Root Mean Square Difference Research Articles

Study on treatment of wastewater with low concentration of ammonia-nitrogen by vacuum plate membrane distillation technology.

The removal of low concentration ammonia-nitrogen in industrial wastewater is necessary before discharged into the environment. In this study, vacuum plate membrane distillation (VPMD) technology was utilized and operating parameters such as pH, feed temperature, vacuum degree, feed flow and time were investigated. Based on the experimental data, the heat and mass transfer mechanism and mathematic model were studied. The experimental results show that low solution pH was significantly beneficial to ammonia-nitrogen removal but permeate flux was nearly changeless. At pH = 4, a removal rate up to 93.33% was achieved. Ammonia-nitrogen mainly exists with NH4+ ions in acidic solution, so only water molecules pass through the membrane to acquire the water product in the permeate side. Increasing the temperature of the solution was disadvantageous to the ammonia-nitrogen removal due to membrane pores expanding and the mass transfer coefficient of NH3 molecules increasing; therefore a low temperature was chosen if possible. Because vapor pressure of the feed solution increases exponentially with temperature and results in membrane surface pressure difference increases, therefore increasing the temperature enhances the permeate flux. Raising the vacuum degree enhanced ammonia removal rate and permeate flux obviously, a vacuum degree of 0.09 MPa was chosen for the experiment. The effect of feed flow rate on ammonia-nitrogen removal instead of permeate flux is weak, the reason is that the boundary layer wears thin when the feed flow rate is increased, which is conducive to permeate flux increasing. In a two-parameter model of Knudsen diffusion, Poiseuille flow was chosen to demonstrate the heat and mass transfers in the process of VPMD in the study. Based on the experimental values of permeate flux, two parameters CK and CP in the model were calculated using a nonlinear fitting method software, which indicated that the Knudsen diffusion model more than the Poiseuille flow model was suitable. The maximum values of the relative average deviation (RAD) and root mean square difference (RMSD) of experimental and calculated values with model equations of the permeate flux at the different temperature, vacuum degree and feed flow rate were no more than 8.7% and 3.20 kg · (m2 · h)-1, respectively.

An improved radiative forcing scheme for better representation of Arctic under-ice blooms

In the present study, a newly designed radiative forcing scheme, i.e., impulsive radiative forcing scheme (IRFS), is applied in the one-dimensional Regional Ocean Modeling System-Carbon Silicate Nitrate Ecosystem (ROMS-CoSiNE) model to reproduce under-ice phytoplankton blooms (UIBs) in the Arctic Ocean. The model results obtained with the IRFS and the traditional continuous radiative forcing scheme (CRFS) are compared with observations of an UIB north of Svalbard. It is found that, the new IRFS performs much better than the traditional CRFS with lower biases, lower root-mean-square difference (RMSD), and a higher correlation coefficient (r), especially at the near-surface layer of the water column (0–15 m). It is also found that in the simulations with the CRFS, the UIB started earlier and maximum chlorophyll concentration was more than twice the observed data. However, the UIB with the IRFS is similar to observations in terms of UIB timing and magnitude. Since the IRFS allows more solar radiation to penetrate through the upper water column, phytoplankton growth is supported at greater water depth (10–40 m). Therefore, this model reproduces a deeper phytoplankton bloom and corresponding deepened subsurface chlorophyll maxima, which agrees well with the observations. Additionally, the introduction of melt ponds into the IRFS model plays a vital role in accelerating spring UIBs because ponded ice can transmit more light. However, the magnitude of integrated chlorophyll-a hardly changes between the model runs with and without melt ponds. This study proposes a more accurate averaging of the light field that simply changes the averaging procedure to take into account horizontal light variability at the sub-grid scale and provides an approach to improve radiative forcing scheme in the Pan-Arctic model case.

