Miracle-dz/UniFAR-FTUs huggingface dataset:
Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia

Objective Genome-wide association studies have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers, suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesised that SNPs previously associated with other cancers may additionally be associated with colorectal cancer. In a large-scale study, we examined 171 SNPs previously associated with 18 different cancers for their associations with colorectal cancer. Design We examined 13 338 colorectal cancer cases and 40 967 controls from three consortia: Population Architecture using Genomics and Epidemiology (PAGE), Genetic Epidemiology of Colorectal Cancer (GECCO), and the Colon Cancer Family Registry (CCFR). Study-specific logistic regression results, adjusted for age, sex, principal components of genetic ancestry, and/or study specific factors (as relevant) were combined using fixed-effect meta-analyses to evaluate the association between each SNP and colorectal cancer risk. A Bonferroni-corrected p value of 2.92×10−4 was used to determine statistical significance of the associations. Results Two correlated SNPs—rs10090154 and rs4242382—in Region 1 of chromosome 8q24, a prostate cancer susceptibility region, demonstrated statistically significant associations with colorectal cancer risk. The most significant association was observed with rs4242382 (meta-analysis OR=1.12; 95% CI 1.07 to 1.18; p=1.74×10−5), which also demonstrated similar associations across racial/ethnic populations and anatomical sub-sites. Conclusions This is the first study to clearly demonstrate Region 1 of chromosome 8q24 as a susceptibility locus for colorectal cancer; thus, adding colorectal cancer to the list of cancer sites linked to this particular multicancer risk region at 8q24.
A Unified Geometric Model for Virtual Slide Image Processing and Classification

In this paper, we use the framework of partial difference equations on weighted graphs as a methodology to address the problem of computer-aided cytology. First, introduced to perform image smoothing and filtering, this framework has recently been extended to address segmentation and semi-supervised clustering of any discrete domain that can be represented by a graph of arbitrary topology. In particular, this framework unifies methods from image processing and machine learning communities within the same formulation. We demonstrate that this method can also be used effectively to unify preprocessing, image segmentation, and data classification for both Feulgen- and Papanicolaou-stained virtual slide processing as well as 3-D confocal microscopy. For evaluation we compare this approach to state-of-the-art algorithms for segmentation and classification.
Potent Antihypertensive Action of Dietary Flaxseed in Hypertensive Patients

Flaxseed contains &ohgr;-3 fatty acids, lignans, and fiber that together may provide benefits to patients with cardiovascular disease. Animal work identified that patients with peripheral artery disease may particularly benefit from dietary supplementation with flaxseed. Hypertension is commonly associated with peripheral artery disease. The purpose of the study was to examine the effects of daily ingestion of flaxseed on systolic (SBP) and diastolic blood pressure (DBP) in peripheral artery disease patients. In this prospective, double-blinded, placebo-controlled, randomized trial, patients (110 in total) ingested a variety of foods that contained 30 g of milled flaxseed or placebo each day over 6 months. Plasma levels of the &ohgr;-3 fatty acid &agr;-linolenic acid and enterolignans increased 2- to 50-fold in the flaxseed-fed group but did not increase significantly in the placebo group. Patient body weights were not significantly different between the 2 groups at any time. SBP was ≈10 mm Hg lower, and DBP was ≈7 mm Hg lower in the flaxseed group compared with placebo after 6 months. Patients who entered the trial with a SBP ≥140 mm Hg at baseline obtained a significant reduction of 15 mm Hg in SBP and 7 mm Hg in DBP from flaxseed ingestion. The antihypertensive effect was achieved selectively in hypertensive patients. Circulating &agr;-linolenic acid levels correlated with SBP and DBP, and lignan levels correlated with changes in DBP. In summary, flaxseed induced one of the most potent antihypertensive effects achieved by a dietary intervention.
On the superiority of defaultness: Hemispheric perspectives of processing negative and affirmative sarcasm

ABSTRACT Defining defaultness in terms of an unconditional, automatic response to a stimulus allows the Defaultness Hypothesis to predict the speed superiority of default over nondefault counterparts. Here we examined the relative contribution of the cerebral hemispheres to the processing of default versus nondefault interpretations (of Hebrew items). Participants performed a lexical decision task on lateralized probes (messy) related to the default/nondefault sarcastic interpretation of their preceding negative/affirmative targets (He is/He is not the most organized student). Consistent with the Defaultness Hypothesis, probes were easier to identify in the default-negative than in the nondefault-affirmative condition. However, this superiority was more pronounced in the left hemisphere (LH) than in the right hemisphere. In particular, whereas both hemispheres reflected the superiority of default negative sarcasm over nondefault affirmative sarcasm when both targets were embedded in equally strong sarcastic contexts (Experiment 2), only the LH exhibited this very same superiority when the negatives and affirmatives were presented in isolation (Experiment 1).
AUTOMATIC HEBREW TEXT VOCALIZATION

