dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods for accuracy evaluation When it comes to underwater 3D metrology, accuracy potential is hard to validate against an independent external standard. The main reason is that logistics is much more complex under than above water and the use of well-known standard protocols for acceptance and verification (like the German VDI/VDE 2634,  2002 ) is seldom feasible. Good figures of the quality of underwater calibrations can be drawn by analysing the bundle adjustment results, such as the standard deviations of calibration parameters, RMS of image residuals and coordinate precision in object space, though this is not sufficient to assess the accuracy in object space. Therefore, it is common knowledge that an external independent check is required. In this study, reference distances of five different lengths (Figure  6 ) were used to evaluate the external accuracy of the calibration datasets.
The reference values were provided by a calibration made in the laboratory of 3DOM-FBK. Possible deformations of the panels are taken into account by analysing the deviations from least square fitted planes for each of the three panels forming the test object. Only those lengths whose potential errors are below 15 µm (chord against arc length) are considered for the accuracy assessment. The length measurement error (LME) and relative length measurement accuracy (RLMA) are computed according to equation 1 and 2 respectively: 𝐿𝑀𝐸 = 𝐷 𝑚 -𝐷 𝑟 (1) 𝑅𝐿𝑀𝐴 = 1: 𝑟𝑜𝑢𝑛𝑑 (| 𝐷 𝑟 𝐷 𝑚 -𝐷 𝑟 |) (2) where Dm and Dr are the measured and reference distances. The theoretical LME of the laboratory reference measurement for the modular test object at 99% confidence level is 0. 078mm. For the dataset of the submerged structure located in the Bay of Rogiolo a reference distance was measured by tape at the main entrance of the rectangular basin.
Underwater calibration datasets The results of calibration datasets are summarised in Tables 5. The main statistics from the BA and the accuracy valuation on 18 known distances on the three panels of the test object (Figure  6 ) in terms of LME and RLMA are reported. Worst RMS point residuals, LME and RLMA are generally observed for the calibration at sea than in the pool. The adoption of the radial weigh results in an expected, increased image point RMS, accompanied with an improvement in the object space, testified by lower LME and higher RLMA values. The achieved improvements are consistent for calibration results of the full-frame DSLR camera with flat port at sea and in the pool. On the contrary, the enhanced accuracy for the GoPro in the pool is doubled than at sea. Figure  8  shows the absolute magnitude of image observation residuals as function of the normalised radial distance from the BA results for the dataset #1_Cal_FLAT Quercianella.
Semi-submerged industrial structure dataset Table  2  reports the results of the open loop strip camera network for the semi-submerged structure. The findings from the calibration datasets are confirmed: when the empirical weighting function is applied, the image point RMS increases, but a significant improvement of about the 50% is obtained in the object space. The LME and RLMA are computed with respect to the loop closure reference length 4. 723m at the entrance of the basin, measured by tape measurement.
The processing in DBAT allowed to factor the weight in the bundle adjustment in a very flexible way and to achieve significant accuracy improvements, particularly for the industrial structure dataset. RMS point residuals at sea are significantly worse than in the pool mainly due to a combination of unfavourable environmental conditions such as water turbidity, swell and lighting. Also, more complex weighting functions are under investigation, to take into account both the worsening of the optical quality towards the image borders and refraction effects by introducing a correction coefficient dependent on the distance between the object point and the centre of projection.
ACKNOWLEDGEMENTS The authors would like to thank:  Dr. Francesco Fassi  from  Politecnico di Milano  and  Barbara de Cillis  for supporting the diving activities during the underwater calibrations at sea; NiMAR (www. nimar. it)which supported this research by providing the photographic underwater equipment and for the useful insights about pressure housings manufacturing techniques;  Sara Messina  and  Stefano Pucher  from the  Associazione Sportiva Dilettantistica Club Subacqueo Rane Nere Trento  who supported the preliminary tests in the swimming pool in Trento;  Mauro Saccone  from  LSIS, I&M Team, Aix-Marseille Universite  for supporting the underwater calibrations of the GOPRO Hero 4 in the testing pool of  Polytech Luminy.
