Single Predictor Research Articles

100 Can in vitro or in situ disappearance predict to in vivo digestibility of forages?

Abstract Though much effort has been made to characterize the in vivo (IV) and in situ (IS) dry matter digestibility (DMD) of southeastern forages, there is a lack of information identifying methods that best represent the in vivo digestibility of these forages. Thus, the objective of our study was to compare IV and IS methods to determine which technique best represents the in vivo DMD of four bermudagrass (Cynodon dactylon [L.] Pers.) cultivars. In an IV experiment, ruminally fistulated heifers (n = 4) were randomly assigned to one of four bermudagrass cultivars (Coastal [COS], Russell [RUS], Tifton 44 [T44], or Tifton 85 [T85]) for four 30-d in vivo periods (21-d adaptation and 9-d collection) in a Latin square design. On d 28 of each period, rumen fluid was collected 4 h post-feeding for an accompanying IV experiment using a completely randomized design with a 4 × 2 factorial treatment structure. Factors in the IV experiment included bermudagrass cultivar (COS, RUS, T44, and T85) and digestibility method (Tilley and Terry [TT] or Goering and Van Soest [GVS]). On d 31 of in vivo periods 3 and 5, an accompanying IS experiment was conducted as a randomized complete block design with three treatment factors (in vivo diet [previous described], bermudagrass cultivar [previously described], and incubation timepoint [0, 0.25, 0.50, 0.75, 1, 2, 3, 4, 8, 12, 16, 20, 24, 48, 72, 96, 120, 144, and 168 h]). Correlations were computed between in vivo DM disappearance and each of the IV (TT and GVS) and IS (24, 48, and 72 h) methods. Regressions were calculated for each of the individual IV and IS measurements, and a stepwise regression was used to determine the predictive value of linear combinations of the measurements. None of the IV or IS DMD values were correlated with in vivo DMD (P ≥ 0.18). Individual IV and IS DMD values did not yield significant models through linear regression (P ≥ 0.18), though the best model (based on AIC) was TT IVDMD (r2 = 0.09; P = 0.26). Stepwise regression revealed that there was no linear combination of IV or IS DMD that improved prediction beyond single predictor models. While both IV and IS experiments remain viable tools to screen forages and make relative comparisons, results from this experiment are interpreted to mean that neither of these methods is suitable for prediction of in vivo performance.

The relationships of sexually harassing behaviors to organizational context factors and working men's dark personality traits.

This research examined the roles of organization contexts factors and dark personality traits in men's (N = 600) self-reports of sexually harassing behaviors toward women in the workplace. Four organization context factors (a permissive climate, a masculinized job/gender context, male/female contact, and Masculinity Contest Culture [MCC] Norms) and four dark personality traits (psychopathy, narcissism, Machiavellianism, and sadism) were examined. While only one organizational context factor, MCCNorms correlated with men's admissions of sexually harassing behaviors at work, all four dark personality traits evidenced significant correlations. In a multiple regression analysis, MCC Norms emerged again as the single organizational context predictor and psychopathy as the single personality predictor of men's admissions of sexually harassing behaviors at work. Moderation analyses showed that a masculinized job/gender context interacted with psychopathy to produce more admissions of sexually harassing behaviors. Mediation analyses showed that psychopathy, narcissism, and Machiavellianism had indirect relationships with admissions of sexually harassing behaviors through MCC Norms. Higher levels on these traits were related to higher levels of these workplace norms which, in turn, predicted more admissions of sexually harassing behavior. This research sheds new light on how both organizational contexts and enduring personal characteristics of men are related to sexual harassment in the workplace.

Sequential stacking link prediction algorithms for temporal networks

Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.

