Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Airborne Disease Prediction: Integrating Deep Infomax and Self-Organizing Maps for Risk Factor Identification

—Asthma poses a significant global public health concern, particularly in urban centers where environmental pollutants and variable weather patterns contribute to heightened prevalence and symptom exacerbation. The Deonar dumping ground, one of Mumbai’s largest landfills, releases a complex mix of particulate matter and hazardous gases, posing a serious threat to local respiratory health. Despite the urgency for comprehensive research integrating patient-specific data with localized weather and air quality metrics, such studies remain limited. This study addresses the critical research gap by investigating asthma risk factors near the Deonar dumping ground. Integrating detailed patient records with precise local weather and air quality measurements, our research aims to unravel the intricate relationship between environmental exposure and respiratory health outcomes. The findings provide crucial insights into the specific risk factors influencing asthma incidence and severity in this region, informing the development of targeted interventions and mitigation strategies. Employing a novel ensemble Deep Info Max - Self-Organizing Map (DIM-SOM) technique, our study compares its performance with various clustering algorithms, including SOM, K-Means, Bisecting K-Means, DBSCAN, and others. The novel ensemble DIM-SOM demonstrated superior performance, achieving a significantly higher Silhouette Score of 0.9234, a lower Davies-Bouldin Score of 0.1276, and a more favorable Calinski-Harabasz Score of 389723.6225 compared to other algorithms. These findings underscore the efficacy of the novel ensemble DIM-SOM approach in generating dense, well-separated, and meaningful clusters, emphasizing its potential to enhance clustering performance compared to traditional algorithms. The study further emphasizes the need for proactive mitigation measures and tailored healthcare interventions based on the identified environmental risk factors.
Human risks to vanadium from naturally growing edible mushrooms and topsoils across Leicestershire, UK

: The aims were: a) to biomonitor vanadium (V) in wild edible mushrooms collected from urban/rural areas across Leicestershire (England); b) to characterise humans risks due to its content in topsoils. Thirty-four mushrooms were collected: 22 Agaricus bitorquis from a green area close to a high traffic area; 4 Marasmius oreades from the NE; 8 Coprinus atramentarius from Bradgate Park, northwest of Leicester city. Moreover, 850 topsoil samples were collected and processed as composite samples (18 urban, 8 rural), which were further processed in duplicate. V was measured twice in each of the 52 composite samples and in cleaned, dried and homogenised mushrooms, by ICP-MS. Significant higher levels of V were found in C. atramentarius [0.856 (0.175-4.338)] than in the edible mushrooms collected in urban areas [ M. oreades 0.305 (<LoD-0.852) and A. bitorquis 0.078 (<LoD-0.187); median and range, in mg/kg dw]. The health risk quotients calculated suggested a minimal risk to V if eaten. T he presence of V in Leicestershire’s topsoils would also not represent a significant risk for the population.
The Inhibition Mechanism of Pancreatic Ductal Adenocarcinoma via LXR Receptors: A Multifaceted Approach Integrating Molecular Docking, Molecular Dynamics and Post-MD Inter-Molecular Contact Analysis

Objective: Pancreatic ductal adenocarcinoma (PDAC) has an unfavorable outlook due to its aggressive characteristics, delayed diagnosis, and limited effective treatment options for advanced stages of the disease. The significant mortality rate has prompted investigations into additional factors that could aid in managing this type of cancer. Liver X receptors, specifically LXRα and LXRβ, are nuclear receptors that oversee the expression of genes related to cholesterol, glucose, lipid metabolism, and inflammatory responses. LXRs have also emerged as potential targets for addressing PDAC, and recent findings have demonstrated that LXR ligands can impede cell proliferation in various cancer forms, notably pancreatic cancer. This comprehensive computational research study involving oncological in silico mechanism discovery explored inhibitory ligands for Liver X receptors (LXRα and LXRβ), which are believed to have prognostic significance in PDAC. Methods: The study utilized Baicalein, Beta-Sitosterol, Polydatin ligands in molecular docking and dynamics and post-molecular Hydrogen bonding contact analyses dynamics to characterize receptor inhibition. Result: The outcomes suggest that Baicalein exhibits versatile inhibitory effects on both receptors, while Beta-Sitosterol emerges as a highly effective inhibitor of LXRβ. Conclusion: Further in vitro and in vivo investigations will be beneficial and would shed light onto the mechanism to decipher the suppression of PDAC evaluating the potential of Baicalein, Beta-Sitosterol, Polydatin natural ligand compounds.
A surprising pattern of Sexual Dimorphism in the Eurasian otter (Lutra lutra) in Sweden.

