Composition Metrics Research Articles

Oropharyngeal Microbiome Analysis in Patients with Varying SARS-CoV-2 Infection Severity: A Prospective Cohort Study.

Patients with COVID-19 infection have distinct oropharyngeal microbiota composition and diversity metrics according to disease severity. However, these findings are not consistent across the literature. We conducted a multicenter, prospective study in patients with COVID-19 requiring outpatient versus inpatient management to explore the microbial abundance of taxa at the phylum, family, genus, and species level, and we utilized alpha and beta diversity indices to further describe our findings. We collected oropharyngeal washing specimens at the time of study entry, which coincided with the COVID-19 diagnosis, to conduct all analyses. We included 43 patients in the study, of whom 16 were managed as outpatients and 27 required hospitalization. Proteobacteria, Actinobacteria, Bacteroidetes, Saccharibacteria TM7, Fusobacteria, and Spirochaetes were the most abundant phyla among patients, while 61 different families were detected, of which the Streptococcaceae and Staphylococcaceae families were the most predominant. A total of 132 microbial genera were detected, with Streptococcus being the predominant genus in outpatients, in contrast to hospitalized patients, in whom the Staphylococcus genus was predominant. LeFSe analysis identified 57 microbial species in the oropharyngeal washings of study participants that could discriminate the severity of symptoms of COVID-19 infections. Alpha diversity analysis did not reveal a difference in the abundance of bacterial species between the groups, but beta diversity analysis established distinct microbial communities between inpatients and outpatients. Our study provides information on the complex association between the oropharyngeal microbiota and SARS-CoV-2 infection. Although our study cannot establish causation, knowledge of specific taxonomic changes with increasing SARS-CoV-2 infection severity can provide us with novel clues for the prognostic classification of COVID-19 patients.

White adipose tissue distribution and amount are associated with increased white matter connectivity.

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.

Abstract 6113: Racial disparity in HER2-positive breast cancer: A single cancer center registry review

Abstract Background: Racial disparity outcomes in HER2-positive breast cancer have been previously described but the literature is limited. We examined data from our cancer center registry to investigate racial disparity in HER2-positive breast cancer. Method: We retrospectively queried patients diagnosed with HER2-positive breast cancer from Houston Methodist Cancer Center registry from 2016 to 2022. Patients’ demographics, cancer staging, treatment and outcomes including recurrence and survival were recorded and analyzed. We used the distressed communities index (DCI), a composite metric of multiple socioeconomic factors based on the patient’s zip code at diagnosis, as a surrogate of socioeconomic status. Multivariable logistic regression and Cox proportional hazards analysis were used. A p value <0.05 is considered statistically significant. Results: From 2016 to 2022, 992 patients were retrieved. 58.4% (579/992) patients were White, 17.3% (172/992) black, 16.2% (161/992) Hispanic and 8.1% (80/992) Asian. Although Black and Hispanic patients lived in more distressed communities, Black and Asian patients were diagnosed with more advanced disease. There were no statistically significant differences in neoadjuvant treatment received and pathologic complete response (pCR) rate among racial groups (table 1). Patients with higher stages are more likely to receive neoadjuvant treatment (OR: 5.49 [CI: 3.61-8.49] and 5.59 [CI: 3.15-10.2] for Stage 2 and 3 respectively). ER-HER2+ subtype was the only predictor of achieving pCR (OR: 0.47, CI: 0.29-0.79). Younger age, receiving neoadjuvant treatment and earlier stages were associated with improved all-cause mortality. There were no differences in receiving neoadjuvant treatment between racial groups, DCI; race and DCI are not associated with pCR or all-cause mortality. Conclusion: Our retrospective study from a single institution revealed no significant racial disparities in the treatment and outcome of HER2-positive breast cancers. Table 1. Characteristics of HER2-positive breast cancer in different racial groups Variables Asian, N = 80 Black, N = 172 Spanish, N = 161 White, N = 579 p-value Age (at diagnosis), mean (SD) 54 (13) 54 (12) 53 (13) 57 (13) <0.001 Distressed communities index, mean (SD) 28 (28) 49 (31) 49 (31) 35 (29) <0.001 HER2 subtype 0.7 ER-HER2+ 33/80 (41%) 64/169 (38%) 55/161 (34%) 204/576 (35%) ER+HER2+ 47/80 (59%) 105/169 (62%) 106/161 (66%) 372/576 (65%) Cancer stage 0.010 0 4/80 (5.0%) 4/172 (2.3%) 8/161 (5.0%) 31/579 (5.4%) 1 34/80 (43%) 51/172 (30%) 52/161 (32%) 227/579 (39%) 2 14/80 (18%) 44/172 (26%) 49/161 (30%) 172/579 (30%) 3 12/80 (15%) 30/172 (17%) 23/161 (14%) 52/579 (9.0%) 4 13/80 (16%) 30/172 (17%) 16/161 (9.9%) 63/579 (11%) Unknown 3/80 (3.8%) 13/172 (7.6%) 13/161 (8.1%) 34/579 (5.9%) Received neoadjuvant 0.074 No 31/66 (47%) 68/136 (50%) 54/134 (40%) 263/497 (53%) Yes 35/66 (53%) 68/136 (50%) 80/134 (60%) 234/497 (47%) Complete pathological response 0.9 No 17/35 (49%) 34/70 (49%) 40/75 (53%) 111/230 (48%) Yes 18/35 (51%) 36/70 (51%) 35/75 (47%) 119/230 (52%) Mortality (all cause) 0.052 Alive 76/80 (95%) 150/172 (87%) 147/161 (91%) 540/579 (93%) Dead 4/80 (5.0%) 22/172 (13%) 14/161 (8.7%) 39/579 (6.7%) Citation Format: Alan Hodges, Cornelius Rosenbaum, Claudia Santana, Polly A. Niravath, Hanh Mai, Dharamvir Jain, Jenny C. Chang, Jiaqiong Xu, Kai Sun. Racial disparity in HER2-positive breast cancer: A single cancer center registry review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6113.

