Miracle-dz/UniFAR-FTUs huggingface dataset:
Marine Scorpaenidae Envenomation in Travelers: Epidemiology, Management, and Prevention.

BACKGROUND The Scorpaenidae are a large family of venomous marine fish that include scorpionfish, lionfish, and stonefish. Although most stonefish are confined to the Indo-Pacific, scorpionfish are distributed in the tropics worldwide, and two species of Indo-Pacific lionfish were inadvertently introduced into the Eastern Atlantic in the 1990s. Since then, lionfish have invaded shallow reef systems in the Eastern Atlantic, Gulf of Mexico, and Caribbean Sea. All of these regions are popular travel destinations for beachcombing, fishing, swimming, and scuba diving-recreational activities that increase risks of Scorpaenidae envenomation. METHODS To meet the objectives of describing species-specific presenting clinical manifestations, diagnostic and treatment strategies, and outcomes of Scorpaenidae envenomation in travelers, Internet search engines were queried with the key words. RESULTS Well-conducted, retrospective epidemiological investigations of Scorpaenidae envenomation case series concluded: (1) most cases occurred in young adult male vacationers visiting endemic regions; (2) victims sought medical attention for pain control within 2 hours of injury and presented with intense pain, edema, and erythema in affected extremities; (3) systemic manifestations and surgical interventions were relatively uncommon following initial management with hot water soaks and parenteral analgesics; (4) all cases required tetanus prophylaxis; deeply penetrating, lacerated, and necrotic wounds required antibiotic prophylaxis; and (5) equine Fab stonefish antivenom does have antigen-neutralizing cross-reactivities with both Indo-Pacific and Atlantic Scorpaenidae species and is indicated in severe scorpionfish and stonefish envenomation worldwide. CONCLUSIONS Travel medicine practitioners should counsel their patients about Scorpaenidae envenomation risks in endemic regions and maintain a high index of suspicion regarding Scorpaenidae envenomation in all travelers returning from tropical beach and ocean holidays and reporting painful fish sting injuries.
TRPM7-Mediated Ca2+ Signals Confer Fibrogenesis in Human Atrial Fibrillation

Rationale: Cardiac fibrosis contributes to pathogenesis of atrial fibrillation (AF), which is the most commonly sustained arrhythmia and a major cause of morbidity and mortality. Although it has been suggested that Ca2+ signals are involved in fibrosis promotion, the molecular basis of Ca2+ signaling mechanisms and how Ca2+ signals contribute to fibrogenesis remain unknown. Objective: To determine the molecular mechanisms of Ca2+-permeable channel(s) in human atrial fibroblasts, and to investigate how Ca2+ signals contribute to fibrogenesis in human AF. Methods and Results: We demonstrate that the transient receptor potential (TRP) melastatin related 7 (TRPM7) is the molecular basis of the major Ca2+-permeable channel in human atrial fibroblasts. Endogenous TRPM7 currents in atrial fibroblasts resemble the biophysical and pharmacological properties of heterologous expressed TRPM7. Knocking down TRPM7 by small hairpin RNA largely eliminates TRPM7 current and Ca2+ influx in atrial fibroblasts. More importantly, atrial fibroblasts from AF patients show a striking upregulation of both TRPM7 currents and Ca2+ influx and are more prone to myofibroblast differentiation, presumably attributable to the enhanced expression of TRPM7. TRPM7 small hairpin RNA markedly reduced basal AF fibroblast differentiation. Transforming growth factor (TGF)-&bgr;1, the major stimulator of atrial fibrosis, requires TRPM7-mediated Ca2+ signal for its effect on fibroblast proliferation and differentiation. Furthermore, TGF-&bgr;1–induced differentiation of cultured human atrial fibroblasts is well correlated with an increase of TRPM7 expression induced by TGF-&bgr;1. Conclusions: Our results establish that TRPM7 is the major Ca2+-permeable channel in human atrial fibroblasts and likely plays an essential role in TGF-&bgr;1–elicited fibrogenesis in human AF.
From interface to content: translingual access and delivery of on-line information

This paper describes our initial implementation of a sys- tem to provide world-wide weather information over the telephone. The information is gathered from several dif- ferent sites on the Web, preprocessed, and cached locally into a relational database to make access both fast and selective. Our natural language tools, originally developed for processing user queries, are used here for under- standing content, and for subsequently translating it into languages other than English. The system is operational, and we have been collecting data from real users via a toll-free number. We report here on an initial evaluation both of the full system in English and of the quality of the responses in German.
Depth judgments by reaching and matching in near-field augmented reality

