Low Standard Error Research Articles

Reliable and accurate prediction of basic pK_a values in nitrogen compounds: the pK_a shift in supramolecular systems as a case study

This article presents a quantitative structure–activity relationship (QSAR) approach for predicting the acid dissociation constant (pK_a) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R^2) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK_a prediction, offering valuable insights for drug design and supramolecular system optimization.Graphical

Determining critical power and W′ in running: Accuracy of different two-point models using the power metric

This study aims to determine the validity of the critical power (CP) and the work capacity over CP (W′) obtained from different two-time trial combinations with respect a five-point model. In a 3-week training period, 15 athletes (age: 23 ± 5 years; height: 166 ± 6 cm; body mass: 58 ± 8 kg; 5 km season-best: 15:29 ± 00:53 mm:ss) performed five time-trials (i.e. 3, 4, 5, 10, 20 min) on a 400 m track, from which the mean power outputs were obtained through the Stryd Power Meter. An acceptable level of agreement was considered if the following criteria were met: low bias and standard error of the estimate (SEE) (<14 W [values corresponding to the ±5% of the mean CP]; W′: <2.0 kJ [values corresponding to the ±10% of the mean W′]), R2 > 0.90, and ICC > 0.81. The CP presented an acceptable SEE for CPwork (1.3 ± 0.5%) and CP1/time (2.7 ± 1.1%) when using the five time-trials. For both CP models, the 3–10 min was the shortest valid combination, whereas the 3–20, 4–20, and 5–20 min showed the greatest level of agreement. The W′ presented a high SEE for CPwork (14.1 ± 5.2%) and CP1/time (13.8 ± 6.2%) when using the five time-trials, therefore, none of the two time-trials combinations were considered. The CP parameter can be accurately estimated from different two time-trial combinations, whereas none reached an acceptable level of accuracy for the determination of W′.

The Influence of Transcranial Magnetic Stimulation Interpulse Interval Duration on Knee Extensor Corticospinal Excitability.

Background/Purpose: To examine the influence of interpulse interval duration on knee extensor corticospinal excitability. Methods: Seventeen college-aged males and females participated in a single laboratory visit, during which 25 single transcranial magnetic stimulation pulses were delivered to the motor cortex with interpulse intervals of 5, 10, 15, and 20 sec. Surface electromyographic signals were detected from the dominant vastus lateralis and rectus femoris. Motor evoked potential amplitude was compared across the four conditions. Results: For the vastus lateralis, the Friedman test indicated significant differences among conditions (chi-squared [3] = 7.80, p = 0.050); however, there were no pairwise differences (p ≥ 0.094) and small effect sizes (d ≤ 0.269). For the rectus femoris, the Friedman test results showed no significant differences among conditions (chi-squared [3] = 2.44, p = 0.487). Across all muscles and conditions, low intraclass correlation coefficients and high standard errors of measurement were suggestive of poor reliability. Conclusion: Unlike resting hand muscles, interpulse interval duration has little influence on corticospinal excitability for the knee extensors during active contractions.

Inclusion of Control Data in Fits to Concentration-Response Curves Improves Estimates of Half-Maximal Concentrations.

Concentration-response curves, in which the effect of varying the concentration on the response of an assay is measured, are widely used to evaluate biological effects of chemical compounds. While National Center for Advancing Translational Sciences guidelines specify that readouts should be normalized by the controls, recommended statistical analyses do not explicitly fit to the control data. Here, we introduce a nonlinear regression procedure based on maximum likelihood estimation that determines parameters for the classical Hill equation by fitting the model to both the curve and the control data. Simulations show that the proposed procedure provides more precise parameters compared with previously prescribed practices. Analysis of enzymatic inhibition data from the COVID Moonshot demonstrates that the proposed procedure yields a lower asymptotic standard error for estimated parameters. Benefits are most evident in the analysis of the incomplete curves. We also find that Lenth's outlier detection method appears to determine parameters more precisely.

The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration.

This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied. Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation. The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect. Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.