The final analysis was done on the first 30 evaluable patients and concluded treatment efficacy if 4 or more patients had therapy response or stable disease. Analyses were performed on the modified intention-totreat population (i. e. patients with at least 4 cycles of therapy). Descriptive analysis was performed using median with range for quantitative variables and percentages for qualitative ones. The median follow-up was determined using the reverse Kaplan Meier method. Survival curves were plotted using the Kaplan Meier. Analyses were performed using SAS 9. 4.
0 (Hammamatsu). For Il-17a analysis, two physicians analyzed the corresponding files independently. The scores were compared, and a third pathologist reviewed the slides when there were inconsistencies in the initial evaluations. IL17a staining was performed using polyclonal goat antibody AF-317 (R&D). Cytotoxic T-cell infiltrates were evaluated using CD8 labelling with the C8/144B clone (Dako) and analyzed with QuPath software.
HRV Analysis By means of Kubios HRV version 2. 2 software (Kubios, Finland), the recorded ECG data were analyzed at a sampling rate of 250 Hz, and several HRV parameters were assessed, divided into time domain parameters, frequency domain factors, and the non-linear domain analysis (Table  1 ). ECG analysis was visually inspected to diagnose for any abnormal signaling, during which manual selection of QRS complexes was done where necessary, along with low to medium artifact corrections to optimize HRV outcomes. Timeline of the 4 h monitoring procedure, displaying the time of device connection one hour prior to examination period. Monitoring covered one hour before exam while doing routine pre-exam activities, 2 h during an exam in routine academic conditions, and one hour after exam while doing routine post-exam activities; after 4 h, the devices were disconnected.
Statistical Analysis SPSS was used for statistical analysis. Values were displayed as mean ± SEM (standard error mean). Differences among several groups were analyzed using one-way analysis of variance (ANOVA) to test interaction between and within subject factors, followed by post-hoc analysis of Tukey to evaluate the significance of difference among means when at least 3 groups were involved. ANOVA of repeated measures was used to analyze the HRV changes in different phases of examination, and two-way ANOVA and three-way ANOVA were performed to study possible factor-factor interactions. Additionally, Chi-squared test was used to determine the presence of any association between categorical parameters among the included sample. P values less than or equal to 0. 05 were considered to be statistically significant.
Demographic and clinical characteristics were obtained for all participants. A pre-established questionnaire was used to collect demographic variables including their medical history, cardiovascular risk factors, and current medications. Diagnosis of arterial hypertension was defined as having ≥140 mmHg systolic blood pressure and/or ≥90 mmHg diastolic blood pressure. 30 ll patients were asked to arrive at the Sleep Unit at 19:00 on the study date. They were instructed to avoid caffeinated or alcoholic beverages 24 h previously to the measurement. PSG monitors and optical probes were placed as explained below. Concurrent optical and PSG data were acquired during the night sleep. If the obstructive apnea or hypopnea index (AHI; number of apneas and hypopneas per hour of sleep) was greater than 30 after about 4 h of sleep, the clinical technician fixed a CPAP mouth-nose mask to find the correct air pressure for preventing apneas (called split-night PSG).
Determination of the Cerebral Blood Flow by Diffuse Correlation Spectroscopy Microvascular CBF during the whole night sleep was continuously assessed by a custom-built DCS system that was previously described. 25, 32, 33 Briefly, the DCS consisted of a modehope free, long-coherence-length, continuous-wave laser at 785 nm and eight single photon avalanche photodiode detectors, whose outputs were fed to a custom-built hardware autocorrelator. DCS uses the intensity autocorrelation of the diffuse light to evaluate the motion of the scatterers, i. e. , the red blood cells. 24 he intensity autocorrelation data are then fitted by a physical model of the photon diffusion in tissues to determine a blood flow index (BFI), which is recorded as a continuous variable. The BFI (cm 2 ∕s) is not a measure of absolute blood flow in traditional units.
38 using the "fda. usc" 37 package and the R function "Outliergram. "  36 fter removing the outliers from our database, all the remaining apneic events for each variable (ΔrCBF, ΔrHR, or ΔSpO 2 ) of all patients were averaged in order to visualize representative cerebral and systemic dynamics of obstructive apneas. The averaging was performed by (1) selecting the start and the end of each apnea based on PSG measurements using the established criteria (see above), (  2 ) calculating the ΔrCBF, ΔrHR, or ΔSpO 2 traces for each apnea, as explained previously, (3) aligning the data considering as the pivot point the start of each individual apnea, as shown in Fig. 1 (a), and (4) grouping and averaging all apneas within a given range of apnea duration. Four groups were used based on their duration; apneas shorter than or equal to 15 s, apneas longer than 15 and up-to 30 s, apneas longer than 30 and up-to 45 s, and apneas longer than 45 and up-to 60 s.
