Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.

BACKGROUND: Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. METHODS: Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. RESULTS: A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. CONCLUSIONS: The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases.

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Calibration of commercial fisheries echo sounders using seabed backscatter for the estimation of fishery resources.

Acoustic methods are often used for fisheries resource surveys to investigate fish stocks in a wide area. Commercial fisheries echo sounders, which are installed on most small fishing vessels, are used to record a large amount of data during fishing trips. Therefore, it can be used to collect the basic information necessary for stock assessment for a wide area and frequently. To carry out the quantification for the fisheries echo sounder, we devised a simple method using the backscattering strength of the seabed to perform calibration periodically and easily. In this study, seabed secondary reflections were used instead of primary reflection because the fisheries echo sounders were not equipped with a time-varied gain (TVG) function, and the primary backscattering strength of the seabed was saturated. It was also necessary to use standard values of seabed backscattering strength averaged over a certain area for calibration to eliminate some of the effects of differences in seabed sediment and vessel motions. By using standard values of the seabed secondary reflections, the fisheries echo sounder was calibrated accurately. Our study can provide a reliable framework to calibrate commercial fisheries echo sounders, to improve the estimation and management of fishery resources.

RFPID: development and 3D-printing of a female physical phantom for whole-body counter.

Whole-body counters (WBC) are used in internal dosimetry forin vivomonitoring in radiation protection. The calibration processes of a WBC set-up include the measurement of a physical phantom filled with a certificate radioactive source that usually is referred to a standard set of individuals determined by the International Commission on Radiological Protection (ICRP). The aim of this study was to develop an anthropomorphic and anthropometric female physical phantom for the calibration of the WBC systems. The reference female computational phantom of the ICRP, now called RFPID (Reference Female Phantom for Internal Dosimetry) was printed using PLA filament and with an empty interior. The goal is to use the RFPID to reduce the uncertainties associated within vivomonitoring system. The images which generated the phantom were manipulated using ImageJ®, Amide®, GIMP®and the 3D Slicer®software. RFPID was split into several parts and printed using a 3D printer in order to print the whole-body phantom. The newly printed physical phantom RFPID was successfully fabricated, and it is suitable to mimic human tissue, anatomically similar to a human body i.e., size, shape, material composition, and density.

Usability of sand samples in TL Radiation Dosimetry.

This study investigated natural sand thermoluminescence (TL) response as a possible option for retrospective high-dose gamma dosimetry. The natural sand under investigation was collected from six locations with selection criteria for sampling sites covering the highest probability of exposure to unexpected radiation on the Egyptian coast. Dose-response, glow curve, chemical composition, linearity, and fading rate for different sand samples were studied. Energy Dispersive X-ray Spectroscopy (EDX) analysis revealed differences in chemical composition among the various geological sites, leading to variations in TL glow curve intensity. Sand samples collected from Ras Sedr, Taba, Suez, and Enshas showed similar TL patterns, although with different TL intensities. Beach sands of Matrouh and North Coastal with a high calcite content did not show a clear linear response to the TL technique, in the dose range of 10 Gy up to 30 kGy. The results show that most sand samples are suitable as a radiation dosimeter at accidental levels of exposure. It is proposed here that for high-dose gamma dosimetry with doses ranging from 3 to 10 kGy, a single calibration factor might be enough for TL measurements using sand samples. However, proper calibration might allow dose assessment for doses even up to 30 kGy. Most of the investigated sand samples had nearly stable fading rates after seven days of storage. The Ras Sedr sands sample was the most reliable for retrospective dose reconstruction.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

Temperature-Wise Calibration Increases the Accuracy of DNA Methylation Levels Determined by High-Resolution Melting (HRM).

High-resolution melting (HRM) is a cost-efficient tool for targeted DNA methylation analysis. HRM yields the average methylation status across all CpGs in PCR products. Moreover, it provides information on the methylation pattern, e.g., the occurrence of monoallelic methylation. HRM assays have to be calibrated by analyzing DNA methylation standards of known methylation status and mixtures thereof. In general, DNA methylation levels determined by the classical calibration approach, including the whole temperature range in between normalization intervals, are in good agreement with the mean of the DNA methylation status of individual CpGs determined by pyrosequencing (PSQ), the gold standard of targeted DNA methylation analysis. However, the classical calibration approach leads to highly inaccurate results for samples with heterogeneous DNA methylation since they result in more complex melt curves, differing in their shape compared to those of DNA standards and mixtures thereof. Here, we present a novel calibration approach, i.e., temperature-wise calibration. By temperature-wise calibration, methylation profiles over temperature are obtained, which help in finding the optimal calibration range and thus increase the accuracy of HRM data, particularly for heterogeneous DNA methylation. For explaining the principle and demonstrating the potential of the novel calibration approach, we selected the promoter and two enhancers of MGMT, a gene encoding the repair protein MGMT.