A Categorical Modelling Approach to Site and Growth of Eucalyptus Stands in Brazilian Eastern Amazon

Theoretical framework: Site index cannot be generalized to different eucalyptus clonal stands, since each clone has a distinct growth and yield pattern, in which categorical variables may add site-specific effects to assess model's interregional variability. 
 
 Objective: This study aimed to assess the statistical performance of site index, as well as growth and yield models in different configurations adding categorical variables. 
 
 Method: The study was carried out in eucalyptus stands in Eastern Brazilian Amazon with three clones of different ages and a different number of trees. Traditional Schumacher’s site model was fitted with the addition of categorical clone variable. Beck-Della Bianca’s model was fitted by ordinary least squares (OLS) and two-stage least squares (2SLS), adding dominant height as site variable and including clone variable. 
 
 Results and discussion: Schumacher’s clone model presented lower standard estimate error (9.50%) and higher adjusted coefficient of determination (0.61), correcting the lack of normality and homoscedasticity. 2SLS was more accurate than OLS for Beck-Della Bianca’s model. This model validation resulted in root-mean-squared error of 2.82% and bias of 0.03%. 
 
 Research implications: Additive and multiplicative effects on site index resulted in polymorphism. Clone variable provided more parsimonious and accurate models to estimate site index and forest growth and yield, in which 2SLS was recommended for forest prognosis.

Prediction of Half-Marathon Power Target using the 9/3-Minute Running Critical Power Test

Running power output allows for controlling variables that have been previously overlooked by relying solely on speed, such as surface, gradient and weight. The ability to measure this external load variable now enables the analysis of concepts that have predominantly been studied in cycling, such as the Critical Power (CP), in the context of running. This study aims to predict the CP target at which trained athletes run a half-marathon and determine whether races of this distance can serve as a valid alternative to update the CP record. A group of nine trained athletes performed the 9/3-minute Stryd CP test and participated in a half-marathon race in two separate testing sessions conducted in the field. The average power during a half-marathon race is a valid alternative method for determining the CP in trained athletes, as evidenced by the agreement (95% CI: -0.11 to 0.37 W/kg) and trivial systematic bias (0.13 W/kg) between methods. The linear regression model half-marathon power = 0.97 + 0.75·CP (W/kg) showed low standard error of estimate (0.29 W/kg) and significant large association between methods (r = 0.88; p = 0.002). Coaches and athletes should be aware that the CP target for a half-marathon race is 97.3% of the CP determined by the 9/3-minute Stryd CP test.

Prediction of Half-Marathon Power Target using the 9/3-Minute Running Critical Power Test.

Running power output allows for controlling variables that have been previously overlooked by relying solely on speed, such as surface, gradient and weight. The ability to measure this external load variable now enables the analysis of concepts that have predominantly been studied in cycling, such as the Critical Power (CP), in the context of running. This study aims to predict the CP target at which trained athletes run a half-marathon and determine whether races of this distance can serve as a valid alternative to update the CP record. A group of nine trained athletes performed the 9/3-minute Stryd CP test and participated in a half-marathon race in two separate testing sessions conducted in the field. The average power during a half-marathon race is a valid alternative method for determining the CP in trained athletes, as evidenced by the agreement (95% CI: -0.11 to 0.37 W/kg) and trivial systematic bias (0.13 W/kg) between methods. The linear regression model half-marathon power = 0.97 + 0.75·CP (W/kg) showed low standard error of estimate (0.29 W/kg) and significant large association between methods (r = 0.88; p = 0.002). Coaches and athletes should be aware that the CP target for a half-marathon race is 97.3% of the CP determined by the 9/3-minute Stryd CP test.