Written Hebrew involves an exceptionally high ambiguity rate due to the lack of vowel markings in the vast majority of modern Hebrew texts. Each letter may be vocalized and pronounced in multiple ways. This letter level ambiguity naturally increases word level ambiguity, which complicates many other natural language tasks. Automatic translation of Hebrew, Hebrew text to speech and many other NLP tasks may bene t from a reliable system which restores vocalization signs. In this research we aim to implement tools that may be used to enhance the capabilities of an automatic vowel restoration system. We focus on handling verbs. The Hebrew verb system has the most complex morphology and vocalization mechanisms of all parts of speech. We rst present a comprehensive generation mechanism, which produces vocalized and morphologically tagged Hebrew verbs given a non-vocalized verb in base-form and an indication of which pattern the verb follows. Given a classi cation of verbs into about 300 distinct clusters, our generation mechanism generates fully vocalized in ected verbal forms. Using our implementation of this method, we have produced a lexical resource for modern Hebrew that includes all in ected forms for over 4,000 distinct verbs. This database contains about 250,000 fully in ected and fully vocalized verbal forms, with an accuracy estimate of over 99.4 In the second part, we address the task of automatic segmentation of vocalized words into syllables. This task is a necessary component of Text to Speech systems. We estimate the accuracy of the syllable segmentation algorithm taught in classical Hebrew grammar books (Behor's 5 rules of shva classi cation). We observe that this algorithm only succeeds in about 80%-85% of the verbs we tested. In the case of verbs, if we introduce additional knowledge, we manage to make this task succeed at over 99.4% accuracy. This success rate requires as additional input to the syllable segmentation algorithm, the vocalized origin form of the verb. This nding provides strong support to indicate that phonological mechanisms in Hebrew rely on a construction mechanism (that is, syllable segmentation starts from a base form + in ection form and not directly from the fully in ected form).
Ontology-based Web Recommendation from tags

With the advent of social networks and tagging systems, The Internet has recently witnessed a big leap in the use of Web Recommendation Systems WRS. Based on users' likings of items and their browsing history on the world wide web, these systems are able to predict and recommend items and future purchases to users. They are being used now in various domains, like news article recommendation, product recommendation, and make-friend recommendation. WRS are still limited by several problems, of which are sparsity, and the new user problem. They also fail to make full use and harness the power of domain knowledge and semantic web ontologies. In this article, we discuss how an ontology-based WRS can utilize relations and concepts in an ontology, along with user-provided tags, to provide top-n recommendations without the need for item clustering or user ratings. For this purpose, we also propose a dimensionality reduction method based on the domain ontology, to solve the sparsity problem.
Fault classification of broken rotor bars in induction motors based on envelope current analysis

In this article a method for the detection of one, two, and three broken bars in induction motors under full load condition is presented. The proposed methodo is based on current envelope analysis. The information obtained from the envelope current is valuable in manifesting and validating the presence of a broken bar fault, since it contains important information about the existence of a fault as well as its severity. The proposed method mainly focuses on the case of steady-state operation under full load. In the established fault diagnosis scheme six features are extracted from the envelope of the current and after the application of a Principal Component Analysis stage are fed to a classifier to perform the diagnosis. Three different classifiers, a linear, a quadratic and a nearest neighbour are investigated for the final stage of the diagnosis. The presented approach manifested promising results using experimental data.
Cross-Lingual Event Tracking

Cross-lingual event tracking from a very large number of information sources (thousands of Web sites, for example) is an open challenge. In this paper we investigate effective and scalable solutions for this problem, focusing on the use of cross-lingual information retrieval techniques to translate a small subset of the training documents, as an alternative to the conventional approach of translating all the multilingual test documents. In addition, we present a new variant of weighted pseudo-relevance feedback for adaptive event tracking. This new method simplifies the assumption and the computation in the best-known approach of this kind, yielding a better result than the latter on benchmark datasets in our evaluations.
A RESTful task allocation mechanism for the Web of Things

The Web of Things extends the Internet of Things with Web technologies to interconnect smart things and build scalable, adaptable, and interoperable applications. In this context, smart things expose their services as RESTful resources, to be combined in composite applications. Though, due to the proliferation of connected objects, multiple smart things often are available to provide a single service, showing the need for a task allocation mechanism for the Web of Things. Our work addresses the task allocation problem, which consists in selecting a set of objects to realize a complex application in a setup where several objects provide the same functionality. We rely on a multi-agent approach to describe this problem as a Distributed Constraint Satisfaction problem. We propose a solution that respects the fundamental principles of the Web by respecting the REST architectural style and design principles. We implemented our solution with a typical smart city scenario to show the applicability of our approach.
Music self-similarity modeling using augmented nonnegative matrix factorization of block and stripe patterns