In this abstract we describe an experiment that measured depth judgments in optical see-through augmented reality (AR) at near-field reaching distances of ~ 24 to ~ 56 cm. The 2×2 experiment crossed two depth judgment tasks, perceptual matching and blind reaching, with two different environments, a real-world environment and an augmented reality environment. We designed a task that used a direct reaching gesture at constant percentages of each participant's maximum reach; our task was inspired by previous work by Tresilian and Mon-Williams [6] that found very accurate blind reaching results in a real-world environment.
Distinct error-correcting and incidental learning of location relative to landmarks and boundaries

Associative reinforcement provides a powerful explanation of learned behavior. However, an unproven but long-held conjecture holds that spatial learning can occur incidentally rather than by reinforcement. Using a carefully controlled virtual-reality object-location memory task, we formally demonstrate that locations are concurrently learned relative to both local landmarks and local boundaries but that landmark-learning obeys associative reinforcement (showing “overshadowing” and “blocking” or “learned irrelevance”), whereas boundary-learning is incidental, showing neither overshadowing nor blocking nor learned irrelevance. Crucially, both types of learning occur at similar rates and do not reflect differences in levels of performance, cue salience, or instructions. These distinct types of learning likely reflect the distinct neural systems implicated in processing of landmarks and boundaries: the striatum and hippocampus, respectively [Doeller CF, King JA, Burgess N (2008) Proc Natl Acad Sci USA 105:5915–5920]. In turn, our results suggest the use of fundamentally different learning rules by these two systems, potentially explaining their differential roles in procedural and declarative memory more generally. Our results suggest a privileged role for surface geometry in determining spatial context and support the idea of a “geometric module,” albeit for location rather than orientation. Finally, the demonstration that reinforcement learning applies selectively to formally equivalent aspects of task-performance supports broader consideration of two-system models in analyses of learning and decision making.
Oncogramme responses of breast tumour cells treated with herceptin correlate with HER2/C-ERB B2 pathological status.

BACKGROUND Among targeted therapies, Herceptin is a monoclonal antibody successfully used on patients with breast cancer expressing Human Epidermal Growth Factor Receptor-2 (HER2 receptors). Oncogramme is a method developed to predict anticancer activity of molecules and thus individualize chemotherapeutic strategies. Before this ex vivo test enters clinical validation, it was desirable to correlate breast cancer cell responses to Herceptin observed through Oncogramme with HER2 expression by these cells. MATERIALS AND METHODS Breast tumour fragments were dissociated and obtained cells were cultured in defined medium. After Herceptin treatment, cytotoxicity was detected by cell death analysis, and responses compared to tumour HER2 status were determined by pathologists. RESULTS Cell responses to increasing doses of Herceptin obtained with Oncogramme were in correlation with HER2 expression. CONCLUSION Comparison between Herceptin responses obtained with Oncogramme and HER2 status of breast tumour cells confirmed that Oncogramme is a reliable method for prediction of patient cell sensitivity to anticancer drugs.
Edicts: implementing features with flexible binding times

In a software product line, the binding time of a feature is the time at which one decides to include or exclude a feature from a product. Typical binding site implementations are intended to support a single binding time only, e.g., compile time or run time. Sometimes, however, a product line must support features with variable binding times. For instance, a product line may need to include both embedded system configurations, in which features are selected and optimized early, and desktop configurations, in which client programs choose features on demand. We present a new technique for implementing the binding sites of features that require flexible binding times. Our technique combines design patterns and aspect-oriented programming: a pattern encapsulates the variation point, and targeted aspects---called edicts---set the binding times of the pattern participants. We describe our approach and demonstrate its usefulness by creating a middleware product line capable of serving the desktop and embedded domains. Our product line is based on JacORB, a middleware platform with many dynamically configurable features. By using edicts to select features at compile time, we create a version of JacORB more suited to resource-constrained environments. By configuring four JacORB subsystems via edicts, we achieve a 32.2% reduction in code size. Our examples show that our technique effectively modularizes binding-time concerns, supporting both compile-time optimization and run-time flexibility as needed.
Protecting the Privacy of Users in Retrieving Valuable Information by a PIR Scheme with Mutual Authentication by RSA Signature Algorithm

While the users retrieve valuable information on the internet, the protection of users' privacy from a database server was not considered feasible until the private information retrieval (PIR) problem was stated and solved. A PIR scheme allows a user to retrieve a data item from an online database while hiding the identity of the item from a database server. In this paper, a new PIR scheme combined with mutual authentication by RSA signature algorithm for protecting the privacy of users is proposed. Using only one server and including the mutual authentication process in the proposed scheme, it is more secure and more practical in the real application compared with previous PIR solutions.
D Morphable Model building from large collections of 2 D hand-labeled databases