Using L-cysteine to enhance calibration range and prevent a memory effect in mercury analysis of complex samples via ICP-OES.

BACKGROUND: Mercury (Hg) is a persistent pollutant occurring in the environment able to transition between different species. It can therefore be found in air, soil and water reservoirs becoming a present concern for the general population but also sensitive populations like pregnant women. Therefore, investigating organ-specific transfer mechanisms of Hg is mandatory for Hg toxicity testing. For this, an in vitro system using microporous inserts to monitor the transfer across an in vitro placental barrier has been used. However, due to the cytotoxicity of Hg only low concentrations (1.26 ×10-4 - 1.36 ×10-2 µg/µL Hg) can be applied, making Hg determination in cell culture medium using inductively coupled plasma-optical emission spectrometry challenging, especially when these trace amounts should be determined alongside other trace elements which are naturally occurring in cells and cell culture medium like the essential metals manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn). Additionally, Hg analysis on an ICP system holds also a number of challenges like a persistent memory effect and instability of Hg standard solutions. METHODS: The development of a rapid and sensitive ICP-OES method to determine Hg in different matrices like cell culture medium and cells has been performed on an Avio 220 Max ICP-OES (Perkin-Elmer) equipped with a cyclonic spray chamber and MicroMist® nebulizer. Cell lysates and cell culture medium were diluted in a mixture of 0.2 % L-cysteine, 2 % HNO3 and 0.1 % HCl and directly introduced into the ICP-OES system. Further method development included the suitability of the analysis of multiple elements like Mn, Fe, Cu, and Zn as well as the determination of the limit of detection and limit of quantification. RESULTS: The combination of 0.2 % L-cysteine, 2 % HNO3 and 0.1 % HCl is able to bind and stabilize Hg ions in standard solutions and in biological matrices over a wide dynamic concentration range (1 - 500 µg/L) also alongside other metals like Mn, Fe, Cu and Zn without losses of sensitivity. A short run time of 3 min enables high throughput analysis. Additionally, the high salt and carbon concentrations in the culture medium do not affect Hg sensitivity using the ICP-OES. CONCLUSION: This method is a useful tool for the quantification of Hg in a variety of complex matrices including cells and cell culture media (high salt and carbon-rich (∼1 % each)) with high sensitivity and minimal sample preparation allowing high throughput. Furthermore, not only Hg can be determined in biological matrices, but even multiple elemental analysis can be carried out to address the effect of Hg on other metals homeostasis.

SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.

In vitro calibration and in vivo validation of phenomenological corrosion models for resorbable magnesium-based orthopaedic implants.

Metallic bioresorbable orthopaedic implants based on magnesium, iron and zinc-based alloys that provide rigid internal fixation without foreign-body complications associated with permanent implants have great potential as next-generation orthopaedic devices. Magnesium (Mg) based alloys exhibit excellent biocompatibility. However, the mechanical performance of such implants for orthopaedic applications is contingent on limiting the rate of corrosion in vivo throughout the bone healing process. Additionally, the surgical procedure for the implantation of internal bone fixation devices may impart plastic deformation to the device, potentially altering the corrosion rate of the device. The primary objective of this study was to develop a computer-based model for predicting the in vivo corrosion behaviour of implants manufactured from a Mg-1Zn-0.25Ca ternary alloy (ZX10). The proposed corrosion model was calibrated with an extensive range of mechanical and in vitro corrosion testing. Finally, the model was validated by comparing the in vivo corrosion performance of the implants during preliminary animal testing with the corrosion performance predicted by the model. The proposed model accurately predicts the in vitro corrosion rate, while overestimating the in vivo corrosion rate of ZX10 implants. Overall, the model provides a "first-line of design" for the development of new bioresorbable Mg-based orthopaedic devices. STATEMENT OF SIGNIFICANCE: Biodegradable metallic orthopaedic implant devices have emerged as a potential alternative to permanent implants, although successful adoption is contingent on achieving an acceptable degradation profile. A reliable computational method for accurately estimating the rate of biodegradation in vivo would greatly accelerate the development of resorbable orthopaedic implants by highlighting the potential risk of premature implant failure at an early stage of the device development. Phenomenological corrosion modelling approach is a promising computational tool for predicting the biodegradation of implants. However, the validity of the models for predicting the in vivo biodegradation of Mg alloys is yet to be determined. Present study investigates the validity of the phenomenological modelling approach for simulating the biodegradation of resorbable metallic orthopaedic implants by using a porcine model that targets craniofacial applications.

