Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 µg/m3 in HS and 5.75 and 17.58 µg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 µg/m3 in HS and 11.72 to 38.86 µg/m3 in NHS.

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.

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.

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.

A biosensor monitoring approach for toxic algae: Construction of calibration curves to infer cell numbers in field material.

A variety of shellfish toxin-producing Harmful Algal Blooms (HABs) occur every year in coastal temperate waters worldwide. These toxic HABs may cause lengthy (months) harvesting bans of mussels and other suspension feeding bivalves exposed to their blooms. To safeguard public health and the shellfish industry, European Union regulations request periodic monitoring of potentially toxic microalgae in seawater and phycotoxins in live bivalve molluscs from shellfish production areas. Monitoring of other toxic microalgae, e.g., fish killers, is based solely on cell counts. Morphological identification and quantification of microalgal cells with light microscopy is time-consuming, requires a good expertise, and accurate identification to species level (e.g., Pseudo-nitzschia species) may require electron microscopy. Toxicity varies among morphologically similar species; there are toxic and non-toxic strains of the same species. Molecular techniques using ribosomal DNA sequences offer a possibility to identify and detect precisely the potentially toxic genus/species. In an earlier project (MIDTAL), specific probes against rRNA sequences of all HAB taxa, known at the time of the project, affecting shellfish areas worldwide were designed, and those affecting Europe were tested and calibrated against rRNA extracts of clonal cultures and field samples. Microarray technology was adopted to relate to cell numbers the fluorescence signal from the reaction of all target species probes spotted in the microarray slides with those present in a single sample extract. The EMERTOX project aimed to develop a more automatic "Lab on a chip" (LOC) technology, including a non- (cell) disruptive water concentration system and biosensors for HAB cells detection. Here, calibration curves are presented against toxic microalgae (cultures and field samples) causing endemic and emerging toxicity events in Galicia (NW Spain) and Portugal. Results here relating cell numbers to electrochemical signals will be used in an early warning biosensor for toxic algae.

Size and fluorescence calibrated imaging flow cytometry: From arbitrary to standard units.

Imaging flow cytometry (IFCM) is a technique that can detect, size, and phenotype extracellular vesicles (EVs) at high throughput (thousands/minute) in complex biofluids without prior EV isolation. However, the generated signals are expressed in arbitrary units, which hinders data interpretation and comparison of measurement results between instruments and institutes. While fluorescence calibration can be readily achieved, calibration of side scatter (SSC) signals presents an ongoing challenge for IFCM. Here, we present an approach to relate the SSC signals to particle size for IFCM, and perform a comparability study between three different IFCMs using a plasma EV test sample (PEVTES). SSC signals for different sizes of polystyrene (PS) and hollow organosilica beads (HOBs) were acquired with a 405 nm 120 mW laser without a notch filter before detection. Mie theory was applied to relate scatter signals to particle size. Fluorescence calibration was accomplished with 2 µm phycoerythrin (PE) and allophycocyanin (APC) MESF beads. Size and fluorescence calibration was performed for three IFCMs in two laboratories. CD235a-PE and CD61-APC stained PEVTES were used as EV-containing samples. EV concentrations were compared between instruments within a size range of 100-1000 nm and a fluorescence intensity range of 3-10,000 MESF. 81 nm PS beads could be readily discerned from background based on their SSC signals. Fitting of the obtained PS bead SSC signals with Mie theory resulted in a coefficient of determination >0.99 between theory and data for all three IFCMs. 216 nm HOBs were detected with all instruments, and confirmed the sensitivity to detect EVs by SSC. The lower limit of detection regarding EV-size for this study was determined to be ~100 nm for all instruments. Size and fluorescence calibration of IFCM data increased cross-instrument data comparability with the coefficient of variation decreasing from 33% to 21%. Here we demonstrate - for the first time - scatter calibration of an IFCM using the 405 nm laser. The quality of the scatter-to-diameter relation and scatter sensitivity of the IFCMs are similar to the most sensitive commercially available flow cytometers. This development will support the reliability of EV research with IFCM by providing robust standardization and reproducibility, which are pre-requisites for understanding the biological significance of EVs.

Multi-site phantomless bone mineral density from clinical quantitative computed tomography in males.

