Despite the importance of wastewater environmental monitoring in disease prevention and response strategies, its use remains poorly documented in Senegal. In addition, there is more onsite sanitation than sewer networks in Dakar, and open drains channel for rainwater are also used as clandestine wastewater discharge into the sea. This study aimed to assess the presence of specific pathogens in wastewater, faecal sludge, and bathing water (the sea). Samples were taken at treatment plants, an open drain, and in the receiving environment (the sea) from June to December 2023. Total nucleic acid was subjected to multiplex qualitative qPCR using SeeGene Allplex™ kits targeting 34 gastrointestinal pathogens. Descriptive statistics, multiple correspondence analysis (MCA) and logistic regression were performed. Considering all matrices, across 51 analysed samples, the results revealed strong bacterial (96.08%, n = 49), parasitic (84.31%, n = 43), and viral (68.63%, n = 35) presence. These results showed high levels of Aeromonas spp. (96.08%), Blastocystis hominis (80.39%), Enterocytozoon (58.82%), and Norovirus GII (74.51%) among bacteria, protozoa, helminths, and viruses, respectively. Moreover, faecal sludge and pumping station samples show more identified pathogen than wastewater treatment plant and seawater samples. The MCA revealed that the dry season is spatially associated with a greater number of pathogens than the rainy season, but the latter showed a greater species diversity. Logistic regression showed that certain physicochemical parameters, including BOD5, turbidity, pH, and suspended solids, influence pathogen detection. However, qualitative detection and sampling period may constitute limitations. These results reveal that wastewater and bathing water can serve as sources of information on the circulation of pathogens of interest with epidemic potential. Therefore, this valuable epidemiological tool could serve as an adjunct to clinical surveillance in order to prevent future epidemics.

The privilege to heal? Mapping patients' unequal mobilisation of health capital in a Nordic welfare context.

A multiple correspondence analysis of necropsy findings in non-caged laying hens that died during the production period

Objective . This study aimed to conduct a comprehensive analysis of the potential of combined use of Amide Proton Transfer (APT) imaging and standardized VASARI (Visually AcceSAble Rembrandt Images) features for predicting molecular characteristics (IDH1 status, 1p19q codeletion) and the grade (differentiating Grade 3 from Grade 4) in patients with diffuse gliomas. Materials and Methods . A prospective pilot study included 40 patients with morphologically verified diffuse gliomas. All patients underwent preoperative MRI on a 3T scanner, including an APT sequence, as well as ASL and DWI (with ADC calculation). Quantitative assessment of APT, ASL, and ADC was performed, along with a qualitative analysis of 25 morphological features according to the VASARI system, followed by multiple correspondence analysis to identify latent components. Machine learning methods (Random Forest, XGBoost, Logistic Regression, Support Vector Machine, Multinomial Regression) were applied for classification tasks. The performance of models based solely on quantitative MRI parameters (mean APT, ADC, ASL values) was compared with models that additionally included VASARI components. Results . APT values significantly differed between gliomas of different grades (p = 0.004), while ASL and ADC showed no significant differences. The extracted VASARI components explained 37.2 % of the variance, with the first and most significant component (VASARI MC1), reflecting the tumor's invasive potential, demonstrating a strong correlation with APT (r = 0.65, p < 0.001). The best results were achieved in predicting IDH1 status: a Random Forest model incorporating VASARI features showed an accuracy = 0.9 and AUC = 0.91, which was significantly higher than models based solely on quantitative parameters (accuracy 0.8, AUC 0.81). Predicting the presence/absence of 1p19q codeletion and differentiating Grade 3 from Grade 4 tumors proved to be more challenging tasks, and the addition of VASARI did not lead to a significant improvement in the models. Conclusion . The combined use of quantitative APT parameters and standardized visual assessment using the VASARI system demonstrates significant potential for the non-invasive prediction of molecular-genetic characteristics of diffuse gliomas, particularly IDH1 status. The integration of qualitative and quantitative MRI data allows for the creation of more accurate diagnostic models, which may contribute to the personalization of patient treatment strategies.

