Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the instrumental Warner-Bratzler Shear force (WBSF). Accordingly, 60 variables including WBSF and belonging to 4 levels of the continuum that are farm-slaughterhouse-muscle-meat were analyzed by Partial Least Squares (PLS) and three decision tree methods (C&RT: classification and regression tree; QUEST: quick, unbiased, efficient regression tree and CHAID: Chi-squared Automatic Interaction Detection) to select the driving factors of beef tenderness and propose predictive decision tools. The former method retained 24 variables from 59 to explain 75% of WBSF. Among the 24 variables, six were from farm level, four from slaughterhouse level, 11 were from muscle level which are mostly protein biomarkers, and three were from meat level. The decision trees applied on the variables retained by the PLS model, allowed identifying three WBSF classes (Tender (WBSF ≤ 40 N/cm2), Medium (40 N/cm2 < WBSF < 45 N/cm2), and Tough (WBSF ≥ 45 N/cm2)) using CHAID as the best decision tree method. The resultant model yielded an overall predictive accuracy of 69.4% by five splitting variables (total collagen, µ-calpain, fiber area, age of weaning and ultimate pH). Therefore, two decision model rules allow achieving tender meat on PDO Maine-Anjou cows: (i) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain ≥ 169 arbitrary units (AU)) AND (ultimate pH < 5.55) THEN meat was very tender (mean WBSF values = 36.2 N/cm2, n = 12); or (ii) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain < 169 AU) AND (age of weaning < 7.75 months) AND (fiber area < 3100 µm2) THEN meat was tender (mean WBSF values = 39.4 N/cm2, n = 30).

This study investigated the knowledge, attitudes, consumption habits, and degree of acceptability of edible insects (EI) or derived products among Portuguese consumers. This work consisted of a questionnaire survey, undertaken on a sample of 213 participants. For the treatment of data, basic descriptive statistics were used, complemented with chi-square tests to assess some associations between categorical variables. Moreover, a tree classification analysis was carried out using a classification and regression tree (CRT) algorithm with cross-validation. The results indicated that people tend to have correct perceptions about the sustainability issues associated with the use of insects as alternative sources of protein; however, the level of knowledge and overall perception about their nutritive value is low. Regarding the consumption of EI, it was found that only a small part of the participants had already eaten them, doing it mostly abroad, by self-initiative, in a restaurant or at a party or event. Additionally, it was found that the reluctance to consume insects is higher if they are whole, but when they are transformed into ingredients used in food formulations, the level of acceptance increases. Furthermore, men have shown to have a better perception about EI, be more informed about sustainability, and have a higher level of acceptability when compared to women. As a final conclusion, it was observed that the Portuguese still show some resistance to adhere to the use of insects as replacements for meat products, but the market of insect based products can be a good alternative to overpass the neophobia associated with this type of food.

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

Ship health checks must be carried out which function to provide a sailing permit. The implementation of ship health checks is carried out in collaboration with the ministry of health and transportation. The implementation of the activity, commonly known as Port Health Quarantine Clearance (PHQC) requires time to check and the ship makes a payment check to be able to issue a sailing permit. The problem that arises in the field is that the ship delays PHQC payments and then impacts on the buildup of ships in the port, besides that officers also need longer time to process the issuance of sailing permits. This of course has an impact on other port services such as dwelling time and scheduled departures that can be delayed. In overcoming this problem, an in-depth study is needed to analyze the trend of late payment of ship health checks, what variables influence it and how treatment is done to overcome these problems. Using logistic regression and decision tree with Classification and Regression Tree algorithm , a model is then developed that determines the variables that affect the delay of the ship making PHQC payments.

Abstract Background: During COVID-19 epidemic, the central sterile supply department (CSSD) staff need to handle a large number of devices, utensils and non-disposable protective articles used by suspected or confirmed COVID-19 patients. This may bring psychological stress to the CSSD staff. However, the mental state of the CSSD staff during COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and the relevant influencing factors during COVID-19 epidemic.Methods: Conduct the questionnaire survey with the general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) among 423 CSSD staff from 35 hospitals in Sichuan Province, China. Analyse the data in SPSS 24.0, use classification and regression tree (CART) to analyse variables, and find variation between groups. Perform chi-square test on enumeration data, and perform t-test and analysis of variance on measurement data.Results: The CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = -0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = -0.477, P < 0.01). The job, age and political status of the CSSD staff were the main factors affecting their mental state. The CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01). Conclusion: During the epidemic, the CSSD staff’s psychological resilience was at a low level, and the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention shall be paid to the mental health of the CSSD staff, and it is necessary to take the protective measures regarding the risk factors at work to ensure they can maintain a good mental state during the epidemic.

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.