Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.

This study aimed to investigate the accuracy of a robotic computer-assisted implant surgery (r-CAIS) for immediate implant placement.
Patients requiring immediate implant placement in the maxillary anterior region were enrolled for r-CAIS. Before surgery, the patients underwent a cone beam computed tomography (CBCT) scan with a positioning marker. Virtual implant placement position and drilling sequences were planned. Following spatial registration and calibration, the implants were placed with the robotic system under supervision. A postoperative CBCT was taken to control the actual implant positions. The DICOM data of the virtually planned and the actually placed implant were superimposed and registered through the accuracy verification software of the robotic system. The accuracy was calculated automatically. The deviation at the mesial-distal, labial-palatal, and apico-coronal directions were recorded.
Fifteen patients with 20 implants were included. No adverse surgical events or postoperative complications were reported. The global platform, apex, and angular deviation were 0.75 ± 0.20 mm (95 % CI: 0.65 to 0.84 mm), 0.70 ± 0.27 mm (95 % CI: 0.57 to 0.82 mm), and 1.17 ± 0.73° (95 % CI: 0.83 to 1.51°), respectively. Moreover, the vertical platform and apex deviation were 0.50 ± 0.31 mm, (95 % CI: 0.35 to 0.64 mm) and 0.48 ± 0.32 mm, (95 % CI: 0.33 to 0.63 mm), respectively. All the placed implant positions were further labial and apical than the planned ones, respectively.
High accuracy of immediate implant placement was achieved with the robotic system.
Our study provided evidence to support the potential of the robotic system in implant placement, even in challenging scenarios.

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used \"robust\" variance estimate of Barlow (Biometrics 50:1064-1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

The LC-MS-based method has emerged as the preferred approach for quantifying food allergens. However, the preparation of a traditional calibration curve (MSCC) is labor-intensive and error-prone. Here, a sensitive and robust LC-MS/MS method for quantifying 10 major food allergens was developed and validated, where the one-sample multipoint external calibration curve (OSCC) was employed instead of MSCC. By employing the multiple isotopologue reaction monitoring (MIRM) technique with only one spiked level in the blank, OSCC can be effectively established. Results demonstrate that the proposed method exhibits excellent performance in selectivity, sensitivity, accuracy, and precision, comparable to that of the traditional MSCC. Additionally, this strategy allows for isotope sample dilution by monitoring the less abundant MIRM channel. Moreover, the developed method was successfully applied to investigate the contamination of 10 food allergens in commercial food products. With its high throughput and robustness, the MIRM-OSCC-LC-MS/MS methodology has many potential applications, especially in the MS-based protein quantification analysis.

Psychometrics is historically grounded in the study of individual differences. Consequently, common metrics such as quantitative validity and reliability require between-person variance in a psychological variable to be meaningful. Experimental psychology, in contrast, deals with variance between treatments, and experiments often strive to minimise within-group person variance. In this article, I ask whether and how psychometric evaluation can be performed in experimental psychology. A commonly used strategy is to harness between-person variance in the treatment effect. Using simulated data, I show that this approach can be misleading when between-person variance is low, and in the face of methods variance. I argue that this situation is common in experimental psychology, because low between-person variance is desirable, and because methods variance is no more problematic in experimental settings than any other source of between-person variance. By relating validity and reliability with the corresponding concepts in measurement science outside psychology, I show how experiment-based calibration can serve to compare the psychometric quality of different measurement methods in experimental psychology.

For the US Food and Drug Administration\'s (FDA) mycotoxin program, the desired application of certified reference materials (CRMs) is primarily for validating methods for the determination of mycotoxins in foods and establishing metrological traceability of measurement results generated by such validated methods. The latter has been an important but insufficiently addressed challenge due to the lack of appropriate protocols and CRMs. Taking advantage of two recently available mycotoxin CRMs, OTAN-1 and Standard Reference Material (SRM) 1565, a protocol was developed for systematically examining uncertainty and establishing metrological traceability of measurement results of ochratoxin A (OTA) in corn through a series of calibration operations using the two CRMs. Instrument and method calibrations were performed using OTAN-1 and SRM 1565. The OTA value and its standard and expanded (k = 2, approximately 95% confidence) uncertainties were estimated from the calibration data and documented. These results demonstrate that the major contributing source of uncertainty is the sample matrix, highlighting the important role of the certified matrix reference material in method calibration. The value of OTA (38.5 ± 7.2 µg/g; 95% confidence interval) in the incurred sample was metrologically traceable to the International System of Units through two CRMs using the multi-laboratory validated liquid chromatography-mass spectrometry FDA compendial method. In addition, an alternative estimation of uncertainty was conducted using a one-point calibration, resulting in comparable uncertainty.

Analysis of low-level organic contaminants in complex matrices is essential for monitoring global food safety. However, balancing sample throughput with complex experimental designs and/or sample clean-up to best reduce matrix effects is a constant challenge. Multiple strategies exist to mitigate these effects, with internal standard-based methods such as isotope dilution mass spectrometry (IDMS) being the most advantageous. Here, multiple internal calibration strategies were investigated for the quantification of ochratoxin A (OTA) in wheat samples by liquid chromatography-mass spectrometry (LC-MS). Internal standard-based quantitation methods such as single (ID1MS), double (ID2MS), and quintuple (ID5MS) isotope dilution mass spectrometry, as well as external standard calibration, were explored and compared. A certified reference material (CRM) of OTA in flour, MYCO-1, was used to evaluate the accuracy of each method. External calibration generated results 18-38% lower than the certified value for MYCO-1, largely due to matrix suppression effects. Concurrently, consistently lower OTA mass fractions were obtained for the wheat samples upon quantitation by external calibration as opposed to ID1MS, ID2MS, and ID5MS. All isotope dilution methods produced results that fell within the expected range for MYCO-1 (3.17-4.93 µg/kg), validating their accuracy. However, an average 6% decrease in the OTA mass fraction was observed from results obtained by ID1MS compared to those by ID2MS and ID5MS. Upon scrutiny, these differences were attributed to an isotopic enrichment bias in the isotopically labelled internal standard [13C6]-OTA that was used for ID1MS, the OTAL-1 CRM. The advantages and limitations of each isotopic method are illustrated.

Process analytical technology (PAT) in late-stage drug product development is typically used for real-time process monitoring, in-process control, and real-time release testing. In early research and development (R&D), PAT usage is limited as the manufacturing scale is relatively small with frequent changes and only a few batches are produced on an annual basis. However, process understanding is critical at early R&D in order to identify process and formulation boundaries, so PAT applications could be particularly useful in early-stage R&D. For oral solid dosage form, conventional HPLC-based content uniformity (CU) methods with sampling of 3 tablets per stratified sampling location in early R&D are typically not sufficient to identify these manufacturing process boundaries and temporal profile. Here, we report a screening CU method based on a multivariate model using transmission Raman spectroscopy (TRS) data on a phase-appropriate calibration set of only 16 tablets. This initial model was used for multiple pre-GMP development batches to provide critical information about blend uniformity and content uniformity (CU). In this work, the precision of the TRS method was evaluated; multiple spectral preprocessing approaches were compared regarding their effects on measurement precision as well as their ability to mitigate the photo bleaching effects during precision experiments. Overall, the TRS-based CU method was much faster than a traditional HPLC-based method allowing a much larger number of tablets to be screened. This larger number of analyzed tablets enabled the processes boundaries and temporal changes in CU to be identified while providing proper statistical assurance on product quality.

Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation.