Correlation Coefficient Research Articles

DNA based phenotype optimization of oryza sativa using machine learning and MolCNN

The prediction of phenotype from the genotype of oryza sativa (rice) is very crucial for optimizing the crop management. utilizing molecular convolutional neural networks (MolCNNs) and machine learning for crop management in oryza sativa provides a data-driven method for phenotype prediction based on DNA data, improving farming techniques. Data gathering, preparation, and integration of phenotypic and DNA data are all part of this process. Meaningful DNA features are extracted by MolCNN, and phenotypic qualities are predicted by a machine learning algorithm. Making educated decisions is ensured by assessing the model’s effectiveness, applying it to crop management, and updating it frequently. Choosing crop varieties, planting schedules, and management techniques are guided by molecular insights, which support sustainable agriculture and increase yields and quality. In the proposed research we are calculating pearson correlation coefficients between anticipated and actual trait values and the model’s performance on a test set. Additionally, it determines the (PCC) for every characteristic in the model and we have received a binary accuracy of 0.9998 in 139 seconds.

Radiation risk during thoracic CT scan for diagnostic and radiotherapy planning procedures in Hassan II, Hospital, Agadir Morocco

Objective: This study aims to establish diagnostic reference levels and radiation-induced risk for the diagnostic CT-scans and the radiotherapy planning CT-scans of the thorax in the regional hospital of Agadir, Morocco. Materials and methods: Data from two groups of patients undergoing thoracic CT-scans with either diagnostic CT-scans (G1, n = 120) or radiotherapy planning CT-scans (G2, n = 120) are collected. All acquisitions were helical. DRLs is calculated for each type of thoracic CT-scan by estimating the 75% percentile of the CTDIvol and the DLP. The total cancer risk RC was calculated according to the ICRP publication 103. The data are statistically analyzed by SPSS Statistics V21.0. The student’s t-test was used to establish the relationship between gender, clinical indication, and effective dose. The Spearman test was used to establish the relationship between age, BMI, and effective dose. Results: DRLs in terms of CTDIvol and DLP for radiotherapy planning of thorax were 19.37 mGy and 851.9 mGy cm, respectively. In diagnostic CT-scans, DRLs in terms of CTDIvol for pulmonary embolism, infectious lung disease, Chronic Obstructive Pulmonary Disease (COPD) were 11.13 mGy, 10.26 mGy, and 7.37 mGy respectively, and DRLs in terms of DLP were 417.73 mGy cm, 451.9 mGy cm and 317.78 mGy cm respectively. The cancer risk for radiotherapy planning CT-scans is ranged between 209 and 1564 with a mean value of 715 per 1 million of CT-scan. For diagnostic CT-scans, the cancer risk is ranged between 199 and 626 with a mean value of 357 per 1 million for pulmonary embolism, between 238 and 668 with a mean value of 369 per 1 million for infectious lung disease, and between 130 and 393 with a mean value of 244 per 1 million for COPD. Conclusion: Optimizing the doses received by patients in medical imaging, particularly CT, has become an obligation. Reviewing practices and procedures and promoting a radiation protection culture can help to better manage the radiation doses received by the patient.

Prediction of Diverse Boreal Summer Intraseasonal Oscillation in the GFDL SPEAR Model

Abstract Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO. Significance Statement The advance of subseasonal-to-seasonal (S2S) prediction largely depends on dynamical models’ ability to predict some major intrinsic modes in the climate system, including the boreal summer intraseasonal oscillation (BSISO). Using a newly developed S2S prediction system, we thoroughly evaluated its performance in predicting BSISO, and revealed the skill dependence on the BSISO propagation diversity. Here we provide physical explanations of what influences the BSISO predictions and identify different precursory signals for two types of BSISO, which have important implications for operational forecasts.

Relationship Between Subjective Questionnaires and Videofluoroscopy of Dysphagia Evaluation: A Systematic Review and Meta-Analysis.

