dataesr/scientific-paragraphs-categorization huggingface dataset:
Predictive modeling For our preliminary analysis, we built 10 models for each of the 12 variations of our dataset to predict responses to the four general health questions and calculated index of general health status by binary classification. The 10 learners chosen for this analysis were 5-Nearest Neighbors (5NN), two versions of C4. 5 decision tree (C4. 5D and C4. 5N), Logistic Regression (LR), Multilayer Perceptron (MLP), Na ïve Bayes (NB), two versions of Random Forest (RF100 and RF500), Radial Basis Functional Network (RBF), and Support Vector Machine (SVM). Detailed descriptions to explain and contrast these algorithms have been described elsewhere  [12]. These were chosen because they represent a variety of different types of learners and because we have had demonstrated success using these in previous experiments.
Moreover, the parameter settings were chosen based on our previous research which showed them to be robust on a variety of different data  [13]. Because this was a preliminary investigation and because our data were limited, further tuning of parameters was not employed as it would have increased the risk of overfitting our models and thus reduced the broader clinical utility beyond these specific data. Each model was built using 10-fold cross validation, and model performance was measured using Area Under the ROC Curve (AUC). Our crossvalidation process began with randomly dividing each of the 12 data sets into 10 equal segments, using nine of these respective segments to train the model and the remaining segment for testing. The number of instances in each segment varied by the size of the respective dataset as indicated in Table  1  (i.
e. , 1/10 of the total number of instances for each dataset) This procedure was repeated 10 times, using a different segment as the test set in each iteration. The results were then combined to calculate the final model's result/performance. For each learner/dataset combination, this entire process was repeated 10 times with the data being split differently each time. Repeating this procedure reduced bias, ensured replicability, and helped in determining the overall model perfor-mance. Differences between learner-specific model performance were examined using ANOVA and Tukey's Honest Significant Difference (HSD) test. In total, 12,000 models were built (12 datasets × 10 learners × 10 runs × 10 folds = 12,000 models).
Each of these data values in Table  5  indicates the aggregate model performance based on the AUC respective mean derived from the 100 models (10 runs × 10 folds) built for each learner/dataset combination, with the statistically overlapping (confidence interval) highest performing learners for each dataset indicated in bold. Logistic regression was generally the top-performing learner in nearly all cases with moderately robust prediction performance for HealthQScore and the general health questions specific to medications and medical conditions affecting thinking (though, only when using the other three health questions responses as independent variables for the latter).
DISCUSSION From the original HAPPYneuron program dataset, we cleaned and analyzed individual measures of episodic memory performance from MemTrax and respective selected demographic information from the M-CRT test. Then, using machine learning, we developed a series of models to separately predict the binary classification responses to four individual general health questions and a calculated binary classification index of implied general health status-HealthQScore. Logistic regression was generally the top-performing learning algorithm indicated by its highest or nearly highest AUC performance on all datasets. Classification prediction for HealthQScore was moderately robust, as were the models for the general health questions specific to medications and medical conditions affecting thinking (when the responses to the other three questions were considered as independent variables for the latter).
Deeper examination of these (or similar) data might prompt select classification algorithm setting changes that would favorably support building more robust models. Interestingly, the models developed for the memory problems question were among the worst performing models for the four general health questions. This was somewhat surprising given that this variation of the dataset contained the most instances, which typically enhances model performance compared to models based on more limited data. Arguably, an underlying reason for this may be that there were still some noisy/faulty data included in the dataset. Further efforts towards additional data cleaning may help improve model performance. Alternatively, while subjective memory complaint can be predictive (in early stages) for future onset and development of dementia, those individuals suffering from or exhibiting cognitive dementia who are diagnosed with AD (beyond mild cognitive impairment) usually deny or are unaware of their memory problems.
