dataesr/scientific-paragraphs-categorization huggingface dataset:
Computer model development: We developed our computer model using MATLAB 2020a (Simscape-Multibody) (MathWorks MA, USA). A de-identified pelvis and lower body CT scan of a male patient without previous THA or lower extremity surgery was used to import all the bones (pelvis, femur, and tibia) into the model. The THA implant components (a full hemispherical acetabular cup without an elevated rim (best fit diameter = 56 mm), polyethylene liner without an elevated rim (diameter = 36 mm), femoral head (diameter = 36 mm), and a triple-taper cementless stem with three different neck shaft angles (127°, 132°, 135°)] were designed in SolidWorks (Dassault Systèmes SolidWorks Corporation, MA, USA) and imported into the MATLAB model as a computer-aided design (CAD) file. The acetabular cup and liner were placed in the acetabulum, and the stem was placed in the proximal femur based on the anatomical orientation as defined below.
J o u r n a l P r e -p r o o f Motion simulation and model verification: Hip implant motion was simulated in standing, pivoting while standing, sitting, sit-to-stand, bending forward, and squatting motions. The closest position of the PA to the edge of the liner during each motion is shown by colored dots on a sample PA motion map (Figure  4 ). The motion map has two groups of dots: Group 1 dots are anterior and represent the motions of the hip in standing and pivoting in extension; group 2 dots are posterior and inferior and represent motion with the hip in flexion, including sitting, sit-tostand, squatting, and bending forward. To verify this model written in MATLAB, an independent model was written in SolidWorks, and the orientation of the implants relative to the reference planes (anterior pelvic plane, horizontal, and vertical planes) and relative to each other were measured and verified in silico.
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day-and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred.
The large datasets generated by high-frequency in situ sensors also present new opportunities for scientists when analysing, modelling, and reporting water quality data  [4, 5]. Consequently, high-frequency datasets collected from in situ sensors can provide a more thorough understanding of water quality dynamics at multiple time scales and help to improve data quality assurance and quality control  [6]. In situ sensors, despite their benefits, are prone to technical errors due to biofouling, power failures, and other issues. These errors can lead to technical anomalies in water quality data and potentially confound the assessment or identification of true changes in water chemistry  [7]. Given that the high frequency and large size of these datasets precludes the use of manual anomaly detection methods (one part of the entire data quality assurance and quality control process), various automated approaches have been proposed.
Slater et al. , 2017 [15] , for example, demonstrated via simulation that the loss in trend detection tends to increase with an increasing size of missing data 'gaps' and decreasing length of time series. Many of the commonly used methods used to reconstruct missing water quality data were developed before the proliferation of high-frequency sensors, such as infilling based on surrounding data  [16, 17] , regression analysis  [18, 19] , state-space models with an estimation maximization algorithm  [20] , or artificial neural networks  [21] [22] [23] , and, therefore, were targeted at data with lower-frequency time steps, such as daily data. More recently, but also based on daily data, various infilling techniques, such as regression, scaling, and equi-percentile approaches  [24] , along with dynamic regression models  [25] , have been used to reconstruct missing streamflow data.
Furthermore, nitrate concentration can vary substantially in space and time in river ecosystems due to instream processes and external inputs  [38, 39]. This high spatial and temporal variation has increased the interest in and use of high-frequency, in situ nitrate sensors in river monitoring programs and, thus, the need to develop appropriate methods to reconstruct missing nitrate concentration data from the resulting time series. While it is important to develop a sound method to confidently reconstruct missing nitrate data for use in environmental management, the use of nitrate data can also serve as a case study to demonstrate the potential for the method to be applied more broadly. As such, the objective of this study was to develop and test a data reconstruction method using both a real time series of high-frequency nitrate concentrations and a simulation study.
Arikaree River Case Study: Applying the Reconstruction Method We applied the missing-data reconstruction method to time series of water-quality data collected from Arikaree River, a small wadeable stream in the semi-arid eastern Colorado plains of the United States of America. The Arikaree River site has a catchment of 2632 km 2 , comprising mainly grasslands and irrigated agricultural land, and is part of the National Ecological Observatory Network (NEON). NEON collects and provides open data from aquatic and terrestrial sites across the United States of America (USA), including data from high-frequency, in situ sensors. NEON conducts standardised configuration, calibration, and preventive maintenance procedures on all their sensors  [43, 44]  and follows in situ measurement and sample analysis protocols as outlined in  [45].
