dataesr/scientific-paragraphs-categorization huggingface dataset:
These results are especially interesting in view of the fact that ruthenium complexes of another KSP inhibitor, monastrol, were shown to exhibit lower KSP inhibitory activity than the parent compound. 58, 59 ocking Study. In order to elucidate contrasting structural features of relevance to protein interaction for the organometallic compounds, molecular modeling was used to investigate the possible binding mode for the ligands (R)and (S)-2 and their half-sandwich complexes 3a-6b as well as the respective diastereomers 3a′, 3b′, 5a′, and 5b′ to KSP. While the complexes may undergo ligand exchange in aqueous solution to form aqua complexes, or in the presence of proteins to form covalent bonds, for the reason of comparability, we decided to dock the chlorido complexes. The KSP cocrystal structure with ispinesib derived from Homo sapiens was obtained from the Protein Data Bank (PDB ID: 4AP0; resolution 2.
■ ASSOCIATED CONTENT * sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. inorgchem. 0c00957. Synthesis and characterization of complexes; general information on methods; characterization; copies of the UV-vis, MS, and NMR spectra; and additional figures (PDF) Accession Codes CCDC 1955610-1955613 and 1979283-1979284 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/ data_request/cif, or by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44 1223 336033.
■ ACKNOWLEDGMENTS This study was financially supported by the  National Science Centre Poland (NCN)  based on decision  UMO-2015/17/B/ ST5/02331. We also thank the  University of Auckland (University of Auckland Doctoral Scholarship  to K. K. -H. T. ), and the  Health Research Council of New Zealand  ( Hercus Fellowship  to M. H. ) for funding. We are grateful to  Tanya Groutso  and  Tony Chen  for collecting the X-ray diffraction and MS data, respectively. We thank  Mie Riisom  for collecting the cytotoxicity data for (R)-and (S)-ispinesib.
Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95. 4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83. 7% accuracy for ten subjects and 68. 2% for a significantly larger group of subjects, i. e. , 60 people.
The falls were not performed for volunteers recorded outside the university of Glasgow in care homes notably the NG homes and West Cumbria data samples. Based on the recorded database, data files have a duration of 10 s for walking and 5 s for other activities, and in total, 1078 samples are collected and analysed for feature extraction and activity classification. Out of all this data, 180 walking samples are used for gait analysis and personnel recognition. Figure  2  presents the steps for the signal pre-processing. The processing starts with reading and reshaping the raw FMCW data in time domain which are the digitized reflected time-delayed and frequency-shifted signals in complex value before the application of Fast Fourier Transform (FFT) to extract the range information. In this project, the data matrix is reshaped into 128×M where 128 is the number of time samples per sweep, which is also the size used for FFT, and M is the number of chirps for one activity sample.
Knowing the resolution of the time axis, the step length l can be calculated by integrating the multiplication of instantaneous velocity v(t) and time t given in Equation  (7). The time for a half swing, as well as the velocity difference of a half swing, is also considered for potential features. Additionally, a binary feature R is calculated using Equation (  8 ): l = v (t) dt (7) R = S binary S spectro (8) R is the ratio between two areas where S spectro denotes the whole surface area of the stable phase walking spectrogram (Figure  10a ) and S binary denotes the area of the white pattern in binary figure (Figure  10b ) converted from the spectrogram (a) using a threshold value which is determined by average pixel values of all the walking spectrograms in the dataset. In this way, a walking pattern with more intense energy and faster velocity will generate a widerspread binary pattern, and the ratio can thus indicate the intensity and the shape of the walking activity in the stable phase.
7% with ensemble classifier, 72% with SVM and 89. 2% with ensemble classifier as a combination. The results show the advantage of the micro-Doppler spectrogram, which can better characterise the kinematic motion properties of activities. Applying additional features in other domains and TSFRESH features increases the dimensionality and complexity of the feature set for classification without adding, in this case, useful information, which can explain the severe decrease in performances. Therefore, a feature selection processing is implemented to improve the prediction performance, increasing prediction speed, cost-effectiveness and providing a better understanding of the data generation process  [30]. Feature selection algorithms are generally divided into the following three categories: • Filter methods, which look into the intrinsic properties of the features regardless of the training model used.
Personnel Recognition Based on Gait Analysis In order to perform personnel recognition on the dataset analysed in this paper, an ensemble classifier with subspace discriminant method was applied to recognise 60 people. In the validation, a 5-fold cross-validation method is implemented for 50 times, and the average values are used calculate the classification accuracy. Table  8  summarises the features that are used for personnel recognition. It contains 75 multidomain features from those mentioned in Table  1  that describe the general motion characteristics of walking activity, 11 gait features mentioned in Table  2  that focus on detailed gait parameters for each person, and 349 TSFRESH features including different coefficients and properties of the velocity-time and energy-time series. In total, 435 features are considered for the analysis of personnel recognition.