Early detection of dysphagia is important for preventing aspiration pneumonia. Although videofluoroscopy is currently the primary diagnostic tool for dysphagia, access to this tool may be limited because of radiation exposure risk, high cost, and other factors. In this study, a meta-analysis was used to determine the strength of the correlation between dysphagia detection outcomes obtained using subjective questionnaires and videofluoroscopy. The PubMed and Embase databases were searched for original articles up to December 2022. Studies published in English that used cross-sectional designs to assess the correlation between subjective questionnaires and videofluoroscopy were considered eligible for inclusion. The search terms used included "dysphagia," "questionnaire," and "videofluoroscopy." Two reviewers critically appraised and extracted the correlation coefficient r values. In addition, a random-effects meta-analysis was conducted. The Q statistic was used to assess the heterogeneity among the included studies. Publication bias was checked using the funnel plot and Egger's tests. Multilevel analysis was used to determine sensitivity to consider within-study correlations. In addition, subgroup analyses were conducted based on type of questionnaire, head and neck cancer, and English-speaking regions. The meta-analysis included five studies and 856 patients using the Eating Assessment Tool-10 and one study and 27 patients using the Sydney Swallow Questionnaire. The results of the random-effects meta-analysis showed a moderate relationship between the subjective questionnaires and videofluoroscopy ( r = .35, 95% CI [0.20, 0.48]). Similar results were also obtained using multilevel analysis ( r = .34, 95% CI [0.25, 0.42]). No publication bias was found for any of the studies ( p = .88). In the subgroup analyses, a moderate relationship between Eating Assessment Tool-10 and videofluoroscopy ( r = .31, 95% CI [0.19, 0.42]) and an ultrahigh relationship between Sydney Swallow Questionnaire and video-fluoroscopy ( r = .74, 95% CI [0.50, 0.87]) were found. Furthermore, moderate associations were observed within each head and neck cancer and English-speaking regions subgroup. However, no significant differences were found between these two subgroups. These results indicate the subjective questionnaires considered in this study share a moderate relationship with videofluoroscopy. Subjective questionnaires may be used as an auxiliary tool by nurses and homecare givers for the early assessment of dysphagia risk in patients.

Sliding-window cross-correlation and mutual information methods in the analysis of solar wind measurements

Context. When describing the relationships between two data sets, four crucial aspects must be considered, namely: timescales, intrinsic lags, linear relationships, and non-linear relationships. We present a tool that combines these four aspects and visualizes the underlying structure where two data sets are highly related. The basic mathematical methods used here are cross-correlation and mutual information (MI) analyses. As an example, we applied these methods to a set of two-month’s worth of solar wind density and total magnetic field strength data. Aims. Two neighboring solar wind parcels may have undergone different heating and acceleration processes and may even originate from different source regions. However, they may share very similar properties, which would effectively “hide” their different origins. When this hidden information is mixed with noise, describing the relationships between two solar wind parameters becomes challenging. Time lag effects and non-linear relationships between solar wind parameters are often overlooked, while simple time-lag-free linear relationships are sometimes insufficient to describe the complex processes in space physics. Thus, we propose this tool to analyze the monotonic (or linear) and non-monotonic (or non-linear) relationships between a pair of solar wind parameters within a certain time period, taking into consideration the effects of different timescales and possible time lags. Methods. Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows. By independently sliding these windows along the time axis of the two time series, this technique can assess the correlation coefficient (and mutual information) between any two windowed data sets with any time lags. Visualizing the obtained results enables us to identify structures where two time series are highly correlated, while providing information on the relevant timescales and time lags. Results. We applied our proposed tool to solar wind density and total magnetic field strength data. Structures with distinct timescales were identified. Our tool also detected the presence of short-term anti-correlations coexisting with long-term positive correlations between solar wind density and magnetic field strength. Some non-monotonic relationships were also found. Conclusions. The visual products of our tool (the SWCC+SWMI maps) represent an innovative extension of traditional numerical methods, offering users a more intuitive perspective on the data. The SWCC and SWMI methods can be used to identify time periods where one parameter has a strong influence on the other. Of course, they can also be applied to other data, such as multi-wavelength photometric and spectroscopic time series, thus providing a new tool for solar physics analyses.

