Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous genotyping of nine polymorphisms in xenobiotic-metabolizing enzymes by multiplex PCR amplification and single base extension.

Studies have reported a large interindividual variation in susceptibility to health effects caused by exposure to xenobiotic compounds such as drugs or chemical carcinogens. There is evidence that this can be partly explained by the existence of genetic polymorphisms in metabolic enzymes, such as cytochrome P450 (CYP450), N -acetyltransferases (NATs), and glutathione S -transferases (GSTM, GSTT, GSTP) [see, e.g., Refs. (1)(2)(3)(4)]. However, studies investigating associations between genetic polymorphisms and disease have reported conflicting results, probably caused by insufficient statistical power (5). Moreover, the majority of these studies focused on single polymorphisms. Regarding the number of genes implicated in the metabolism of xenobiotics and the large number of polymorphisms present in the human genome (6), these approaches fail to fully determine the role of genetic variation in an individual’s susceptibility to xenobiotic exposures. Such observations underline the need for methodologies that allow for high-throughput, low-cost genotyping of multiple polymorphisms in large populations (7)(8). In this study we describe the development, validation, and application of a cost-effective and rapid method for simultaneous genotyping of nine polymorphisms in five key enzymes involved in metabolism of xenobiotics: CYP1A2, GSTM1, GSTP1, GSTT1, and NAT2. The fragments containing the nine single-nucleotide polymorphisms (SNPs) were amplified in one sevenplex and one duplex PCR reaction (Table 1⇓ ). Primers were obtained from Qiagen. For the sevenplex PCR, a 50-μL reaction mixture was prepared containing PCR buffer, 0.2 mM deoxynucleotide triphosphates, 0.5 mM MgCl2, 1.25 U of Platinum® Taq Polymerase (Invitrogen), and 200 ng of template DNA. The final primer concentrations were 0.22 μM (for GSTP1 * 3 , GSTT1 , NAT2 * 6 , and NAT2 * 7 ), 0.45 μM (for CYP1A2 * 1F and NAT2 * 5 ), and 0.16 μM (for GSTP1 * 2 ). PCR was …
A Context-Aware Pain Trajectory Framework for Low Back Pain Management

Current advances in mobile and sensor technologies have provided new opportunities for many fields of research, especially in healthcare. Chronic pain is one such field, where low back pain is a common problem that affects 20% of the population, and is also a major contributor to disability. Unfortunately, not much is yet known about the contributing factors, nor the nature of low back pain itself. Existing research does not collect data frequently - with most studies only collecting pain data monthly, or half yearly. Experts agree that there is a need for the increase in frequency of data collection, and to study the context of the patient’s pain experience in order to understand the nature of pain. Currently, there are not any research that attempts to include the context around the patient’s pain experience, to collect and analyze data for correlations on an individual patient basis. This research will propose a context-aware pain trajectory approach capitalizing on the opportunities that arise from advances in mobile and sensor technologies, to increase the frequency of data collection, and enable the collection and integration of the patient’s context into current low back pain models using day to day pain trajectories.
The effect of buffering on the performance of R-trees

Past R tree studies have focused on the number of nodes visited as a metric of query performance. Since database systems usually include a buffering mechanism, we propose that the number of disk accesses is a more realistic measure of performance. We develop a buffer model to analyze the number of disk accesses required for spatial queries using R trees. The model can be used to evaluate the quality of R tree update operations, such as various node splitting and tree restructuring policies, as measured by query performance on the resulting tree. We use our model to study the performance of three well known R tree packing algorithms. We show that ignoring buffer behavior and using number of nodes accessed as a performance metric can lead to incorrect conclusions, not only quantitatively, but also qualitatively. In addition, we consider the problem of how many levels of the R tree should be pinned in the buffer.
Using Growing Neural Gas Networks to Represent Visual Object Knowledge

We present a so-called Neural Map, a novel memory framework for visual object recognition and categorization systems. The properties of its computational theory include self-organization and intelligent matching of the image features that are used to build their object models. Its performance for representing the visual object knowledge comprised by these models and for recognizing unknown objects is measured using three different types of image features, which extract different granularity of information from object views of the ETH-80 image set. The obtained experimental results slightly outperform previous ones using PCA-based methods on the same image set, and they suggest that the medium-sized image features maximize the object models' informativeness and distinctiveness.
Inclusion of large input corpora in Statistical Machine Translation