: Sexual dimorphism is a very common strategy in polygamous mammals. In mustelids, sexual size dimorphism (SSD) is present in several species, while sexual shape dimorphism (SShD) is not as common. The Eurasian otter is a semiaquatic mustelid, exclusively feeding on aquatic prey, mainly fish, amphibians, and crustaceans. It is well-known that male otters tend to be larger, whereas information on SShD is contrasting. We used a 3D geometric morphometric approach to investigate SSD and SShD in three genetically distinct populations of otters located in Northern, Central and Southern Sweden. We analyzed 71 otter skulls (M = 41, F = 30), using photogrammetric techniques to produce 3D models of each skull, placing 30 3D landmarks on each model. We found that sexual dimorphism varies among populations. Neither SSD nor SShD occurred in the North, only SSD occurred in the Centre, and both SSD and SShD are present in the South of Sweden. Allometric trajectories of males and females from the North population differed from those of the other two populations. Our results highlighted a great variability in sexual dimorphism in Swedish Eurasian otters, that probably derives from different responses to prey availability and historical contingencies.
Quantifying the temporal dynamics of the protective effect of forests against rockfall after disturbances

Increasing disturbances may signiﬁcantly impact the long-term protective eﬀect of forests against natural hazards. Quantifying the temporal evolution of the protection service of forests after disturbances is thus crucial for ﬁnding optimal management strategies. In this study, we quantiﬁed the long-term protective eﬀect of spruce, beech and ﬁr forests against rockfall after severe disturbances based on a straightforward modeling approach. We therefore modelled stand growth of the three tree species based on data of the Swiss National Forest Inventory (NFI) and parameterized the potential rockfall energy dissipation capacity of these stands as a function of their age. We then quantiﬁed the evolution of their protective eﬀect for varying rockfall dispositions. The recovery of the protective
SUSTAIN captures category learning, recognition, and hippocampal activation in a unidimensional vs information-integration task

There is a growing interest in alternative explanations to the dual-system account of how people learn category structures varying in their optimal decision bounds (unidimensional and information-integration structures). Recognition memory performance and hippocampal activation patterns in these tasks are two interesting ﬁndings, which have not been formally explained. Here, we carry out a formal simulation with SUS-TAIN (Love, Medin, & Gureckis, 2004), an adaptive model of category learning, which had great success in accounting for recognition memory performance and fMRI activity patterns. We show, for the ﬁrst time, that a formal single-system model of category learning can accommodate recognition performance after learning and is consistent with fMRI data obtained while participants learned these structures.
Design and methods for a cluster randomized trial of the Sunless Study : a skin cancer prevention intervention promoting sunless tanning among beach visitors

Background: Skin cancer is the most prevalent yet most preventable cancer in the US. While protecting oneself from ultraviolet radiation (UVR) can largely reduce risk, rates of unprotected sun exposure remain high. Because the desire to be tan often outweighs health concerns among sunbathers, very few interventions have been successful at reducing sunbathing behavior. Sunless tanning (self-tanners and spray tans), a method of achieving the suntanned look without UVR exposure, might be an effective supplement to prevention interventions. Methods and Design: This cluster randomized trial will examine whether a beach-based intervention that promotes sunless tanning as a substitute for sunbathing and includes sun damage imaging and sun safety recommendations is superior to a questionnaire only control group in reducing sunbathing frequency. Female beach visitors (N = 250) will be recruited from 2 public beaches in eastern Massachusetts. Beach site will be the unit of randomization. Follow-up assessment will occur at the end of the summer (1-month following intervention) and 1 year later. The primary outcome is average sunbathing time per week. The study was designed to provide 90% power for detecting a difference of . 70 hours between conditions (standard deviation of 2.0) at 1year with an intra-cluster correlation coefficient of 0.01 and assuming a 25% rate of loss to followup. Secondary outcomes include frequency of sunburns, use of sunless tanning products, and sun protection behavior. Discussion: Interventions might be improved by promoting behavioral substitutes for sun exposure, such as sunless tanners, that create a tanned look without exposure to UVR. Trial registration: NCT00403377 Published: 5 February 2009 BMC Public Health 2009, 9:50 doi:10.1186/1471-2458-9-50 Received: 6 January 2009 Accepted: 5 February 2009 This article is available from: http://www.biomedcentral. com/1471-2458/9/50 © 2009 Pagoto et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BMC Public Health 2009, 9:50 http://www.biomedcentral. com/1471-2458/9/50
Vaccine protection against simian immunodeficiency virus in monkeys using recombinant gamma-2 herpesvirus

