Composition Parameters Research Articles

Polyvinyl Alcohol-Based Membranes: A Review of Research Progress on Design and Predictive Modeling of Properties for Targeted Application.

This review provides a comprehensive evaluation of the current state of polyvinyl alcohol (PVA)-based membranes, emphasizing their significance in membrane technology for various applications. The analysis encompasses both experimental and theoretical research articles, with a focus on recent decades, aiming to elucidate the potential and limitations of different fabrication approaches, structure-property relationships, and their applicability in the real world. The review begins by examining the advanced polymeric materials and strategies employed in the design and processing of membranes with tailored properties. Fundamental principles of membrane processes are introduced, with a focus on general modeling approaches for describing the fluid transport through membranes. A key aspect of discussion is the distinction between the membrane performance and process performance. Additionally, an in-depth analysis of PVA membranes in various applications is presented, particularly in environmental fields (e.g., fuel cell, water treatment, air purification, and food packaging) and biomedical domains (e.g., drug delivery systems, wound healing, tissue engineering and regenerative medicine, hemodialysis and artificial organs, and ophthalmic and periodontal treatment). Special attention is given to the relationship between membranes' characteristics, such as material composition, structure, and processing parameters, and their overall performance, in terms of permeability, selectivity, and stability. Despite their promising properties, enhanced through innovative fabrication methods that expand their applicability, challenges remain in optimizing long-term stability, improving fouling resistance, and increasing process scalability. Therefore, further research is needed to develop novel modifications and composite structures that overcome these limitations and enhance the practical implementation of PVA-based membranes. By offering a systematic overview, this review aims to advance the understanding of PVA membrane fabrication, properties, and functionality, providing valuable insights for continued development and optimization in membrane technology.

Wear Characteristics and Optimization Measures of Disc Cutters During Large-Diameter Slurry Tunnel Boring Machine Advancing in Soil-Rock Composite Strata: A Case Study

The large-diameter slurry tunnel boring machine (TBM) is widely used in the construction of tunnels across rivers and seas. However, cutter wear has become a critical issue that severely limits the tunnelling efficiency. Taking the Qingdao Jiaozhou Bay Second Subsea Tunnel Project as the background, the wear patterns of disc cutters on the atmospheric cutterhead of a large-diameter slurry TBM under complex geological conditions were analyzed. The flat wear of disc cutters induced by factors such as rock chip accumulation in front of the cutterhead, the jump trajectory when changing disc cutters, alloy-insert disc cutter mismatch, cutter barrel clogging, and severe wear of scrapers is discussed. Furthermore, the impacts of measures such as slurry circulation to remove rock chips during TBM stoppage, clay dispersant injection into the slurry chamber, cutter barrel flushing, and the wear resistance optimization of cutters and cutter barrels on reducing cutter wear were investigated. Based on numerical simulations and field data, a methodology for determining the optimal timing for cutter replacement is proposed. The results indicate the following: The circulation system effectively reduces accumulation, minimizing secondary wear of the disc cutters and lowering the risk of clogging in the cutter barrel. Adopting measures such as shield shutdown, a circulation system to carry away the slag, cutter barrel flushing, and soaking in 2% dispersant for 8 h can effectively reduce the accumulation of rock chips and mud cakes on the cutterhead, which in turn reduces the flat wear of the disc cutter. Measures such as making the cutter body and cutter ring rotate together and adding wear-resistant plates to the cutter barrel greatly improve the life of the cutter. The sharp increase in composite parameters can serve as an effective marker for assessing cutter conditions. The findings of this study can provide valuable insights into reducing cutter wear in similar projects.

Looking at or beyond the tumor - a systematic review and meta-analysis of quantitative imaging biomarkers predicting pancreatic cancer prognosis.