Multi-objective design of the energy storage-based combined heat and power off-grid system to supply of thermal and electricity consumption energies

Simultaneous consumption of electrical and thermal energy management, together with economic and technical advantages of close-to-consumption feeding of consumers, has demonstrated the importance of multi-carrier energy systems planning in recent years. Therefore, this paper presents the fuzzy decision-based multi-objective planning of an islanded hybrid system (IHS) with renewable energy sources (RESs), combined heat and power system (CHP), boiler, electric and thermal energy storage systems (ESSs) to provide a clean energy for electric and thermal consumers. Scheme is responsible for minimizing total weighted annual operating costs and pollution consumption of system. It is subjected to planning-operation constraints of sources and storages based on the electrical and thermal energy management. To obtain optimal solution with a low standard deviation error, a hybrid algorithm based on ant-lion optimizer and krill herd optimization is developed. Novelties include extraction of IHS with electric and thermal sources and ESSs, provision of clean energy to electrical and thermal consumers, extraction of scheme based on hybrid algorithm. By implementing scheme on data of Rafsanjan city in Iran and investigating numerical results, ability of design to obtain optimal hybrid combination of sources and storages to supply electric and thermal consumers from an economic and environmental point of view is confirmed. So that storage system (especially thermal storage) is able to reduce the planning cost and pollution level about to 1 %–6 % in compared with case including sources to supply thermal and electricity consumption energies. The aforementioned solution algorithm is able to extract the most optimal solution compared to non-combined algorithms and the standard deviation of its response is around 0.96. Fuzzy decision-making has obtained an optimal point for the aforementioned objective functions, which are about 20 % to 23 % away from their minimum value.

Biomechanical evaluation of additively manufactured patient-specific mandibular cage implants designed with a semi-automated workflow: A cadaveric and retrospective case study

ObjectiveMandibular reconstruction using patient-specific cage implants is a promising alternative to the vascularized free flap reconstruction for nonirradiated patients with adequate soft tissues, or for patients whose clinical condition is not conducive to microsurgical reconstruction. This study aimed to assess the biomechanical performance of 3D printed patient-specific cage implants designed with a semi-automated workflow in a combined cadaveric and retrospective case series study. MethodsWe designed cage implants for two human cadaveric mandibles using our previously developed design workflow. The biomechanical performance of the implants was assessed with the finite element analysis (FEA) and quasi-static biomechanical testing. Digital image correlation (DIC) was used to measure the full-field strains and validate the FE models by comparing the distribution of maximum principal strains within the bone. The retrospective study of a case series involved three patients, each of whom was treated with a cage implant of similar design. The biomechanical performance of these implants was evaluated using the experimentally validated FEA under the scenarios of both mandibular union and nonunion. ResultsNo implant or screw failure was observed prior to contralateral bone fracture during the quasi-static testing of both cadaveric mandibles. The FEA and DIC strain contour plots indicated a strong linear correlation (r = 0.92) and a low standard error (SE=29.32με), with computational models yielding higher strain values by a factor of 2.7. The overall stresses acting on the case series’ implants stayed well below the yield strength of additively manufactured (AM) commercially pure titanium, when simulated under highly strenuous chewing conditions. Simulating a full union between the graft and remnant mandible yielded a substantial reduction (72.7±1.5%) in local peak stresses within the implants as compared to a non-bonded graft. ConclusionsThis study shows the suitability of the developed semi-automated workflow in designing patient-specific cage implants with satisfactory mechanical functioning under demanding chewing conditions. The proposed workflow can aid clinical engineers in creating reconstruction systems and streamlining pre-surgical planning. Nevertheless, more research is still needed to evaluate the osteogenic potential of bone graft insertions.

How Do Different Weighting Methods Affect the Overall Effect Size in Meta-Analysis? : An Example of Science Attitude in Türkiye Sample

There is increasing interest in meta-analysis in different fields due to the need to combine the results of primary research. One of the crucial concepts in combining results is weighting. This study examines how Hunter and Schmidt's method, weighting by sample size; Hedges and Vevea's method, weighting by inverse variance; and Osburn and Callender's method, unweighting, affect the overall effect size in meta-analysis. In this context, for meta-analysis, the search was done for studies examining the effects of alternative measurement and assessment techniques and methods in science education on science attitudes. The databases of the HEI National Thesis Center, Web of Science, ERIC, EBSCO, Google Scholar, and DergiPark were searched between 2010 and 2021. Eleven studies (with 14 effect sizes) that met the criteria were included in the meta-analysis. In line with the study's findings, it was observed that the overall effect sizes were significant and did not change much in the weighting methods. Besides, it was found that the method with the lowest standard error was unweighted. The weighting methods of Hunter and Schmidt and Hedges and Vevea gave similar results in terms of standard error. When the correlation coefficient between the weighting methods was examined, it was seen that all correlation coefficients were greater than 0.90.

