dataesr/scientific-paragraphs-categorization huggingface dataset:
1) STRIPE NOISE REMOVAL Stripe noise is usually encountered for remotely sensed hyperspectral images obtained using wiskbroom and pushbroom sensors. It also arises during inconsistent response from image detectors and when sensors go out of the radiometric range  [60]. In order to simulate the effect of stripe noise (as faced in real HSI data), we made use of the method proposed in  [60] ,  [61]. The data is corrupted by a combination of Gaussian+stripe noise. Image from CAVE dataset  [62]  is used in our simulation experiments. The data is corrupted with three different levels of Gaussian noise (20 dB, 15 dB and 10 dB) followed by three different levels of stripe noise (10%, 15% and 20%); respectively. Percentage levels in stripe noise indicate the percentage of total number of layers randomly selected as potential candidates for stripe noise.
C. EXPERIMENTS ON REAL DATA For experiments over real data; two datasets are used. First is the Urban dataset 4  (Fig. 7 ). It is obtained in the wavelength range 440 nm to 2500 nm with 210 spectral bands and 307 × 307 pixels in the spatial resolution covering a ground area of 2 m × 2 m. Our second dataset is Salinas dataset 5  obtained from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor covering a ground area of 3. 7m 2 with a spatial resolution of 512 × 217 pixels for 224 spectral bands. Three zoomed sections of Urban dataset are visualized in Fig. 7  depicting the reconstructed image quality on lowand high-textured regions. On the one hand, high level of artefacts can be seen in HSI-SDeCNN while LRMR is unable remove horizontal stripes often encountered when sensors go out of the radiometric range.
On the other hand, HSIPrior    introduces false colour (not present in the original data) into the resultant denoised image. However, our proposed technique is capable of removing noise while still keeping the details intact. Results on Salinas dataset are visualized in Fig. 8. Insufficient denoising capability is quite evident in data restored using LRMR. Deep HSI Prior removes noise but blur is introduced in the end result. LRTF-DFR and HSI-SDeCNN recover the image quite well but colour reproduction is not the same as present in the original data. Additionally, artefacts (visible as pixelated image in the zoomed image sections) in visual result denoised using HSI-SDeCNN suggest limited denoising performance of the technique. Finally, the results produced by our proposed method is capable of restoring the data with sufficiently good visual quality without introducing any post-processing artefacts.
1) RESULTS ON CLASSIFICATION ACCURACY Since Urban dataset is essentially designed as a benchmark dataset for spectral unmixing techniques, it cannot be used to    its classification accuracy (shown in the ground truth CM highlighted in red colour in top left corner). Overall, from the predicted class labels, we can observe that the classifier trained and tested on the denoising results of the proposed technique is able to classify most of the class labels to their correct regions. It is important to mention that results are obtained using the model trained under heterogeneous noise levels. To quantify the classification results, values of overall accuracy (OA) and kappa coefficient (KC) are tabulated in Table  6. The metrics results are in direct agreement with visual results in Fig. 9  with Deep HSI Prior performing worst and LLRGTV and LRTF-DFR giving better results.
D. EVALUATION OF RUNNING TIME All the experiments on model-driven techniques (LRMR, LLRGTV, LRTF-DFR, LRTDTV, NAILRMA and   7. Our proposed method was more efficient for CAVE and ICVL dataset. However, the efficiency of LRTF-DFR was better for WDC and Salinas dataset. This can be considered an exception when we take into account the overall visual and metric results of the proposed model against competing methods. At the other extreme, Deep HSI Prior is least efficient in running time because of the denoising mechanism used in the approach; which required around 4000 iterations before any useful result could be obtained.
VI. CONCLUSION In this paper, we have designed a discriminative learning framework with a Bayesian viewpoint towards using a loss function. More formally, we have theoretically formulated the data fidelity terms according to Gaussian-impulse corrupted observation in HSI data. Ablation study and experimental results over wide range of noise levels and eight benchmark techniques make the proposed deep learning framework suitable for HSI image restoration on homogeneous and heterogeneous noise scenarios. Incorporating more robust statistical assumptions like Gaussian mixture model and network conditioning can provide further insights towards Bayesian formulation in a learning-based framework.