Self-similarity matrices have been widely used to analyze the sectional form of music signals, e.g. enabling the detection of parts such as verse and chorus in popular music. Two main types of structures often appear in self-similarity matrices: rectangular blocks of high similarity and diagonal stripes off the main diagonal that represent recurrent sequences. In this paper, we introduce a novel method to model both the block and stripe-like structures in self-similarity matrices and to pull them apart from each other. The model is an extension of the nonnegative matrix factorization, for which we present multiplicative update rules based on the generalized Kullback-Leibler divergence. The modeling power of the proposed method is illustrated with examples, and we demonstrate its application to the detection of sectional boundaries in music.
Better together? A hospitality case for umbrella branding

Purpose While the topic of “umbrella branding” strategies for manufacturers’ products in the business-to-business literature has received attention, much less has been written about umbrella branding strategies in the hospitality industry. With the aid of a theoretical framework, this paper aims to explore three types of behavioural characteristics: alliance attribute, communication behaviour and alliance management, to examine cost and service benefits for alliance success within one umbrella organisation in the German hospitality industry. The theoretical framework of the paper built on the model of Vanpoucke and Vereecke (2012), incorporating a top management perspective to test and extend an umbrella brand. Design/methodology/approach Semi-structured interviews with a sample of senior managers were carried out in Germany at the headquarters of Ringhotels e.V. Content analysis of the data collected was implemented to increase understanding of the research phenomenon with regard to relationships and the conceptual framework applied. The results were presented in the tables with discussions about the qualitative research. Findings The results of the study showed that behavioural characteristics played a significant role in explaining overall alliance success on cost and service benefits. A good level of quality presented in Ringhotels’ services, marketing, risk and coordination was found to be a better predictor of success when absence of management and lack of trust hampered good performance. Originality/value The study offers insights into the management of relationships within Ringhotels e.V. and how these can be better managed. The main contribution of the work fills in a gap currently existing in the literature about umbrella branding within the hospitality industry.
Inertial sensing-based method for characterization of activities with walking assitive devices

INTRODUCTION Measurement of the evolution of the physical activity in assistive devices (ADs) of daily use might increase the flexibility of the rehabilitation programmes [1]. Also, while walking ADs become more a more autonomous and intelligent [2], reliable and simple methods to assess and adapt performance are demanded. MEMs-based Inertial sensors are called to become standard devices for human motion capture.
Oracle ® Streams for Near Real Time Asynchronous Replication

Replication users typically have two very conflicting needs. They require the online transaction workload to proceed seamlessly, as if there was no replication. At the same time, they need the latency of the replica to be extremely small, or, in other words, near real time. Now, synchronous replication guarantees no latency, but it does so at tremendous cost to the transaction throughput and system availability. Thus, it is not surprising that asynchronous replication is the more popular choice. The challenge is to design an asynchronous replication system that can guarantee a small, fixed latency while at the same time keeping up with the full transaction throughput supported by the database. This paper discusses the design of Oracle® Streams (as configured for replication) to provide near real-time asynchronous replication at throughputs close to the maximum.
THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM

The paper introduces OMNeT++, a C++-based discrete event simulation package primarily targeted at simulating computer networks and other distributed systems. OMNeT++ is fully programmable and modular, and it was designed from the ground up to support modeling very large networks built from reusable model components. Large emphasis was placed also on easy traceability and debuggability of simulation models: one can execute the simulation under a powerful graphical user interface, which makes the internals of a simulation model fully visible to the person running the simulation: it displays the network graphics, animates the message flow and lets the user peek into objects and variables within the model. These features make OMNeT++ a good candidate for both research and educational purposes. The OMNeT++ simulation engine can be easily embedded into larger applications. OMNeT++ is opensource, free for non-profit use, and it has a fairly large user
A novel interpolated N-gram language model based on class hierarchy

In this paper, we propose a novel interpolated language model that combines the interpolation and the backing-off along hierarchical classes based on class hierarchy. And the corresponding approach to the estimation of interpolation coefficients is also presented. We use the Minimum Discriminative Information (MDI) method to cluster the vocabulary into a word-clustering tree hierarchically. The tree is used to balance the generalization ability of classes' and word specificity when estimating the likelihood of a n-gram event. Experiments are performed on Reuter's corpus using a vocabulary of 27,000 words. Results show a reduction on the test perplexity over the standard Modified KN n-gram approach by 12%.
DFIG for Wind Energy Generation Using Stator Voltage Orientation Control – A Review