Data limitations and outstanding questions Our initial exploration and assessment of the overall dataset revealed several issues and challenges. Notably, numerous instances of missing information across many features may have compromised the accuracy of our current (and would for any future) models trained on these data. Specifically, the markedly large difference in the number of users who answered whether they were having memory problems compared to the prevalence of responses to the other three general health questions, suggests the need to examine when in the process these questions were presented to the participants and how the users were prompted. Whereas our analysis showed significant differences between some features, filter-based modeling (i. e. , training models only on a subset of top-ranked features) did not demonstrate meaningful improvement, and thus was not included in the current methods or discussed.
ACKNOWLEDGMENTS We recognize the work of  J. Wesson Ashford ,  Curtis B. Ashford , and colleagues for developing the on-line continuous recognition task and tool (Mem-Trax) utilized here and the numerous patients with dementia who contributed to the critical foundational research. We also thank  Franck Tarpin-Bernard , his colleagues and assistants, and the participants of the  HAPPYneuron program  who provided their valuable time and commitment in taking the  M-CRT  and providing the data for us to evaluate in this study. J. Wesson Ashford  has filed a patent application for the use of the specific continuous recognition paradigm described in this paper for general testing of memory. He also owns the URL for http://www. memtrax. com. MemTrax, LLC is a company owned by  Curtis Ashford , and this company is managing the memory testing system described in this paper and the http://www.
Methods: We developed a new macro script for Fiji-ImageJ to automatically assess human fiber morphometry in digital images of the entire muscle. We tested the functionality of our method in deltoid muscle biopsies from a heterogeneous population of subjects with histologically normal muscle (male, female, old, young, lean, obese) and patients with dermatomyositis, necrotizing autoimmune myopathy, and anti-synthetase syndrome myopathy. Results: Our macro is fully automated, requires no user intervention, and demonstrated improved fiber segmentation by running a series of image pre-processing steps before the analysis. Likewise, our tool showed high accuracy, as compared with manual methods, for identifying the total number of fibers (r = 0. 97, p < 0. 001), fiber I and fiber II proportion (r = 0.
Here, we provide a reproducible and accurate automatic image analysis macro script that runs in an open-access platform (Fiji-ImageJ) and allows for the fast quantification and differentiation of human muscle fiber composition and histomorphometry in different physiological and pathological conditions. Our macro was designed to assess fiber morphometry in digital images of the entire muscle. We tested the functionality of our method in deltoid muscle biopsies from a heterogeneous population of patients with histologically normal muscle (male, female, old, young, lean, obese), patients with pathological muscle, and a small set of pectoral samples. Thus, our method provides a potentially powerful tool for research in the skeletal muscle field and medical diagnostics.
Automated morphometric analyses using our macro in Fiji Morphometric analyses were conducted using Fiji (Fiji is just ImageJ), an open-source image processing package based on ImageJ®  [12] , in the digitized images of the entire muscle section. To automatically detect and quantify the myofibres in the sections, we generated a customized macro script for the assessment of human histological samples in collaboration with the IMRB image platform (Creteil, France).
Overview of the developed macro The macro is entirely automated and requires no user intervention or clicking in dialog boxes during analysis. The macro can be run in Fiji in a computer with available Java 8 runtime. The image processing is compatible with Windows 64-bit (XP, Vista, 7, 8, and 10), Mac OS (X 10. 8), Mac OS X, and Linux (× 64 and × 86 architectures). The pipeline of the macro instructions is depicted in Fig. 1. After selection of a folder containing the individual channel images (one for the membrane: C1; one for type II fibers: C2) of each sample, the program will automatically load the files within the selected folder to conduct the analysis. Image processing is conducted using mathematical morphology. The first steps consist of automatic image enhancement by (1) eliminating background noise (despeckle filter), (2) increasing the contrast of the image, and (3) subtracting the background (clear out).