Vaccine protection against simian immunodeficiency virus in monkeys using recombinant gamma-2 herpesvirus" (2011). Preventive and Behavioral Medicine Publications and Presentations. Paper 284. RC. Vaccine protection against simian immunodeficiency virus in monkeys using recombinant gamma-2 herpesvirus.
CLFM: Contrastive Learning and Filter-attention Mechanism for Joint Relation Extraction

—Relation extraction is a fundamental task in natural language processing, which involves extracting structured information from textual data. Despite the success of joint methods in recent years, most of them still have the propagation of cascade errors. Specifically, the error in former step will be accumulated into the final combined triples. Meanwhile, these methods also encounter another challenges related to insufficient interaction between subtasks. To alleviate these issues, this paper proposes a novel joint relation extraction model that integrates a contrastive learning approach and a filter-attention mechanism. The proposed model incorporates a potential relation decoder that utilizes contrastive learning to reduce error propagation and enhance the accuracy of relation classification, particularly in scenarios involving multiple relationships. It also includes a relation-specific sequence tagging decoder that employs a filter-attention mechanism to highlight more informative features, alongside an auxiliary matrix that amalgamates information related to entity pairs. Extensive experiments are conducted on two public datasets and the results demonstrate that this approach outperforms other models with the same structure in recall and F1. Moreover, experiments show that both the contrastive learning strategy and the proposed filter-attention mechanism work well.
Predicting the Performance of Foundation Models via Agreement-on-the-Line

Estimating out-of-distribution performance is critical to safely deploy machine 1 learning models. Recently, Baek et al. showed that the phenomenon “agreement-2 on-the-line” can be a reliable method for predicting the OOD accuracy of models 3 in an ensemble consisting largely of CNNs trained from scratch. However, it is 4 now increasingly common to lightly fine-tune foundation models, and it is unclear 5 whether such fine-tuning is sufficient to produce enough diversity in model predic-6 tions for such agreement-based methods to work properly. In this paper, we develop 7 methods for reliably applying agreement-on-the-line-based performance estimation 8 to fine-tuned foundation models. In particular, we first study the case of fine-tuning
Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

Purpose Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making. Originality/value Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.
Eocene-Oligocene magnetostratigraphy from the central part of the SE margin of the Ebro Basin

Previous magnetostratigraphic studies focused in the Eocene successions of the eastern Ebro Basin have provided some absolute age for the basin infill. These results were based on some biostratigraphic constraints wich have been recently challenged. Our new results from the approximately 3000 m thick Montserrat and Maians-Rubió magnetostratigraphic sections have been integrated into the both marine and continental biochronology data, providing a more independent chronology. The resulting absolute chronology of the Montserrat-Rubió composite section represents about 10 My, covering the Lutetian to Rupelian (approximately 40.7 to 30.5 Ma). This leads to the re-evaluation of the age of the La Salut Fm. (Lutetian to Bartonian) and the Montserrat conglomerates (Bartonian to Priabonian). The last marine regression in the central part of the SE margin of the Ebro Basin yields an approximate age of about 36.0 Ma (Priabonian).
Experimental Implementation of Molecule Shift Keying for Enhanced Molecular Communication

Molecular communication is a communication paradigm inspired by biological systems, where chemical signals are used to encode and transmit information. MoSK (Molecule Shift Keying) is proposed as a modulation technique that utilizes different types of signaling molecules to encode digital information. A prototype platform for MoSK implementation is presented, including a transmitter with infusion and selection valves, and a fluorescence-based receiver. The receiver detects and decodes fluorescence signals emitted by Graphene Quantum Dots (GQDs), which are water-soluble and fluorescent molecular messengers. The fluorescence signals of Blue-GQDs and Cyan-GQDs are acquired by the receiver, and the performance of the system is evaluated in terms of synchronization, detection threshold, and symbol recognition using Principal Component Analysis (PCA). The results demonstrate the successful detection and recognition of different symbols, even at lower concentrations. PCA proves to be an efficient method for qualitative recognition of molecular messengers in MoSK-based molecular communication systems.
Mature Women and Higher Education : Reconstructing Identity and Family Relationships