International Intercomparison of In Situ Chlorophyll-a Measurements for Data Quality Assurance of the Swedish Monitoring Program

Chlorophyll-a is an essential climate variable. Chlorophyll-a in situ measurements are usually used for the validation of satellite images. Previous intercomparisons have shown that there can be substantial differences between in situ laboratories. In order to shed light on these differences, we arranged international chlorophyll-a intercomparisons with eight participating laboratories during 1–2 July 2021. We performed two dedicated transects through Bråviken bay (NW Baltic proper) and sampled four stations in each transect along a chlorophyll-a gradient. We took three surface replicates per laboratory and per station, i.e., 24 samples per laboratory. The samples were filtered through Whatman GF/F filters, and filters were frozen in liquid nitrogen and distributed in dry ice to all laboratories together with chlorophyll-a standards. The results between labs compared quite well. The mean normalized bias (MNB) of the standard measurements ranged between −23% and +19% for all laboratories and −7% to +19% for the Baltic Sea laboratories compared to high-performance liquid chromatography. The MNB of the two Bråviken transects ranged between −23 and +17% for all laboratories (compared to the median of all spectrophotometric and fluorometric measurements) and between −2 and +17% for the Baltic Sea laboratories. On average, the chlorophyll-a concentrations measured by the fluorometric method were about 13% higher than those measured by spectrophotometry, and fluorometry samples tended to have more scatter. The largest uncertainties seem to be caused by variable storage and extraction methods and are not fully captured in this intercomparison. This is demonstrated by analyzing historical comparisons revealing very large uncertainties (root mean square difference (RMSD) up to 109% and bias up to 68%), possibly due to too low filtration volumes and due to different extraction and storage methods. Our recommendation is to flash-freeze samples in liquid nitrogen and store them at −80°C. After storage, they should be extracted and measured at room temperature within 6–24 h. Our results also indicate that ethanol is much more efficient in extracting Chl-a than acetone. Last but not least, we would like to point out that the uncertainties in measuring chlorophyll-a by satellite are now within the range of in situ data, as shown here by comparing the in situ results from this study with published remote sensing results from the literature.

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3.

Synchronous Assimilation of Tidal Current-Related Data Obtained Using Coastal Acoustic Tomography and High-Frequency Radar in the Xiangshan Bay, China

To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by CAT and the radial velocity that was observed by HFR were, for the first time, synchronously assimilated into a three-dimensional barotropic ocean model. Compared with acoustic Doppler current profile data, the velocities of the model assimilating both CAT and HFR data had the highest accuracy according to root mean square differences (RMSDs), ranging from 0.05 to 0.08 m/s for all the vertical layers. For the models individually assimilating CAT and HFR, the values in the vertical layers ranged from 0.07 to 0.12 m/s and 0.08 to 0.13 m/s, respectively. A harmonic analysis of the model grid data showed that the spatial mean amplitudes of the M2, M4, and residual currents were 0.66, 0.14, and 0.09 m/s, respectively. Furthermore, the standing wave characteristics of the M2 current and M4 associated-oscillation in the inner XSB, mouth of the Xiangshan fjord, were better captured by the model assimilating both CAT and HFR. Our study demonstrates the advances in three-dimensional tidal current analysis using a model that assimilates both CAT and HFR data, especially in regions with complex coastal geography.

The Relationship Between Sexual Activity and Heart Rate Variability in Menopausal Women.

Background:Menopause is an important life stage for women, which can bring along sexual and cardiac problems. Increased heart rate variability is an indicator of parasympathetic activity and is associated with mental and physical health and life expectancy. This study aimed to evaluate the effect of sexual activity (only penile–vaginal intercourse but not masturbation or non-coital sex with a partner) on heart rate variability in healthy menopausal women.Methods:We evaluated 130 menopausal patients aged 45-60 years, without chronic disease. The average weekly sexual activity numbers remembered in the last 1 year were questioned. The patients were divided into 2 groups according to the presence of sexual activity. The sexually active group was divided into subgroups as 1 per week and 2 or more per week. Menopause Rating Scale was applied for menopausal symptoms. Heart rate variability was analyzed from the 24-hour electrocardiography Holter recording.Results:Heart rate variability parameters were higher in the sexually active group than in the sexually inactive group (mean of the standard deviations of all the NN intervals for each 5 min segment of a 24-hour heart rate variability recording: P = .004; root mean square of differences between adjacent normal RR intervals, expressed in ms: P = .001; number of NN intervals exceeding 50 milliseconds: P = .011; percentage of adjacent RR intervals with a difference of duration >50 ms: P = .009; low frequency: P = .011; high frequency: P = .008, low frequency/high frequency: P = .018). When assessed by multiple linear regression analysis by adjusting for age, body mass index, and menopause duration, the variables mean of the standard deviations of all the NN intervals for each 5 min segment of a 24-hour heart rate variability recording, root mean square of differences between adjacent normal RR intervals, expressed in ms, and low frequency were independently associated with the number of sexual activities per week (B = 2.89 ± 1.02, 95% CI = 0.866-4.91, P = .005; B = 4.57 ± 1.83, 95% CI = 0.94-8.2, P = .014; and B = 1174.9 ± 592.2, 95% CI = 2.9-2346.9, P = .049, respectively).Conclusion:In healthy menopausal women, continued sexual activity with penile–vaginal intercourse is associated with better health outcomes on cardiac autonomic function through higher heart rate variability, an index of parasympathetic activity.