Volumetric bone mineral density (vBMD) is commonly assessed using QCT. Although standard vBMD calculation methods require phantom rods that may not be available, internal-reference phantomless (IPL) and direct measurements of Hounsfield units (HU) can be used to calculate vBMD in their absence. Yet, neither approach has been systemically assessed across skeletal sites, and HU need further validation as a vBMD proxy. This study evaluated the accuracy of phantomless methods, including IPL and regression-based phantomless (RPL) calibration using HU to calculate vBMD, compared to phantom-based (PB) methods. vBMD from QCT scans of 100 male post-mortem human subjects (PMHS) was calculated using site-specific PB calibration at multiple skeletal sites throughout the body. A development sample of 50/100 PMHS was used to determine site-specific reference material density for IPL calibration and RPL equations. Reference densities and equations from the development sample were used to calculate IPL and RPL vBMD on the remaining 50/100 PMHS for method validation. PB and IPL/RPL vBMD were not significantly different (p > .05). Univariate regressions between PB and IPL/RPL vBMD were universally significant (p < 0.05), except for IPL Rad-30 (p = 0.078), with a percent difference across all sites of 6.97% ± 5.95% and 5.22% ± 4.59% between PB and IPL/RPL vBMD, respectively. As vBMD increased, there were weaker relationships and larger differences between PB vBMD and IPL/RPL vBMD. IPL and RPL vBMD had strong relationships with PB vBMD across sites (R2 = 97.99, R2 = 99.17%, respectively), but larger residual differences were found for IPL vBMD. As the accuracy of IPL/RPL vBMD varied between sites, phantomless methods should be site-specific to provide values more comparable to PB vBMD. Overall, this study suggests that RPL calibration may better represent PB vBMD compared to IPL calibration, increases the utility of opportunistic QCT, and provides insight into bone quality and fracture risk.

Comparison of Four Severity Assessment Scoring Systems in Critically Ill Patients for Predicting Patient Outcomes: A Prospective Observational Study From a Single Tertiary Center in Central India.

Background and aim A variety of scoring systems are employed in intensive care units (ICUs) with the objective of predicting patient morbidity and mortality. The present study aimed to compare four different severity assessment scoring systems, namely, Acute Physiology and Chronic Health Evaluation II (APACHE II), Rapid Emergency Medicine Score (REMS), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiologic Score II (SAPS II) to predict prognosis of all patients admitted to a mixed medical ICU of a tertiary care teaching hospital in central India. Methods The prospective observational study included 1136 patients aged 18 years or more, admitted to the mixed medical ICU. All patients underwent severity assessment using the four scoring systems, namely APACHE II, SOFA, REMS, and SAPS II, after admission. Predicted mortality was calculated from each of the scores and actual patient outcomes were noted. Receiver operating curve analysis was undertaken to identify the cut-off value of individual scoring systems for predicting mortality with optimum sensitivity and specificity. Calibration and discrimination were employed to ascertain the validity of each scoring model. Bivariate and multivariable logistic regression analyses among the study participants were conducted to identify the best scoring system, after adjusting for potential confounders. Results Final analysis was done on 957 study participants (mean (±SD) age-58.4 (±12.9) years; males-62.2%). The mortality rate was 14.7%. APACHE II, SOFA, SAPS II, and REMS scores were significantly higher among the non-survivors as compared to the survivors (p<0.05). SAPS II was found to have the highest AUC of 0.981 (p<0.001). SAPS II score >58 had 93.6% sensitivity, 94.1% specificity, 73.3% PPV, 98.8% NPV, and 94.0% diagnostic accuracy in predicting mortality. This scoring system also had the best calibration. Binary logistic regression showed that all four scoring systems were significantly associated with ICU mortality. After adjusting for each other, only SAPS II remained significantly associated with ICU mortality. Conclusion Both SAPS II and APACHE II were observed to have good calibration and discriminatory power; however, SAPS II had the best prediction power suggesting that it may be a useful tool for clinicians and researchers in assessing the severity of illness and mortality risk in critically ill patients.

Comparison of pSOFA with PRISM III and PIM 2 as Predictors of Outcome in a Tertiary Care Pediatric ICU: A Prospective Cross-sectional Study.