Reduction and Adsorption of Hexavalent Chromium by Iron Sulfide Loaded Biochar: Isotherms, Kinetics and Thermodynamics

AbstractThe removal of noxious Cr(VI) from water has gained worldwide attention. In this study, biochar derived from macadamia nut shells (MHP) was embedded with iron sulfide (FeS) nanoparticles produced through a solid‐phase reduction of pyrite. The MHP‐FeS composite was evaluated for the adsorption of Cr(VI) and its subsequent reduction to Cr(III). The scanning electron microscope images revealed that the particles were of irregular shape, while the energy dispersive spectroscopy results showed the existence of Fe, S, O, and C atoms. X‐ray diffraction peaks at 2=29.8°, 33.7°, 43.7°, and 53.1° were characteristic of the formation of FeS nanoparticles. A maximum Cr(VI) adsorption capacity of 4.95 mg/g was obtained at pH 3, 60 min, and 25 °C. The higher correlation coefficient (R2>0.873) and lower residual standard error (RSE=1.43) values pointed to the adsorption process favoring Langmuir and pseudo‐second‐order models. As per thermodynamic reaction calculations, the remediation process was spontaneous and endothermic. With further optimization, this material could be a potential adsorbent and reductant for Cr(VI) in water.

Bayesian Network Design for Crude Palm Oil (CPO) Price Prediction Driven by Fluctuation Patterns and Trends

Crude Palm Oil (CPO) becomes the alternative to petroleum because it can be refined into different products, such as biodiesel. The price of crude palm oil can be considered the future direction of the world economy. CPO prices are based on market demand and the oil supply from the CPO producer, where they fluctuate and have a huge influence on economy. The price pattern of CPO requires a variety of factors to predict accurately. It needs a lot of data analytics to predict and respond in quick time to the highly variable CPO market. In this work, a dynamic probability model is proposed to predict CPO prices using the Bayesian rule. The rule is formed to be effective in responding to changeable market demands and supplies. It formulates the approximation factors for indicating the expected price of the CPO for 12 months. The dynamic training data has been modelled to fit the Bayesian rule. The process of Prediction and hypothesizing price fluctuations is identified according to several level of Bayesian rule. It is then used to generate a measure of deviation between the actual and probable prices. Our Bayesian-based prediction pricing model is able to predict the price pattern that are comparable to existing benchmark data and shows a lower standard error of regression. The prediction approach can help the traders have a better analysis of price fluctuations in CPO demand and supply. Using Bayesian probability not only improves prediction rules, but it can also forecast CPO trades in high fluctuation situations.

Improved Correlations for the Unstretched Laminar Flame Properties of Mixtures of Air with Iso-octane and Gasoline Surrogates TRF86 and TRF70

<div>Laminar flame properties embody the fundamental information in flame chemistry and are key parameters to understanding flame propagation. The current study focuses on two parameters: the unstretched laminar flame speed (LFS) and <i>ϕ<sub>m</sub> </i> (the equivalence ratio at which the LFS reaches its maximum). Most existing correlations for LFS are either only applicable within a narrow range of conditions or built on a large number of coefficients. Few correlations are available for <i>ϕ<sub>m</sub> </i>. Thus, the objectives of the current study are to provide accurate, while concise, correlations for both properties for a wide range of working conditions in internal combustion (IC) engines, including dilution effects. The original results were obtained for iso-octane and gasoline surrogates from one-dimensional (1D) simulations for a range of 300–950 K for unburned temperature, 1–120 bar for system pressure, 0.6–1.4 for equivalence ratio, and 0–0.5 for diluent mass fraction, and then were correlated using an improved power law method and an improved Arrhenius form method. Comparisons with the literature show that the predicted LFSs from both methods and <i>ϕ<sub>m</sub> </i>s are close to the experimental measurements for a wide range of conditions. The predicted dilution factor has a similar trend with others, but fewer coefficients are needed. Overall, the improved Arrhenius form is recommended to calculate the LFS for future use, considering its lower standard errors. The experimental measurements at very high temperatures and pressures are limited, and thus the predictions under these conditions need further validation.</div>