INVESTIGATING THE INTERPLAY: UNRAVELLING THE IL-6 CONNECTION AND INTRAVENOUS METHYLPREDNISOLONE'S IMPACT ON RECTUS MUSCLE IN THYROID EYE DISEASE

Abstract
 Introduction & Objectives : Intravenous corticosteroid has been used in management of Thyroid Eye Diseases (TED). Many previous studies have shown the therapeutic benefits of corticosteroid therapy. Recently, the efficacy of high-dose intravenous corticosteroid pulse therapy has been reported. Recent studies have highlighted the potential role of interleukin-6 (IL-6) in the pathogenesis of TED. However, the correlation between IL-6 levels and the effects of IVMP on rectus muscles in patients with TED remains unclear. This study aims to investigate the correlation between IL-6 levels and the effects of IVMP on rectus muscles in patients with TED
 Methods : Retrospective analysis were used in the data collection of patients with active Thyroid Eye Disease who undergone high-dose intravenous methylprednisolone pulse therapy (500mg per week for 6 successive weeks) at Sardjito General Hospital between January 2022 and December 2022. Data on the Level of IL-6 were obtained at the start of treatment, and quantitative computed tomography (CT) of the orbit and were performed before and after treatments. Demographics data were presented in tables and graphs.
 Results : This study included 62 eyes of 31 patients (61,3% female) with mean age of 37.8+13.47 years old. There was significant inverse correlation in levels of IL-6 and ?SR (P=0.037) which implies a reduced effect of IVMP on SR. However, other statistical analyses on different rectus muscle groups bare no statistical significance.
 Conclusion : The use of IL-6 as a single predictor of responsiveness of the treatment of high dose intravenous methylprednisolone is not endorsed by the result of this study. However, further studies with better established level of IL-6 both pre and post treatment, and more inclusive parameters are needed to better provide better understanding on the role of IL-6.

INVESTIGATING THE INTERPLAY: UNRAVELLING THE IL-6 CONNECTION AND INTRAVENOUS METHYLPREDNISOLONE’S IMPACT ON RECTUS MUSCLE IN THYROID EYE DISEASE

Introduction & Objective: Intravenous corticosteroid has been used in management of Thyroid Eye Diseases (TED). Many previous studies have shown the therapeutic benefits of corticosteroid therapy. Recently, the efficacy of high-dose intravenous corticosteroid pulse therapy has been reported. Recent studies have highlighted the potential role of interleukin-6 (IL-6) in the pathogenesis of TED. However, the correlation between IL-6 levels and the effects of IVMP on rectus muscles in patients with TED remains unclear. This study aims to investigate the correlation between IL-6 levels and the effects of IVMP on rectus muscles in patients with TED Methods : Retrospective analysis were used in the data collection of patients with active Thyroid Eye Disease who undergone high-dose intravenous methylprednisolone pulse therapy (500mg per week for 6 successive weeks) at Sardjito General Hospital between January 2022 and December 2022. Data on the Level of IL-6 were obtained at the start of treatment, and quantitative computed tomography (CT) of the orbit and were performed before and after treatments. Demographics data were presented in tables and graphs. Results : This study included 62 eyes of 31 patients (61,3% female) with mean age of 37.8±13.47 years old. There was significant inverse correlation in levels of IL-6 and ?SR (P=0.037) which implies a reduced effect of IVMP on SR. However, other statistical analyses on different rectus muscle groups bare no statistical significance. Conclusion : The use of IL-6 as a single predictor of responsiveness of the treatment of high dose intravenous methylprednisolone is not endorsed by the result of this study. However, further studies with better established level of IL-6 both pre and post treatment, and more inclusive parameters are needed to better provide better understanding on the role of IL-6.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of “relative expression reversals”. Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble.

Medicare Part D low-income subsidies expanded in January 2024, but more needs to be done to ensure that eligible beneficiaries enroll.