Moduli of Continuity in Metric Models and Extension of Livability Indices

Index spaces serve as valuable metric models for studying properties relevant to various applications, such as social science or economics. These properties are represented by real Lipschitz functions that describe the degree of association with each element within the underlying metric space. After determining the index value within a given sample subset, the classic McShane and Whitney formulas allow a Lipschitz regression procedure to be performed to extend the index values over the entire metric space. To improve the adaptability of the metric model to specific scenarios, this paper introduces the concept of a composition metric, which involves composing a metric with an increasing, positive and subadditive function ϕ. The results presented here extend well-established results for Lipschitz indices on metric spaces to composition metrics. In addition, we establish the corresponding approximation properties that facilitate the use of this functional structure. To illustrate the power and simplicity of this mathematical framework, we provide a concrete application involving the modeling of livability indices in North American cities.

Landscapes of risk: A comparative analysis of landscape metrics for the ecotoxicological assessment of pesticide risk to bees

Abstract Pesticide use in agricultural landscapes creates environmental contamination that is heterogenous in space and time. Mobile organisms, such as bees, are exposed to multiple contamination sources when visiting patches that vary in the amount, timing and toxicity of pesticides used. Yet, environmental risk assessments (ERA) typically fail to consider this heterogeneity, in part because of the complexities of estimating exposure to different pesticides, and subsequent risk at organism‐relevant scales. We use pesticide assays of 269 bee‐collected pollen samples to understand the spatiotemporal variability of risk across a network of 41 field sites in southern Sweden. Observed bee pesticide risk is calculated based on compound‐specific residue quantifications in pollen and standardized toxicity data. We then compare the ability of three classes of landscape‐scale variables to predict this risk: (1) landscape composition and configuration metrics, (2) landscape load based on national pesticide use data and (3) predictions from a newly developed bee pesticide exposure model. Based on use data, 10 crops account for 81% of the total risk. We detected 49 pesticide compounds in bee‐collected pollen. Although herbicides and fungicides constitute the bulk of detected pesticides, both in frequency and amount quantified, unsurprisingly, insecticides contribute the most to risk. Landscape composition and configuration metrics did not predict observed pesticide risk, and interactions with bee species indicate taxa‐dependency in predictions. Landscape load predicted observed risk consistently between taxa. Risk estimates from our exposure model were strongly predictive but only when considering realized risk (i.e., risk estimates based on prior pesticide use information). Synthesis and applications. Predicting pesticide risk based on landscape patterns could enable landscape‐scale ERA. However, simple metrics of landscape pattern, such as proportion of agricultural land, are not sufficient. We found that risk observed in bee‐collected pollen was best predicted when integrating spatialized pesticide use in the pesticide exposure model, underscoring the importance of such data for research, monitoring and mitigation. Further, we propose a guidance framework for future landscape ecotoxicological risk analyses that clarifies data needs relative to risk prediction goals.

