Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia.

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

HR-pQCT cross-calibration using standard vs. Laplace-Hamming binarization approach.

High-resolution peripheral quantitative computed tomography (HR-pQCT) has emerged as a powerful imaging technique for characterizing bone microarchitecture in the human peripheral skeleton. The second-generation HR-pQCT scanner provides improved spatial resolution and a shorter scan time. However, the transition from the first-generation (XCTI) to second-generation HR-pQCT scanners (XCTII) poses challenges for longitudinal studies, multi-center trials, and comparison to historical data. Cross-calibration, an established approach for determining relationships between measurements obtained from different devices, can bridge this gap and enable the utilization and comparison of legacy data. The goal of this study was to establish cross-calibration equations to estimate XCTII measurements from XCTI data, using both the standard and Laplace-Hamming (LH) binarization approaches. Thirty-six volunteers (26-85 yr) were recruited and their radii and tibiae were scanned on both XCTI and XCTII scanners. XCTI images were analyzed using the manufacturer's standard protocol. XCTII images were analyzed twice: using the manufacturer's standard protocol and the LH segmentation approach previously developed and validated by our team. Linear regression analysis was used to establish cross-calibration equations. Results demonstrated strong correlations between XCTI and XCTII density and geometry outcomes. For most microstructural outcomes, although there were considerable differences in absolute values, correlations between measurements obtained from different scanners were strong, allowing for accurate cross-calibration estimations. For some microstructural outcomes with a higher sensitivity to spatial resolution (eg, trabecular thickness, cortical pore diameter), XCTII standard protocol resulted in poor correlations between the scanners, while our LH approach improved these correlations and decreased the difference in absolute values and the proportional bias for other measurements. For these reasons and due to the improved accuracy of our LH approach compared with the standard approach, as established in our previous study, we propose that investigators should use the LH approach for analyzing XCTII scans, particularly when comparing to XCTI data.

Ensemble surrogate modeling of advective-dispersive transport with intraparticle diffusion model for column-leaching test.

Column-leaching tests are a common approach for assessing the leaching behavior and resulting environmental risks of contaminated soils and waste materials, which are frequently reused for various construction purposes. The observed breakthrough curves of the contaminants are influenced by the complex dynamics of solute transport and kinetic inter-phase mass transfer. Disentangling these interactions necessitates numerical models. However, inverse modeling and sensitivity analysis can be time-consuming, especially when sorption kinetics are explicitly described by intraparticle diffusion, which requires discretizing the domain both in the flow direction along the column axis and inside the grains. To circumvent the need for such computationally intensive models, we have developed two different ensemble surrogate models. These models employ two separate ensemble methods: random forest stacking and inverse-distance weighted interpolation. Each method is applied to base surrogate models that cover different parts of the parameter space. The base surrogate models use the method of Extremely randomized Trees (ExtraTrees). The defined parameter range is based on the German standard for column-leaching tests. To optimize the base surrogate models, we utilized adaptive-sampling methods based on three distinct infill criteria: maximizing the expected improvement, staying within a certain Mahalanobis distance to the best estimate (both for exploitation), and maximizing the standard deviation (for exploration). The ensemble surrogate model demonstrates excellent performance in emulating the behavior of the original numerical model, with a relative root mean squared error of 0.09. We applied our proposed ensemble surrogate model to estimate the complete posterior parameter distribution using Simulation-Based Inference, specifically Neural Posterior Estimation, to determine the full parameter distribution conditioned on copper-leaching data from two different soils. Samples drawn from the posterior distribution align perfectly with the observed data for both the surrogate and original models.

Paleopteran molecular clock: Time drift and recent acceleration.

