dataesr/scientific-paragraphs-categorization huggingface dataset:
Currently, the ERROR dosimetry software  [87] , based on GATE, aims to optimize dosimetry during pediatric CT examinations by modeling and validating clinical commercial CT scanners. The process of simulating a CT protocol with GATE software is summarized in Fig. 1. The proposed methodology is to create a simulated dosimetry database using a large population of several pediatric models. Thus, the database could be used in clinical practice by matching every new patient to the closest anatomical pediatric model and to extract the precalculated absorbed doses per organ for the specific imaging protocol and for the specific CT scanner  [48] ,  [49]. MC simulation techniques appear the most precise and advanced approach for internal dose calculations  [124] -  [126]. These techniques are also getting into the clinical practice with the development of more user-friendly software, such as the Minerva, Celldose, RADAR, DOSIMG, 3D-RD, OEDIPE and DPM  [127] -  [133].
For research purpose different teams have different code for dosimetry of NM, such as the MCNP  [134] -  [137] , FLUKA  [138] , and Geant4  [139] ,  [140]. At last, GATE MC code  [22]  has been widely used for internal dosimetry  [89] ,  [141] -  [147]. Fig. 2  shows the procedure for the simulation of an NM study in GATE. There is also a radiotherapy study comparing MC techniques with a clinical Treatment Planning System, for Proton therapy dose distribution to pediatric patients  [148]. In another radiotherapy study, several commercial BT seeds were modeled and validated which could be directly applied in MC treatment simulations for pediatric purposes. Initial results were presented by Chatzipapas et al. [149]. Fig. 3  presents the phantom used for the BT simulation and the corresponding isodose curves that were depicted.
Absorbed fractions for photons at discrete energies were published for these phantoms, which contained approximately 25 source and target regions. The S value datasets were made available in the MIRDOSE computer software  [155]. Lee et al. [156]  proposed six more computational phantoms of pediatric patients. They are tomographic computational phantoms which provide distinct advantages over stylized phantoms, such as the MIRD. The UF hybrid reference phantoms  [157]  are well suited for manual adjustment to match anthropometry. Researchers have developed methods to systematically scale phantoms to various body morphometries using the software Rhinoceros. The handbook of Anatomical Models for Radiation Dosimetry has been published reviewing the whole history and progress of anatomical computational models  [21].
Xu et al. [158]  proposed a different approach than MIRD, leading to an image-based whole-body adult male model, constructed using color photographs, the VIP-Man. DUKE University has developed a series of realistic 4-D anthropomorphic models (XCAT model  [159] ) based on the nonuniform rational b-spline (NURBS) and subdivision surfaces. In 2009, the ITIS foundation developed and published the "virtual family"; a series of surface-based anatomical models, including two children. Two years later they extended their dataset with a "virtual classroom," including four more children based on healthy clinical MR data  [160]. Recently some realistic phantoms have been published  [161]. In this paper, 80 deformable pediatric reference NURBS computational phantoms are described, ranging from newborn to 15-year-old, male and female.
V. CONCLUSION In this paper, a review on the evolvement of pediatric dosimetry has been applied, referring the medical protocols used in clinical practice. A large number of medical societies and agencies are working together toward the promotion of radiation protection for children through updated guidelines, good strategies, improved instrumentation, and reconstruction algorithms. The rapid evolution of computer technology in combination with the clinical needs of modern medicine for personalized protocols, have emerged the growth of numerous pediatric models, fast simulation procedures (GPU, HPC, etc. ) and new deep learning techniques that can predict dosimetry using large databases of presimulated data. The individual anatomical and functional characteristics play crucial role in the assessment of accurate dosimetry and modern medicine and the scientific community aim to overcome obstacles for such an issue of pediatric dosimetry.
Description of population After screening the database of The Holy Family Specialist Hospital in Rudna Mała (Poland), 111 subjects operated in 2015-2016 for a rotator cuff tear and therefore meeting the inclusion criteria, were invited to participate in this study. The subjects were contacted by telephone and informed about the research objective and procedure. They were asked to complete the questionnaires, which were sent to them by post, in accordance with the enclosed testing procedure instructions. 37. 8% (N = 42, 24 males, 18 females, mean age = 56. 5) of the initially enrolled subjects refused to participate in this study. 62. 2% of the subjects (N = 69, 49 males, 20 females, mean age = 55. 5) completed and sent back the questionnaires. Each of the subjects provided answers for all questions included in the OSS-PL.
Photo analysis and HB scoring The counting of dead brood removal was performed by analysing the pictures that were taken for each tested area at time zero and after 24 h. Image analysis was performed with the help of the counter tool of the software ImageJ  (Schneider et al. 2012). HB was recorded as the proportion of removed dead larvae in 24 h: HB ¼ 1-sealed cells T 24 sealed cells T 0 HB was scored in the most conservative way, i. e. , if the cell was only partly uncapped or if it was uncapped but the dead larva was only partially removed, the cell was considered sealed.