In engineering environments characterized by uncertainty and a lack of structural information, traditional risk management is ineffective. However, it remains unknown how to transform qualitative tacit knowledge into reliable predictive models when stable statistical distributions are inaccessible. This study addresses this gap by developing a hybrid artificial intelligence system that bridges the divide between unstructured experiences and digital decision-making. By integrating Case-Based Reasoning (CBR) with K-medoids clustering and Multiple Correspondence Analysis, we created a framework to classify and reuse qualitative knowledge assets. We evaluated this model across four industrial case studies in volatile construction supply chains involving 212 expert participants. The analysis showed that the model achieved a 72.92% agreement rate with expert judgement in real-world settings. Beyond simple accuracy, the framework maintained robust precision and recall metrics of 0.80, successfully identifying critical risks, such as information security gaps and bureaucratic bottlenecks, that human project managers frequently overlooked. Additionally, validation via the Analytic Hierarchy Process (AHP) confirmed the practical efficiency of the suggested strategies, with a performance rating exceeding 4.25 out of 5. Ultimately, this methodology establishes a generalizable foundation for risk management in data-sparse industries and offers a practical path toward improving project resilience in unstable economies.

Motor competence (MC) can be defined as the level of skilled performance in different motor tasks. However, the influence of variables related to body composition and anthropometric measures has not yet been fully clarified. Therefore, the aim of this study was to evaluate the association between MC and sociodemographic, biological, and physical activity factors in children. This cross-sectional study was conducted with 172 children of both sexes, aged 6 to 10 years, from a municipal school. Motor competence was assessed using the Körperkoordinationstest für Kinder (KTK). Height, weight, waist circumference, leg length, foot length, and wingspan were measured. Body Mass Index (BMI) and waist-to-height ratio (WHtR) were calculated. Body composition was assessed by bioelectrical impedance analysis, providing body fat percentage, fat mass, and muscle mass. Bivariate and multivariate analyses were performed using Multiple Correspondence Analysis (MCA) and Multiple Linear Regression, with a significance level of 5%. The mean MC score was 42.66 (± 10.97) points. Significant associations were observed with age, body fat percentage, WHtR, leg length, wingspan, and participation in extracurricular physical activity. MCA indicated a moderate association between higher MC, older age, adequate body fat percentage, and physical activity practice. Multiple regression analysis demonstrated positive associations of MC with age, BMI, and wingspan, and negative associations with body fat percentage and waist circumference. It is concluded that motor competence shows independent associations with biological factors, especially age, wingspan, and body composition indicators. These findings reinforce the need for multifactorial approaches in the assessment and intervention of motor competence in children, considering sociodemographic, biological, and behavioral aspects.

Background: The aim of this study was to identify patient clusters based on acute symptom profiles and individual characteristics most likely to develop pediatric post-acute sequelae of SARS-CoV-2 infection (PASC), as well as clusters among patients with PASC based on post-acute sequelae and associated characteristics. Methods: This multicenter cohort study in 12 Italian pediatric units enrolled patients aged 0-17 years within three months of a laboratory-confirmed SARS-CoV-2 infection. Participants who completed at least two surveys developed by the ISARIC over one year were analyzed. PASC was defined per WHO criteria. Multiple Correspondence Analysis and Hierarchical Clustering were performed. Results: Of 1137 children enrolled, 850 (76%) completed at least two surveys. The most prevalent age group was older children (6-11 years) (46%); adolescents (12-17) and young children (0-5) were numerically similar. Males were more represented (51.9%), except for the adolescent group (45.1%). PASC occurred in 32.8% of participants, with the distribution of sequelae types varying by age. Clustering in COVID-19 cases identified three clusters: young children mainly presented with respiratory symptoms and with a higher risk of hospitalization, while older children were spared in both acute and post-acute phases. Adolescents, particularly females, reported more pronounced acute symptoms and developed PASC more frequently. Clustering analysis of cases with PASC identified three clusters, confirming these age-related patterns. Young children still exhibited respiratory sequelae, and older children confirmed good recovery with minimal complications, while adolescents, especially females, remained the most affected subgroup, reporting persistent neuropsychological sequelae such as fatigue and insomnia. Conclusions: Findings support age-tailored follow-up, emphasizing respiratory monitoring for young children and targeted neuropsychological care for adolescents, particularly girls.