In recent years, the availability of large, parallel, bilingual corpora has gone untapped by the statistical machine learning community. The crux of the problem lies in the inherent linearity of the traditional machine-translation algorithms, which impedes easy inclusion of new, large input corpora. However, it has been speculated [1] that there exists a log-linear relationship between the training corpora size and performance in machine-translation tasks. In this paper, we cast the IBM Model-1 algorithm into three formats that allow it to use large input corpora, and then verify the performance-gain claims. The models we introduce provide a scalable architecture for incorporating further corpora easily and cheaply, which we then show to translate into higher performance in machine-translation tasks.
The thin hot plume beneath Iceland

SUMMARY We present the results of a seismological investigation of the frequency-dependent amplitude variations across Iceland using data from the HOTSPOT array currently deployed there. The array is composed of 30 broad-band PASSCAL instruments. We use the parameter t*, de¢ned in the usual manner from spectral ratios (Halderman & Davis1991), to compare observed S-wave amplitude variations with those predicted due toboth anelastic attenuation and diiraction eiects.Four teleseismic events at a range of azimuths are used to measure t*. A 2-D vertical cylindrical plume model with a Gaussian-shaped velocity anomaly is used to model the variations. That part of t* caused by attenuation was estimated by tracing a ray through IASP91, then superimposing our plume model velocity anomaly and calculating the path integral of 1/oQ. That part of t* caused by diiraction was estimated using a 2-D ¢nite diierence code to generate synthetic seismograms.The same spectral ratio technique used for the datawas then used to extract a predictedt*. Thet* variations caused by anelastic attenuation are unable to account for the variations we observe, but those caused by diiraction do.We calculate the t* variations caused by diiraction for diierent plume models and obtain our best-¢t plume, which exhibits good agreement between the observed and measured t*. The best-¢t plume model has a maximum S-velocity anomaly of {12 per cent and falls to 1/e of its maximum at 100 km from the plume centre. This is narrower than previous estimates from seismic tomography, which are broadened and damped by the methods of tomography. This velocity model would suggest greater ray theoretical traveltime delays than observed. However, we ¢nd that for such a plume, wave-front healing eiects at frequencies of 0.03^0.175 Hz (the frequency range used to pick S-wave arrivals) causes a 40 per cent reduction in traveltime delay, reducing the ray theoretical delay to that observed.
A Simple Framework for Natural Animation of Digitized Models

We present a versatile, fast and simple framework to generate animations of scanned human characters from input optical motion capture data. Our method is purely mesh- based and requires only a minimum of manual interaction. The only manual step needed to create moving virtual people is the placement of a sparse set of correspondences between the input data and the mesh to be animated. The proposed algorithm implicitly generates realistic body deformations, and can easily transfer motions between human subjects of completely different shape and proportions. We feature a working prototype system that demonstrates that our method can generate convincing lifelike character animations directly from optical motion capture data.
Coarse-Grain Data Gathering in Continuous Query for Periodical Phenomena in Wireless Sensor Networks

This paper presents a query optimization method for continuous data gathering in sensor networks. The method is based on a coarse-grain data collection approach by using the setting of thresholds and the analysis on the content to be sent over the network. In particular, the paper concentrates on the situation where phenomena under monitoring appear a periodical feature. An initiative data model is first created, and then at each single sensor node, instead of direct delivery of each result data, data communication is needed only when the current data is out of the range constrained by the periodical data model. The performance of the proposed algorithms shows that the proposed methods can achieve better performance than without performing the optimization.
Endoplasmic Reticulum Stress Response in Arabidopsis Roots

Roots are the frontier of plant body to perceive underground environmental change. Endoplasmic reticulum (ER) stress response represents circumvention of cellular stress caused by various environmental changes; however, a limited number of studies are available on the ER stress responses in roots. Here, we report the tunicamycin (TM) -induced ER stress response in Arabidopsis roots by monitoring expression patterns of immunoglobulin-binding protein 3 (BiP3), a representative marker for the response. Roots promptly responded to the TM-induced ER stress through the induction of similar sets of ER stress-responsive genes. However, not all cells responded uniformly to the TM-induced ER stress in roots, as BiP3 was highly expressed in root tips, an outer layer in elongation zone, and an inner layer in mature zone of roots. We suggest that ER stress response in roots has tissue specificity.
Inter-organizational Relations in Cyberspace : Legitimacy Problems in the Diffusion Of Web-based Outsourcing as a Hybrid Form By Annetta Fortune Fuqua School of Business

