dataesr/scientific-paragraphs-categorization huggingface dataset:
FRoG's fast yet computationally intensive patient recalculations were made feasible by a GPU-based architecture. Over the last decade, a surge in compact, affordable computational systems like the GPU have found their way into scientific research to perform parallelized computation as opposed to the traditional CPU cluster  20. As Moore's law persists, demand for IT and computer science specialists in radiation oncology clinics and medical physics related fields will endure. FRoG's analytical dose calculation accuracy on the other hand stems from the dual pencil-beam (DPB) model in conjunction with superposition PB splitting. Past approaches to PB splitting are based on a single Gaussian model. This work marks the first technique to separately handle multiple Gaussians during PB splitting, made possible by FRoG's inherently MC-driven physics database.
Methods Platform Architecture. For FRoG, the pencil-beam algorithm  43  with GPU optimized Siddon raytracing  44  , which provided runtime reductions by up to a factor of 6 in previous works, was employed into the framework. To extract medical data inputs necessary for the dose algorithm, the Python programming language along with packages and toolkits for DICOM and GPU compatibility were implemented. Platform features include physical and biological dose calculation using DG  45, 46  or TG  29, 30  parameterization with the base data generated via FLUKA MC simulation  [47] [48] [49]. All GPU-based recalculations in FRoG were performed on a high-end consumer grade graphics card (NVIDIA Tesla V100). Raytracing is performed at the resolution of the planning CT, whereas dose can be calculated on a downsized grid, following the clinical procedure at HIT.
). When using higher order lateral parameterization models for improved accuracy like TG in place of DG, the computation time increases by a factor of ~3. Particle beam database. The FRoG physics database was generated in silico with the FLUKA MC development version 2016 for all four ions available at HIT, which incorporated a detailed geometry of the HIT beamline  50. The HADROTHErapy default setting was selected, with a particle transport and delta ray production threshold set to 100 keV. The production and evaporation of energetic heavy fragments was activated via the COALESCE and EVAPORATION cards, respectively. Patient dose calculations with helium, carbon and oxygen ions required application of a ripple filter as described in previous works  51, 52. For physical dose calculation, the FRoG database included parameters such as integral depth dose (IDD), and Gaussian parameters (sigmas and relative weights of the Gaussians, respectively) for both DG and TG parameterization.
During database generation in FLUKA, primary particles crossing boundaries of the tungsten wiring in the MWPC were flagged. Scoring of these flagged primaries was performed separately from primaries which did not interact with tungsten wiring, yielding a pair of pencil beams for each energy in the HIT database. In summary, DPB model was constructed for proton and helium ions, while a conventional single PB model was implemented for carbon and oxygen ions. To account for variable lateral dose evolution in the presence of anatomical heterogeneity, PB splitting was incorporated into FRoG's framework following the mathematical procedures of beamlet superposition  56. In a 2D grid space, the splitting method involves a superposition of N equally-spaced sub-Gaussians (bounded by ±3. 5σ) with equivalent FWHM but variable weighting.
25. The second weighting factor for the secondary PB measured in air at the isocenter varies from roughly 22% to 24% and 17% to 24% for all available proton and helium ion beam energies, respectively. As a first approximation, the effect of the MWPC on the PB is neglected for the heavier ions, carbon and oxygen, since the degree of large angle scattering decreases with particle mass, resulting in reduced second and third Gaussian sigmas and weights. Therefore, a conventional PB model using a single database was justified for carbon and oxygen ions. Platform validation in a homogenous water phantom. For preliminary testing and characterization, FRoG forward dose calculations were performed in QA-mode (without PB splitting) for a homogeneous water phantom with the surface situated at isocenter.
Materials and Methods Sampling and genotyping. Samples from 82 European wild boars (Austria, Bosnia and Herzegovina, Estonia, Hungary, Poland, Serbia and Slovakia) and 60 domestic pigs (Croatia, Estonia and Poland) were provided by local hunters and veterinarians and collected according to National laws, no animal was specifically killed for this research. These 142 individuals were genotyped with the Porcine SNP60 Beadchip  16  , according to the manufacturer's instructions (http://www. illumina. com/products/porcineSNP60_dna_analysis_kit. ilmn) at GenoSkan A/S (Denmark). The resulting 60 K genotypes were merged with publicly available data from Near Eastern (N = 19) and European (N = 334) wild boar and domestic pigs (N = 318)  18, 19, 24. The dataset was analysed with PLINK 1. 9  25  for filtering according to quality (call rate >0.