The Inflation Reduction Act of 2022 extended full low-income subsidies (LIS) to a small group of Medicare Part D recipients with limited assets and incomes between 135% and 150% of the Federal Poverty Level beginning in January 2024. This policy may result in small enrollment gains among beneficiaries eligible for the new benefits, but the biggest problem with the current LIS program is underenrollment across all eligibility groups. Prior research has shown that underenrollment has been a persistent problem since the LIS program began in 2006, yet little has been done to correct the situation. To identify individual-level factors associated with failure to enroll among low-income beneficiaries eligible for both full subsidies and partial subsidies under the LIS program. We used 2019 Medicare Current Beneficiary Survey data for the study. The Medicare Current Beneficiary Survey is uniquely suited for this work because it contains administrative data on LIS enrollment plus extensive survey information on financial resources necessary to establish program eligibility. We conducted descriptive and multivariate analyses to identify factors associated with failure to enroll when eligible for either full or partial subsidies. Explanatory variables included sociodemographic characteristics, economic resources, work status, health variables, and source of prescription coverage (for nonsubsidized beneficiaries). In 2019, 73% of beneficiaries eligible for full subsidies under pre-Inflation Reduction Act LIS provisions were enrolled, compared with only 25% eligible for partial subsidies. The number of those estimated to be eligible for full subsidies but not enrolled (N = 3.9 million) was more than double that of those eligible but not enrolled for partial subsidies (N = 1.5 million). Factors associated with failure to enroll (older age, male sex, White race, married, higher education, higher income and assets, and excellent/very good health status) were similar for both groups. In multivariate analyses, the single strongest predictor of failure to enroll was receipt of income from work (odds ratio = 5.50; P < 0.001). Among the nonenrolled, 64% eligible for full subsidies and 75% eligible for partial subsidies maintained unsubsidized Part D coverage. Significant numbers of low-income Medicare beneficiaries are eligible for Part D subsidies but fail to enroll. Common characteristics distinguishing nonenrollees from enrollees include older age with higher proportions of White individuals, married individuals, higher income and assets, and better overall health. Two promising targets for increasing LIS enrollment are evidence of work income and unsubsidized Part D coverage.

The Ratio Between the Infrarenal and Suprarenal Aortic Diameter Is a Predictor of Durable Proximal Seal After Endovascular Aneurysm Repair.

The aim of this study was to assess whether the ratio of the mean infrarenal neck diameter to the suprarenal aortic diameter is a predictor for a durable proximal seal after endovascular aneurysm repair (EVAR). A total of 439 patients who underwent elective EVAR between 2004 and 2018 in a single vascular referral center met our inclusion criteria. Ratios were calculated by dividing the mean infrarenal neck diameter by 4 different suprarenal aortic diameters. Patients who developed a late type 1A endoleak (n=20) or proximal neck dilatation mandating revision (n=8) were compared with the 411 patients without long-term proximal seal complications. Patients who developed a late type 1A endoleak had more frequently hypertension, a shorter infrarenal neck length, and a larger mean infrarenal neck diameter. The ratio of the mean infrarenal neck diameter to all 4 suprarenal aortic diameters was higher in the late type 1A endoleak group compared with the group without a late type 1A endoleak. Least absolute shrinkage and selection operator (LASSO) logistic regression identified a combination of 6 variables as the best combination to predict a late type 1A endoleak: presence of hypertension, increased mean infrarenal neck diameter, decreased aneurysm neck length, larger ratio of the mean infrarenal neck diameter to the aortic diameter proximal to the superior mesenteric artery (SMA), larger ratio of the mean infrarenal neck diameter to the aortic diameter proximal to the upper renal artery, and increased β-angle. Of these, based on both the univariate area under the curve (AUC) and optimal LASSO model restricted to a single predictor, the ratio of the mean infrarenal neck diameter to the aortic diameter proximal to the SMA (AUC, 0.770; cutoff value, 0.997) was considered the best prognostic variable. The ratio of the mean infrarenal neck diameter to the aortic diameter proximal to the SMA is a good predictor for a late type 1A endoleak. Patients with mean infrarenal neck diameter larger than the diameter proximal to the SMA (ratio >1) are at risk for a late type 1A endoleak. In this single-center, retrospective cohort study, we found that the ratio of the mean infrarenal neck diameter to the aortic diameter proximal to the SMA is a good predictor for a late type 1A endoleak. We conclude that the suprarenal diameter must be taken into account before assessing endovascular aortic aneurysm repair eligibility. Patients with a ratio >1 may not be the best candidates for a durable result after EVAR and may be better off with fenestrated EVAR or open repair.

Very low dose glucocorticoid therapy in rheumatoid arthritis: Impact of b/tsDMARDs initiation timing on glucocorticoid withdrawal.

