Miracle-dz/UniFAR-FTUs huggingface dataset:
Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group

Electrical impedance tomography (EIT) has undergone 30 years of development. Functional chest examinations with this technology are considered clinically relevant, especially for monitoring regional lung ventilation in mechanically ventilated patients and for regional pulmonary function testing in patients with chronic lung diseases. As EIT becomes an established medical technology, it requires consensus examination, nomenclature, data analysis and interpretation schemes. Such consensus is needed to compare, understand and reproduce study findings from and among different research groups, to enable large clinical trials and, ultimately, routine clinical use. Recommendations of how EIT findings can be applied to generate diagnoses and impact clinical decision-making and therapy planning are required. This consensus paper was prepared by an international working group, collaborating on the clinical promotion of EIT called TRanslational EIT developmeNt stuDy group. It addresses the stated needs by providing (1) a new classification of core processes involved in chest EIT examinations and data analysis, (2) focus on clinical applications with structured reviews and outlooks (separately for adult and neonatal/paediatric patients), (3) a structured framework to categorise and understand the relationships among analysis approaches and their clinical roles, (4) consensus, unified terminology with clinical user-friendly definitions and explanations, (5) a review of all major work in thoracic EIT and (6) recommendations for future development (193 pages of online supplements systematically linked with the chief sections of the main document). We expect this information to be useful for clinicians and researchers working with EIT, as well as for industry producers of this technology.
Phrase Pair Mappings for Hindi-English Statistical Machine Translation

In this paper, we present our work on the creation of lexical resources for the Machine Translation between English and Hindi. We describes the development of phrase pair mappings for our experiments and the comparative performance evaluation between different trained models on top of the baseline Statistical Machine Translation system. We focused on augmenting the parallel corpus with more vocabulary as well as with various inflected forms by exploring different ways. We have augmented the training corpus with various lexical resources such as lexical words, synset words, function words and verb phrases. We have described the case studies, automatic and subjective evaluations, detailed error analysis for both the English to Hindi and Hindi to English machine translation systems. We further analyzed that, there is an incremental growth in the quality of machine translation with the usage of various lexical resources. Thus lexical resources do help uplift the translation quality of resource poor langugaes.
Meta data to support context aware mobile applications

The greater sophistication and availability of mobile computing devices is enabling the application of computing to new tasks and applications to be experienced in a wide range of contexts. To facilitate such applications it is necessary to adapt the applications and the data they display to operate within the limitations of the context of use, take advantage of the strengths of the devices and best support the users in their tasks. A part of the solution to this issue is the description of the data available to the applications. Meta data may describe the structure and properties of information and data. We achieve this by separating the semantic elements of the information from the syntactic variants which realise them in order to form documents. In this paper we describe a new meta data model which can encode this description. We provide example meta data and illustrate how this may lead to better application usability than current simplistic or constrained approaches, through contextual mediation.
genenames.org: the HGNC resources in 2011

The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique gene symbol and name to every human gene. The HGNC database currently contains almost 30 000 approved gene symbols, over 19 000 of which represent protein-coding genes. The public website, www.genenames. org, displays all approved nomenclature within Symbol Reports that contain data curated by HGNC editors and links to related genomic, phenotypic and proteomic information. Here we describe improvements to our resources, including a new Quick Gene Search, a new List Search, an integrated HGNC BioMart and a new Statistics and Downloads facility.
Security Protocols in Service-Oriented Architecture

In this paper, a comprehensive Quality of Security Service (QoSS) model for addressing security within a Service-Oriented Architecture (SOA) is proposed. We define a detailed SOA security model that supports and incorporates a number of networking security techniques and protocols. It utilizes symmetric keys, public keys and hash functions techniques, in order to provide different levels of QoSS agreements to satisfy the requirements of both the services providers and requesters. These levels are based on core networking security requirements such as Mutual Authentication, Session keys, Anonymity, and Perfect forward Secrecy. In addition, the proposed model forms a strong line of defense against Replay, Man-in-the-Middle, and Denial-of-Services attacks.
Evaluating Term Concept Association Mesaures for Short Text Expansion: Two Case Studies of Classification and Clustering