Abstract Background: While Advance Care Planning (ACP) is critical for respecting patient autonomy, a significant gap exists in the quantitative understanding of the specific discordance in values and preferences between Japanese cancer patients and their families. This study aimed to deconstruct these potential conflicts by moving beyond item-by-item comparisons to an analysis of their underlying conceptual structures. Methods: Self-administered questionnaires were distributed at Breast and Thyroid Center, Jyoban Hospital, from August 1, 2024, to May 31, 2025. A total of 470 outpatients with breast cancer and 324 family members responded to the survey. For the primary dyadic analysis, a final matched sample of 218 patients and their 324 family members was used. Descriptive statistics were first calculated for all variables. Generalized Linear Mixed-Effects Models (GLMM) were employed to examine differences in preferences while accounting for the one-to-many dyadic structure, with intraclass correlation coefficients confirming model appropriateness. Chi-squared tests with Bonferroni correction were used to analyze the association between preferences and the patient’s choice of surrogate. Multiple Correspondence Analysis (MCA) was then performed to identify the latent structures of “Life Priorities and Values” and “End-of-Life Care Preferences. Results: The initial descriptive analysis of the full 470-patient cohort revealed that "Independence in Activities of Daily Living (ADL)" (85.7%) and "Not being a burden to family" (76.4%) were the most frequently endorsed priorities. Within the dyadic sample, the most prevalent preference among family members was "Engagement in enjoyable and meaningful activities" (78.4%). Subsequent GLMM analysis confirmed significant discordance. Patients systematically prioritized autonomy and avoiding burden, with the odds of family members endorsing these values being substantially lower (e.g., for Independence in ADL, OR = 0.20, 95% CI [0.12, 0.34]; for Not being a burden to family, OR = 0.31, 95% CI [0.20, 0.48]; both p < .001). Conversely, families prioritized life prolongation and intervention, with the odds of endorsing "Longevity" being more than double those of patients (OR = 2.15, 95% CI [1.42, 3.24], p < .001) and the odds for "Receiving treatment until personal satisfaction is met" being 4.5 times higher (OR = 4.50, 95% CI [2.75, 7.37], p < .001). Subsequent GLMM analysis confirmed significant discordance. Patients systematically prioritized autonomy and avoiding burden (e.g., for Independence in ADL, OR for family vs. patient = 0.20), while families prioritized life prolongation and intervention (e.g., for Longevity, OR = 2.15; both p < .001). Furthermore, Chi-squared tests revealed that the designation of a spouse—but not a child—as a surrogate was significantly linked to distinct preference patterns. For instance, the choice of a spouse was significantly associated with the preference for "Prognostic awareness" within both the patient cohort (Cramér's V = 0.25, p < .001) and the family cohort (Cramér's V = 0.15, 95% CI [0.04, 0.26], p = .009). Crucially, MCA visualized this fundamental structural divergence: on a map of values, patients were oriented towards "self-reliance and not being a burden," while families were oriented towards "survival, even with dependency." Conclusion: This study is one of the first in Japan to quantitatively reveal, through dyadic modeling and conceptual mapping, that patients prioritize social independence and dignity, while families emphasize life preservation—a structural gap likely contributing to clinical conflict. Effective ACP should move beyond treatment choices and promote deeper dialogue to bridge these underlying worldviews and truly honor patient values. Citation Format: A. Ozaki, K. Gonda, A. Hara, A. Ajitomi, M. Wada, K. Tachibana, M. Arai, K. Konuma, T. Sawano, Y. Kanemoto, H. Miyatake, T. Kurokawa, Y. Kouchi, N. Kanzaki, Y. Kotera, Y. Takebayashi, M. Murakami, T. Ohtake, H. Shimmura. Discordance in End-of-Life Decision-Making: A Quantitative Study on the Values and Preferences of Japanese Cancer Patients and Their Family Members [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr RF1-06.