We investigated the effectiveness and safety of very low-dose (<5 mg daily) glucocorticoids (GCs) in patients with RA treated with biological and targeted synthetic disease-modifying anti-rheumatic drugs (b/tsDMARDs). In this prospective cohort study, we included all RA patients who started their first b/tsDMARDs at our institution between 2015 and 2020 and were monitored every 6 months for 3 years. Relationships between exposure to very low-dose GCs and disease activity were examined through multivariable logistic regression and repeated-measures analysis of variance. The impact of very low-dose GCs on safety was also evaluated. We enrolled 229 RA patients, of whom 68% were prescribed very low-dose GCs and 32% received no GCs. After three years on b/tsDMARDs, 32% had never abandoned, 20% had gone on and off, and 23% had permanently discontinued very low-dose GCs, while 25% had never taken GCs. Shorter disease duration at b/tsDMARD initiation was the single modifiable predictor of very low-dose GCs cessation (OR 1.1, 95% CI 1.03-1.14 for any 1-year decrease; p= 0.001). A significant association existed between ongoing utilization of very low-dose GCs and persistent moderate disease activity. Use of very low-dose GCs was associated with hypertension (20% vs 11%) and myocardial infarction (2.3% vs 0%). A substantial proportion of RA patients treated with b/tsDMARDs continue to receive very low-dose GCs without significantly improving disease control. However, this appears to increase cardiovascular morbidity.

Branch retinal vein occlusion treated with anti-VEGF: to switch or not to switch?

ObjectiveTo evaluate visual outcomes after switching from bevacizumab to ranibizumab or aflibercept in patients with macular edema (ME) secondary to branch retinal vein occlusion (BRVO). DesignA retrospective, multi-center, observational study. ParticipantsPatients diagnosed with BRVO and were treated with at least 3 bevacizumab injections, before anti VEGF switch. MethodsThe follow-up period was 36 months, and the primary study outcomes assessed changes in best corrected visual acuity (BCVA) after anti VEGF switch. ResultsA total of 263 eyes of 263 patients with a mean age of 71.5 ± 11.2 years of which 50% were of male gender met the inclusion criteria. Of them, 175 eyes did not undergo switch, whereas 88 eyes underwent anti-VEGF switch. There was not a significant difference in mean age (p = 0.634) and gender (p = 0.269) between the groups. Baseline BCVA of the no-switch group was 0.47 ± 0.43 logMAR (20/59 Snellen) versus 0.6 ± 0.49 logMAR (20/79 Snellen) (p = 0.031) in the switch group, and at 36-months it was 0.41 ± 0.39 (20/51 Snellen) logMAR versus 0.54 ± 0.49 logMAR (20/69 Snellen) (p = 0.035), respectively. The difference between the rate of change in BCVA per year was insignificant between groups (p = 0.414). In multivariate analysis, baseline BCVA was the single significant predictor for switch (beta 0.137, p = 0.035). Patients with more than one anti-VEGF switch suffer from decrease in BCVA. ConclusionsWorse baseline BCVA is a significant predictor for anti-VEGF switch execution, though the switch has no significant impact on the change in BCVA over time. Multiple anti-VEGF switch is not recommended.

Disproportional oxidation rates of ammonia and nitrite deciphers the heterogeneity of fertilizer-induced N2O emissions in agricultural soils

Nitrous oxide (N2O) is a potent greenhouse gas and ozone-depleting chemical. Although nitrogen (N) fertilizer rate is the best single predictor of N2O emissions, the spatial heterogeneity of the emission factor is striking and poorly understood. Nitrite (NO2−) dynamics have been reported to be correlated with N2O production in many soils; however, NO2− is often overlooked owing to its rare accumulation, and the underlying mechanisms of NO2− accumulation and the relationship with spatiotemporal heterogeneity of N2O emissions in response to N addition remains unclear. Here, we collected upland agricultural soils that cover the main upland agricultural production regions of China and investigated the soil-specific effects on N2O emissions, changes in microbial abundances and community structures of nitrifiers, and co-occurrence networks for ammonium (NH4+)-based fertilizer addition. Our results showed that high NO2− accumulation and high N2O emissions were generally correlated with low nxrA/AOB gene ratios (R2 = 0.90 and 0.86, respectively, P < 0.01). Further network analyses revealed that NH4+ addition increased the network links of the nitrification functional communities by 23% and enhanced the interrelationship between AOB and nitrite oxidizers. By synthesizing our data with previous studies in the literature, we found that soil N2O emission rate and nxrA/AOB gene ratio exhibit a threshold behavior worldwide, suggesting that disproportionality of NH3 and NO2− oxidations rates are involved in the heterogeneity of fertilizer-induced N2O emissions in agricultural soils. These findings enhance our understanding of spatiotemporal heterogeneity in NO2− accumulation and N2O emissions within upland agricultural soils in view of nitrification functional communities.