Statistical analysis The total number of fibers per sample was normalized by dividing this value by the tissue area (computed by the macro in μm 2 ) and then multiplying this result by 10 7 , hence obtaining the number of fibers in a 10-mm 2 area. For each sample, the averages for CSA, perimeter, and major and minor diameters were computed and used for all, type I, and type II fiber analyses. Because sex-related differences were found to affect muscle architecture in our population (Additional file 1: Table  S1 ), we removed the confounding effects of gender on fiber size and number by conducting a gender-adjusted two-way analysis of covariance (ANCOVA). The main effects of age, BMI, and their interactions were assessed for all, type I, and type II fibers. The data are presented as least square means with a standard error of the mean (LSMeans ± SEM).
Tukey-Kramer post hoc test was used to determine differences between groups. Pearson's correlation coefficients were conducted to establish significant associations among the parameters of interest. Statistical analyses were performed using SAS Enterprise Guide OnDemand (SAS Institute, Inc. , Cary, NC), and significance was set at p < 0. 05. For the analysis of Fig. 2  Example of automated analysis results. Human deltoid muscle cross-section analyzed with our macro in Fiji-ImageJ. a Cross-sections are labeled for dystrophin and spectrin (green-channel 1) to outline the fibers and for myosin heavy chain (MyHC) (red-channel 2) to identify type II myofibers; type I myofibers are unlabeled. Images were acquired with a Zeiss epifluorescence microscope using a × 10 objective and saved in a 16-bit TIF format.
Results Immunofluorescence staining and automated morphometric analyses using Fiji One of the challenges of conducting automated analyses using immunofluorescence-stained samples is that high fluorescence signals are required and the myofibers membrane need to be completely closed to be properly identified. Moreover, some images need to be pre-treated to increase brightness and contrast using different image editing software before analysis, which can be very time-consuming. We overcame these problems in two steps: First, the immunostaining protocol used was carefully optimized in muscle frozen sections (i) to increase the fluorescence signal without increasing the background and (ii) to ensure closure of the membrane, and its following identification by our automated program, by combining two different antibodies to target the sarcolemma.
CSA cross-sectional area, BMI mody mass index through the Fiji software project. The functionality of the macro was demonstrated in deltoid muscle from young, old, lean, and obese patients, as well as in patients with different pathological conditions and in pectoral muscle, thus confirming that our tool is flexible and broadly applicable to the analyses of a great variety of samples and supporting its value for research purposes as well as in clinical settings. The output color-coded maps based on the fiber's size proved to be an advantageous feature for the fast and easy visual identification of location-specific atrophy or hypertrophy and a potentially powerful tool for medical diagnosis.
Acknowledgements The authors would like to thank the  Association Française contre les Myopathies (AFM)  via  TRANSLAMUSCLE  ( PROJECT 19507 ),  Labex REVIVE  ( ANR-10-LABX-73 ),  Agence Nationale pour la Recherche (ANR)  grant  MyoStemVasc  ( ANR-17-CE14-0018-01 ), and  RHU CARMMA  ( ANR-15-RHUS-0003 ).
We present in this work an improvement of the computing performance of such simulations using ultraparallel processing on a graphical processing unit (GPU). Methods: A new Monte Carlo simulator named MPEXS-DNA, allowing high computing performance by using a GPU, has been developed for track structure and radiolysis simulations at the subcellular scale. MPEXS-DNA physics and chemical processes are based on Geant4-DNA processes available in Geant4 version 10. 02 p03. We have reimplemented the Geant4-DNA process codes of the physics stage (electromagnetic processes of charged particles) and the chemical stage (diffusion and chemical reactions for molecular species) for microdosimetry simulation by using the CUDA language. MPEXS-DNA can calculate a distribution of energy loss in the irradiated medium caused by charged particles and also simulate production, diffusion, and chemical interactions of molecular species from water radiolysis to quantitatively assess initial damage to DNA.