Intra- and inter-rater reliability, agreement, and minimal detectable change of the handheld dynamometer in individuals with symptomatic hip osteoarthritis.

The handheld dynamometer has been validated to measure muscle strength in different muscle groups. However, to date, it has not been tested in individuals who experience pain induced by hip osteoarthritis. The current study aimed to evaluate the intra- and inter-rater reliability, agreement, and minimal detectable change of the Lafayette handheld dynamometer, model 1165, to assess the peak force (Pk) and average peak force (Af) of hip muscles in individuals with symptomatic hip osteoarthritis. Twenty participants with hip osteoarthritis (mean ± SD age: 58.7±15.3 years; body mass index: 28.8±4.2 kg/m2) and pain intensity on the Visual Analogue Scale ≥ 4 (8.05±1.2) were recruited to participate in this study. Pk and Af of hip flexors (seated position), abductors and adductors (supine position), and extensors (prone position) were collected in a single day by two independent raters, each one obtaining test and retest in randomly ordered separate sessions. The intra-rater intraclass correlation coefficient (ICC) was classified as good (>0.75) or excellent (≥0.90) for all muscle groups and all inter-rater ICCs were classified as excellent. Rater A had a lower standard error of measurement compared to rater B, ranging from 0.15 to 0.58 kilogram-force (Kgf) compared with 0.34 to 1.25 kg, respectively. However, the inter-rater comparison showed a minimal detectable change (MDC) of < 10% for all Pk and Af measures for hip adductors and extensors. Finally, the inter-rater Bland-Altman analysis demonstrated good agreement for abductors, adductors, and extensors. Despite pain and dysfunction related to hip osteoarthritis, the mean of two measures using a handheld dynamometer was shown to be a reliable tool to assess hip muscle strength, with good to excellent intra- and inter-rater ICCs, satisfactory agreement, and small values for MDC.

Genetic and genomic evaluation for resistance to white spot syndrome virus in post-larvae of Pacific white shrimp (Litopenaeus vannamei)

The objective of the present study was to compare genetic evaluations with pedigree- and genomic-based for the time of death of post-inoculation (TD) and survival considered as a binary variable (1 = survivor, 0 = dead) (BS) to white spot syndrome obtained in a controlled experimental challenge. In seven tanks were seed a total 6132 shrimps, of which the genotypes of 1685 were obtained using a commercial 50 K SNP panel. A total of 176 families were formed from a Sire: Dam ratio of 1:2. In the challenge test, the organisms were inoculated per os with macerated shrimp tissue with a viral load of 1 × 106 copies/g of tissue. The inoculum was supplied in two doses (first of 6% and second of 4% of the biomass in each tank) whit 12 h of difference. Four approaches were used for estimating variance components and calculating genetic or genomic estimated breeding values: 1) PBLUP-1 is an animal model fitted using a pedigree-based kinship matrix using only parent-progeny relationships from individuals from the latest generation; 2) PBLUP-All is the same as above but using the genealogy from 19 generations; 3) GBLUP is an animal model fitted using a genomic-based relationship matrix including genotyped animals only, and 4) ssGBLUP is an animal model fitted using a blend between the pedigree and genomic relationship matrix. The heritability values obtained for TD and BS with the four approaches ranged from 0.072 to 0.105 and 0.069 to 0.095, respectively. The heritability estimators obtained with genomic information were slightly higher than those obtained with only pedigree information. PBLUP-All and ssGBLUP present proportionally lower standard errors than PBLUP-1 and GBLUP. The use of genomic information (GBLUP and ssGBLUP) increased the mean accuracy of estimated breeding values by an average of 38% and 54% concerning methods that only used phenotypic information (PBLUP-1 and PBLUP-ALL). Using a 50 K SNP genotyping panel and deep pedigree information increased heritability estimation of traits associated with resistance to white spot syndrome virus in Pacific white shrimp. Using genomic information substantially improves the accuracy of estimated breeding values when using ssGBLUP compared to pedigree-based methods. Genomic selection will allow individual selection within-family by predicting the genetic values of animals not exposed to the white spot syndrome virus.