9, missing genotypes <10%) and relatedness (identity by descent) criteria. Whenever possible, without compromising the sample size, we removed one individual from closely related pairs showing a high degree of relatedness (first order relatives). Additionally, as unequal sample size could potentially bias the estimates of diversity measures, levels of differentiation and cluster inference, the number of individuals in each population was equalized. We randomly removed, in R 3. 5. 0  26 , individuals from large populations to obtain a maximum sample size of 25. The resulting pruned dataset consisted of 16 Near Eastern and 290 European wild boar and 299 pigs belonging to five international (N = 44) and 22 local breeds (N = 255), genotyped at 47,148 (47 K) autosomal loci (Fig. 1  and Table  1 ; for additional details see Table  S1 ).
Variability levels of the populations were assessed by computing minor allele frequencies (MAF, which indicates the abundance of rare alleles through the genome), as well as expected (H e ) and observed (H o ) heterozygosities (which are measurements of genetic variability) within clusters with PLINK. Genetic differentiation among populations was estimated by calculating pairwise F ST values with Arlequin 3. 5  28. For this analysis, loci in linkage disequilibrium (r 2 > 0. 5) were removed with PLINK, to reduce bias due to physical linkage between loci  29  , resulting in a reduced dataset of 29,802 (30 K) SNPs. We performed an initial assessment of population structure based on the 30 K dataset and all individuals with the maximum likelihood approach implemented in Admixture v1. 23  30. Default settings plus a bootstrap of 1000 and a cross-validation of 10 for values of K from 1 to 30 were used in this analysis.
2a. The number of polymorphic loci ranged from 28,575 (60. 6%) in Near Eastern wild boar to 44,104 (93. 5%) in commercial pigs, Near Eastern wild boar (0. 136) and commercial pigs (0. 262) also represent the two extremes of the MAF range (Table  1 ). With the exception of DP-CE and DP-CN all populations showed higher H e than H o , with H e ranging from 0. 186 (Near Eastern wild boar) to 0. 344 (Commercial breeds) and H o in the range 0. 165 (Sardinian wild boar) -0. 333 (DP-CN) (Table  1 ). Pairwise F st values, calculated on the 30 K SNPs dataset, were lower among domestic breeds than among wild boar populations (Mann-Whitney U test, t: 2. 715, p: 0. 009). Values ranged from 0. 045 (commercial breeds-DP-CW) to 0. 152 (Balkan breeds-DP-CE), whereas the most divergent population was Near Eastern wild boar (range 0.
Identification of hybrids. Admixture analyses were initially based on all the available samples and the 30 K dataset. The cross-validation error showed a decreasing tendency without reaching a plateau, with a first levelling off at K = 27. Such results highlight the complexity of both the wild and domestic populations (Fig. 3a ). The observed substructure could be an important tool for animal traceability, allowing the monitoring of natural expansion -like in the observed case of German wild boar with French or WB-CNE ancestry, as well as Balkan wild boar among the Italian sample -and, potentially, the identification of translocated individuals. The analyses by geographic area allowed us to infer the contributions of local breeds to the hybridization events and evaluate   S3 ). For example, we observed the presence of multiple clusters at the within-country level (e.
g. : France, the Netherlands), but also areas showing a gradient among clusters (e. g. Balkans, Carpathians). This, incidentally, also highlights the importance of including references from as many sources of gene-flow as possible, as applied in our study (Fig. S1 ). Such a strategy is particularly helpful in characterizing population substructure within both wild and domestic populations, as earlier suggested by Steyer et al. 31. The analysis with Admixture of the 1 K dataset was highly congruent with the 30 K dataset for K values between 2 and 4. At K = 2 wild boar were roughly separated from pigs although most populations, including commercial breeds, show high levels of introgression. At K = 3, the dataset was divided in wild boar, local and commercial pig breeds, while at K = 4 the distinction was among Western European, Near Eastern plus Balkan-Carpathian wild boar, local and commercial breeds (Fig.