This study aimed to characterize hunting dog packs (rehalas) and identify management typologies within a Mediterranean mountain system (Sierra Morena region of Córdoba). An ethno-demographic survey was designed and completed by 30 breeders. Descriptive statistics were used for general characterization, while variability assessment and typology identification were performed using multiple correspondence analysis and hierarchical clustering. The typical dog pack breeder was a 48-year-old man with extensive experience (28.5 years) and basic formal education. Dog packs comprised an average of 51.9 dogs, predominantly of the Large-sized Podenco Andaluz breed, participating in approximately 40 hunting events per year. Feeding practices commonly combine commercial feed with supplementary food items. Health management included routine deworming every six months, and 43% of breeders reported concern about leishmaniasis. Training generally began at around 14.5 months of age and followed regular weekly routines. Ten factors explained 82.4% of the observed variability, allowing the identification of three typologies: traditional, pragmatic, and non-organized. These findings underline the cultural, genetic, and socio-ecological relevance of dog packs as working groups in Mediterranean rural systems. The long-term sustainability of these systems depends on reinforcing selective breeding, improving health management, and safeguarding traditional practices adapted to each identified typology.

ABSTRACT The study of how parents represent the father's role is critical for a deeper understanding of fatherhood in contemporary families, as well as its contribution to family dynamics. This study aims to explore: (1) how fathers and mothers represent what is ‘being a father’ in Portugal; and (2) how these representations modulate in how and in what fathers are involved with their children, through a mixed-methods approach. Participants were mothers (n = 487) and fathers (n = 376) of children aged between 2 and 6 years old (M = 4.45; SD = 1.86; 54.2% boys). Parents were invited to present five words about the father. Fathers also reported to a father involvement scale. A similitude analysis and multiple correspondence analysis were performed. Findings of the similitude analysis revealed distinct parents' representations about the father. Mothers offered positive and negative characteristics, valuing the father's role as caregiver and playmate. Fathers only provided positive attributes, valuing themselves as moral guides and educators. The multiple correspondence analysis uncovered that only fathers' representations were related to greater involvement. Findings are critical to identify what makes fathers differently involved with their children.

Abstract This study conducts a comprehensive examination of the interrelationships among artificial intelligence (AI), trust, entrepreneurship, and decision-making in organizational settings, employing a Multiple Correspondence Analysis (MCA) of 31 peer-reviewed articles published between 2020 and 2025. Building on a systematic literature review and the HOMALS technique, the research identifies four thematic clusters that structure the contemporary conceptual space of AI in business environments: ethical-social governance, strategic-technological core, digital infrastructure, and decision-oriented automation. The findings expose a latent structural tension between technocratic efficiency and ethical legitimacy, highlighting the need for hybrid models that integrate algorithmic capabilities with principles of responsible governance. Within this framework, variables such as user acceptance, transparency, and hybrid cognitive architectures emerge as key dimensions for understanding and designing AI-mediated decision processes in entrepreneurial contexts. The study proposes a conceptual model that articulates these dimensions and provides a theoretical basis for future investigations and practical initiatives aimed at the reflexive, responsible, and sustainable implementation of intelligent technologies in contemporary business ecosystems.

Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline, with no known cure. Early detection of dementia, a primary manifestation of Alzheimer's disease, is critical to enable timely intervention and treatment planning. This study introduces ensemble learning models for predicting Alzheimer's disease and presents a comparative analysis between traditional machine learning and advanced ensemble models. The evaluation is conducted using the "Open Access Series of Imaging Studies" 2 (OASIS-2) dataset. Traditional models, including logistic regression, decision tree, support vector machine, and random forest, are benchmarked against ensemble models such as adaptive boosting, extreme gradient boosting, and a hyperparameter-tuned majority voting ensemble models. Performance is assessed using accuracy, precision, and the area under the receiver operating characteristic curve. Results show that ensemble models, particularly the optimized majority voting classifier, consistently outperform traditional methods. To complement the supervised comparison, exploratory unsupervised methods were applied using multiple correspondence analysis and k-means clustering to uncover latent structures in the dataset. By categorizing all variables, these unsupervised methods highlight patterns of clinical and demographic similarity. Unlike prior studies that focus solely on predictive accuracy, this work integrates supervised classification, ensemble learning, and unsupervised exploratory analysis within a unified framework. This combined approach enables both robust performance comparison and deeper insights into latent data structures relevant to Alzheimer's disease. All computational experiments were conducted using the Python programming language.