The proliferation of Web applications based on short texts represents both an opportunity and a challenge to text mining algorithms, because of sparse representations and lack of shared context. To address this problem, we investigate a term expansion approach based on analyzing the relationships between the term concepts present in the concept lattice associated with the document corpus. We define five term concept association measures: proximity, concept similarity, connection strength, damping-weighted proximity, proximity&strength. By means of two case studies, we evaluate the effectiveness of these measures for expansion-enhanced K-NN classification and K-Means clustering of short texts. The results suggest that the five measures are highly competitive, with the best measure showing a clear improvement over the corresponding unenhanced K-NN and K-Means algorithms, as well as over two alternative term expansion enhancements (i.e., based on Wordnet and on pseudo-relevance feedback).
The Small Subunit of Snapdragon Geranyl Diphosphate Synthase Modifies the Chain Length Specificity of Tobacco Geranylgeranyl Diphosphate Synthase in Planta[W]

Geranyl diphosphate (GPP), the precursor of many monoterpene end products, is synthesized in plastids by a condensation of dimethylallyl diphosphate and isopentenyl diphosphate (IPP) in a reaction catalyzed by homodimeric or heterodimeric GPP synthase (GPPS). In the heterodimeric enzymes, a noncatalytic small subunit (GPPS.SSU) determines the product specificity of the catalytic large subunit, which may be either an active geranylgeranyl diphosphate synthase (GGPPS) or an inactive GGPPS-like protein. Here, we show that expression of snapdragon (Antirrhinum majus) GPPS.SSU in tobacco (Nicotiana tabacum) plants increased the total GPPS activity and monoterpene emission from leaves and flowers, indicating that the introduced catalytically inactive GPPS.SSU found endogenous large subunit partner(s) and formed an active snapdragon/tobacco GPPS in planta. Bimolecular fluorescence complementation and in vitro enzyme analysis of individual and hybrid proteins revealed that two of four GGPPS-like candidates from tobacco EST databases encode bona fide GGPPS that can interact with snapdragon GPPS.SSU and form a functional GPPS enzyme in plastids. The formation of chimeric GPPS in transgenic plants also resulted in leaf chlorosis, increased light sensitivity, and dwarfism due to decreased levels of chlorophylls, carotenoids, and gibberellins. In addition, these transgenic plants had reduced levels of sesquiterpene emission, suggesting that the export of isoprenoid intermediates from the plastids into the cytosol was decreased. These results provide genetic evidence that GPPS.SSU modifies the chain length specificity of phylogenetically distant GGPPS and can modulate IPP flux distribution between GPP and GGPP synthesis in planta.
Tissue-Engineered Endometrial Model for the Study of Cell–Cell Interactions

Endometrial stromal and epithelial cell cross talk is known to influence many of the dynamic changes that occur during the menstrual cycle. We modified our previous model and embedded telomerase-immortalized human endometrial stromal cells and Ishikawa adenocarcinoma epithelial cells in a collagen–Matrigel hydrogel to create a tissue-engineered model of the endometrium. Comparisons of single and cocultured cells examined communication between endometrial stromal and epithelial cells, which were cultured with 0 or 10 nmol/L 17β estradiol; conditioned medium was used to look at the production of paracrine factors. Using this model, we were able to identify the changes in interleukin 6 (IL-6) and active matrix metalloproteinase 2, which appear to be due to paracrine signaling and differences in transforming growth factor β1 (TGF-β1) that do not appear to be due to paracrine signaling. Moreover, IL-6, TGF-β1, and DNA content were also affected by the presence of estradiol in many of the tissues. These results indicate that paracrine and endocrine signaling are involved in human endometrial responses and support the use of coculture models to further investigate cell–cell and cell–matrix interactions.
Multifunctional SENSING using 3D printed CNTs/BaTiO3/PVDF nanocomposites

