Efficient Test Research Articles

Interrogating post-transplant donor HLA-specific antibody characteristics and effector functions using clinical bead assays

Single antigen bead (SAB) assays are the most common and sensitive method used to detect and monitor post-transplant donor specific HLA antibodies (DSA). However, a direct comparison across traditional and modified SAB assays to improve routine DSA monitoring using pre-treated IgG sera to eliminate interference has not been performed. We performed a technical comparison of 251 post-transplant DSA from n = 91 serum samples tested neat (pre-treated, undiluted), at a single 1:16 dilution, in the C1q bead assay, and for IgG subclasses (IgG1, IgG2, IgG3, IgG4) with IgG-enriched sera. We found that DSAs that are detectable by 1:16 dilution and/or C1q are associated with higher IgG MFI values and results could be predicted by testing neat sera. DSA detected at 1:16 dilution correlated with >7000 IgG MFI in neat sera and identified DSA that exceeded the SAB linear range for semiquantitative measurements. C1q positive DSA correlated with >15,000 IgG MFI in neat sera. C1q binding correlated most strongly with total IgG MFI (Spearman r = 0.82, p = 0.002) and not specific subclasses, demonstrating that DSA C1q binding capacity in this cohort is driven by HLA-specific IgG concentration. Evaluation of engineered pan-HLA class I-specific human IgG1 and IgG2 subclass monoclonal antibodies by SAB C1q and C3d assays revealed that IgG2 antibodies can bind complement at higher concentrations. The strengths and limitations of modified SAB assays must be considered to optimize efficient testing and accurate clinical interpretation.

Long-distance hand tractor controlled by android system in Wetland Field

Modernization of agricultural practices is essential to overcome challenges such as decreasing agricultural labor and decreasing agricultural land in Indonesia. This research examines the development and performance of a hand tractor that is controlled remotely via an Android-based system using the NodeMCU ESP8266 module and a WiFi network. This study focuses on the technical and operational performance of hand tractors in wetlands. Modifications were made to the tractor, by installing three DC motors to control the clutch and gas levers. Tests were carried out on the control system accuracy and response time to assess the system's suitability for field operations. The research results show that the Android system has 100% accuracy in executing all commands given, but the response time decreases as the distance between the Android device and the WiFi module increases, with an average delay reaching 6,8 seconds for a maximum 100 m distance. Field efficiency testing shows that a tractor with Android control has a work efficiency of 70.48% in the plowing process and 75.33% in the harrowing process, lower than with manual control which reaches an efficiency of more than 95%. Despite response delays and reduced efficiency, the system is an important step toward agricultural mechanization, especially in labor-intensive wetlands.

Enhancing integration testing efficiency through AI-driven combined structural and textual class coupling metric

Integration testing, a critical and resource-intensive phase in the software development lifecycle, can account for up to a high percentage of the total testing cost. Identifying classes with high coupling is crucial for efficient integration testing, as these classes are more susceptible to the impact of maintenance-related changes. This research introduces a novel metric called <i>combined structural and textual class coupling</i> (CSTCC), which harnesses the power of artificial intelligence (AI) techniques to predict and rank the most critical classes in an object-oriented software system. CSTCC integrates structural coupling metrics with <i>latent semantic indexing</i> (LSI)-based textual coupling, providing a comprehensive measure of class coupling. LSI, an information retrieval technique, analyses the semantic relationships between classes based on their textual content, enabling CSTCC to capture both structural and conceptual dependencies, resulting in a more accurate identification of high-risk classes. The effectiveness of the proposed approach is rigorously evaluated using mutation testing on four Java open-source projects, and the results demonstrate that test cases developed based on CSTCC achieve high mutation scores, indicating their ability to detect a significant percentage of integration faults. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The results demonstrate that test cases developed based on CSTCC achieve high mutation scores, ranging from 98% to 100%, indicating their ability to detect a significant percentage of integration faults. Additionally, the approach results in substantial efficiency gains, with a notable reduction in the number of test cases needed, saving up to 33.3% of the testing effort in some cases. By focusing testing efforts on high-coupling classes identified by CSTCC, developers can potentially save time and cost during integration testing. The CSTCC metric provides a novel and effective approach to prioritize testing resources and improve the efficiency of integration testing in object-oriented software systems.