The validation of MPEXS-DNA physics and chemical simulations was performed by comparing various types of distributions, namely the radial dose distributions for the physics stage, and the G-value profiles for each chemical product and their linear energy transfer dependency for the chemical stage, to existing experimental data and simulation results obtained by other simulation codes, including PARTRAC. Results: For physics validation, radial dose distributions calculated by MPEXS-DNA are consistent with experimental data and numerical simulations. For chemistry validation, MPEXS-DNA can also reproduce G-value profiles for each molecular species with the same tendency as existing experimental data. MPEXS-DNA also agrees with simulations by PARTRAC reasonably well. However, we have confirmed that there are slight discrepancies in G-value profiles calculated by MPEXS-DNA for molecular species such as H 2 and H 2 O 2 when compared to experimental data and PARTRAC simulations.
In Section 2, we first recall the principle of modeling ionizing radiation in physical and chemical processes in liquid water, the main component of biological medium. We then describe how such processes have been implemented in MPEXS-DNA and verify their implementation by comparison to Geant4-DNA simulations of absorbed radial dose distributions around ion tracks and radiochemical yields as a function of time. In Section 3, comparisons to experimental data on radial doses and chemical yields as a function of time and linear energy transfer (LET) are presented as well as the results of validation of the code. Finally, gains in terms of computing performance are presented. MPEXS-DNA allows a speedup of three orders of magnitude compared to a single CPU approach.
2. C. Track structures and radiolysis simulations by MPEXS-DNA MPEXS-DNA has been developed as an extension of MPEXS for the simulation of ionizing radiation effects at the DNA scale. It currently simulates the three stages described in Section 2. A: (1) the physical stage, (2) the physicochemical stage, and (3) the chemical stage. The biological stage is not yet implemented in MPEXS-DNA. The processing of the three stages by MPEXS-DNA essentially follows the approach employed in the Geant4-DNA extension package designed to perform track structures and radiolysis simulations available in Geant4 10. 02 p03. Details of the simulation of each phase are given in the following paragraphs.
Comparison of the computing performance achieved by MPEXS-DNA to Geant4-DNA is thus of great significance. Table  XVI  shows the calculation environment utilized in our benchmark. TITAN V, a GPU device manufactured by NVIDIA, was utilized in the MPEXS-DNA benchmark. To maximize the computing performance of MPEXS-DNA, L1 cache was enabled, and the fast-math option, which ensures specialized calculations such as trigonometric and logarithm functions at high speed, was used. The Geant4-DNA performance was measured using a single Intel â Xeon â CPU (6core model). The time required for initialization that was performed prior to the simulation was negligible compared to the simulation time in both Geant4-DNA and MPEXS-DNA. In these benchmark tests, the initialization time was excluded from both the datasets, so that only the processing time from the physics stage to the chemical stage was assessed.
CONCLUSION MPEXS-DNA is a new track structure and radiolysis simulation code that is based on the Geant4-DNA package available in Geant4 10. 02 p03. It simulates the electromagnetic interactions of charged particles in liquid water, calculates the track structure of particles in a microregion at nanometer level, and allows calculation of the localized energy loss distribution. In addition, it simulates diffusion and chemical reactions for molecular species produced by the radiolysis of water and allows the determination of the distribution of molecular species within a target region. In this paper, initial particles such as electrons and protons were used to irradiate a water target, and the energy loss distributions and G-value time profiles for molecular species were calculated. We have confirmed that the simulation results obtained by MPEXS-DNA are consistent with the existing experimental data and simulations performed using state-of-the-art codes such as PAR-TRAC, which indicates the validity of simulations performed by the MPEXS-DNA.
ACKNOWLEDGMENTS This work was partly supported by  JSPS KAKENHI  Grant Numbers  17H01084  and  25246044 , the  Japan-U. S. cooperative program  in high-energy physics, and the  Associated International Laboratory KEK (Japan) -CNRS (France) -CEA (France) "France-Japan Particle Physics Laboratory, Toshiko Yuasa Lab. (FJPPL-TYL)". The authors gratefully thank  Dr. Nicholas Henderson  for his excellent contribution to the development of the MPEXS framework. The authors thank  Prof. Margot Gerritsen  from  Stanford University ,  Dr. Makoto Asai , and  Dr. Andrea Dotti  from  SLAC National Accelerator Laboratory  for their helpful advice and continuous support. The authors also thank  Dr. Dennis Wright  for proofreading to ensure the English quality of the manuscript. The authors thank NVIDIA for their generous support for the project.
