Conditional Inference Tree Model Research Articles

Waste to protein: A systematic review of a century of advancement in microbial fermentation of agro-industrial byproducts.

Increasing global consumption of protein over the last five decades, coupled with concerns about the impact on emissions of animal-based protein production, has created interest in alternative protein sources. Microbial proteins (MPs), derived through the fermentation of agro-industrial byproducts, present a promising option. This review assesses a century of advancements in this domain. We conducted a comprehensive review and meta-analysis, examining 347 relevant research papers to identify trends, technological advancements, and key influencing factors in the production of MP. The analysis covered the types of feedstocks and microbes, fermentation methods, and the implications of nucleic acid content on the food-grade quality of proteins. A conditional inference tree model and Bayesian factor were used to ascertain the impact of various parameters on protein content. Out of all the studied parameters, such as type of feedstock (lignocellulose, free sugars, gases, and others), type of fermentation (solid, liquid, gas), type of microbe (bacteria, fungi, yeast, and mix), and operating parameters (temperature, time, and pH), the type of fermentation and microbe were identified as the largest influences on protein content. Gas and liquid fermentation demonstrated higher protein content, averaging 52% and 42%, respectively. Among microbes, bacterial species produced a higher protein content of 51%. The suitable operating parameters, such as pH, time, and temperature, were also identified for different microbes. The results point to opportunities for continued innovation in feedstock, microbes, and regulatory alignment to fully realize the potential of MP in contributing to global food security and sustainability goals.

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Changes in inter-city car travel behavior over the course of a year during the COVID-19 pandemic: A decision tree approach

This study examined how inter-city car travel behavior changed due to the COVID-19 pandemic over the three pandemic waves in South Korea. We used daily freeway traffic volume data from 2019 and 2020 to represent inter-city car travel behavior and compiled a dataset that includes information on the COVID-19 pandemic, weather conditions, holidays, and socio-economic factors. A conditional inference tree model was employed to classify and understand changes in inter-city car travel during the pandemic. The modeling result demonstrates that inter-city travel-related responses to the pandemic varied over time but had few variations across regions. The main findings can be summarized as follows: i) inter-city car travelers responded more sensitively to the number of national confirmed cases than the number of regional confirmed cases; ii) the threshold of travelers' sensitivity to the number of confirmed cases rose as the pandemic continued; iii) inter-city travel behavior exhibited a more resilient recovery as the pandemic prolonged; and iv) travelers tend to resume their inter-city car travels before social distancing rules were actually eased. The findings in this study provide useful insights to suggest a transportation-related policy strategy for effectively managing possible future pandemics at the national level.

High-grade cervical lesions diagnosed by JAM3/PAX1 methylation in high-risk human papillomavirus-infected patients.

Currently, traditional cervical cancer screening methods, such as high-risk human papillomavirus testing and liquid based cytology (LBC), still possess limitations. This study aims to identify new diagnostic biomarkers to achieve the goal of "precision screening" via exploring the clinical value of DNA methylation [ΔCtP: paired box gene 1 (PAX1)and ΔCtJ: junctional adhesion molecule 3 (JAM3)] detection in cervical exfoliated cells for the diagnosis of high-grade cervical lesions. A total of 136 patients who underwent gynecological examinations in the vaginal room of the Department of Gynecology at the Third Xiangya Hospital of Central South University from June 2021 to June 2022 were retrospectively studied. Among them, 122 patients had non-high-grade cervical lesions, and 14 patients had high-grade cervical lesions. The variables included general information (age, body mass index, and menopause status), LBC, high-risk human papillomavirus, cervical tissue pathology, vaginal examination results, and the ΔCt values of JAM3 and PAX1 gene methylation. Logistic regression analysis was used to identify the factors affecting the diagnosis of high-grade cervical lesions, followed by correlation analysis and construction of a conditional inference tree model. Logistic regression analysis showed that the methylation ΔCt values of PAX1 and JAM3 genes and LBC detection results were statistically significant between the high-grade cervical lesions group and the non-high-grade cervical lesions group (all P<0.05). Correlation analysis revealed a negative correlation between cervical pathological changes and ΔCtP (r=-0.36, P<0.001), ΔCtJ (r=-0.448, P<0.001), LBC (r=-0.305, P<0.001), or bacterial diversity (r=-0.183, P=0.037). The conditional inference tree showed that when ΔCtJ>10.13, all of patients had non-high-grade cervical lesions, while ΔCtP>6.22, the number of non-high-grade lesions accounted for 97.5% (117/120), and high-grade lesions accounted for only 2.5% (3/120). When ΔCtJ>8.61 and LBC were atypical squamous cell of undetermined significance or negative for intraepithelial lesions or malignancy (NILM), 105 (99.1%) patients were non-high-grade cervical lesions, only 1 (0.9%) patient was high-grade lesion. When the results of LBC were high-grade lesions, only 9 patients' histopathological examination was the high-grade lesions and 3 non-high-grade lesions. When LBC indicated low-grade lesions, atypical squamous cell of undetermined significance, no intraepithelial lesions, and ΔCtP>6.22, 117 (97.5%) of patients' histopathological examination was the non-high-grade lesions. The JAM3/PAX1 gene methylation test can be used independently for the stratified diagnosis of high-grade/non-high-grade cervical lesions in women with high-risk human papillomavirus infection, independent of the cytological results of cervical excision. The JAM3/PAX1 gene methylation test can also be used in combination with LBC to make up for the shortcomings of low sensitivity of LBC. In addition, the application of methylation kit in large-scale cervical cancer screening in the future will be good to the detection of more patients with high-grade cervical lesions, and achieve early screening and early treatment for cervical lesions/cancer.