Electromyographical Analysis of Table Tennis Forehand Stroke Using Different Ball Material

Research purpose. This study aimed to determine the EMG analysis of forehand topspin using different ball materials. 
 Materials and methods. For the purposes of this study, seven male university-level table tennis players who were right-hand dominant players (age 21.1 ± 2.4 years, height 162.6 ± 6.8 cm, mass 61.8 ± 3.2 kg) with at least 6 years of playing experience were selected. The selected participants were physically active and had no physical injuries. The study was approved by the research committee. Pectoralis Major (PM), Deltoid Anterior (DA), Latissimus Dorsi (LD), Biceps Brachii (BB), Extensor Carpi Radialis (ECR), Flexor Carpi Radialis (FCR) muscles were selected for the electromyographical study. One of the EMG signals variable Root Mean Square was measured in all muscles, through which the maximum muscle involvement was evaluated. For each assessed muscle and both ball materials, the mean value of Root Mean Square EMG signals was computed. For descriptive statistics, minimum, maximum, mean, and standard deviation were used, and a t-test was used as a statistical technique. 
 Results. The level of significance was set at 0.05. Results of the study showed no significant difference in EMG variable Root Mean Square between celluloid and poly plastic ball material on selected muscles. 
 Conclusions. We conclude that the biceps brachii and extensor carpi radialis are important muscles for the forehand topspin because they are responsible for flexion of the arm and work strongly in the action of gripping in the performance of quick forehand spin strokes.

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains.

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings.Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%.Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%.Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

Abstract 460: Tools for collecting pathologist annotations and understanding interobserver variability

Abstract Background: The High Throughput Truthing (HTT) project is assessing pathologist agreement estimates of stromal tumor-infiltrating lymphocytes (sTILs) density in hematoxylin and eosin (H&E) stained breast cancer biopsy slides. The HTT project will create a validation dataset for artificial intelligence and machine learning (AI/ML) algorithms in digital pathology fit for a training, proficiency testing, and regulatory purpose. Methods: The pilot study crowdsourced pathologists to estimate sTIL density in 640 regions of interest (ROIs) across 64 slides via two modalities: an optical microscope (eeDAP) and two digital platforms (caMicroscope and PathPresenter). eeDAP is a hardware-software interface that presents the observer with pre-defined fields of view on H&E slides that corresponds to the ROI on a whole slide image. The PathPresenter and caMicroscope web-applications replicate the eeDAP workflow on the whole slide image without microscope hardware. In the workflow, pathologists evaluated the eligibility of an ROI for sTILs content then estimated the densities of tumor-associated stroma and sTILs in the ROI. Inter-pathologist agreement within ROIs was characterized with the root mean-squared difference. Using 72 of the highest variability ROIs selected from the pilot study, seven practicing pathologists participated in a subsequent focus group to improve the clinical training and data-collection workflows. Results: The pilot study collected 7,373 sTIL density estimates from 35 pathologists between February 2020 and May 2021. The focus group provided an additional 411 evaluations on 72 ROIs and in-depth discussions to identify pitfalls, gaps in training, and workflow improvements. Installation of eeDAP for physical data collection guided improvements in documentation and operation capabilities. Updated training materials refine the definition of tumor-associated stroma, provide reference images to differentiate sTILs from other cell types, and provide feedback during training. Digital and microscope platforms benefitted from enforcing registration and training, standardizing workflows, and accelerating eeDAP slide-image registration. Conclusions: The slides, images, and annotations provided by volunteer collaborators and participants for our pilot study led to improvements in data collection tools and crowdsourcing workflows to ensure consistency and minimize annotation variability. Our pilot dataset and analysis methods are available on a public HTT Github repository to allow open access to our methodology and feedback from the digital pathology and statistics communities. These data-collection and analysis methods are applicable to other quantitative biomarkers for validation of AI/ML algorithms. The lessons learned from this work will be applied to the HTT pivotal study and inform future quality data-collection methods of pathologist annotations. Citation Format: Katherine N. Elfer, Kim Blenman, Sarah N. Dudgeon, Victor Garcia, Anna Ehinger, Xiaoxian Li, Amy Ly, Dieter Peeters, Bruce Werness, Matthew Hanna, Roberto Salgado. Tools for collecting pathologist annotations and understanding interobserver variability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 460.