To evaluate the prognostic value of quantitative imaging biomarkers derived from computed tomography (CT) and magnetic resonance imaging (MRI) for pancreatic cancer (PC), with a particular focus on body composition parameters beyond the traditional intrinsic features of the tumor. PubMed, EMBASE, and Cochrane Library databases were searched for articles on quantitative imaging biomarkers obtained from CT or MRI in predicting PC prognosis published between January 2014 and August 2024. The Newcastle-Ottawa scale was used to assess the quality of the included studies. Survival outcomes, such as overall survival (OS) and recurrence-free survival (RFS), were evaluated. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a random-effects model. In case of high heterogeneity, subgroup analyses and sensitivity analyses were performed to identify potential sources of heterogeneity among the studies. We performed a meta-analysis of ten imaging biomarkers investigated in 43 included studies. Larger tumor size, lower skeletal muscle radiodensity, lower skeletal muscle index (SMI), presence of sarcopenic obesity, lower psoas muscle index (PMI), higher visceral to subcutaneous adipose tissue area ratio, and lower visceral adipose tissue index were associated with significantly worse OS. In particular, lower SMI and lower PMI had relatively high HRs (1.65 for SMI, 95% CI 1.39-1.96, and 2.20 for PMI, 95% CI 1.74-2.78). Patients with lower SMI exhibited poorer RFS (HR 1.78, 95% CI 1.46-2.18). Subgroup analyses identified the origin region of the study and intervention type as potential factors of heterogeneity for SMI in predicting OS. Imaging biomarkers indicating body composition at PC diagnosis may play an important role in predicting patient prognosis. Further prospective multi-center studies with large sample sizes are needed for validation and translation into clinical practice.

Using body composition to predict treatment-related adverse events and disease-free survival in patients with gastrointestinal stromal tumors treated with imatinib: a retrospective cohort study.

Imatinib (IM) is the primary treatment for Gastrointestinal stromal tumor (GIST), but it faces significant challenges with resistance and a high incidence of adverse events. This study aims to assess the predictive value of baseline body composition parameters on treatment-related adverse events and disease-free survival (DFS) in GIST patients treated with imatinib. A single-center retrospective analysis was conducted on 107 moderate or high-risk stratification GIST patients diagnosed from 2014 to 2020 at the First Affiliated Hospital of Nanjing Medical University. Body composition parameters, including skeletal muscle index (SMI), myosteatosis, cachexia index (CXI), and Fat-Free Mass (FFM), etc. were obtained using abdominal CT images and clinical data. Logistic and COX regression models were used to analyze the relationship between these indicators and treatment-related adverse events and DFS. Multivariate analysis revealed that myosteatosis (OR=7.640, P<0.001) and drug dose (OR=1.349, P=0.010) were independent risk factors for adverse events, while a higher CXI (OR=0.983, P=0.017) was protective. Additionally, LAMA/SMA% (OR=1.072, P=0.028) was identified as an independent risk factor for dose-limiting toxicity (DLT). Independent predictors of DFS included sarcopenia (HR=3.067, P=0.013), myosteatosis (HR=6.985, P=0.024), risk stratification (HR=9.562, high-risk vs. moderate-risk, P=0.003), and C-KIT mutation (HR=3.615, C-KIT exon 9 mutation vs. 11, P=0.013). Baseline body composition parameters, particularly myosteatosis, effectively predict the adverse events and DFS in patients taking imatinib. Personalized treatment, such as targeted nutritional and exercise interventions, and close monitoring of patients with myosteatosis or sarcopenia can enhance compliance and improve survival rates.

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies

Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessing steps to extract body composition measures from routine computed tomography (CT) scans. In 337 surgical oncology patients, total skeletal muscle tissue (SMtotal), psoas muscle tissue (SMpsoas), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were quantified both manually and using the pipeline. Automated and manual measurements showed strong correlations (SMpsoas: r = 0.776, VAT: r = 0.993, SAT: r = 0.984; all P < 0.001). Measurement discrepancies primarily resulted from segmentation errors, anatomical anomalies or image irregularities. SMpsoas measurements showed substantial variability depending on slice selection, whereas SMtotal, averaged across all L3 levels, provided greater measurement stability. Overall, SMtotal performed comparably to SMpsoas in predicting overall survival (OS). In summary, body composition measures derived from the pipeline strongly correlated with manual measurements and were prognostic for OS. The increased stability of SMtotal across vertebral levels suggests it may serve as a more reliable alternative to psoas-based assessments. Future studies should address the identified areas of improvement to enhance the accuracy of automated segmentation models.

Time for Change: A 3-Year Prospective Study on Mediterranean Diet Adherence and Body Composition in Kidney Transplant Recipients.