A conditional inference tree model for predicting cancer risk of non-mass lesions detected on breast ultrasound.

To generate and validate a prediction model based on imaging features for cancer risk of non-mass lesions (NMLs) detected on breast ultrasound (US). In this single-center study, consecutive women with 503 NMLs detected on breast US between 2012 and 2019 were retrospectively identified. The lesions were randomly assigned to the training or testing dataset with a 70/30 split. Age, symptoms, lesion size, and US features were collected. Multivariate analyses were employed to identify risk factors associated with malignancy. The predictive model was developed by using conditional inference trees (CTREE). There were 498 patients (50.9 ± 13.29years; range, 22-88years) with 503 NMLs with histopathologic results or > 2-year follow-up, including 224 (44.5%) benign and 279 (55.5%) malignant lesions. At multivariate analysis, age (odds ratio (OR) = 1.08, 95% confidence interval (CI), 1.06-1.11, p < 0.001), NMLs with focal mass effect (OR = 3.03, 95% CI, 1.59-5.81, p = 0.001), indistinct glandular-fat interface (GFI) (OR = 4.23, 95% CI, 2.31-7.73, p < 0.001), geographic (OR = 3.47, 95% CI, 1.20-10.8, p = 0.022) and mottled (OR = 3.67, 95% CI, 1.32-10.21, p = 0.013) patterns, and calcifications (OR = 2.15, 95% CI, 1.16-4.01, p = 0.016) were associated with malignancy. The GFI status, architectural patterns, general morphology, and calcifications were consistently identified as the strongest US predictors of malignancy using CTREE analysis. Based on these factors, individuals were stratified into six risk groups. The predictive model showed an area under the curve of 0.797 in the testing dataset. The CTREE model efficiently aids in interpreting and managing ultrasound-detected breast NMLs, overcoming BI-RADS limitations by refining cancer risk stratification. The CTREE model allows for the reclassification of BI-RADS categories into subgroups with varying malignancy probabilities, thus providing a valuable enhancement to the BI-RADS assessment for the diagnosis of ultrasound-detected NMLs, with the potential to minimize unnecessary biopsies. • The indistinct glandular-fat interface (GFI) status, NML with focal mass effect, geographic or mottled patterns, and calcifications are the strongest imaging predictors of malignant non-mass lesions (NMLs) detected on breast US. • A practical system has been created to categorize NMLs found in breast US; each classification is associated with a degree of diagnostic certainty. • The model may contribute to patient stratification by determining the relative likelihood of malignancy and thus support clinical decision-making and evidence-based management.

Quantifying COVID-19 policy impacts on subjective well-being during the early phase of the pandemic: A cross-sectional analysis of United States survey data from March to August 2020.

To stop the spread of COVID-19, a number of public health policies and restrictions were implemented during the pre-vaccination phase of the pandemic. This study provides a quantitative assessment of how these policies impacted subjective well-being (SWB) in the United States over a 6-month period spanning March to August 2020. We study two specific research objectives. First, we aim to quantify the impacts of COVID-19 public health policies at different levels of stringency on SWB. Second, we train and implement a conditional inference tree model for predicting individual SWB based both on socio-demographic characteristics and policies then in place. Our results indicate that policies such as enforcing strict stay-at-home requirements and closing workplaces were negatively associated with SWB, and that an individual's socio-demographic characteristics, including income status, job, and gender, conditionally interact with policies such as workplace closure in a predictive model of SWB. Therefore, although such policies may have positive health implications, they also have secondary environmental and social implications that need to be taken into account in any cost-benefit analysis of such policies for future pandemic preparedness. Our proposed methodology suggests a way to quantify such impacts through the lens of SWB, and to further advance the science of pandemic preparedness from a public health perspective.

