Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymorphic Cytochrome P450 Enzymes (CYPs) and Their Role in Personalized Therapy

The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen.
Applying aspect-oriented programming to music computing

Computer programs for the composition, performance and analysis of music generally involve the tangled interaction of many dimensions of musical and extra-musical concern. In this paper we introduce the concepts of Aspect-Oriented Programming (AOP) to Music Computing and argue that AOP and related techniques and technologies form an appropriate solution to the separation and composition of such concerns. We motivate our argument with simple examples from the musical domain, but argue that the underlying principles may be applied to a wide and expressive range of musical applications
L and U Slot Loaded UWB Microstrip Antenna: C-Band/WLAN Notched

This paper introduces a dual band-notched, ultra-wideband printed monopole antenna. The antenna uses L & U slot to create dual band-notched characteristics for WLAN and C-band satellite communication systems, respectively. The proposed antenna has the novel suitability to utilize it for both uplink and downlink band notching applications in C-band satellite. The proposed UWB antenna has overall size of 26×30×1.6mm3 and it works over 3 to 11 GHz with VSWR <; 2. Characteristics of the proposed antenna like, group delay, radiation pattern, return loss and vswr have been measured and presented along with the simulated results.
Theory-based evaluations: Framing the existence of a new theory in evaluation and the rise of the 5th generation

In this article we defend the idea that theory-based evaluations—contribution analysis, logic analysis, and realist evaluation—are complementary components of a new theory in evaluation. We also posit that we are currently observing the emergence of a fifth generation in evaluation: the explanation generation. Theory-based evaluations have featured prominently in the discourse of evaluators since the mid-1980s. They have developed mainly in response to the need for evaluation of complex interventions. In this article we analyze certain approaches that have matured in their design and application. We use the framework of Shadish et al. to analyze the ontological, epistemological, and methodological foundations of various theory-based approaches in evaluation to appraise their similarities and differences. We observe that all these approaches are grounded in critical realism. Similarities seen in their ontological, epistemological, and methodological positionings, as well as their complementarity in terms of the evaluative questions they address, suggest we may be observing the consolidation of a new theory in evaluation and the emergence of a fifth generation.
JBigin: A Java Package for Bayesian Inference of Genetic Interaction Networks from Micro-Array Data

Bayesian Network (BN) has been proposed to infer the gene regulatory networks (GNs) based on differential expressions of its members under various physiological conditions. However, its practical use is limited by the lack of data and the overwhelmingly large number of candidate structures to compare. Here we present JBigin as an integrated tool to infer GN. It allows specification of prior beliefs on the expected GN structure and therefore limits the search space and alleviated the need for expression data. JBigin offers an integrated and extensible API library as well as a biologist-friendly GUI. The usefulness of this package was exemplified by an inference task based on 11 expression profiles. With three expected edges specified as belief, JBigin inferred ten edges, out of which eight edges had been reported in literature. JBigin and its documents can be downloaded at http://www.cls. zju.edu. cn/rlibs/software/jbigin.html.
Analysis Strategies for Software Product Lines

Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a set of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, testing, and formal verification, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analysis techniques is both broad and diverse such that it is difficult for researchers and practitioners to understand their similarities and differences (e.g., with regard to variability awareness or scalability), which hinders systematic research and application. We classify the corpus of existing and ongoing work in this field, we compare techniques based on our classification, and we infer a research agenda. A short-term benefit of our endeavor is that our classification can guide research in product-line analysis and, to this end, make it more systematic and efficient. A long-term goal is to empower developers to choose the right analysis technique for their needs out of a pool of techniques with different strengths and weaknesses.
Component Certification and System Prediction: Is there a Role for Formality?

