Miracle-dz/UniFAR-FTUs huggingface dataset:
Traumatic Ulcer Associated with Iatrogenic Trauma After Basal Cell Adenoma Surgery

Traumatic ulcer is an oral cavity lesion characterized by loss of epithelial lining, reaching the lamina propria. This condition presents a single lesion with irregular borders, surrounded by diffuse erythema, and covered with pseudo-membrane. In specific case, the traumatic ulcer was caused by iatrogenic trauma after basal cell adenoma surgery. This case report aimed to discuss the causes of traumatic ulcer. A 39-year-old female was referred to the Oral Medicine Clinic from the Ear, Nose, and Throat Department, complaining of pain in the oral mucosa after basal cell adenoma surgery. On extraoral examination, the facial symmetry was intact along with dry and exfoliative lips. A solitary, oval, yellowish-white intraoral ulcer covered with pseudomembrane, irregular borders surrounded by diffuse erythema on the palate, left buccal mucosa and upper labial mucosa was present. The patient was diagnosed with traumatic ulcer due to iatrogenic trauma after surgery for basal cell adenoma located within the parotid gland that extended to the left palate. The treatment administered comprised instructions to clean the oral cavity using gauze and a cotton stick soaked in 0.2% chlorhexidine gluconate. Additionally, compressing the affected areas with gauze soaked in 0.9% NaCl and applying a thin coating of Vaseline album on the lips were part of the therapeutic measures. Given the iatrogenic nature of traumatic cancer, comprehensive therapy was considered essential to prevent secondary infection and address issues related to speech, appearance, and masticatory functions.
Duration of amantadine benefit on dyskinesia of severe Parkinson's disease.

BACKGROUND Recent short-term studies suggested that amantadine (Ama) might ameliorate dyskinesia in patients with Parkinson's disease. A double-blind study programmed over 12 months was designed to assess the duration of the antidyskinetic effect of amantadine on levodopa induced dyskinesia. METHODS 40 patients treated for 7.5 (2.2) years with levodopa (729.3 (199.4) mg/day) and dopaminoagonists, having peak dose or dyphasic dyskinesia with or without pain, were assessed with the Unified Parkinson's Disease Rating Scale subscale IV, Items 32-34, the Dyskinesia Rating Scale and Investigator Global Assessment. Twenty patients received amantadine chloridrate (100 mg) and 20 received a placebo. The Ama or placebo could be withdrawn when scores indicated worsening of dyskinesia, after agreement with the patient. RESULTS After 15 days of amantadine treatment there was a reduction by 45% in the total dyskinesia scores. All patients in the placebo group were withdrawn in 1-3 months and all patients in the Ama group were withdrawn in 3-8 months (p = 0.01, p<0.001). Ama withdrawal induced a rebound with increase of dyskinesia by 10-20% in 11 patients. CONCLUSION 300 mg amantadine reduces dyskinesia in Parkinson's disease by approximately 45% but the benefit lasted less than eight months.
Is cystatin C a reliable renal marker in trauma?

Cystatin C is a small, non-glycosylated basic protein belonging to the cystatin superfamily of cysteine protease inhibitors and is thought to function as a modulating factor in the intracellular catabolism of proteins (1, 2). It is formed by all nucleated cells at a constant rate, is freely filtered by the renal glomeruli, and is completely reabsorbed and catabolized by the proximal tubular cells (3). Cystatin C concentration is not affected by factors such as diet, infection, liver dysfunction, malignancy or body fat ratio (4). Glomerular filtration rate (GFR, determined by inulin) or radionuclide markers such as Cr-EDTA, I-labelediothalamate and Tc-DTPA are accepted as gold standard tests of renal function. Recently, it was reported that serum cystatin C has a higher correlation with renal function in comparison with these ‘‘gold standard’’ tests and has been suggested as a more sensitive indicator of GFR than serum creatinine and creatinine clearance (CrCl) (5). In trauma, the inhibitor of lysosomal cysteine proteinases, cystatin C, has not been studied yet. Thus, we investigated whether cystatin C would also be a good marker of renal function in the case of trauma. We studied 92 patients admitted to the emergency department within 1 h of exposure to trauma. Injuries were identified and assigned an injury severity score (ISS) (6), which was used to classify patients into two groups. Group 1 included mildly injured patients that had an ISS trauma score below 15 (ns60) and group 2 comprised moderately injured patients that had an ISS trauma score above 15 (ns32). Group 1 included 35 male (43.4"18.4 years) and 25 female (44.0"18.7 years) patients, whereas group 2 included 24 male (45.3"15 years) and 8 female (34.0"19.7 years) patients. Blood specimens from 30 healthy volun-
L0 Regularized Stationary Time Estimation for Crowd Group Analysis

We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are used to detect four types of stationary group activities and analyze crowd scene structures. We provide the first public benchmark dataset for stationary time estimation and stationary group analysis.
Nonstationary Consistency of Subspace Methods

In this paper, we study ldquononstationary consistencyrdquo of subspace methods for eigenstructure identification, i.e., the ability of subspace algorithms to converge to the true eigenstructure despite nonstationarities in the excitation and measurement noises. Note that such nonstationarities may result in having time-varying zeros for the underlying system, so the problem is nontrivial. In particular, likelihood- and prediction-error related methods do not ensure consistency under such situation, because estimation of poles and estimation of zeros are tightly coupled. We show in turn that subspace methods ensure such consistency. Our study carefully separates statistical from nonstatistical arguments, therefore, enlightening the role of statistical assumptions in this story.
An ANT+ Protocol Based Health Care System

In remote health care Body Area Networks (BAN) are very popular but demand low energy consumption due to very constrained resources. For it several protocols, such as ZigBee, BlueTooth, WiFi etc, have been proposed but non has delivered the optimum results. These systems also demand vast interoperability among devices. Recently a propriety protocol ANT+ provides such features and strengthens the goals for Internet of Things (IOT). The authors describe a software architecture which flexibly integrates ANT+ protocol enabled sensors to deliver health care services. The approach is validated on a health care application that integrates heart rate, cadence, distance, foot steps and environmental temperature sensors. Described architecture is modular, flexible, scalable and possess several features.
Investigations on Phrase-based Decoding with Recurrent Neural Network Language and Translation Models

This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural models. In this paper, we apply approximations and use caching to enable RNN decoder integration, while requiring reasonable memory and time resources. We analyze the effect of caching on translation quality and speed, and use it to integrate RNN language and translation models into a phrase-based decoder. To the best of our knowledge, no previous work has discussed the integration of RNN translation models into phrase-based decoding. We also show that a special RNN can be integrated efficiently without the need for approximations. We compare decoding using RNNs to rescoring n-best lists on two tasks: IWSLT 2013 German→English, and BOLT Arabic→English. We demonstrate that the performance of decoding with RNNs is at least as good as using them in rescoring.
Co-clustering of biological networks and gene expression data

MOTIVATION Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.
Designing for the invisible: user-centered design of infrastructure awareness systems

Infrastructure awareness systems reveal invisible aspects of infrastructures to their existing or potential users. Designing such systems is challenging as it requires making visible the hidden activity of infrastructures while providing information of interest to the users. To address this challenge we introduce the AMC technique (for Awareness Model Cards). This technique relies conceptually on awareness model's concepts of nimbus and focus. The main objective is to match the users' interests to the information the infrastructure awareness systems can provide, through the use of card matching. This technique provides three benefits: 1) evaluate how relevant is the information displayed by infrastructure awareness systems; 2) identify which of users' interests infrastructure awareness systems do not take into account; 3) identify elements of re-design in the infrastructures themselves, so to improve their adoption.
Keratin Dynamics: Modeling the Interplay between Turnover and Transport

Keratin are among the most abundant proteins in epithelial cells. Functions of the keratin network in cells are shaped by their dynamical organization. Using a collection of experimentally-driven mathematical models, different hypotheses for the turnover and transport of the keratin material in epithelial cells are tested. The interplay between turnover and transport and their effects on the keratin organization in cells are hence investigated by combining mathematical modeling and experimental data. Amongst the collection of mathematical models considered, a best model strongly supported by experimental data is identified. Fundamental to this approach is the fact that optimal parameter values associated with the best fit for each model are established. The best candidate among the best fits is characterized by the disassembly of the assembled keratin material in the perinuclear region and an active transport of the assembled keratin. Our study shows that an active transport of the assembled keratin is required to explain the experimentally observed keratin organization.
SLEDDED: A Proposed Dataset of Event Descriptions for Evaluating Phrase Representations