Objective sleep quality predicts subjective sleep ratings

In both clinical and observational studies, sleep quality is usually assessed by subjective self-report. The literature is mixed about how accurately these self-reports track objectively (e.g. via polysomnography) assessed sleep quality, with frequent reports of little to no association. However, previous research on this question focused on between-subject designs, which may be confounded by trait-level variables. In the current study, we used the novel Budapest Sleep, Experiences and Traits Study (BSETS) dataset to investigate if within-subject differences in subjectively reported sleep quality are related to sleep macrostructure and quantitative EEG variables assessed using a mobile EEG headband. We found clear evidence that self-reported sleep quality in the morning is influenced by within-subject variations in sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. These effects were replicated if detailed sleep composition metrics (percentage and latency of specific vigilance states) or two alternative measures of subjective sleep quality were used instead. We found no effect of the number of awakenings or relative EEG delta and sigma power. Between-subject effects (relationships between individual mean values of sleep metrics and subjective sleep quality) were also found, highlighting that analyses focusing only on these may be erroneous. Our findings show that while previous investigations of this issue may have been confounded by between-subject effects, objective sleep quality is indeed reflected in subjective sleep ratings.

The association of Neuromedin U levels and non-alcoholic fatty liver disease: A comparative analysis

This comprehensive study delves into the potential link between Neuromedin U (NmU) serum levels and the development of non-alcoholic fatty liver disease (NAFLD), a condition of increasing global prevalence and significant public health concern. The research provides a nuanced understanding of the disease's etiology by examining a cohort of 112 participants, including individuals with and without NAFLD. The study meticulously considers a spectrum of variables such as demographic factors, body composition metrics, and blood parameters. Advanced diagnostic tools like Fibroscan® are employed to ascertain NAFLD presence, ensuring accurate and reliable results.The investigation reveals a noteworthy correlation between NAFLD and several risk factors, notably obesity, increased waist and neck circumferences, hypertriglyceridemia, and insulin resistance. These findings underscore the multifactorial nature of NAFLD and its intricate connection with metabolic syndromes. Intriguingly, the study observes lower NmU levels in individuals diagnosed with NAFLD. However, the role of NmU as an independent risk factor for NAFLD remains inconclusive, warranting further investigation. Although triglyceride level was observed to be an independent risk factor for NAFLD, this relationship was not associated with NmU.This research contributes significantly to the existing knowledge on NAFLD, highlighting the disease's complexity and the interplay of various risk factors. It also opens up new avenues for future research, particularly in exploring the role of NmU within the metabolic pathways associated with NAFLD. The insights gained from this study could guide the development of novel diagnostic and therapeutic strategies for NAFLD, addressing a crucial need in contemporary healthcare.In conclusion, the findings of this study not only enhance the understanding of NAFLD's pathophysiology but also emphasize the importance of comprehensive risk factor analysis in the management and prevention of this growing health concern.

Removing Heavy Metals: Cutting-Edge Strategies and Advancements in Biosorption Technology.

This article explores recent advancements and innovative strategies in biosorption technology, with a particular focus on the removal of heavy metals, such as Cu(II), Pb(II), Cr(III), Cr(VI), Zn(II), and Ni(II), and a metalloid, As(V), from various sources. Detailed information on biosorbents, including their composition, structure, and performance metrics in heavy metal sorption, is presented. Specific attention is given to the numerical values of the adsorption capacities for each metal, showcasing the efficacy of biosorbents in removing Cu (up to 96.4%), Pb (up to 95%), Cr (up to 99.9%), Zn (up to 99%), Ni (up to 93.8%), and As (up to 92.9%) from wastewater and industrial effluents. In addition, the issue of biosorbent deactivation and failure over time is highlighted as it is crucial for the successful implementation of adsorption in practical applications. Such phenomena as blockage by other cations or chemical decomposition are reported, and chemical, thermal, and microwave treatments are indicated as effective regeneration techniques. Ongoing research should focus on the development of more resilient biosorbent materials, optimizing regeneration techniques, and exploring innovative approaches to improve the long-term performance and sustainability of biosorption technologies. The analysis showed that biosorption emerges as a promising strategy for alleviating pollutants in wastewater and industrial effluents, offering a sustainable and environmentally friendly approach to addressing water pollution challenges.