3D Morphable Models (3DMMs) are a popular tool to model the shape and appearance of faces in images. Although extensively used, current methods to build 3D face models have several drawbacks: (1) The number of available 3D databases is limited, and there is a lack of expression variability (e.g. typically databases include only neutral expressions). (2) Most common approaches use laser scans to get 3D face shapes, requiring complex setups. (3) Finding correspondences and registering the 3D model is a labor intensive and error prone process. This paper proposes a method to learn generic 3DMMs from large collections of 2D hand-labeled databases containing many subjects under different expressions and poses. We use an in-cremental Structure-from-Motion (SfM) approach to build 3D generic face models from images. The algorithm is able to incorporate prior information in order to get realistic shape reconstructions from a reduced set of views. Experimental results show the effectiveness and robustness of the proposed technique to build generic 3DMMs able to fit previously unseen subjects.
The Blood Oxygen Level–Dependent Functional MR Imaging Signal Can Be Used to Identify Brain Tumors and Distinguish Them from Normal Tissue

BACKGROUND AND PURPOSE: In neuro-oncology, a major problem is clear identification of tumor from the surrounding normal tissue. We hypothesized that we could use the blood oxygen level–dependent functional MR imaging (BOLD fMRI) signals from tumors and normal brain to identify the tumors and distinguish them from the surrounding brain. MATERIALS AND METHODS: Fourteen patients with meningiomas, gliomas, and metastatic tumors were scanned before surgery. All subjects performed a motor task; 2 subjects were also scanned while in a resting state. The BOLD signals were taken from selected points within the tumor and from the surrounding normal brain and were analyzed by using correlation analysis to determine how closely they were related. RESULTS: The BOLD signals from all of the tumors were significantly different from those in the surrounding normal tissue. In meningiomas and gliomas, selection of a voxel in the tumor for signal-intensity analysis highlighted the entire tumor mass while excluding the normal tissue. The BOLD signal intensity was the same whether the subjects were motionless or finger tapping. CONCLUSIONS: Analysis of the BOLD signal intensity provides a relatively simple and straightforward method for identifying brain tumors and distinguishing them from normal tissue. This approach may be of use in neurosurgery.
On Ambiguity Detection and Postprocessing Schemes using Cluster Ensembles

In this paper, we explore the cluster ensemble problem and propose a novel scheme to identify uncertain/ambiguous regions in the data based on the different clusterings in the ensemble. In addition, we analyse two approaches to deal with the detected uncertainty. The first, simplest method, is to ignore ambiguous patterns prior to the ensemble consensus function, thus preserving the non-ambiguous data as good “prototypes” for any further modelling. The second alternative is to use the ensemble solution obtained by the first method to train a supervised model (support vector machines), which is later applied to reallocate, or “recluster” the ambiguous patterns. A comparative analysis of the different ensemble solutions and the base weak clusterings has been conducted on five data sets: two artificial mixtures of five and seven Gaussian, and three real data sets from the UCI machine learning repository. Experimental results have shown in general a better performance of our proposed schemes compared to the standard ensembles.
Gene expression of vasoactive intestinal contractor/endothelin-2 in ovary, uterus and embryo: comprehensive gene expression profiles of the endothelin ligand-receptor system revealed by semi-quantitative reverse transcription-polymerase chain reaction analysis in adult mouse tissues and during late 

Vasoactive intestinal contractor (VIC)/endothelin-2 (ET-2) is a 21 amino acid intestinal peptide characterized as a potent vasoactive and intestinal smooth muscle-contracting compound. To investigate the physiological roles of VIC/ET-2 further, we characterized the specificity of VIC gene expression relative to that of other members of the endothelin (ET) ligand-receptor system in adult mouse tissues and during embryonic development. Gene expression of ET-1, ET-3, ETA and ETB was ubiquitous in almost all tissues we examined while gene expression of VIC was localized to certain tissues. A high level of VIC gene expression was observed in ovary and uterus. The gene expression of VIC, relative to that of glyceraldehyde-3-phosphate dehydrogenase, was approximately 2.0%, 0.4%, and 2.3% in ovary, uterus, and intestine respectively, and was approximately 1.6 and 7. 1 times higher than that of ET-1 in ovary and intestine respectively. Thus, VIC may have some physiological role in adult ovary and uterus as well as intestine. In embryonic development, VIC gene expression sharply increased between 11 and 15 days post coitus and decreased after birth, suggesting an involvement in the later stages of embryonic development.
Ontology-based Multi-agent Systems Support Human Disease Study and Control