The relationship between the accuracy and n reveals the benefit of repeatedly recording HB. The accuracy for each method was calculated by the following formula (see Appendix for the derivation): a ¼ σ C ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi σ 2 C þ σ 2 e n r The trends of the accuracy of each method and the phenotypic correlation between the two methods were plotted as a function of the number of records. Statistical analyses were performed using the computing environment R (R Core Team 2015). Mixed models were fitted using the R package lme4  (Bates et al. 2015).
Colonies and phenotyping Heritability and repeatability were estimated only for the FKB* method, from data collected in 2017 and 2018. FKB* was used to phenotype a cohort of 151 colonies made available by Melyos, an Italian bee-breeding and beekeeping company. The colonies were kept in two apiaries, near Zelo Buon Persico, Lodi, Italy, during 2017 and in one apiary in 2018 located in Lesmo, Monza, Italy. The tested group was composed of colonies headed by groups of sister queens with known pedigree, all naturally mated at an isolated mating station hosting one paternal line which was different for the two groups tested in the 2 years. Each colony was managed in the context of a breeding programme, therefore in the most standardised way. The colonies were phenotyped twice for HB during productive season of 2017 and of 2018.
Statistical analysis For the analysis, estimates of the genetic relationships between groups of workers and queen in colonies are required, and also those with the groups of drone-producing queens with which queens are mated. We used the methods of  Brascamp and Bijma (2014)  to estimate these relationships. The pedigree file was built following the procedure described in  Brascamp et al. (2016). To estimate heritability and repeatability, the statistical package ASReml and the pin function of the nadiv package were used in the computing environment R  (Butler 2009; Wolak 2012; R Core Team 2015). Only the genetic effect of the workers was included, as the paucity of data did not allow us to simultaneously estimate the queen and the worker's effect. Following  Brascamp et al. (2018) , we used the additive genetic variance of worker groups to calculate phenotypic variance.
Another important issue is that parametric methods will return a partition even if data do not contain any structure. Table. 1 shows the resulting clustering using three well-known parametric methods in the solar energy literature, namely the Kmeans, the partition around medoids (PAM) and hierarchical clustering (HC). In this example, we use 10-minute averaged set of Sioux Falls, USA, as a training set (See Section. 2 for more details about data). To choose the optimal number of classes, we maximize the silhouette score and Dunn score  (Brock et al. , 2008)  under different distance measures for the same algorithm. Table. 1 clearly shows that the number of classes varies from model to model, as such, the correct number of classes is highly subjective. It depends on the algorithm used and the measuring parameter of similarity, which makes the appropriate choice ambiguous.
One of the optimal approaches to solve these problems involves the use of the nonparametric paradigm. It is able to establish automatically the correct number of classes and even able to detect the absence of partition in the data structure, without having to perform any model comparisons  (Gershman and Blei, 2012). The nonparametric method prescribes the use of an infinite-dimensional parameter space rather than a fixed dimension. Furthermore, a common practice is to combine the Bayesian approach with the nonparametric framework to provide more robust models based on the prior knowledge  (Frimane et al. , 2018) , which can be of interest for different climates and geographical differences at various sites around the world. Traditionally, SES studies and simulations are primarily conducted on the basis of historical solar irradiance measurements or through public databases such as typical meteorological years.
Main ideas and contribution This paper presents a new clustering-based methodology for generation of temporal 1-min synthetic irradiance data as downscaled from 10-min to 20-min real data such is the standard availability from next generation satellite databases. We state that the method proposed within is not limited to GHI, though this is the only irradiance component that it is applied to, but could also be applied to direct and diffuse irradiance also. The new method aims to satisfy all the key opportunities identified from literature. Firstly, a Dirichlet process Gaussian mixture model (DPGMM) is employed that aims to provide a new consistent nonparametric Bayesian (NPB) framework for automatic classification, commonly referred to as clustering of daily clearness index distributions. The DPGMM investigates the use of an overarching probability function; i.
From the computational viewpoint, our algorithm requires less input data (just a time-series of solar irradiance) and is more computationally straightforward since it is Bayesian. The computation task is based on the generation of drawings from the posterior distributions using Markov chain Monte Carlo procedure, specifically Gibbs sampling  (Neal, 2000). Most importantly, reproducibility of this work can be achieved by downloading the R-package methodology from https://github. com/frimane/SolarClusGnr. The structure of this paper is as follows. Section. 2 provides the description of the solar irradiation databases used in this study. The methodology is fully detailed in Section. 3. Section. 4. 1 and Section. 4. 2 discuss the experimental results of classification and presents a detailed analysis of the resulting classes and class sequences re-.