Measuring the semantic relatedness of phrase pairs is important for evaluating compositional distributional semantic representations. Many existing phrase relatedness datasets are limited to either lexical or syntactic alternations between phrase pairs, which limits the power of the evaluation. We propose SLEDDED (Syntacti-cally and LExically Divergent Dataset of Event Descriptions), a dataset of event descriptions in which related phrase pairs are designed to exhibit minimal lexical and syntactic overlap; for example, a decisive victory — won the match clearly . We also propose a subset of the data aimed at distinguishing event descriptions from related but dissimilar phrases; for example, vow-ing to ﬁght to the death — a new training regime for soldiers , which serves as a proxy for the tasks of narrative generation, event sequencing, and summarization. We describe a method for extracting candidate pairs from a corpus based on occurrences of event nouns (e.g. war ) and a two-step annotation process consisting of expert annotation followed by crowdsourcing. We present examples from a pilot of the expert annotation step.
QoS-Aware Dynamic MAP Selection in HMIPv6 Architectures

The main problem dealt with in this paper is the creation of a protocol for improved QoS-aware mobility management support in cellular all-IP networks, whereby we propose a new algorithm for QoS-aware mobility management, based on multidimensional QoS metrics. An analytical framework for performance evaluation was presented as well. The proposed algorithm for QoS-aware dynamic MAP selection relies on multidimensional QoS metrics, defined in QoS-preference spaces of the mobile node and QoS-ability spaces of MAP candidates, in the decision-making process. The metric is chosen to achieve the desired QoS level through three parameters: bandwidth, delay, and reliability, while retaining the balance of MAP's loads in the entire network. For purposes of performance evaluation of the proposed model, we used: algorithm convergence, traffic class distribution by MAP's, and handover delay. Results showed that the standard deviation for each component of the QoS-ability vector is two orders of magnitude smaller than the deviation in the static MAP selection scenario. We achieved a total handover delay decrease from 20 ms to several hundred milliseconds, by simplifying DAD procedures preserving the simplicity of architecture.
Topic Modeling of Document Metadata for Visualizing Collaborations over Time

We describe methods for analyzing and visualizing document metadata to provide insights about collaborations over time. We investigate the use of Latent Dirichlet Allocation (LDA) based topic modeling to compute areas of interest on which people collaborate. The topics are represented in a node-link force directed graph by persistent fixed nodes laid out with multidimensional scaling (MDS), and the people by transient movable nodes. The topics are also analyzed to detect bursts to highlight "hot" topics during a time interval. As the user manipulates a time interval slider, the people nodes and links are dynamically updated. We evaluate the results of LDA topic modeling for the visualization by comparing topic keywords against the submitted keywords from the InfoVis 2004 Contest, and we found that the additional terms provided by LDA-based keyword sets result in improved similarity between a topic keyword set and the documents in a corpus. We extended the InfoVis dataset from 8 to 20 years and collected publication metadata from our lab over a period of 21 years, and created interactive visualizations for exploring these larger datasets.
Message in a Bottle

This project aims to develop a culture of affection, with a view to humanization and the development of the feeling of belonging in the relations of the school community, based on the exercise of citizenship. Messages, prepared by students in Portuguese classes, were placed in reused plastic bottles, decorated for this purpose in Visual Education classes. The messages were translated by students to English, French and German classes and by various elements of the school community, totalling nine different languages. The bottles were successfully delivered by the school to health care institutions. This project has been implemented since the 2015-2016, and 416 messages were distributed to 20 stakeholders. We want messages to be sent to as many people as possible, both at regional, national and international level.
Quorum-sensing autoinducers resuscitate dormant Vibrio cholerae in environmental water samples

Cholera epidemics have long been known to spread through water contaminated with human fecal material containing the toxigenic bacterium Vibrio cholerae. However, detection of V. cholerae in water is complicated by the existence of a dormant state in which the organism remains viable, but resists cultivation on routine bacteriological media. Growth in the mammalian intestine has been reported to trigger “resuscitation” of such dormant cells, and these studies have prompted the search for resuscitation factors. Although some positive reports have emerged from these investigations, the precise molecular signals that activate dormant V. cholerae have remained elusive. Quorum-sensing autoinducers are small molecules that ordinarily regulate bacterial gene expression in response to cell density or interspecies bacterial interactions. We have found that isolation of pathogenic clones of V. cholerae from surface waters in Bangladesh is dramatically improved by using enrichment media containing autoinducers either expressed from cloned synthase genes or prepared by chemical synthesis. These results may contribute to averting future disasters by providing a strategy for early detection of V. cholerae in surface waters that have been contaminated with the stools of cholera patients or asymptomatic infected human carriers.
Unlocking Pathways to Employment: Understanding Factors Influencing Youth Employability in Tanzania's Informal Sector