TRAINING DATA Two separate models are trained. Under homogeneous case, every layer in 3D training sample is corrupted by same noise level and under heterogeneous case, every layer in a training sample is corrupted by a different noise level. For training data preparation, the range of Gaussian noise is varied between [20 dB • • • 5 dB] and impulse noise is varied VOLUME 9, 2021between [5% • • • 25%]. Images used for training are obtained from CAVE 1 and Interdisciplinary Computer Vision Laboratory (ICVL) [52] 2 multi-/hyperspectral datasets. Images from CAVE dataset are obtained from Cooled CCD camera (Apogee Alta U260) which is a multi-spectral camera acquiring images of size 512 × 512 × 31 in the wavelength range 400 nm to 700 nm in steps of 10 nm. There are in total 32 images in the dataset obtained in artificial lighting conditions in the 16-bit format.
Images in ICVL are acquired from Specim PS Kappa DX4 hyperspectral camera which contain more than 200 images (at the time of preparation of this manuscript). The dataset accommodates images from varying acqusition environments: urban, sub-urban, indoor and plant life. Size of every image is 1392 × 1300 × 519 over wavelength range 400 nm to 1000 nm with increments of 1. 25 nm. 15 images from CAVE and 15 images from ICVL are used for training and rest are used for testing. 20% of the training data is used as the validation set. Motivated by the efficient restoration under patch-based training [53], [54], in this work we have extracted 3D patch-size of dimension 40×40×6. Since non-overlapping patches are used, total size of training data is 12690(= (512/40 × 512/40 × 31/6) × 15) for CAVE dataset and 978310(= (1390/40 × 1300/40 × 519/6) × 15) for ICVL dataset.
B. TRAINING DATA Two separate models are trained. Under homogeneous case, every layer in 3D training sample is corrupted by same noise level and under heterogeneous case, every layer in a training sample is corrupted by a different noise level. For training data preparation, the range of Gaussian noise is varied between [20 dB • • • 5 dB] and impulse noise is varied VOLUME 9, 2021between [5% • • • 25%]. Images used for training are obtained from CAVE 1 and Interdisciplinary Computer Vision Laboratory (ICVL) [52] 2 multi-/hyperspectral datasets. Images from CAVE dataset are obtained from Cooled CCD camera (Apogee Alta U260) which is a multi-spectral camera acquiring images of size 512 × 512 × 31 in the wavelength range 400 nm to 700 nm in steps of 10 nm. There are in total 32 images in the dataset obtained in artificial lighting conditions in the 16-bit format.
11, 12 Our group previously used a risk stratification approach to distinguish between patients with IPLC at high versus low risk of SPLC using the Surveillance, Epidemiology, and End Results database. 13 owever, this cohort did not contain data on potentially important risk factors such as tobacco smoking. In this study, we leveraged data from the Multiethnic Cohort (MEC) to identify risk factors for SPLC among IPLC cases with a focus on tobacco smoking. We validated these findings with the following two additional cohorts: the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the European Prospective Investigation into Cancer and Nutrition (EPIC). Finally, in a subset analysis in MEC, we evaluated the impact of smoking cessation on reducing SPLC risk.
MEC: Statistical Analyses Cause-specific proportional hazards models evaluated the associations between candidate risk factors and SPLC, accounting for the competing risk of death from all causes. 16 The cause-specific function was selected because it can estimate the effects of factors on SPLC risk with sustained power in multiple competing risk scenarios while minimizing type I error. 17 All models evaluated SPLC risk from the date of IPLC diagnosis. The proportional hazards assumption was confirmed for all models. In light of previous data indicating that IPLC age at diagnosis, stage, and histology are relevant predictors for SPLC,  13  we adjusted for these as covariates in all analyses. To account for multiple testing, we implemented a Bonferroni threshold for the primary analyses (p ¼ 0.