TSFRESH features 24 After feature extraction, classification and feature selection, the model for personnel recognition is obtained with 68. 2% accuracy in identifying 60 people. Then a one-versus-one personnel recognition is performed on all 60 people in order to evaluate the performance of data samples recorded from each individual person. Figure  18  provides the 60 × 60 confusion matrix of the classification. It can be observed that data samples recorded for some specific people with ID 10 to 12 perform poorly during the classification, and the samples from these people cannot be identified in most of the one-versus-one combinations. Three main reasons can be accountable for these poor classification results. A well-recorded data sample (Figure  19a ) contains a full acceleration phase, stable phase, and deceleration phase of walking and also has a clear track of the motion of torso, feet, and arms.
However, some data samples like Figure  19b  have an incomplete waveform of a walking period in the allocated time of 10 s for the recording, which can cause trouble in stable phase extraction and the loss of data for feature extraction. Furthermore, some data (e. g. , Figure  19c ) is noisy, which can obscure the contribution of the torso and the measure of the step length. Additionally, since each person only recorded three walking samples, which can be insufficient given 60 classes needed to be recognised, the limited training data may not provide enough consistent features. For instance, walking speed and step length in 3 samples of one person can vary to a certain extent and create confusion with samples from other people with similar features and thus the error can occur in intra-subject gait samples.
Conclusions In this study, human activity classification and individual personnel recognition were performed on a large dataset of radar signatures collected at the University of Glasgow. A large set of diverse features was considered, namely empirical-based features, transform-based features, image features in multiple radar formats, and TSFRESH features which, to the best of our knowledge, have not been used for radar-based human signatures analysis. The most suitable features for human activities discrimination, both separately and overall, were discussed. Additionally, a hierarchical classification structure was designed to further improve the results by taking advantage of designing customised combinations of features and models for each activity, and the results reached 95. 4%, up from 91.
Therefore, more customised features can be developed to capture the key differences and identify these specific activities in the dataset. Additionally, the influence of pre-processing choices on the classification accuracy could be investigated, such as the use of windowing at different stages of the pre-processing chain, or the efficiency of a zero-phase moving target indocator compared to a classic moving target indicator filter. For gait analysis, more data from more people and more repetitions should be collected to further evaluate the robustness of the trained model and measured gait features. This will also include situations where the aspect angle between the radar line of sight and the walking trajectory is unfavourable, or where the walking segments are more fragmented with stop-and-go patterns.
, , 2007;; Montety et al. , 2007; Bertrand et al. , 2013; Marc et al. , 2017). Artificial and isotopic tracing techniques are also classically used to help dissociate the origin of waters in an unstable slope  (Binet et al. , 2007; Deiana et al. , 2020). The datasets to be integrated can be numerous and are sometime heterogeneous. The consistency of temporally and spatially disparate multi-source data with sometimes uneven reliability is an important issue for the characterization and conceptualization of landslide functioning  (Bichler et al. , 2004; Travelletti and Malet, 2012; Lissak et al. , 2014b; Fressard et al. , 2016; Denchik et al. , 2019). This is particularly the case for long-term studied landslides, for which the data are sometimes discontinuous and scattered. The study site discussed in this paper is a typical case of a complex landslide under hydrological control with a high sensitivity due to the presence of clays.
The containers were filled over a stop-time period, and the quantities were measured in graduated test tubes. The locations of the measurement points were determined using a Trimble Juno 3B lightweight GPS field device. These measurements allow for characterization of all areas where the aquifer is outcropping on the landslide. The locations of the measurement points were coupled to a set of 34 piezometric measurements carried out over the entire site. The currently operational network is composed of 13 piezometers and 7 inclinometers, followed by continuous piezometric sensors or a manual contact gauge. In addition, data from 14 other piezometric measurement locations could be retrieved and integrated into the analysis. These data are partly from old piezometers no longer in use and partly from exploratory drilling enabling spot measurements of the groundwater level.
The latter only provide point information on the piezometric levels. Because we know that the variations in the water table are low on a seasonal scale but exhibit strong site variability  (Lafenetre, 2010; Lissak, 2012) , it seems interesting to include them in the analysis. The objective here is to characterize not the temporal variability but rather the spatial variability of the unconfined aquifer, which is directly related to the lithostructure. This is a first approach to try to highlight the preferential water flow paths over the unstable area. A total of 97 groundwater measurements were used to produce a map of the mean piezometric surface. An ordinary kriging probabilistic interpolation method was applied, which considers the spatial dependency of the data while minimizing prediction errors  (Matheron, 1963; Oliver and Webster, 1990; Baillargeon, 2005).
Conclusion The objective of this study was to assess the hydrogeologic behaviour of the Cirque des Graves using spatially and temporally heterogeneous datasets to improve the limited initial knowledge. Structural and piezometric datasets acquired over the long term were first used to assess the predisposition of the massif to underground flows. This allows the highlighting of litho-stratigraphic disparities explaining contrasts regarding water table levels and the distribution of runoffs on the surface. The wide heterogeneity of the geological formations encountered (variation in thickness, reworking and discontinuities due to landslide dynamics, varying density and hydraulic conductivity of the materials, etc. ) therefore explains the uneven functioning of the slope. Second, geophysical and physicochemical data acquisition aimed to explain this variability by characterizing the hydrokinetics of the slope.
Acknowledgements This research was supported by the  ANR  project " RICOCHET: Multi-risk assessment on coastal territory in a global change context  ( 2017-2021 )" ( ANR-16-CE03-0008 ), funded by the  French National Research Agency (ANR). This work was also conducted with the help of data collected during the  ANR Risk-Nat  project " SISCA : Monitoring System of landslide crises (e. g. , acceleration, fluidization of large muddy landslides) ( 2009-2011 )" ( ANR-08-RISKNAT-009 ). The Villerville landslide, locally managed by  LETG-Caen laboratory , is a permanent site instrumented as part of the  OMIV-INSU observatory (French Observatory on Landslides ) led by the  EOST laboratory at the University of Strasbourg. The authors thank all of the contributors to this work during the field campaigns from the  LETG-Caen  and BRGM teams.
One of the ways to improve outcomes could be the combination of treatments based on synergies and mechanisms of resistance to ICIs. 16 Among the potential leads in the treatment of HCC, combinations of ICIs with anti-angiogenic  antibodies are about to change our practices. Recently the combination of bevacizumab plus atezolizumab, an anti PD-L1, showed an objective response of 33%, and a gain in survival (PFS and OS) and quality of life compared with sorafenib. 17 Other combinations of ICIs with tyrosine kinase inhibitors are under investigation (NCT03713593; NCT01658878). Given the highly heterogeneous nature of HCC-ICC, it is essential to identify the predictive biomarkers of immunomodulatory treatments in order to select patients who will benefit from this type of therapy. Our patient presented overexpression of PD-L1 in immunohistochemistry at 85%.
ACKNOWLEDGMENTS We acknowledge financial support from the  National Science Center, Poland  ( 2019/35/B/ST5/02666 ), and the  EU  project  PAMS  ( 610446 ). The STM experiments were carried out using equipment purchased with financial support from the  European Regional Development Fund  within the framework of the  Polish Innovation Economy Operational Program  (Contract No. POIG. 02. 01. 00-12-023/08 ). A. J. acknowledges funding from the  Foundation EXPERIENTIA  and from  ERDF/ESF  " UOCHB MSCA Mobility " (No. CZ. 02. 2. 69/ 0. 0/0. 0/17_050/0008490 ). R. Z. acknowledges support received from the  National Science Center, Poland  ( 2017/24/ T/ST5/00262 ).
Data analysis Population were described in terms of %, mean, standard deviation, range and median values. The test data was analyzed in the Department of Infectiology. The specificity and sensitivity of the rapid test kits compared to test of reference (RT-PCR) were calculated according to the following formulas: Specificity (%) = 100 x [Negative / (Negative + Positive)]. Sensitivity (%) = 100 x [Positive/ (Positive + Negative)] Confidence intervals for sensitivity were produced with the Wilson score method  [6].
We appreciate professional advice from  José R. Crespo ,  López-Urrutia , and  Hendrik Bekker. This work is part of and supported by the  German Research Foundation (DFG) Collaborative Research Centre SFB 1225 (ISOQUANT)  and by the  DFG Research UNIT   FOR 2202. P. I. acknowledges partial support from  NIST. Laboratoire Kastler Brossel (LKB)  is " Unité Mixte de Recherche de Sorbonne Université, de ENS-PSL Research University, du Collège de France et du CNRS  No. 8552. "P. I. , Yu. N. , and K. B. are members of the  Allianz Program of the Helmholtz Association , Contract No. EMMI HA-216  " Extremes of Density and Temperature: Cosmic Matter in the Laboratory. "P. I. wishes to thank  Jean-Paul Desclaux  for his help improving the MCDFGME code. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under Grant Agreement No.
Acknowledgements FM acknowledges funding from the  European Research Council  under the  H2020 framework program / ERC  grant agreement # 637503  (Pristine) and financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), and the  ANR  through a chaire d'excellence  Sorbonne Paris Cité.
