dataesr/scientific-paragraphs-categorization huggingface dataset:
Certain assemblies of fibers, called fiber bundles, play a crucial role in the statistical macroscale properties of fibrous structures like natural or artificial materials. Based on the concept of using idealized statistical fiber bundle cells (FBCs) as model elements, the software named FiberSpace was developed by us earlier for the phenomenological modeling of the tensile test process of real fibrous structures. The model fibers of these FBCs had been considered linear elastic, which was suitable for modeling certain textiles and composites. However, the biological tissues are multilevel structures with fiber-like building elements on every structural level where the fiber elements on the dominant level are statistical bundles of elementary fibers. Hence, their modeling required us to introduce model fibers of nonlinear mechanical behavior and derive the proper mathematical formulas for the calculation of the expected tensile force processes of the FBCs.
2012; Vas and Tamás 2008). Using FBCs for creating theoretical structural-mechanical models of fibrous structures provides a way to describe the statistical mechanical behavior of fibrous structures for different damage modes during mechanical tests such as tensile or bending tests. Results of testing and analyzing woven fabric samples showed that instead of linear fiber elements, nonlinear elements had to be used for modeling tensile behavior  (Vas et al. 2013). The use of such type of bundles can be found in some papers dealing with other structures as well  (Goh et al. 2013; He and Wang 2004). We had similar results when testing biological (plant, animal, and human) tissues  (Hangody et al. 2017). Hence, in order to model the tensile behavior of human or animal tissues, we developed the concept of nonlinear FBC modeling and a general approximation method based on nonlinear E-bundles in a new version of the FiberSpace software.
Formulas for calculating the expected tensile force process of nonlinear FBCs Knowing the relationship between the bundle (u) and fiber strains (ε), one can calculate the expected value of the tensile force of the FBCs ( E(F) = F(u) ) as a sum of the single fiber forces, using the suitable formulas developed. Dividing the expected value by the mean breaking force of fibers, the normalized tensile force of the bundle is calculated as follows: where n, F B , and B are the number, the mean breaking force, and the strain of fibers, respectively, and z is the bundle strain normalized by the mean breaking strain of fibers, while F1 is the bundle force related to one fiber. Accord- ingly, normalizing the strain quantities in Eq. (  1 ) with B defines a new function: Earlier we had developed mathematical formulas for calculating the expected value of the tensile force processes of linear fiber bundles  (Vas and Rácz 2004)  and built their numerical realization in the software named FiberSpace  (Vas and Tamás 2008).
Modeling and decomposing tensile test results with parallel-connected FBCs We have developed an updated version of the program package named FiberSpace as the numerical realization of the nonlinear FBC-based modeling procedure, in order to assist the construction of a suitable FBC model for a given material or facilitate studying the behavior of model structures. The identification of the different statistical fiber bundle cells (FBCs) to be applied has been based on the minimization of the squared deviation between the measured force-strain curve and the expected tensile force process of the FBC model, which was created as the parallel connection of number-weighted fiber bundle cells.
Numerical realization of nonlinear FBCs by FiberSpace In Fig. 8 , the normalized expected tensile force process of a nonlinear E-bundle can be seen together with the window for setting the tensile characteristic parameters of fibers as displayed by the novel version of FiberSpace. The parameters (a, b, c) are the same as in Eq. (  4 ). The window reveals the shape of the characteristic function as well. In addition, besides the exponential type characteristic by Eq. (  4 ), FiberSpace offers two other modes for setting nonlinear fiber characteristics. One of them is a polynomial Fig. 8  The tensile force process of a nonlinear E-bundle and the setting window for the nonlinear fiber characteristic as visible in the FiberSpace program screen of a degree of maximum 5, the other is a numerical method where the measured data can be imported from MS Excel.