Computational turf model validation with Clegg Impact Soil Tester experimental impacts on a wide range of synthetic turf constructions

The use of synthetic turf has been increasing in professional sports, where evaluation of novel turf designs for mechanical response, consistency, and athlete playability currently requires time-consuming and complex physical testing on physical turf, and more efficient drop-weight impact testing. A variety of drop tests are used to measure the impact response and surface stiffness of synthetic turf, including the Clegg Impact Soil Tester (CIST). Finite element computational models offer an opportunity to assess new turf designs prior to the construction of physical turfs but require validation for a range of turf constructions. In the present study, 14 different synthetic turf designs with varying infill and fibers were constructed and experimentally evaluated using the CIST device. Peak accelerations and acceleration-versus-time responses were measured. The turf constituent materials were mechanically characterized, FE models of all the turfs were created, and the CIST tests were simulated. The peak acceleration from the simulation followed the experimental trend for the wide range of turf designs, with an average difference of 9.7%. When considering conventional turf constructions, the average difference in peak acceleration was 8.8%, indicating the models provided a good prediction of turf impact response and can be used to assess new turf designs virtually, potentially reducing design times and physical test requirements.

Effective Determination of Diosmin Using Nitrogen Doped Carbon Dots as Probe Based on Internal Filtering Effect.

The establishment of a convenient and efficient testing method is crucial and needed for the monitoring of diosmin. In this study, nitrogen doped carbon dots (N-CDs) with the particle size distribution of 2.5-5.7nm and the average diameter of 4.1nm were successfully synthesized using a simple strategy. N-CDs exhibited excellent and stable fluorescence performance with the quantum yield of 22.33%. Correspondingly, a fluorescence analytical method was developed for diosmin determination using N-CDs as probe. UV-vis absorbance spectroscopy and the evaluation of internal filtering parameters verified that the mechanism causing the quenching of N-CDs was an internal filtering effect (IFE). The concentration of diosmin can be directly evaluated based on the quenched fluorescence intensities. After optimizing experimental conditions, it was found that the fluorescence quenching efficiency ((F0-F)/F0) of N-CDs exhibited a good linear relationship with the concentration of diosmin (CDiosmin) in the range of 3.0-50µg/mL. The limit of detection (LOD) was 0.86µg/mL based on 3σ/slope (n = 13). This method was successfully applied to accurately determine the content of diosmin in diosmin tablets and human plasma samples with good reproducibility. It stands out for its simplicity, speed, and acceptable determination performance.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

Genetic Perspectives on Feed Event, Meal and Feed Efficiency Traits in Bos taurus indicus Beef Cattle.

Electronic feeders record feeding behaviour as feed events by tracking the animal's in-out visits to the feeder. Another way to measure feeding behaviour is based on meals. However, the two approaches provide different outcomes. The objectives of this study were to estimate genetic parameters (heritabilities and genetic and phenotypic correlations) for feed event and meal traits, and their genetic and phenotypic correlations with feed efficiency traits in Nellore cattle. The present study analysed six feed event traits (DMIFE: dry matter intake per feed event, FED: feed event duration, TBFE: time between feed events, FTd: feeding time per day, FEd: feed events per day, and FR: feeding rate), six meal traits (DMIME: DMI per meal, MED: meal duration, TBME: time between meals, MC: meal criterion, MTd: meal time per day, and MEd: meals per day), and three feed efficiency traits (ADG: average daily gain, DMI, and RFI: residual feed intake). The traits were measured in feed efficiency tests of Nellore cattle (age = 280 ± 41 days and body weight = 258 ± 47 kg at enrolment). The MC was calculated for each animal and ranged from 1.70 to 64.0 min, i.e., any pair of feed events separated by less than the MC value was considered part of the same meal. The heritabilities and correlations were estimated by fitting univariate and bivariate animal models, respectively, using single-step genomic BLUP. The highest heritabilities for feed event traits were 0.35 ± 0.06 (FED), 0.39 ± 0.06 (FTd), and 0.50 ± 0.05 (FTd), and for meal traits were 0.31 ± 0.06 (MED) and 0.45 ± 0.06 (MTd). The genetic correlation between feed event traits and meal traits were weak. FR, FED, and FTd had moderate genetic correlations with RFI (-0.56 ± 0.11, 0.44 ± 0.11, 0.60 ± 0.08, respectively). These results indicate that more efficient animals spent less time at the feeder per feed event and per day, and eat faster compared to less efficient animals. In conclusion, feed event and meal traits must be treated as distinct groups of traits since the genetic and phenotypic correlations were, in general, weak to moderate. Among feed event versus meal traits, feed event traits are more favourable to explain the genetic relationships of feeding behaviour with feed efficiency-related traits.

