dataesr/scientific-paragraphs-categorization huggingface dataset:
We didn't included patients operated for simultaneous bilateral TKA, revision of TKA, primary unicompartemental knee arthroplasty (UKA) and revision of UKA, mmultiple joint replacements within the same admission and bone tumor. We included 410 observations in this database. Study procedures: all patients had preoperative assessment three months maximum before surgery. Patients had standard monitoring during surgery. Spinal anesthesia was performed unless contraindication after loading with 500 mL saline. Single shot spinal anesthesia using 12 mg of hyperbaric bupivacaine 0. 5% and 5 micrograms of sufentanyl is performed in patients aged less than 65 years, otherwise we used continuous spinal anesthesia using boluses of 2. 5 mg isobaric bupivacaine 0. 5% and 2. 5 micrograms of sufentanyl. General anesthesia was indicated in case of no indication of spinal anesthesie or its failure.
7; 95%CI, 1. 5 to 4. 8). In A large database, most risk factors for embolism events were: cerebrovascular disease, obesity, older age, female sex and bilateral surgery, surgery time > 2 hours  [25]. In our study, we didn't find increased risk of PE with increased BMI which was similar to other studies  [26]. Pulmonary embolism at a rate of 2. 7% of patients exceeds reported incidence in previous studies that is usually around 1%. We think that, besides the above mentioned statistically significant factors, there are other potential causes. Eventually, our thrombophylaxis strategy based on 40 mg of enoxaparine (60 mg for morbidly obese patient) may not be best suitable for this particular embolism high risk surgery comparative to the Food and Drug Administration (FDA) and other American colleges guidelines of 30 mg twice a day for TKA (40 mg twice daily if BMI ≥40 kg/m 2 ).
following TKA in Tunisia. Data of TKA were obtained from local registry of Kassab which is unique in Tunisia. One important fact to consider is that this study was performed in a single institution where standardized protocols are in place and patients receive a thorough preoperative medical evaluation and are followed by intensivist postoperatively. However, several limitations of this study should be noted. First, our study is a single-center and have relied on local register. Second, our population included a small number of patients compared with large published series. Finally, the short follow-up of patients (three postoperative days) didn't allow detection of complication that occurred after discharge. Concerning the same topic the dataset did not provide information regarding long-term outcomes and other clinical outcomes such as peripheral nerve injury.
Myocardial infarction as defined by European societies of cardiology and anesthesiology. Respiratory Kassab TKA register database were divided into: Demographic characteristics: age, gender, BMI, autonomy physical status and metabolic equivalent of task (MET), ASA (American Society of Anesthesiology) classification, cardiovascular and respiratory co morbidities, diabetes mellitus, chronic renal failure defined by a clearance of creatinine less than 60 milliliters/minute and preoperative hemoglobin.
Data We construct a comprehensive dataset of international squash team confrontations from 1998 to 2016 12  that includes 2,039 national team matches. We consider 55 international team tournaments, including Men's and Women's World Team Championships, Men's and Women's Asian Team Championships and Women's European Team Championships. 13 The World Team Championships are organized by the World Squash Federation (WSF). The competition is held once every two years, each time in a different venue. The men's and women's events are held sepa- 11 See Section S1 of the WSF Regulations, Section L1 of the ESF Regulations and Section T1 of the ASF Regulations for more details. rately in different years. 14 The Asian Team Championships are organized by the Asian Squash Federation (ASF) and take place every two years.
The tested econometric specification is as follows. A 2 wins battle 2 = β 0 +β N on-neutrality ×A 1 won battle 1+f (Ranking A 2 , Ranking B 2 )+ AB2 , where f (Ranking A 2 , Ranking B 2 ) refers to one of the three measures of players' relative ability described in the previous section: i) ranking modalities, (ii) interacted ranking modalities, (iii) the ratio of rankings. We integrate these measures of players' ability taking into account the symmetric structure of the dataset: the outcome of a battle depends on the characteristics of the two players. Hence, when we use ranking modalities and interacted modalities, we break down every battle into two observations and weight each observation by 1 2 so as to adjust standard errors correctly. When we use the ratio of rankings as a proxy for relative ability, we define player A i as the player who has the better ranking, so that the ratio of rankings always lies between 0 and 1.