Fourier approximation and the final model parameters Since the FBC model may use up to 29 parameters, determining them by minimizing the deviation according to Eq. (  19 ) needs suitable initial parameter values. The measured and normalized load-strain curve is approximated by Fourier regression so that its shape features can be recognized. On the basis of our experiences, the load-strain curves can be well approximated with a Fourier polynomial of 8 terms besides the constant. The FiberSpace system uses the gradient method for seeking the global minimum which depends on the initial values of the model parameters. In order to find the adequate initial position, we built in the so-called nearest neighbor searching system. The already determined Fourier coefficient vectors (f = [a 0 , a 1 ,…, a n , b 1 ,…, b n ] T ) and the related parameter vectors (m ∈ R P ) containing the P = 29 FBC model parameters are stored in database Data.
Human facial nerve samples The tensile tests were performed with a Zwick Z005 computer-controlled tensile tester with custom grips, where the nerves were rigidly fixed on steel slit rods. Test speed was 10 mm/min. In Fig. 14a , the arrangement of the tensile test of some facial nerves (Table  1 ) of a 59-year-old man (Fig. 14a ) can be seen, while Fig. 14b  shows the results. Table  2  contains the geometrical and mechanical data of the specimens. The force-elongation curves in Fig. 14. b are significantly different, hence averaging them point by point would not (  42 ) u 1 ∶ 1 2 k u 1 = F u , u * ∶ max u F(u) = F(u * ). (43) F * < k(u * ) < F N = k u N ≤ k u 1 ;u * < u N ≤ u 1. give a reasonably usable result for modeling or analyzing. Therefore, both their evaluation and modeling are to be performed on the individual measurements.
Decomposition into nonlinear FBCs As mentioned above, the tensile test result of the left side facial nerve code   BB was modeled and analyzed with the aid of the Fiber-Space software. The first step before creating an FBC model is the normalization of the measured force-elongation curve, which needs the normalizing parameters according to Eq. (  39 ). On the basis of Paragraph 2. 5, for example, they can be estimated by fitting a simple shifted linear E-bundle to the measured data, as shown in Fig. 16 , where the tensile characteristic was approximated by the inflection tangent of the rising part (its Equation is y = 4. 65 + 1. 3(x-6. 61)). From fitting the linear E-bundle (a = 0), we obtained that E(ε B )≈ε N = 12. 80 mm and the tangent gave at this place that E(F B )≈F N = 12. 69 N, hence the shifting of the E-bundle response came at about 3.
03 mm. Figure  17  shows the final approximation (red) of the measured and normalized force-elongation relationship (green) obtained by FBC modeling in the FiberSpace environment, together with the sine and cosine Fourier amplitudes as parameters (olive and green columns of the measured sample, and purple and blue columns of the approximating FBC), which were stored in the learning process of the software. The goodness properties of approximation were RMSE = 3. 39% and R 2 = 0. 988. Figure  18  shows the component curves belonging to FBCs and the parameters are summarized in Table  4. The tensile characteristic parameters of all the fibers were the same (a = -1, b = 1, c = 1) while ET contraction parameters were Ca = Cb = 5, meaning a very strong contraction by the reduction of the free volume among fibers.
Conclusions The idealized statistical nonlinear fiber-bundle-cells (FBCs) and the modeling methods developed are suitable for phenomenological modeling of multilevel fibrous structures such as human tissues and make it possible to analyze them on the basis of evaluating measurements and determining the ratio of fibers or their bundles of different geometrical properties. The software FiberSpace uses composite bundles constructed as the parallel connection of different nonlinear FBCs for modeling the tensile load-strain curve of real fibrous structures. The expert system based on Fourier approximation and load-strain curve classes makes it possible to find the best model parameters fast. The system can learn and develop itself by improving mapping and/or establishing new subclasses on the basis of the modeled results.