Species-specific allometric models for reducing uncertainty in estimating above ground biomass at Moist Evergreen Afromontane Forest of Ethiopia

An allometric equation is used to convert easily measured tree variables into biomass. However, limited species-specific biomass equations are available for native tree species grown in various biomes of Ethiopia. The available pantropic generic equation has resulted in biases owing to the uncertainty of the generic model estimation due to the difference in tree nature and response to growth conditions. The objective of the study is, thus, to develop a species-specific allometric equation for reducing uncertainty in biomass estimation at the Moist Evergreen Afromontane Forest in south-central Ethiopia. Five tree species were selected for model development, these selected trees were harvested and weighed in the field. The measured above-ground biomass data related to easily measured tree variables: diameter at stump height, diameter at breast height (dbh), crown diameter, and total tree height. The developed model evaluated and compared with previously published model by using measures of goodness of fit such as coefficient of determination (R2), total relative error, mean prediction error, root mean square error, and Akaike information criteria. The analysis showed that a model with dbh as a single predictor variable was selected as the best model for the estimation of above-ground biomass. It gives the highest R2 for Syzygium guineense (0.992) and the lowest for Bersama abyssinica (0.879). The additions of other tree variables did not improve the model The pantropic model by Brown overestimates the biomass by 9.6–77.8% while both Chave models resulted in an estimation error of 12–50.3%. Our findings indicated that species-specific allometric equations outperformed both site-specific and pantropic models in estimating above-ground biomass by giving 0.1% up to 7.9% estimation error for the respective tree species.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Modelling urban freight generation using linear regression and proportional odds logit models

Receivers and intermediary establishments typical to urban areas have unique freight behaviour and are understudied, particularly in developing countries. This paper uses establishment-based freight survey data to examine their freight generation patterns in the Chennai metropolitan area, India. Three sets of Simple Linear Regression (SLR) models, three single predictor proportional odds logit models, one multiple linear regression model, and one multiple predictor proportional odds logit model with business size (employment, area, and operational age) and indicators of establishment category as regressors are developed each for both freight production and freight attraction. Partial proportional odds logit models are developed in a few cases to overcome the limitations of proportional odds logit models. The best SLR model varied with the establishment category for freight production and attraction. The establishment area model is the best among single predictor proportional odds logit models. The multiple predictor proportional odds logit models marginally improved the fit over single predictor models. The proportional odds logit model results show that the establishment category has a greater impact on freight generation levels than business size variables. Since earlier studies rarely focused on receivers and intermediary establishments, policymakers may benefit from the developed models and study insights while estimating freight demand and developing freight policies.

Biometric variability and sexual size dimorphism in the Great Knot Calidris tenuirostris

The Great Knot is a species from the Scolopacidae family of waders migrating within the East Asian-Australasian Flyway, and studies on this species have contributed greatly to understanding of migration ecophysiology and migration strategies in long-distance migrants. In this paper, we provide the first description of biometric variability and sexual size dimorphism in Great Knots. During the study on the Kamchatka Peninsula, 683 adults and 229 juveniles were measured and sexed molecularly. In adults, the mean measurements of females were larger than in males, except for tarsus length. In juveniles, at the early stage of migration from breeding to wintering grounds, apparently growth was not complete. Sexual dimorphism was small, with only wing length being significantly longer in females than in males. All dimensions of juveniles were smaller than those of adults, especially in bill length. The most sexually dimorphic trait in both adults and juveniles was wing length, and the most effective discriminant function with wing length as a single predictor correctly identified the sex of 76% of birds in both age classes. However, molecular sexing is the method of choice for reliable sexing, especially in juveniles.

Incidence of Suicidal Ideation in a Civil Servants Cohort During the COVID-19 Pandemic in Brazil: Insights from the ELSA-Brasil Study.