Experimental comparison of cylindrical and plane parallel ionization chambers for reference dosimetry in continuous and pulsed scanned proton beams.

Objective. In this experimental work we compared the determination of absorbed dose to water using four ionization chambers (ICs), a PTW-34045 Advanced Markus, a PTW-34001 Roos, an IBA-PPC05 and a PTW-30012 Farmer, irradiated under the same conditions in one continuous- and in two pulsed-scanned proton beams.Approach. The ICs were positioned at 2 cm depth in a water phantom in four square-field single-energy scanned-proton beams with nominal energies between 80 and 220 MeV and in the middle of 10 × 10 × 10 cm3dose cubes centered at 10 cm or 12.5 cm depth in water. The water-equivalent thickness (WET) of the entrance window and the effective point of measurement was considered when positioning the plane parallel (PP) ICs and the cylindrical ICs, respectively. To reduce uncertainties, all ICs were calibrated at the same primary standards laboratory. We used the beam quality (kQ) correction factors for the ICs under investigation from IAEA TRS-398, the newly calculated Monte Carlo (MC) values and the anticipated IAEA TRS-398 updated recommendations.Main results. Dose differences among the four ICs ranged between 1.5% and 3.7% using both the TRS-398 and the newly recommendedkQvalues. The spread among the chambers is reduced with the newlykQvalues. The largest differences were observed between the rest of the ICs and the IBA-PPC05 IC, obtaining lower dose with the IBA-PPC05.Significance. We provide experimental data comparing different types of chambers in different proton beam qualities. The observed dose differences between the ICs appear to be related to inconsistencies in the determination of thekQvalues. For PP ICs, MC studies account for the physical thickness of the entrance window rather than the WET. The additional energy loss that the wall material invokes is not negligible for the IBA-PPC05 and might partially explain the lowkQvalues determined for this IC. To resolve this inconsistency and to benchmark MC values,kQvalues measured using calorimetry are needed.

Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data.

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.

Improved Mass Accuracy and Precision for Multi-Attribute Methods Using a New Internally Calibrated High Resolution Orbitrap Mass Detector.

In the development of biotherapeutics, a thorough understanding of a molecule's product quality attributes (PQAs) and their effect on structure-function relationships and long-term stability is essential for ensuring the safety and efficacy of the product. First published in 2015, the multi-attribute method (MAM), based on LC-MS peptide mapping and automation principles, can be used to support biotherapeutic process and product development. The MAM provides simultaneous site-specific detection, identification, quantitation, and quality control monitoring of selected PQAs. In this article, a low-maintenance MAM-ready mass detector with a small footprint was evaluated for its ability to monitor PQAs on proteolytically digested proteins with high mass accuracy and precision. Optimized source parameters enable robust relative quantitation of attributes with high sensitivity and minimal in-source fragmentation. A combination of a built-in one-point mass calibration procedure prior to data acquisition and Scan-to-Scan on-the-fly mass correction allows monitoring of most peptides for at least 54 days with sub-1 ppm mass accuracies at high-resolution (180,000 at m/z 200). This enables the use of <3 ppm mass tolerances for peptide monitoring, supporting high method specificity and robustness. LC-MS based MAM data from this instrument compares well to data collected by earlier MAM systems and conventional HPLC profile-based drug substance release assays.

Simultaneous in-air and underwater 3D kinematic analysis of swimmers: Feasibility and reliability of action sport cameras.

This study explored the potential of reconstructing the 3D motion of a swimmer's hands with accuracy and consistency using action sport cameras (ASC) distributed in-air and underwater. To record at least two stroke cycles of an athlete performing a front crawl task, the cameras were properly calibrated to cover an acquisition volume of 3 m in X, 8 m in Y, and 3.5 m in Z axis, approximately. Camera calibration was attained by applying bundle adjustment in both environments. A testing wand, carrying two markers, was acquired to evaluate the three-dimensional (3D) reconstruction accuracy in-air, underwater, and over the water transition. The global 3D accuracy (mean absolute error) was less than 1.5 mm. The standard error of measurement and the coefficient of variation were smaller than 1 mm and 1%, respectively, revealing that the camera calibration procedure was highly repeatable. No significant correlation between the error magnitude (percentage error during the test and the retest sessions: 1.2 to 0.8%) and the transition from in-air to underwater was observed. The feasibility of the hand motion reconstruction was demonstrated by recording five swimmers during the front crawl stroke, in three different tasks performed at increasing efforts. Intra-class correlation confirmed the optimal agreement (ICC>0.90) among repeated stroke cycles of the same swimmer, irrespective of task effort. Skewness, close to 0, and kurtosis, close to 3.5, supported the hypothesis of negligible effects of the calibration and tracking errors on the motion and speed patterns. In conclusion, we may argue that ASCs, equipped with a robust bundle adjustment camera calibration technique, ensure reliable reconstruction of swimming motion in in-air and underwater large volumes.