Aims and background: Severity scores are used to predict the outcome of children admitted to the intensive care unit. A descriptive score such as the pediatric sequential organ failure assessment (pSOFA) may be useful for prediction of outcome. This study was planned to compare the pSOFA score with these well-studied scores for prediction of mortality. Materials and methods: This prospective cross-sectional study was conducted at the pediatric intensive care units (PICU) of a tertiary care hospital. Children aged from 1 month to 12 years were enrolled sequentially. The pediatric index of mortality (PIM 2) score was calculated within 1 hour, and pediatric risk of mortality (PRISM) III and pSOFA scores were calculated within 24 hours of PICU admission. The pediatric sequential organ failure assessment score was recalculated after 72 hours. The primary outcome variable was hospital mortality, and secondary outcome variables were duration of PICU stay, need for mechanical ventilation, and occurrence of acute kidney injury (AKI). Appropriate statistical tests were used. Results: About 151 children with median (IQR) age of 36 (6, 84) months were enrolled. Mechanical ventilation was required in 87 (57.6%) children. Mortality was 21.2% at 28 days. The median (IQR) predicted mortality using PRISM III and PIM 2 score were 3.4 (1.5%, 11%) and 8.2 (3.1%, 16.6%) respectively. Area under ROC for prediction of mortality was highest for pSOFA 72 with a cut-off of 6.5 having sensitivity of 83.3% and specificity of 76.9%. Conclusion: The pSOFA score calculated at admission and at 72 hours had a better predictive ability for the PICU mortality compared to PRISM III and PIM 2 score. How to cite this article: Agrwal S, Saxena R, Jha M, Jhamb U, Pallavi. Comparison of pSOFA with PRISM III and PIM 2 as Predictors of Outcome in a Tertiary Care Pediatric ICU: A Prospective Cross-sectional Study. Indian J Crit Care Med 2024;28(8):796-801.

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.

Advancing Glaucoma Diagnosis: Employing Confidence-Calibrated Label Smoothing Loss for Model Calibration.

Objective: The aim of our research is to enhance the calibration of machine learning models for glaucoma classification through a specialized loss function named Confidence-Calibrated Label Smoothing (CC-LS) loss. This approach is specifically designed to refine model calibration without compromising accuracy by integrating label smoothing and confidence penalty techniques, tailored to the specifics of glaucoma detection. Design: This study focuses on the development and evaluation of a calibrated deep learning model. Participants: The study employs fundus images from both external datasets-the Online Retinal Fundus Image Database for Glaucoma Analysis and Research (482 normal, 168 glaucoma) and the Retinal Fundus Glaucoma Challenge (720 normal, 80 glaucoma)-and an extensive internal dataset (4639 images per category), aiming to bolster the model's generalizability. The model's clinical performance is validated using a comprehensive test set (47 913 normal, 1629 glaucoma) from the internal dataset. Methods: The CC-LS loss function seamlessly integrates label smoothing, which tempers extreme predictions to avoid overfitting, with confidence-based penalties. These penalties deter the model from expressing undue confidence in incorrect classifications. Our study aims at training models using the CC-LS and comparing their performance with those trained using conventional loss functions. Main Outcome Measures: The model's precision is evaluated using metrics like the Brier score, sensitivity, specificity, and the false positive rate, alongside qualitative heatmap analyses for a holistic accuracy assessment. Results: Preliminary findings reveal that models employing the CC-LS mechanism exhibit superior calibration metrics, as evidenced by a Brier score of 0.098, along with notable accuracy measures: sensitivity of 81%, specificity of 80%, and weighted accuracy of 80%. Importantly, these enhancements in calibration are achieved without sacrificing classification accuracy. Conclusions: The CC-LS loss function presents a significant advancement in the pursuit of deploying machine learning models for glaucoma diagnosis. By improving calibration, the CC-LS ensures that clinicians can interpret and trust the predictive probabilities, making artificial intelligence-driven diagnostic tools more clinically viable. From a clinical standpoint, this heightened trust and interpretability can potentially lead to more timely and appropriate interventions, thereby optimizing patient outcomes and safety. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Calibrating acceleration transmitters to quantify the seasonal energetic costs of activity in lake trout.

Bioenergetics models are powerful tools used to address a range of questions in fish biology. However, these models are rarely informed by free-swimming activity data, introducing error. To quantify the costs of activity in free-swimming fish, calibrations produced from standardized laboratory trials can be applied to estimate energy expenditure from sensor data for specific tags and species. Using swim tunnel respirometry, we calibrated acceleration sensor-equipped transmitting tags to estimate the aerobic metabolic rates (MO2) of lake trout (Salvelinus namaycush) at three environmentally relevant temperatures. Aerobic and swim performance were also assessed. Like other calibrations, we found strong relationships between MO2 and acceleration or swimming speed, and jackknife validations and data simulations suggest that our models accurately predict metabolic costs of activity in adult lake trout (~5% algebraic error and ~20% absolute error). Aerobic and swim performance metrics were similar to those reported in other studies, but their critical swimming speed was lower than expected. Additionally, lake trout exhibited a wide aerobic scope, suggesting that the avoidance of waters ≥15°C may be related to selection for optimal growing temperatures. The ability to quantify the free-swimming energetic costs of activity will advance our understanding of lake trout ecology and may yield improvements to bioenergetics model.

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

The dosimeter response should be calibrated in a reference field near the user's radiation environment. Environments around nuclear reactors and radition 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 (JAEA) and National Metrology Institute of Japan (NMIJ) have high-energy reference fields with energies above several MeV 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 laboratories, were experimentally and computationally evaluated for each high-energy field. These results agreed well with 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 can be verified in the two high-energy reference fields. .

Multivariate curve resolution-alternating least-squares and second-order advantage in first-order calibration. A systematic characterisation for three-component analytical systems.

BACKGROUND: Recent interest has been focused on the application of multivariate curve resolution-alternating least-squares (MCR-ALS) to systems involving the measurement of first-order and non-bilinear second-order data. The latter pose important challenges to bilinear decomposition models, due to the phenomenon of rotational ambiguity in the solutions, even under the application of the full set of chemical constraints that is usually employed in MCR-ALS calibration. RESULTS: After the analysis of several simulated and experimental datasets, important conclusions regarding the role of the selectivity patterns in the constituent spectra have been drawn concerning the achievement of the second-order advantage. Theoretical considerations based on the calculation of the areas of feasible solutions helped to support the observations regarding the predictive ability of MCR- ALS in the various datasets. SIGNIFICANCE: The understanding of the impact of rotational ambiguity in obtaining the second-order advantage with both first-order and non-bilinear second-order data is of paramount importance in the future development of analytical protocols of complex samples.

Predicting Drug-Target Interactions by Measuring Confidence with Consistent Causal Neighborhood Interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Establishment and Validation of a Nomogram for Identifying False Positives in Xpert MTB/RIF Rifampicin Resistance Test.

Purpose: This study aimed to establish and validate a diagnostic nomogram for identifying false positives in the Xpert MTB/RIF (Xpert) for detection of rifampicin resistance (RIF-R). Patients and Methods: In this retrospective study, we collected basic patient characteristics and various clinical information from the electronic medical record database. Patients were randomly divided into training and validation groups in a 7:3 ratio. LASSO regression was used to screen variables and construct a diagnostic nomogram. The ROC curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 384 patients were included in the study, with 268 and 116 patients in the training and validation cohorts, respectively. Finally, probe mutations and probe delay were identified as the independent influencing factors. Using the mutation of probe E as a reference, probes A or C (OR = 51.07, P<0.001), probe D (OR = 7.48, P<0.001), and multiple probes (OR = 4.42, P=0.029) were identified as factors influencing false positives in Xpert for detection of RIF-R. Taking probe delay ΔCT <4 as a reference, ΔCT (4-5.9) (OR = 17.06, P=0.005) and ΔCT (6-7.9) (OR = 36.67, P<0.001) were noted to be the factors influencing false positives in Xpert for detection of RIF-R. Based on these two variables, we constructed a diagnostic nomogram. The area under the curve of the nomogram model was 0.847 and 0.850 for the training and validation groups, respectively. The calibration curves were consistent. The DCA revealed that the model achieved the greatest net benefit when the threshold probability was set between 6% and 71% in the training cohort and 6% and 70% in the validation cohort. Conclusion: The nomogram constructed can identify false positives in Xpert for detection of RIF-R and provides basis for clinicians to formulate diagnosis and treatment plans.