Acknowledgements This work was supported by  Hungarian National Research, Development and Innovation Office (NKFIH)  through grant  OTKA K 116189  and  OTKA K 138472 , by the  Higher Education Excellence Program  of the  Ministry of Human Capacities in the framework of the Nanotechnology research area of the Budapest University of Technology and Economics (BME FIKP-BIO ) and by the  National Research, Development and Innovation Fund  ( TUDFO/51757/2019-ITM ,  Thematic Excellence Program ).
a b s t r a c t Water stable isotope analysis using Cavity Ring-Down Spectroscopy (CRDS) has a strong between-sample memory effect. The classic approach to correct this memory effect is to inject the sample at least 6 times and ignore the first two to three injections. The average of the remaining injections is then used as measured value. This is in many cases insufficient to completely compensate the memory effect. We propose a simple approach to correct this memory effect by predicting the asymptote of consecutive repeated injections instead of averaging over them. The asymptote is predicted by fitting a y = a x + b relation to the sample repetitions and keeping b as measured value. This allows to save analysis time by doing less injections while gaining precision. We provide a Python program applying this method and describe the steps necessary to implement this method in any other programming language.
These correction coefficients correspond to the proportion of residual water vapor from the previous sample. Wassenaar et al. [8]  propose a particular order of analysis and a software (Lims for Lasers) which allow to quantify the memory effect in between two samples by comparing the result of a standard analyzed after a contrasting standard to the result of a repeated analysis of the same standard. They use the average of non-ignored injections as only information for each sample. Gröning  [1]  proposes an excel-based software-tool estimating the memory effect for each injection of the sample analysis by estimating the effect of the previous sample on each injection of the following sample for each pair of two consecutive samples. Van Geldern & Barth  [6]  use a similar approach, estimating memory coefficients from a series of known standards and update these memory coefficients using daily repetition of the series of standards.
The application of this approach does not require profound knowledge of the analytical system and is hardly more complex than averaging over the last samples but adds precision and requires less injections per sample. In addition to the memory effect, the instrument's drift over time needs to be corrected for. In contrast to the memory effect, this correction is relatively straight forward and does not differ much between the different studies. It is estimated by regularly measuring known standards and quantifying their trend over time and can then be corrected through linear approximation. A simple drift correction is also briefly described at the end of the methods section and included in the Python program provided with this article.
The procedure of the inv method is to optimize a and b to obtain a good fit for measured value vs. injection number for each of the analyzed samples. We use Python's scipy module and its optimize. curve_fit function  [7]. The program is provided in the supplementary material. This method is applied to any analyzed sample (i. e. standards and unknown samples). The memory correction can be combined with any calibration workflow. See the end of this section for details on the full workflow of the provided Python program. No matter which programming language is used, we strongly recommend either fitting a linear function to 1/x-transformed data or using increasing weight/decreasing uncertainty with number of injection when fitting directly on the data-scale. If all points are equally weighted, the first injections have a stronger influence on the fit as they are further away from the mean and thus weigh stronger in the fitting algorithms cost function.
Taking into account the necessity of regularly reanalyzing the standards due to possible drift of the device, the time gain is increased as more samples may be analyzed in between two repetitions of the standards. The step-by-step calibration workflow of the provided Python program is as follows: 1. For each set of standards (in our example data there is one in the beginning and one in the end). a. Inv method is applied to estimate each standard's measured value. b. A calibration is performed by fitting a linear relation to real vs. measured values of the standards. c. The two fitted parameters (intersection and slope) are saved together with the central time of analysis of this set of standards (i. e. the middle time between first injection of the first standard and last injection of the last standard).
Acknowledgment We would like to thank  Niels Munksgaard  for sharing some of his expertise. We also thank  Mathieu Sebilo  for enabling us to do the analyzes. The analyzes were funded by OHMCV observatory, which is part of  OZCAR Resarch Infrastructure  ( https://www. ozcar-ri. org/) that is supported by the  French Ministry of Research, French Research Institutions and Universities. We thank three anonymous reviewers for their constructive comments.
Immediately hereafter, the plasma tubes were gently mixed, placed on board and TG was measured. The samples were run in duplicate and the ST Genesia embedded software analyzed the data automatically and gave a mean result for the duplicates. The reference plasma run in parallel to the samples, comes with specific assigned ranges provided on a barcoded flier and helps to normalize results  (26, 28). The ST Genesia normalizes the results for each sample automatically by applying the following formula: [patient sample result / reference plasma result • activity assigned for the particular lot and parameter of this reference plasma].