Intra-rater and inter-rater reliability of 3D facial measurements

Three-dimensional (3D) body scanning technology has applications for obtaining anthropometric data in human-centered and product development fields. The reliability of 3D measurements gathered from 3D scans must be assessed to understand the degree to which this technology is appropriate for use in place of manual anthropometric methods. The intra- and inter-rater reliabilities of 3D facial measurements were assessed among four novice raters using 3D landmarking. Intraclass correlation coefficient (ICC) statistics were calculated for the 3D measurement data collected in three phases to assess baseline reliabilities and improvements in reliabilities as the result of additional training and experience. Based on the results of this study, the researchers found that the collection of 3D measurement data, by multiple raters and using 3D landmarking methods, yielded a high percentage of ICC statistics in the good to excellent (>0.75 ICC) reliability range. Rater training and experience were important considerations in improving intra- and inter-rater reliabilities.

Identifying Reliability High-Correlated Gates of Logic Circuits With Pearson Correlation Coefficient

Identifying Reliability High-Correlated Gates of Logic Circuits With Pearson Correlation Coefficient

Reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting.

Many people worldwide suffer from chronic pain. Improving our knowledge on chronic pain prevalence and management requires methods to collect pain self-reports in large populations. Smartphone-based tools could aid data collection by allowing people to use their own device, but the measurement properties of such tools are largely unknown. To assess the reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting. We recruited people with fibromyalgia, rheumatoid arthritis, and/or osteoarthritis and access to a smartphone and the internet. Data collection included the Global Pain Scale at baseline and follow-up, and 30 daily pain drawings completed on a 2-dimensional, gender-neutral manikin. After deriving participants' pain extent from their manikin drawings, we evaluated convergent and discriminative validity, test-retest reliability, and responsiveness and assessed findings against internationally agreed criteria for good measurement properties. We recruited 131 people; 104 were included in the full sample, submitting 2185 unique pain drawings. Manikin-derived pain extent had excellent test-retest reliability (intraclass correlation coefficient, 0.94), moderate convergent validity (ρ, 0.46), and an ability to distinguish fibromyalgia and osteoarthritis from rheumatoid arthritis (F statistics, 30.41 and 14.36, respectively; P < 0.001). Responsiveness was poor (ρ, 0.2; P, 0.06) and did not meet the respective criterion for good measurement properties. Our findings suggest that smartphone-based manikins can be a reliable and valid method for pain self-reporting, but that further research is warranted to explore, enhance, and confirm the ability of such manikins to detect a change in pain over time.

Association between trace element contents in normal human breast and age investigated using inductively coupled plasma mass spectrometry

The etiology of breast cancer remains largely unclear; however, it is well known that the incidence of this disease increases with age. In the presented work, for the first time, the age-related changes of Al, As, B, Ba, Bi, Cd, Ce, Cr, Cu, La, Li, Mg, Mn, Nb, Ni, Pb, Rb, Sb, Sn, Sr, Ti, W, and Zn content in the mammary gland of women aged 16-60 years was investigated. For this purpose, a method based on inductively coupled plasma mass spectrometry (ICP-MS) was employed, which makes it possible to determine the mass fractions of these trace elements in microsamples (mass from 10 mg) of breast tissue. With the help of this method, the material obtained during the autopsy of 38 practically healthy women aged 16-60 years who died suddenly was studied. Trace element mass fractions were determined in two age groups of women: 16-40 and 41-60 years old. Using the parametric Student's t-test and the non-parametric Wilcoxon-Mann-Whitney U-test to compare two age groups, as well as Pearson's correlation coefficients between age and trace elements mass fractions, it was found that the Cu, Mg, Rb, and Zn mass fractions in normal breast tissue decrease with age, while the As mass fraction increase. The phenomenon of the age-related changes of trace elements mass fractions in the normal mammary gland, discovered for the first time, requires further detailed study.