Additionally, we included local breeds as, in principle, they are expected be the main source of porcine introgression into wild boars, whereas industrially raised pigs would have a much less relevant role. Furthermore, as highlighted by both the complexity of our Admixture results (Fig. 3 ) and the within-cluster gradient in the PCA (Fig. 2 ), local breeds can display a strong genetic differentiation when compared to commercial lines and their inclusion is of paramount importance for the estimation of recent hybridization levels. The incorporation in the dataset of multiple potential sources of hybridization, and the recent shared common ancestry between wild boar and pigs, makes the identification of admixed individuals challenging, as shown by the Admixture analyses at K = 2. An additional difficulty is to define non-admixed individuals i.
e. any wild boar population may present some level of ancient or recent introgression from pigs. Conceivably, the group least likely to have experienced recent introgression is the commercial lines, as their breeding history is recorded. An assumption of no introgression is commonly required by most admixture analyses. However, Pilot et al. 35  recently reported that the identification of hybrids, was not greatly affected by the analytical approach employed in their identification and the composition of the dataset. To account for potential biases, and in agreement with previous studies suggesting the importance of combining different approaches  36  , we chose to consider as hybrids only those individuals that were concordantly identified as such by multiple analyses. This led to the identification of an overall 11.
Such observations can be due to the introductions and/or escape of farmed individuals  17, 41. More specifically, two populations deserve particular attention as our results deviated from expectations: the Netherlands and Austria. Although we started with the dataset of Dutch individuals analysed by Goedbloed at al. 15  , where they reported 10% hybridization, we did not observe any signal of admixture. Several factors could have led to this lack of concordance. First of all, to equalize sample size across populations, we strongly reduced the number of analysed wild boar (from 88 to 25 individuals). This random selection may have left out introgressed individuals by chance. However, by repeating the analyses with different individuals we obtained the same results and we can thus rule out such an explanation.
25 and 0. 5  35. In our wild boar dataset we mostly observed pig ancestry ranging from 0 to 0. 25, although we only considered as hybrids those wild boars with a domestic ancestry above 10%. Interestingly, we detected seven individuals with pig ancestry ranging between 0. 27 and 0. 39 and two samples, clearly identifiable in the PCA plots, whose genomes were ca. 90% of pig origin. Our results confirm that for these populations, hybridization with the domestic counterpart is an ongoing process, possibly strongly related to pig farming and wild boar management practices. The observation of almost pure pigs within the wild boar sample suggests the presence of released or escaped animals which became feral. Future research, ideally comprising additional samples from Eastern Europe and the Balkan area, could help identify the possible existence of broader geographical patterns relevant for evolution and conservation management, and the potential influence of the different breeding systems established in Eastern and Western Europe.
Acknowledgements Research was supported by the  Aalborg Zoo Conservation Foundation (AZCF). L. I. received funding from the  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Action  (Grant Agreement no. 656697 ). A. V. S. was supported by the  Danish Natural Science Research Council  (Postdoctoral Grant  1337-00007 ). R. O. and U. S. were supported by the institutional research funding (grant  IUT20-32 ) from the  Estonian Ministry of Education and Research. We are grateful to  K. T. Nielsen  for help with scripting.
The number of factors was chosen based on the Kaiser criterion. Factors items were considered valid when loadings were greater or equal to 0. 5. The fit to the data was evaluated by CFA in comparison with well established PANSS models using fit indexes such as: NNFI (Non-Normed Fit Index), NFI (Normed fit Index), CFI (Comparative Fit Index), RMEA (Root-Mean-Square Error of Approximation). SPSS 23. 0 and R version 3. 2. 2 were used for statistical analyses. Results: Demographic data showed that, when compared with NTRS, patients with TRS showed an earlier age of onset, a longer duration of illness, higher PANSS positive scores, a higher duration of persistent positive and negative symptoms. There were no differences between groups in terms of the duration of untreated psychosis. The EFA yielded almost the same five-factor structure in both groups namely Negative, Positive, Affective, Disorganized/Cognitive and Excitation factors.