Applying BEAST v1.10.4, we constructed a Bayesian Inference tree comprising 322 taxa, primarily representing Paleoptera (Odonata and Ephemeroptera; Pterygota), Zygentoma and Archaeognatha (Apterygota; paraphyly), and Neoptera (Plecoptera; Pterygota), based on a 2685 bp sequence dataset. Our analyses revealed that robust dating required the incorporation of both Quaternary and pre-Quaternary dates. To achieve this, our dating incorporated a 1.55 Ma (Quaternary) geological event (the formation of the Ryukyu Islands) and a set of chronologically well-founded fossil dates, spanning from up to 400 Ma (Devonian) for the stem Archaeognatha, 320 Ma (Carboniferous) for the crown of Paleoptera, 300 Ma (Carboniferous) for the crown Ephemeroptera, and 280 Ma (Permian) for the crown Odonata, down to 1.76 Ma (Quaternary) for Calopteryx japonica, encompassing a total of 22 calibration points (events: 6, fossils: 16; Quaternary: 7, pre-Quaternary: 15). The resulting dated tree aligns with previous research, albeit with some dates being overestimated. This overestimation was mainly due to the lack of Quaternary calibration and the exclusive dependence on pre-Quaternary calibration, though the application of maximum age constraints also played a role. Our minimum age dating demonstrates that the molecular clock did not uniformly progress, rendering rate dating an inapplicable approach. We observed that the base substitution rate is time-dependent, with an exponential increase evident from around 20 Ma (Miocene) to the present time, exceeding an order of magnitude. The extensive radiation and speciation of Insecta and Paleoptera, potentially resulting from the severe climatic changes associated with the Quaternary, including the commencement of glacial and interglacial cycles, may have significantly contributed to this increase in base substitution rates. Additionally, we identified a potential peak in base substitution rates during the Carboniferous period, around 320 million years ago, possibly corresponding to the Late Paleozoic Ice Age.

Open, Active-Controlled Clinical Study to Evaluate the Correlation between Whole Body DEXA and BIA Muscle Measurements.

BACKGROUND: Dual energy X-ray absorptiometry (DXA) is the gold standard for diagnosing sarcopenia. However, comparative studies using bioelectrical impedance analysis (BIA) would be required in the Korean population. This study aimed to evaluate the correlation between total-body bone density measuring devices (Hologic and GE Lunar) and a bioelectrical impedance measurement device (InBody 970) as well as the correlation between upper body muscle mass. METHODS: A total of 119 participants were involved in this study, aged 20 to 70 years, with specific body mass index ranges and no severe health conditions used both DXA (or DEXA) and BIA technologies to assess body composition. The participants were scanned using a Hologic QDR-4500W DXA scanner and GE-Lunar Prodigy DXA systems, and the InBody 970 type of multi-frequency BIA machine. Statistical analysis was performed to determine the correlation between the devices, with a coefficient of at least 0.8. RESULTS: The muscle mass measurement comparisons between the InBody 970 and Hologic devices demonstrated remarkably high correlation coefficients (exceeding 0.9) across all limbs. Similarly, the muscle mass comparison between the Inbody 970 and GE Lunar devices also revealed substantial correlation coefficients, ranging from 0.83 upwards, across all limbs. CONCLUSIONS: Limb muscle mass measurements using Hologic and GE Lunar whole-body DXA and Inbody 970 BIA demonstrated particularly high levels of concordance. In addition, a conversion formula that bridges limb muscle mass measurements from two widely used whole-body DXA machines and a BIA machine will facilitate sarcopenia research and patient management.

Adaptation to Reverberation for Speech Perception: A Systematic Review.

In everyday acoustic environments, reverberation alters the speech signal received at the ears. Normal-hearing listeners are robust to these distortions, quickly recalibrating to achieve accurate speech perception. Over the past two decades, multiple studies have investigated the various adaptation mechanisms that listeners use to mitigate the negative impacts of reverberation and improve speech intelligibility. Following the PRISMA guidelines, we performed a systematic review of these studies, with the aim to summarize existing research, identify open questions, and propose future directions. Two researchers independently assessed a total of 661 studies, ultimately including 23 in the review. Our results showed that adaptation to reverberant speech is robust across diverse environments, experimental setups, speech units, and tasks, in noise-masked or unmasked conditions. The time course of adaptation is rapid, sometimes occurring in less than 1 s, but this can vary depending on the reverberation and noise levels of the acoustic environment. Adaptation is stronger in moderately reverberant rooms and minimal in rooms with very intense reverberation. While the mechanisms underlying the recalibration are largely unknown, adaptation to the direct-to-reverberant ratio-related changes in amplitude modulation appears to be the predominant candidate. However, additional factors need to be explored to provide a unified theory for the effect and its applications.

