Miracle-dz/UniFAR-FTUs huggingface dataset:
Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.
The DrosDel Collection

We describe a collection of P-element insertions that have considerable utility for generating custom chromosomal aberrations in Drosophila melanogaster. We have mobilized a pair of engineered P elements, p{RS3} and p{RS5}, to collect 3243 lines unambiguously mapped to the Drosophila genome sequence. The collection contains, on average, an element every 35 kb. We demonstrate the utility of the collection for generating custom chromosomal deletions that have their end points mapped, with base-pair resolution, to the genome sequence. The collection was generated in an isogenic strain, thus affording a uniform background for screens where sensitivity to genetic background is high. The entire collection, along with a computational and genetic toolbox for designing and generating custom deletions, is publicly available. Using the collection it is theoretically possible to generate >12,000 deletions between 1 bp and 1 Mb in size by simple eye color selection. In addition, a further 37,000 deletions, selectable by molecular screening, may be generated. We are now using the collection to generate a second-generation deficiency kit that is precisely mapped to the genome sequence.
Rank-Aware Subspace Clutering for Structured Datasets

In online applications such as Match.com and Trulia.com users define structured profiles in order to find potentially interesting matches. Typically, profiles are evaluated against large datasets and produce thousands of matches. In addition to filtering, users also specify ranking in their profile, and matches are returned in the form of a ranked list. Top results in ranked lists are typically homogeneous, which hinders data exploration. For example, a user looking for 1or 2-bedroom apartments sorted by price will see a large number of cheap 1-bedrooms in undesirable neighborhoods before seeing any apartment with different characteristics. An alternative to ranking is to group matches on common attribute values (e.g., cheap 1-bedrooms in good neighborhoods, 2-bedrooms with 2 baths). However, not all groups will be of interest to the user given the ranking criteria. We argue here that neither single-list ranking nor attributebased grouping is adequate for effective exploration of ranked datasets. We formalize rank-aware clustering and develop a novel rank-aware bottom-up subspace clustering algorithm. We evaluate the performance of our algorithm over large datasets from a leading online dating site.
Identifying Webpage Semantics for Search Engine Optimization

The added-value of search engines is, apparently, undoubted. Their rapid evolution over the last decade has transformed them into the most important source of information and knowledge. From the end user side, search engine success implies correct results in fast and accurate manner, while also ranking of search results on a given query has to be directly correlated to the user anticipated response. From the content providers’ side (i.e. websites), better ranking in a search engine result set implies numerous advantages like visibility, visitability, and profit. This is the main reason for the flourishing of Search Engine Optimization (SEO) techniques, which aim towards restructuring or enriching website content, so that optimal ranking of websites in relation to search engine results is feasible. SEO techniques are becoming more and more sophisticated. Given that internet marketing is extensively applied, prior quality factors prove insufficient, by themselves, to boost ranking and the improvement of the quality of website content is also introduced. Current paper discusses such a SEO mechanism. Having identified that semantic analysis has not been widely applied in the field of SEO, a semantic approach is adopted, which employs Latent Dirichlet Allocation techniques coupled with Gibbs Sampling in order to analyze the results of search engines based on given keywords. Within the context of the paper, the developed SEO mechanism LDArank is presented, which evaluates query results through state-of-the-art SEO metrics, analyzes results’ content and extracts new, optimized content.
A Process-Oriented Interactive Design Environment for Automatic User-Interface Adaptation