This study investigated the incidence of suicidal ideation and its associated risk factors in the São Paulo state of ELSA-Brasil cohort during the COVID-19 pandemic. During a pre-pandemic ELSA-Brasil onsite assessment in 2016-2018 (wave 3) and a pandemic online assessment in May-July 2020 (wave COVID), we assessed suicidal ideation using the Clinical Interview Scheduled-Revised (CIS-R). Single and multi predictor logistic regressions were performed using sociodemographic characteristics, household finance impact during pandemic, presence of previous chronic diseases, alcohol abuse, adverse childhood experiences (ACE), living alone, and previous CMD as predictors. Suicidal ideation incidence was used as outcome. Out of 4191 participants of wave 3, 2117 (50.5%) answered wave COVID. There was a threefold increase in suicide ideation, from 34 (1.8%) to 104 (5.6%).In multiple predictor models, we found that previous CMD (OR 7.17; 95% CI 4.43 - 11.58) and ACE (OR 1.72; 95% CI 1.09 - 2.72) increased the odds of incident suicidal ideation. The sociodemographic predictors female sex, younger age and low income were significant risk factors only in the single predictor model. Conclusions These findings underscore the importance of monitoring and supporting individuals who suffered ACE and have a history of mental health disorders. This is especially critical in times of heightened societal stress, such as the COVID-19 pandemic. These findings underscore the importance of monitoring and supporting individuals who suffered ACE and have a history of mental health disorders. This is especially critical in times of heightened societal stress, such as the COVID-19 pandemic.

Age-related Pancoronary Characteristics in Patients with ST-segment Elevation Myocardial Infarction

Background: Age-related vulnerable characteristics of pancoronary plaques in patients with ST-segment elevation myocardial infarction (STEMI) have not been systemically evaluated by optical coherence tomography (OCT). Therefore, we sought to explore the discrepancies in pancoronary characteristics between younger and older patients with STEMI through OCT. Methods: This retrospective single-center study included 588 patients who had STEMI and underwent three-vessel OCT through emergency percutaneous coronary intervention between October 2016 and September 2018. With a median age of 56 years as a cutoff, the patients were divided into a younger group (≤56 years, n = 298) and an older group (&gt;56 years, n = 290). Results: A total of 795 non-culprit plaques were found in 298 of the younger patients, whereas 858 non-culprit plaques were identified in 290 of the older patients. Fewer high-risk OCT plaques (15.8% vs. 23.1%; P = 0.025), as well as other structures (cholesterol crystals, P = 0.001; microchannels, P = 0.032; calcifications, P &lt; 0.001; spotty calcifications, P &lt; 0.001; large calcifications, P &lt; 0.001; and thrombi, P = 0.001) were identified in younger patients than older patients, at the patient level. In addition, pancoronary vulnerability in younger patients was independently predicted by culprit plaque rupture {CLIMA-defined high-risk plaques (odds ratio [OR]: 3.179; 95% CI: 1.501 to 6.733; P = 0.003), non-culprit rupture (OR: 3.802; 95% CI: 1.604 to 9.014; P = 0.002), non-culprit thin-cap fibroatheroma (OR: 3.536; 95% CI: 2.051 to 6.094; P &lt; 0.001)}, hypertension (OR: 1.920; 95% CI: 1.099 to 3.355; P = 0.022), and total cholesterol (OR: 1.094; 95% CI: 1.002 to 1.195; P = 0.045). In older patients with STEMI, the predictor was male sex (OR: 3.031; 95% CI: 1.352 to 6.795; P = 0.007). Conclusions: Among patients with STEMI, younger patients had limited vulnerable plaque characteristics, and pancoronary vulnerability was associated with culprit plaque rupture, hypertension, and total cholesterol. In contrast, older patients had greater pancoronary vulnerability with the single predictor of male sex, thus suggesting that traditional risk factors have limited applicability in predicting pancoronary vulnerability in older patients.

A Machine Learning Model to Predict Risk for Hepatocellular Carcinoma in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease

BACKGROUND & AIMSHepatocellular carcinoma (HCC) incidence is increasing and correlated with metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD), even in patients without advanced liver fibrosis who are more likely to be diagnosed with advanced disease stages and shorter survival time, and less likely to receive a liver transplant. Machine learning (ML) tools can characterize large datasets and help develop predictive models that can calculate individual HCC risk and guide selective screening and risk mitigation strategies. METHODSTableau and KNIME Analytics were used for descriptive analytics and machine learning tasks. ML models were developed using standard laboratory and clinical parameters. Sci-kit learn algorithms were used for model development. Data from UC Davis was used to develop and train a pilot predictive model, which was subsequently validated in an independent dataset from UC San Francisco (UCSF). MASLD and HCC patients were identified by ICD-9/10 codes. RESULTSOf the patients diagnosed with MASLD (n=1,561 training; n=686 validation), HCC developed in 14% (n=227) of the UC Davis training cohort and 25% (n=176) of the UCSF validation cohort. Liver fibrosis determined by the non-invasive FIB-4 score was the strongest single predictor for HCC in the model. Using the validation cohort, the model predicted HCC development at 92.06% accuracy with an AUC of 0.97, F1-score of 0.84, 98.34% specificity, and 74.41% sensitivity. CONCLUSIONSML models can aid physicians in providing early HCC risk assessment in patients with MASLD. Further validation will translate to cost-effective, personalized care of at-risk patients.

A novel multivariate combined power load forecasting system based on feature selection and multi-objective intelligent optimization

The ongoing transition in the electricity market, coupled with the rising penetration of renewable energy sources, has spurred demand for enhanced load forecasting accuracy. Traditional single predictors exhibit limited robustness and diversity, rendering them unsuitable for most forecasting situations. Moreover, weather conditions, holidays, and social events significantly influence load demand, making forecasting techniques that account for these factors increasingly relevant. To bridge the gaps and further enhance predictive performance, in this paper, a novel adaptive multivariate combined forecasting system is proposed, incorporating redundant feature elimination, adaptive model selection, multi-objective intelligent optimization with non-negative constraints, and reliable interval prediction theory. The proposed system employs non-linear feature selection to identify effective load features and develops an advanced Multi-Objective Optimization Strategy (MOS) for combining multiple optimal predictors under various scenarios. The weight coefficients of sub-predictors are theoretically demonstrated to be Pareto optimal, and the MOS exhibits superior distribution and convergence. Simulation results reveal that the developed system substantially outperforms benchmark models, achieving reliable uncertainty quantification, which facilitates the rational allocation of spinning reserves and enhances dispatching efficiency.

Prediction of One Repetition Maximum in Free-Weight Back Squat Using a Mixed Approach: The Combination of the Individual Load-Velocity Profile and Generalized Equations.

Fitas, A, Santos, P, Gomes, M, Pezarat-Correia, P, Schoenfeld, BJ, and Mendonca, GV. Prediction of one repetition maximum in free-weight back squat using a mixed approach: the combination of the individual load-velocity profile and generalized equations. J Strength Cond Res 38(2): 228-235, 2024-We aimed to develop a mixed methods approach for 1 repetition maximum (1RM) prediction based on the development of generalized equations and the individual load-velocity profile (LVP), and to explore the validity of such equations for 1RM prediction. Fifty-seven young men volunteered to participate. The submaximal load-velocity relationship was obtained for the free-weight parallel back squat. The estimated load at 0 velocity (LD0) was used as a single predictor, and in combination with the slope of the individual LVP, to develop equations predictive of 1RM. Prediction accuracy was determined through the mean absolute percent error and Bland-Altman plots. LD0 was predictive of 1RM ( p < 0.0001), explaining 70.2% of its variance. Adding the slope of the LVP to the model increased the prediction power of 1RM to 84.4% ( p < 0.0001). The absolute percent error between actual and predicted 1RM was lower for the predictions combining LD0 and slope (6.9 vs. 9.6%). The mean difference between actual and estimated 1RM was nearly zero and showed heteroscedasticity for the LD0 model, but not for the combined model. The limits of agreement error were of 31.9 and 23.5 kg for LD0 and LD0 combined with slope, respectively. In conclusion, the slope of the individual LVP adds predictive value to LD0 in 1RM estimation on a group level and avoids error trends in the estimation of 1RM over the entire spectrum of muscle strength. However, the use of mixed methods does not reach acceptable accuracy for 1RM prediction of the free-weight back squat on an individual basis.