The purpose of this paper is to explore the effects of HE participation on identity change for mature women students and their families. It reports on an aspect of my doctorate research with a small group of Early Years Foundation degree mature students and their husbands 1 focusing on identity change for the women, effects on long term relationships and consequential effects on families. The main findings demonstrated that HE changed the women’s identity, as their early years knowledge base increased, their confidence grew and they became more analytical and reflective. This had consequential effects on the family for 2 reasons: 1. The woman’s changing identity and early years knowledge acquisition affected her parenting – this enhanced the child/children’s all round development (including education). 2. Role changes in the family, due to the mother’s study taking up a large proportion of her time, meant that most of the husbands took on a more active role with childcare and household tasks – this enhanced their relationships with their children.
Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

—The Internet of Things (IoT) data is the main component for finding the basis that allows decisions to be made intelligently and enables other services to be explored and used. Data originates from smart things that have the capabilities to connect and share data enormously with other things in the IoT ecosystem. However, the level of intelligence obtained and the type of services provided, all depend on whether the data is trusted or not. High-quality data is the most trusted;, it can be used to extract meaningful insights from an event and can also be used to provide good services to humans. Therefore, decisions based on high-quality and trusted data could be good, whereas those based on low-quality or untrusted data are not only bad but could also have severe consequences. The term Quality of Data (QoD) is used to represent data trustworthiness and is used throughout this paper. To the best of our knowledge, this work is the first to coin the term QoD. The problems that hinder QoD are identified and discussed. One if it is an outlier, it is a major feature of the data that degrades its overall quality. Several machine-learning techniques that detect outliers have been studied and presented, with few data-cleaning techniques. This paper aims to present the elements necessary to ensure QoD by presenting the overview of the IoT state-of-the-art. Then, data quality, data in IoT, and outliers are studied, and some quality assurance techniques that maintain data quality is presented. A comprehensive taxonomy is shown to provide state-of-the-art data in IoT. Open issues and future directions were suggested at the end of the paper
Move Away From Me! User Repulsion Under Proximity-Induced Interference in OWC Systems

As communication systems shift towards ever higher frequency bands, the propagation of signal between a user device and an infrastructure becomes more susceptible to nearby obstacles, including other users. As an extreme case, we consider such proximity-induced channel impairments in indoor optical wireless communication (OWC) systems. We set up a model, where the achievable OWC data rate depends not only on the relative position between a user device and an infrastructure access point, but also on the location of other users modeled as proximal interferers. We use a reinforcement learning (RL) approach to enable users to ﬁnd suitable positions, both relative to the access point and to each other, that maximise the sum-rate capacity of the system. Our initial results demonstrate a feasibility of RL-based approach that enables indoor OWC users to ﬁnd suitable balance between establishing high-rate direct link while remaining distant from proximal interferers.
Using Mereotopology for Automated Spatial Inference in Task and Motion Planning

Earlier work has introduced ontologies to improve the performance of task and motion planning. However, these approaches have not explicitly used first-order axiomatizations for ontologies that are typically used for the specification of spatial constraints, such as mereotopologies. In this paper we demonstrate the application of new mereotopologies to automatically generate spatial constraints that prune the search space of task and motion planning, thereby enabling the semantic coupling of task and motion planning.
An Efficient Honeycomb Lung Segmentation Network Combining Multi-Paradigms Representation and Cascade Attention

—Honeycomb lung is a pulmonary manifestation that occurs in the terminal stage of various lung diseases, which greatly threatens patients. Due to the different locations and irregular shapes of lesions, the accurate segmentation of the honeycomb region is an essential and challenging problem. However, most deep learning methods struggle to effectively utilize both global and local information from lesion images, resulting in cannot to accurately segment the lesion. In addition, these methods often ignore some semantic information that is necessary for the segmentation of lesion location and shape in the decoding stage. To alleviate these challenges, in this paper, we propose a dual-branch encoder and cascaded decoder network (DECDNet) for segmenting honeycombs lesions. First, we design a dual-branch encoder consisting of ResNet34 and Swin-Transformer with different paradigm representations to extract local features and long-range dependencies respectively. Next, to further combine the different paradigm features, we develop the feature fusion module to obtain richer representation information. Finally, considering the problem of information loss during the decoder, a cascaded attention decoder is constructed to aggregate the multi-stage encoder information to get the final segmentation result. Experimental results demonstrate that our method outperforms other methods on the in-house honeycomb lung dataset. Notably, compared with the other nine universal methods, the proposed DECDNet obtains the highest IoU (86.34%), Dice (92.66%), Precision (93.21%), Recall (92.13%), F1-Score (92.66%), and achieves the lowest HD95 (7.33) and ASD (2.30). In particular, our method enables precisely segmenting lesions under different clinical scenarios as well. Our code and dataset are available at https://github.com/ybq17/DECDNet.