In the design of user interfaces that adapt automatically to end-users and usage contexts, designers should be prepared to cope with large design spaces; these spaces will be necessary to accommodate design constraints posed by the diversity in the target user population and the various contexts of use. Adaptation-oriented design is considered a powerful design recipe addressing the compelling requirements of customization, accessibility, and high quality of interaction. Under this perspective, it must be carefully planned, designed, accommodated, and conducted within the life cycle of interactive systems, from the early exploratory phases of design and prototyping to evaluation, implementation, and deployment. Despite recent progress, the practice of designing automatic user-interface adaptations remains difficult, due to intrinsic complexity of the task and the current limited expertise of designers and practitioners. Toward overcoming such a difficulty, this article presents MENTOR, a tool providing (a) practical integrated support for all phases of adaptation design, through appropriate editing facilities; (b) practical support for a "smooth transition" from design to development through the availability of automated verification mechanisms for the designed adaptation logic, as well as the automated generation of "ready-to-implement" interface specifications; and (c) support for the progressive accumulation of design cases and of the related design experience and knowledge, in particular regarding adaptation.
Sleep, stress and compensatory behaviors in Australian nurses and midwives.

OBJECTIVE To describe sleep, stress and compensatory behaviors in nurses and midwives. METHODS The study included 41 midwives and 21 nurses working in Australian hospitals between 2005 and 2009. Participation was voluntary. All participants recorded on a daily basis their work and sleep hours, levels of stress and exhaustion, caffeine intake and use of sleep aids for a month (1,736 days, 1,002 work shifts). RESULTS Participants reported moderate to high levels of stress and exhaustion on 20-40% of work days; experienced sleep disruption on more than 50% of work days; struggled to remain awake on 27% of work days; and suffered extreme drowsiness or experienced a near accident while travelling home on 9% of workdays. Age, perceived sleep duration and work hours were significant predictors of caffeine intake. About 60% of participants reported using sleep aids (about 20% reported taking prescription medications and 44% of nurses and 9% of midwives reported alcohol use as a sleep aid at least once during the study). Stress and workdays were significant predictors of sedative use. Overall, 22% reported being indifferent or mildly dissatisfied with their job. CONCLUSIONS Sleep problems, high levels of stress and exhaustion and low job satisfaction are prevalent among nurses and midwives. The use of alcohol and sleeping pills as sleep aids, and the use of caffeine to help maintain alertness is also common. Nurses and midwives may use caffeine to compensate for reduced sleep, especially on workdays, and sleeping pills to cope with their daily work-related stress.
PolyUHK: A Robust Information Extraction System for Web PersonalNames

Personal information extraction is an important component of advanced information retrieval. There are two problems needed to be solved in this practical task: personal name ambiguity and extraction of personal information for a specific person. For personal name ambiguity, which is a very common phenomenon in the fast growing Web resource, we propose a robust system which extracts features with a totally unsupervised approach from resources beyond the given Web corpus. The experiments show that these broad features not only can improve performances, but also increase the robustness of a disambiguation system. For personal information extraction, a rule-based information extraction system is introduced, which is able to re-use current well-developed tools effectively and identify the properties of Web data. The experiments show that the system can achieve state-of-the-art performances, especially the high precision.
Information Systems for Competitive Advantage : Implementation of an Organisational Strategic Management Process

In order to guarantee the degree to which the Infor mation Technology (IT) mission, objectives and plan s support and are supported by business mission, obje ctives and plans; scholars and researchers (e.g. Ac ur et al. 2012) emphasised the need of organisations to a ligning their business strategy with Information Technology strategy if they desire to achieve great er business value from their business and IT invest m n s. However; investigating exact conditions on applying specific type of strategy still uncovered (Tallon a d Pinsonneault, 2011). Consequently, for firms to ach ieve a competitive advantage through continuous sup port from both IT and business managers and executives, it is essential to explore the underlying condition s and risks associated with implementing IT systems, spec ifically Enterprise Recourse Planning (ERP). Furthe r, since strategy formulation is part of the strategic management process in order to achieve a firm’s competitive advantage by creating more economic val ue than its rivals for the organization’s vision; t he benefits, drawbacks, and risks accompanying the imp le entation of ERP systems are presented in this research.
Targeting more relevant, contextual recommendations by exploiting domain knowledge