As such, the Arikaree River site provided us with a suitable time series of water quality data for the purposes of this study. Several water quality variables are available from each NEON aquatic site (Table  A1 ). Nitrate concentration  [46]  is measured in µmol/L using a 10 mm path length SUNA V2 UV light spectrum sensor. The SUNA V2 collects data reported as a mean value from 20 measurements made during a sampling burst every 15 min. The published nitrate resolution is 0. 1 µmol/L and the manufacturer's stated sensor accuracy is approximately 2 µmol/L or 10% of the reading above 20 µmol/L. We, therefore, report units of measurement for nitrate in µmol/L (1 µmol nitrate/L = 0. 062 mg nitrate/L). Other co-located sensors report specific conductance (µS/cm), dissolved oxygen (mg/L), water temperature (°C), and turbidity (Formazin Nephelometric Units, FNU) data as one-minute instantaneous measurements  [47, 48].
Water elevation data (i. e. , water level as meters above sea level) are published as five-minute averaged measurements from data sampled at 1 min intervals  [49]. For this study, we used a two-year period of nitrate concentration data from one of two sites (the downstream site) on the Arikaree River, from October 2018 to October 2020 (n = 73,056 nitrate concentration observations), in which there were already missing point data and missing periods of data (14,283 missing observations of nitrate concentration = 20% of the nitrate data in total) (Figure  2 ). These data had all been removed from the time series as part of the NEON data quality assurance and quality control process. For example, there was a technical issue with the nitrate sensor during winter 2019 so no nitrate concentration measurements were available for the first three months of 2019.
As such, the Arikaree River site provided us with a suitable time series of water quality data for the purposes of this study. Several water quality variables are available from each NEON aquatic site (Table  1 ). Nitrate concentration  [46]  is measured in µmol/L using a 10 mm path length SUNA V2 UV light spectrum sensor. The SUNA V2 collects data reported as a mean value from 20 measurements made during a sampling burst every 15 min. The published nitrate resolution is 0. 1 µmol/L and the manufacturer's stated sensor accuracy is approximately 2 µmol/L or 10% of the reading above 20 µmol/L. We, therefore, report units of measurement for nitrate in µmol/L (1 µmol nitrate/L = 0. 062 mg nitrate/L). Other co-located sensors report specific conductance (µS/cm), dissolved oxygen (mg/L), water temperature ( • C), and turbidity (Formazin Nephelometric Units, FNU) data as one-minute instantaneous measurements  [47, 48].
Missing data were also present in the time series of the covariates as a result of quality control and assurance processing: 29% of the temperature time series, 35% of the specific conductance time series, 14% of the dissolved oxygen time series, 47% of the turbidity time series, and 11% of the elevation time series. We considered nitrate concentration as our Y and the other water quality variables (specific conductance, dissolved oxygen, water temperature, turbidity, and water elevation) as the covariates X that could be related to Y  [50]. Visual examination of the distributions of the response and covariates indicated that turbidity had a strongly rightskewed distribution and was, therefore, log-transformed (i. e. , log (turbidity + 1)) prior to analysis  [51]. We also included two additional covariates to account for temporal autocorrelation in the time series, as determined by the AIC.
Simulation Study: Performance Evaluation To evaluate the performance of our reconstruction method, we then repeatedly and randomly removed different combinations of data (both point observations and sequences of contiguous observations) from the two-year time series of nitrate concentration from Arikaree River. For the missing point data, we randomly removed 20%, 30%, and 40% of the observations from the nitrate concentration time series and repeated this process 100 times each (Simulations 1, 2, and 3). For the missing sequences of data, we randomly removed ten individual days (10 × 24 h worth) of observations, repeating the process 100 times (Simulation 4), as well as ten individual weeks of observations, again repeating the process 100 times (Simulation 5). For each simulation, we then calculated the root-mean-square error (RMSE) and the proportion of reconstructed data within the precision interval of the nitrate sensor (PWPI), i.