A second part of the survey focused on the profile of physicians: region of practice, sex, age, medical specialty (clinician or embryologist), working in private or public institutions. The online questionnaire was sent by email to 676 French specialists in ARTs, who represented the majority of physicians in the discipline. The listing was obtained from a French database of all clinicians and biologists specialized in ARTs. In order to obtain the highest possible response rate, three e-mails were sent on May 15, May 23 and May 31, 2017. Respondents were able to reply only once and anonymously. A specific e-mail address was created to answer any questions or comments from participants. Data were expressed as numbers and percentages and all the results were analyzed according to the characteristics of the participants using a Cochran-Mantel-Haenszel test.
Acknowledgements The authors thank the  Reproductive Medicine Centre of Pellegrin hospital , all gynecologists, embryologists, midwives, secretaries, and other team members working in the Reproductive Centre. The authors also thank French specialists in ART for their valuable participation to this survey. manuscript. M. G. actively participated in the critical revision of the manuscript. L. C. D, J. T. , S. F. , A. P. R, C. J. , N. C. and C. H. participated in the review of the manuscript.
Results For each patient, total range of laxity was calculated separately for the healthy and operated elbow. The absolute difference between the laxity ranges of both elbows was then calculated. Fig. 3  The measuring device conducted a cycle of transitions of the forearm between maximal valgus and varus deviations according to the predetermined protocol Two coloured dots show mean valgus and varus deviation value. It can be appreciated that the valgus dot is located on the maximal torque line, while the varus dot is slightly lower. This shows that whereas the valgus deviation was registered upon reaching maximal torque value, the varus deviation was recorded when maximal torque derivative was reached As stated earlier, analogous data had been previously obtained in healthy volunteers. Wilcoxon signed-rank test was used to verify the hypotheconcerning mean values of absolute differences between healthy elbows and between healthy and operated elbows by comparing the mean differences with the values of 1°and 2°.
Discussion Column procedure is a well-established and efficient technique in elbow surgery associated with favourable results. Although joint instability is not recognized as a common complication of this type of surgery, this issue has received little attention. The aim of our study was to analyse in an objective manner whether column procedure affects elbow laxity, which could in turn lead to joint instability. The vast experience of our centre allowed us to operate in a reproducible manner and to obtain surgical results concomitant with those of other authors, which additionally validates our conclusions. According to our knowledge and thorough search through the available medical databases, there were only two reports involving similar studies conducted in cadaver labs. Nielsen et al. examined the effect of anterior and posterior capsulectomy on elbow laxity in seven cadaver specimens, finding no convincing influence.
Data and settings The IST was a large, multi-centre trial assessing the effect of Aspirin and Heparin on a primary (composite) outcome of death or dependency at six months after stroke, using a 2 x 2 factorial design. 12 It enrolled 19,435 adults with acute ischaemic stroke from 36 countries and collected over 99% complete follow-up data. The dataset from this RCT was recently released under an open-access license on behalf of the International Stroke Trial Collaborative Group. 19 As we performed a secondary analysis, our study was exempt from patient consent form collection. The trial originally reported a non-significant, moderate average risk reduction in the primary outcome at six months in the Aspirin group (62. 2% versus 63. 5%, two-tailed P = 0. 07). 12 To estimate the individualised responses to Aspirin, we used the methodology of clinical prediction models under a counterfactual framework.
In order to carry out this work, GH quantification at certain measuring sites was conducted using the EMT. The aim of the present paper is to expand on this previous work, especially by integrating the results of in-situ measurements with seismic data. This is achieved through a detailed review of the different steps followed in order to use the EMT, with special emphasis on the establishment of a petrophysical model and the derivation of mineralogical profiles from in-situ acoustic attenuation data. This was carried out by performing correlations between different laboratory and in-situ data before comparing the similarities and the differences between reference sites without GH and GH-bearing sites. This proved effective in detecting and quantifying GH based on in-situ acoustic measurements even for sediments where accurate mineralogical characterisation is not available.
Numerous oceanographic campaigns, aiming to study GH distribution, have been carried out along the Gulf of Guinea, which is known to accommodate dense accumulations of shallow GH  (Cunningham and Lindholm, 2000; Sultan et al. , 2010; Wei et al. , 2015). The study area is also characterised by the presence of different seabed deformations known as pockmarks. However, little has been discussed concerning the distribution of GH below the seabed and its impact on the shapes and sizes of these pockmarks. As a step towards identifying links between GH and free gas and the geometrical features of different pockmarks, the second aim of this paper is to correlate seismic data profiles and seabed morphologies with GH content. Analysing the geophysical signatures of GH accumulations and extrapolating all the available in-situ data using seismic profiles would indeed be helpful in the process of (1) characterising the sub-seafloor distribution of GH and (2) determining the relationship between hydrate distribution and seafloor depressions.