The aim of this prospective follow-up study was to evaluate changes in body composition parameters and Mediterranean diet (MeDi) adherence among kidney transplant recipients (KTRs) over a three-year period. Additionally, this study sought to investigate the associations between these changes and clinical parameters, including laboratory parameters, new onset of diseases, and death outcome. A total of 116 KTRs were initially assessed in 2019 and subsequently re-evaluated in 2022. The Mediterranean Diet Serving Score (MDSS) was used to assess dietary adherence to the MeDi at baseline and follow-up assessments. Bioelectrical impedance analysis was used to assess body composition and clinical outcomes were assessed by the data available from medical charts. After three years, MeDi adherence significantly decreased (p = 0.028) from 15 (18.29%) to 5 (6%), with dominantly lower adherence for vegetable, fruit, legume, red meat, olive oil and fish intake. Regarding body composition parameters, the most prominent change was seen in muscle mass, which deteriorated from 41.77% (IQR 6.46) to 39% (IQR 6.14; p = 0.004). However, changes in fat mass level were not significant in the follow-up period. Furthermore, cereal intake, fasting blood glucose (FBG), cholesterol level, level of low-density lipoprotein cholesterol (LDL), triglyceride leve and presence of diabetes mellitus lwere shown to be predictive for the decline of skeletal muscle mass. There were no significant changes in the estimated glomerular filtration rate (eGFR) or albuminuria level during the follow-up period. Associations with the death outcome were found for the MeDi-advised intake of eggs (β = -1.06, HR = 0.35, CI (0.14-0.87), p = 0.023), phase angle (PhA) (β = -2.68, HR = 0.07, CI (0.01-0.43), p = 0.004), cholesterol level (β = 0.95, HR = 2.60, CI (1.40-4.70), p = 0.001) and calcium level (β = -7.21, HR = 0.00, CI (0.00-1.50), p = 0.063). This study highlights a significant decline in MeDi adherence and skeletal muscle mass among KTRs over a three-year follow-up period, with no notable changes in fat mass or kidney function. The predictors of muscle mass loss and associations with mortality underscore the importance of dietary and clinical management in this population.

Expression Patterns of miR‐29b and miR‐148a in Colostrum of Awassi Sheep With Higher Immunoglobulin G Levels

ABSTRACTThis study aimed to investigate the compositional parameters and expression patterns of miR‐29b and miR‐148a in sheep colostrum containing low and high levels of Immunoglobulin G (IgG). Blood and colostrum samples were collected within the first 6 h following birth from 24 pregnant Awassi sheep. On the basis of the determined colostrum IgG levels, low (Low IgG [LIGG], 44.83 ± 4.97 mg/mL) and high (High IgG [HIGG], 80.71 ± 3.31 mg/mL) groups were formed. In addition to measuring colostrum compositional parameters, expressions of miR‐29b and miR‐148a were determined in plasma and colostrum. Although somatic cell score (SCS), pH and electrical conductivity (EC) were higher, fat‐free dry matter (FFDM), protein, lactose and freezing point were lower in HIGG group. Compared to LIGG, miR‐29b was downregulated approximately 5‐fold in the HIGG, whereas miR‐148a was downregulated more than 3‐fold in colostrum. There were negative correlations between colostrum miR‐148a and colostrum IgG, SCS and pH. Colostrum IgG was positively correlated with pH and freezing point and negatively correlated with FFDM, protein and lactose. Area under the curve (AUC) values of SCS, pH, FFDM, protein, lactose, freezing point, EC and miR‐148a (colostrum) were significant. Sensitivity and specificity of pH were 100% and 66.7%, of SCS were 81.8% and 66.7% and of miR‐148a (Colostrum) were 75% and 75% in HIGG and LIGG. According to the results, miR‐29b might be an important molecular target for lamb development, whereas miR‐148a could be a potential biomarker for colostrum quality. Moreover, the study showed that SCS and pH might be useful diagnostic parameters for IgG status because of higher sensitivity rates and AUC values.

