Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Advanced Squamous Cell Carcinoma of the Vulva

Simple Summary Vulvar cancer is a rare gynaecological malignancy that has an increasing incidence, particularly in younger women. Early vulvar cancer can be treated and cured with surgical excision. Approximately 30% of women present with advanced disease, which requires treatment either with mutilating surgery or a combination of chemotherapy and radiotherapy, which is an effective treatment but has many side effects. Current research is focused on new less morbid approaches to treatment, in which drugs that target various steps on the biological pathway from pre-cancer to cancer are used, with the aim of preventing the growth of vulvar cancers. This review is an update of the current management of women with advanced vulvar cancer. Abstract Vulvar cancer is a rare gynaecological malignancy, accounting for 2–5% of cancers of the female genital tract. Squamous cell carcinoma is the most frequently occurring subtype and, historically, has been a disease of older post-menopausal women, occurring with a background of lichen sclerosus and other epithelial conditions of the vulvar skin that may be associated with well-differentiated vulvar intra-epithelial neoplasia (dVIN). An increase in human papillomavirus (HPV) infections worldwide has led to an increase in vulvar squamous carcinomas in younger women, resulting from HPV-associated high-grade vulvar squamous intra-epithelial lesions (vHSIL). Surgical resection is the gold standard for the treatment of vulvar cancer. However, as approximately 30% of patients present with locally advanced disease, which is either irresectable or will require radical surgical resection, possibly with a stoma, there has been a need to investigate alternative forms of treatment such as chemoradiation and targeted therapies, which may minimise the psychosexual morbidity of radical surgery. This review aims to provide an update on management strategies for women with advanced vulvar cancer. It is hoped that investigation of the molecular biologies of the two different pathways to vulvar squamous cell carcinoma (HPV-associated and non-HPV-associated) will lead to the development of targeted therapeutic agents.
Authentication in Wireless Body Area Network: Taxonomy and Open Challenges

: Wearable body area network (WBAN) aids the communication between the health providers and patients by supporting health monitoring services. It assists the users to maintain their health status records by collecting the body signals and transmitting them for further processing measurements. However, sensor data are publicly transferred through insecure network that facilitates the attacker malicious acts like performing masquerading attack, man in the middle, and snooping. Several authentication techniques were suggested to levitate the security of the communication channels to preserve the user data from exposure. Moreover, authentication schemes aid plenty of security issues related to user and data privacy, anonymity, repudiation, confidentiality, and integrity, but they lack performance efficiency. On the other hand, it is very hard to find the balance between security and efficiency in most of the authentication schemes, especially for the WBAN platform that consists of memory and processing constraint devices. Therefore, this paper surveys and discusses the latest authentication schemes types, techniques, and system features. Also, it highlights their strengths and weaknesses towards common knowingly attacks and provides a comparison between the popular scheme validation proofs and simulation tools. Thence, this paper draws a path for the new direction of the authentication technologies, the authentication schemes open issues, and the potential future evolution in this area.
Effects of Biologically Synthesized Iron Oxide Nanoparticles on Rhizospheric Microorganisms Associated with Tomato (Solanum lycopersicum L.)

In this study, the effect of iron oxide nanoparticles on soil rhizospheric microbial communities of tomato was investigated. Iron oxide nanoparticles were biologically synthesized using plant extract from Azadirachta indica, and characterized using a UV-VIS spectrophotometer. Varying concentrations (25, 50, 75, or 100 %) of biosynthesized iron oxide nanoparticles or precursor solution was rhizoinjected into soils in which tomato plants were grown. Plate count method was used to analyse population size and community structure of test subjects. Quantitative analysis of the bacterial and fungal community was determined and diversity indicies were calculated. The results obtained from the analysis revealed that the addition of iron oxide nanoparticles to the soil changed bacterial and fungal community with respect to the control. Also, the bacterial and fungal abundance were changed. Some tolerant microorganisms such as Micrococcus, Stapylococcus, Aspergillus, Trichoderma and Penicillium could withstand high concentrations of iron oxide nanoparticles. Shannon diversity indices showed that there was difference in the diverisity of each concentration of iron oxide nanoparticles for both fungal and bacterial communties. The study's findings showed that high concentration of iron oxide nanoparticles in the soil had adverse effect on both the tomato and the microorganisms associated with the root of the tomato. Further study needs to be conducted to ascertain the magnitude of impact iron oxide nanoparticles will have on plants and rhizosphere microbiome.
Deep Learning-enabled Detection of Acute Ischemic Stroke using Brain Computed Tomography Images