ABSTRACT This article investigates the evolution of the Turkish rap field, analyzing strategies for legitimacy and recognition. Dividing the field’s development into three periods (2002–2023), the study employs Multiple Correspondence Analysis (MCA) to examine the trajectories of 89 rappers featured on Spotify’s annual lists (2020–2023). The analysis structures the field along two axes: specific 'Turkish rap capital' and 'capital of renown.' The findings suggest four hypotheses: the diminishing significance of specific rap capital in favor of renown, increasing permeability of field boundaries, easier entry and exit, and a fundamental transformation of the field’s specific capital and nomos over time.

Older adults are increasingly taking up caregiving roles due to the mismatch between available formal care services and growing demands. We aimed to identify profiles of older caregivers according to their quality of life (QoL), and to explore the associations of such profiles with loneliness and social isolation. A cross-sectional analysis was conducted using cohort data from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). The study included a total of 994 unique caregivers aged 60 and above, assessed between 2001 and 2016. Multiple correspondence analysis and cluster analysis were used to obtain caregiver profiles according to the items of SF-12 QoL instrument. Multinomial logistic regressions with robust standard errors were performed to study the associations between QoL, loneliness and social isolation. Three distinct QoL profiles were identified: good (57.9%), moderate (34.8%), and moderate physical, poor mental (7.3%) QoL. The latter profile was characterized by the predominance of female spousal caregivers, who provided the most hours of care. Loneliness and social isolation were independently associated with higher odds of being in the moderate physical, poor mental QoL profile, compared to the good QoL profile. Men with higher social isolation levels were more likely to be in the worse QoL profile compared to women with similarly high levels of isolation. Our findings emphasize that a substantial proportion of older caregivers have suboptimal QoL, and that those with poor mental QoL also experience higher levels of loneliness and social isolation. Targeted policies to reduce caregiver burden and enhance their QoL are essential.

This article addresses the issue of gender-based violence – specifically male violence against women – through the perspective of children, highlighting the need for their involvement in the prevention and detection of such violence. The study explores children’s views on the conceptualisation of gender-based violence and feminist arguments, and identifies groups of children with similar positions – either recognising or denying gender-based violence – while examining associations with personal and school-related variables. A total of 1170 children (aged 9–16) in Catalonia (Spain) provided free-text responses to a questionnaire. These were analysed in two ways: (a) a content analysis and (b) a multiple correspondence analysis. The results show that children who recognise the existence of gender-based violence have different profiles from those who deny it: lower recognition of the problem was found among secondary school students, children in urban settings, boys and those in schools classified as low complexity. We conclude by emphasising the need for a more preventive socio-educational approach, recognising gender-based violence and underscoring the strategic role of the schools in tackling this social problem.

Prevalence and associated plasmid resistant genome of ESBL and AmpC producing commensal E. coli isolated from lactating cows and pre-weaned calves on dairy farms in Western Australia.

Mapping antibiotic use and calf-rearing practices in Brazilian dairy farms.

Preoperative intrinsic capacity phenotypes forecast adverse physical resilience trajectories following surgery in older bladder cancer patients.

Udder health management, bacteriological culturing, and antimicrobial use: Knowledge, implementation, attitude, and opinion among Flemish bovine dairy producers.

Meaning, Identity, and Practice in Nipple-Areola Complex Tattooing Among Breast Cancer Survivors: A Scoping Review With a Data Mining Approach.