The association between obesity severity and food reward in adolescents with obesity: a one-stage individual participant data meta-analysis.

Food reward and cue reactivity have been linked prospectively to problematic eating behaviours and excess weight gain in adults and children. However, evidence to date in support of an association between degree of adiposity and food reward is tenuous. A non-linear relationship between reward sensitivity and obesity degree has been previously proposed, suggesting a peak is reached in mild obesity and decreases in more severe obesity in a quadratic fashion. To investigate and characterise in detail the relationship between obesity severity, body composition, and explicit and implicit food reward in adolescents with obesity. Data from seven clinical trials in adolescents with obesity were aggregated and analysed in an independent participant data meta-analysis. Linear and curvilinear relationships between the degree of obesity and explicit and implicit reward for sweet and high fat foods were tested in fasted and fed states with BMI-zscore as a continuous and discrete predictor using clinically recognised partitions. Although positive associations between obesity severity and preference for high-fat (i.e. energy dense) foods were observed when fasted, none reached significance in either analysis. Conversely, adiposity was reliably associated with lower reward for sweet, particularly when measured as implicit wanting (p = 0.012, ηp2 = 0.06), independent of metabolic state. However, this significant association was only observed in the linear model. Fat distribution was consistently associated with explicit and implicit preference for high-fat foods. A limited relationship was demonstrated between obesity severity and food reward in adolescents, although a lower preference for sweet could be a signal of severe obesity in a linear trend. Obesity is likely a heterogenous condition associated with multiple potential phenotypes, which metrics of body composition may help define. NCT02925572: https://classic. gov/ct2/show/NCT02925572 . NCT03807609: https://classic. gov/ct2/show/NCT03807609 . NCT03742622: https://classic. gov/ct2/show/NCT03742622 . NCT03967782: https://classic. gov/ct2/show/NCT03967782 . NCT03968458: https://classic. gov/ct2/show/NCT03968458 . NCT04739189: https://classic. gov/ct2/show/NCT04739189 . NCT05365685: https://www. gov/study/NCT05365685?tab=history .

Body Composition Indicators Jointly Predict Metabolic Unhealthy Phenotypes in Young and Middle-Aged Obese Individuals: A Cross-Sectional Quantitative Computed Tomography Study.

The main aim of this study is to analyze the relationship between body composition indices and metabolic unhealthy phenotypes in young and middle-aged obese patients and to assess their joint predictive ability. A cross-sectional study method was used to select 207 patients who were proposed to undergo weight loss surgery for morbid obesity from March to November 2022. Total adipose tissue (TAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), liver fat content (LFC), cross-sectional area (CSAmuscle), and intermuscular adipose tissue (CSAIMAT) of paraspinal muscles were measured using quantitative computed tomography. Participants were categorized into two groups: metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). The receiver operating characteristic curve comprised body composition variables that correlated with MUO, and the area under the curve (AUC) was calculated to compare their prediction capacity for MUO. There were 71 patients with MHO (34.3%) and 136 patients with MUO (65.7%). The VAT, VAT/TAT ratio, LFC, and CSAmuscle was higher in MUO patients than in MHO (all P < 0.001), and SAT was lower than in MHO (P = 0.008). And all of these metrics were correlated with MUO (all P < 0.05). Inclusion of these body composition metrics in the ROC analysis showed that the AUC values for SAT, VAT, VAT/TAT ratio, LFC and CSAmuscle were 0.615, 0.663, 0.727, 0.694, 0.671, respectively, and the combination of the VAT/TAT ratio and the LFC had the ability to predict MUO best (AUC=0.746, P = 0.025). The combined use of VAT/TAT ratio and LFC is superior to the use of these two metrics alone in terms of their ability to predict the MUO, providing a more accurate approach to the management and prevention of obesity-related metabolic risk.