05/9 ¼ 0. 005). For all other analyses, statistical significance was defined at a two-sided p value less than 0. 05. Cumulative incidences were calculated using Gray's method. 18 We handled missing data by performing multiple imputation  19, 20  and evaluated the robustness of the findings using complete cases (Supplementary Methods). 1] [12]  Additional sensitivity analyses evaluated the smoking-SPLC associations among ever smokers, within major IPLC histologic subtypes (i. e. , adenocarcinoma, squamous cell carcinoma), and among advanced-stage IPLC cases. For continuous smoking variables (i. e. , pack-years, CPD), we evaluated their potential nonlinear effects using natural cubic splines. 21 All analyses were performed using R version 4. 0. 2 (Vienna, Austria).
PLCO and EPIC: Validation Cohorts We validated the SPLC associations using the following two additional epidemiologic cohorts: PLCO (N ¼ 3423 IPLC cases with 110 SPLC) and EPIC (N ¼ 4731 IPLC cases with 16 SPLC). Detailed information on these cohorts is included in the Supplementary Methods. As with the discovery cohort, cause-specific proportional hazards models evaluated the SPLC associations in PLCO and EPIC. To pool the SPLC associations across the three cohorts (discovery and validation), we used random effects meta-analysis. 22 tatistical significance was defined at a two-sided p value less than 0. 05. Smoking cessation was not evaluated in these cohorts because longitudinal smoking data were unavailable for this collaborative study.
Discussion In this study, we found that tobacco smoking is a prominent risk factor for SPLC among IPLC cases on the basis of three epidemiologic cohorts. We also revealed in a landmark analysis that smoking cessation after IPLC diagnosis is associated with a substantial reduction in the risk of SPLC. To the best of our knowledge, this is the first population-based study to leverage multiple epidemiologic cohorts in identifying and validating risk factors for SPLC, including tobacco smoking. We evaluated  several candidate SPLC risk factors that have not been previously examined (e. g. , personal history of cancer, family history of lung cancer) and evaluated detailed smoking exposures through multiple measures, including smoking status, pack-years, intensity, and years since cessation. Importantly, these data support tobacco smoking as a modifiable risk factor for SPLC, with smoking cessation after IPLC diagnosis associated with a significant reduction in SPLC risk.
Nonetheless, these deficiencies were accounted for appropriately in the meta-analyses using random effects models. Demographic and environmental data were collected through self-reported questionnaires in all cohorts; thus, misclassification is possible, but the consistency of the SPLC associations strengthens these findings. The smoking cessation analysis consisted of a small subset of MEC participants and requires further validation in independent cohorts, which is currently underway. Finally, one relevant question concerns the relationship between tobacco smoking and overall survival in patients with IPLC, specifically regarding the impact of SPLC on survival and how tobacco smoking contributes to survival differences. Future directions should aim to elucidate these relationships, which are beyond the scope of the present study.
Acknowledgments This work was supported by the  National Institutes of Health  ( 1R37CA226081 ,  U01 CA164973 ) and a  Stanford Medical Scholars research grant. The funders had no role in the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Where authors are identified as personnel of the  International Agency for Research on Cancer/WHO , the authors alone are responsible for the views expressed in this article and do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/ WHO. Cancer incidence data have been provided by the  Alabama Statewide Cancer Registry ,  Arizona Cancer Registry, Colorado Central Cancer Registry, District of Columbia Cancer Registry ,  Georgia Cancer Registry ,  Hawaii Cancer Registry, Cancer Data Registry of Idaho ,  Maryland Cancer Registry ,  Michigan Cancer Surveillance Program ,  Minnesota Cancer Surveillance System, Missouri Cancer Registry, Nevada Central Cancer Registry, Ohio Cancer Incidence Surveillance System ,  Pennsylvania Cancer Registry, Texas Cancer Registry, Utah Cancer Registry ,  Virginia Cancer Registry , and  Wisconsin Cancer Reporting System.
So far we have only used a semi-explicit time scheme with an ad-hoc time-step restriction. Moreover, a more in-depth study should be carried on to evaluate the accuracy of the thermal fluxes through the interface related to the use of the Ghost-Fluid method. This work takes a step forward in our general ongoing R&D effort to provide models and numerical methods for simulating the thermal-hydraulic behavior of a corium pool composed of different chemically reactive liquid phases with, possibly, liquid/solid phase change at their boundaries. The presented CFD results and, more importantly, the future ones will be used for engineering applications following an up-scaling methodology (see  [31]  for instance) in order to enhance modeling in the industrial integral code PROCOR  [25].