Evaluation of Website Performance and Usability Using GTMetrix, Usability Testing, and System Usability Scale (SUS) Methods

This study was conducted to measure the performance of IAIN Metro's website in terms of performance and user perception to ensure that the campus website can adequately support visitors' needs. This study aims to determine things that need to be improved to improve the performance of the IAIN Metro website. To get comprehensive results from the performance of the website, this research uses GTMetrix to analyse the technical performance of the IAIN Metro website, and then, to test user perceptions, researchers use Usability Testing and System Usability Scale methods. In usability testing, several aspects will be measured to determine usability problems, namely learnability and efficiency, while the System Usability Scale questionnaire will be used to test the satisfaction level. Based on the test results using GTMetrix, after testing, several aspects of the access speed of the IAIN Metro website need to be improved. Although, in general, from the test results, Usability Testing and System Usability Scale users still consider the performance of the website to be acceptable, the results of the first task on Time Based Efficiency testing show that initial access to the main page metrouniv.ac.id, takes a relatively long time compared to other tasks. This is also evident from the GTMetrix score on the performance aspect, which has a low presentation of 25%. Therefore, optimisation is needed on the main page to improve website performance.

Translation Efficiency Test Using Polysome Profiles Under Heat Stress.

Translational control of different genes under heat stress is a critical step for plant adaptation to the environment. Assessing the translational activities of various genes can help us understand the molecular mechanisms underlying plant resilience, contributing to the development of crops with enhanced stress tolerance in the face of global climate change. This paper presents a detailed methodology for assessing translation efficiency through polysome profiling in plants exposed to heat stress. The procedure is divided into three parts: heat stress treatment for Arabidopsis, translation efficiency test using polysome profiles, and calculation of translation efficiency by isolating non-polysomal and polysomal RNA based on the profile. In the first part, Arabidopsis plants are subjected to controlled heat stress conditions to mimic environmental challenges. The treatment involves exposing the plants to high temperatures for specified durations, ensuring consistent and reproducible stress induction. This step is crucial for studying the plant's physiological and molecular responses to heat stress. The second part involves the translation efficiency test using polysome profiling. Polysomes are extracted through sucrose gradient centrifugation, which separates mRNAs based on ribosomal loading. This allows for the examination of ribosome occupancy on mRNAs, providing insights into the translational control mechanisms under stress conditions. In the third part, RNA is isolated from both polysomal and non-polysomal fractions. Spike-in RNA is used to accurately measure the amount of RNA in each fraction. The calculation of translation efficiency is performed by comparing the distribution of mRNAs across these fractions under normal and heat stress conditions. The translation activities of specific genes are further assessed by performing quantitative real-time PCR (qRT-PCR) with ribosome-associated RNA and total RNA. This methodology focuses exclusively on the effects of heat stress, providing a detailed protocol for analyzing translational regulation in plants.

