dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Population The study was conducted from January 2012 to December 2018 in the ART unit at the reproductive medicine center of our university-based institution. Infertile patients who underwent in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles in a freeze-all strategy  (15, 16)  in our center with a prolonged embryo culture leading to at least one elective blastocyst viable for implantation were considered. Each woman was included only once (first ART cycle performed in our center). The data had been collected and recorded in a registered database. The inclusion criteria were as follows: 1) a requirement for ART with IVF or ICSI; 2) age %42 years at the time of the oocyte retrieval; 3) at least one expanded blastocyst electively vitrified at D5 or D6. The exclusion criteria were: 1) having both D5 and D6 blastocysts vitrified; and 2) having been included previously.
Data Analysis and Statistics The general characteristics of the patients in both of the groups were recorded prospectively during their medical consultations (Tables  1  and 2 ). All of the data were compiled into a digital database and analyzed with the use of SPSS software. A P value of <. 05 was considered to be statistically significant. For univariate statistical analysis, we used the Pearson c 2 test or Fisher exact test for qualitative variables and the Mann-Whitney test for quantitative variables. To identify factors associated with having an entire cohort of D6 blastocysts, we performed a logistic regression analysis. The factors were tested by means of univariate analysis and added in a multiple logistic regression model. We included all of the available factors that could be linked to our outcome in the multiple logistic regression model.
According to a number of authors, the dose of recombinant FSH, the E 2 level on the day of hCG trigger, and the type of GnRH analogue used during ovarian stimulation affect early embryo developmental kinetics  (24, 25). Bodri   (27). Another strength of the present study is that we performed a multivariate analysis after adjustment for several confounders to isolate independently associated factors related to the timing of blastocele expansion at D5 versus D6. Strength also lies in the methodologic design, with a large number of patients (1,103 women) included. Thus, our study's sample size is likely to have minimized statistical errors. In addition, numerous epidemiologic variables were collected prospectively through face-to-face interviews before ART treatment (regarding infertility data and ovarian stimulation characteristics) to avoid memorization bias and to allow multivariate analysis for adjustment on several confounders.
Despite the precautions taken, our study may nonetheless be subject to certain shortcomings and biases. One limitation is the self-reporting of smoking (based on personal interviews)  (19). Thus, there could be a risk of underestimation in this regard because we have no other information about the quantity of tobacco consumption  (28). In addition, it cannot be ruled out that other female or male clinical predictive factors are involved. Other lifestyle factors (e. g. , alcohol consumption, recreational drug use) could have influenced blastulation time. Unfortunately, those are not exhaustively available in our database. For example, the proportion of male smokers was not known. A male partner who smokes has been associated with detrimental effects on ART outcomes, although the results are still controversial  (23, 29).
Simulation of ODE models Time course analyses were conducted in MATLAB 2017a by using the ode15s solver or in Python 2. 7 by using the scientific python (SciPy) version 1. 1. 0, Numeric Python (NumPy) v1. 14. 1, Design Space Toolbox Python module v0. 3. 0a4, and the related C toolbox v0. 3. 0a6. In the Python scripts, a sequence of integrators was set up in case that one of the methods would fail to integrate accurately. The sequence implemented was the following: lsoda, bdf, and dopri. The Python and MATLAB code used to generate all our analyses are provided in the Supplementary Code Repository.
Finding oscillatory phenotypes by using the Systems Design Space Toolbox The Design Space Toolbox V2 for Python 2. 7 (ref. 31  ) was used to apply the SDS methodology to the 11 network designs considered in this work. The functionality of the toolbox was first tested by reproducing previously published results by Savageau and colleagues  27. Subsequently, their pipeline to analyze the phenotypic repertoire for oscillatory phenotypes was implemented in Python. The uniqueness of our implementation is two-fold: (i) the generalization of this pipeline, to be applicable to any predefined kinetic model in terms of a set of equations in SciPy notation; (ii) the approach of Savageau and colleagues was improved to include extensive parameter sampling within the boundaries of phenotypes characterized by potential oscillations (oscillatory phenotypes), i.