SCADA data monitoring relies on the numerous variables measured mainly for production supervision purposes. Fault monitoring approaches based on SCADA data differ in the way the data can be merged to synthesize a fault indicator residual, and can be categorized in "mono-turbine methods" or "multi-turbine methods", as proposed in  [9]. Mono-turbine methods combine variables recorded on a unique wind turbine while multi-turbine methods combine variables coming from all the turbines within a wind farm or subset of a wind farm. In this work, it is proposed to use 10-minutes SCADA data in an innovative way, combining a mono-turbine (or turbine-level) and a multi-turbine (or farm-level) information processing. Wind turbines SCADA variables are highly non-stationary because of the frequent changes in operational conditions and of the variations in external conditions.
e. they use data measured at one sampling time  [17, 18]  or dynamic, such as ARX models  [19, 20]. They can be simple linear regression models or more complex artificial neural networks  [21, 22, 23, 24, 25]. Let note at this point that several published works follow a classical model-based fault detection and isolation approach (model-based FDI) to build fault indicators sensitive to faults, but robust to disturbances, for wind turbine monitoring,  [26, 27, 28, 29, 30]. In these works, the residuals are built using state observers or parity equations,  [2]. The proposed methods are usually implemented on wind turbine benchmarks, emulated by differential equations models where different faults can be simulated. They use high frequency SCADA data, recorded every second. Though the results obtained are very interesting, the problem addressed in these works is very different in nature from the one addressed in the present paper where 10-minutes SCADA data are used and the methods are validated on real data.
Indeed, turbines of the same farm are submitted to the same weather conditions so variables should evolve in a similar way, somehow. [32]  compares the behavior of different turbines in the same farm using curves displaying the temperature of a drive train bearing as a function of the produced power. The temperatures and powers are measured during a period of time and the curves from all the turbines are plot on the same graph. Faults can be identified visually when one of the curves deviates from the others. [10]  measures the difference between the power curve given by the manufacturer and an actual power curve built using data gathered during a current period of time. The difference between the two curves can exhibit a loss in performance. The differences measured for all the turbines in the farm are compared one with each other to detect a turbine with a larger loss of performance, which can be due to a fault.
To address these issues and fill this gap, we propose in this paper a hybrid multi-level synthesis method to take benefits of both approaches -mono-and multi-turbine -and to build fault indicators combining the two approaches. At the turbine level, residuals obtained with a mono-turbine model learnt during a normal condition period are first calculated for each turbine. At the farm level, these residuals are compared to a wind farm residual reference, in a multi-turbine approach. The use of mono-turbine residuals enables the influence of the operating conditions to be reduced while the use of a wind farm reference enables the changes in the environmental conditions to be accounted for. The performance of the method proposed is evaluated and compared to methods proposed in the literature on a large data set made of SCADA data recorded on a French wind farm during four years.
In theory, in this classical setting, when no information is available on the properties of the fault indicator F j in presence of a fault, the value of this threshold can be determined using the estimated or assumed statistical properties of F j when no fault is present in order to guarantee, for example, a false alarm rate. In our approach, we want to avoid any additional hypothesis on the distribution of F j under the "no fault " assumption, which could be different for each turbine and could possibly lead to a different threshold for each turbine. Consequently, in the following, our approach is to set the detection threshold for the whole wind farm, on the basis of the whole available historical 4-years database for the farm in order to get a given false alarm level.