Stroke is the second leading cause of death globally. Computed Tomography plays a significant role in the initial diagnosis of suspected stroke patients. Currently, stroke is subjectively interpreted on CT scans by domain experts, and significant interand intra-observer variation has been documented. Several methods have been proposed to detect ischemic brain stroke automatically on CT scans using machine learning and deep learning, but they are not robust and their performance is not ready for clinical practice. We propose a fully automatic method for acute ischemic stroke detection on brain CT scans. The system’s first component is a brain slice classification module that eliminates the CT scan’s upper and lower slices, which do not usually include brain tissue. In turn, a brain tissue segmentation module segments brain tissue from CT slices, followed by tissue contrast enhancement using the Extreme-Level Eliminating Histogram Equalization technique. Finally, the processed brain tissue is classified as either normal or ischemic stroke using a classification module, to determine whether the patient is suffering from an ischemic stroke. We leveraged the use of the pre-trained ResNet50 model for slice classification and tissue segmentation, while we propose an efficient lightweight multi-scale CNN model (5S-CNN), which outperformed state-of-the-art models for brain tissue classification. Evaluation included the use of more than 130 patient brain CT scans curated from King Fahad Medical City (KFMC). The proposed method, using 5-fold cross-validation to validate generalization and susceptibility to overfitting, achieved accuracies of 99.21% in brain slice classification, 99.70% in brain tissue segmentation, 87.20% in patient-wise brain tissue classification, and 90.51% in slice-wise brain tissue classification. The system can assist both expert and non-expert radiologists in the early identification of ischemic stroke on brain CT scans. Keywords—Acute ischemic brain stroke; deep learning; convolutional neural network; CT brain slice classification; brain tissue segmentation; brain tissue contrast enhancement; brain tissue classification
Multi-Source Domain Generalization Using Domain Attributes for Recurrent Neural Network Language Models

SUMMARY Most conventional multi-source domain adaptation techniques for recurrent neural network language models (RNNLMs) are domain-centric. In these approaches, each domain is considered independently and this makes it di ﬃ cult to apply the models to completely unseen target domains that are unobservable during training. Instead, our study exploits domain attributes, which represent common knowledge among such di ﬀ erent domains as dialects, types of wordings, styles, and topics, to achieve domain generalization that can robustly represent unseen target domains by combining the domain attributes. To achieve attribute-based domain generalization system in language modeling, we introduce domain attribute-based experts to a multi-stream RNNLM called recurrent adaptive mixture model (RADMM) instead of domain-based experts. In the proposed system, a long short-term memory is independently trained on each domain attribute as an expert model. Then by integrating the outputs from all the experts in response to the context-dependent weight of the domain attributes of the current input, we predict the subsequent words in the unseen target domain and exploit the speciﬁc knowledge of each domain attribute. To demonstrate the e ﬀ ectiveness of our proposed domain attributes-centric language model, we experimentally compared the proposed model with conventional domain-centric language model by using texts taken from multiple domains including di ﬀ erent writing styles, topics, dialects, and types of wordings. The experimental results demonstrated that lower perplexity can be achieved using domain attributes.
Análise Estatística do Algoritmo LMS Filtrado Modificado

—This paper presents a statistical analysis of the modified filtered-x least-mean-square (MFxLMS) algorithm. The MFxLMS algorithm is a modified version of the standard FxLMS aiming to improve the convergence speed for active noise control and vibration applications. Analytical models for the first moment of the adaptive filter weights and learning curve are derived. These models are obtained without invoking the independence theory, which is a classical assumption for the modeling of LMS algorithms. The models here derived allow for a slow adaptation condition. Results of Monte Carlo (MC) simulations show a very good agreement with those obtained from the proposed model for both white and colored Gaussian input data.
Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona's Traffic with Empirical Data

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.
Mesoporous Silica Nanoparticles in Chemical Detection: From Small Species to Large Bio-Molecules

A recompilation of applications of mesoporous silica nanoparticles in sensing from the last five years is presented. Its high potential, especially as hybrid materials combined with organic or bio-molecules, is shown. Adding to the multiplying effect of loading high amounts of the transducer into the pores, the selectivity attained by the interaction of the analyte with the layer decorating the material is described. Examples of the different methodologies are presented.
Nipah virus detection at bat roosts following spillover events in Bangladesh, 2012–2019

Knowledge of the dynamics and genetic diversity of Nipah virus circulating in bats and at the human-animal interface is limited by current sampling efforts, which produce few detections of viral RNA. We report on a series of investigations at bat roosts identified near human Nipah cases in Bangladesh between 2012 and 2019. Pooled bat urine samples were collected from 23 roosts; seven roosts (30%) had at least one sample with Nipah RNA detected from the first visit. In subsequent visits to these seven roosts, RNA was detected in bat urine up to 52 days after the presumed exposure of the human case, although the probability of detection declined rapidly with time. These results suggest that rapidly deployed investigations of Nipah virus shedding from bat roosts near human cases could increase the success of viral sequencing compared to background surveillance and enhance our understanding of Nipah virus ecology and evolution.
The use of zebrafish to evaluate neuropharmacology of the gold nanoparticles