High throughput thrombin dynamics analysis Thrombin dynamics analysis was performed in Matlab (R2016a, Mathworks, Eindhoven, the Netherlands) using the Matlab "Statistics and Machine Learning Tool Box" (version 10. 2, Mathworks, Eindhoven), and theMatlab "Curve Fitting Toolbox" (version 3. 5. 3, Mathworks, Eindhoven, the Netherlands) and to ensure a high throughput computation of thrombin dynamics parameters. Analyses were performed on a HP probook (HP, Amstelveen, the Netherlands) equipped with a Microsoft Windows 10 pro operating system. The equations described in the sections above were implemented in an semiautomized data analysis script, which analyzes around 50 curves per minute.
Statistics The GraphPad Prism software (version 8. 4. 2, San Diego, CA) was used to determine statistical significance of the results. Data are presented as median ± interquartile range (IQR) as indicated. Data were checked for normality by the Shapiro-Wilk test. As not all groups passed normality, the Friedman test was used to compare paired data in combination with the Dunn's post-hoc test for multiple comparison. Similarly, for unpaired data, the Kruskal-Wallis test was used in combination with Dunn's post hoc test for multiple comparison. Reference ranges were established by calculating the 2. 5th and 97. 5th percentile in the dataset of 112 healthy subjects. A p-value < 0. 05 was considered statistically significant.
Similarly to the ETP, T-AT, and T-α 2 M complexes increased with the TF concentration (Figures  1E, G ). T-AT was, respectively for the STG-BleedScreen, STG-ThromboScreen and STG-DrugScreen, 860, 897, and 955 nM, while T-α 2 M was 28, 30, and 33 nM. The effect of TM was measured using the STG-ThromboScreen kit, which as previously described contains TG trigger with and without TM. PC tot , T-AT, and T-α 2 M complex formation are strongly inhibited by the addition of TM (-44, -43, and -48%, respectively, Figures  1B, F, H ), whereas PC max was affected less, but still significantly (-24%, Figure  1D ). The reference values for each TD parameter and STG reagent were calculated as the 2. 5th and 97. 5th percentile of the distribution using the data from all 112 healthy donors and are shown in Table  2.
Acknowledgements-We are grateful to the  National Research Foundation  and the  Ministry of Education, Singapore  for financial support. The authors acknowledge the use of the  IBM  Quantum Experience devices for this work. This work is supported by a  Samsung GRC  project and the  UK Hub in Quantum Computing and Simulation , part of the  UK National Quantum Technologies Programme  with funding from  UKRI EPSRC  grant  EP/T001062/1.
Afterwards, the mathematical relation between melt pool morphology and process parameters is established for different melting states based on physical mechanisms including the melt pool energy conservation, mass exchange and temperature field principles. Then melt pool structure could be calculated by process parameters through the developed model. A considerable experimental data sets of melt pool for distinct materials, machines and process parameters reported from literature are employed for comparative verification of its feasibility and robustness. Additionally, to further verify its effectiveness, the proposed model is contrasted with other existing empirical models. The applicability of the model in melt pool classification and prediction is also elaborated. The model could effectively and rapidly compute the pivotal morphological dimensions of melt pool, further reconstruct melt pool shape, and meanwhile reduce experimental trial-and-error costs, which provides potential use for melt pool defect identification and geometry simulation for the virtually SLM printed part which is cumulatively formed by melt pools.
Calculation procedure The calculation flow of melt pool morphology is shown in Fig. 6. According to the defined process parameter index, a preliminary qualitative analysis of melt pool status could be conducted. Then the ratio coefficients of melt pool shape are estimated based on the mechanisms of melt pool temperature field and mass conversion. Thereafter the critical geometries of melt pool could be calculated, containing the width, height, depth and length based on energy transfer principle. Lastly, the melt pool morphology status can be evaluated. Furthermore, melt pool classification and melt pool morphology prediction are realized. And the proposed calculation approach is implemented in Matlab.