New approach for near-infrared wavelength selection using a combination of MIC and firefly evolution.

Full-length spectral data analysis has a big problem that the variables are highly in collinearity and correlation. Spectral wavelength selection is a continuing hot topic in quantitative or qualitative analysis. In this paper, we propose a new approach for near-infrared (NIR) wavelength selection. The novel strategy mainly refers to the modification of maximum information coefficient (MIC) method and an improvement of firefly evolutionary algorithm. We introduce the orthogonal decomposition to modify the MIC method, so as to search the informative signals conceived in projection vectors. We also raise the common firefly algorithm (FA) as in the discretized mode, and design a novel adaptive mapping function to improve its intelligent computing effect. In experiment, the modified MIC (MICm) method and the adaptive discrete FA algorithm (DFAadp) are joint together for combined optimization of the NIR calibration model. The proposed combined modeling strategy is applied for quantitative analysis of the fishmeal samples, in the concern to select their informative variables/wavelengths. Experimental results indicate that the combination of MICm and DFAadp perform better than traditional MIC method and common DFA. We conclude that the proposed combined optimization strategy is beneficial for wavelength selection in NIR spectral analysis. It is anticipated to be validated for further applications in a wide range.

Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses.

The objective of this study was to develop calibration models against rib eye traits and independently validate the precision, accuracy, and repeatability of the Frontmatec Q-FOM™ Beef grading camera in Australian carcasses. This study compiled 12 different research datasets acquired from commercial processing facilities and were comprised of a diverse range of carcass phenotypes, graded by industry identified expert Meat Standards Australia (MSA) graders and sampled for chemical intramuscular fat (IMF%). Calibration performance was maintained when the device was independently validated. For continuous traits, the Q-FOM™ demonstrated precise (root mean squared error of prediction, RMSEP) and accurate (coefficient of determination, R2) prediction of eye muscle area (EMA) (R2 = 0.89, RMSEP = 4.3 cm2, slope = 0.96, bias = 0.7), MSA marbling (R2 = 0.95, RMSEP = 47.2, slope = 0.98, bias = -12.8) and chemical IMF% (R2 = 0.94, RMSEP = 1.56%, slope = 0.96, bias = 0.64). For categorical traits, the Q-FOM™ predicted 61%, 64.3% and 60.8% of AUS-MEAT marbling, meat colour and fat colour scores equivalent, and 95% within ±1 classes of expert grader scores. The Q-FOM™ also demonstrated very high repeatability and reproducibility across all traits.

Development and validation of an alternative procedure for quantitative quality control analysis of 99mTc-radiopharmaceuticals using a Geiger Müller counter.

BACKGROUND AND OBJECTIVES: In a hospital radiopharmacy with 2a operational level, including the preparation of radiopharmaceuticals from prepared and approved reagent kits, it is common to have a single activimeter or dose calibrator for labeling and fractionation, and to perform the quality controls of the 99mTc-radiopharmaceuticals. In certain cases, the accumulation of radioactive material or accidental contamination of the work area causes the background to exceed the limits to carry out the radiochemical purity analyses and it is necessary to look for viable alternatives. In this work, a Geiger Müller detector (equipped with a probe for measuring surface contamination) frequently used for radioprotection purposes, was validated as an alternative and its performance was compared against the activimeter for 99mTc-radiopharmaceuticals. MATERIALS AND METHODS: Using [99mTc]pertechnetate, systematic studies of error analyses and detector response to activity concentration, activity and measurement time were carried out in liquid matrices and in paper. The results were compared against an activimeter calibrated for [99mTc]Tc. RESULTS: The developed method was used to determine the radiochemical purity of the compounds [99mTc]Tc-MDP and [99mTc]Tc-MIBI by ascending paper chromatography tests, obtaining comparable values to those measured with an activimeter in the same system (within 1% uncertainty) and using the method of vial partitioning in a dedicated equipment. CONCLUSIONS: This work demonstrates that a Geiger Müller detector with a probe for measuring surface contamination can be adequately used to replace other equipment in the control of radiochemical purity in the hospital radiopharmacy.

Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement.

BACKGROUND: Cone-beam CT (CBCT) with non-circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time. PURPOSE: We propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations. METHODS: The algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C-arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints. RESULTS: Qualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D-2D registration-based geometric calibration algorithm. CONCLUSIONS: We have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.