Another major issue is the cost of false alarms. A false alarm results in a useless maintenance action: a maintenance team has to be sent to the production site to inspect the supposedly faulty turbine. Because wind turbines are usually located in remote sites, the inspection takes time and consequently costs a lot of money. So, in this paper, we propose 3 performance evaluation indicators, able to make a fair and objective assessment of the indicator usefulness: the detection time before failure, the useless maintenance action number and the indicator persistence, which gives to the operator some indications about the relevance and performance of the fault detection process based on the proposed indicator. Performance evaluation indicators are computed from a data base gathered on a wind farm during a period of length D, where faults occurred.
Implementation of fault indicators Four fault indicators are implemented and compared using the performance evaluation indicators presented in Section 2. 2 calculated on the 4 years of data : the first considered fault indicator corresponds to the proposed combined mono-and multi-turbine approach, the three others are more classical and are implemented for comparison purposes. Each fault indicator is calculated every 10 minutes, using a sliding window of size W = 144 samples, ie 24 hours. N W L is thus set to 72 and N T L to 4. When relevant, the limit production threshold T h p , above which the temperature can be predicted, is set to 50 kW, mainly because production levels below 50 kW correspond to starting or shutting down transient behaviors. In the following, depending on the considered fault to monitor, T j can be either the bearing temperature or the stator temperature.
The mono-turbine residuals are further post processed in a multiturbine approach, which enables the impact of the variations in the environmental conditions to be reduced. Indeed, turbines in a same wind farm are part of a fleet. They are of the same make and model, and their variables evolve in a similar way since they are submitted to the same environmental conditions. New performance evaluation criteria were proposed to analyze the indicator performances for fault detection purpose, keeping in mind the cost of any useless maintenance action. The performance of the combined mono-and multi-turbine fault indicators were compared to more traditional mono-turbine indicators on a data base made of SCADA data recorded every ten minutes from a real wind farm on a four year period. As far as we know, this is the first time a paper presents such an extensive analysis of fault indicators performance for fault detection, on real data with a wind farm production manager point of view.
Structural analysis Fig. 2  illustrates the ω-2θ scan of MQW structures were observed using HRXRD. Thicknesses and In compositions of the InGaN MQWs were obtained from the symmetric scans of (0 0 0 2) plane. Using the epitaxy smooth fit software (Fig. 2 (III)), the InGaN well and GaN barrier thickness were determined as 3 ± 0. 5 nm and 18 ± 0. 5 nm respectively. The In compositions were estimated around ~16-18%. From the obtained HRXRD results, the high intense peaks manifests that, the grown n-GaN/GaN is oriented along (0 0 0 2) plane. The satellite peaks observed illustrates good periodicity in the MQW layers (Fig. 2 (II)). It is worth to note that, the absence of satellite peaks in one period InGaN/ GaN QW (Fig. 2(I) ), emphasize the non-uniformity in the InGaN/GaN interface which is evident from its surface roughness  [27].
Fig. 3  shows the reciprocal space mapping (RSM) corresponding to the asymmetric (1 0-1 5) plane of InGaN/GaN MQW structures for one, three and five periods respectively. The high intensity GaN peak is observed on the intense contour lines in all the RSM studies and the InGaN/GaN MQW peaks are observed below the GaN peak. Using the epitaxy smooth fit software, the reciprocal lattice point (RLP) of Q x values was found to be 0. 279046 rlu and 0. 277604 rlu for the GaN buffer layer and corresponding peaks of three and five periods of InGaN/GaN MQWs respectively which indicates that the GaN layer, InGaN well and GaN barriers which accommodating on identical inplane lattice constants. As the critical thickness of the barrier and wells in the InGaN/GaN MQW structures did not exceed, the overlying InGaN MQWs are coherently strained with respect to the GaN base layer  [5].