You’re so mean but I like it – Metapragmatic evaluation of mock impoliteness in Danmaku comments

Mock impoliteness, a term encompassing a wide array of phenomena (e.g., banter, teasing, mocking, jocular mockery, jocular abuse/insults, humour, etc.), has long been grounded in the framework of (im)politeness. However, the research on the participants’ metapragmatic evaluations of mock impoliteness is scarce, with the exception of Sinkeviciute (2017). This research aims to investigate the third-party participants’ metapragmatic evaluation in Danmaku comments in a Chinese online talk show Roast! that features mock impoliteness speech events. Danmaku, as a commenting system that displays users’ synchronous comments within the video stream, is widely used in Asian countries, especially in China and Japan (Wu & Ito, 2014). Danmaku comments provide easy access to a vast amount of third-party participants’ evaluations of mock impoliteness, which is an ideal data source for this research. Such metapragmatic evaluations offer invaluable insight to the first-order understanding of mock impoliteness, which resonates with the discursive approaches to (im)politeness that advocates first-order understanding of (im)politeness interactions (Eelen, 2001; Locher and Watts, 2005; Locher, 2006, 2012, 2015; Mills, 2003). By qualitatively categorizing the information provided in the Danmaku comments, a data-driven coding scheme is created, which captures different aspects of information: (i) in-text reference (Referent and Speech Event); (ii) pragmatic phenomena that is relevant to mock impoliteness (Impoliteness and Funniness), and (iii) metapragmatic evaluation (positive/negative Evaluation). Then a conditional inference tree model (Hothorn et al., 2006; Tagliamonte and Baayen, 2012; Tantucci and Wang, 2018) was fitted to investigate to what extent the above factors contribute to third-party participants’ metapragmatic evaluations of mock impoliteness. This method generated clear data visualization by displaying the ranking of contributing factors to the metapragmatic evaluations. Such quantitative results were then interpreted through qualitative analysis of typical examples from the data. The analysis concludes that funniness and impoliteness are the two most statistically significant factors contributing to Danmaku users’ qualitative evaluations. This conclusion, in return provides solid empirical evidence for second-order theoretical underpinning of mock impoliteness.

Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy.

COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. This study has two objectives. First, we quantify the associations between common socio-demographic variables (including, but not limited to, age, ethnicity, and income) and vaccine acceptance in the U.S. Second, we use a conditional inference tree to quantify and visualize the interaction and conditional effects of relevant socio-demographic variables, known to be important correlates of vaccine acceptance in the U.S., on vaccine acceptance. We conduct a retrospective analysis on a COVID-19 cross-sectional Gallup survey data administered to a representative sample of U.S.-based respondents. Our univariate regression results indicate that most socio-demographic variables, such as age, education, level of household income and education, have significant association with vaccine acceptance, although there are key points of disagreement with the global survey. Similarly, our conditional inference tree model shows that trust in the (former) Trump administration, age and ethnicity are the most important covariates for predicting vaccine hesitancy. Our model also highlights the interdependencies between these variables using a tree-like visualization.

Sociodemographic, environmental and biological factors affecting uses of plants from open ecosystems: Insights for improved livelihoods and biodiversity conservation

The sustainable management of open ecosystems requires the committed participation of the neighboring people. Attention should be given to plant species ecology, which conditions the rate of vegetation growth. Sociocultural and ecological factors contribute to both the loss and the conservation of fragile ecosystems. Owing to the importance of traditional medicine in Benin, a focus has been made on the selection of species in this field as a case study for highlighting the factors that drive the selection of plant species in Benin. Further inferences highlight the conservation status of the selected woodlands and savannas. The study assessed the effects of people’s sociodemographic status and species’ ecological parameters on the variation in people’s knowledge associated with species’ usefulness in traditional medicine in Benin. An ethnobotanical survey was conducted through semi-structured individual interviews with 590 specialists in traditional medicine. A conditional inference tree model was combined with a generalized linear model to determine the factors associated with traditional medicinal knowledge (TMK). It was found that TMK varied with informant ethnicity, source of knowledge, membership position, age, instruction level, and professional activity. As for the ecological and species accessibility factors, phytodistrict, mode of reproduction of the species, reasons for the species’ disappearance, availability, and conservation status influenced TMK. When combining the two categories of factors, phytodistrict, habitat of species conservation, source of knowledge, and membership position of the informants were highlighted as the principal factors that influenced TMK. These identified factors should be integrated into a comprehensive management plan for TMK conservation and biodiversity management, and the sustainable use of savanna resources. Through this, sustainable management of open ecosystems, resulting in biodiversity conservation, livelihood improvement, and climate change mitigation, will be achieved. Such a plan should be based on the mix of ethnicities and the diversity of the species’ habitat in each phytodistrict.

Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer.

Accurate diagnosis of invasion depth for T1 colorectal cancer is of critical importance as it decides optimal resection technique. Few reports have previously covered the effects of endoscopic morphology on depth assessment. We developed and validated a novel diagnostic algorithm that accurately predicts the depth of early colorectal cancer. We examined large pathological and endoscopic databases compiled between Jan 2015 and Dec 2018. Training and validation data cohorts were derived and real-world diagnostic performance of two conditional interference tree algorithms (Models 1 and 2) was evaluated against that of the Japan NBI-Expert Team (JNET) classification used by both expert and non-expert endoscopists. Model 1 had higher sensitivity in deep submucosal invasion than that of JNET alone in both training (45.1% vs. 28.6%, p < 0.01) and validation sets (52.3% vs. 40.0%, p < 0.01). Model 2 demonstrated higher sensitivity than Model 1 (66.2% vs. 52.3%, p < 0.01) in excluding deeper invasion of suspected Tis/T1a lesions. We discovered that machine-learning classifiers, including JNET and macroscopic features, provide the best non-invasive screen to exclude deeper invasion for suspected Tis/T1 lesions. Adding this algorithm improves depth diagnosis of T1 colorectal lesions for both expert and non-expert endoscopists.

Super responders: Predicting language gains from JASPER among limited language children with autism spectrum disorder.

Early intervention can provide a great benefit for children with autism spectrum disorder (ASD). However, no single intervention is effective for all children. Even when an intervention is effective overall, individual child response varies. Some children make incredible progress, and others make slow or no progress. Therefore, it is important that the field move towards developing methods to personalize intervention. Operationalizing meaningful change and predicting intervention response are critical steps in designing systematic and personalized early intervention. The present research used improvement in expressive language to group children that received a targeted social communication early intervention, Joint Attention, Symbolic Play, Engagement, and Regulation (JASPER), into super responders and slow responders. Using baseline data from traditional standardized assessments of cognition and behavioral data from validated experimental measures of play and social communication, we used conditional inference tree models to predict responder status. From a sample of 99 preschool age, limited language children with ASD, play diversity was the most significant predictor of responder status. Children that played functionally with a wider variety of toys had increased odds of being a super responder to JASPER. A combination of lower play diversity and impairments in fine motor abilities increased the odds of children being slow responders to JASPER. Results from the present study can inform future efforts to individualize intervention and systematic approaches to augmenting treatment in real time. LAY SUMMARY: To help us answer the question of for whom an intervention works best, we examined 99 children, age three to five, who qualified as being limited spoken language communicators, and received a targeted intervention for social communication and language. We used child characteristics before intervention to predict which children would improve their language the most and found that the ability to play appropriately with a wider variety of toys predicted the best improvements in expressive language. These findings will help better inform future work to individualize intervention based on the unique needs of each child.

Salience-simplification strategy for markedness of causal subordinators: “Because” and “since” in argumentative essays

Previous studies have discriminated between different causal subordinators by assessing their lexico-semantics, clausal positioning, and the potential for separating punctuation marks. Distinguishing between the subordinators “because” and “since” in argumentative essays, this study adds to this line of research by a) exploring more contextual factors around these two causal markers, b) incorporating the effective factors in multifactorial models to measure their weights in discriminating between these two causal markers, and c) examining the different weights borne by individual factors from the perspective of markedness correspondence strategies. We retrieved 976 causal complex sentences (488 for each subordinator) from student argumentative essays and applied conditional inference tree models to these data. The results revealed that six factors were effective in discriminating between causal subordinators. The semantic factor clausal relationship indicated preferred clausal relationships introduced by “because” and “since,” while five syntactic factors demonstrated form correspondence to the markedness of the subordinators. Furthermore, conditional inference tree models revealed the factors’ relative weights in discriminating between “because” and “since,” suggesting that writers adopt the salience-simplification strategy to match the markedness of the subordinators in argumentative essays.

