dataesr/scientific-paragraphs-categorization huggingface dataset:
Its extension to the extraction of optic radiations of any other subject is straightforward and consists of 3 simple steps: 1) matching the new subject's T1w anatomy to the MNI ICBM152 template space; 2) reconstructing the whole brain connectogram of the subject from any HARDI dMRI dataset using a probabilistic fiber tracking approach; 3) applying our novel MNI-based optical radiation bundle atlas to identify the fibers belonging to optic radiations. Both the MNI-based optical radiation bundle atlas and corresponding post-processing pipeline are freely available from the Ginkgo toolbox GitLab repository accessible at https://framagit. org/cpoupon/gkg.
V. DATA AND SIMULATION SAMPLES The data samples used in this analysis correspond to integrated luminosities of 36. 3, 41. 5 and 59. 7 fb -1 collected in 2016, 2017 and 2018, respectively, for a total of 138 fb -1 collected during Run 2 of the CERN LHC at a proton-proton (pp) center-of-mass collision energy of 13 TeV  [54] [55] [56]. MC simulation is used to model signal and background processes in pp interactions at the LHC and their reconstruction in the CMS detector. All MC samples are interfaced with the PYTHIA  [57]   [58]. Following the formalism discussed in Sec. II, the samples with the SM and anomalous H couplings in VBF and VH production are generated with the JHUGen program at leading order (LO) in quantum chromodynamics (QCD). All the simulated scenarios are reweighted to model any other set of H couplings using the MELA package.
4. Jet momentum is determined as the vector sum of all particle momenta in the jet, and is found from simulation to be, on average, within 5% to 10% of the true momentum over the whole p T spectrum and detector acceptance. Pileup interactions can contribute additional tracks and calorimetric energy depositions to the jet momentum. To mitigate this effect, charged particles identified to be originating from pileup vertices are discarded and an offset correction is applied to correct for the remaining contributions. Jet energy corrections are derived from simulation to bring the measured response of jets to that of particle level jets on average. In situ measurements of the momentum balance in dijet, photon þ jet, Z þ jet, and multijet events are used to account for any residual differences in the jet energy scale between data and simulation  [84].
The jet energy resolution amounts typically to 15%-20% at 30 GeV, 10% at 100 GeV, and 5% at 1 TeV  [84]. Additional selection criteria are applied to each jet to remove jets potentially dominated by anomalous contributions from various subdetector components or reconstruction failures. In this analysis, jets are required to have p T > 30 GeV and jηj < 4. 7, and to be separated from the reconstructed visible τ decay products by a distance parameter of at least 0. 5, where ϕ is the azimuthal angle in radians. Data collected in the most forward region of the ECAL endcaps were affected by large amounts of noise during the 2017 run, which led to disagreements between simulation and data. To mitigate this effect, jets used in the analysis of the 2017 data are discarded if they have p T < 50 GeV and 2.
For the Drell-Yan, W þ jets, and H events estimated from simulation, corrections are applied to the p miss T based on the vector difference of the measured p miss T and the total p T of the neutrinos from the Z, W, or H decay products ("recoil corrections"). (vi) The Z boson mass and p T spectra in simulation are corrected to better match the data. To this purpose the Z mass and p T are measured in data and simulation in dimuon events. The observed differences between the data and simulations are taken as event weights that are subsequently applied to the simulated Z=γ Ã → ll events. The size of the corrections are typically less than 20%. For t t events, the top quark p T spectra are also reweighted to match the p T spectra in data. The procedure used to derive these corrections is described in Ref.
Lastly, D ggH CP , defined in Eq. (  17 ), provides sensitivity to the interference between the CPodd and CP-even contributions: D VBF 2jet ¼ P ggH SM þ P ggH 0- P ggH SM þ P ggH 0-þ P VBF SM ; ð15Þ D ggH 0-¼ P ggH SM P ggH SM þ P ggH 0- ; ð16Þ D ggH CP ¼ P ggH SM-0- P ggH SM þ P ggH 0- : ð17Þ The results of the analysis are extracted with a global maximum likelihood fit based on signal-sensitive observables. We summarize the observables utilized in the analysis of the ggH production in Table  V. As the same set of observables is used in the HVV study, except for the superscripts of anomalous coupling specific MELA discriminators, we define them in Table V as well. We use four observables in total to construct fitted distributions in the VBF category: D ggH 0-, D ggH CP , D NN , and D VBF 2jet.