Application service providers (ASPs) represent an innovative inter-organizational relationship because of the virtual tie that exists between an ASP and its customers. Rogers' (1995) diffusion of innovation framework and the concept of legitimacy from institutional theory provide the theoretical context for our discussion of the ASP phenomenon and the factors inhibiting the diffusion of this innovative business model. Industry surveys and case interviews reveal that the key factors influencing the diffusion of the ASP model are a perceived lack of compatibility and a low degree of observability. A strong trade association and collective action by key players may enable the industry to overcome the cognitive and socio-political factors now inhibiting its growth. Executive Summary Application service providers (ASPs) are an innovative way for firms to outsource business functions. ASPs utilize the Internet in the provision of business outsourcing by providing customers with access to software as a service via the Internet for a monthly fee. Consequently, the ASP business model has the potential to level the playing field by providing firms, especially SMEs, with cutting edge technology without the initial investment and cash-outlay of developing comparable functionality in-house. The potential impact of the ASP business model has generated an emerging industry that includes ASPs, software and hardware firms, infrastructure providers, trade associations, information disseminating organizations, and many other players. However, despite the excitement surrounding the emerging ASP industry, industry experts have noted that the diffusion of the ASP model been slower than they anticipated. To explore the sluggish diffusion of the ASP model, we turn to the literature on innovation and to institutional theory. The defining aspect of an innovation is its novelty to the parties involved. We consider the ASP business model to be an innovation for two reasons. First, the Internet plays an integral role of in the provision of business services. Second, ASPs create a virtual, boundary-spanning inter-organizational relationship between themselves and their customers. In other words, the ASP model introduces a new dimension to the traditional concept of outsourcing through the incorporation of the Internet and a continuous boundary-spanning relationship. 4 Similar to other innovations, the new ASP model must face and overcome the challenges generated by a lack of legitimacy associated with its novelty. Following the framework proposed by Aldrich and Fiol (1994), the ASP industry is faced with the challenge of gaining cognitive and sociopolitical legitimacy. Cognitive legitimacy refers to the acceptance of the …
Bird Species Recognition by Wavelet Transformation of a Section of Birdsong

In this study, a method for birdsong recognition is proposed. In this method, after detecting the range of each syllable, birdsong sections containing a period of syllables were segmented. For each syllable of a birdsong section, the first five orders MFCCs were computed, and the same order MFCCs of all syllables were aligned so that wavelet transformation can be applied to compute the feature vector of the birdsong section. By using neural network as the classifier, the proposed method was applied to recognize the birdsongs of 420 bird species.
Recognition and gendered identity constructions in labour activation

Few studies have considered how labour activation programmes affect participants’ identity construction, particularly from a gender comparison perspective. Using qualitative data and recognition theories, this exploratory study of the Norwegian Qualification Program examined how gender may affect labour activation recipients’ identity construction and sense of social value. The findings suggest that women experience labour activation as an enabling process, facilitating an enhanced sense of social value and status. In contrast, men experience either no such change or a diminution of their sense of worth and status. The study shows how cultural values regarding gender, work and employment are embedded in social work practice and activation policy implementation. Underscoring how activation may be intertwined in such cultural values and norms, the study calls for further research to understand these processes, as they may affect outcomes in labour activation policy.
The Construction of 'reality' in the Robot: Constructivist Perspectives on Situated Artificial Intelligence and Adaptive Robotics

This paper discusses different approaches in cognitive science and artificial intelligence research from the perspective of radical constructivism, addressing especially their relation to the biologically based theories of von Uexküll, Piaget as well as Maturana and Varela. In particular recent work in 'New AI' and adaptive robotics on situated and embodied intelligence is examined, and we discuss in detail the role of constructive processes as the basis of situatedness in both robots and living organisms.
An Enhanced Objective Cluster Analysis-Based Fuzzy Iterative Learning Approach for T-S Fuzzy Modeling

This work presents an Enhanced Objective Cluster Analysis-based fuzzy iterative learning approach for T-S fuzzy modeling. In this method, the Enhanced Objective Cluster Analysis including the Dipole Partition, the Relative Dissimilarity Measure and the Enhanced Consistency Criterion are incorporated with the Fuzzy - Means algorithm for the robust and compact fuzzy partition in the input space. For improving accuracy of the model, iterative learning strategy with Covering Measure is adopted to repartition the dissatisfying fuzzy subspaces according to the user's requirement. By the Stable Kalman Filter algorithm, the consequent parameters are efficiently estimated. The Box-Jenkins example demonstrates the power of our method.
A systematic approach to practically efficient general two-party secure function evaluation protocols and their modular design