Threshold effect of ecosystem services in response to climate change, human activity and landscape pattern in the upper and middle Yellow River of China

Identifying spatiotemporal heterogeneity and impact mechanism of ecosystem service value (ESV) is critical for enhancing ecosystem management and regional sustainability. However, the existing findings have failed to investigate the synergetic effect of multiple drivers on ESV, nor have considered the threshold effects under different conditions, which is important to understand the complex and frequently interacting ecosystem. In this study, we applied the improved ESV calculation method, Pearson correlation analysis and conditional inference tree model to depict the dynamic changes of ESV, to explore separate and synergetic effects of various factors, and further to identify key thresholds under combined effects in urban agglomerations along the upper and middle Yellow River area (UMYRA). The results showed that: (1) The total ESV of the study area increased by 5936.63 million CNY over the study period. With the exception of the urban agglomeration along the Yellow River in Ningxia (YRNX), four types of sub-services in Hulunbeier-Baotou-Erdos-Yulin urban agglomeration (HBEY) and Taiyuan urban agglomeration (TY) showed a rising trend. Spatially, the greater growth of ESV mainly concentrated in the northern desert belt, whereas the major grain production areas and urban areas presented different degrees of degradation. (2) Climate factors and land use intensity are the main factors affecting the total ESV, followed by economic density and terrain condition, while landscape metrics exert a relatively small impact on ESV. But the influences of these factors on single ESV show distinct internal differences. (3) Synergetic effects of multi-factors showed that: ①When the evapotranspiration and precipitation are at a certain threshold (1277 mm＜PET＜1317 mm and PRE＞417.55 mm), rising temperature can effectively promote the growth of ESV. The same effect is also observed on steep slopes under matched hydrothermal conditions; ②When economic development is at a high level, intensifying human activities within a certain range (0.11＜HAI＜0.29 and 184.32＜LUI＜210.6) could greatly promote the ESV, but the opposite effect was also true when the thresholds were breached; ③Fragmented landscapes with low diversity (SHDI＜0.86) can promote ESV improvement, but high diversity fragmented landscapes (PD＞0.14 or SHDI＞0.85) would lead to ESV decline. Our research sheds light on the key thresholds under synergetic effect of multiple factors on ESV from a novel perspective, which provide a specific and practical guidance for achieving sustainable management.

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

Classification and analysis of outcome predictors in non-critically ill COVID-19 patients.

BackgroundEarly detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected patients who could develop a severe form of COVID‐19 must be considered of great importance to carry out adequate care and optimise the use of limited resources.AimsTo use several machine learning classification models to analyse a series of non‐critically ill COVID‐19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome.MethodsWe retrospectively analysed non‐critically ill patients with COVID‐19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected.ResultsIn the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64–0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction‐inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors.ConclusionsIn non‐critically ill COVID‐19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID‐19, such as age or dementia, influence clinical outcomes.

Research on main transformer defect detection methods based on Conditional Inference Tree and AdaBoost Algorithm

With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.

Castanea spp. Agrobiodiversity Conservation: Genotype Influence on Chemical and Sensorial Traits of Cultivars Grown on the Same Clonal Rootstock.

A large species diversity characterises the wide distribution of chestnuts in Asia, North America, and Europe, hence reflecting not only the adaptation of the genus Castanea to diverse environmental conditions, but also to different management strategies encompassing orchards. The characterisation and description of chestnut populations and cultivars are crucial to develop effective conservation strategies of one of the most important Italian and European fruit and wood species. Chestnut cultivars grown in the same pedoclimatic conditions and on the same clonal rootstock were characterised with sensory, spectrophotometric, and chromatographic analysis to determine the phytochemical composition and nutraceutical properties. A multivariate approach, including principal component analysis and conditional inference tree models, was also performed. The ease of peeling, seed colour, and intensity of sweetness were the sensory descriptors that allowed us to differentiate C. sativa cultivars. Antioxidant capacity ranged from 9.30 ± 0.39 mmol Fe+2 kg−1 DW (‘Bouche de Bètizac’) to 19.96 ± 1.89 mmol Fe+2 kg−1 DW (‘Garrone Rosso’). Monoterpenes represented the main component, reaching 88% for hybrids, followed by polyphenols (10–25% for hybrids and chestnuts, respectively). A multivariate approach showed that phenolic acids and tannins were the bioactive classes with the highest discriminating power among different genotypes, and that genotype is a significant variable (p < 0.05). In addition, most of the analysed chestnut cultivars showed a content of bioactive compounds similar to or higher than the main hazelnut, walnut, and almond varieties. Chestnut agrobiodiversity could be intended as strictly associated to the genotype effect and underlines the large variability within the genus Castanea, and therefore, the importance of in farm and ex situ conservation of local germplasm is part of a global strategy aimed at increasing the levels of agrobiodiversity.