Abstract A029: Metabolomic profiles among African-American and White breast cancer survivors: An exploratory comparison of correlations with body weight and body composition

Abstract Objective: Obesity, a strong risk and prognostic factor for postmenopausal breast cancer, is associated with numerous disruptions to the metabolome. However, it remains unclear whether obesity-metabolite associations vary when considering direct measures of adiposity versus body weight, particularly among breast cancer survivors. This analysis aims to compare correlations of body mass index (BMI) and dual-energy x-ray absorptiometry (DXA) derived measures of adiposity (percent fat, total fat) with global serum metabolites. Methods: A cross-sectional analysis was conducted among 60 postmenopausal breast cancer survivors, including 25 African-American and 35 White women within five years of their breast cancer diagnosis who were seen for care at the University of Maryland (UM) Greenebaum Comprehensive Cancer Center (Baltimore, Maryland) and the Cancer Institute at the UM St. Joseph Medical Center (Towson, Maryland). Measured height and weight were used to calculate body mass index (kg/m2); percent fat, total fat and lean mass were quantified using DXA. Global metabolomic profiling in EDTA plasma samples was performed by liquid chromatography-tandem mass spectrometry at Metabolon, Inc. Metabolites were log transformed and partial correlations of BMI, percent fat and total fat (adjusted for age, race and fasting status) were estimated. Analyses of total fat also adjusted for lean mass. The Benjamini Hochberg procedure was used to control the false discovery rate for multiple comparisons. Results: A total of 1353 metabolites, including those with known and unknown identities, were detected. After excluding metabolites with a coefficient of variation ³ 25% or with ³ 30% of values below the limit of detection, 978 metabolites remained for analyses. The median BMI among all women (n=60) was 29.7 kg/m2 ranging from 20.7-46.7 kg/m2. Percent fat ranged from 30.5 – 54.3% with a median of 41.1% while the median total fat was 27.9 kg (17.2-62.7 kg). BMI was strongly correlated with %fat (r=0.8, p=6.6e-08) and total fat after adjustment for lean mass (r=0.6, p=5.7e-05). In analyses of all women, after adjustment for age, race and fasting status, 73 metabolites correlated with BMI, 110 with percent fat and 92 with total fat adjusted for lean mass (q-value &amp;lt;.15). Of those, 27 metabolites correlated with all three metrics while 39 metabolites were distinctly correlated with BMI only, 10 with percent fat only and 21 with total fat only. BMI, percent fat and total fat were most commonly correlated with lipids and amino acids. Conclusions: Preliminary analyses suggest characterizing obesity with metrics of body composition may provide further insight into obesity-metabolite associations among breast cancer survivors. Analyses are ongoing to assess differences in correlations by race and to examine metabolite patterns via metabolite set enrichment analyses. Citation Format: Cher M Dallal, Li Feng, Charles Ma, Katherine Tkaczuk, Stephen Thomas, Joanne F. Dorgan. Metabolomic profiles among African-American and White breast cancer survivors: An exploratory comparison of correlations with body weight and body composition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A029.

Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics.

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.

DECODING MICROBIOME DYSBIOSIS THROUGH METAGENOMIC ALPHA DIVERSITY

Background: Sarcoidosis is a chronic inflammatory disease that can affect multiple organs. The aetiology of sarcoidosis is not fully understood, but there is increasing evidence that the microbiome may play a role. The blood microbiome is a collection of microorganisms that live in the bloodstream. It is a complex and dynamic community that is influenced by a variety of factors, including the host’s lifestyle and pathology. Recent studies have shown that people with sarcoidosis have alterations in their blood microbiome. These alterations include changes in the diversity, richness, and evenness of the microbial community. The abundance measures by which the blood microbiome diversity may detect instances of dysbiosis related to sarcoidosis aetiology. It should be clearly distinguished from microbiome changes related to unspecific inflammation or sepsis. However, the available evidence suggests that the microbiome may be a promising target for therapeutic interventions. Aim: The primary goal of this review was to assess and compare the existing metrics of microbiome composition and diversity as established by metagenomic analyses. Additionally, we aim to elucidate the potential causal relationship between these measures, the phenomenon of blood microbiome dysbiosis and the pathogenesis of sarcoidosis. Conclusion: In the present review, we investigated alpha diversity measures as characteristics of microbiome communities, examining their potential as indicators of dysbiosis, and the probablemechanisms of microbiome participation. A descriptive qualitative comparison was conducted between lung microbiome data of sarcoidosis patients and blood microbiome data of healthy adults. This comparison elucidates common taxa between the two microbiomes and identifies taxa potentially involved in sarcoidosis.