One-step copper deposition-induced signal amplification for multiplex bacterial infection diagnosis on a lateral flow immunoassay device

The lateral flow immunoassay (LFIA) is predominant in rapid diagnostic tests owing to its cost-effectiveness and operational simplicity. However, the conventional LFIA exhibits limited sensitivity and is susceptible to human variance for the result readout, impacting result interpretation. In this study, we introduced a novel one-step copper deposition-induced signal amplification lateral flow immunoassay (osa-LFIA) that markedly enhances the detection sensitivity for Staphylococcus aureus (protein A) and Pseudomonas aeruginosa (exotoxin A). Utilizing gold nanoparticles (AuNPs) as a catalyst, this approach employs ascorbic acid to reduce Cu2+ to Cu0, depositing on AuNPs at the test line and amplifying the signal. A user-friendly design features a three-dimensional paper structure incorporating pre-dried reagents, enabling a streamlined, efficient testing process. The osa-LFIA significantly lowers detection limits to 3 ng mL−1 for protein A and 10 ng mL−1 for exotoxin A, offering a tenfold improvement over conventional LFIA. Additionally, we developed a portable grayscale detection device, achieving less than 10% error in quantitative analysis compared to the data acquired and analyzed in the lab. This entire process, from detection to signal amplification, is completed in just 20 min. For the clinical trial, we utilized the osa-LFIA to test synovial fluid samples infected with Staphylococcus aureus. We also successfully detected different concentrations of the exotoxin A in parallel, with a recovery value of 96%–110%. Our findings demonstrate the osa-LFIA's potential as a rapid, highly sensitive, and simple-to-use diagnostic tool for detecting various pathogens, significantly advancing the field of rapid diagnostic testing.

No evidence of improvements in energy metabolism after 1week of nitrate and citrulline co-supplementation in elite rowers.

Citrulline (CIT) and beetroot extract (BR) supplements positively impacts exercise performance in elite rowers. However, its influence on metabolic outcomes such as whole-body volumes of oxygen consumption (VO2) and carbon dioxide production (VCO2), substrate oxidation, energy expenditure (EE), and gross efficiency remains unknown. We studied the effects of 1week of daily co-supplementation of 3.5g BR (500mg NO3-) plus 6g CIT on VO2 and VCO2 kinetics, substrate utilization, EE, and gross efficiency in elite male rowers compared to a placebo and to a BR supplementation. Twenty elite rowers participated in this randomized, double-blind, placebo-controlled crossover trial completing 1week of supplementation in each group of study: Placebo (PLAG); BRG; and BR-CITG. Efficiency (70% VO2max) and performance (incremental maximal) tests were performed, and gas-exchange data were collected via indirect calorimetry. Analysis of covariance (ANCOVA) showed no mean between-condition differences on respiratory exchange ratio (RER), EE, and gross efficiency in the efficiency test (all P > 0.06), and in the performance test (all P > 0.28). Moreover, in both tests no interaction Time × Supplement effects were observed for VO2, VCO2, RER, EE, substrate oxidation, and, gross efficiency (all P > 0.12). After 1week, no effects on energy metabolism and substrate utilization were observed after the daily co-ingestion of BR extract plus CIT supplement, therefore longer (> 7days) and higher doses of supplementation might be needed to influence metabolism.

Experimental determination of soil thermal conductivity for design of underground cables

The dimensioning of underground extra-high voltage direct current transmission lines (HVDC) requires detailed knowledge of soil properties, particularly thermal conductivity, to optimise cable spacing and minimise project costs and environmental impact. Given the absence of an internationally standardised procedure for testing soil thermal conductivity, a novel approach was developed as part of the SuedLink project - a 700 km long underground power line which will transmit renewable wind energy between northern and southern Germany. The unique aspect of this methodology lies in its ability to deliver reproducible results of the thermal conductivity of a soil across the entire water content spectrum. This allows the use of realistic thermal conductivity values, as opposed to the current use of overly conservative values in the design of underground power lines. Additionally, the highly efficient testing configuration enables the test to effectively be rolled out on a large scale as was shown with over 6000 tests done on the SuedLink project. This article explains the thermal conductivity test procedure and the evaluation of the raw-data obtained from these tests.