This study investigates the determinants of employability in the informal sector among youth in Tanzania using data from the Integrated Labor Force Survey (ILFS) conducted in 2020–2021, utilizing probity regression analysis. The model, statistically significant at a 1% level, indicates that variables including sex, capital access, income tax, social security, technical and computer skills, literacy and numeracy skills, physical skills, business registration, ownership, age, marital status, readiness for work, and education level collectively account for 4.4% of variations in youth employability. Positive impacts on employability are associated with capital access, numeracy and computer skills, paid employment, and level of education. The negative impacts are being male, income tax, social security coverage, literacy skills, physical skills, registered and owned businesses, married individuals, age, and readiness for work. The study suggests that attending school and possessing computer skills increases the likelihood of informal sector employment, while being male, subjected to income tax, covered by social security, having literacy skills, physical skills, registered or owned businesses, being married, aging, and being ready for work decrease the probability. The findings emphasize the importance of targeted interventions to enhance employability for youth in the informal sector, with considerations for gender, skills development, and business practices. The research recommended that enhancing access to education, programs in skill development, and improving numeracy and computer skills for youth will foster employability.
Estimating the effectiveness of spectrum-based fault localization

Spectrum-Based Fault Localization (SBFL) techniques calculate risk values to predict buggy units in a program,but they may cause heavy manual work when the calculated risk values are not reasonable on some application scenarios. In this paper, presents a preliminary study to estimate the effectiveness of SBFL before manual code walk through, so that we can decide whether to adopt SBFL for a given application.
Salivary gland-like tumours of the breast: surgical and molecular pathology

Breast glands and salivary glands are tubulo-acinar exocrine glands that can manifest as tumours with similar morphological features, but that differ in incidence and clinical behaviour depending on whether they are primary in breast or salivary glands. Salivary gland-like tumours of the breast are of two types: tumours with myoepithelial differentiation and those devoid of myoepithelial differentiation. The first and more numerous group comprises a spectrum of lesions ranging from “bona fide” benign (such as benign myoepithelioma and pleomorphic adenoma), to low grade malignant (such as adenoid cystic carcinoma, low grade adenosquamous carcinoma, and adenomyoepithelioma), to high grade malignant lesions (malignant myoepithelioma). The second group comprises lesions that have only recently been recognised, such as acinic cell carcinoma, oncocytic carcinoma of the breast, and the rare mucoepidermoid carcinoma.
Neurotropic properties of pseudorabies virus: uptake and transneuronal passage in the rat central nervous system

Uptake, replication, and transneuronal passage of a swine neurotropic herpesvirus (pseudorabies virus, PRV) was evaluated in the rat CNS. PRV was localized in neural circuits innervating the tongue, stomach, esophagus and eye with light microscopic immunohistochemistry. In each instance, the distribution of PRV-immunoreactive neurons was entirely consistent with that observed following injection of cholera toxin- horseradish peroxidase conjugate (CT-HRP). Injections of the tongue resulted in retrograde transport of PRV and CT-HRP to hypoglossal motor neurons, while preganglionic neurons in the dorsal motor vagal nucleus or somatic motor neurons in the nucleus ambiguus were labeled following injections of the stomach or esophagus, respectively. At longer times after infection, viral antigens were found in astrocytes adjacent to infected neurons and their efferent axons and second-order neuron labeling became apparent. The distribution of second-order neurons was also entirely dependent upon the site of PRV injection. Following tongue injection, second-order neurons were observed in the trigeminal complex, the brain-stem tegmentum and in monoaminergic cell groups. Injection of the stomach or esophagus led to second-order neuron labeling confined to distinct subdivisions of the neucleus of the solitary tract and monoaminergic cell groups. Comparative quantitative analysis of the number of PRV immunoreactive neurons present in the diencephalon and brain stem following injection of virus into both the eye and stomach musculature of the same animal demonstrated that retrograde transport of PRV from the viscera was more efficient and occurred at a much faster rate than anterograde transport of virus. These data demonstrate projection-specific transport of PRV in the nervous system and provide further insight into the means through which this neurotropic virus infects the nervous system.