Geographical Variations in Egg Nutrient Density: A Comparative Study of Sustainable Layer Hen Systems in Ohio and Indiana in Late Fall and Early Winter Months

Regenerative, pasture-raised layer hen systems create synergistic relationships between the hens, forage, soil, and weather, fostering a sustainable system. However, the influence of these factors on egg nutrient profile and quality may vary by region. This study took place over two years; samples were collected from a farm in Ohio during the first year and a farm in Indiana during the second year. Egg (n = 12) and forage (n = 3) samples were collected monthly from September to December. Fatty acids were quantified using gas chromatography-mass spectrometry, micronutrients were assessed in a commercial laboratory, and carotenoids and polyphenols were analyzed colorimetrically. Ohio eggs had significantly darker yolks (average yolk fan score: 8.5 ± 1.8 vs. 3.9 ± 2.0; p &lt; 0.0001), a higher beta carotene and carotenoid content, and a lower n-6:n-3 fatty acid ratio across the season. Indiana eggs had a lower average total phenolic content (0.07 ± 0.00 mg GAE/g vs. 0.14 ± 0.10 mg GAE/g) and lower average total cholesterol (5.35 ± 2.9 mg/g vs. 10.99 ± 1.54 mg/g). Forage composition and soil parameters varied between farms. Regional variations in forage composition, soil nutrients, and regional weather can significantly influence the nutrient density of eggs produced in sustainable, pasture-raising systems. This research highlights how local environmental factors can shape the nutritional profiles of eggs in different regions. Future studies should explore this relationship in more regions.

Study on the degradation mechanism of mechanical properties of red sandstone under static and dynamic loading after different high temperatures

To study the effect of high temperature on the mechanical properties of red sandstone, YNS600 electro-hydraulic servo universal testing machine and pneumatic separating Hopkinson press bar (SHPB) device were respectively used to conduct static and dynamic load loading tests on red sandstone after 500~1000 ℃ action, and X diffraction detection. The damage pattern and mechanical property index changes before and after high temperature action were compared, and the relationship between mineral composition, microstructure and mechanical parameters of the specimens and temperature was analyzed, and a theoretical model was constructed. With the increase of temperature, the peak strength and elastic modulus under static and dynamic loading decreased significantly, and compared with the static stress-strain curve, the dynamic stress-strain curve did not have an obvious compaction phase, and the mass loss rate, volume expansion rate, and longitudinal wave velocity attenuation rate increased above 600 ℃. Minerals such as zeolite and pyrite within the red sandstone specimen gradually reacted after high temperature. The theoretical model explains the degradation mechanism of the mechanical properties of red sandstone under the action of idealized high temperature. The research results can provide a reliable scientific basis for the assessment and prediction of rock engineering after high-temperature fire.

Neurofilament Light Chain Levels, Skeletal Muscle Loss, and Nutritional Decline: Key Prognostic Factors in Amyotrophic Lateral Sclerosis

ABSTRACTIntroduction/AimsHypermetabolism and weight loss are established negative prognostic factors in amyotrophic lateral sclerosis (ALS). However, the role of individualized body composition parameters in predicting ALS progression has been underexplored. This study aimed to investigate the correlation between nutritional parameters, neurofilament light chain (NfL) levels, and disease progression in ALS patients.MethodsThe Global Leadership Initiative on Malnutrition criteria were used to define malnutrition in this study. Nutritional status was assessed using body mass index and bioelectrical impedance analysis. The rate of disease progression was defined by the change in the Revised ALS Functional Rating Scale score (ΔFRS). NfL was quantified using single molecule array technology. Spearman's analyses were used to assess correlations.ResultsSixty of 110 ALS patients were classified as malnourished. There was a strong positive correlation between NfL and ΔFRS (r = 0.71), and a moderate negative correlation with disease duration (r = −0.55). The correlations between NfL and body composition parameters were statistically significant, although weak. NfL levels were significantly higher in fast progressors (p &lt; 0.0001 compared to slow progressors) and in malnourished patients (p = 0.0001). Of the 34 fast progressor patients, 28 (82%) exhibited some degree of malnutrition.DiscussionOur findings indicate that poor nutritional status, particularly reduced skeletal muscle mass—both independently and in combination with fat mass loss—is associated with elevated NfL levels and faster ALS progression. NfL, combined with nutritional parameters, could serve as a valuable biomarker for disease severity. Further research is warranted to clarify the role of skeletal muscle abnormalities in ALS progression.