Zebrafish (Danio rerio) is a vertebrate animal used in animal model research with complex brains and behaviors similar to humans and associate with low coast become a model attractive for the academic community to seek zebrafish for scientific research. Studies on diseases of the central nervous system (CNS) have advanced and news therapeutic agents were developed for treatment these disorders. Reports suggest that the zebrafish model supports the neurodegenerative studies due functional conservation between human genes implicated in neurodegenerative disorders. The discovery of therapeutic compounds for CNS using the zebrafish model allows to show a neuroprotective action or neurotoxicity that might alter the behavioral changes. Neurotoxicity tests might perform in zebrafish’s embryos into 96 multi-well plates, which reduces the amount of substances used and cost. The bioactive compounds able to penetrate the blood-brain barrier (BBB) have important role physicochemical properties that might be desirable pharmacological effects and zebrafish trials allow if the substances might penetrate BBB and to exert central activity. The assays zebrafish are used to analyze nanoparticles that are small molecules used to explore variety applications in human health. Gold nanoparticles (AuNPs) has important properties which are extremely interest for pharmaceutical area such as drug delivery, cellular imaging, diagnostics, and therapeutic agents. Gold nanoparticles enhances Parkinson symptoms and improved neuroinflammation. Some studies show zebrafish might use to evaluate gold nanoparticles for human health hazard and toxicity studies. There is enormous potential for zebrafish in preclinical assays due to predict pharmacological and toxicity effects. Specific guidelines focused on methodologies in the zebrafish are needed to ensure adequate reproducible trials.
A mathematical analysis of an activator-inhibitor Rho GTPase model

Recent experimental observations reveal that local cellular contraction pulses emerge via a combination of fast positive and slow negative feedbacks based on a signal network composed of Rho, GEF and Myosin interactions [22]. As an examplary, we propose to study a plausible, hypothetical temporal model that mirrors general principles of fast positive and slow negative feedback, a hallmark for activator-inhibitor models. The methodology involves (ⅰ) a qualitative analysis to unravel system switching between different states (stable, excitable, oscillatory and bistable) through model parameter variations; (ⅱ) a numerical bifurcation analysis using the positive feedback mediator concentration as a bifurcation parameter, (ⅲ) a sensitivity analysis to quantify the effect of parameter uncertainty on the model output for different dynamic regimes of the model system; and (ⅳ) numerical simulations of the model system for model predictions. Our methodological approach supports the role of mathematical and computational models in unravelling mechanisms for molecular and developmental processes and provides tools for analysis of temporal models of this nature.
Therapeutic Potential of Croton Blanchetianus for the Treatment of Gastric Ulcers: A Brief Review

Natural products are considered one of the main sources that contribute to advances in research in medical science, in such a way that the elucidation of the mechanism of action enables the industry to design new drugs, providing new applications, inputs, and alternatives for the treatment of various pathologies. Studies show the importance of secondary metabolites, presenting pharmacological, microbiological, and food functions. Given the above, this review aims to describe the importance of studies with natural products, emphasizing the gastroprotective activity of the Croton genus. The genus Croton (Euphorbiaceae) is characteristic of the Brazilian biome, especially in the semiarid climate. The species of the genus have anti-inflammatory and curative properties, correlating their gastroprotective effect. Among the plurality of species, one that has therapeutic and economic potential is Croton blanchetianus. The population widely uses the species as it has medicinal properties, used to prepare teas and compresses, helping with inflammatory processes and pain. Thus, it was observed that Croton species have great potential in anti-inflammatory activity and gastroprotection. Therefore, studies with the Croton blanchetianus species should be deepened regarding this activity, providing greater knowledge about this plant.
Three-phase bidirectional dc/ac converter using a six-leg inverter connected to a direct ac/ac converter

In this study, a three-phase bidirectional dc/ac converter is proposed using a direct ac/ac converter and a six-leg converter, to avoid the use of dc-link capacitors and to increase the current capability at the dc side. To link the six-leg inverter to the direct ac/ac converter, three single-phase high-frequency transformers are implemented to simplify the topology, which will attract the industry. The direct ac/ac converter used in the study demonstrates reduced complexity and simpler modulation techniques compared with a conventional matrix converter. The analysis starts with the description of the proposed dc/ac converter for single phase, which is subsequently extended to a three-phase dc/ac converter. A 20 kW prototype was built to verify and to validate the theoretical study of the proposed converter.
Fluid Restriction Negatively Influences Perceived Morning Alertness and Visuomotor Ability