This research studied multifunctional sensing capabilities on nanocomposites composed of poly(vinylidene) fluoride (PVDF), BaTiO3 (BT), and multiwall carbon nanotubes (CNTs) fabricated by fused-deposition modeling 3D printing. To improve the dielectric property within BT/PVDF composites, CNTs have been utilized to promote ultrahigh polarization density and local micro-capacitor among BT and polymer matrix. The 3D printing process provides homogeneous dispersion of nanoparticles, alleviating agglomeration of nanoparticles, and reducing micro-crack/voids in matrix which can enhance their dielectric property. In this research, we demonstrated that by utilizing unique advantages of this material combination and a 3D printing technique, sensing capabilities for temperature and strain can be engineered with different content variations of included BT and CNTs. It is observed that the sensing capability for temperature change with respect to a 25–150℃ range can be improved as both BT and CNTs’ inclusions increase and is maximal with 1.7 wt. % CNTs/60 wt. % BT/PVDF nanocomposites, while the sensing capability for strain change in a 0–20% range is maximal with 1 wt. % CNTs/12 wt. % BT/PVDF nanocomposites. In addition, it is found that the best combination for mechanical toughness is 1 wt. % CNTs/12 wt. % BT/PVDF with 24.2 MPa and 579% in ultimate tensile strength and failure strain, respectively. These results show the technique to 3D print multifunctional nanocomposites with temperature and strain sensing capabilities as well as increased mechanical property. Furthermore, this research demonstrated the feasibility for large-scale multifunctional sensor device manufacturing with freedom of design, low-cost, and an accelerated process.
Sicherheit in Mobilen Ad hoc Netzwerken

Mobile Ad hoc Netzwerke (MANETs) sind Netzwerke aus mobilen Knoten, die ganzlich ohne fest installierte Infrastruktur auskommen. Damit ermoglichen sie den Einsatz verteilter Anwendungen an Orten oder in Situationen, in denen der vorherige Aufbau einer Infrastruktur zur Vernetzung von Computern oder anderen elektronischen Geraten nicht moglich oder wunschenswert ist. Bisher konzentrierten sich die Anstrengungen primar auf die Entwicklung geeigneter Routingprotokolle, welche die Verkehrslenkung in MANETs organisieren. Ein Aspekt, der bisher nur teilweise untersucht wurde, ist die Absicherung solcher Netze. Hier treten eine Vielzahl von neuen Fragestellungen auf. Wegen der besonderen Struktur von MANETs lassen sich die Losungen aus dem Bereich herkommlicher Netze nicht direkt auf MANETs ubertragen. Manche Aspekte sind auch ganzlich neu und treten nur in MANETs auf. Ein Beispiel dafur ist die Bekampfung von egoistischen Knoten. Diese Dissertation versucht, ein umfassendes Sicherheitsrahmenwerk fur Ad hoc Netze zu entwerfen. Nach den einleitenden Kapiteln, welche die Grundlagen von Sicherheit, Kryptographie und mobiler Datenkommunikation darlegen, folgt eine ausfuhrliche Sicherheitsanalyse fur Mobile Ad hoc Netze. Als Losung der aufgefundenen Probleme wird die Sicherheitsinfrastruktur SAM (Security Architecture for Mobile Ad hoc Networks) vorgeschlagen, welche aus den Komponenten "Identifizierung und Pseudonyme", "Sicheres Routing und Schlusselaustausch" und einem "Mobilen Intrusion Detection System" besteht. Diese werden in eigenen Kapiteln ausfuhrlich vorgestellt. Den funktionalen Beschreibungen schliest sich ein Analysekapitel an, in welchem die Komponenten von SAM auf ihre Funktionsfahigkeit, auf verbleibende Angriffsmoglichkeiten und auf ihre Effizienz hin untersucht werden. Die Arbeit schliest mit einer Zusammenfassung der geleisteten Beitrage und einem Ausblick auf mogliche zukunftige Arbeiten.
Joint Learning of Correlated Sequence Labeling Tasks Using Bidirectional Recurrent Neural Networks