This reveals that the InGaN/GaN MQWs are pseudomorphic to the GaN layer. The obtained results comply with the Fast Fourier Transformation (FFT) analysis. Fig. 4  represents the cross-sectional HRTEM images of hexagonal GaN (ICSD#25676) layer and sapphire substrate (ICSD#9770) indexed using java electron microscopy software (JEMS)  [32]. From figure, it is clear that, the interface between the layer and substrate is atomically flat. The SAED pattern signifies the epitaxial relationship of hexagonal GaN layer on sapphire substrate. The obtained SAED patterns complement the HRXRD results. The GaN/InGaN/GaN interface has exhibited surface morphology that is predominantly free from pits and stacking faults. The cross-sectional bright field TEM images Fig. 5(I, II ) show high resolution image of InGaN/GaN MQW (three periods) which further displays the interfaces between the InGaN well (dark) and GaN (bright) barrier layer in InGaN/GaN MQW structures.
Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening.
Benchmarking studies show that the quality of prediction is variable, although significant progress has been made over the past 15 years. The weakness of docking lies mainly in the scoring functions  [4, 5]. The widely used docking programs are generally able to predict a ligand/protein three-dimensional (3D) structure similar to that observed by X-ray crystallography, but their scoring function does not necessarily reward it as the best. Logically, scoring functions are not very effective in more difficult exercises, such as distinguishing between active and inactive molecules on a target protein and ranking active molecules by binding affinity  [6, 7]. A recent review by Guedes et al. provides a good overview of empirical functions and their recent and future developments, while discussing the evolution in the design of test datasets, the contribution of learning machines and challenging topics  [8].
In this study, we propose to combine the multiple experimental binding modes into a single reference, in order to make the rescoring approach more robust, faster and applicable to a larger number of cases. become a useful tool for drug discovery  [11]. In 2013, we proposed to encode the binding mode by an interaction pattern graph, so that the similarity score also takes into account the spatial relationships of interacting atoms. This method, called GRIM, is overall more efficient than our inhouse interaction fingerprint (IFP) in pose prediction and in virtual screening, but is also more costly in computation time  [12, 13]. The advantages of GRIM rescoring with respect to standard energybased scoring functions have notably been acknowledged for various targets in two recent international docking contests  [14, 15].
The pose prediction was based on a high quality dataset  [17]. Each protein was described by at least 20 3D structures containing diverse drug-like ligands. This allowed us to quantify the minimum number of 3D structures needed to create a single useful reference. For three of the proteins, free protein 3D structures with crystallization additives in the binding site were available. We have therefore assessed whether crystallization additives alone are sufficient to create a useful reference. Virtual screening was performed on eight target sets of the DUD-E dataset  [18]  meeting the LID requirements, i. e. , those described by several experimental reference 3D structures. For one of the proteins, we also performed a virtual screening on a more challenging dataset which was created from the results of an experimental screening  [19].
In this article, we describe the method, called Local Interaction Density or LID. LID and GRIM being based on the same representation of the binding mode, LID's performance in pose prediction and virtual screening are compared to that of GRIM. The pose prediction was based on a high quality dataset  [17]. Each protein was described by at least 20 3D structures containing diverse drug-like ligands. This allowed us to quantify the minimum number of 3D structures needed to create a single useful reference. For three of the proteins, free protein 3D structures with crystallization additives in the binding site were available. We have therefore assessed whether crystallization additives alone are sufficient to create a useful reference. Virtual screening was performed on eight target sets of the DUD-E dataset  [18]  meeting the LID requirements, i.
LID's Performance in Pose Prediction Is LID able to recognize, among the docking poses selected by the scoring function, those that are close to the crystallographic structure of the ligand-protein complex? To answer this question, we sought to reproduce the crystallographic structures of 1382 ligand-protein complexes. These 3D structures have been carefully selected in the Protein Data Bank (PDB) to meet strict quality criteria (e. g. , no mutation in the site, agreement between atomic coordinates and electron density), while being globally adapted to the docking approach (drug-like ligand, ligandable site). The test dataset, called the LID dataset, describes 19 proteins (Table  S1 ). On average, a protein is described by about fifty different 3D structures. There are almost as many different ligands in complex with a protein as there are 3D structures of that protein (there can be more than one structure of the same complex).