e. areas in the parameter space with two non-negative eigenvalues. Our computational pipeline is shown in Fig. 2. The pipeline consists of a Python script containing model definitions, a set of newly written Python functions that are wrapped around the Design Space Toolbox V2, and several Jupyter notebooks  73  that analyze a model and properties of any given limit cycle, respectively. All files are available in the Supplementary Code Repository. The main Jupyter notebook reads the model to be analyzed (defined in GMA form), and then proceeds to (i) set up the design space for the model, (ii) identify all valid phenotypes, (iii) identify the stability of each valid phenotype indicated by the presence of two complex conjugate eigenvalues with non-negative real part, by sampling each valid phenotype for a user defined number of times (in this study 250 parameter samples were collected), and (iv) retrieve limit cycle behavior by integrating the full kinetic model in the GMA form (Supplementary Information, Section 4) for the sampled parameter set.
Principal Component Analysis PCA projects a set of N-dimensional vectors along a new coordinate axis specified by orthogonal and uncorrelated vectors which are ranked according to how much of the variance in the data they explain. These "principal components" are linear combinations of the original parameters and are of unit length. Each principal component is associated with a percentage of the variance in the original dataset that it explains. As a result we can visualize features (parameter values in this case) in twodimensional space in such a way that parameter sets that are "close together" (i. e. not showing much variation) will appear together on the PCA plot. Observations that are far apart in the PCA plot are separated in the original space along the directions specified by the principal components if there was significant deviation within the original dataset to begin with.
However, parameter sets that are far apart in the original data may sometimes cluster closely together on the PCA plots if the first principal components do not explain a lot of the variance in the dataset. We first normalized the parameter sets by dividing by the standard deviation and subtracting the mean so that each parameter has a mean of 0 and a variance of 1 across the different limit cycle parameter sets before applying the PCA algorithm. This helps avoid bias from the multiple orders of magnitude covered in the parameter values.
ACKNOWLEDGEMENTS The authors thank  Michael A. Savageau  for helpful discussions,  Jason Lomnitz  for help with the  System Design Space Toolbox ,  Irene Zorzan  for checking the mathematical derivation of the quasi-steady-state approximation, and the anonymous reviewers for insightful comments. This work was supported by the  Systems Biology Grant of the University of Surrey  to M. B. and by the  SILS Starting Grant  of the  University of Amsterdam  to M. B. The corresponding author can also be contacted at matteo@barberislab. com.
Third, it is non parametric ('model free' in this sense), like our neural network schemes again, and as opposed to various parameterizations such as SABR or SSVI that are used as standard in various segments of the industry, but come without theoretical justification for robustness, are restricted to specific industry segments on which they play the role of a market consensus, and are all implied volatility based. Fourth, an efficient numerical implementation of the Tikhonov method (as we call it for brevity hereafter), already put to the test of hundreds of real datasets in the context of  Crépey (2004) , is available through  Crépey (2002). Fifth, this method is itself benchmarked to other (spline interpolation and constrained stochastic control) approaches Section 7 of  Crépey (2002). Our training sets were prepared using daily datasets of DAX index European vanilla options of different available strikes and maturities, listed on the 7th, 8th (by default below), and 9th, August 2001, i.
e. , same datasets as already used in previous work  Crépey (2002 Crépey ( , 2004)) , for benchmarking purposes. The corresponding values of the underlying were S = 5752. 51, 5614. 51 and 5512. 28. The associated interest rate and dividend yield curves were constructed from zero-coupon and forward curves, themselves obtained from quotations of standard fixed income linear instruments and from call/put parity applied to the option market prices. Each training set was composed of about 200 option market prices plus the put payoffs for all strikes present in the training grid. For each day of data (see e. g. , Figures  3  and 4 ), a test set of about 350 points was generated by computing, thanks to a trinomial tree, option prices for a regular grid of strikes and maturities, in the local volatility model calibrated to the corresponding training set by the benchmark Tikhonov calibration method of  Crépey (2002).