In today's mobile applications, it becomes more and more important to have a broader view on knowledge about a certain domain when generating contextual and semantic recommendations. Data that provides additional and useful information to the traditional User x Item representation, such as taxonomies, implicit and indirect knowledge about a user's preferences or location information can immensely enhance the quality of recommendations. For this purpose, the generic recommender system of Fraunhofer Institute FOKUS, the SMART Recommendations Engine, has been extended by the SMART Ontology Extension and the Proximity Filter, which enable the recommender to use domain knowledge included in semantic ontologies and contextual information in the recommendation process in order to generate much more precise recommendations. The functionality of the extensions are demonstrated in the scope of a food purchase scenario.
An Evaluation of Readily Usable Automatic Video Shot Segmentation Techniques

Shot segmentation is a fundamental task in video systems, being a prior step to scene detection and summarization. Although the literature provides large number of automatic shot segmentation techniques, most of them cannot be easily used by researchers simply because they aren't available to download, use legacy technology or are paid. This paper provide an evaluation of easily obtained video shot segmentation software in a benchmark composed by a set of movies and documentaries. Results, although may not be comparable to state-of-the-art, have significant meaning because these methods could be actually used by anyone.
Circulating Lipids Are Associated with Alcoholic Liver Cirrhosis and Represent Potential Biomarkers for Risk Assessment

Liver disease is the greatest cause of death related to alcohol and a major public health problem. While excessive alcohol intake results in hepatosteatosis in most individuals, this can progress in some to more severe forms of liver disease including fibrosis and cirrhosis. An ongoing challenge in the management of alcoholic liver disease is the identification of liver injury early in the disease process such that intervention strategies can prevent serious long term outcomes. Given that excessive alcohol consumption results in dysregulation of lipid metabolism we applied lipid profiling technology to characterise and compare serum lipid profiles from excessive chronic drinkers with no liver disease to those with advanced alcoholic cirrhosis. In a cohort of 59 excessive drinkers (31 with liver cirrhosis and 28 with no evidence of liver disease) we used electrospray ionisation tandem mass spectrometry to measure over 300 individual lipid species in serum, including species of the major phospholipid, sphingolipid, glycerolipid and sterol classes. Six of the 25 lipid classes and subclasses were significantly associated with alcoholic liver cirrhosis; these included dihexosylceramide, trihexosylceramide, alkylphosphatidylcholine, lysoalkylphosphatidylcholine, phosphatidylinositol and free cholesterol. Multivariate classification models created with only clinical characteristics gave an optimal model with an AUC of 0.847 and an accuracy of 79.7%. The addition of lipid measurements to the clinical characteristics resulted in models of improved performance with an AUC of 0.892 and accuracy of 81.8%. The gain in AUC and accuracy of the combined models highlight the potential of serum lipids as markers of liver injury in alcoholic liver disease.
Radiation detection and measurement

introduces the reader to the DARE P simulation system. The DARE P system is an equation-oriented continuous system simulation language developed by the authors at the University of ArizonaIt is designed for batch-mode processing, and the system programs are coded in ASNI Fortran IV. Several examples from the disciplines of electrical circuits, control systems, and aerospace engineering are given to illustrate the versatility of the DARE P system. In Chapter 5 the design and application of minicomputer interactive stimulation systems is described with particular reference to the DARE/ELEVEN system. Chapter 6 describes methods for increasing the computing speed of the simulation language. A comparison of the performance of different simulation-language systems is also given. Appendix A contains a discussion of integration routines. Appendix B contains Fortran subroutines while Appendix C discusses numerical techniques. Since the general purpose digital computer is replacing the analog computer as the primary tool of engineers and scientists for the simulation of continuous time systems, a book on this subject is important to the scientific community. This book is valuable in that it gives a simple overview and a logical development of the subject, and together with the DARE package enables the user to obtain a digital simulation of a continuous system without becoming overly involved in the intracacies of the simulation language itself. In our opinion this book provides a good introduction to the subject of digital simulation of continuous systems. It is not suitable as a classroom textbook. However, to those who avail themselves of the DARE system (the programs are available from the authors at a nominal cost) it would serve as a valuable companion to the users’ manual. Unfortunately the book is difficult to read. The authors use specific terms and phrases without adequately defining them. In many instances the narration is too brief and confusing. The technique of providing explanations under the figures themselves rather than in the main body of the text makes for disjointed reading.
A Dual Sparse Decomposition Method for Image Denoising