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring.

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

Repeatability of two methods for estimating scapular kinematics during dynamic functional tasks.

Best practices for scapular motion tracking are still being determined. The repeatability of different scapular kinematic procedures needs to be evaluated. The purpose of this study was to assess the test-retest reliability of two scapular kinematic procedures: double calibration with AMC (D-AMC) and individualized linear modelling (LM). Ten healthy participants had their upper body movement tracked with optical motion capture in two identical sessions. Five scapular calibration poses were performed, and seven dynamic functional tasks were tested. Scapular angles were calculated from both procedures (D-AMC vs LM). The D-AMC approach uses two poses (neutral and maximum elevation) and tracks the scapula with a rigid cluster, while the LM approach predicts scapular positioning from humeral angles based on equations built from the calibration pose data. Angle waveforms and repeatability outcomes were compared. Internal and upward rotation angle waveforms were significantly different (p < 0.05) between kinematic procedures for some tasks, with maximum mean differences up to 17.3° and 23.2°, respectively. Overall, repeatability outcomes were similar between procedures, but the LM approach was slightly better for tilt and the D-AMC approach was notably improved for upward rotation in certain tasks. For example, minimal detectable changes during the Forward Transfer ranged from 6.9° to 11.9° for the D-AMC and 8.9° to 25.3° for the LM. Discrepancies between procedures may be a function of the calibration poses chosen. Additional calibration poses may improve the comparisons between procedures.

Magnification calibration of X-ray 3D microscopy using micro-line structures.

X-ray microscopy using computed tomography (CT) is an excellent three-dimensional imaging instrument. Three-dimensional X-ray microscopy (3DXRM) is a nondestructive imaging technique used to inspect internal and external structures in units of submicrometers or less. The 3DXRM, although attractive, is mostly used as an observation instrument and is limited as a measurement system in quantitative evaluation and quality control. Calibration is required for use in measurement systems such as coordinate measurement systems, and specific standard samples and evaluation procedures are needed. The certified values of the standard samples must ideally be traceable to the International System of Units (SI). In the 3DXRM measurement system, line structures (LSs) are fabricated as prototype standard samples to conduct magnification calibration. In this study, we evaluated the LS intervals using calibrated cross-sectional scanning electron microscopy (SEM). A comparison of the evaluation results between SEM and 3DXRM for the LS intervals provided the magnification calibration factor for 3DXRM and validated the LSs, whereby the interval methods and feasibility of constructing an SI traceability system were evaluated using the calibrated SEM. Consequently, a magnification calibration factor of 1.01 was obtained for 3DXRM based on the intervals of the LSs evaluated by SEM. A possible route for realizing SI-traceable magnification calibration of 3DXRM has been presented.

Cavity ionization chamber responses in the JAEA and the NMIJ high-energy photon reference fields for radiation protection.

A dosimeter should ideally be calibrated in a reference field with similar energy and doserate to that which the dosimeter is being used to measure. Environments around nuclear reactors and radiation therapy facilities have high-energy photons with energies exceeding that of60Co gamma rays, and controlling exposure to these photons is important. The Japan Atomic Energy Agency and National Metrology Institute of Japan have high-energy reference fields with energies above several megaelectronvolts for different types of accelerators. Their reference fields have different fluence-energy distributions. In this study, the energy dependencies of the two-cavity ionization chambers, which are often used by secondary standard laboratories, were experimentally and computationally evaluated for each high-energy field. These results agreed well within the relative expanded uncertainties (k= 2), and their capabilities for air kerma measurements in each high-energy reference field were confirmed. Therefore, the capabilities of the air-kerma measurements were verified in the two high-energy reference fields.

Calibration-free and ready-to-use wearable electroanalytical reporting system (r-WEAR) for long-term remote monitoring of electrolytes markers.