A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019

Abstract. Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. The passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellites, while the coarse resolution (usually >∼10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to the public that meet the qualification of 1 km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2-based (Advance Microwave Scanning Radiometer of the Earth Observing System and its successor) SSM at 36 km, covering all on-orbit times of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal-infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model so that the “all-weather” quality was achieved for the 1 km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product compares well against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased root mean square difference (RMSD) ranging from 0.052 to 0.059 vol vol−1. Moreover, the evaluation results also show that the developed product outperforms the SMAP (Soil Moisture Active Passive) and Sentinel (active–passive microwave) combined SSM product at 1 km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used by the hydrological community, by the agricultural industry, and for water resource and environment management. The new product is available for download at https://doi.org/10.11888/Hydro.tpdc.271762 (Song and Zhang, 2021b).

The Effects of Acute Cannabidiol on Autonomic Balance

A recent poll indicates that at least one third of Vermont state’s population has used hemp‐based products such as Cannabidiol (CBD). This non‐psychoactive compound is one of 700 chemicals derived from the Cannabis sativa plant and is both legal and widespread for distribution in the state of Vermont, however very little work has been done to investigate its physiological effects at recreationally‐relevant doses. We hypothesized that in apparently healthy adults, acute CBD favorably affects the autonomic nervous system and that this would be evident by an increase in heart rate variability (HRV). HRV is a non‐invasive method used to analyze autonomic balance. Though scientific studies are lacking, anecdotal reports of athletes self‐administering CBD have indicated a positive effect on HRV. Using a randomized, double‐blind, placebo‐controlled crossover study, we examined a narrow age range of adults (average 20.9 ± 0.3 yrs), administered varying acute recreationally‐relevant doses of CBD (0, 25mg, 50mg, 200mg), and characterized baseline cardiovascular variables (heart rate, blood pressure, HRV) at rest. Results indicate that such doses do not affect resting heart rate or blood pressure, however increasing dose appears to increase standard deviation of interbeat intervals (SDNN) and root mean square of successive differences (RMSSD). Such changes reflect an increase in HRV, indicative of elevated parasympathetic activity. This work contributes to the framework of understanding potential therapeutic uses and/or risk factors of CBD, particularly as it relates to healthy individuals.

Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses

The application of diffusion theory and Monte Carlo lidar radiative transfer simulations presented in Part I of this series of study suggests that snow depth can be derived from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. These methods are now applied to the satellite ICESat-2 lidar measurements over the Arctic sea ice and land surfaces of Northern Hemisphere. Over the Arctic sea ice, the ICESat-2 retrieved snow depths agree well with co-located IceBridge snow radar measured values with a root-mean-square (RMS) difference of 7.8 cm or 29.2% of the mean snow depth. The terrestrial snow depths derived from ICESat-2 show drastic spatial variation of the snowpack along ICESat-2 ground tracks over the Northern Hemisphere, which are consistent with the University of Arizona (UA) and Canadian Meteorological Centre (CMC) gridded daily snow products. The RMS difference in snow depths between ICESat-2 and UA gridded daily snow products is 14 cm, or 28% of the mean UA snow depth. To better understand these results, we also discuss the possible sources of errors in ICESat-2 derived snow depths, including surface roughness within the laser footprint, atmospheric forward scattering, solar background noise, and detector dark current. Simulation results indicate that the snow depth errors would be less than 5 cm if the standard deviation of pulse spreading due to surface roughness is within 50 cm. Our results demonstrate that the ICESat-2 lidar measurements can be used to reliably derive snow depth, which is a critical geophysical parameter for cryosphere studies including sea ice thickness estimation and also provides important constraints in the modeling of terrestrial hydrological processes.