Accuracy of anthropometric-based predictive equations for tracking fat mass over a competitive season in elite female soccer players: a validation study

Background and aimsBody fat is a key body composition parameter monitored in soccer. Identifying reliable alternatives to laboratory techniques for assessing body fat during the competitive period is essential. This study aimed to evaluate the cross-sectional and longitudinal validity of anthropometric prediction equations in elite female soccer players.MethodsEighteen female soccer players (age: 26.6 [3.8] years; height: 168 [6.3] cm; body mass: 64.1 [7.4] kg; body mass index: 22.7 [1.9] kg/m²) from an Italian Serie A team were assessed at four time points during a competitive season. Fat mass was estimated using anthropometric equations by Evans and Warner and compared to dual-energy X-ray absorptiometry (DXA), which served as the reference method.ResultsCross-sectional agreement analysis revealed a bias of -4.5% with Warner’s equation, while Evans’s equation showed no bias compared to DXA, with coefficient of determination (R²) values of 0.69 and 0.70, respectively. Both methods showed a negative association (Evans: r = -0.53, Warner: r = -0.63) when the difference between the values and the mean with DXA were correlated. Longitudinal agreement analysis showed no significant differences in fat mass changes between the anthropometric equations and DXA, with R² values ranging from 0.68 to 0.83. The 95% limits of agreement between the methods for individual changes in fat mass ranged from − 3.3 to 3.2%. Furthermore, no significant changes (p > 0.05) in fat mass were observed over the season with any method.ConclusionsAt the group level, Evans’s equation provides valid estimates of fat mass, whereas it may overestimate values in players with low body fat and underestimate them in those with high fat mass. The Warner equation showed the same trend as Evans at the individual level, also resulting in poor accuracy at the group level. Despite this, both anthropometric equations are valid alternatives to DXA for monitoring fat mass changes during the season, with Evans’s equation showing superior overall performance.

Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus.

This study developed a predictive model for Post-Transplant Diabetes Mellitus (PTDM) by integrating clinical and radiological data to identify at-risk kidney transplant recipients. In a retrospective analysis across three Mayo Clinic sites, clinical metrics were combined with deep learning analysis of pre-transplant CT images, focusing on body composition parameters like adipose tissue and muscle mass instead of BMI or other biomarkers. Among 2,005 nondiabetic kidney recipients, 335 (16.7%) developed PTDM within the first year. PTDM patients were older, had higher BMIs, elevated triglycerides, and were more likely to be male and non-White. They exhibited lower skeletal muscle area, greater visceral adipose tissue (VAT), more intermuscular fat, and higher subcutaneous fat (all p < 0.001). Multivariable analysis identified age (OR: 1.05, 95% CI: 1.03-1.08, p < 0.0001), family diabetes history (OR: 1.55, CI: 1.14-2.09, p = 0.0061), White race (OR: 0.43, CI: 0.28-0.66, p < 0.0001), and VAT area (OR: 1.37, CI: 1.14-1.64, p = 0.0009) as predictors. The combined model achieved C-statistic of 0.724 (CI: 0.692-0.757), outperforming the clinical-only model (C-statistic 0.68). Patients with PTDM in the first year had higher mortality than those without PTDM. This model improves predictive precision, enabling accurate identification and intervention for at risk patients.

Effects of Soil Attributes on Floristic Composition and Structure of Dry Forests in the Brazilian Savanna

ABSTRACTSoil attributes significantly influence community patterns within dry forests, yet the factors driving regional community formation in the Brazilian savanna remain poorly understood. This study aimed to describe and compare the floristic composition, vegetation structure and soil properties across dry forests, specifically examining the impact of soil attributes on the floristic composition and structure of woody vegetation. The research was conducted in four dry forests within Goiás State, including two deciduous forests (DF) and two semideciduous forests (SF), with 25 permanent plots (20 × 20 m) established in each forest type. We inventoried tree species with a diameter at breast height (DBH) greater than or equal to 10 cm and analysed the physicochemical properties of the soil. Principal component analysis of the soil variables accounted for 86% of the floristic composition variation, whereas cluster analysis distinctly separated deciduous forests from semideciduous. The deciduous forests presented more fertile soil, whereas the semideciduous forests presented greater sand contents. Differences in floristic composition and structural parameters were evident, with the semideciduous forest at Itajá showing the highest species richness and diversity. The Jataí semideciduous forest exhibited greater structural development. We found substantial effects of soil attributes on vegetation parameters, with pH, effective cation exchange capacity, calcium content, potential acidity and sand percentage being the primary correlates of variation in floristic and structural characteristics between the two forest physiognomies. Our results highlight the relevance of soil characteristics as determinants in the differentiation of forest communities in the Cerrado, highlighting the need to deepen the understanding of soil–vegetation relationships to guide preventive conservation strategies.

