lead by the minister of education of the  Canton Vaud, Cesla Amarelle. Funding Open Access funding provided by  EPFL Lausanne. This document is the result of the research project funded by the  NCCR Robotics, Switzerland.
Including data from Bombus queens and Psithyrus females would have been a plus. Unlike Bombus queens, Psithyrus females do not forage to feed their colony but only for themselves, their incentive to forage is thus very different although both have also offspring to produce, the comparison would thus have been very interesting. I noticed that females of certain species of bumble bees like B. vestalis are regularly observed foraging while others like B. rupestris females are rarely encountered foraging on flowers although both species population density seem pretty similar (interestingly it's opposite for males, as you show in your dataset). I believe females of B. rupestris feed directly on the resources gathered by their hosts and don't bother much foraging, so the parasitic strategy can I'm sure explain differences in foraging behaviours between cuckoo and free-living bumble bees but also between the different cuckoo species.
The trial was registered (NCT01483027). TARE-specific grade $ 3 AEs included radiation pneumonitis (n 5 1), cholecystitis (n 5 2), and duodenal ulcer (n 5 1). No arterial dissections were reported. There were 12 grade 5 AEs: eight TARE (radiation-induced liver disease, hepatic failure, and portal hypertension at 3, 3, and 5 months, respectively, deemed possibly related; intestinal obstruction, myocardial Abbreviations: CEA, carcinoembryonic antigen; LLN, lower limit of normal; TARE, transarterial yttrium-90 radioembolization.
DATA SHARING STATEMENT Boston Scientific may share patient-level data from registered clinical trials with qualified health care practitioners or academic researchers in response to a formal clinical research proposal, and when the request is in the same therapeutic area as the original study. Clinical trial data may be shared when not in conflict with all other applicable regulations, laws, or Boston Scientific policies and/or written agreements. Data may be provided for regulated and approved product 6 months following manuscript publication and after the posting of the study results on clinicaltrials. gov. If made available, data will be accessible for 12-18 months following the end of the trial. There will be limited data availability for trials before January 1, 2018. Boston Scientific will disposition requests consistent with these and other internal company criteria for data sharing.
Study Population All confirmed patients of IBD (UC or CD) presenting to Department of Gastroenterology at Dayanand Medical College and Hospital, Ludhiana, between January 1, 1991 and December 31, 2015 were included. Diagnosis of IBD was established on the basis of Copenhagen (clinical, laboratory, radiological, and pathological) criteria. 8, 9 The medical record of each patient was retrieved from prospectively maintained database and the patients' demographic profile (including age at diagnosis, gender), disease history including disease duration, disease behavior and severity, extent of disease, complications, drugs prescribed and requirement of surgery were recorded. Montreal classification was used to assess the disease phenotype in adults with IBD  10  whereas Paris classification was used for patients in pediatric age group ( < 17 years) (Supplementary Tables  1, 2 ).
15  The mean age of patients diagnosed with IBD decreased over the study period (45. 7 ± 12. 1 in the first cohort to 38. 4 ± 13. 8 in the last cohort). This was primarily due to a gradual decrease in age at presentation for UC as there was no significant change in the mean age of diagnosis for patients with CD over the time cohorts. However, when we look at proportion of patients with pediatric ( < 17 years) or elderly onset ( > 60 years) IBD, it remained consistent indicating that the majority of patients (88. 8% to be precise) belong to 18-60 years age group. This may however not be an accurate observation because there was no pediatric gastroenterologist or geriatric physician involved as all the patients in electronic database had reported to adult gastroenterology clinics. Another interesting finding/ observation was UC presenting at a younger age than CD.
Only 0. 6% of patients developed CRC in the entire cohort. This is significantly lower compared to the rates of malignancy in Western literature. 26 Studies from India estimate a lower prevalence of CRC in UC. The lower prevalence of CRC in our cohort can be due to low risk of sporadic CRC in Indians or an inherent limitation of retrospective analysis of database records. 27 Lack of implementation of an effective CRC screening programme could also have contributed to lower detection rates of malignancy. The data from our study is based on a large cohort of more than 2,400 patients with IBD and is likely a true representation of the changing epidemiological trends in IBD. The limitations include retrospective analysis at the time of diagnosis of IBD. Patients with missing data were not included in the analysis.
Clinical contextual factors. We conducted an analysis of the contextual factors that could influence the feasibility of our newly developed intervention, using an exploratory qualitative approach  (Gray et al. , 2017). The full report is presented elsewhere  (de Goumoëns et al. , 2020). We conducted eight semi-structured interviews, with nurses and allied health professional managers. The interview guide was developed based on the theory of FSN. The questions focused on the health professional managers' perception about family care for ABI patients hospitalized in their clinical settings. We analyzed our data with an inductive and deductive content analysis method  (Mayring, 2004).
Hyperspectral images (HSI) are corrupted by a combination of Gaussian and impulse noise. Successful denoising of HSI data increases the accuracy of high-level vision operations like classification, target tracking and land-cover problem. On the one hand, the traditional approach of handling the denoising problem using maximum a posteriori (MAP) criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result. On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training. A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results.
In this paper, we design the training paradigm emphasizing the role of loss functions in neural network; similar to as observed in model-based optimization methods. Further, Bayesian motivated loss functions also act as priors to constrain the solution space to the types of noise observed in hyperspectral image acquisition process. As a result, loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance. Extensive analysis and experimental results on synthetically corrupted and real hyperspectral datasets suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings. Classification results over the real dataset and associated metrics like kappa coefficient and overall accuracy used as the task-based evaluators further support our hypothesis made in the proposed methodology.
