Search Results (3,195)

This article presents a novel mathematical formalism for advanced manifold–metric pairs, enhancing the frameworks of geometry and topology. We construct various D-dimensional manifolds and their associated metric spaces using functional methods, with a focus on integrating concepts from mathematical physics, field theory, topology, algebra, probability, and statistics. Our methodology employs rigorous mathematical construction proofs and logical foundations to develop generalized manifold–metric pairs, including homogeneous and isotropic expanding manifolds, as well as probabilistic and entropic variants. Key results include the establishment of metrizability for topological manifolds via the Urysohn Metrization Theorem, the formulation of higher-rank tensor metrics, and the exploration of complex and quaternionic codomains with applications to cosmological models like the expanding spacetime. By combining spacetime generalized sets with information-theoretic and probabilistic approaches, we achieve a unified framework that advances the understanding of manifold–metric interactions and their physical implications. Full article

Introduction: Primary hyperparathyroidism (pHPT) is an endocrine disorder characterized by excessive parathyroid hormone production, typically due to adenomas, hyperplasia, or carcinoma. Preoperative imaging plays a critical role in guiding surgical planning, particularly in selecting patients for minimally invasive procedures. While first-line imaging techniques, such as ultrasound and 99mTc-sestamibi scintigraphy, are standard, advanced second-line imaging modalities like 18F-fluorocholine PET/CT (FCH-PET) and four-dimensional computed tomography (4D-CT) have emerged as valuable tools when initial diagnostics are inconclusive. Methods: This article provides an updated review and recommendations of the role of these advanced imaging techniques in localizing parathyroid adenomas. Results: FCH-PET has shown exceptional sensitivity (94% per patient, 96% per lesion) and is particularly useful in detecting small or ectopic adenomas. Despite its higher sensitivity, it can yield false positives, particularly in the presence of thyroid disease. On the other hand, 4D-CT offers detailed anatomical imaging, aiding in the identification of parathyroids in challenging cases, including recurrent disease and ectopic glands. Studies suggest that FCH-PET and 4D-CT exhibit similar diagnostic performance and could be complementary in preoperative planning of most difficult situations. Conclusions: This article also emphasizes a multimodal approach, where initial imaging is followed by advanced techniques only in cases of uncertainty. Although 18F-fluorocholine PET/CT is favored as a second-line option, 4D-CT remains invaluable for its high spatial resolution and ability to guide surgery in complex cases. Despite limitations in evidence, these imaging modalities significantly enhance the accuracy of parathyroid localization, contributing to more targeted and minimally invasive surgery. Full article

Background: Acute kidney injury (AKI) frequently occurs following major liver resection, adversely affecting both short- and long-term outcomes. This study aimed to determine the incidence of AKI post-hepatectomy and identify relevant pre- and intraoperative risk factors. Our secondary objectives were to develop a predictive score for postoperative AKI and assess the associations between AKI, chronic kidney disease (CKD), and 1-year mortality. Methods: This was a retrospective study in a cancer referral center in Marseille, France, from 2018 to 2022. Results: Among 169 patients, 55 (32.5%) experienced AKI. Multivariate analysis revealed several independent risk factors for postoperative AKI, including age, body mass index, the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, time to liver resection, intraoperative shock, and bile duct reconstruction. Neoadjuvant chemotherapy was protective. The AKIMEBO score was developed, with a threshold of ≥15.6, demonstrating a sensitivity of 89.5%, specificity of 76.4%, positive predictive value of 61.8%, and negative predictive value of 94.4%. AKI was associated with increased postoperative morbidity and one-year mortality following major hepatectomy. Conclusion: AKI is a common complication post-hepatectomy. Factors such as time to liver resection and intraoperative shock management present potential clinical intervention points. The AKIMEBO score can provide a valuable tool for postoperative risk stratification. Full article