The positive extrema are referenced as "peak" values, and the negative as "drop" values. The time windows to find these extrema were from -5 to 15 s for the first extremum on ΔrCBF [see Fig. 1 (b) as an example], from 0 to 15 s for the ΔrHR, and from 5 to 35 s for the ΔSpO 2. In order to visualize the possible link between the hypoxemia present in these patients and the CBF, also the second extremum that was outside this window was considered for ΔrCBF. These time windows were selected from the literature  10, 39  and also by visual observation of all the apneas plotted together from -30 s to 60 s in order to include the majority of the peak/ drop values. This analysis was performed with MATLAB 2012a (Mathworks, Massachusetts). The association between the calculated ΔrCBF, ΔrHR, and ΔSpO 2 extrema to the apnea duration (from the PSG) was analyzed by adjusting a linear mixed-effect model.
40 The patient identifier was used as a random factor, the parameter apnea duration was the fixed effect, and the positive and negative extrema (previously defined as "peaks" and "drops") of the apnea time response on variables ΔrCBF, ΔrHR, and ΔSpO 2 were the predictors. The linear mixed-effect analysis was carried out in the R programming language and environment 40 using the "nlme" package. The associations between the mean of the previously calculated ΔrCBF, ΔrHR, and ΔSpO 2 extrema responses for each patient with gender, age, and body mass index (BMI) (one by one) were analyzed by performing simple linear models. The demographic parameters were the fixed effects and the mean calculated extrema were the predictors. The residuals of the models were checked for linearity by plotting the standard residuals versus the predicted means.
Acknowledgments The authors thank  Dr. Raquel Delgado Mederos ,  Dr. Joan Martí Fàbregas ,  Ivan García Domínguez ,  Ignasi Jorba ,  Nuray Aysan , and  Rosa Maria Miralda  for their discussions and contributions to some of the software used for analysis. This work was funded by  Fundació CELLEX Barcelona, Institució CERCA/ Generalitat de Catalunya, Ministerio de Economía y Competitividad/FEDER  (Grant Nos. PHOTODEMENTIA,  DPI2015-64358-C2-1-R ,  DPI2015-68820-R , and  FIS2015-71851-P ),  Instituto de Salud Carlos III/FEDER  (Grant Nos. MEDPHOTAGE and  DTS16/00087 ), the " Severo Ochoa" Programme for Centres of Excellence in R&D  (Grant No. SEV-2015-0522 ), the  Obra Social "la Caixa" Foundation  (Grant Nos. LlumMedBcn,  Programa de Matemàtica Col•laborativa) ,  AGAUR-Generalitat  (Grant Nos.
8c  (donut shape, pancake-like), are also observed. Similar shapes of liposomes have been reported also in other studies  (Liang, Mao et al. 2004 , Ruozi, Tosi et al. 2007 ). Also, some liposomes are located in deeper layers in the mica, which indicates absorption by mica. In conclusion, both System 1 and 2 exhibit similar morphologies. Subsequently, the diameter of the liposomes was measured to evaluate an average size. The comparative measurement of the diameter of the two types of liposomes was performed using the AFM tapping mode and the dimensions of the liposomes were measured from the topographic images using the AFM SpmLabAnalysis software. The diameter of the liposomes is calculated from the spatial width of the peak from its base according to Fig. S6. The measurement of the width of the diameters (green arrows) and the height (blue) of the liposomes using the SpmLabAnalysis software can be seen in the supplementary 2 3/2 3 (1 ) 4 v dF E R dz - = material.
Acknowledgements We acknowledge computational time granted from the  Greek Research & Technology Network (GRNET)  in the National HPC facility -ARIS under project  ID pr001026-MAG-NANO-MEM. We also acknowledge  Dr. Eleni Efthimiadou  for providing us the Zetasizer for the Dynamic Light Scattering experiments and  Giorgia Giacometti  for her ancillary help in the liposome preparation. ZC was co-funded by the  European Commission  under the  H2020 Research Infrastructures Contract No. 675121  (project  VI-SEEM ).