High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models.

Omics data generated from high-throughput technologies and clinical features jointly impact many complex human diseases. Identifying key biomarkers and clinical risk factors is essential for understanding disease mechanisms and advancing early disease diagnosis and precision medicine. However, the high-dimensionality and intricate associations between disease outcomes and omics profiles present significant analytical challenges. To address these, we propose an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream advanced machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test, forming the comprehensive High-dimensional Feature Importance Test (HiFIT) framework. Through extensive numerical simulations and real-world applications, we demonstrate HiFIT's superior performance in both outcome prediction and feature importance identification. An R package implementing HiFIT is available on GitHub ( https://github.com/BZou-lab/HiFIT ).

Image super-resolution based on improved ESRGAN and its application in camera calibration

Camera calibration is a key technique in the field of computer vision. The Zhang’s method is currently the most commonly used camera calibration method, but its accuracy is mainly dependent on the image quality, thusly constrained by camera hardware conditions. Considering that high-performance cameras are often expensive, a cost-effective method to improve the quality of calibration images is of great practical value. Without changing the existing hardware conditions, image super-resolution can be considered to enhance the quality of the calibration image. Image super-resolution (SR) is the process of reconstructing an image from low resolution (LR) to high resolution (HR). It can enhance the clarity and detail of the calibration images, which will be beneficial in improving the accuracy of camera calibration. However, there is very little research on the application of image super-resolution to camera calibration. Therefore, this study innovatively proposed a method and process for applying image super-resolution in camera calibration. Firstly, the ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) was optimized and improved, followed by the proposal of the MMRSRGAN (Multi-scale Multi-adaptive-weights Residual Super-Resolution Generative Adversarial Networks). Two methods for local image super-resolution were also proposed for efficient training and testing of datasets, and two evaluation metrics based on reprojection error were proposed evaluate the effectiveness of the proposed model. Training and testing experiments of image super-resolution for camera calibration were conducted on both virtual and real image datasets. The proposed MMRSRGAN was compared with several advanced image super-resolution networks developed in recent years across multiple dimensions and indicators. The positive results have demonstrated the feasibility of applying image super-resolution to camera calibration and showcased the good performance of the MMRSRGAN.

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.

In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods. Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

Hardware Testing Methodologies for Wide Bandgap High-Power Converters

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor-based converter bring-up before the control validation phase steps in. The proposed methods enable thorough evaluation and validation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilized the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT was further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test was introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements were conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

Use of NIR in COVID-19 Screening: Proof of Principles for Future Application.

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Development and validation of a model for early prediction of residual feed intake in beef cattle using plasma biomarkers

Identification of plasma biomarkers for feed efficiency in growing beef cattle offers a promising opportunity for developing prediction models to improve precision feeding strategies. However, these models must accurately predict feed efficiency at early stages of fattening. Our study aimed to evaluate the reliability of candidate biomarkers previously identified in late-fattening cattle when analyzed during early fattening stages and to develop diet-specific prediction equations for residual feed intake (RFI). From a total of 364 Charolais bulls across 7 cohorts, we selected 64 animals with extreme RFI values. The animals were fed either a corn‑ or grass-silage diets. These animals were chosen from 4 out of available 7 cohorts. Animals from three cohorts (24 high-RFI and 24 low-RFI, having a mean RFI difference of 1.48 kg/d) were used for biomarker confirmation and prediction model training. Animals from a fourth cohort (8 high-RFI and 8 low-RFI, having a mean RFI difference of 0.98 kg/d) were used for model external validation. Blood samples were collected at the beginning of the feed efficiency test (333±20 days), and plasma underwent targeted metabolomic for 630 metabolites, natural abundance of 15N (δ15N), insulin, and IGF-1 analysis. Seven previously identified plasma biomarkers for RFI in late-fattening beef cattle still kept their capability for discriminating low and high RFI animals when analyzed during early fattening stages (P < 0.05). Among these confirmed biomarkers, five were common for both grass- and corn-fed animals (creatinine, β-alanine, triglyceride TG18:0_34:2, symmetric dimethyl-arginine and phosphatidylcholine PC aa C30:2) while two were diet-specific (insulin-like growth factor 1 for grass silage-based diet, and isoleucine for corn silage-based diet. No new plasma biomarkers of RFI were identified at early-fattening stages (false discovery rate, FDR>0.05). Prediction models were developed based on seven confirmed RFI biomarkers analyzed during early-fattening. Two logistic regression models incorporating creatinine and either IGF-1 (for grass silage-based diet) or PC aa C30:2 (for corn silage-based diet) effectively distinguished between high and low-RFI animals with high sensitivity and specificity (area under the curve, AUC > 0.80). The biomarkers used in the models showed moderate to high repeatability between early and late fattening stages (45 < r <65). The models were successfully externally validated, with more than 85% of animals from the fourth cohort correctly classified. Once validated in larger cohorts and utilizing cost-effective and rapid analytical methods, these models could support precision feeding and breeding programs, aiming to reduce the cost of raising beef cattle.