1 ). Since its first public release in 2008, the project has evolved in a dramatic fashion and its development and user base has greatly expanded. Within the following ten years, the original paper was cited more than 500 times -considerable for a pure MBPT code -and the code has been used in many high impact studies spanning a wide range of novel materials and exciting technologies. The highest cited applications cover graphene derivatives  [10] [11] [12] [13] , metal-halide perovskites  [14, 15] , van der Waals bonded layered compounds  [16] [17] [18] [19] , Li-air and K-ion batteries  [20, 21] , and TiO 2 photocatalytic surfaces  [22, 23] , to select just a handful. yambo has moreover helped advance fundamental understanding of physical phenomena such as excitonic Bose-Einstein condensation  [24] , excitonic insulators  [25] , the influence of zero point motion  [26] , charge transfer excitations  [27] , etc.
A full list of publications can be found through our website  [28] , http://www. yambo-code. org. Part of yambo's popularity and success may be ascribed to the code's user-friendliness: thanks to an intelligent command line interface, a full GW-BSE calculation on an unfamiliar material can in principle be carried out launching a single command. Extensive user documentation is provided on our website  [28]. This includes descriptions of the fundamental theory, command line interface, and input variables, and provides a wide range of tutorials directed at explaining different functionalities of the code across a number of systems with different dimensionalities. Support is given by the developers through a forum. In addition to the website  [28, 29] , the theory and use of yambo has been disseminated through a number of international schools and workshops including a dedicated biennial CECAM event run by the developers and aimed at showcasing the latest developments.
The first major release, version 3. 2. 0, was described in detail in Marini et. al. [4]  (henceforth referred to as CPC2009), and therefore the basic methodology, formalism, and code structure will not be repeated here. Instead we describe the main additions made to the code up to and including version 4. 4. 0. Much development of the code has been driven by its status as a key ab initio spectroscopy code of the European Theoretical Spectroscopy Facility (ETSF)  [30]  and as a flagship code of the MaX European Centre of Excellence for Materials Design at the Exascale [31] and of the Nanoscience Foundries and Fine Analysis -Europe user infrastructure  [32]. With regard to the broad strategy outlined above, yambo now includes the possibility to compute the following state-of-the-art physical phenomena discussed later: • Electron-phonon and exciton-phonon interaction: influence of temperature on electronic structure and optical spectra (section VI); • Real-time propagation of the density matrix (section VII A) and Bloch states for nonlinear optics (section VII B); • Spin-orbit coupling and Kerr effect within a fully noncollinear BSE framework (section V B).
e. linear response, GW, BSE) computed by yambo have very different behaviours in terms of performance, scalability, and memory distribution, it is important to outline the different approaches -ultimately controlled by the user -adopted by the code in each case. Last, yambo has been almost completely rewritten since the first major release in order to follow modern software design practices such as modularity, reuse of routines and libraries, and so on, and the project as a whole has been expanded to include rigorous self-testing and automation frameworks. Here we highlight a few key features: • Test-suite and benchmarking scripts (section IX C); • The yambopy python scripts for automation and analysis (section IX A); • Plugin for workflow management via AiiDA (section IX B); • Wide use of standard libraries (section II A).
• Maintenance and distribution through GitHub. In the following section we recall the structure of the yambo software package and outline new features in its installation environment and interface with external codes and libraries. Sections III-VII outline new features implemented relating to improved algorithms and new capabilities. Section VIII discusses the new parallelism paradigm and performance issues. Sec. IX introduces new scripting and automation tools. Following some general conclusions, various technical information is presented in the appendices along with a useful glossary of acronyms.