Monte Carlo Backtesting Repricing Error Finally, we evaluate the performance of the models in a backtesting Monte Carlo exercise. Namely, the options in each testing grid are repriced by Monte Carlo with 10 5 paths of 100 time steps in the model dS t S t = (r(t)q(t)) dt + σ(t, S t )dW t , (5) using differently calibrated local volatility functions σ(•, •) in (  5 ), for each of the 7th, 8th, and 9th August dataset. The residual gap between the Monte Carlo RMSEs of the (even best) neural network local volatility and of the Tikhonov local volatility can seem disappointing. However we should keep in mind that the neural network can evaluate quickly a local volatility on any node outside the training grid, whereas Tikhonov then requires a further layer of interpolation (or a new calibration). Furthermore, any vanilla option price can be accurately and quickly obtained by neural prediction (better than by Monte Carlo repricing as above).
funded by the  Research Initiative  "Modélisation des marchés actions, obligations et dérivés", financed by  HSBC France  under the aegis of the  Europlace Institute of Finance , and by a public grant as part of investissement d'avenir project, reference  ANR-11-LABX-0056-LLH LabEx LMH. The views and opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views or policies of  HSBC Investment Bank , its subsidiaries or affiliates. § The research of  Matthew Dixon  benefited from the support of  Intel Corp.
The enrollment criteria, clinicopathological data collection, and postoperative surveillance were according to our previous study  [16]. Overall survival (OS) was calculated as the time interval between the date of hepatectomy and death or last follow-up. Recurrence-free survival (RFS) was determined from the date of hepatectomy to tumor recurrence or last follow-up. Written informed consent was obtained from all patients involved in our study, and our study was approved by the research ethics committee of Zhongshan Hospital, Fudan University.
Antibodies and reagents The following antibodies were used in our study: anti-SLFN11 (Atlas antibodies, HPA023030), anti-GAPDH (Sigma-Aldrich), anti-RPS4X (Abcam), anti-phospho-RPS6 or anti-phospho-S6 (Ser235/236; Cell Signaling Technology), anti-RPS6 (or S6; Cell Signaling Technology), anti-phospho-eIF4E (Ser209; Cell Signaling Technology), anti-eIF4E (Cell Signaling Technology), anti-Bax (Cell Signaling Technology), anti-Bak (Cell Signaling Technology), anti-Bcl-2 (Cell Signaling Technology), anti-Ki-67 (Abcam), anti-Flag (Cell Signaling Technology), Normal rabbit IgG (Cell Signaling Technology), Alexa Fluor 488 goat anti-mouse IgG (Invitrogen), Alexa Fluor 594 goat anti-rabbit IgG (Invitrogen), anti-SLFN11 (Santa Cruz, sc-374339), and BrdU Cell Proliferation Assay Kit (Cell Signaling Technology).
Wound healing assays HCC cells were seeded in a six-well plate. When the cells formed a tight cell monolayer, a 200-µl plastic pipette tip was used to make a scratch. To remove the cell debris, we washed the cells in PBS three times. After we removed the complete DMEM, we added serum-free DMEM into each well. Wound photographs were recorded at the indicated times by an IX71 inverted microscope (Olympus Corporation) and analyzed by ImageJ software. All assays were conducted three times in our study.
Cell viability assays HCC cells were seeded into a 96-well plate at 3,000 cells/well with 100 µl of 10% FBS DMEM. After overnight incubation, the complete medium containing different concentrations of cisplatin  (1, 5, 10, 20, 40, 60 , 80, 100 μM) replaced the original medium of each group for 72 h. Then the cell viability was determined by CCK-8 assay. The drug half-maximum inhibitory concentration (IC50) was calculated by GraphPad Prism 8 Software (San Diego, CA, USA).
Mascot (Matrix Science, London, UK; version 2. 3. 0), an engine that can search the uniprot_human database, which contains 20205 protein sequences, was used for analyzing the LC-MS/MS data. The Mascot parameters were set as follows: Enzyme = "Trypsin", fragment mass tolerance = "± 0. 05 Da", mass values = "Monoisotopic", variable modifications = "Oxidation (M)", peptide mass tolerance = "10 ppm", instrument type = "ESI-FTCR", max missed cleavages = "1", fixed modifications = "Carbamidomethyl (C), P value < 0. 05, ion score > 20". In the end, Ingenuity Pathway Analysis (IPA) software (http://www. ingenuity. com/products/ipa) was applied for analyzing the significant pathways and functions involved in the identified proteins.