This article addresses the image denoising problem in the situations of strong noise. We propose a dual sparse decomposition method. This method makes a sub-dictionary decomposition on the over-complete dictionary in the sparse decomposition. The sub-dictionary decomposition makes use of a novel criterion based on the occurrence frequency of atoms of the over-complete dictionary over the data set. The experimental results demonstrate that the dual-sparse-decomposition method surpasses state-of-art denoising performance in terms of both peak-signal-to-noise ratio and structural-similarity-index-metric, and also at subjective visual quality.
Wormhole solutions to Hořava gravity

We present wormhole solutions to Hořava nonrelativistic gravity theory in vacuum. We show that, if the parameter A is set to one, traversable wormholes connecting two asymptotically de Sitter or anti-de Sitter regions exist. In the case of arbitrary λ, the asymptotic regions have a more complicated metric with constant curvature. We also show that, when the detailed balance condition is violated softly, traversable and asymptotically Minkowski, de Sitter or anti-de Sitter wormholes exist.
TKDDI group at NTCIR10-RITE2: Recognizing Texual Entailment Based on Dependency Structure Alignment

This paper discribes the TKDDI system which participated in NTCIR10-RITE2. We propose a method for recognizing textual entailment by not using only word alignment, but also using syntactic dependency structure alignment. Entailment can then be recognized by the overlap of the dependency structures. Our system achieved a macro f1 of 63.83 on JA-BC, 49.08 on JA-ExamBC and 74.00 on JAUnitTest.
The VENEX corpus of anaphora and deixis in spoken and written Italian

The VENEX corpus is a corpus of Italian annotated with information about anaphora and deixis, created in a joint project between the Universita di Venezia and the University of Essex. The corpus includes both texts (articles from a financial newspaper) and dialogues (an Italian version of the MapTask corpus). The annotation scheme is an almost complete implementation of the scheme proposed in MATE, and the markup scheme is the simplified form of standoff adopted in the MMAX annotation tool.
Learning to Search for Recognizing Named Entities in Twitter

We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.
A study on primary path switching strategy of SCTP

As a variety of communication devices emerging, we can easily install two or more communication interfaces on a PC or a PDA and make a multi-home environment. To utilize this multi-home environment effectively, SCTP (stream control transmission protocol) has been proposed and standardized. Extending functions of the original SCTP, a strategy which can adapt SCTP to real-time communications has been proposed. However, the typical existing strategy has not enough considered the switching conditions from the viewpoint of transmission delay, so there is room to improve them. In this paper, we clarify the drawbacks of the existing strategy and their influences on the performance. Then, we propose a new strategy of path switching to reduce the delay and to increase the amount of data successfully delivered. The effect of the proposed strategy is discussed and confirmed through experimental evaluations.
Minutiae based thermal face recognition using blood perfusion data

This paper describes an efficient approach for human face recognition based on blood perfusion data from infra-red face images. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to optical image data. Blood perfusion data are related to distribution of blood vessels under the face skin. A distribution of blood vessels are unique for each person and as a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. For classification, a five layer feed-forward backpropagation neural network has been used. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block sizes. Experiments have been performed on the database created at our own laboratory. The maximum success of 91.47% recognition has been achieved with block size 8×8.
Noun/Verb Inference

We present a system which combines logical inference with a semantic calculus producing normalised semantic representations that are robust to surface dierences which are irrelevant for entailment detection. We focus on the detection of entailment relations between sentence pairs involving noun/verb alternations and we show that the system correctly predicts a range of interactions between basic noun/verb predications and semantic phenomena such as quantication, negation and non factive contexts.