Comparison with experimental observations The geometrical dimensions of melt pool were computed according to the calculation procedure from Section 2. 3 for distinct materials, and the calculation outcomes were compared with the experimental data sets from the literature correspondingly for SLM process. Table  2  represents the error results of melt pool morphologies between calculation and experiment for IN718, TC4 and 316L materials. And sufficient groups of melt pool samples from literature were utilized and contrasted. Results indicate that the calculated melt pool dimensions are in good agreement with the experimental data for distinct materials, machines and process conditions. The adopted material parameters of IN718, TC4 and 316L for calculation are shown in Table  3  in Appendix A.
Background: Breast cancer is the most common cancer among women worldwide. Most breast cancers are estrogen receptor-positive and epidermal growth factor receptor 2 negative (ER+/HER2-). A small proportion of these cases are metastatic, and cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are the mainstay of treatment in this scenario. Evidence supports the existence of differences between the three approved cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) in preclinical and clinical settings. However, literature regarding the use of a CDK4/6i in metastatic breast cancer patients (MBC) previously treated with another CDK4/6i is scarce. Methods: A search will be performed on the PubMed, Embase, and Web of Science databases following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines.
Methods To our knowledge, no previous systematic reviews and meta-analyses have evaluated the available evidence regarding the e cacy and safety of CDK4/6i treatment in MBC patients previously treated with another CDK4/6i. Therefore, a speci c protocol for a new systematic review and meta-analysis was developed. The present protocol has been registered in the PROSPERO database (registration number: CRD42022330355) and is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement  (11).
Search strategy Our PubMed search strategy is the following: (abemaciclib OR ribociclib OR palbociclib OR cyclin-dependent kinase inhibitor OR cyclin-dependent kinase inhibitor OR iCDK OR CDKi OR CDK inhibitor OR CDK4/6i OR CDK4/6 inhibitor) AND (Rechallenge OR previous OR prior OR after OR retreatment OR readministration OR restart OR resume OR reinduction OR reintroduction OR continuing) AND (breast cancer OR breast malignancy OR breast neoplasm). The reviewers will adapt this search strategy for the Web of Science database.
Studies without full accessibility. 4. Duplicate studies. 5. Articles written in other languages, not English. Six reviewers (JBA, RS, MA, CF, ALL, CT) will independently screen the titles and abstracts of these studies for potential inclusion. Potentially eligible studies will be con rmed by an evaluation of the full text. A third-party investigator (FM or JA) will resolve any uncertainty or discrepancy. References of all articles included or excluded at the full-text review stage will be entered into EndNote 20. 2. Details of the study screening and selection process will be shown in Figure  1.
Data Extraction A standardised data extraction template for the study will be developed using Microsoft Excel. Six reviewers will apply data extraction separately (JBA, RS, MA, CF, ALL, and CT). Whenever possible, the original authors will be contacted for any missing data. The following information will be retrieved: Studies: publication year, name of the rst author, study design, sample size, country, whether singlecenter or multicentre, follow-up length, follow-up method, and quality of the study. Participants: age, sex, ethnicity, performance status, menopausal status, tumour pathology characteristics (histological type, hormone receptor, HER2 status, Ki67), extent of disease, presence of visceral metastases, previous lines of treatment with start and nish date, previous chemotherapy use, time until CDK4/6i use since prior CDK4/6i, PFS obtained with prior CDK4/6i, best response achieved with prior CDK4/6i, and previous AEs with these treatments.
Interventions: CDK4/6i treatment start and nish date and dosage. Outcome measures: HR for OS, PFS, and/or survival curves, allowing estimation of the HR for OS and/or PFS, ORR, DCR, and AEs. The reviewers will also collect other information regarding the data extraction process (e. g. reviewer, date of data extraction, record number, and missing data). The capacity of this procedure to collect the intended data will be tested in a few studies before performing complete data extraction of all included studies. Once data extraction is completed, reviewers will compare their results, and a third author (FM or JG) will mediate in case of disagreement.