Measurement of inferior facial angle and prefrontal space ratio in first trimester fetuses with aneuploidies: a retrospective study

Objective To determine whether the measurement of inferior facial angle (IFA) and prefrontal space ratio (PFSR) in two-dimensional (2D) ultrasound images in the first trimester of pregnancy is reliable and to describe these markers in normal and aneuploid fetuses. Methods IFA and PFSR were measured in stored 2D midsagittal images of 200 normal and 140 aneuploid fetal profiles between 11 + 0 and 13 + 6 weeks of gestation. Limits of agreement (LOAs) and intraclass correlation coefficients (ICCs) for inter- and intraobserver differences were calculated. Results The mean IFA in normal fetuses was 76.5° ± 6.3. Between the two measurement rounds of the same observer, the LOAs were −5.4 to 7.1 (obs. 1) and 7.4 to 8.4 (obs. 2). For IFA measurements by the same observer the ICC was 0.88 (obs. 1) and for measurements by two different observers the ICC was 0.74. The mean PFSR was 0.76 ± 0.40 and the intraobserver LOAs were −0.372 to 0.395 (obs. 1) and −0.555 to 0.667 (obs. 2). For PFSR measurements by the same observer the ICC was 0.89 (obs. 1) and for measurements by two different observers the ICC was 0.65. Among aneuploid fetuses, IFA was below the normal range in one third of the cases with trisomy 18. PFSR was below the 95% prediction limit in 16.2% of fetuses with trisomy 21% and 17.9% of fetuses with trisomy 18. Conclusion IFA can be reliably measured in 2D ultrasound images in the first trimester of pregnancy with a high interobserver agreement and may provide information about retrognathia associated with various syndromes and aneuploidies at early stages of pregnancy.

Comprehensive ensemble in QSAR prediction for drug discovery

Background Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. Results The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/. Conclusions We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.

An Investigation Into the Prediction of Forming Limit Diagrams for Normal Anisotropic Material Based on Bifurcation Analysis

In this paper, formula derivation for bifurcation analysis based on a constitutive model including Hill 48 yield criterion with normal anisotropy of a pointed vertex on subsequent yield loci to predict the entire forming limit diagram (FLD) is carried out. Proportional loading, total deformation theory of plasticity, and power law relation are assumed. Predicted limit strains for Hill’s zero and minimum extension of localized neck orientation is derived. The dominancy of zero extension and minimum extension on the left-hand side of FLDs for different work hardening components and r-values are investigated in detail. An implicit four order rational function equation for major strain, which preferred that the orientation of neck correspond to minimum value of limit strain, is found by a developed optimization method. Optimized predicted limit strains for typical work hardening components and different r-values are obtained and discussed. Limit strains vary directly on the left and reversely on the right-hand side of FLD when r-value increases. Comparison between the predicted and experimental results exhibits a better agreement compared with those from the isotropic material. In addition, on the left-hand side, the resulted prediction limit strains represent a full dependency to assumed yield criterion. A comparison between the current work and Chow et al. results are performed and discussed in detail.

A Simple Normal Approximation for Weibull Distribution with Application to Estimation of Upper Prediction Limit

We propose a simple close-to-normal approximation to a Weibull random variable (r.v.) and consider the problem of estimation of upper prediction limit (UPL) that includes at leastlout ofmfuture observations from a Weibull distribution at each ofrlocations, based on the proposed approximation and the well-known Box-Cox normal approximation. A comparative study based on Monte Carlo simulations revealed that the normal approximation-based UPLs for Weibull distribution outperform those based on the existing generalized variable (GV) approach. The normal approximation-based UPLs have markedly larger coverage probabilities than GV approach, particularly for small unknown shape parameter where the distribution is highly skewed, and for small sample sizes which are commonly encountered in industrial applications. Results are illustrated with a real dataset for practitioners.

Surveillance of ambulance dispatch data as a tool for early warning

Early detection of disease outbreaks is essential for authorities to initiate and conduct an appropriate response. A need for an outbreak detection that monitored data predating laboratory confirmations was identified, which prompted the establishment of a novel symptom surveillance system. The surveillance system monitors approximately 80% of the Danish population by applying an outbreak detection algorithm to ambulance dispatch data. The system also monitors both regional and national activity and has a built-in, switch-on capacity for implementing symptom surveillance reporting in case of an alert. In an evaluation with outbreak scenarios it was found that decreasing the outbreak detection sensitivity from a prediction limit of 95% to one of 99% moderately reduced the time to detection, but considerably diminished the number of false alerts. The system was able to detect an increased activity of influenza-like illness in December 2003 in a timely fashion. The system has now been implemented in the national disease surveillance programme.

Relationship between Cortisol Increment and Basal Cortisol: Implications for the Low-Dose Short Adrenocorticotropic Hormone Stimulation Test

Background: We analyzed the low-dose (1 μg) rapid adrenocorticotropic hormone test (LDST) in 17 patients with a normal hypothalamic-pituitary-adrenal axis to determine reference intervals for the LDST on the basis of poststimulation cortisol increments. Methods: We analyzed test results for 17 patients (14 females and 3 males; age range, 18–46 years) who had received a 2-mL aliquot of low-dose (1 μg) adrenocorticotropic hormone prepared from one 250-μg vial of Synacthen diluted in 500 mL of sterile normal saline solution. Sampling took place at 0, 20, 30, and 60 min post stimulation. The cortisol increment was plotted against basal cortisol. Results: We observed a marked interdependence of the basal cortisol concentration with the increase in cortisol concentration. The relationship was inverse and linear with the best fit observed at 30 min post stimulation. The lower 95% prediction limit for basal cortisol at the zero increment was 400 nmol/L with a mean concentration of 600 nmol/L. Conclusions: We propose that a peak cortisol concentration <400 nmol/L is a sufficient single criterion for abnormal adrenal function as assessed by the LDST. Concentrations of 400–600 nmol/L are in the gray area, and those >600 nmol/L confirm normal adrenal function. Repeat analyses with larger sample sizes are warranted to confirm these observations.