Exposure and recovery: The effect of different dilution factors of treated and untreated metal mining effluent on freshwater biofilm function and structure

Abandoned mines generate effluents rich in heavy metals, and these contaminants are released uncontrolled into the nearby aquatic ecosystems, causing severe pollution. However, no real solution exists, leaving a legacy of global pollution. In this study, the efficiency of the treatment technologies in reducing the ecological impacts of mining effluents to freshwater ecosystems with different dilution capacities was tested using biofilm communities as biological indicators. The functional and structural recovery capacity of biofilm communities after 21 days of exposure was assessed. With this aim, we sampled aquatic biofilms from a pristine stream and exposed them to treated (T) and untreated (U) metal mining effluent from Frongoch abandoned mine (Mid Wales, UK). Additionally, we simulated two different flow conditions for the receiving stream: high dilution (HD) and low dilution (LD). After exposure, the artificial streams were filled with artificial water for 14 days to assess the biofilm recovery. Unexposed biofilm served as control for biofilm responses (functional and structural) measured throughout time. During the exposure, short term effects on biofilm functioning (photosynthetic efficiency, nutrient uptake) were observed in T-LD, U-HD, and U-LD, whereas long term effects (community composition, chl-a, and diatom metrics) were observed on the structure of all biofilms exposed to the treated and untreated mining effluent. On the other hand, metal accumulation occurred in biofilms exposed to the mining effluents. However, a functional recovery was observed for all treatments, except in the U-LD in which biofilm structure did not present a significant recovery after the exposure period. The results presented here highlight the need to consider the dilution capacity of the receiving stream to assess the real efficiency of treatment technologies applied to mining effluents to mitigate the ecological impact on freshwater ecosystems.

Impact of Neighborhood Deprivation and Social Vulnerability on Outcomes and Interventions in Patients with Cleft Palate.

To examine whether neighborhood disadvantage impacts length of follow-up, interventions, and outcomes for patients with cleft palate. Retrospective cohort. Cleft Palate Craniofacial Institute Database at St. Louis Children's Hospital. Patients with cleft palate following in St. Louis Children's Hospital Cleft Palate Multidisciplinary Team Clinic. Primary palatoplasty between 2012-2017. Patients were divided into quartiles across area deprivation index (ADI) and social vulnerability index (SVI), two validated, composite metrics of neighborhood disadvantage, to examine whether living in neighborhoods from different deprivation quartiles impacts outcomes of interest. Follow-up through age 5, surgeries and surgical complications, speech, developmental, and behavioral outcomes. 205 patients were included. 39% of patients belonged to the most deprived ADI quartile, while 15% belonged to the most vulnerable SVI quartile. There were no differences between ADI or SVI quartiles in number of operations received (p ≥ 0.40). Patients in the most deprived ADI quartile were significantly more likely to have speech/language concerns (OR 2.32, 95% CI [1.20-4.89], p = 0.01). Being in a more vulnerable SVI quartile was associated with developmental delay (OR 2.29, 95% CI [1.04-5.15], p = 0.04). ADI and SVI quartile did not impact risk of loss to follow-up in the isolated and combined cleft lip and palate subgroups (p ≥ 0.21). Neighborhood disadvantage impacts speech and developmental outcomes in patients with cleft palate despite comparable length of follow-up in multidisciplinary team clinic.