The purpose of this study was to assess the effect of two fluid intake protocols on alertness and reaction time before and after fluid intake. Healthy college-age males (n = 12) followed two fluid intake protocols on separate occasions: (1) prescribed fluid (PF) and fluid restricted (FR). In PF, participants were instructed to consume 500 mL of fluid the night prior to and the morning of data collection. In FR, participants were instructed to refrain from the consumption of fluid for 12 h. To assess hydration status, urine specific gravity and urine color were measured. Participants perceived level of thirst and alertness were also recorded. Participants then completed visuomotor reaction time tests using the Dynavision LED board, using both a central visuomotor test and a peripheral visuomotor test (PVRT) prior to (1) and following (2) the ingestion of 100 mL of water. Participants displayed significantly improved PVRT in PF state as compared to FR (PF1 = 1.13 ± 0.16, PF2 = 1.04 ± 0.14; FR1 = 1.27 ± 0.27, FR2 = 1.18 ± 0.20; p = 0.038, ηp2 = 0.363). Both CVRT and PVRT improved over time, following the ingestion of 100 mL of fluid. Participants in the PF state were also significantly more alert than participants in the FR state (PF = 4 ± 2, FR = 5 ± 2; p = 0.019, ES = 0.839). Collectively, perceived alertness and PVRT were negatively impacted by FR.
Robustesse de l'estimation de fiabilité d'une structure : un nouvel algorithme valorisant la monotonie, illustré sur un exemple industriel

The structural integrity assessment of nuclear reactor pressure vessel under accidental transients has been assessed for years including probabilistic mechanics besides deterministic studies. Even with a simple physical model coupled with probabilistic uncertainties on material properties and the thermohydraulic transient, determination of the failure probability represents a computing dilemma: approximations are necessary to keep the computing time industrially-reasonable, either on the thermomechanical model to compute it quicker, or on the probabilistic side to limit the number of computations; both threaten the a priori theoretical conservatism. A new algorithm is introduced along with a simplified industrial example, entirely based on monotony properties: it allows the robust control of probabilistic approximations (FORM-SORM-like) classically used on the thermo-mechanical model.
Ada Destinasyonların Turist Çekim Potansiyellerine İlişkin Ölçek Geliştirme Çalışması: Gökçeada Örneği (A Scale Development on Tourist Attraction Potentials of Island Destinations: The Case of Gökçeada)

Turistlerin seyahat kararı süreci, turizmde ilgi çekici bir araştırma alanı olmaya devam etmektedir. Konuyla ilgili araştırmalar, destinasyonların sahip olduğu çekicilik potansiyelinin destinasyon imajında ve seyahat motivasyonunda etkili olduğunu göstermektedir. Destinasyonların çekiciliklerinin ortaya çıkarılması rekabet avantajı elde etmelerinde önemli bir unsurdur. Bu çalışmanın temel amacı, turistlerin seyahat deneyimi sonrasında ada destinasyonların çekicilik potansiyelini değerlendirmeyi sağlayacak bir ölçek ortaya koymaktır. Çalışma nicel araştırma deseniyle gerçekleştirilmiştir. Verilerin elde edilmesi sürecinde seyahatini tamamlayan ziyaretçilerin görüşlerine ulaşmak esas alınmıştır. Veriler 2021 yılının Ekim ayında Gökçeada’yı ziyaret eden ve araştırmaya katılmayı kabul eden 400 ziyaretçiden yüz yüze anket yoluyla elde edilmiştir. Araştırma kapsamında alanyazın incelemesi sonucu hazırlanan anket ölçeğe açıklayıcı faktör analizi ve doğrulayıcı faktör analizi yapılarak ölçeğin geçerliği sağlanmış, son olarak da ölçek boyutlarına tek yönlü ANOVA analizi uygulanmıştır. Analiz sonuçlarına göre; adayı farklı sayılarda ziyaret eden turistlerin ‘destinasyon çekiciliği’ ve ‘fiyat boyutlarına ilişkin algılarında anlamlılık söz konusudur.
Learning in a Mixed Reality System in the Context of ‚Industrie 4.0‘

This contribution in the field of innovative approaches to training and education in technical subjects focuses on the potential of modern teaching and learning environments. The contribution is based on a theoretical introduction to Mixed Reality Systems and virtual teaching and learning systems, and as such provides an overview of current research regarding modern learning environments. In particular, it takes a close look at motivational effects in the context of web-based learning structures, human-object interactions, gamification and immersion. The article discusses both technical, user-relevant and pedagogical aspects as well as suggestions for further research in the context of Ausbildung 4.0. Keywords: Industry 4.0, Vocational Training 4.0, Mixed Reality System, virtual learning Acknowledgement The author would like to thank the China Scholarship Council (CSC) for the financial support (No. 201406030091).