Also, it has been observed that total variation (TV) as image prior in varied forms is investigated along with lowrank assumption in HSI data. Authors in  [6]  lexicographically ordered 3D cube in 2D matrix based on the assumption that clean HSI data is low-rank. In a method proposed by He et al. [18] , authors proposed a three-fold approach for the recovery of clean data. This includes the application of nuclear norm for exploiting spectral low-rank property and TV regularization for denoising piecewise-smooth regions in the spatial bands. Further 1 -norm is utilized for the removal of sparse noise, stripes and dead pixels. However, both these methods  [6] ,  [18]  consider similarity only in the spectral dimension but pixels in each layer can themselves be of low-rank. Local redundancy and correlation (RAC) in the spectral dimension and global RAC in the spatial dimension is employed in  [21]  for modeling the similarity among data pixels.
The resultant optimization setup is solved using proximal alternating minimization method. In general, we can conclude that 2D low-rank based methods cause large spectral distortion along the channel dimension. This issue becomes less worse with tensor-based optimization methods; but the resultant computational cost is severe. Limited by the manual tuning of filtering parameters in filter-based approaches and hand-crafted priors and iterative design strategy in statistical approaches, there is a third category; which is essentially data-driven. It tries to overcome the stated limitations. More precisely, these datadriven approaches estimate true data from its corrupted version by learning a large set of parameters over input-target datasets. In recent works, it is observed that learning-based techniques are utilized for noise reduction in HSI data.
A self-supervised learning approach using separable image prior is explored in  [26]. However, since most of these models  [25] -  [29]  are not trained over mixed noise in corrupted HSI data; they yield sub-optimal results when these techniques are tested over real HSI data. However, there are some notable works  [31] ,  [32]  which do consider mixed-case scenario. For training the residual learning model, authors in  [31]  fed image gradients in the spatial and adjacent spectral dimensions to consider the effect of noise in neighbouring pixels. In  [32] , a convolutional neural network (CNN) is modelled as constraint non-negative matrix factorization strategy. Taking the benefits of fast testing speed of trained neural networks and prior assumption of image degradation in a statistical Bayesian setting using a model-driven strategy; in this paper, we design a training approach which synchronises with the image degradation observed in HSI data and prior introduced by weights learning in the end-to-end training of noisy-clean image pairs.
arg min f h -f 1 (7b) Hence, we can directly observe that putting p = 2 and p = 1 (as discussed in Eq. (  5 ) yields 2 (Eq. (  6b )) and 1 -norm (Eq. (7b)) respectively in the likelihood solution. As discussed in section I, noise in HSI data is the combined effect of two different sources; namely Gaussian and impulse noise handled using 2 and 1 terms respectively as the data fidelity terms. If a neural network (NN) ϑ θ (h) is used for learning parameters θ, the cumulative loss ( 2 + 1 ) can be used to guide the learning process according to MAP estimator. Further, the convolutional nature of the NN is capable of learning complex prior information p(f ) in the data  [40]. Hence, the collective loss is given by: L 2 + 1 θ = 1 N N i=1 α 2 ϑ θ (h i )-f i 2 2 + (1-α) ϑ θ (h i )-f i 1 ( 8 ) where N is the number of noisy-clean image pairs in a single batch; for model training using any stochastic gradient-based optimization method.
cs. columbia. edu/CAVE/databases/multispectral/ 2 http://icvl. cs. bgu. ac. il/hyperspectral/ (Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation)  [58]  and NMoG-LRMF (Denoising Hyperspectral Image With Non-i. i. d. Noise Structure)  [5]  are model-driven approaches. Deep HSI Prior  [27]  and HSI-SDeCNN (A Single Model CNN for Hyperspectral Image Denoising)  [29]  are data-driven techniques.
B. EXPERIMENTS ON SYNTHETIC DATA Washington DC (WDC) dataset 3  obtained from Hyperspectral Digital Imaging Collection Experiment (HYDICE) sensor  [59]  is a representative HSI dataset with 191 spectral bands. Similarly, one CAVE dataset (512 × 512 × 31) and an ICVL dataset obtained in artificial and natural settings respectively are tested in our synthetic experiments. Layerwise PSNR and SSIM for homogeneous (G, I ) = (10 dB, 20%) and heterogeneous noise levels for WDC dataset are shown in Fig. 2. With exceptional intermittent drops in metric values (PSNR and SSIM), our proposed solution is able to maintain considerable gap with competing methods under both homogeneous and heterogeneous noise scenarios. Further, we can observe that LLRGTV and Deep HSI Prior are second and third best performing techniques.
Visual analysis under different techniques are presented in Fig. 3  and Fig. 4  for homogeneous and heterogeneous noise settings respectively for WDC dataset. Zoomed sections in images show the blurring effect in Deep HSI Prior and artefacts prominent in HSI-SDeCNN. LRMR is unable to remove grainy texture from noisy data. For all the three datasets and eight comparing techniques; our proposed method is able to remove noise while retaining details in the image. This is attributed to the TV term being augmented to the original loss function during training. TV is known for its edge preserving capability at high-frequency regions in the image while removing noise from piece-wise constant regions. To measure the reconstruction accuracy of the proposed denoising model against competing methods, we have plotted the spectral signature of all techniques in Fig.