Implementation Simulation and imputation were performed with the base packages within the R statistical software  [52]. Modelling was undertaken using the car  [53] , gam  [54] , mgcv  [39] , and forecast  [55]  packages. The R script used to implement the analyses is provided in the GitHub repository available online at https://github. com/Claire-K/nitrate_time_serie_reconstruction (last accessed 4 December 2021) and the Arikaree data are available from NEON  [46] [47] [48] [49]  (see Table  A1  for data product numbers). We considered nitrate concentration as our Y and the other water quality variables (specific conductance, dissolved oxygen, water temperature, turbidity, and water elevation) as the covariates X that could be related to Y  [50]. Visual examination of the distributions of the response and covariates indicated that turbidity had a strongly right-skewed distribution and was, therefore, log-transformed (i.
Implementation Simulation and imputation were performed with the base packages within the R statistical software  [52]. Modelling was undertaken using the car  [53] , gam  [54] , mgcv  [39] , and forecast  [55]  packages. The R script used to implement the analyses is provided in the GitHub repository available online at https://github. com/Claire-K/nitrate_time_serie _reconstruction (accessed on 4 December 2021) and the Arikaree data are available from NEON  [46] [47] [48] [49]  (see Table  1  for data product numbers).
Arikaree River Case Study Model performance varied according to the characteristics of the missing data. For demonstration purposes, we focus here on four different cases: (a) a 12-day sequence in which data were sporadically missing, (b) a one-day peak flow event containing sporadically missing data, (c) a full day of missing data, and (d) a three-month sequence of missing data (Figure  3 ). Our method performed well at predicting values of nitrate concentration where point observations were sporadically missing from the time series. In other words, the predicted values followed the pattern of the surrounding data closely and prediction intervals were narrow compared to the sensor precision interval (e. g. , Figure  3a, b ). RMIT Classification: Trusted.
Arikaree River Case Study Model performance varied according to the characteristics of the missing data. For demonstration purposes, we focus here on four different cases: (a) a 12-day sequence in which data were sporadically missing, (b) a one-day peak flow event containing sporadically missing data, (c) a full day of missing data, and (d) a three-month sequence of missing data (Figure  3 ). Our method performed well at predicting values of nitrate concentration where point observations were sporadically missing from the time series. In other words, the predicted values followed the pattern of the surrounding data closely and prediction intervals were narrow compared to the sensor precision interval (e. g. , Figure  3a, b ). However, the method performed less well when periods of contiguously missing observations were reconstructed.
Simulations 1, 2, and 3: Missing Point Data In terms of the reconstruction performance of our method, the RMSE values from simulations 1, 2, and 3 (20%, 30% and 40% of randomly missing point data in the nitrate time series, respectively) were all similar and rarely >0. 2 µmol/L, even with 40% of the data having been removed (Figure  4a ). Furthermore, the method predicted more than 95% of the missing nitrate values with an RMSE of 0. 2 µmol/L. Nevertheless, as the proportion of missing data increased, so did the maximum RMSE. Overall, 72% of the reconstructed nitrate values were within the precision interval of the sensors (Figure  4b ). The performance of our method in reconstructing missing point data can also be demonstrated by looking more closely at different periods of the simulated Arikaree River time series, including typical baseflow and storm event behaviours of nitrate concentration.
In all cases, the predicted data followed the pattern of nitrate concentration closely, including a peak event that occurred over a period of less than 24 h (Figure  5 ). The performance of our method in reconstructing missing point data can also be demonstrated by looking more closely at different periods of the simulated Arikaree River time series, including typical baseflow and storm event behaviours of nitrate concentration. In all cases, the predicted data followed the pattern of nitrate concentration closely, including a peak event that occurred over a period of less than 24 h (Figure  5 ).
Discussion Data from low-cost, in situ water quality sensors provide unprecedented opportunities to better understand spatial and temporal water quality dynamics. However, in situ sensors are prone to technical issues, which presents a challenge for the processing and analysis of environmental data. The study presented here demonstrates that it is possible to predict these missing data for reconstruction of high-frequency environmental time series using appropriate statistical methods. To our knowledge, our study is among the first to reconstruct missing nitrate data from high-frequency data collected by in situ sensors. This may be in part due to the relatively recent, standard use of such sensors for measuring nitrate concentrations in river networks and makes comparison of our findings with other studies and methods difficult.
Reconstruction of high-frequency runoff data showed that a new machine learning method, "nu-support vector machines," outperformed other machine learning methods  [57]. Performance of the new method was evaluated in terms of the correlation (R 2 ) between observed and simulated data, with their method achieving values between 0. 75 and 0. 95. Applying the same R 2 coefficient to our simulations, we achieved R 2 values between 0. 976 and 0. 997 when 40% of the dataset was removed and between 0. 119 and 0. 994 (first quartile = 0. 84) when sequences of 10 days were removed, indicating that our method attains a comparatively good performance, particularly for point and short periods of missing data. Blending ARIMA forecasts and backcasts has also shown promise for reconstruction of sensor-based water quality data, including temperature, pH, specific conductance, and dissolved oxygen  [58].