Posidonia oceanica seagrass, endemic to the Mediterranean Sea, provides ecological goods and ecosystem services of paramount importance. In shallow and sheltered bays, P. oceanica meadows can reach the sea surface, with leaf tips slightly emerging, forming fringing and barrier reefs. During the 20th century, P. oceanica declined conspicuously in the vicinity of large ports and urbanized areas, particularly in the north-western Mediterranean. The main causes of decline are land reclamation, anchoring, bottom trawling, turbidity and pollution. Artificial sand nourishment of beaches has also been called into question, with sand flowing into the sea, burying and destroying neighbouring meadows. A fringing reef of P. oceanica, located at Saint-Mandrier-sur-Mer, near the port of Toulon (Provence, France), is severely degraded. Analysis of aerial photos shows that, since the beginning of the 2000s, it has remained stable in some parts or continued to decline in others. This contrasts with the trend towards recovery, observed in France, thanks to e.g., the legally protected status of P. oceanica, and the reduction of pollution and coastal developments. The sand nourishment of the study beach, renewed every year, with the sand being washed or blown very quickly (within a few months) from the beach into the sea, burying the P. oceanica meadow, seems the most likely explanation. Other factors, such as pollution, trampling by beachgoers and overgrazing, may also play a role in the decline. Full article

Efflux is one of the key mechanisms used by Gram-negative bacteria to reduce internal antibiotic concentrations. These active transport systems recognize and expel a wide range of toxic molecules, including antibiotics, thereby contributing to reduced antibiotic susceptibility and allowing the bacteria to acquire additional resistance mechanisms. To date, unlike other resistance mechanisms such as enzymatic modification or target mutations/masking, efflux is challenging to detect and counteract in clinical settings, and no standardized methods are currently available to diagnose or inhibit this mechanism effectively. This review first outlines the structural and functional features of major efflux pumps in Gram-negative bacteria and their role in antibiotic resistance. It then explores various strategies used to curb their activity, with a particular focus on efflux pump inhibitors under development, detailing their structural classes, modes of action, and pharmacological potential. We discuss the main obstacles to their development, including the structural complexity and substrate promiscuity of efflux mechanisms, the limitations of current screening methods, pharmacokinetic and tissue distribution issues, and the risk of off-target toxicity. Overcoming these multifactorial barriers is essential to the rational development of less efflux-prone antibiotics or of efflux pump inhibitors. Full article

Genetic testing in amyotrophic lateral sclerosis (ALS) often reveals variants of uncertain significance (VUS), which are frequently omitted from diagnostic reports or reported with limited clinical interpretation. To address this gap, we developed a rapid functional assessment pipeline in collaboration with FILSLAN, the French ALS care network, combining in vitro and in vivo neurogenetic assays. We illustrate this approach through the reclassification of the SOD1 p.Val120Leu variant, identified in an ALS patient, as pathogenic. Functional studies demonstrated that this variant leads to cytoplasmic aggregation, reduced neurite outgrowth, and abnormal motor behavior in zebrafish. These results support the systematic use of functional assays to clarify the pathogenicity of uncertain variants, thereby improving diagnostic accuracy, preventing misdiagnosis, and enabling timely therapeutic interventions in ALS. Full article

Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological system. A fully connected multilayer perceptron with a shallow structure was used to reduce complexity and limit overfitting. The inputs of the ANN were determined by rainfall–water level cross-correlation analysis. For each optimizer, the hyperparameters of the ANN were selected using a grid search and the cross-validation score on a novel criterion (PERS PEAK) mixing the persistency (PERS) and the quality of flood-peak restitution (PEAK). For an extreme and unseen event used as a test set, LM outperformed AD by 25% on all performance criteria. The peak water level of this event, 66% greater than that of the training set, was predicted by 92% after more training iterations were done by the LM optimizer. This shows that the ANN can predict beyond the ranges of the training set, given the right optimizer. Nevertheless, the LM training time was up to five times longer than that of AD during grid search. Full article

Simian foamy virus (SFV) is a retrovirus that infects primates. However, epidemiological studies of SFV are often limited to captive populations. The southeastern Brazilian Atlantic Forest is home to both an endemic, endangered species, Leontopithecus rosalia, and an introduced species, Leontopithecus chrysomelas, to which no data on SFV exist. In this study, we assessed the molecular prevalence of SFV, their viral load, and their phylogenetic relationship in these two species of primates. Genomic DNA was extracted from 48 oral swab samples of L. chrysomelas and 102 of L. rosalia. Quantitative PCR (qPCR) was performed to diagnose SFV infection and quantify viral load. SFV prevalence was found to be 23% in L. chrysomelas and 33% in L. rosalia. No age-related differences in prevalence were observed; however, L. rosalia showed a higher mean viral load (3.27 log10/10⁶ cells) compared to L. chrysomelas (3.03 log10/10⁶ cells). The polymerase gene sequence (213 pb) of L. rosalia (SFVlro) was clustered within a distinct SFV lineage found in L. chrysomelas. The estimated origin of SFVlro dated back approximately 0.0836 million years ago. Our study provides the first molecular prevalence data for SFV in free-living Leontopithecus populations while offering insights into the complex evolutionary history of SFV in American primates. Full article