Structural synaptic imaging was performed on a custom-built two-photon microscope with a Chameleon Ultra II Ti:Sapphire laser (Coherent), a 40ϫ objective (0. 8 NA, Olympus), and ScanImage software  (Pologruto et al. , 2003). Mice were anesthetized for imaging sessions with isoflurane (1%-2% maintenance v/v) and kept warm with an electric heating blanket (Harvard Apparatus). Throughout the imaging sessions (typically Ͻ1 h), mice were monitored for taxed breathing and proper coloration of paws/ ears. Z stacks with 1 m steps were collected at 512 ϫ 512 pixels, and a ScanImage zoom of 1ϫ for low-magnification images (0. 71 m/pixel) (see Fig. 1D  1 ,D 2 ) and 5ϫ for dendrite and axon imaging (0. 153 m/ pixel) (see  Figs. 1, 2, 4, 5). Imaging sessions were performed in the evening at P14 and in the morning at P15 after being housed with their dam and littermates in either standard cages or enriched cages (see EE).
EE cage dimensions were 13-1/4 inch ϫ 16 -19/32 inch ϫ 7-3/8 inch (1800 Mouse Cage, Lab Products) and contained brightly colored balls and plastic tunnels, a running wheel, beaded necklaces (Mardi Gras beads) strewn from the top of the cage, and a variety of toys, multiple nest pads, and a layer of bedding (see Fig. 3B ). Statistical analysis. Spine imaging data were analyzed using the semiautomated ScanImage  (Pologruto et al. , 2003)  spine analysis software in MATLAB (The MathWorks). Raw, unprocessed images were used for the analysis. If a time point was missing or one or more imaging sessions was of poor quality, the FOV was excluded from the analysis for time-series calculations. Spines Ͼ5 m were excluded from analysis (Ͻ1% of all spines). Dendritic and axonal segments were analyzed only in regions Ͼ10 m from branch points and distal tips, and only if the segment was at least 15 m in length, and the segment was isolated (i.
e. , it had only minimal overlap with other processes). Custom-written code in MATLAB was used to determine EPB volume. Z stacks with 1 m steps were collected at 512 ϫ 512 pixels and a ScanImage zoom 5ϫ for imaging (0. 153 m/pixel) (see  Figs. 1, 2, 4, 5). To minimize background signal interference, slices above and below ROIs were deleted from stacks. Raw images were first aligned using the StackReg plugin in ImageJ and then sum-projected. Images were manually binarized before skeletonization. Then, the signal baseline along the skeleton was estimated on the projected images using an asymmetric least-squares smoothing algorithm  (Eilers, 2003). Peaks along the skeleton of the axon were detected using the Fast 2D Peak Finder algorithm version 1. 7 written in MATLAB  (Natan, 2013). This algorithm is based on the smoothing of the noisy point spread function associated to each peak.
This was achieved through median filtering and convolution-based smoothing of the input image, so that each peak corresponds to the local maximum of the point spread function. The threshold for EPB detection was empirically set to detect what visibly appeared as a plausible EPB, rather than an irregularity of the axon shaft (see Fig. 2E ). The volume of EPBs was defined as the sum of the pixel intensities of the subregions containing the peaks detected by the Fast 2D Peak Finder algorithm, divided by the baseline local value for normalization. Statistical analysis was performed using MATLAB for each peak along the axonal shaft. In Results, n c and n as indicate the number of cells or axonal segments analyzed, respectively, and N indicates the number of animals used. In total, we tracked and analyzed 7969 individual spines in 165 cells over a total dendritic length of 6.
For within-group comparisons of EPB density and volume, Wilcoxon signed-rank tests were used (with significance set at p Ͻ 0. 05, two-tailed). All statistics were performed with SPSS 24 (IBM). Graphs were generated using Excel 2017 (Microsoft) and SigmaPlot 13 (Systat). Code accessibility. Access to the EPB volumetric analysis MATLAB code will be made available upon request.
The fourth study evaluated the impact of social desirability on responses to the WRMI, and simultaneously investigated its test-retest reliability. The fifth study analyzed the measurement invariance of the inventory across several individual characteristics (i. e. , gender, marital status, contract, age, and tenure). Finally, study 6 examined the predictive validity of the WRMI. Data were gathered from 2015 to 2017. Apart from the first qualitative study, participants completed the scales via an online inventory. The ethical conditions were the same for all the studies. According to local regulations, no formal ethical scrutiny was required, as no ethics committee existed in the institution at the time of these studies. However, participation was voluntary and participants gave their informed consent.