The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.
Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery

IKONOS 1-m panchromatic and 4-m multispectral images were used to map mangroves in a study site located at Punta Galeta on the Caribbean coast of Panama. We hypothesized that spectral separability among mangrove species would be enhanced by taking the object as the basic spatial unit as opposed to the pixel. Three different classification methods were investigated: maximum likelihood classification (MLC) at the pixel level, nearest neighbour (NN) classification at the object level, and a hybrid classification that integrates the pixel and object-based methods (MLCNN). Specifically for object segmentation, which is the key step in object-based classification, we developed a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. A comparison of BD values at the pixel level and a series of larger scales not only supported our initial hypothesis, but also helped us to determine an optimal scale at which the segmented objects have the potential to achieve the best classification accuracy. Among the three classification methods, MLCNN achieved the best average accuracy of 91.4%. The merits and restrictions of pixel-based and object-based classification methods are discussed.
Query-sensitive Feature Selection for Lazy Learners

Feature selection contributes to increasing many learners' accuracy by identifying and removing irrelevant features in multidimensional datasets. Conventional feature selection methods determine the optimal feature subset independently from and prior to the introduction of a new query. In general, some features will be relevant only in certain tasks. We argue that a query, as an indicator of the attention focus and current task, is a major part of the context and should be involved in the determination of the final feature subset. In this paper we attempt to propose a query-sensitive feature selection model, present two algorithms for applying such a feature selection method, and test their effectiveness by comparing their performances to those of the conventional selection algorithms. Our experiments are executed under a nearest neighbor classification environment and the results show a consistent improvement in the classification performance when a query-sensitive feature subset is selected and used for measuring similarities between the query and other instances. The results suggest that the performance of a lazy learner has the potential to increase through query-sensitive feature selection
Device and Imaging-Specific Volumetric Analysis of Clot Lysis after Percutaneous Mechanical Thrombectomy for Iliofemoral DVT

Purpose: To determine the most accurate method of assessing clot lysis after percutaneous mechanical thrombectomy for iliofemoral deep vein thrombosis (DVT) and to evaluate the effectiveness of two different pharmacomechanical thrombectomy devices. Methods: Between 2004 and 2009, 33 patients (18 women; mean age 47 years) with iliofemoral DVT underwent pharmacomechanical thrombectomy using the AngioJet (n = 18) or Trellis (n = 15) devices with 10 mg of tenecteplase. Intravascular ultrasound (IVUS) and venography were performed over the iliofemoral segments before and after treatment. Cross-sectional vessel and lumen diameters were measured from the IVUS scans and the post-procedure anteroposterior and lateral venograms at 3 points (proximal, mid-section, and distal) along each iliofemoral vein by 2 independent observers blinded to the treatment method. Volumes of the recanalized segments were calculated and compared to volumes of the original venous segments to assess clot lysis with each PMT device. IVUS scans and venograms were also compared for their ability to identify residual lesions or clot in need of treatment. Repeatability between and among observers was analyzed using the Bland and Altman method. Results: All procedures were successfully completed; there were only 2 minor bleeding complications. The mean volume of the recanalized segment was 2255±66 mm3 by IVUS, representing 80% lysis of the clot compared to what was perceived as >90% lysis with venography (p<0.05). IVUS was able to delineate significant residual thrombus, stenosis, or May-Thurner anatomy requiring ancillary interventions in 100% of patients versus 48% (16/33) on the venograms (p<0.01). Quantitative assessments of the diameters of the involved venous segments from the venograms and IVUS were consistent between and among observers. Comparing the similar patient subgroups, AngioJet resulted in greater clot lysis (88%) versus the Trellis device (72%; p<0.01), corresponding to recanalized venous segment volumes of 2486±74 and 2025±57 mm3 and total venous segment volumes of 2826±84 and 2813±79 mm3, respectively. Conclusion: IVUS is superior to venography for detection of residual thrombus and underlying venous pathology after pharmacomechanical thrombectomy. While greater clot lysis was seen with the AngioJet system, both the AngioJet and Trellis devices resulted in excellent clinical clot lysis.
Spina bifida and hydrocephalus.