The medical milieu is an open environment characterized by a variety of distributed, heterogeneous and autonomous information resources. Coordination, cooperation and exchange of information is important to the medical community. Efficient storage and acquisition of medical knowledge requires structured and standardized organization of data. We design a new ontology, called Generic Human Disease Ontology (GHDO), for the representation of knowledge regarding human diseases. The concepts of the GHDO ontology are organized into the following four 'dimensions': Disease Types, Symptoms, Causes and Treatments. We align and merge existing ontologies against the four dimensions of GHDO. The designed ontology makes our query system suitable for different user categories. The process of problem decomposition into smaller sub-problems within a multi-agent system becomes much easier as well. We also design a multi-agent system framework over different information resources. The multi-agent system uses the common GHDO ontology for query formulation, information retrieval and information integration. This intelligent dynamic system provides opportunities to collect information from multiple information resources, to share data efficiently and to integrate and manage scientific results in a timely manner.
Bone marrow fat composition as a novel imaging biomarker in postmenopausal women with prevalent fragility fractures

The goal of this magnetic resonance (MR) imaging study was to quantify vertebral bone marrow fat content and composition in diabetic and nondiabetic postmenopausal women with fragility fractures and to compare them with nonfracture controls with and without type 2 diabetes mellitus. Sixty‐nine postmenopausal women (mean age 63 ± 5 years) were recruited. Thirty‐six patients (47.8%) had spinal and/or peripheral fragility fractures. Seventeen fracture patients were diabetic. Thirty‐three women (52.2%) were nonfracture controls. Sixteen women were diabetic nonfracture controls. To quantify vertebral bone marrow fat content and composition, patients underwent MR spectroscopy (MRS) of the lumbar spine at 3 Tesla. Bone mineral density (BMD) was determined by dual‐energy X‐ray absorptiometry (DXA) of the hip and lumbar spine (LS) and quantitative computed tomography (QCT) of the LS. To evaluate associations of vertebral marrow fat content and composition with spinal and/or peripheral fragility fractures and diabetes, we used linear regression models adjusted for age, race, and spine volumetric bone mineral density (vBMD) by QCT. At the LS, nondiabetic and diabetic fracture patients had lower vBMD than controls and diabetics without fractures (p = 0.018; p = 0.005). However, areal bone mineral density (aBMD) by DXA did not differ between fracture and nonfracture patients. After adjustment for age, race, and spinal vBMD, the prevalence of fragility fractures was associated with −1.7% lower unsaturation levels (confidence interval [CI] −2.8% to −0.5%, p = 0.005) and +2.9% higher saturation levels (CI 0.5% to 5.3%, p = 0.017). Diabetes was associated with −1.3% (CI –2.3% to −0.2%, p = 0.018) lower unsaturation and +3.3% (CI 1.1% to 5.4%, p = 0.004) higher saturation levels. Diabetics with fractures had the lowest marrow unsaturation and highest saturation. There were no associations of marrow fat content with diabetes or fracture. Our results suggest that altered bone marrow fat composition is linked with fragility fractures and diabetes. MRS of spinal bone marrow fat may therefore serve as a novel tool for BMD‐independent fracture risk assessment.
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach

The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of ‘1’ (positive) or ‘0’ (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are large either in the number of instances, or the number of attributes. In this paper. we introduce an algorithm that achieves faster learning by restricting the search space. This iterative algorithm restricts the parents of each variable to belong to a small subset of candidates. We then search for a network that satisfies these constraints. The learned network is then used for selecting better candidates for the next iteration. We evaluate this algorithm both on synthetic and real-life data. Our results show that it is significantly faster than alternative search procedures without loss of quality in the learned structures
Playing the Web Search Game * Figure 1. System Overview * Submitted to Ieee Intelligent Systems, Special Issue on Agents and Markets

Distributed heterogeneous search environments are an emerging phenomenon in Web search. Together with new promises they bring new challenges. We describe COUGAR, a learning system that competes in such environments. A heterogeneous search environment consists of multiple specialised search engines and metasearchers . All these components can be independently owned and controlled. Specialised search engines provide focused search services in a specific domain (e.g. on a particular topic). Different search engines can specialise in different domains (hence, the heterogeneous environment). Metasearchers help to process user queries effectively and efficiently by distributing them only to the most suitable search engines for each query. A typical scenario works as follows. To find a “suitable” search engine, users submit search requests to the metasearcher. The metasearcher has a database with index content summaries of known search engines. For each search query, the metasearcher selects the search engine(s) that provides the best quality (most relevant) results. Fig. 1 illustrates the interactions in a heterogeneous search environment.