Background: The impact of pretreatment factors on immune checkpoint inhibition in platinum-refractory advanced urothelial cancer (aUC) deserves further evaluation. The aim was to study the association of Bellmunt risk factors, time from last chemotherapy (TFLC), previous therapy and PD-L1 expression with atezolizumab efficacy in platinumrefractory aUC. Patients and methods: This was a post-hoc analysis of patients who had received prior cisplatin or carboplatin in the prospective, single-arm, phase IIIb SAUL study (NCT02928406). Patients were treated with 3-weekly atezolizumab 1200 mg intravenously. The primary outcome was overall survival (OS). Relationships were analysed using Cox regression and long-rank test. Results: Of 997 patients in SAUL, 969 were eligible for this analysis. The number of Bellmunt risk factors was associated with OS (P < 0.
Importantly, the study included difficult-to-treat populations, such as non-urothelial carcinomas and patients with autoimmune diseases, which are underrepresented in clinical trials but frequently present in everyday practice, yet relevant clinical evidence is lacking. Therefore, we used the SAUL database to investigate the prognostic value of patient-related characteristics, factors related to previous chemotherapy as well as PD-L1 expression among patients with aUC treated with atezolizumab following failure of first-line, platinum-based chemotherapy.
Patients De-identified data including demographic, clinicopathologic, laboratory and outcomes from the complete SAUL database were provided by the sponsor. The data cut-off for the primary analysis was 16 September 2018. The database was not updated for this analysis. All patients provided written informed consent and the trial was approved by the institutional review board or ethics committee at each site before study start. Inclusion criteria have been previously published. 14 riefly, SAUL (NCT02928406) is a single-arm, phase IIIb safety and efficacy study of atezolizumab in locally advanced or metastatic urothelial or non-urothelial carcinoma of the urinary tract. All patients were required to have ECOG PS 2 and disease progression during or following one to three prior treatments for inoperable, locally advanced or metastatic disease.
Statistical analysis All time-to-event data (OS, PFS) were calculated from day 1, cycle 1 of study treatment and were summarised using KaplaneMeier estimates and medians reported with corresponding 95% confidence intervals (CIs). TFLC was calculated from the last day of the last chemotherapy line administered before study entry on day 1, cycle 1 of study treatment. Post-hoc subgroup analyses of efficacy according to the following factors were carried out: previous platinum compound (cisplatin versus carboplatin; patients who received both were categorised in the cisplatin group), number of previous lines of systemic therapy (0 versus 1), TFLC (cut-off points at 3, 6, 9 and 12 months), PD-L1 expression [tumour-infiltrating immune cell (IC) score of 0/1 versus 2/3], baseline haemoglobin (<10 versus 10 g/ dl), liver metastases (yes versus no), ECOG PS (0 versus 1/2), number of Bellmunt risk factors (0 versus 1 versus 2/3) and adjuvant or neoadjuvant therapy (yes versus no).
Of the 600 patients who had received previous lines of chemotherapy for advanced/ metastatic disease, 94 (16%) had received perioperative chemotherapy. Among the 969 patients, 100 had ECOG PS of 2, 45 had non-urothelial histology, 14 had CNS disease, 2 were HIV positive, 34 had a history of autoimmune disease and 38 were on steroid treatment at study entry. One hundred and forty-eight patients (15%) received some form of anticancer therapy after atezolizumab (Supplementary Table  S1 , available at https://doi. org/10. 1016/j. esmoop. 2021. 100152). No patient received erdafitinib or enfortumab vedotin. At the time of database lock, median follow-up was 12. 8 months (95% CI: 11. 8-13. 3), 539 (55. 6%) patients had died, 492 (91. 3%) due to urothelial cancer, while 212 (21. 9%) were still on study treatment.