Four of the proteins are represented by more than 100 3D structures: cyclin-dependent kinase 2 (CDK2) with 156 ligands, carbonic anhydrase 2 (CAH2) with 155 ligands, beta-secretase 1 (BACE1) with 152 ligands, and heat shock protein 90-alpha (HSP90A) with 106 ligands. Table  1  gives the number of ligands per protein. Protein descriptors indicate whether the binding site is rather large or small, hydrophilic or hydrophobic, rigid or flexible. All cases are encountered in the dataset. In four proteins, the binding site contains a metal cation. The 19 proteins exhibit a variable proportion of interaction types with bound ligands (Table  S2 ). Table  1. LID dataset description: binding site (a), pose prediction (b) and virtual screening (c). Proteins are sorted by the number of 3D structures in descending order (HET codes).
All the ligands of a protein were docked into the same site, using all 3D structures of the protein, except that of the self crystallographic complex. In other words, we did non-native or cross-docking. For each docking job, the 10 poses with the highest docking scores were evaluated with LID. Up to 45,200 poses were compared for the same complex. The docking poses were also evaluated with GRIM. The docking was carried out with the PLANTS software, which has the advantage of being freely distributed, and which, in our hands, reproduces the diversity of poses obtained with the GOLD program (using "enable the generate diverse solutions" and disabling "allow early termination")  [21]. Tested on the LID dataset, PLANTS found a pose close to the crystallographic structure in 1313 out of the 1382 complexes (Figure  3a , see "Max" labelled bar).
distributed, and which, in our hands, reproduces the diversity of poses obtained with the GOLD program (using "enable the generate diverse solutions" and disabling "allow early termination")  [21]. Tested on the LID dataset, PLANTS found a pose close to the crystallographic structure in 1313 out of the 1382 complexes (Figure  3a , see "Max" labelled bar). Here, we evaluated the similarity between the docked pose and the crystallographic structure using the RMSD calculated on the non-hydrogen atoms of the ligand, and we considered that the docked pose is correct whether the RMSD is below 2 Å. The PLANTS default scoring function, namely ChemPLP, placed a correct pose in the first position in only 42% of the cases (Figure  3a , see "ChemPLP" labelled bar), while GRIM did so in 60% (Figure  3a , see "GRIM" labelled bar).
For each protein in the dataset, we tested all combinations of n reference 3D structures, with n ranging from 1 to 20 (the maximal number of tested combinations is equal to 1000). We thus generated up to 1000 × 21 new LID maps per protein. GRIM and LID best performance was observed for 10 or more reference 3D structures (Figure  3b ). The decrease of the median RMSD however differed between GRIM and LID. GRIM curve shows a regular downward slope while LID curve shows a steep initial slope that becomes more gradual from three reference 3D structures on (Figure  3b ). This suggests that the binding modes of three randomly chosen ligands may provide sufficient information to guide docking using LID. Moreover, from seven reference 3D structures on, LID achieves its best performance level for the majority of the tested cases.
Application of LID to "apo" Proteins LID and GRIM have been designed for already well characterized protein structures, for which binding mode information is available for at least one ligand. Is it possible to use LID for a protein whose structure has been resolved at the atomic level, in the absence of drug-like ligands but in the presence of diverse crystallization additives? To answer this question, we searched the LID dataset for proteins that are represented in the PDB in the form "apo" (i. e. , in the absence of drug-like ligand) but whose site is not empty. Three proteins have at least three different additives in their binding site: CAH2, macrophage metalloelastase (MMP12) and glutamate receptor (GRIA2) (Figure  4a ). Glycerol, sulfate, acetate, carbon dioxide, bicarbonate or cyanic acid are found in CAH2 (carbon dioxide and bicarbonate are ligands of the enzyme that have a key role in regulating cell pH).