We next compared the genetic diversity and structure of viral populations in male organs, as well as in semen and blood cells and plasma for three animals with compartmentalized semen strains. Our data revealed that a number of male genital organs harbor viral strains phylogenically distinct from those in blood and other genital organs, resulting in the presence of multiple distinct viral populations within the male genital tract. These results were then confirmed by our compartmentalization analyses (Tables  8  and 10 ). These findings are in contrast with those of Fieni et al. , who failed to observe compartmentalized strains in testis, epididymis, seminal vesicles, prostate, and semen from 5 rhesus macaques infected with SIVmac251 for 3 months (4). Differences in experimental procedure, such as duration of infection, male genital organs analyzed (vas deferens and urethra were not included in that study and the section of epididymis.
The second-round PCR was carried out using 1 l of the first-round product with primers SIVmacEnvF2 (5=-TATAATAGACATGGAGACACCCTTGAGGGAGC-3=) and SIVsmEnvR2 (5=-ATGAGACATRTCTATTGCCAATT TGTA-3=) and the same PCR mixture as in the first round. The cycling conditions were 98°C for 45 s followed by 40 cycles of 98°C for 15 s, 55°C for 30 s, and 72°C for 5 min and a final extension of 72°C for 10 min. Amplicons were detected (or inspected) using the long amplicon melt profiling (LAMP) method described previously  (16). Briefly, the melting analysis was carried out in a 96-well plate with 1. 2 l of second-round PCR product, 2 l of IQ SYBR green supermix, and 0. 16 l of ROX passive reference dye (Bio-Rad) in an 8-l reaction mixture using a 7500 real-time PCR system instrument (Applied Biosystems). The following melt curve run method was used: 95°C for 15 s and 60°C for 1 min, followed by an increase to 95°C with a ramp rate of 1% and continuous data acquisition.
Positive reactions were isolated based on the specific melting profile of the full-length env amplicon. PCR products were directly sequenced on both strands as partially overlapping fragments by Eurofins Company. Individual sequence fragments for each amplicon were manually examined for multiple peaks (to exclude sequences arising from multiple variant templates) and assembled using the Contig Editor program from GeneStudio. Sequence alignment and phylogenetic reconstruction. A total of 708 full-length SIV env sequences of semen and blood from 5 animals and genital tract tissues from 3 animals were selected (24 to 187 sequences per macaque). Nucleotide sequences were translated into amino acids and aligned in MAFFT (58) using the global algorithm (G-INS-i). Alignments were visually inspected and adjusted if needed and then translated back to nucleotides.
Population dynamics were inferred in BEAST (67) using the Gaussian Markov random field (GMRF) skyride model, the HKY substitution model with gamma-distributed among-site rate heterogeneity, and a relaxed clock (log-normal) model of rate of substitution. We used the date of infection to calibrate the analysis. We performed two runs 2 ϫ 10 7 generations long. Intrahost phylogeographic history (viral dispersal patterns and divergence times), while accommodating phylogenetic uncertainty, was inferred between body compartments using asymmetric discrete phylogeographic diffusion models  (68). Results were summarized with a maximum clade credibility (MCC) tree, using TreeAnnotator. The MCC tree was visualized with FigTree (http://tree. bio. ed. ac. uk/software/figtree). Parameter uncertainty was summarized in the 95% highest posterior density (HPD) intervals.
All output generated by BEAST was analyzed in Tracer (http://tree. bio. ed. ac. uk/software/tracer/) to test for convergence and mixing. Compartmentalization detection. Compartmentalization among body compartments in each macaque was assessed using several approaches, including distance-based (F ST and nearest neighbor [S nn ]) and tree-based (Slatkin-Maddison [SM], association index [AI], correlation coefficients [r and r b ]) estimators implemented in HyPhy (see references therein). Viral populations were considered compartmentalized when all tests converged. Statistical analysis. Statistical analyses were performed using commercially available software GraphPad Prism version 5. 03. Spearman's rank correlation test was used to assess nonparametric associations between virus and cytokine RNA concentrations.