erefore, studying the effect of material static mechanical properties on rail fastening clips is of great significance since these findings are helpful for suppliers to provide theoretical guidance to implement proper heat treatments in the manufacturing process. To fill these gaps in knowledge, based on previous research  [27] [28] [29] [30] , this paper takes the ω-shaped fastening clip of the Vossloh 300-1 fastener system, which is commonly used for high-speed railways, as the research subject. A series of uniaxial tensile tests were first performed on the fastening clip material 38Si7 at three different stages. en, based on the measured performance data of the fastening clip material, a finite element (FE) model of the complete fastener system was constructed. e critical plane method was subsequently employed to identify the critical plane at the critical point, and the fatigue crack initiation life of the fastening clip was predicted following the Brown-Miller criterion.
B A C K G R O U N D Traditionally, renal registries collect and report populationbased epidemiological data on patients with kidney failure who are treated by kidney replacement therapy (KRT), i. e. dialysis or transplantation. Over the past decade, a number of these registries have started to widen the inclusion of patients to those with kidney failure treated with comprehensive conservative management and in some cases to earlier stages of chronic kidney disease (CKD), leading to, for example, CKD Stages 4-5 registries. As a result, they are increasing their value by not only providing numbers on those receiving or refraining from extremely expensive therapies, but also to stages of CKD in which kidney failure may still be prevented. The European Renal Association (ERA) Registry currently collects data on patients treated by KRT and uses them for comparison and collaborative research.
C K D R E G I S T R I E S I N E U R O P E Information was collected from six existing CKD registries and one in preparation. Most had been started by the boards of the national KRT registry with which they formed one registry, but in Romania the CKD registry was separate from that on KRT (Table  1 ). The Czech registry was fully funded by nongovernmental sources, whereas five others-those in Frenchspeaking Belgium, France, Norway, Romania and Swedenmanaged to secure at least partial funding from their ministries of health or other healthcare system authorities, mostly for the set-up or maintenance of their web-based data collection platforms. In the majority of cases, data collection was voluntary.
D A T A C O L L E C T I O N All registries collected demographic data, primary renal disease (PRD), comorbidities and height and weight at baseline, but in France and Norway this was extended to information on care plans (Table  2 ). Most collected medications, often accompanied by their Anatomical Therapeutic Chemical codes. Additionally, all registries gathered baseline and follow-up data on estimated glomerular filtration rate (eGFR). In contrast, only three registries collected data on urinary albumin:creatinine ratio. All countries with functioning registries collected at least some laboratory test results (Table  3 ), frequently by linkage to national or regional laboratory databases. The outcomes studied included CKD progression, dialysis and transplantation (including pre-emptive transplantation), date and cause of death, sometimes supplemented with data on (pre-emptive) transplant waitlisting, hospitalization and complications.
C O N C L U S I O N Given the importance of obtaining knowledge on patients with advanced CKD, it is not unexpected but still disappointing that the results of this inventory show that in Europe only six countries or large regions have engaged in routine data collection on patients with CKD Stages 4-5 who are under the care of nephrologists and Finland is making preparations to do so. Most are collecting data on a growing number of patients while facing challenging issues in registry management, such as the efforts needed for data collection. As a next step, we will explore whether the data quality and potential differences in methods and definitions used by the countries will allow collaboration in a European CKD registry under the umbrella of the ERA Registry with the purpose of joint scientific analyses to advance our knowledge of treatments and outcomes in advanced CKD.
The second possibility, called "inverted ordering", corresponds to taking  3 <  1 <  2 which effectively means that the   would not be the lightest neutrino. The measurement of the NMO is on the agenda of several existing and planned neutrino experiments. JUNO  [4, 5]  and KM3NeT/ORCA  [6]  are two next-generation neutrino detectors that plan to address this question. Both detectors are already under construction and both expect to be fully functional and taking data by 2025. ORCA, the low-energy branch of the KM3NeT neutrino telescope, will determine the NMO by probing Earth matter effects on the oscillations of atmospheric neutrinos. JUNO is a medium-baseline reactor neutrino experiment which is sensitive to the NMO through the precise measurement of the fast oscillations in the spectrum of the reactor electron anti-neutrino spectra, where matter effects play only a small role.