Examining the Insight Impacts of Indian Ocean Pressure on Autumntime Precipitation Variability over Tasmania through Singular Value Decomposition Analysis

This study explores further the impacts of the relationship between autumntime mean sea level pressure (MSLP) over the Indian Ocean and autumntime precipitation over Tasmania. We employ a combination of cross-correlation analysis, Singular Value Decomposition (SVD), and covariance measures to unravel this intricate connection. Our results reveal that MSLP over the Indian Ocean significantly influences Tasmania’s autumn precipitation, particularly in the region extending from 125°E eastward and from 35°S southward. This MSLP-precipitation link is statistically significant, characterized by a Pearson’s coefficient of correlation of 0.63 (p &lt; 0.05). In addition, we calculate the Root Mean Square Covariance (RMSC) to gauge the strength of this association. The RMSC value, calculated at 0.33, surpasses the threshold of 0.1, emphasizing a robust and significant connection between autumntime MSLP and precipitation in Tasmania. Our results emphasize that the first leading mode of SVD accounts for 84% of the variability in both MSLP over the Indian Ocean and precipitation in Tasmania. These results contribute to a more profound understanding of the factors governing autumn precipitation in Tasmania and offer valuable insights for meteorological and climate science.

In English

Currently, textile and food industries produce a significant volume of sewages containing azo dyes and other organic pollutants. These effluents are serious environmental threats, so new methods for their treatment and the degradation of azo dyes are attracting much attention. Composite materials based on TiO2 modified by noble metals and nanoceria show high activity in the photodegradation of organic contaminates and are proposed for hydrogen synthesis as well. To optimize the treatment of contaminants, different processes can combine including the strategies of adsorption, photoluminescence, photocatalysis, etc. The synthesized TiO2-based nanomaterials (sols, powders) will be exploited for bioremediation due to their small size and surface plasmon resonance from noble metals. Binary nanocomposites based on TiO2 were obtained by the chemical co-precipitation method from solutions of titanium tetraisopropoxide (TTIP) and inorganic salts of cerium, silver, and palladium. It has been stated that TiO2 is represented by anatase with primary particle size (CSR) from 8.5 to 16.8 nm, depending on the nature and concentration of the dopant. It is shown that Ag is reduced on the surface of anatase particles and blocks their growth, while Pd and Ce penetrate the titanium dioxide matrix in the form of small clusters with the deformation of the anatase crystal lattice. Nanocomposite particles formed loose and fragile aggregates, which spontaneously dispersed in solutions of dyes with the formation of colloid-stable sols, required the use of a centrifugal field for their sedimentation. Nanoparticles of TiO2&amp;Pd were electronegative and others were electropositive according to the values 4.1÷9.6 of ZPC (zero point of charge). It was shown that the particles of all composites sorbed Methylene Blue (MB) without photocatalytic activity under the visible light to any dye. Moreover, anionic dyes such as Orange-G (Or-G) and Methyl Orange (MO) were excellently discolorated in the presence of TiO2&amp;Pd system; cationic dyes of MB and Rhodamine B (RhB) discolorated too with the TiO2, TiO2&amp;CeO2, and TiO2&amp;Ag systems under UV light action. As such, photocatalysis tests showed that Orange-G’s and MO’s discoloration was higher for TiO2&amp;Pd (2 wt. %) and TiO2 systems with the correlation coefficient R2 0.999.