A major bottleneck in the development of wearable ion-selective sensors is the inherent conditioning and calibration procedures at the user's end due to the signal's instability and non-uniformity. To address this challenge, we developed a strategy that integrates three interdependent materials and device engineering approaches to realize a Ready-to-use Wearable ElectroAnalytical Reporting system (r-WEAR) for reliable electrolytes monitoring. The strategy collectively utilized (1) finely-configured diffusion-limiting polymers to stabilize the electromotive force in the electrodes, (2) a uniform electrical induction in electrochemical cells to normalize the open-circuit potential (OCP), and (3) an electrical shunt to maintain the OCP across the entire sensor in the r-WEAR. The approaches jointly enable fabrication of homogeneously stable and uniform ion-selective sensors, eliminating common conditioning and calibration practices. As a result, the r-WEAR demonstrated a signal's variation down to ±1.99 mV with a signal drift of 0.5 % per hour (0.12 mV h-1) during a 12-h continuous measurement of 10 sensors and a signal drift as low as 13.3 µV h-1 during storage. On-body evaluations of the r-WEAR for four days without conditioning and re-/calibration further validated the sensor's performance in realistic settings, indicating its remarkable potential for practical usage in a user operation-free manner in wearable healthcare applications.

Microphone calibration estimation for mobile audiological tests with resonating bottles.

OBJECTIVE: Audiological tests on smartphones require consistent microphone recordings across device types with a reasonable standard uncertainty (2-3 Decibel (dB)) of the sound pressure level at the microphone. However, the calibration of smartphone microphones by the non-expert user is still an unsolved issue. We show that whistling on standardized glass bottles permits a coarse sound level calibration with an uncertainty that is smaller than the standard uncertainty of clinical audiograms (4.9dB) and enough for mobile health (mHealth) products. DESIGN: We define and test a calibration procedure with bottle-whistles for smartphones. The empirical sound pressure levels are used to calculate the mean and standard deviation of a single measurement. STUDY SAMPLE: Two uncalibrated studies with a total of 30 participants, one calibrated study with 11 participants. RESULTS: The mean maximal sound pressure level of 330 ml Vichy-shape bottle-whistles at 50 cm distance is 92.8 ± 1.6dB sound pressure level (SPL). The sound pressure level variation of a single measurement is 3.0dB SPL. CONCLUSIONS: In comparison to other possible ways of level calibration estimates for smartphones (e.g. level of own voice, level of common environmental sounds), the current method appears to be robust in background noise and easily reproducible with glass bottles of defined dimensions.

Enhancing surgical navigation: a robust hand-eye calibration method for the Microsoft HoloLens 2.

PURPOSE: Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts. METHODS: We propose a simple and robust hand-eye calibration process for the depth camera of the Microsoft HoloLens 2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool. RESULTS: Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65 mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67 mm in an average time of 42 s. CONCLUSION: This work enables real-time hand-eye calibration for the Microsoft HoloLens 2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.

A framework for understanding selection bias in real-world healthcare data.

Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.

Calibration and Comparison of SIR, SEIR/SLIR, and SLAIR Models for Influenza Dynamics: Insights from the 2016-2017 Season in the Valencian Community, Spain.

Influenza and influenza-like illnesses (ILI) pose significant challenges to healthcare systems globally. Mathematical models play a crucial role in understanding their dynamics, calibrating them to specific scenarios, and making projections about their evolution over time. This study proposes a calibration process for three different but well-known compartmental models - SIR, SEIR/SLIR, and SLAIR - using influenza data from the 2016-2017 season in the Valencian Community, Spain. The calibration process involves indirect calibration for the SIR and SLIR models, requiring post-processing to compare model output with data, while the SLAIR model is directly calibrated through direct comparison. Our calibration results demonstrate remarkable consistency between the SIR and SLIR models, with slight variations observed in the SLAIR model due to its unique design and calibration strategy. Importantly, all models align with existing evidence and intuitions found in the medical literature. Our findings suggest that at the onset of the epidemiological season, a significant proportion of the population (ranging from 29.08% to 43.75% of the total population) may have already entered the recovered state, likely due to immunization from the previous season. Additionally, we estimate that the percentage of infected individuals seeking healthcare services ranges from 5.7% to 12.2%. Through a well-founded and calibrated modeling approach, our study contributes to supporting, settling, and quantifying current medical issues despite the inherent uncertainties involved in influenza dynamics. The full Mathematica code can be downloaded from https://munqu.webs.upv.es/software.html.