a very large proportion of morphological defects in the newborn. The most common of these-spina bifida cystica-has a wide range of incidence in different countries, being highest in the western hemisphere, where in some areas the incidence is 4 per 1,000 births. A general practitioner in Britain with 2,000 patients on his list could expect a new case to occur in his practice once every five years. Prompt and accurate advice to the family with a newborn child with such a major lesion and efficient handling of the infant will lay a good foundation for the future management of the child and of its family, whether or not the infant survives. Vacillation and changes of plan based on the apparent expediency of the moment and on emotion will undermine confidence and create guilt and anxiety so deep rooted that the family may breakdown. Conversely 80% of infants with myelomeningocele also exhibit hydrocephalus. The diagnosis will not be in doubt if the head size is big enough to impede labour, but most children with hydrocephalus have a normal head circumference at birth. Measurement of the head circumference (Fig. 1) should always be part of the routine neonatal examination, and the most valuable clue to congenital hydrocephalus is the disproportionate in-
Effects of the factor Xa inhibitor, fondaparinux, on the stability of atherosclerotic lesions in apolipoprotein E-deficient mice.

BACKGROUND Atherosclerosis is a progressive inflammatory disease that can lead to sudden cardiac events by plaque rupture and subsequent thrombosis. Factor Xa (FXa) not only occupies a crucial position in the coagulation cascade responsible for thrombin generation, but also has pro-inflammatory effects. The hypothesis that Fondaparinux, the selective FXa inhibitor, attenuates plaque progression and promotes stability of atherosclerotic lesions was assessed. METHODSANDRESULTS Fondaparinux (5 mg/kg body weight/day) or 0.9% saline was intraperitoneally administered for 4 weeks to apolipoprotein E-deficient mice (n=12 per group) with established atherosclerotic lesions in the innominate arteries. Fondaparinux did not remarkably decrease the progression of atherosclerosis development in apolipoprotein E-deficient mice, but increased the thickness of fibrous cap (P=0.049) and decreased the ratio of necrotic core (P=0.001) significantly. Moreover, Fondaparinux reduced the staining against Mac-2 (P=0.017), α-SMA (P=0.002), protease-activated receptor (PAR)-1 (P=0.001), PAR-2 (P=0.003), CD-31 (P=0.024), MMP-9 (P=0.000), MMP-13(P=0.011), VCAM-1 (P=0.041) and the mRNA expression of inflammatory mediators (P<0.05) significantly, such as interleukin (IL)-6, MCP-1, IFN-γ, TNF-α, IL-10 and Egr-1. CONCLUSIONS Fondaparinux, the selective FXa inhibitor, can promote the stability of atherosclerotic lesions in apolipoprotein E-deficient mice, possibly through inhibiting expression of the inflammatory mediators in plaque and reduced synthesis of MMP-9 and MMP-13.
Deep Motif: Visualizing Genomic Sequence Classifications

This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task. To make the model understandable, we propose an optimization driven strategy to extract "motifs", or symbolic patterns which visualize the positive class learned by the network. We show that our system, Deep Motif (DeMo), extracts motifs that are similar to, and in some cases outperform the current well known motifs. In addition, we find that a deeper model consisting of multiple convolutional and highway layers can outperform a single convolutional and fully connected layer in the previous state-of-the-art.