Now a days as one of the most important renewable source of energy is wind energy that is gaining interest due to its ecofriendly nature. This paper is about the study on control of wind driven doubly fed induction generator. A converter setup that is back to back PWM with stator voltage oriented vector control is used to control the DFIG. The back to back PWM converter set up consists of the Rotor Side Converter and the Grid Side Converter with intermediate DC link. Decoupled control of the DC link voltage and the grid reactive power is obtained through Voltage oriented control of GSC. Similarly independent control of power from wind and reactive power of DFIG is carried out by stator voltage oriented vector control of RSC. Coordinated control of the back to back converter setup in both sub synchronous and super synchronous mode is presented.
A High Order Operator Splitting Method for the Degasperis–Procesi Equation

The Degasperis–Procesi equation is split into a system of a hyperbolic equation and an elliptic equation. For the hyperbolic equation, we use the high order finite difference WENO-Z scheme to approximate the nonlinear flux. For the elliptic equation, the wavelet collocation method is employed to discretize the high order derivative. Due to the combination of the WENO-Z reconstruction and the wavelet collocation, the splitting method shows an excellent ability in capturing the formation and propagation of shockpeakon solutions. The numerical simulations for different solutions of the Degasperis–Procesi equation are conducted to illustrate high accuracy and capability of the proposed method. AMS subject classifications: 65M10, 78A48
A time series clustering based framework for multimedia mining and summarization using audio features

Past work on multimedia analysis has shown the utility of detecting specific temporal patterns for different content genres. In this paper, we propose a unified, content-adaptive, unsupervised mining framework to bring out such temporal patterns from different multimedia genres. We formulate the problem of pattern discovery from video as a time series clustering problem. We treat the sequence of low/mid level audio-visual features extracted from the video as a time series and perform a temporal segmentation. The segmentation is based on eigenvector analysis of the affinity matrix constructed from statistical models estimated from the subsequences of the time series. We are thus able to detect transitions and outliers from a sequence of observations from a stationary background process. We define a confidence measure on each of the detected outliers as the probability that it is an outlier. Then, we establish a relationship between the mining parameters and the confidence measure. Furthermore the confidence measure can be used to rank the detected outliers in terms of their departures from the back-ground process. Our experimental results with sequences of low and mid level audio features extracted from sports video show that "highlight" events can be extracted effectively as outliers from a background process using the proposed framework. We proceed to show the effectiveness of the proposed framework in bringing out patterns from surveillance videos without any a priori knowledge. Finally, we show that such temporal segmentation into background and outliers, along with the ranking based on the departure from the background, can be used to generate content summaries of any desired length
Substance P Signaling Contributes to Granuloma Formation in Taenia crassiceps Infection, a Murine Model of Cysticercosis

Cysticercosis is an infection with larval cysts of the cestode Taenia solium. Through pathways that are incompletely understood, dying parasites initiate a granulomatous reaction that, in the brain, causes seizures. Substance P (SP), a neuropeptide involved in pain-transmission, contributes to inflammation and previously was detected in granulomas associated with dead T. crassiceps cysts. To determine if SP contributes to granuloma formation, we measured granuloma-size and levels of IL-1β, TNF-α, and IL-6 within granulomas in T. crassiceps-infected wild type (WT) mice and mice deficient in SP-precursor (SPP) or the SP-receptor (neurokinin 1, NK1). Granuloma volumes of infected SPP- and NK1-knockout mice were reduced by 31 and 36%, respectively, compared to WT mice (P < .05 for both) and produced up to 5-fold less IL-1β, TNF-α, and IL-6 protein. Thus, SP signaling contributes to granuloma development and proinflammatory cytokine production in T. crassiceps infection and suggests a potential role for this mediator in human cystercercosis.
Neighbour Discovery for Transmit Power Adjustment in IEEE 802.15.4 Using RSSI

Neighbour discovery (ND) is an important process for self-configuration as it provides useful information, such as neighbour nodes with some power saving parameters, for other processes. In wireless sensor networks, transmission power adjustment is one of the possible ways to minimise energy consumption and prolong network lifetime. This paper proposes the ND technique which provides the received signal strength indicator (RSSI) value for dynamic transmit power adjustment in IEEE 802.15. 4. This work focuses on a single-hop network of wireless devices with different maximum transmission powers, where an asymmetric wireless link is very common. The energy consumption and packet loss rate are investigated through testbed experimentation. The proposed algorithm shows transmission efficiency which does not affect the packet loss rate. Moreover, it supports both environment change and the change of distance between nodes.