Merging and Downscaling Soil Moisture Data From CMIP6 Projections Using Deep Learning Method

Soil moisture (SM) is an important variable in mediating the land-atmosphere interactions. Earth System Models (ESMs) are the key tools for predicting the response of SM to future climate change. Many ESMs provide outputs for SM; however, the estimated SM accuracy from different ESMs varies geographically as each ESM has its advantages and limitations. This study aimed to develop a merged SM product with improved accuracy and spatial resolution in China for 2015-2100 through data fusion of 25 ESMs with a deep-learning (DL) method. A DL model that can simultaneously perform data fusion and spatial downscaling was used to analyze SM’s future trend in China. Through the model, monthly SM data in four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) from 2015 to 2100, with a high resolution at 0.25°, was obtained. The evaluation metrics include mean absolute error (MAE), root mean square difference (RMSD), unbiased root mean square difference (ubRMSD), and coefficient of correlation (r). The evaluation results showed that our merged SM product is significantly better than each of the ESMs and the ensemble mean of all ESMs in terms of accuracy and spatial distribution. In the temporal dimension, the merged product is equivalent to the original data after deviation correction and equivalent to reconstructing the fluctuation of the whole series in a high error area. By further analyzing the spatiotemporal patterns of SM with the merged product in China, we found that northeast China will become wetter whereas South China will become drier. Northwest China and the Qinghai-Tibet Plateau would change from wetting to drying under a medium emission scenario. From the temporal scale of the results, the rate of SM variations is accelerated with time in the future under different scenarios. This study demonstrates the feasibility and effectiveness of the proposed procedure for simultaneous data fusion and spatial downscaling to generate improved SM data. The merged data have great practical and scientific implications.

Effects of Weight-Bearing on Tibiofemoral, Patellofemoral, and Patellar Tendon Kinematics in Older Adults.

Quantification of natural knee kinematics is essential for the assessment of joint function in the diagnosis of pathologies. Combined measurements of tibiofemoral and patellofemoral joint kinematics are necessary because knee pathologies, such as progression of osteoarthritis and patellar instability, are a frequent concern in both articulations. Combined measurement of tibiofemoral and patellofemoral kinematics also enables calculation of important quantities, specifically patellar tendon angle, which partly determines the loading vector at the tibiofemoral joint and patellar tendon moment arm. The goals of this research were to measure the differences in tibiofemoral and patellofemoral kinematics, patellar tendon angle (PTA), and patellar tendon moment arm (PTMA) that occur during non-weight-bearing and weight-bearing activities in older adults. Methods: High-speed stereo radiography was used to measure the kinematics of the tibiofemoral and patellofemoral joints in subjects as they performed seated, non-weight-bearing knee extension and two weight-bearing activities: lunge and chair rise. PTA and PTMA were extracted from the subject’s patellofemoral and tibiofemoral kinematics. Kinematics and the root mean square difference (RMSD) between non-weight-bearing and weight-bearing activities were compared across subjects and activities. Results: Internal rotation increased with weight-bearing (mean RMSD from knee extension was 4.2 ± 2.4° for lunge and 3.6 ± 1.8° for chair rise), and anterior translation was also greater (mean RMSD from knee extension was 2.2 ± 1.2 mm for lunge and 2.3 ± 1.4 mm for chair rise). Patellar tilt and medial–lateral translation changed from non-weight-bearing to weight-bearing. Changes of the patellar tendon from non-weight-bearing to weight-bearing were significant only for PTMA. Conclusions: While weight-bearing elicited changes in knee kinematics, in most degrees of freedoms, these differences were exceeded by intersubject differences. These results provide comparative kinematics for the evaluation of knee pathology and treatment in older adults.