This article examines two Caribbean texts which use 20th-century journeys on passenger ships as opportunities to investigate ways in which colonial anxieties of race and gender are worked out through nautical desires. Mayra Montero’s erotic novel La última noche que pasé contigo (1991) and Claude McKay’s Romance in Marseille (2020) both wrestle with the imagined and material consequences of pervasive anti-blackness. They also raise crucial questions about embodied practices of struggle for survival. My analysis seeks to answer the following questions. What happens when anti-blackness masquerades as desire? How do we read and represent an anti-blackness that seeks to consume parts of the Caribbean and then dispense as refuse with what it sees as superfluous? What reading practices might we adopt in order to make sense of Caribbean bodies dehumanized on their own shores, and what narrative solutions might Caribbean fiction propose that might begin to restore humanity and value to these bodies? Full article

Differential equations need boundary conditions (BCs) for their solution. It is widely acknowledged that differential equations and BCs are representative of independent physical processes, and no correlations between them are required. Two recent studies by Hilhorst, Chung et al. argue instead that, in the specific case of diffusion equations (DEs) in bounded systems, BCs are uniquely constrained by the form of transport coefficients. In this paper, we revisit how DEs emerge as fluid limits out of a picture of stochastic transport. We point out their limits of validity and argue that, in most physical systems, BCs and DEs are actually uncorrelated by virtue of the failure of diffusive approximation near the system’s boundaries. When, instead, the diffusive approximation holds everywhere, we show that the correct chain of reasoning goes in the direction opposite to that conjectured by Hilhorst and Chung: it is the choice of the BCs that determines the form of the DE in the surroundings of the boundary. Full article

Background and Objectives: this pilot study aimed to evaluate the diagnostic accuracy of ChatGPT-4o in analyzing oral mucosal lesions from clinical images. Materials and Methods: a total of 110 clinical images, including 100 pathological lesions and 10 healthy mucosal images, were retrieved from Google Images and analyzed by ChatGPT-4o using a standardized prompt. An expert panel of five clinicians established a reference diagnosis, categorizing lesions as benign or malignant. The AI-generated diagnoses were classified as correct or incorrect and further categorized as plausible or not plausible. The accuracy, sensitivity, specificity, and agreement with the expert panel were analyzed. The Artificial Intelligence Performance Instrument (AIPI) was used to assess the quality of AI-generated recommendations. Results: ChatGPT-4o correctly diagnosed 85% of cases. Among the 15 incorrect diagnoses, 10 were deemed plausible by the expert panel. The AI misclassified three malignant lesions as benign but did not categorize any benign lesions as malignant. Sensitivity and specificity were 91.7% and 100%, respectively. The AIPI score averaged 17.6 ± 1.73, indicating strong diagnostic reasoning. The McNemar test showed no significant differences between AI and expert diagnoses (p = 0.084). Conclusions: In this proof-of-concept pilot study, ChatGPT-4o demonstrated high diagnostic accuracy and strong descriptive capabilities in oral mucosal lesion analysis. A residual 8.3% false-negative rate for malignant lesions underscores the need for specialist oversight; however, the model shows promise as an AI-powered triage aid in settings with limited access to specialized care. Full article