Quantitative Comparison of Liver Volume, Proton Density Fat Fraction, and Time Burden between Automatic Whole Liver Segmentation and Manual Sampling MRI Strategies for Diagnosing Metabolic Dysfunction-associated Steatotic Liver Disease in Obese Patients.

The performance of automatic liver segmentation and manual sampling MRI strategies needs be compared to determine interchangeability. To compare automatic liver segmentation and manual sampling strategies (manual whole liver segmentation and standardized manual region of interest) for performance in quantifying liver volume and MRI-proton density fat fraction (MRI-PDFF), identifying steatosis grade, and time burden. Fifty patients with obesity who underwent liver biopsy and MRI between December 2017 and November 2018 were included. Sampling strategies included automatic and manual whole liver segmentation and 4 and 9 large regions of interest. Intraclass correlation coefficient (ICC), Bland-Altman, linear regression, receiver operating characteristic curve, and Pearson correlation analyses were performed. Automatic whole liver segmentation liver volume and manual whole liver segmentation liver volume showed excellent agreement (ICC=0.97), high correlation (R2=0.96), and low bias (3.7%, 95% limits of agreement, -4.8%, 12.2%) in liver volume. There was the best agreement (ICC=0.99), highest correlation (R2=1.00), and minimum bias (0.84%, 95% limits of agreement, -0.20%, 1.89%) between automated whole liver segmentation MRI-PDFF and manual whole liver segmentation MRI-PDFF. There was no difference of each paired comparison of receiver operating characteristic curves for detecting steatosis (P=0.07-1.00). The minimum time burden for automatic whole liver segmentation was 0.32 s (0.32-0.33 s). Automatic measurement has similar effects to manual measurement in quantifying liver volume, MRI-PDFF, and detecting steatosis. Time burden of automatic whole liver segmentation is minimal among all sampling strategies. Manual measurement can be replaced by automatic measurement to improve quantitative efficiency.

Tree barriers height information extraction under power line based on watershed segmentation and local maximum algorithm

ABSTRACT In view of the problem that the traditional power line inspection cannot accurately obtain the height of the tree barriers under the power line, this study takes a forest area in Guilin City and a power line corridor in Yinchuan City as the research object, and combines watershed segmentation algorithm and local maximum algorithm to study the height of the tree barriers under power line. The results show that the watershed segmentation algorithm and the local maximum algorithm can accurately extract the height of tree barriers in power lines. In the Guilin experiment, there is a good correlation between the extracted height of different trees and the measured height, The correlation coefficients (R2) for osmanthus, eucalyptus, and pine trees are 0.9368, 0.8923, and 0.8227, respectively. In the experiment in Yinchuan, the tree height extracted by unmanned aerial vehicles (UAV) and LiDAR also has a good correlation with the measured height, with correlation coefficients (R2) of 0.926 and 0.952, respectively. These two experiments have verified the feasibility of watershed segmentation algorithm and local maximum algorithm in extracting the height of tree barriers under power lines, which is of great significance for monitoring and warning of tree barriers under power lines.

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.

Experimental study on the use of the ARM Cortex M7 processor for measuring far-field blast waves

AbstractThe ongoing study of blast waves and blast wave mitigation continues to play an essential role in protecting structures and personnel. The methodology, however, for capturing far-field blast waves in large-scale tests has remained largely unchanged for three decades, relying on large arrays of pressure transducers connected by hundreds of meters of cabling and requiring a considerable amount of time to set up. This paper evaluates the use of a modern low-cost microprocessor with high computational power to capture blast waves with sufficient fidelity to provide scientists and engineers with credible data. The system utilizes an ARM Cortex M7 processor as an experimental data acquisition (DAQ) system for measuring far-field blast waves in an open-air blast arena at sampling speeds of up to 1.8 Msps (megasamples per second). The experimental system’s performance was evaluated by comparing it to a traditional commercial system used for measuring blast waves. The comparison showed an average Spearman correlation coefficient r of 0.928 between the two systems, suggesting a low variance between the commercial and experimental DAQ systems. This suggests that, despite its simplicity, the experimental system is an effective and low-cost alternative for accurately measuring blast waves.