General two-party Secure Function Evaluation SFE allows mutually distrusting parties to correctly compute any function on their private input data, without revealing the inputs. Two-party SFE can benefit almost any client-server interaction where privacy is required, such as privacy-preserving credit checking, medical classification, or face recognition. Today, SFE is a subject of immense amount of research in a variety of directions and is not easy to navigate.In this article, we systematize the most practically important works of the vast research knowledge on general SFE. We argue that in many cases the most efficient SFE protocols are obtained by combining several basic techniques, e.g., garbled circuits and additively homomorphic encryption.As a valuable methodological contribution, we present a framework in which today's most efficient techniques for general SFE can be viewed as building blocks with well-defined interfaces that can be easily combined into a complete efficient solution. Further, our approach naturally allows automated protocol generation compilation and has been implemented partially in the TASTY framework.In summary, we provide a comprehensive guide in state-of-the-art SFE, with the additional goal of extracting, systematizing and unifying the most relevant and promising general SFE techniques. Our target audience are graduate students wishing to enter the SFE field and advanced engineers seeking to develop SFE solutions. We hope our guide paints a high-level picture of the field, including most common approaches and their trade-offs and gives precise and numerous pointers to formal treatment of its specific aspects.
Journal of Humanistic Mathematics Journal of Humanistic Mathematics An Introduction to Fourier Analysis with Applications to Music An Introduction to Fourier Analysis with Applications to Music

Synopsis In our modern world, we are often faced with problems in which a traditionally analog signal is discretized to enable computer analysis. A fundamental tool used by mathematicians, engineers, and scientists in this context is the discrete Fourier transform (DFT), which allows us to analyze individual frequency components of digital signals. In this paper we develop the discrete Fourier transform from basic calculus, providing the reader with the setup to understand how the DFT can be used to analyze a musical signal for chord structure. By investigating the DFT alongside an application in music processing, we gain an appreciation for the mathematics utilized in digital signal processing.
DEFLATE Compressed Data Format Specification version 1.3

This specification defines a lossless compressed data format that compresses data using a combination of the LZ77 algorithm and Huffman coding, with efficiency comparable to the best currently available general-purpose compression methods. The data can be produced or consumed, even for an arbitrarily long sequentially presented input data stream, using only an a priori bounded amount of intermediate storage. The format can be implemented readily in a manner not covered by patents.
Context-Dependent Acoustic Modelling of Croatian Speech

This paper presents experiments of Croatian speech modelling used in speech recognition as well as in speech synthesis. The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. For speech recognition and speech synthesis experiments a common Croatian speech corpora is used. The experiments have shown that Croatian speech corpora, Croatian phonetic rules and hidden Markov models as the modelling formalism can be used to develop speech recognition and speech synthesis systems in parallel. The proposed procedures for Croatian acoustic modelling were developed as speech interfaces in a spoken dialog system. Kontekstno odvisno akustično modeliranje hrvaškega govora V članku so opisani postopki akustičnega modeliranja, ki so bili uporabljeni pri razpoznavanju in sintezi hrvaškega govora. Predlagani akustični model temelji na kontekstno odvisnih trifonskih modelih in na fonetičnih pravilih hrvaškega govora. Za učenje akustičnih modelov je izbran formalizem prikritih Markovovih modelov in korpus hrvaškega govora. Razviti postopki za razpoznavanje in sinetezo hrvaškega govora so del enotnega sistema za govorni dialog.
Survey on Enhancement of Performance of Video Streaming over Ad Hoc Wireless Network

With the development of communication technology and the extensive use of mobile devices, streaming video content through wireless network is becoming popular application. Several works have been conducted to address its challenges as well as improve its performance. This paper will re-observe the problem of video streaming over ad hoc wireless network. It explains the background concept of ad hoc network as well. Subsequently, various proposed improvement methods are discussed. It finally summarizes how these methods answer the challenges and improve the performance of video stream on ad hoc wireless network.
Mova: Interactive Movement Analytics Platform

There is an increasing interest in analyzing, extracting, and representing human movements in terms of a set of spatial, temporal, and qualitative characteristics for applications such as human-computer interactions and sports and health movement analysis. Information visualization techniques can be used to help people better understand the contents of movements. While all the characteristics of movement may not always be visible or detectable by humans, visualizations can illustrate detailed information about the characteristics of the movement. We present the prototype of an interactive movement analytics framework, called Mova, for feature extraction, feature visualization, and analysis of human movement data. Integrated with a library of feature extraction methods, this platform can be used to anaylze movement qualities and investigate the relationships between its characteristics. In addition, Mova can be used to develop and validate new feature extraction methods with the help of parallel visualization of multiple features. We discuss test-cases in which Mova can be used and detail the road-map for its further development. Link to the platform: http://www.sfu. ca/~oalemi/mova