We checked Events / bin Observation (Best fit) W W o H bkg. W W mis-ID h W o jet Other Uncertainty =0) ggH a3 (f W W o ggH =1) ggH a3 (f W W o ggH h W h W boosted, jj VBF high-m CMS (13 TeV) -1 138 fb < 80 GeV W W m d 50 < 100 GeV W W m d 80 < 115 GeV W W m d 100 < 150 GeV W W m d 115 < 200 GeV W W m d 150 0 1 2 2 4 3 6 4 8 6 0 Bin number 0. 5 1 1. 5 Obs. /Exp. FIG. 5. Observed and predicted 2D distributions after the fit to data in the VBF high-m jj boosted category in the τ h τ h channel. The total H signal, including VBF, ggH, and VH processes, is shown stacked on top of the background in the solid red histogram. The ggH signal for the CP-even (CP-odd) scenario is also shown overlaid by the red (blue) line. The uncertainty band accounts for all sources of systematic uncertainty in the signal and background predictions.
X. ANALYSIS OF VBF PRODUCTION As MELA -based observables offer superior sensitivity and discrimination among different possible anomalous HVV couplings in the VBF and VH productions compared to single kinematic observables, such as Δϕ jj , the VBF study is conducted with MELA -based observables only. As the sensitivity to VH anomalous effects is small with the present dataset, the analysis is optimized for the VBF process. However, we allow the anomalous couplings to modify the VH kinematics in the fit to data. We employ the same neural networks to separate VBFlike signal and background processes as in the ggH analysis, while MELA discriminants offer optimal separation between different signal hypotheses (shown in Table  V ). We use D VBF 2jet , defined in Eq. (  15 ), to separate SM ggH production from the SM VBF production.
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MES  and  NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI  and  NSTDA (Thailand) ;  TUBITAK  and  TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Analysis Platform The analysis was performed on Omics Box BioBam Version 3. 0. The bioinformatics software programme Omics Box, created by BioBam, offers a user-friendly platform for the study of omics data, including datasets from the genome, transcriptome, and metagenome. We used Omics Box's metagenomic analysis tools for our analysis. The identi cation and characterisation of microbial communities as well as their potential for functional function in a speci c environment are made possible by metagenomic analysis. Kraken 3. 0, an Omics box tool, was used to classify the metagenomic sequences based on their taxonomic composition. Kraken 3. 0 assigns taxonomic labels to the sequences using a database of reference genomes, giving important details on the make-up of the microbial community present in the sample (BioBam Bioinformatics, 2023; OmicsBox, 2019).
Plots and Graph To create a wide range of graphical representations, we used Microsoft Excel and the R programming language. We used these technologies speci cally to create observant representations like pie-of-pie charts, bar charts, stacked bar charts, and conventional pie charts. Our procedure in R includes getting the data ready and utilising the ggplot2 package, a exible data visualisation toolkit. Microsoft Excel furthermore provided a user-friendly framework for creating these visualisations. We arranged our data in spreadsheets and created necessary charts using special capabilities.
RESULTS AND DISCUSSION In our study, we employed the OmicsBox software developed by BioBam (OmicsBox, 2019) to conduct comprehensive analyses of 40 Polycystic Ovary Syndrome (PCOS) samples in comparison to 8 control samples. The genetic sequences for our analysis were sourced from the NCBI project (PRJNA775858). Through metagenomic analysis, we aimed to identify both shared and distinctive microorganisms present within the control and subject groups. Our analysis was conducted at the Genus level to determine the prevalence of various microorganisms.
Microorganisms identi ed in the control group The current study utilized 8 control sequences obtained from the NCBI project with the accession number PRJNA775858. In the course of our analysis, we identi ed the existence of 63 different microbial genera within the control cohort, which consisted of 8 individuals in good health. It is worth noting that out of these 63 genera, 13 microorganisms (Figure  -1 ) were found to be exclusive, signifying that they were present only in the control and not in the subject group. This observation suggests potential microbial differences between the control and subject groups. 3. 2 Role of the unique microorganism identi ed in the control group. Sulfate-reducing Gram-negative bacteria belong to the Desulfovibrio genus. Desulfovibrio frequently appears in aquatic habitats with a lot of organic matter.
Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development. Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel. Methods: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity.
To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed. Conclusions: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients.
Fundus Reading Center at Moorfields Eye Hospital The Moorfields Eye Hospital Reading Center at Moorfields Eye Hospital National Health Service Foundation Trust, London, United Kingdom, evaluated all the study images using established standardized protocols. All anonymized images were submitted to the Moorfields Reading Centre using a safe transfer protocol. The images were uploaded to their respective image viewing software. Those subjects deemed to have MacTel following a clinician adjudication process (A. C. B. , T. P. , C. A. E. , F. B. S. , T. F. C. H. ) were released to the grading queue. Image grading was completed by trained and certified personnel (T. P. , F. B. S. , I. L. ) using a prespecified protocol and data were entered into the MacTel Study's database. Each image was evaluated independently without the knowledge of the fellow eye or prior study visit gradings.