Exploring social networks through stochastic multilayer graph modeling

Several graph models are available today to model online social networks. These graph models are used to analyze the structural properties of the online social network, such as detecting communities, finding the influential spreader and predicting the behavior of the network. However, these models are based on deterministic single-layer graphs that may not be appropriate when online users use multiple social networks at the same time and social networks provide specific services. Moreover, because of the unknown and dynamic nature related to the behaviors and activities of online users, as well as structural and behavioral parameters, which may vary over time, stochastic multi-layer models could be applied to better capture and represent this phenomenon, as well as the dynamic nature of social networks. For example, in recommender systems, users' interests are unknown parameters and vary over time. Therefore, stochastic multilayer graph modeling can be used to develop recommender systems by considering different layers for different types of interests or preferences. In this paper, we propose a stochastic multilayer graph in which the edges are associated with random variables as a potential graph model for the analysis of online social networks. Therefore, after redefine some network measures related to stochastic multilayer graphs, we propose a Cellular Goore Game (CGG) based algorithm to computes the redefine network measures. A CGG-based algorithm computes defined network measures by learning automata from the edges of stochastic multilayer graphs. The experimental results show that the new CGG-based algorithm requires fewer samples from the edges of stochastic multilayer graphs than the standard sampling method in network measures calculation. Furthermore, the obtained results demonstrate that, from an evaluation perspective, the CGG-based algorithm provides superior results in terms of Kolmogorov-Smirnov (KS-test), Pearson Correlation Coefficient (PCC), Normalized Root Mean Square Error (NRMSE) and Kullback–Leibler divergence (KL-test).

Concurrent Validity and Reliability of Video-Based Approach to Assess Physical Function in Adults with Knee Osteoarthritis.

The aim of this study was to determine the concurrent validity and reliability of the Osteoarthritis Research Society International recommended performance-based tests delivered by video-based in adults with knee osteoarthritis. Thirty-two participants (aged 40 to 70 years; 15 men) undertook assessments of 4 performance-based tests via both video-based (real-time and recorded) and face-to-face approaches, on the same day. Outcome measures were performance-based test and the number of technical issues encountered. The performance-based tests included the 40-Meter Fast-Paced Walk Test (velocity, m/s), the 30-Second Chair Stand Test (number of repetitions), the Stair Climb Test (time, seconds), and the Timed "Up & Go" Test (TUG; time, seconds). The Bland-Altman limit of agreement measures, standard error of measurement (SEM), coefficient of variation (CV), minimal detectable changes (MDCs), and bias (mean difference) were employed to analyse the concurrent validity between video-based and face-to-face approaches of the performance-based tests. Reliability was measured using intraclass correlation coefficients (ICC), CV, and SEM. A high degree of concurrent validity for the TUG (bias = -0.22), the 30-Second Chair Stand Test (bias = -0.22), the Stair Climb Test (bias = -0.31), and the 40-Meter Fast-Paced Walk Test (bias = -0.06) was found. SEM and CV values were within the acceptable level for concurrent validity. There was a high degree of reliability demonstrated for all tests analysed. ICC measures ranged from 0.95 to 1.00 for intrarater reliability, and from 0.95 to 0.99 for interrater reliability. Video-based assessment of physical performance tests is a valid and reliable tool for measuring physical function among adults with knee osteoarthritis via Microsoft Teams (Microsoft Corp, Redmond, WA, USA). Video-based assessment is a promising public health tool to measure physical function in adults with knee osteoarthritis.