Optimized Design and Fabrication of Polyethylene Glycol 1000/Polyamide 6 (PEG1000/PA6) Nanofibers for Phase Change Materials (PCMs) Application

Ultrafine phase change nanofibers based on polyethylene glycol 1000 (PEG1000) as phase change material (PCM) and polyamide 6 (PA6) as a supporting material were prepared in a systematic manner planned by the Design-Expert® software using the uniaxial electros-pinning. Research surface methodology (RSM) was carried out to optimize the parameters and conditions leading to minimize the fiber diameter. The effect of PEG content, applied voltage, needle gauge, and flow rate on the fiber characteristics was studied by a central composite design (CCD). The minimum diameter of nanofibers was predicted by a quadratic model to be 64.33 nm and the actual fibers diameter prepared under optimal condition showed a very low relative standard error (RSE). It was shown that the PEG/PA6 mass ratio has the dominant effect on the fibers diameter. The results from FTIR and FE-SEM images confirmed well encapsulated PEG in PA6 and no leakage and morphology alterations were observed after heating tests. To further investigate morphological structure and the quality of PEG1000 encapsulation in PA6 matrices, the composite fibers underwent a solvent treatment using ethanol. The results proposed a new innovative method to control operational electrospinning conditions for encapsulating phase change materials in polymer matrices which is very important in thermal energy saving/retrieving applications.

From purple corn waste (pericarp) to polyphenol-rich extract with higher bioactive contents and superior product qualities using two-step optimization techniques

The pericarp of purple corn has traditionally been thought of as being a byproduct. However, it is an excellent source of high-value polyphenols (such as anthocyanins, proanthocyanins, etc.) that could be extracted and used as nutraceuticals, cosmeceuticals, and valuable starting material for synthesizing food colorants and food additives. Hence, this study aimed to optimize the recovery of anthocyanins, phenolic acids, and condensed tannins (CT) from purple corn pericarp using a two-stage multivariate design and evaluated the phenolic profiles, antioxidants, product quality, and structural characteristics of the recovered extracts. According to Plackett-Burman design (PBD) screening, the key extraction variables identified were acetic acid concentration, extraction time, and ethanol concentration. This was followed by optimization using Box-Behnken design (BBD), facial central composite design (FCCD), and full-factorial design (FFD), and the best design was identified after comparative analysis. The models were found to be acceptable, as evidenced by the desirability values of 0.836–0.933. A comparison of the BBD, FCCD, and FFD was done, and it was found that the PBD-BBD produced a lower residual standard error (RSE) than the PBD-FCCD for most of the responses and yielded the lowest extraction time (∼8.6 h), proving that the BBD could forecast the data more accurately. To evaluate the extracts' bioactive extraction efficiency, antioxidant properties, product quality, and structural parameters, samples from a single factor (SF) experiment and BBD were compared. The BBD extract had better color properties (lower ΔE, higher a*, C, and H0) and a greater extraction yield (17.5%) than SF and was confirmed by the principal component and hierarchical cluster analysis. Although the scanning electron microscopy and the optical profilometer for BBD revealed irregular, loosely arranged granules, it had a less adverse effect on product quality and aligned with the Fourier transform infrared spectroscopy results. The BBD samples had lower levels of phenolic acids but higher levels of total phenolic content (118.86 ± 3.60 g GAE/kg), condensed tannins (231.74 ± 8.45 g EE/kg), antioxidants, cyanidin-3-glucoside (90.46 ± 4.47 mg/g), naringin (7.68 ± 0.58 mg/g), and cyanidin-chloride (49.81 ± 1.78 mg/g). This shows that BBD can effectively be used to optimize the extraction of polyphenolic compounds from purple corn pericarp using optimal extraction conditions, resulting in a higher yield of polyphenols having superior antioxidant properties and product quality in a comparatively shorter extraction time.

Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry.

Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its preparation involves several unit operations, including fermentation, which is essential in gari production. Fermentation brings about specific biochemical changes in cassava starch due to the actions of lactic acid bacteria. Consequently, it gives rise to organic acids and a significant reduction in the pH. Consumer preferences for gari are influenced by these changes and impact specific functional characteristics, which are often linked to cassava genotypes. Measurement of these functional characteristics is time-consuming and expensive. Therefore, this study aimed to develop high-throughput and less expensive prediction models for water absorption capacity, swelling power, bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy (NIRS). Gari was produced from 63 cassava genotypes using the standard method developed in the RTB foods project. The prediction model was developed by dividing the gari samples into two sets of 48 samples for calibration and 15 samples as the validation set. The gari samples were transferred into a ring cell cup and scanned on the NIRS machine within the Vis-NIR range of 400-2,498nm wavelength, though only the NIR range of 800-2,400nm was used to build the model. Calibration models were developed using partial least regression algorithms after spectra pre-processing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. Results showed an excellent coefficient of determination in calibrations (R2 Cal) of 0.99, 0.97, 0.97, and 0.89 for bulk density, swelling power, dispersibility, and water absorption capacity, respectively. Also, the performances of the prediction models were tested using an independent set of 15 gari samples. A good prediction coefficient (R2 pred) and low standard error of prediction (SEP) was obtained as follows: Bulk density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and solubility index (0.62), respectively. Therefore, NIRS prediction models in this study could provide a rapid screening tool for cassava breeding programs and food scientists to determine the food quality of cassava granular products (Gari).

Level of Agreement between the MotionMetrix System and an Optoelectronic Motion Capture System for Walking and Running Gait Measurements

Markerless motion capture systems (MCS) have been developed as an alternative solution to overcome the limitations of 3D MCS as they provide a more practical and efficient setup process given, among other factors, the lack of sensors attached to the body. However, this might affect the accuracy of the measures recorded. Thus, this study is aimed at evaluating the level of agreement between a markerless MSC (i.e., MotionMetrix) and an optoelectronic MCS (i.e., Qualisys). For such purpose, 24 healthy young adults were assessed for walking (at 5 km/h) and running (at 10 and 15 km/h) in a single session. The parameters obtained from MotionMetrix and Qualisys were tested in terms of level of agreement. When walking at 5 km/h, the MotionMetrix system significantly underestimated the stance and swing phases, as well as the load and pre-swing phases (p &lt; 0.05) reporting also relatively low systematic bias (i.e., ≤ −0.03 s) and standard error of the estimate (SEE) (i.e., ≤0.02 s). The level of agreement between measurements was perfect (r &gt; 0.9) for step length left and cadence and very large (r &gt; 0.7) for step time left, gait cycle, and stride length. Regarding running at 10 km/h, bias and SEE analysis revealed significant differences for most of the variables except for stride time, rate and length, swing knee flexion for both legs, and thigh flexion left. The level of agreement between measurements was very large (r &gt; 0.7) for stride time and rate, stride length, and vertical displacement. At 15 km/h, bias and SEE revealed significant differences for vertical displacement, landing knee flexion for both legs, stance knee flexion left, thigh flexion, and extension for both legs. The level of agreement between measurements in running at 15 km/h was almost perfect (r &gt; 0.9) when comparing Qualisys and MotionMetrix parameters for stride time and rate, and stride length. The agreement between the two motion capture systems varied for different variables and speeds of locomotion, with some variables demonstrating high agreement while others showed poor agreement. Nonetheless, the findings presented here suggest that the MotionMetrix system is a promising option for sports practitioners and clinicians interested in measuring gait variables, particularly in the contexts examined in the study.