Photochemical corneal cross-linking: Evaluating the potential of a hand-held biopen.

Photochemical corneal cross-linking: Evaluating the potential of a hand-held biopen.

Optimization of a cyclone combustor in a flameless combustion using producer gas

Flameless combustion of producer gas offers significant environmental and efficiency advantages, but its implementation requires careful optimization of both the combustor design and operating conditions. This article is aimed to design a combustion chamber utilizing producer gas for achieving flameless combustion using SOLID-WORKS, computational fluid dynamics (CFD) ANSYS-FLUENT simulation and Design of Experiment (DOE) from Minitab software. The design varied inlet nozzle diameters from 20 to 50 mm and combustor heights from 500 to 800 mm. Computational fluid dynamics (CFD) simulations is utilized to explore the impact of altering chamber height and inlet diameter in order to achieve efficiency in flameless combustion. The producer gas composition and simulation parameters were based on prior studies. The evaluation is focused on CO, NOx emissions and the Damköhler number, and using Design of Experiment (DOE) methodology for optimization. Results showed that chamber height and inlet diameter had limited effects on combustion and CO emission. Therefore, increasing chamber height raised NOx emissions due to prolonged fuel exposure to high temperatures, while varying inlet nozzle diameter has no effect on Damköhler number, but chamber’s height does. Finally, Minitab optimization suggested a chamber with 30 mm inlet nozzle diameter and 600 mm height for desirable flameless combustion, and operating on an equivalence ratio of φ=0.7 which resulted in the lowest CO and NOx emissions, with values of 71.5 ppm and 5.05 ppm, respectively.

Harnessing machine learning approach for hardness optimization of Al-Si alloy composites reinforced with coconut shell ash

Abstract Purpose. The purpose of this study was to utilize Machine Learning (ML) to optimize the hardness of aluminum-silicon (Al-Si) alloy composites reinforced with Coconut Shell Ash (CSA). Specifically, the study focused on leveraging ML techniques to accurately predict material properties and streamline optimization. Also, the study intended to optimize the composition and process parameters simultaneously through the ML approach. Methodology. The study used Minitab’s Automated Machine Learning (AutoML), specifically the TreeNet model, to develop a predictive model for hardness. The input parameters included Al-Si alloy content, CSA content, melting temperature, and stirring speed. The model was trained and validated using experimental data, and hyperparameter tuning was performed to improve accuracy. The optimal settings were then experimentally verified to assess the model’s reliability. Findings. The optimal composition for maximizing hardness was 90 wt% Al-Si alloy and 10 wt% CSA, with a melting temperature of 800 °C and a stirring speed of 800 rpm. The experimental results validated the model’s predictions, with a hardness value of 70.9 BHN. The study demonstrated that CSA can be an effective, eco-friendly reinforcement for Al-Si composites, enhancing mechanical properties and promoting sustainability. Practical Implications. The findings have significant implications for industries such as automotive, aerospace, and defense, where lightweight, high-strength materials are critical. The ML-based approach used in this study can reduce the need for extensive experimental testing, offering a practical and efficient method for optimizing composite materials. Using CSA as a reinforcement also contributes to sustainable manufacturing by utilizing agricultural waste. Originality. This study presents an innovative approach by integrating ML into optimizing metal matrix composites, specifically using an eco-friendly reinforcement material. The article offers a novel contribution to materials science and sustainable engineering through a novel and structured step-by-step AutoML approach.