Remote estimation of phytoplankton primary production in clear to turbid waters by integrating a semi-analytical model with a machine learning algorithm

Remote estimation of phytoplankton primary production has long been recognized as an important method for investigating the responses of aquatic ecosystems to global climate change. The theory-based primary production model (TPM), one of the earlier proposed models, is potentially applicable to a variety of water bodies because of its semi-analytical nature. Its accuracy is highly dependent on whether the photophysiological response of phytoplankton is adequately parameterized, specifically the assimilation number (PmaxB) and the light saturation parameter (Ek). The remote assignment of PmaxB and Ek is acknowledged to be a challenging task, and the limited progress has impeded extensive use of the TPM. In this study, we proposed a machine learning algorithm, the enhanced random forest regression (ERFR), to retrieve PmaxB and Ek from satellite observations. The ERFR were then integrated with the TPM (together termed as TPMERFR) to estimate daily depth-integrated primary production (IPP) in clear to turbid waters. The ERFR was trained and validated using in situ datasets from a broad range of trophic and biogeographic conditions, covering oceanic, coastal, and inland water bodies. Evaluations with independent in situ data and matchup data showed that the ERFR outperformed conventional empirical and semi-analytical algorithms, and it could better capture the variability of PmaxB and Ek than look-up-table methods. The root mean square difference (RMSD) of the satellite-based IPP estimates from the TPMERFR remained within 0.27. In contrast, the benchmark models generally yielded IPP estimates with RMSDs of 0.27–0.62. The TPMERFR was then implemented to climatological satellite products (2010–2019) to reassess global IPP. Reasonable spatial distributions of IPP were preliminarily demonstrated, especially in polar, coastal, and inland waters. These results indicated the potential utility of the TPMERFR to generate seamless IPP distributions in clear to turbid waters.

Evaluation of Heart Rate Variability Parameters During Awake and Sleep in Refractory and Controlled Epileptic Patients.

BackgroundAlterations of heart rate variability (HRV) in epileptic patients were the field of interest of several studies for many reasons, particularly the contribution toward sudden unexpected death in epilepsy (SUDEP).AimWe aimed at evaluation of autonomic dysfunction in epileptic patients during awake and sleep in addition to studying the association between SUDEP risk with different Holter parameters.Patients and MethodsThe study included eighty epileptic patients (40 controlled epileptic patients and 40 refractory epileptic patients) compared to 30 volunteers as control group. They underwent detailed epileptic history, Chalfont seizure severity scale, sudden unexpected death in epilepsy (SUDEP)-7 risk score and 24 hour Holter monitoring to assess HRV parameters.ResultsPatients with refractory epilepsy had longer duration of epilepsy with increased number of used AEDs compared to controlled epileptic group. Both controlled and refractory epileptic patients had significantly higher average heart rate (AV.HR), sympatho-vagal ratio (low-frequency/high-frequency (LF/HF) ratio in 24 hours, daytime, and nighttime), and LF and HF values compared to controls. The rMSSD (the root mean square of difference between successive normal intervals), Tri.Index (triangular index), and pNN50 (percentage of the number of pairs of consecutive beat-to-beat intervals that varied by 50 ms) were significantly reduced in both epileptic groups compared to controls. Among refractory epileptic patients, patients with generalized epilepsy had significantly higher severity epileptic scale, average heart rate, minimum heart rate, and LF/HF night, in addition to lower rMSSD and pNN50 compared to patients with focal epilepsy. We found positive correlation between the following Holter indices (LF/HF 24, LF/HF day, and LF/HF night) and the duration of the epilepsy, while negative correlations between Tri.Index, LF, and HF and the epileptic duration were detected. SUDEP-7 risk was negatively correlated with pNN50 and rMSSD; meanwhile, it was positively correlated with LF/HF 24. The severity of epilepsy among refractory epileptic patients was positively correlated with average heart rate but negatively correlated with pNN50 and rMSSD. Using linear regression analysis, we found that pNN50 and rMSSD could predict SUDEP-7 risk and severity of epilepsy in refractory epileptic patients.ConclusionEpileptic patients (particularly refractory patients with generalized EEG findings and long duration) had reduced heart rate variability and hence impairment of parasympathetic activity with increased susceptibility for adverse outcomes. Moreover, pNN50 and rMSSD could be used as predictors for SUDEP-7 risk as well as severity of epilepsy in refractory epileptic patients.