Clinician adjudication took place when the graders required further input and the clinician decision was taken as final. This process resulted in the agreed grading results for all imaging modalities to be uploaded to the MacTel study database in order to integrate with the rest of subject related data. At no time did the graders have access to any other medical information but the images. Agreement on grading characteristics was moderate to substantial for all fields. The reading center personnel graded for MacTel features on the CF photographs, fluorescein angiograms, OCTs, and FAF images. Color fundus photograph were assessed for transparency in the perifoveal retina, pigment epithelial changes, and subretinal neovascularization. The fluorescein angiographic images were graded for abnormal vessels temporal to the fovea and subsequently in the perifoveal capillaries.
Statistical Analyses The Classification and Regression Trees (CART)  24  is a predictive algorithm used in machine learning. The CART algorithm is nonparametric and can be used for any type of data. The CART methodology uses recursive partitioning to split the data into several groups based on values of predictor variables that create the best homogeneous group when splitting the data. To appropriately conduct CART the dataset was split into 2 sets, a training set and validation set. The training data set used to build this decision tree model was based upon the right eyes of the MacTel participants in the natural history study while the validation data set used to determine the appropriate tree size needed to achieve the optimal final model was based on the left eyes of MacTel participants.
Discussion In this analysis we have used data from MacTel Study participants that incorporates state-of-the-art imaging modalities, including SD-OCT. The CART analyses demonstrated 3 important features key to the construction of this classification: OCT hyper-reflectivity, pigment, and EZ discontinuity (break). The primary target of interest for the algorithm development by CART was the decline in BCVA. This classification was built using the data from the right eye and targeting the loss of VA over time. The validation studies using the left eyes of the NHOS and both eyes from the NHOS last study visit provided further evidence of support for this classification. It is not surprising that this classification shares features with the Gass classification (Table  1 ), including features of the most severe end of the scale, the presence of pigmentation and exudative neovascularization, as these events caused the most severe loss of vision.
This may be a disadvantage because eyes are highly correlated. Ideally, a training set, a validation set, and a test set would be conducted using both eyes of this dataset and divided by participants. It is also important to be able to validate this algorithm on an entirely separate dataset involving different participants. Such an external validation data set of a longitudinal assessment of cohort of  participants with the diagnosis of this somewhat rare disease, MacTel, is not available. However, a limited dataset for the future may be the sham-treated participants who are currently enrolled in our Phase II  31  and III clinical trials of ciliary neurotrophic factor studies (ClinicalTrials. gov identifier (NCT number: NCT03319849 and NCT03316300), which we expect to complete follow-up in the last quarter of 2022.
Aims. We analyse unpublished Spitzer observations of the thermal phase-curve of WASP-121 b, a benchmark ultra-hot Jupiter. Methods. We adopted the wavelet pixel-independent component analysis technique to remove challenging instrumental systematic effects in these datasets and we fit them simultaneously with parametric light-curve models. We also performed phase-curve retrievals to better understand the horizontal and vertical thermal structure of the planetary atmosphere. Results. We measured planetary brightness temperatures of ∼2700 K (dayside) and ∼700-1100 K (nightside), along with modest peak offsets of 5. 9 • ± 1. 6 (3. 6 µm) and 5. 0 •+3. 4 -3. 1 (4. 5 µm) after mid-eclipse. These results suggest inefficient heat redistribution in the atmosphere of WASP-121 b. The inferred atmospheric Bond albedo and circulation efficiency align well with observed trends for hot giant exoplanets.
Interestingly, the measured peak offsets correspond to a westward hot spot, which has rarely been observed. We also report consistent transit depths at 3. 6 and 4. 5 µm, along with updated geometric and orbital parameters. Finally, we compared our Spitzer results with previous measurements, including recent JWST observations. Conclusions. We extracted new information on the thermal properties and dynamics of an exoplanet atmosphere from an especially problematic dataset. This study probes the reliability of exoplanet phase-curve parameters obtained from Spitzer observations when state-of-the-art pipelines are adopted to remove the instrumental systematic effects. It demonstrates that Spitzer phase-curve observations provide a useful baseline for comparison with JWST observations, and shows the increase in parameters precision achieved with the newer telescope.
Raw photometry extraction We downloaded the basic calibrated data (BCD, files extension '_bcd. fits') from the Spitzer Heritage Archive  (Wu et al. 2010). The BCD are flat-fielded and flux-calibrated frames  (Fazio et al. 2004; IRAC Instrument & Instrument Support Teams 2021). We extracted the pixel time series from 5 × 5 arrays where the central pixel records the highest flux in most frames within an AOR. The raw light curves were computed as the sum of pixel time series from the 5x5 arrays. We note that the selected array could vary between AORs within the same visit. We also attempted to use a single array for each entire visit, but this choice increased the photometric offsets between AORs and degraded the performance of our data detrending method. We flagged and corrected outliers in the raw light curves through the following procedure.
Finally, we binned all the light curves by a factor of four, corresponding to an integration time of 8 s, to speed up the following data analysis. The chosen bin size is a conservative one, being much smaller than the occultation timescales  (Kipping 2010). We could have adopted a larger bin size for the parts outside the occultations, based on the phase curve A54, page 2 of 14  Morello, G. , et al. : A&A proofs,  Table  2. Spitzer/IRAC datasets analysed for this study. Filter (i)  Prog. ID AORs (ii)  UT start date (iii)  N frames Mode (iv) Pip. Notes. (i) IRAC channel and central wavelength. (ii) Astronomical observation requests. (iii) Timestamp of the first frame of the AOR. (iv) Readout mode and frame time in seconds. (v) Pipeline version of the basic calibrated data. timescales, but our choice also minimizes the impact of correlated noise  (Morello et al.
2022). Figure  1  shows the binned raw light curves analysed in this work. For illustrative purposes only, we calculated the coordinates of the stellar centroid using the centre-of-light method, as implemented by  Morello (2015). Figure  2  shows the x and y coordinates obtained for both visits. It appears by eye that the centroids describe different loci in the x-y plane for each AOR. Pointing is stable mostly within 1-2 tenths of the pixel side during an AOR, then jumps abruptly by up to more than half a pixel when starting a new AOR. The larger-than-usual discontinuities likely make these datasets especially challenging to analyse compared to other Spitzer/IRAC phase-curve observations (e. g. Stevenson et al. 2017; Morello et al. 2019).
2. Centroid coordinates of the Spitzer/IRAC images, using different colours for each AOR (as in Fig. 1 ). Note: there are pointing offsets of few tenths of pixels between consecutive AORs. technique, which instead used pixel light curves in the time domain  (Morello et al. 2014 (Morello et al. , 2015;; Morello 2015). As in previous papers, here we applied a single-level discrete wavelet transform (DWT) to the pixel light curves, but adopting the Haar wavelet  (Haar 1910 ) instead of the more complex Daubechies-4 one  (Daubechies 1992). We checked, however, that the choice of wavelet function does not noticeably affect the ICA transform. The adopted ICA algorithm is MULTICOMBI  (Tichavsky et al. 2008 ), as always. This time we wrapped the original MATLAB source code for use in a Python script.
We initially tried to concatenate the pixel light curves from multiple AORs to form a single set of input signals to be transformed with ICA for each visit. Morello et al. (2019)  successfully adopted this approach to detrend Spitzer/IRAC phase curves of WASP-43 b. The same approach failed on the WASP-121 b data presented here, most likely due to larger pointing jumps between consecutive AORs. Therefore, we decided to perform individual ICA transforms for each AOR. We excluded the second AORs from each visit. In fact, these AORs do not contain an astrophysical signal with a well recognizable shape, such as a transit or an eclipse. The lack of morphology makes it difficult to separate the astrophysical component from the instrumental ones. For all other AORs, we identified the first ICA component as the astrophysical one, containing a clear transit or eclipse signal.
2009; Perna et al. 2012; Mendonça et al. 2016; Deitrick et al. 2020). Although they are rare, westward hot-spot offsets have been previously reported for a few hot Jupiters  (Armstrong et al. 2016; Dang et al. 2018; von Essen et al. 2020 ) and predicted theoretically  (Rogers & Komacek 2014; Rogers 2017; Hindle et al. 2019). We performed phase-curve retrievals with the phase-curve plugin  (Changeat & Al-Refaie 2020; Changeat et al. 2021 ) of TauREx 3. 1  (Al-Refaie et al. 2021 , 2022) , the latest version of the TauREx software  (Waldmann et al. 2015a,b). For the atmosphere, we assumed three homogeneous regions: hot spot, dayside, and nightside. We computed the emitted flux at given phases using a quadrature integration scheme and fit all the phases for both channels in a single run. Each region is described by a plane-parallel atmosphere composed of 100 layers with pressures ranging from 10 to 10 -6 bar in log scale.