Nutritional response to water hyacinth (Eichhornia crassipes) challenges via blood biochemical profiles in goats and sheep

Eichhornia crassipes remains a significant threat to aquatic ecosystems and poses economic challenges globally. Interestingly, its high nutritional value and abundance in water bodies, making it a promising and cost-effective source of animal feed. The purpose of the study was to see how varying inclusion rates of E. crassipise affected the blood biochemical profiles of Doyogena sheep and Woyto-Guji goat local breeds. Twelve sheep and twelve goats were used in a 2*4 randomized crossover design with two species, four diets, and four phases (15-day adaptation plus 7-day experimental diets), and on the last day blood sample collected. The dietary treatments included E. crassipise (0, 25, 50, and 75%) as a substitute for commercial concentrate. The data were analyzed using SAS software tool PROC GLM, and Pearson's correlation coefficient between serum biochemical indices was computed. Results of AST, ALB, GLB, AST/ALT, and A/G showed significant (P < 0.0001), ALP (P < 0.005), and GLU (P < 0.05) differences between species of animals, except for ALT, CREAT, TP, and UREA. Sheep had higher values for AST, ALP, GLB, AST/ALT, CREAT, and UREA, except for A/G, ALB, ALT, and TP. Among treatments and treatment species interaction effect did not show variation in all studied parameters. Positive correlations were observed between ALT and AST, TP and ALB, and A/G and ALB, negative correlations were observed between ALT and AST/ALT, TP and A/G; GLB and A/G in sheep. Furthermore, positive correlations were observed between AST/ALT with ALT and AST and ALB with TP and A/G; however, negative correlations were observed between ALB with TP and A/G in goats. It was concluded that substituting E. crassipise with concentrate had no adverse effect on the serum biochemical profile.

Classifying alkaliphilic proteins using embeddings from protein language model

Alkaliphilic proteins have great potential as biocatalysts in biotechnology, especially for enzyme engineering. Extensive research has focused on exploring the enzymatic potential of alkaliphiles and characterizing alkaliphilic proteins. However, the current method employed for identifying these proteins that requires web lab experiment is time-consuming, labor-intensive, and expensive. Therefore, the development of a computational method for alkaliphilic protein identification would be invaluable for protein engineering and design. In this study, we present a novel approach that uses embeddings from a protein language model called ESM-2(3B) in a deep learning framework to classify alkaliphilic and non-alkaliphilic proteins. To our knowledge, this is the first attempt to employ embeddings from a pre-trained protein language model to classify alkaliphilic protein. A reliable dataset comprising 1,002 alkaliphilic and 1,866 non-alkaliphilic proteins was constructed for training and testing the proposed model. The proposed model, dubbed ALPACA, achieves performance scores of 0.88, 0.84, and 0.75 for accuracy, f1-score, and Matthew correlation coefficient respectively on independent dataset. ALPACA is likely to serve as a valuable resource for exploring protein alkalinity and its role in protein design and engineering.

A low-profile high-isolation endfire MIMO antenna based on spoof surface plasmon polaritons for X-band applications

This work proposes a three-port, low-profile, coplanar endfire multiple-input-multiple-output (MIMO) antenna based on spoof surface plasmon polaritons (SSPPs). The proposed antenna comprises a coplanar waveguide (CPW)-fed SSPPs transmission line (TL). A transition to transform the CPW quasi-TEM mode to the SSPPs mode. The radiator section with asymmetric discontinuities converts the bounded SSPPs waves into radiating waves. The proposed MIMO antenna exploits the inherent feature of high isolation in the SSPPs TL due to its strong field confinement competency. Wider beamwidths for ample azimuth and elevation coverage with a stable pattern in the endfire direction are achieved in the 9.52–11.23 GHz operating band. In the operating band, the MIMO antenna has an average realized gain of 6 dBi. Mainly, it has high isolation below -35 dB (measured) in the operating range, a maximum envelope correlation coefficient (ECC) of 1.5 × 10−3, and a total active reflection coefficient (TARC) value below 0.3. The measured results are closely matched with the simulated ones in terms of S-parameters, radiation pattern, ECC, TARC, and CCL.