Method Data were collected from 40 French workers (24 men and 16 women), aged 55-62 years (M age = 59. 1 years; SD age = 1. 72). They worked in various sectors (e. g. , business, industry or service). To ensure that the final version of the WRMI would be sensitive to the experiences of people undergoing the transition from working life to retirement, we only recruited individuals who estimated they were close to retirement (within 5 years). The interviewers were four psychology graduates trained in interviewing techniques. Questions were based on the Push Pull Anti-push Anti-pull model. The participants were asked the four following open questions: (a) "What reasons would lead you to end your professional career?, " (b) "What aspects of life would make you want to retire?, " (c) "What reasons would encourage you to continue working?, " and (d) "What particular worries do you have about retiring?" The interviews lasted approximately 1 h during which the participants were encouraged to speak freely about their transition from vocational activity.
The interviews were taperecorded and transcribed to ensure accuracy and to provide extensive data. Content analysis techniques were applied to classify the textual information into a smaller number of relevant content units  (Krippendorff, 2004). Categorization was based on a phenomenographic approach, which investigates the qualitatively different ways of perceiving a phenomenon  (Fülop, 2004). A panel of three researchers specialized in career termination used previous literature in this research area based on the Push Pull framework (e. g. ,  Shultz et al. , 1998; Brougham and Walsh, 2007; Potocnik et al. , 2009)  to identify and categorize raw data into significant themes and patterns that could represent possible reasons for staying in or terminating the vocational career.
Statistical Analysis and Results In accordance with the recommendations of  Tabachnick and Fidell (2013) , univariate (i. e. , |z| < 3. 29, p < 0. 001, two-tailed test) outliers were excluded from the analyses. In this way, 236 participants were included in the analyses described below. Some of the participants did not respond to all 88 items of the WRMI, but the percentage of missing values was lower than 5% and thus did not represent a problem (e. g. ,  Graham and Hofer, 2000). When testing for missing values, Little's chi-square test for MCA was not significant (p > 0. 05), Chi² (244) = 295. 692 indicating that data are missing completely at random. The expectation maximization (EM) algorithm was thus used to impute missing values using SPSS software (version 18). First, we carried out an exploratory factor analysis with oblique rotation.
Statistical Analysis and Results Descriptive statistics are presented in Table  3. Fourteen univariate (i. e. , |z| < 3. 29, p < 0. 001, two-tailed test) and two multivariate outliers (Mahalanobis distance lower than χ 2 (4) = 18. 47, p < 0. 001) were excluded, leaving 359 participants for the analyses. The sample had <5% of missing values, and analysis indicated that these were completely random (i. e. , Little's χ 2 = 174. 26, df = 613, p = 1. 00). Therefore, the EM algorithm was used to impute missing values using SPSS software. Since the variables were abnormal (z values for kurtosis ranging from -2. 71 to 1. 07; z-values for skewness ranging from -2. 77 to 0. 81; and Mardia's normalized skewness coefficient = 50. 22 with a criterion ratio of kurtosis multinormality above 1. 96), confirmatory factor analyses were performed using Bootstrapped Maximum Likelihood estimation with AMOS 20.
0 software  (Arbuckle, 2011). Thus, all fit values provided in this study were based on AMOS 20. 0 Bollen-Stine bootstrap p-value and bootstrap adjusted chi-square and goodness-of-fit indexes  (Fouladi, 2000; Yuan and Hayashi, 2003). Assessment of fit was based on multiple indicators: the Bollen-Stine χ 2 , the B-Sχ 2 /df ratio, the comparative fit index (CFI;  Bentler, 1990) , the Tucker-Lewis Index (TLI;  Tucker and Lewis, 1973) , the standardized root mean square residual (SRMR;  Jöreskog and Sörbom, 2001) , the root mean square error of approximation (RMSEA;  Browne and Cudeck, 1989)  and the 90% confidence interval of the RMSEA (RMSEA 90% CI) and its associated p-value (for RMSEA >0. 05). To compare models, two additional fits were calculated, the Akaike information criterion (AIC;  Akaike, 1987)  and the modified expected cross-validation index (MECVI;  Browne and Cudeck, 1989, 1993).