This perspective paper explores the future potential of “conversational intelligence” by examining how Large Language Models (LLMs) could be combined with GRAPHYP’s network system to better understand human conversations and preferences. Using recent research and case studies, we propose a conceptual framework that could make AI reasoning transparent and traceable, allowing humans to see and understand how AI reaches its conclusions. We present the conceptual perspective of “Matching Game Preferences through Dialogical Large Language Models (D-LLMs),” a proposed system that would allow multiple users to share their different preferences through structured conversations. This approach envisions personalizing LLMs by embedding individual user preferences directly into how the model makes decisions. The proposed D-LLM framework would require three main components: (1) reasoning processes that could analyze different search experiences and guide performance, (2) classification systems that would identify user preference patterns, and (3) dialogue approaches that could help humans resolve conflicting information. This perspective framework aims to create an interpretable AI system where users could examine, understand, and combine the different human preferences that influence AI responses, detected through GRAPHYP’s search experience networks. The goal of this perspective is to envision AI systems that would not only provide answers but also show users how those answers were reached, making artificial intelligence more transparent and trustworthy for human decision-making. Full article

Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer signatures available in high-frequency electrical data include many harmonic orders that have the potential to advance disaggregation. This has been explored to some extent, but not comprehensively due to a lack of an appropriate public dataset. This paper presents the development of a cost-effective energy monitoring system scalable for multiple entries while producing detailed measurements. We will detail our approach to creating a NILM dataset comprising both aggregate loads and individual appliance measurements, all while ensuring that the dataset is reproducible and accessible. Ultimately, the dataset can be used to validate NILM, and we show through the use of machine learning techniques that high-frequency features improve disaggregation accuracy when compared with traditional methods. This work addresses a critical gap in NILM research by detailing the design and implementation of a data acquisition system capable of generating rich and structured datasets that support precise energy consumption analysis and prepare the essential materials for advanced, real-time energy disaggregation and smart energy management applications. Full article

High-resolution remote sensing imagery plays an essential role in urban management and environmental monitoring, providing detailed insights for applications ranging from land cover mapping to disaster response. Semantic segmentation methods are among the most effective techniques for comprehensive land cover mapping, and they commonly employ ImageNet-based pre-training semantics. However, traditional fine-tuning processes exhibit poor transferability across different downstream tasks and require large amounts of labeled data. To address these challenges, we introduce Denoising Diffusion Probabilistic Models (DDPMs) as a generative pre-training approach for semantic features extraction in remote sensing imagery. We pre-trained a DDPM on extensive unlabeled imagery, obtaining features at multiple noise levels and resolutions. In order to integrate and optimize these features efficiently, we designed a multi-layer perceptron module with residual connections. It performs channel-wise optimization to suppress feature redundancy and refine representations. Additionally, we froze the feature extractor during fine-tuning. This strategy significantly reduces computational consumption and facilitates fast transfer and deployment across various interpretation tasks on homogeneous imagery. Our comprehensive evaluation on the sparsely labeled dataset MiniFrance-S and the fully labeled Gaofen Image Dataset achieved mean intersection over union scores of 42.7% and 66.5%, respectively, outperforming previous works. This demonstrates that our approach effectively reduces reliance on labeled imagery and increases transferability to downstream remote sensing tasks. Full article

Two-component dental materials are commonly used by the dentist for various applications (cementation of indirect restorations, filling of a cavity without layering, etc.). These materials are cured by redox polymerization. The (hydro)peroxide/thiourea/copper salt redox initiator system is well established and can be found in a wide range of commercially available dental materials. The thiourea is a key component of the initiator system. This study explores the influence of the nature of the thiourea reducing agent on the reactivity and efficiency of redox initiator systems. In this work, six different thiourea structures were investigated, in combination with copper(II) acetylacetonate and cumene hydroperoxide (CHP), to understand their impact on polymerization kinetics and mechanical properties of methacrylate-based materials. Various experimental techniques, including mass spectrometry (MS) and spectroscopic analyses, were employed to elucidate the underlying mechanisms governing these redox systems. The results highlight that thiourea plays a dual role, acting both as a reducing agent and as a ligand in copper complexes, affecting radical generation and polymerization efficiency. Structural modifications of thiourea significantly influence the initiation process, demonstrating that reactivity is governed by a combination of factors rather than a single property. Self-cure dental flowable composites exhibiting excellent flexural strength (>100 MPa) and modulus (>6000 MPa) were obtained using hexanoyl thiourea, N-benzoylthiourea, or 1-(pyridin-2-yl)thiourea as a reducing agent. The adjustment of the Cu(acac)₂ enables to properly set the working time in the range of 100 to 200 s. These findings provide valuable insights into the design of the next generation of redox initiating systems for mild and safe polymerization conditions. Full article