However, we are unable to compare our results with this work given that performance of the correction method was assessed by comparing the ARIMA-based results with corrections done manually by technicians. Our method was able to predict all missing values present in a real-time series of nitrate concentration data. Although the prediction intervals for some predicted values were relatively wide, the median prediction interval was very low (1. 34 µmol/L nitrate), indicating that many missing data had a 95% prediction interval lower than the sensor accuracy (i. e. , at least 2 µmol/L) and, therefore, were precise enough for the intended use of the data. We also showed via simulation that even when 40% of the initial dataset (point observations) was missing, our method was able to accurately recover approximately 70% of the data.
When day-long sequences of contiguously missing data were simulated, mimicking, for example, a persistent sensor outage or prolonged periods of quality-flagged data, performance of the method was similarly efficient. However, for week-long periods of missing data, the percentage of accurately recovered data decreased, indicating that data reconstruction is more impacted by long sequences of missing data in a row than by multiple but sporadically missing data. Consideration of different periods of the nitrate concentration time series provided insight into the overall utility of the method and why the method may not accurately reconstruct all missing data. For example, excessive nitrate can create eutrophication issues in aquatic systems and, therefore, for the purposes of environmental management, it is important to know (i) whether the presence of high nitrate concentrations is real or anomalous, and (ii) that accurate reconstruction of the real concentrations can be achieved in a timely fashion, particularly during floods.
The method has currently only been applied in a binary fashion depending on the existence of covariates (other environmental data that can be used as predictors). For any one missing nitrate observation, GAM was used to predict the observation when data for all covariates were available, and ARIMA was used when data for at least one of the covariates were missing. Several avenues of study are envisaged from this work. First, future work could aim to develop a method whereby one or more environmental variables could be used as the covariate(s) according to their availability, such that ARIMA is only used when no covariate data are available. A second avenue would be to use different types of models in the framework, bearing in mind that the characteristics of time series data can influence the forecasting method that should be run.
Conclusions Measurement errors or missing observations are recurrent and, in some cases, may reduce user perception of data quality, thereby preventing data from underpinning management actions. Here, we developed a method to successfully reconstruct missing nitrate concentration data from high-frequency in situ sensors in fresh waters, thereby adding value to the literature on anomaly detection and fulfilling a critical management need in the environmental domain. To mimic sporadically missing observations, both point data and sequences of data were removed from a two-year time series of nitrate concentration data. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although the predictive ability declined when sequences of missing data occurred.
The models also had stronger predictive ability when other water variables (covariates) were available. This suggests there may be advantages to deploying co-located sensors to measure covariates, even when there is a single constituent of concern, such as nitrate, by enabling a more reliable reconstruction of the nitrate time series. Our study is an important first step towards environmental data reconstruction in the information age and sets a benchmark against which future datasets and methodological developments can be compared. While we believe the general methodology presented here is generalizable to rivers in other ecosystems  [59] , the relationships between other water quality variables of interest may differ. Thus, future research should also focus on understanding these relationships so that co-located sensors can be optimally deployed.
This will ensure that near real-time water quality data produced by low-cost in situ sensors are trustworthy and reliable enough to underpin data-enabled management decisions. Funding: Funding was provided by the Energy Environment Solutions (E2S-UPPA) consortium through an international research chair. This study was part of a project funded by an Australian Research Council (ARC) Linkage grant (LP180101151) "Revolutionising water-quality monitoring in the information age".
Acknowledgements This research was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 681818 IMPACT  to RC) and under the  Marie Skłodowska-Curie program  (grant agreement no. 750901  ABISSE to FS), and by the  Research Council of Norway through its Centres of Excellence funding scheme , project number  223272. We acknowledge access to the  GENCI supercomputers (Occigen at CINES, Ada and Jean-Zay at IDRIS , and  Curie and Irene at TGCC)  through the stl2816 series of eDARI computing grants, and the  PRACE  grant  RA4947.
Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for high-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The epiMEIF model is fitted on a group of potential causal SNPs and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets.