Postoperative Dietary Intake Achievement: A Secondary Analysis of a Randomized Controlled Trial

Sufficient postoperative dietary intake is crucial for ensuring a better surgical outcome. This study aimed to investigate the postoperative dietary intake achievement and predictors of postoperative dietary intake among gynecologic cancer patients. A total of 118 participants were included in this secondary analysis. Postoperative dietary data was pooled and re-classified into early postoperative dietary intake achievement (EDIA) (daily energy intake (DEI) ≥ 75% from the estimated energy requirement (EER)) and delay dietary intake achievement (DDIA) (DEI < 75% EER) There was a significant difference in postoperative changes in weight (p = 0.002), muscle mass (p = 0.018), and handgrip strength (p = 0.010) between the groups. Postoperative daily energy and protein intake in the EDIA was significantly greater than DDIA from operation day to discharged (p = 0.000 and p = 0.036). Four significant independent postoperative dietary intake predictors were found: preoperative whey protein-infused carbohydrate loading (p = 0.000), postoperative nausea vomiting (p = 0.001), age (p = 0.010), and time to tolerate clear fluid (p = 0.016). The multilinear regression model significantly predicted postoperative dietary intake, F (4, 116) = 68.013, p = 0.000, adj. R2 = 0.698. With the four predictors’ recognition, the integration of a more specific and comprehensive dietitian-led supportive care with individualized nutrition intervention ought to be considered to promote functional recovery.

Vocal Acoustic Analysis

Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.

Is Coronary Artery Ectasia a Progressive Disease? A Self-Controlled Retrospective Cohort Study

Objective: It is essential to understand whether coronary artery ectasia (CAE) progresses over time because the patients might be under the risk of coronary rupture, and stent implant should be avoided if ectatic changes progress.Methods: A consecutive series of 99 CAE patients who had undergone coronary angiography at least twice were enrolled and followed up for 1–16 years until they received a second angiogram. Subjects were divided into two groups (1–5 vs. 5–16 years of follow-up), then the basic clinical characteristics and coronary artery images were compared over time.Results: (1) All CAE patients exhibited atherosclerosis, and a majority presented with acute myocardial infarction. Most baseline clinical characteristics were relatively stable. (2) Atherosclerosis (indicated by the distribution of stenosis in coronary vessels) and the Gensini scores progressed significantly. Ectasia extent showed minimal changes as indicated by blood vessel involvement, Markis type, coronary blood flow, ectasia diameter, and ectasia length. (3) Multilinear regression analysis revealed that the underlying factors related to stenosis evolution indicated by fold of Gensini score were: longer time interval, lower baseline Gensini score, and higher hypersensitive C-reactive protein concentration. (4) There was a relationship between the ectatic diameter and the extent of stenosis.Conclusions: For CAE patients with atherosclerosis followed for 1–16 years, there was minimal CAE progression, while the atherosclerosis progressed and the ectasia extent was related to degree of stenosis. The results indicate that prevention and treatment of atherosclerotic changes might have more clinical significance than addressing ectatic changes.

Internet Use and Cognitive Functioning at the Interaction of Age and Gender Among Older Adults

The positive relationship between internet use and cognition has long been documented in the gerontology literature, and researchers are consistently finding that internet use engages the brain in a way that improves cognitive functions such as multitasking, information processing and executive thinking. While there are numerous studies examining this association, few studies have explored the three-way interaction between age, gender, and internet use on cognition. This study aimed to examine the gendered moderation effect of age on the relationship between internet use and cognition among older adults. The study sample was derived from the 2016 Health and Retirement study, which is a biennial longitudinal panel study of adults aged 50 and older in the United States. Multilinear regression models were used to examine the three-way interaction of age, sex and internet use on cognition while controlling for other covariates. Results showed that women gained a greater increase in cognition as a result of internet use as they became older, while men had the same amount of increase in cognition as a result of internet use regardless of age. This indicates that internet use can be a positive agent in improving cognition among older adults regardless of age and sex, and interventions should focus on increasing internet use among older adults, to ensure equitable access to the benefits of internet use on cognition.

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m2), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R2 of EOY% = 0.929, R2 of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R2 of EOY% = 0.553, R2 of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m2, NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m2, NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs.

The microbial metabolic activity on carbohydrates and polymers impact the biodegradability of landfilled solid waste

Biological waste degradation is the main driving factor for landfill emissions. In a 2-year laboratory experiment simulating different landfill in-situ aeration scenarios, the microbial degradation of solid waste under different oxygen conditions (treatments) was investigated. Nine landfill simulation reactors were operated in triplicates under three distinct treatments. Three were kept anaerobic, three were aerated for 706 days after an initial anaerobic phase and three were aerated for 244 days in between two anaerobic phases. In total, 36 solid and 36 leachate samples were taken. Biolog® EcoPlates™ were used to assess the functional diversity of the microbial community. It was possible to directly relate the functional diversity to the biodegradability of MSW (municipal solid waste), measured as RI4 (respiration index after 4 days). The differences between the treatments in RI4 as well as in carbon and polymer degradation potential were small. Initially, a RI4 of about 6.5 to 8 mg O2 kg−1 DW was reduced to less than 1 mg O2 kg−1 DW within 114 days of treatment. After the termination of aeration, an increase 3 mg O2 kg−1 DW was observed. By calculating the integral of the Gompertz equation based on spline interpolation of the Biolog® EcoPlates™ results after 96 h two substrate groups mainly contributing to the biodegradability were identified: carbohydrates and polymers. The microbial activity of the respective microbial consortium could thus be related to the biodegradability with a multilinear regression model.

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated; they also appeared to be the most transferable approach when applied to field data collected in Malawi. We compared calibrated CO observations to remote sensing data in two regions in Malawi and found qualitative agreement in spatial and annual trends. However, the monthly mean surface observations were 2 to 4 times higher than the remote sensing data, possibly due to proximity to small-scale combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70 %) conditions and influence from emissions from nearby biomass cookstoves. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi; however, overall data recovery was limited by insufficient power and access to technical resources at deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regulatory grade regional infrastructure.

Spatial analysis of public residential housing's electricity consumption in relation to urban landscape and building characteristics: A case study in Singapore

In a highly populated country like Singapore, a significant percentage of our gross annual electricity consumption stems from our domestic electricity usage in our residential houses. Analyzing and understanding factors that could influence such patterns is thus essential in order to derive effective measures to reduce usage. In this research, 16 identified variables were calculated and considered in the spatial analyses based on various buffer sizes. Both multilinear regression (MLR) and geographically weighted regression (GWR) based analyses were conducted using each residential housing's Energy Unit Intensity (EUI) as the dependent variable. The analyzed results have shown that building characteristics variables have more significant influences towards energy consumption patterns as compared to urban landscape variables. Although little difference was observed across different buffer sizes, more reliable results were obtained from a smaller buffer size of 50 m, suggesting its suitability in using these obtained values for further prediction model analysis and development. Results obtained from the GWR-based analysis have shown a significant improvement in the goodness-of-fit value compared to the MLR-based analysis, effectively indicating that GWR performs better in this context, apart from its better explanation on the contribution of these identified variables to the EUI in this case study.

Internationalizing Malawian Higher Education: towards more beneficial academic mobility.

Aim: This study investigated the intentions, opportunities, and barriers to engaging in a meaningful internationalization of higher education in Malawi.Methods: This was cross-sectional research that was done between June and October 2021. Using a purposive (judgmental) sampling, we recruited 212 respondents from various institutions of higher education in Malawi. Multilinear regression analysis was used to analyze the factors with the P-value set at 0.05 level of statistical significance. Results: The results indicated that the majority of the respondents were males (63.7%) who fell into 30 years age bracket. Further, the results from the multilinear regression analysis indicate that Institutional collaboration (ß=0.326, p=0.000, CI=0.27—0.383), clear Policy on Mobility (ß=0.146, p=0.0.004, CI=0.047-0.246), experience (ß=0.083, p=0.117, CI=-0.021-0.186), academic rank (ß=0.114, p=0.000, CI=0.069-0.159) were positively statistically significant variables, whereas on the other hand, Occupation (ß=-0.131, p=0.002, CI=-0.213-0.49), academic qualification (ß=-0.106, p=0.013, CI=-0.19-0.023 and mobilityImportance (ß=-0.116, p=0.022, CI=-0.215-0.017) were negatively significant variables respectively.Conclusion and Recommendations: institutions need to invest in international and inter-institutional collaboration, clarify policy direction regarding academic mobility, keep track and linkages with mobile faculty, create a conducive social and formal institutional culture that attracts back mobile faculty, and reduce staff turnover.