Solar global horizontal irradiance data Four data-sets of GHI time-series distributed throughout to the world were used to validate the method described in this paper. All irradiance data is gathered from the Baseline Surface Radiation Network (BSRN) an affiliate of the World Meteorological Organization (WMO). All the irradiance data collected underwent rigorous quality control following the well established convention described by  Long and Shi (2006). These checks test the measured values against their physical limits for acceptability. Measured GHI data from the BSRN is not taken directly from the measured instrument directly, but instead calculated as the sum of the direct horizontal irradiance and diffuse horizontal irradiance as is the convention in solar engineering  Gueymard and Myers (2009).
Prior setting This section describes the prior settings around which the DPGMM is based. All quantities within the DPGMM (e. g. k t , parameters, hyperparameters, etc. ) are random variables, and so they are represented by distributions. We define the distribution of k t by allowing an infinite number of Gaussian distributions that, when combined, form the overall distribution. We adopt a Bayesian approach and so we must establish a prior set of parameters that define these infinite number of Gaussian distributions before they are later updated in the posterior. Prior settings provide the first description of the classes that exist within the data before it takes into account any input data or other evidence. Because the parameters that define the overall k t distribution are also random variables, they in turn are represented by their own set of parameters-we call these additional sets of parameters 'hyperparameters'.
Hyperparameters are again random variables, however, we do not extend the hierarchy beyond this level of complexity; the hyperparameters are fixed by the empirical mean and the covariance of the input data. The explicit expression and parameterization of each probability function used in this section is listed in Appendix A. For the DPGMM, the prior setting begins by having an infinite number of classes. What this means is that every dataset analysed by this methodology does not have a limit on the number of classes identified as statistically. Therefore, each daily distribution of k t (h t ) is generated by an infinite mixture of multivariate Gaussian distributions (MGDs). These concepts are denoted in what follows: h t |{π j , µ j , Λ j } ∞ j=1 ∼ ∞ ∑ j=1 π j N j (h t |µ j , Λ j ), (1) where π = {π j } ∞ j=1 is the mixing proportions vector, Θ = {µ j , Λ j } ∞ j=1 are the parameters of the MGDs components; µ j is the mean vector for the MGD component N j and Λ j its precision matrix.
Posterior inference In the posterior inference, we decide whether the observation under analysis belongs to an existing class or whether a new class must be created. In our application, we are concerned with the distribution of k t from an entire day. Our prior settings are therefore updated with the data to determine which class the k t distribution belongs. We can arbitrarily name these classes depending on their distribution characteristics, e. g. a distribution with a very high peak at a large k t would be indicative of a clear day. The prior distribution of c t -the prior distribution of the class label that the day t takes-can be obtained by integrating out the mixing proportions π conditional on all other random variables. It has the following limits at infinity. For more details, see  Neal (2000) : P(c t = j; j already seen| c -t , α) -→ n -t, j N -1 + α , (7a) P(c t = j; j new| c -t , α) -→ α N -1 + α , (7b) where c -t is the set of all observed indicator variables except c t , n -t, j is the number of days associated with the class label j excluding the day t, and N is the total number of days.
ICDF is obtained by integrating the complementary ECDF of the cumulative increments. It gives the sum of all possible cumulative increments that characterizes the day. SSN indicates the frequency of the Sun occurrence in the sky. It is calculated using a random time-dependent binary variable that uses direct normal irradiance (DNI) values and zenith angle, for more details see  Blaga and Paulescu (2018). Lastly, FD of a solar irradiance timeseries is a measure of how much it is jagged. It ranges between 1 and 2. FD of value 1 correspond to a straight line and a FD of value 2 correspond to a wiggly line that is totally fills up a plan. Interested readers can find more detailed information in  Harrouni (2008). The validity of our methodology is examined by statistically quantifying the sameness of the empirical cumulative distribution functions (CDF) of each quantifier calculated from the synthesized data and the corresponding 1-min measured time-series.
Geographically speaking, this makes our methodology more flexible in many respects despite the dependency on training data. This does raise the question as to what can be fully considered synthetic data and which should be considered downscaling? Our methodology is perhaps a hybrid, as it can be used for purely synthetic time series without the guidance of the input data, however, it can also very successfully attempt to downscale the data by gap filling with 1-min resolution that has the statistical properties of real data, even if it is not expected to match. Grantham et al. (2017)  and  Bright (2019)  proposed all the reasons why this type of methodology is useful, however, we have avoided the need for storing a significant database of sequences in order to downscale. Our classification technique avoids the clear pitfalls of their sequence-based methodology whereby ramps were facilitated during actually clear periods because the mean hourly k t was maintained even under highly variable skies.