As previous studies reported, it was necessary for us to inhibit mTORC1 effectors (RPS6 and eIF4E) completely in hepatocarcinogenesis  [44]. Hsieh et al. reported that INK128 completely restored phosphorylation of the 4E-BP1/eIF4E and p70S6K/RPS6 axes in a prostate cancer preclinical study  [45] , which means that INK128 may have effects in HCC. Currently, INK128 is undergoing an international phase 1/2 clinical trial to determine its efficacy in patients with advanced or metastatic HCC (NCT02575 339, ClinicalTrials. gov). Our findings demonstrated that INK128 successfully suppresses the mTOR signaling pathway and attenuates tumor growth and the degree of lung metastasis induced by low SLFN11 expression.
Acknowledgements We thank the  China Scholarship Council  for supporting  Dr. Chenhao Zhou  as a visiting PhD candidate at The  University of Texas MD Anderson Cancer Center. We also thank  Tamara Locke  at the  Department of Scientific Publications at the University of Texas MD Anderson Cancer Center  for editing this manuscript, and  Dr. Jingyang Yin  at the  Department of Hepatobiliary Surgery of Chengdu Fifth People's Hospital  for providing the color hand-drawn illustration of liver. This work was supported by grants from the  Shanghai International Science and Technology Collaboration Program  ( 1841 0721900 ), the  National Natural Science Foundation of China  ( 81472672 ), the  National Key Research and Development Program of China  ( 2017YFC1308604  and  2017YFC0908402 ), and the  Shanghai Sailing Program  ( 19YF1407600 ).
the aim of the study was to develop and assess the performance of a video-based augmented reality system, combining preoperative computed tomography (ct) and real-time microscopic video, as the first crucial step to keyhole middle ear procedures through a tympanic membrane puncture. Six different artificial human temporal bones were included in this prospective study. Six stainless steel fiducial markers were glued on the periphery of the eardrum, and a high-resolution CT-scan of the temporal bone was obtained. Virtual endoscopy of the middle ear based on this CT-scan was conducted on Osirix software. Virtual endoscopy image was registered to the microscope-based video of the intact tympanic membrane based on fiducial markers and a homography transformation was applied during microscope movements.
During experiments, estimated slow (<5 mm/s) or rapid (5-10 mm/s) translations, rotations and pitches were applied to the microscope. Please refer to the Phacon Inc. website for further details about the temporal bone phantoms (https://www. phacon. de/en/hno/felsenbein). microneedle was introduced into the middle ear through a puncture hole in the tympanic membrane. It was controlled by a micromanipulator (DC3314R, World Precision Instruments, Sarasota, FL, USA) with 3 degrees of freedom and a 37 × 20 × 20 mm 3 workspace with 0. 1 mm precision. AR was implemented by combining real-time video images of the external auditory canal and the tympanic membrane to the 3D CT-scan reconstruction of the middle ear cavity (Fig. 1 ). The software was developed using OpenCV, Eigen libraries and Ximea API in XCode (C + + ).
This paper presents a Model Predictive Control-based Energy Management System for compliance with the day-ahead power dispatching plan of a hybrid power plant connected to the Guadeloupe Island electrical grid. The hybrid power plant combines a wind farm and a Li-ION battery energy storage system. The proposed EMS handles several operation rules, in order to solve the optimization problem while considering the production forecasting data as well as the battery lifespan. A simulation study is implemented via PowerFactory and Matlab.
To test the strategy, realistic simulation scenarios were considered with different forecasting errors to prove the ability of the proposed method to compensate them. The complete system was implemented in PowerFactory while the control algorithm is executed in MATLAB. This paper is organized as follows: Section 2 presents a description of the Grid-connected hybrid power plant. Section 3 introduces the HPP PowerFactory model. In Section 4 the MPC strategy proposed is developed and finally in Section 5 the results are shown and discussed.
For that, the amperes-hour delivered Q and the discharge current I are varied. The four storage units comprising the BESS implemented in PowerFactory is shown in Fig. 4. It was also added the respective converters, two 2-winding transformers, one 3winding, three busbars, and three underground lines. 2018 ). For this, a current input was imposed on both system representations (Matlab and PowerFactory) to observe the dynamic of battery states during the charging and discharging process. DC BUS 4 LV BUS 4 DC BUS 3 LV BUS 3 DC BUS 2 LV BUS 2 DC BUS 1 LV BUS 1 PCC LOCAL LI-ION MV BUS 3-4 MV BUS 2 MV BUS 1 V LI-ION 4 CV 4 V LI-ION 3 CV 3 2-Winding. transfo c. V LI-ION 2 CV 2 2-Winding. transfo c. V LI-ION 1 CV 1 LINE 3-4 NA2YSY 1x240rm 12/20kV it LINE 2 NA2YSY 1x240rm 12/20kV it LINE 1 NA2YSY 1x240rm 12/20kV it 3-Winding.