Radiotherapy with protons and heavier ions landmarks a novel era in the field of high-precision cancer therapy. To identify patients most benefiting from this technologically demanding therapy, fast assessment of comparative treatment plans utilizing different ion species is urgently needed. Moreover, to overcome uncertainties of actual in-vivo physical dose distribution and biological effects elicited by different radiation qualities, development of a reliable high-throughput algorithm is required. To this end, we engineered a unique graphics processing unit (GPU) based software architecture allowing rapid and robust dose calculation. FRoG, Fast Recalculation on GPU, currently operates with four particle beams available at Heidelberg Ion Beam Therapy center, i. e. , raster-scanning proton ( 1 H), helium ( 4 He), carbon ( 12 C) and oxygen ions ( 16 O).
Recent works undertake this issue by developing secondary systems in-house like matRad 7 and FoCa 8 , educational MATLAB ® -based tools for had- rontherapy treatment planning. Such platforms are usually benchmarked against their model center's clinical treatment planning system (TPS), demonstrating good agreement. Other platforms for particle therapy include TRiP 9 and Astroid 10 ; however, the accuracy of such pencil-beam (PB) algorithms, the crucial element of every particle therapy dose calculation, should be under scrutiny in cases of severe patient geometry heterogeneity  [11] [12] [13].
Here, the dose calculation engine is validated, making way for clinic workflow integration and future retrospective study with the HIT patient database as shown in Fig. 1 , such as linking physical, delivery or biological uncertainties in particle therapy to clinical outcome. Recent works provided evidence of variable relative biological effective (RBE) in a subset of patient follow-up MR scans, revealing a hidden complexity in biological track damage when using therapeutic proton beams  23. With this in mind, a major goal of a fast computation engine like FRoG is to perform high-throughput patient calculations and use clinical outcome as an endpoint to develop data-driven biological effect models. Further efforts in FRoG development will focus on clinical accessibility and enhancement of computational performance, as well as implementation of research and clinical RBE models.
Discussion The aim of project FRoG was to establish a user-friendly forward calculation method for both clinical and research purposes, providing MC-like accuracy with clinical TPS-like calculation speed. Table  1  presents a summary of the patient case calculations, including calculation times for FRoG and time gain factors in place of FLUKA MC simulation. Despite the use of ~300 CPUs in each MC calculation, large time gain factors are observed using an analytical code on a single GPU. Ideally, determining time gain factors of performing dose calculation on CPU versus GPU would require development of a CPU-based FRoG engine. Nevertheless, these gain time factors express the power of parallelized computing on the GPU. To gauge FRoG's speed with commercially available platforms, forward calculations were performed with the three proton patient cases (A, B and C) using Syngo's CPU-based PB algorithm (HIT's current clinical system) and the RayStation (RaySearch Laboratories AB, Stockholm, Sweden) GPU-accelerated PB algorithm.
A better comparison of FRoG's calculation engine and FLUKA would involve incorporating MC beam evolution data into FRoG for interpolation of foci values at the source-to-skin distance (SSD) instead of the database of experimental measurements. Despite this issue, agreement was excellent considering FRoG's calculation times were <4 minutes, as opposed to several hours required for MC codes  14. The two main reasons for discrepancy between an analytical calculation and FLUKA MC simulation are as follows: limitations due to a simplified lateral dose evolution model, as shown in Fig. 2  in the SOBP cases, and inadequate description of PB deformation in heterogeneous conditions when employing techniques such as PB splitting  12. The effect of the former in the case of protons can be considered less pronounced than for the heavier ions, attributed to increased dose distribution complexity stemming from nuclear interactions.
As for the latter, PB splitting attempts to improve lateral range variation agreement; however, improvements in accuracy are restricted by the model's fixed lateral spread parameters such as Gaussian sigmas and weights of the decomposed beamlets, spreading dose tangentially from the central axis as the homogenous PB model in water. Unlike the dose calculation assessment in a homogenous phantom which served to scrutinize ray tracing procedures and lateral dose parameterization methods, dose calculation with patient datasets assessed the performance of the PB model and splitting method in heterogeneous anatomy. In most cases, FLUKA and FRoG line profiles (Fig. 4 ) and DVH plots (Fig. 3 ) are in excellent agreement. For proton patient cases, slight discrepancies between FLUKA and FRoG occurring at the distal edge can be attributed to the accuracy limits of PB splitting in describing lateral PB distortion.