This is a weakness in the model in that there must be a large enough sample size is required for optimal performance. In practise, we would use-and strongly recommend-sources of coarser resolution data which tend to have a more significant history. This is entirely suitable as most satellite-derived bankable solar irradiance databases extend to significant historical duration. The recent findings of  Bright (2019)  that sites matched by climate did not provide the best synthetic generation than other sites that inherently contained a more appropriate statistical similarity. It was suggested that variability was the crucial component. Furthemore,  Schwarz et al. (2018)  mapped the whole world in regards to spatial vari-ability correlation and presented fascinating maps of variability similarity.
The experimental databases are recorded at four solar irradiance monitoring stations around the world. To assess the consistency of the DPGMM, we have summarized the daily clearness indexes k t as daily equal width histograms for different databases at different temporal resolutions. A good agreement between the resulting classes and their sequences is obtained. Based on the resulting class distributions and the transition rules encoded by transition rate matrices, combined with a Markov model, we generated synthetic 1-min GHI time-series with minimal error compared to measured data. The global applicability of this methodology was demonstrated by training the methodology on one site and testing at a different location that shared similar climatic properties. We observed very good performance at a globally applicable synthetic irradiance generation through our advanced clustering approach.
location than the training data, we apply testing to sites based on similar climate conditions. Each two similar climate databases are compared by the same manner described above. From Table. 2 we compare ASP to TAM, SFX to TOR. Fig. 9display the CDFs of each quantifier over the generated and measured data for each pair of locations. The CDFs are correlated. Table. 5 confirms this correlation by showing high values for the OVC index and lower values for the KLD measure. This confirms that there is a good agreement when applying the methodology in a synthetic irradiance generation application. With only the training data from a few locations, it is suggested that the methodology can be applied to any other similar climate with reasonable results. This is very noticeable between ASP and TAM ground stations whereby the training and testing datasets are 14,783 km separated on different continents and hemispheres.
We see fantastic reproducibility across all metrics. This can be also observed between SFX and TOR, two ground stations located also on two other different continents. This very much implies global applicability with an appropriate database of climate types to train on. The implications of this are that, for a given location, high-temporal resolution synthetic irradiance data can be generated so long as data from a similar climate exists, which it invariably does on account of the extensive collections from the BSRN. The predictive accuracy in terms of normalized root mean square error (nRMSE) and normalized mean bias error (nMBE) are depicted in Table. 6. It is observed that in terms of nRMSE, DPGMM shows a good similarity in the GHI means for both daily and monthly time scales with all values < 0.
While these perioperative benefits are generally accepted, the differential impact of RARC compared to ORC on survival outcomes remains debated with widely diverging opinions  [4, 11, 12]. The RAZOR trial  [13] , a randomized, open-label, non-inferiority, phase 3 trial comparing ORC and RARC, found that RARC was non-inferior to open cystectomy for 2-year progression-free survival but did not report overall survival. Given the shortage of prospective randomized trials comparing RARC to ORC, controlled data regarding the oncological risks and benefits are needed from well-designed retrospective multicenter studies. Therefore, to address this unmet need, we collected complete data from BCa patients treated at academic centers to determine the impact of on survival outcomes of RARC compared to the standard ORC.
Patients and Methods We collected the data from 9757 patients treated with RC for non-metastatic UCB at 25 institutions. Patients were staged preoperatively with cross sectional imaging (mostly computerized tomography), bone scan when indicated and chest X-ray. Surgical specimens were processed according to standard pathologic procedures at each institution. Tumors were staged according to the 2009 American Joint Committee on Cancer-Union Internationale Centre le Cancer (AJCC/UICC) TNM classification. Tumor grade was assigned according to the 2003 WHO/International Society of Urologic Pathology (ISUP) consensus classification. STSM was defined as the presence of tumor at inked areas of soft tissue on the RC specimen  [14, 15]. Urethral and ureteral margins were not considered as STSM. Lymphovascular invasion (LVI) was defined as the presence of tumor cells within an endothelium-lined space without underlying muscular walls  [16, 17].
The variables adjusted for were administration of neoadjuvant chemotherapy (NAC), grade, pathological T stage, lymph node status and age at surgery Subgroup analyses were performed. Statistical significance was considered at p < 0. 05. Statistical analyses were performed using SPSS v. 22. 0 (IBM Corp. , Armonk, NY, USA) and STATA 14 (Stata Corp. , College Station, TX, USA).
This manuscript follows two previous publications [10,21] from the same collaboration, evaluating for the first time the impact of survival and on peri-operative outcomes demonstrating an advantage of RARC in blood loss and length of stay. New centers were added to this manuscript in respect of the previous publications and the match of the final database was performed separately for each study on the bases of the main aim of each project. We found that RARC and ORC share similar survival outcomes, both on univariable and multivariable analyses controlled for established prognostic factors. We performed propensity matching to minimize the risk of selection bias adjusting for pathological stage, lymph node status, and age at surgery. Even in this setting we confirmed that the RARC approach is associated with similar recurrence and CSM rates compared to ORC.