조건부 추론나무모형을 이용한 KLPGA우승자와 비우승자의 경기력비교분석

Purpose: The purpose of this study is to compare the performance factors of the KLPGA winners and the non-winners and identify the winner’s performance factors. This study is intended to help establish the training direction of athletes by presenting the performance factors to be trained to win the KLPGA.<BR> Method: This study used statistical program R to analyze the performance factors of the winners and non-winners who are the top 60 players among 177 in the prize rankings of KLPGA in 2017-2019. To give an overview of the distribution of performance factors, graphs were compiled by year using descriptive statistics and ggplot2 package of R. In addition, Conditional Inference Tree model was utilized using the partykit package of R.<BR> Results: The results of this study are as follows. 1) In the case of all players, the variables affecting the prize ranking were in the order of the par break rate (p<.001), the par save rate (p<.001), and the average birdie rate (p<.001). 2) In the case of the winner, the variables affecting the prize ranking were in the order of the par save rate, the number of participation in the competition, and the average birdie rate. 3) The variables affecting the ranking of non-winners were the average buddy rate and the par save rate. 4) As a result of applying the above performance factors to active players, it was proved valid.<BR> Conclusion: In conclusion, this study is able to identify the required performance factors according to the player’s ranking. The winners should pay attention to the par save rate, the number of participants in the competition, and the average birdie rate. The non-winners should pay attention to the average birdie rate and par save rate.

Machine Learning Prediction of Extracapsular Extension in Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma.

To determine whether machine learning (ML) can predict the presence of extracapsular extension (ECE) prior to treatment, using common oncologic variables, in patients with human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). Retrospective database review. National Cancer Database study. All patients with HPV-associated OPSCC treated surgically between January 1, 2010, and December 31, 2015, were selected from the National Cancer Database. Patients were excluded if surgical pathology reports did not include information regarding primary tumor stage, number of metastatic regional lymph nodes, size of largest metastatic regional lymph node, and tumor grade. The data were split into a random distribution of 80% for training and 20% for testing with ML methods. A total of 3753 adults with surgically treated HPV-associated OPSCC met criteria for inclusion in the study. Approximately 38% of these patients treated with surgical management demonstrated ECE. ML models demonstrated modest accuracy in predicting ECE, with the areas under the receiver operating characteristic curves ranging from 0.58 to 0.68. The conditional inference tree model (0.66) predicted the metastatic lymph node number to be the most important predictor of ECE. Despite a large cohort and the use of ML algorithms, the power of clinical and oncologic variables to predict ECE in HPV-associated OPSCC remains limited.

Patterns of signs, symptoms, and laboratory values associated with Zika, dengue, and undefined acute illnesses in a dengue endemic region: Secondary analysis of a prospective cohort study in southern Mexico

ObjectivesDengue and Zika infections cause illnesses with overlapping clinical manifestations. The aim of this study was to explore the association of each of these infections with single or grouped clinical and laboratory parameters. MethodsClinical and laboratory data were collected prospectively from a cohort of patients seeking care for symptoms meeting the Pan American Health Organization’s modified case-definition criteria for probable Zika virus infection. Zika and dengue were diagnosed with RT-PCR. The relationship of clinical characteristics and laboratory data with Zika, dengue, and undefined acute illness (UAI) was examined. ResultsIn the univariate models, localized rash and maculopapular exanthema were associated with Zika infection. Generalized rash, petechiae, and petechial purpuric rash were associated with dengue. Cough and confusion/disorientation were associated with UAI. Platelets were significantly lower in the dengue group. A conditional inference tree model showed poor sensitivity and positive predictive value for individual viral diagnoses. ConclusionsClusters of signs, symptoms, and laboratory values evaluated in this study could not consistently differentiate Zika or dengue cases from UAI in the clinical setting at the individual patient level. We identified symptoms that are important to Zika and dengue in the univariate analyses, but predictive models were unreliable. Low platelet count was a distinctive feature of dengue.