Chlorobenzenes and PCDD/Fs emissions from a pilot-scale plasma melting incinerator of simulated radioactive waste: Impacts of waste composition and operational parameters

Chlorobenzenes and PCDD/Fs emissions from a pilot-scale plasma melting incinerator of simulated radioactive waste: Impacts of waste composition and operational parameters

A benchmark analysis of feature selection and machine learning methods for environmental metabarcoding datasets.

Next-Generation Sequencing methods like DNA metabarcoding enable the generation of large community composition datasets and have grown instrumental in many branches of ecology in recent years. However, the sparsity, compositionality, and high dimensionality of metabarcoding datasets pose challenges in data analysis. In theory, feature selection methods improve the analyzability of eDNA metabarcoding datasets by identifying a subset of informative taxa that are relevant for a certain task and discarding those that are redundant or irrelevant. However, general guidelines on selecting a feature selection method for application to a given setting are lacking. Here, we report a comparison of feature selection methods in a supervised machine learning setup across 13 environmental metabarcoding datasets with differing characteristics. We evaluate workflows that consist of data preprocessing, feature selection and a machine learning model by their ability to capture the ecological relationship between the microbial community composition and environmental parameters. Our results demonstrate that, while the optimal feature selection approach depends on dataset characteristics, feature selection is more likely to impair model performance than to improve it for tree ensemble models like Random Forests. Furthermore, our results show that calculating relative counts impairs model performance, which suggests that novel methods to combat the compositionality of metabarcoding data are required.

Longitudinal association between statins and changes in CT-derived body composition in patients with abdominal aortic aneurysm.

Loss of skeletal muscle mass and systemic inflammation may offer prognostic value in patients with abdominal aortic aneurysm (AAA). The longitudinal progression of abnormal body composition parameters and their determinants is poorly reported. Statins are widely used medications that improve the prognosis of cardiovascular disease and interact with both muscle tissue and systemic inflammation. The present study aimed to describe the association between statin therapy and both pre-operative and longitudinal CT-derived body composition in patients undergoing elective intervention for AAA. A total of 756 consecutive patients undergoing elective intervention for AAA at three centres were retrospectively recruited. Body composition analysis was performed on pre-operative and follow-up CTs at L3 to generate subcutaneous adipose tissue index, visceral adipose tissue index and skeletal muscle index and density (SMI and SMD). Systemic inflammation was assessed using the systemic inflammatory grade. A total of 756 patients (702 [93%] males, median [interquartile range, IQR] age 73.0 [11.0] years) were included, with a median (IQR) follow-up of 67.0 (32) months and 235 deaths during the follow-up period. There were 582 patients (77%) receiving statin therapy and 174 patients (23%) not receiving statin therapy. Follow-up CTs were available for 273 patients. From pre-operative to follow-up CTs, there was a decrease in median SMI (P<0.001) and SMD (P<0.001) and an increase in the comparative prevalences of low SMI (43% vs. 50%, P<0.01) and low SMD (64% vs. 88%, P<0.001). There were no differences in baseline clinicopathological characteristics, systemic inflammation or pre-operative CT-derived body composition parameters between patients with and without >10% loss of skeletal muscle mass. In patients with ≤10% loss of SMI, mean (95% confidence interval) survival was 91.6 (87.2-95.9) months versus 89.3 (80.4-98.2) months in patients with >10% loss of SMI (P=0.58). Patients receiving statin therapy had a higher American Society of Anesthesiologists grade (P<0.001), a higher body mass index (BMI) (P<0.05) and a greater prevalence of normal pre-operative SMI (P<0.001). In patients with AAA, skeletal muscle mass and density appear to progressively decline despite treatment of AAA, though specific determinants of this are uncertain, and statin use does not appear to predispose to either muscle loss or preservation. Statin therapy appears to be associated with a lower rate of pre-operative low skeletal muscle mass, despite greater comorbidity and BMI. Further investigation of the progressive changes in muscle mass and quality, statin therapy and systemic inflammation is warranted.

Association between body composition parameters and treatment-related toxicities in patients with metastatic breast cancer receiving cyclin-dependent kinase 4 and 6 inhibitors.

Association between body composition parameters and treatment-related toxicities in patients with metastatic breast cancer receiving cyclin-dependent kinase 4 and 6 inhibitors.