A hybrid multi-criteria meta-learner based classifier for imbalanced data

Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of generalization and fairness stem from the existence of underrepresented classes with sensitive characteristics. Additionally, defining an optimal criterion is crucial when dealing with imbalanced data. To address this, it is common in practice to employ composite metrics and loss functions that evenly distribute the cost across each class.However, relying solely on a single metric or loss function, frequently yields sub-par outcomes due to the tendency of the optimization criteria to be overly responsive to imbalanced data, resulting in excessive adaptation and flawed generalization. In order to overcome these limitations, various heuristics can be applied to optimize the criterion.To this end, we aim at addressing the problem of imbalanced data classification by leveraging a multicriteria ensemble procedure, the Hybrid Multi-criteria Meta-learner (HML). The suggested approach focuses on optimizing precision, recall together with balanced accuracy in the Multi-Objective optimization phase and brings forth optimal ensembles, from which the concluding Meta-learner can be chosen after a clustering step, based on a multicriteria decision-making aspect contingent on the selected metrics. Comprehensive experiments portrayed the effectiveness of HML, offering both quality assurance and addresses a vast array of imbalanced data problems.

Habitat configuration influences mammal populations at a wider spatial extent than habitat composition: a meta-analysis of forest mammal datasets

ContextThe spatial extent at which landscape variables most strongly influences wildlife populations (i.e., scale of effect) is of key importance to ecology and conservation. Many factors can influence scales of effect, but these relationships are poorly understood. In particular, the way scales of effect vary with different landscape metrics has not been extensively tested.ObjectivesOur study tested whether metrics of landscape configuration have wider or narrower scales of effect than habitat composition. We also examined how species traits and landscape context influence the magnitude of differences in scales of effect between metrics.MethodsWe calculated scales of effect for four metrics of habitat configuration and one metric of composition directly from species presence/absence data in online repositories including 102 species of forest mammals from 33 studies across the globe. We then compared the magnitudes and variability of scales of effect using Hedge’s g.ResultsTwo metrics of configuration (flux and patch density) had significantly wider scales of effect than habitat composition, while the differences between the other two metrics of configuration and composition were not statistically significant. The magnitude of the difference between metrics varied among samples, but none of the factors tested explained this variability.ConclusionsFor forest mammals, the scale of effect of a configuration metric can be expected to be 15–20% wider than a composition metric. However, this pattern may not hold for metrics of configuration that do not quantify attributes of the landscape that mediate species dispersal or that are correlated with the metric of composition.

The Significance of Name-Based Racial Composition in Analyzing Neighborhood Disparities

Contemporary sociological research emphasizes the need to analyze inequality beyond nominal categories. Although research has grown in this regard at the individual level, little research has pursued this approach with neighborhoods. This article explores how names can serve as a measure of the perceived typicality associated with race and how names are associated with neighborhood characteristics. Analyses on data with the names of over 300 million Americans demonstrate that name-based racial composition more fully explains socioeconomic disparities among neighborhoods than conventional survey-based racial composition metrics. Neighborhoods with the most Black-sounding names demonstrate greater socioeconomic disadvantage than neighborhoods with the most individuals self-identifying as Black. Additionally, naming patterns explain variation in socioeconomic inequality within both predominantly nominally Black neighborhoods and predominantly nominally White neighborhoods—where little nominal racial variation exists. This research suggests that infracategorical measures of race can provide additional predictive power to nominal measures of racial composition when analyzing neighborhood inequalities

Deep CNN and LSTM Approaches for Efficient Workload Prediction in Cloud Environment

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively.

Comparison of body composition and muscle mass by age in amateur soccer players.

[Purpose] This study aimed to elucidate the changes in body composition components associated with aging in amateur male soccer players. Specifically, we investigated the alterations in the phase angle and regional muscle mass distribution. [Participants and Methods] The study included a cohort of 163 male participants categorized into three age groups: U15 (12-15 years), U18 (16-18 years), and O19 (≥19 years). Precise body composition assessments were performed, employing the InBodyS10 body composition scale. [Results] The findings revealed substantial age-related disparities in various body composition parameters. Data revealed a consistent trend of increasing basic body composition metrics with age. Notably, the body fat percentage progressively increased with age. Muscle mass and phase angle exhibited age-related increases with nuanced variations in different anatomical regions. [Conclusion] In the general Japanese population, muscle mass tends to decrease with age after 18 years. However, in this study on amateur soccer players, we observed a plateau in the height and lower limb phase angle around the age of 18 years, whereas muscle mass exhibited an increasing trend.