The irtQ R package: a user-friendly tool for item response theory-based test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package, which simplifies IRT-based analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Early Warning Models Using Machine Learning to Predict Sepsis-Associated Chronic Critical Illness: A Study Based on the Medical Information Mart for Intensive Care Database.

Background Patients with chronic critical illness (CCI) experience poor prognoses and incur high medical costs. However, there is currently limited clinical awareness of sepsis-associated CCI, resulting in insufficient vigilance. Therefore, it is necessary to build a machine learning model that can predict whether sepsis patients will develop CCI. Methods Clinical data on 19,077 sepsis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. Predictive factors were identified using the Student's t-test, Mann-Whitney U test, or χ 2 test. Six machine learning classification models, namely, the logistic regression, support vector machine, decision tree, random forest, extreme gradient enhancement, and artificial neural network, were established. The optimal model was selected on the basis of its performance. Calibration curves were used to evaluate the accuracy of model classification, while the external validation dataset was used to evaluate the performance of the model. Results Thirty-seven characteristics, such as elevated alanine aminotransferase, rapid heart rate, and high Logistic Organ Dysfunction System scores, were identified as risk factors for developing CCI. The area under the receiver operating characteristic curve (AUROC) values for all models were above 0.73 on the internal test set. Among them, the extreme gradient enhancement model exhibited superior performance (F1 score = 0.91, AUROC = 0.91, Brier score = 0.052). It also exhibited stable prediction performance on the external validation set (AUROC = 0.72). Conclusion A machine learning model was established to predict whether sepsis patients will develop CCI. It can provide useful predictive information for clinical decision-making.

Calibration-Free and Highly Sensitive Potentiometric Enzyme-Based Biosensors.

Enzyme-based amperometric biosensors have become popular for healthcare applications. However, they have been under constant pressure for technological innovation to improve their sensitivity and usability. An ideal biosensor has high sensitivity and calibration-free characteristics. This study aims to report enzyme-based glucose and lactate sensors that utilize a proposed "time-derivative of potential (dOCP/dt)" method, with a further aim being to prove theoretically and experimentally that dOCP/dt values are proportional to substrate concentration. High sensitivity is obtained regardless of the electrode size because the electrode potential is independent of the electrode area in the biosensor. Importantly, because the substrate diffusion determines the enzyme reaction rate on the sensors, the dOCP/dt biosensors can essentially eliminate external influences such as temperature and pH. The result is the successful realization of a biosensor that is calibration-free, making it a much more practical option.

Collaborative Study for the Calibration of the Ph.Eur. Prekallikrein Activator in Albumin Biological Reference Preparation batches 8, 9 and 10.

An international collaborative study was organised by the European Directorate for the Quality of Medicines & HealthCare (EDQM, Council of Europe) to calibrate replacement batches for the current European Pharmacopoeia (Ph. Eur.) Prekallikrein Activator (PKA) in albumin Biological Reference Preparation (BRP) whose stocks were dwindling. The study was run in the framework of the Biological Standardisation Programme (BSP) of the Council of Europe and the European Union (EU) Commission. Twenty-four laboratories from official medicines control authorities and manufacturers in Europe and outside Europe took part in the study. Three candidate replacement batches were produced with albumin solutions artificially spiked with a PKA concentrate to increase their PKA level. Participants were requested to evaluate the candidate batches against the 3rd World Health Organization (WHO) International Standard (IS) for Prekallikrein activator in albumin (16/364) using their routine assay method. The Ph. Eur. PKA in albumin BRP batch 7 (BRP7) was also included in the test panel to ensure the continuity of the consecutive BRP batches. The 3 candidate replacement batches were considered suitable for their intended use as BRPs. The study confirmed the stability of the PKA content of the current BRP7. Thermal stress study on the candidate batches confirmed the stability of their PKA activity. In December 2023, the Ph. Eur. Commission officially adopted the 3 candidate batches as Ph. Eur. PKA in albumin BRP batches 8, 9 and 10 with assigned potencies of 37 IU/vial, 33 IU/vial and 34 IU/vial, respectively. The activity of the 3 new batches of Ph. Eur. PKA in albumin BRP will be regularly monitored.

Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration.

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487-0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001).