TRAINING METHODS FOR ATHLETES’ NEUROLOGICAL REACTION TIMES

ABSTRACT Introduction: By observing the characteristics of the development of competitive tennis in the world, it is not difficult to find that tennis requires extremely fast reaction times from players. Objective: To explore the relationship between athlete's reaction time and different training methods. Methods: 48 outstanding female tennis players were selected. They received four weeks of regular training (from March 2 to March 28, 2020) and two weeks before the competition (from June 8 to June 20, 2020). After the training, the Omega Wave system was used for testing, and urine samples were taken for catecholamine testing. Results: In the test of 48 people before the competition, 30 people experienced central fatigue, from which 24 people (80%) were in the regular training stage, and only six people (20%) were in the training stage before the competition. HRV indicators: In the regular training stage, the results of time-domain indicators such as SDNN (NN interval standard deviation), SDSD (NN interval difference standard deviation), RMSSD (NN interval difference root mean square), and frequency-domain indicators such as LF (low frequency) and HF (high frequency) in the second and third week of the athletes were lower than those in the first and fourth week, showing the characteristics of first falling and then rising. However, there was no significant change. Conclusion: Different training methods can improve athletes› competitive level and reaction speed. Level of evidence II; Therapeutic studies - investigation of treatment results.

CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence

Abstract Objective: This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor. Methods: A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results: The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t = −3.55 , P = 0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t = 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t = 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t = −9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t = −2.74, P = 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion: The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

Future shifts in the phenology of table grapes on Crete under a warming climate

Climate change is expected to pose major challenges to viticulture. The projected increase in temperature in the Mediterranean region due to climate change is likely to influence the timing of grapevine phenological stages. This study developed and validated a phenological model, from budbreak to maturation phenological stages, based on the growing degree days for the Sultana grapevine cultivar on Crete Island, Greece, and used the model to assess potential changes in future phenology timing employing different climate change scenarios. A dataset of unpublished phenological observations from 20 locations spanning four decades was used to validate the phenological model. The root mean squared difference (RMSD) of the calibration-validation procedure was estimated between 5.4 and 11.5 days, depending on the phenological stage. The model outperformed for the flowering and maturity stages. The highest RMSD was found for the shoot development stages. Projections determined an earlier occurrence of the different phenological stages. Near future climate (2020–2060) projections indicate budbreak advancement by 7 to 8 days and maturity by 4 to 5 days on average. For the far future (2060–2100), the respective changes are 11 to 18 and 7 to 9 days earlier. Discussion on the underlying uncertainty sources is provided.

The capabilities of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) in seagrass and algae species differentiation using spectral reflectance

Abstract. This paper assesses the reflectance difference values between the respective spectral bands in the visible and near-infrared (VNIR) of Sentinel 2A/2B Multi-Spectral Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeast of Bahrain Island in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninervis and Halophila stipulacea), and algae (green and brown). In addition, an experimental mode was established in a goniometric laboratory to simulate the marine environment, and spectral measurements were performed using an Analytical Spectral Devices (ASD) spectroradiometer. Measured spectra and their transformation using the continuum-removed reflectance spectral (CRRS) approach were analyzed to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVIs) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and near-infrared (NIR) wavelengths for seagrass and algae detection and likely discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Instead of the red band, the integration of the blue or the green band in WVI increases their power to discriminate submerged aquatic vegetation (SAV), particularly the water adjusted vegetation index (WAVI), water enhanced vegetation index (WEVI), and water transformed difference vegetation index (WTDVI). These results corroborate the spectral and the CRRS analyses. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to more scattering in this part of the spectrum. Furthermore, statistical fits (p<0.05) between the reflectance in the respective VNIR bands of MSI and OLI revealed excellent linear relationships (R2 of 0.999) with insignificant root mean square difference (RMSD) (≤ 0.0015). Important agreement (0.63 ≤ R2 ≤ 0.96) was also obtained between respective WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environments, provided that rigorous data pre-processing issues are addressed.