RESULTS To test the robustness of our strategy, a 24h profile of wind power production from Guadeloupe Island was analyzed. Two scenarios representing 3-hours commitment profiles were chosen (as can be seen in Fig. 7 ). These results deal with the electrical grid of Guadeloupe as well as the HPP modeled under PowerFactory environment, while the MPC and the optimization strategy was executed in Matlab. • Case 1: In Fig. 8  a 3-hours commitment profile with steps of 30 minutes is shown, a prediction horizon (N p ) of 15 seconds was defined. In this first case, greater importance was given to respect of the injection band over the SOC maximization. Fig. 8b  shows that the injected power P IN J stays inside the tolerance region of 2 MW above and bellow of the commitment profile, (P SCHED ). Between t = 3.
CONCLUSION In this work, an energy management strategy for the power dispatch of a wind-BESS HPP injecting power into the electrical grid of Guadeloupe Island is proposed. For that, a model predictive control and optimization strategy were developed to drive the storage system with respect to two optimization objectives: respect of a defined region for the power injection, and maximization of the storage system SOC. The strategy uses the model of the BESS to find optimal solutions that bring the system's predicted output close to a trajectory of future power injections defined. The controller performance was verified in co-simulation PowerFactory/Matlab using real wind speed data collected in Guadeloupe Island. The methodology proved its ability to efficiently manage the power output of the hybrid power plant and meet generation schedules with respect to the constraints defined.
Contribution: The aim of this paper is to present two new techniques for SMLM. We start in section 3 by introducing a reformulation of the 0 norm. We rewrite the norm as a convex minimization problem by introducing an auxiliary variable. Thus, we can reformulate (2) and (3) as a mathematical program with equilibrium constraint (MPEC). Based on the reformulation of the problem, we define a Lagrangian cost function G ρ. The function G ρ : R N × R N → R is biconvex. In section 4, we propose a Constrained Biconvex (CoBic) algorithm and a Penalized Biconvex algorithm (PeBic) to minimize the new objective function. The algorithms are based on already existing and well-known algorithms, and thus easy to implement. In section 5, we test the algorithms on the problem of SMLM and compare them to the state-of-the-art methods Deep-STORM  [26]  and IRL1-CEL0  [27] , as well as the well-known IHT algorithms  [28].
The comparison done on the real dataset confirms the efficiency of CoBic and PeBic. Section 7 gives the mathematical justification of the algorithm. Note that the reformulation of the 0 norm was presented in  [25] , and slight variations have been previously presented in  [22] [23] [24] [25]. Our work differs from their work, as  [23]  and  [22]  did not work on the minimization problem (3) and (  2 ). [24, 25]  study the minimization of the sum of Lipschitz continuous data term and a sparsity constraint. In this paper, the data term is the square norm, which is not Lipschitz continuous. Notations: • The 2 norm of a vector x is denoted • 2 and defined as i x 2 i. • To simplify the notation we will set • = • 2. • The 1 norm of a vector x is denoted • 1 and defined as i |x i |. • A is a matrix in R P×N , P<N.
Numerical experiments In this section, we perform numerical experiments of CoBic and PeBic and compare them to other grid-based localization algorithms. They are compared on two datasets from the ISBI 2013 SMLM challenge  [6]. A more recent challenge was launched in 2016. We decided to use the 2013 challenge as the data is denser in the 2013 challenge (220 fluorophores per acquisition in 2013 compared to 12 in the 2016 challenge). The algorithms are coded on MATLAB2015 with a computer running on Linux, with CPU INTEL core i7-3920XM, except Deep-STORM that was launched on a computer running on Linux, with CPU Intel Xeon E5-2687WV3. The code for PeBic and CoBic, as well as an example can be found at: https://github. com/abechens/CoBicand-PeBic-SMLM/ CoBic and PeBic are compared with four algorithms; the IRL1-CEL0  [27] , Deep-STORM  [26] , as well as the standard Iterative Hard Thresholding (IHT)  [28]  applied to the constrained and penalized form.