ONLINE PRACTICE TEST BANK SOLUTION: A WEB-BASED REVIEW PLATFORM FOR LIBRARY AND INFORMATION SCIENCE PRACTITIONERS

Test construction and test administration in most institutions still adopt the traditional pen-and-paper method of examination, which is very tedious (Kotwal et al., 2016). Its lengthy process can limit the time instructors in devoting to their proactive instruction and assessment. The general purpose of the study is to develop an Online Practice Test bank for Library and Information Science Practitioners. To actualize the Online Practice Test bank, the researcher followed the three phases of the modified rapid application model: Planning or Analyze Phase, Design and Development or Prototyping Phase, and Testing. The result of the study revealed that the developed system was able to execute its different operations. Specifically, by providing an avenue to the contributors/review masters and the reviewees to communicate and interact regarding the different reviewers provided. The reviewees can also track their progress through the learning analytics feature of the system. The contributors/review masters can monitor if the review materials they have provided has efficient test items by accessing the test item analysis. The system also has undergone functionality testing through conducting test cases, which have also gained positive remarks. Based on the results of the developed Online Practice Test bank, it is concluded that the system attained its three objectives in having integrated features that would be a great help, especially to the future licensed professional librarians as they will have a platform where they do their review and assess their knowledge on the six core subjects of library science. It is therefore recommended that mobile or android-based user modules must be developed for it to be more accessible to the users. And also an automated intervention or scaffolding for coping with the results from the performance of the review. And it must have an option or platform for contributors outside the institution who want to share their review materials relevant to the Library and Information Science profession.

Metal-organic framework-derived hierarchical flower-like DyCo-layered double hydroxide amalgamated nitrogen-doped graphene for diphenylamine detection in fruit samples: Theoretical density functional theory interpretation

Diphenylamine (DPA) is an environmental pollutant that can be potentially toxic. As a result, it is crucial to use basic and affordable analytical techniques to detect DPA. Electrochemical detection of DPA is a cost-effective and simple method. Modifying the electrodes with nanomaterials can enhance the electrochemical characteristics and sensitivity of the sensor. Herein, metal-organic framework (MOF) derived dysprosium cobalt-layered double hydroxide integrated nitrogen-doped graphene (DyCo-LDH/NG) is reported for the fabrication of the DPA sensing platform. The electrochemical oxidation of DPA is enhanced by the exceptional electrocatalytic activity and electron transfer properties of the DyCo-LDH/NG nanocomposite. Interestingly, the glassy carbon electrode (GCE) modified with DyCo-LDH/NG nanocomposite demonstrates a large linear detection range (0.05-470μM) and a low limit of detection (0.012µM). The density functional theory (DFT) study is employed to examine the energy levels and electron transfer sites of DPA during the electro-oxidation process. Furthermore, the practical efficiency test of the developed DPA sensor demonstrates a substantial recovery in fruit samples.