Ecological niche modeling of Lactarius deliciosus using kuenm R package: Insights into habitat preferences.

Understanding species habitat preferences is essential for conservation and management efforts, as it enables the identification of areas with a higher likelihood of species presence. Lactarius deliciosus (L.) Gray, an economically important edible mushroom, is influenced by various environmental variables, yet information regarding its ecological niche remains elusive. Therefore, in this study, we aim to address this gap by modeling the fundamental niche of L. deliciosus. Specifically, we explore its distribution patterns in response to large-scale environmental factors, including long-term temperature averages and topography. We employed 242 presence-only georeferenced points in Europe obtained from the Global Biodiversity Information Facility (GBIF). Utilizing the Kuenm R package, we constructed 210 models incorporating five sets of environmental variables, 14 regularization multiplier values, and three feature class combinations. Evaluation metrics included statistical significance, predictive power, and model complexity. The final model was transferred to Turkiye, with careful consideration of extrapolation risk using MESS (multivariate similarity surface) and MoD (most dissimilar variable) metrics. In alignment with all three evaluation criteria, the algorithm implemented in Kuenm identified the best model as the linear-quadratic combination with a regularization multiplier of 0.2, based on variables selected by the contribution importance method. Results underscore temperature-related variables as critical determinants of L. deliciosus habitat preferences within the calibration area, with solar radiation also playing a significant role in the final model. These results underscored the effectiveness of ecological niche modeling (ENM) in understanding how climatic patterns may alter the distribution of species like L. deliciosus. The findings contribute to the development of informed conservation strategies and decision-making in dynamic environments. Emphasizing a comprehensive approach to ecological modeling is crucial for promoting sustainable forest management.

Tutorial on phantoms for photoacoustic imaging applications.

Significance: Photoacoustic imaging (PAI) is an emerging technology that holds high promise in a wide range of clinical applications, but standardized methods for system testing are lacking, impeding objective device performance evaluation, calibration, and inter-device comparisons. To address this shortfall, this tutorial offers readers structured guidance in developing tissue-mimicking phantoms for photoacoustic applications with potential extensions to certain acoustic and optical imaging applications. Aim: The tutorial review aims to summarize recommendations on phantom development for PAI applications to harmonize efforts in standardization and system calibration in the field. Approach: The International Photoacoustic Standardization Consortium has conducted a consensus exercise to define recommendations for the development of tissue-mimicking phantoms in PAI. Results: Recommendations on phantom development are summarized in seven defined steps, expanding from (1) general understanding of the imaging modality, definition of (2) relevant terminology and parameters and (3) phantom purposes, recommendation of (4) basic material properties, (5) material characterization methods, and (6) phantom design to (7) reproducibility efforts. Conclusions: The tutorial offers a comprehensive framework for the development of tissue-mimicking phantoms in PAI to streamline efforts in system testing and push forward the advancement and translation of the technology.

Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth.

A portable ultra-wideband microwave system (MiS) coupled with an antipodal slot Vivaldi patch antenna (VPA) was used as an objective measurement technology to predict sheep meat carcase GR tissue depth, tested against AUS-MEAT national accreditation standards. Experiment one developed the MiS GR tissue depth prediction equation using lamb carcasses (n = 832) from two slaughter groups. To create the prediction equations, a two layered machine learning stacking ensemble technique was used. The performance of this equation was tested within the dataset using a k-fold cross validation (k = 5), which demonstrated excellent precision and accuracy with an average R2 of 0.91, RMSEP 2.11, bias 0.39 and slope 0.03. Experiment two tested the prediction equation against the AUS-MEAT GR tissue depth accreditation framework which stipulates predictions from a device must assign the correct fat score, with a tolerance of ±2 mm of the score boundary, and 90% accuracy. For a device to be accredited three measurements captured within the same device, as well as measurements across three different devices, must meet the AUS-MEAT error thresholds. Three MiS devices scanned lamb carcases (n = 312) across three slaughter days. All three MiS devices met the AUS-MEAT accreditation thresholds, accurately predicting GR tissue depth 96.1-98.4% of the time. Between the different devices, the measurement accuracy was 99.4-100%, and within the same device, the measurement accuracy was 99.7-100%. Based on these results MiS achieved AUS-MEAT device accreditation as an objective technology to predict GR tissue depth.