In this paper, we specifyan openproblem: predictable component assembly, andstatea positiononcompositional reasoningtechniquesthat webelieveare necessaryfor this purpose. 1 A Rhetorical Question? Yes, naturally, the questionin the title is meantto be rhetorical.However, by formality I do not meanthatwhich is exemplifiedby existing formal methods.Rather , I mean theuseof a priori reasoning, insteadof theprevalenta posteriori reasoningusedfor verification-basedsoftwareconstruction. 2 What is A Priori Reasoning? Verification-basedmethodstake the ‘posit-and-see ’ approach:given the specificationfor a program,first posit a program,thenseeif theprogramis correct(wrt to thegiven specification);similarly, to constructa specifiedcomposite, first posit the componentsandtheir composition,thensee if their compositionmeetsthe given specification.This is whatI call a posteriorireasoning: reasoningaboutcorrectness(or otherproperties)takesplaceafter the programor componenthasbeenconstructed.In other words, it does not offer any helpwith theconstructionbeforehand. By contrast,a priori reasoningtakesplacesbeforethe constructiontakesplace. In the context of CBSE,a priori reasoningassumesthat it is possibleto show a priori that theindividualcomponentsin questionarecorrect(wrt their own specifications). It thenoffershelpwith reasoningabout the compositionof thesecomponents,both to guide their compositionin order to meetthe specificationof a larger system,andto predicttheprecisenatureof any composite, sothatthecompositecanin turnbeusedasaunit for further composition. In otherwords,whereasa posteriorireasoningtakesthe posit-and-seeapproach, a priori reasoningtriesto leaveout thepositelementaltogether , andreplacetheseeelementby preciseprediction. 3 A Priori ReasoningDemandsFormality Unlike a posteriori reasoning,which may or may not beformal (theformercorrespondsto posit-and-see , andthe latterposit-and-pr ove), a priori reasoningdemandsformality becauseit is just not possibleotherwise,aswill become plain in Section6.3. 4 Can Formality Succeedin CBSE? But why shouldanyonebelievethatformality canbeemployedsuccessfullyin CBSEwhenexistingformalmethods have not madean impacton currentsoftwareengineering practice? Well, obviouslyI cannotbeabsolutelycertainthat it canbe,but I am reasonablyconvincedthata priori reasoning is practicable(andhencethe associatedformality) andit canhelpCBSEsucceed, 5 Why doesCBSENeedA Priori Reasoning? My answerto thisquestionis simply thatI believethata priori reasoningcandeliverthekey pre-requisitesfor CBSE to achieve its ultimategoalof third-part assembly . 6 What are the KeyPre-Requisitesfor CBSE? I believe they arethefollowing: a standard semanticsof componentsandcomponent composition(andhencereuse); good(component)interfacespecifications ; agoodassemblyguidefor selectingtheright componentsfor building a specifiedsystem. 6.1 Standard Semantics Without a standardsemantics,it is not possibleto achieve astandard(universallyunderstoodandaccepted)definition, which in turn is essential for achieving theultimategoalof third-partyassemblyof components.
Topological median filters

This paper describes the definition and testing of a new type of median filter for images. The topological median filter implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional median filter, the extraction of edges in noise. The concept of alpha-connectivity is defined and used to create an algorithm for computing the degree of connectedness of a pixel to all the other pixels in an arbitrary neighborhood. The resulting connectivity map of the neighborhood effectively disconnects peaks in the neighborhood that are separated from the center pixel by a valley in the brightness topology. The median of the connectivity map is an estimate of the median of the peak or plateau to which the center pixel belongs. Unlike the conventional median filter, the topological median is relatively unaffected by disconnected features in the neighborhood of the center pixel. Four topological median filters are defined. Qualitative and statistical analyses of the four filters are presented. It is demonstrated that edge detection can be more accurate on topologically median filtered images than on conventionally median filtered images.
A Comparative Study on Bioinformatics Feature Selection and Classification

paper presents an application of supervised machine learning approaches to the classification of the colon cancer gene expression data. Established feature selection techniques based on principal component analysis (PCA), independent component analysis (ICA), genetic algorithm (GA) and support vector machine (SVM) are, for the first time, applied to this data set to support learning and classification. Different classifiers are implemented to investigate the impact of combining feature selection and classification methods. Learning classifiers implemented include K-Nearest Neighbors (KNN) and support vector machine. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for use in high dimension domains. This research also shows that feature selection helps increase computational efficiency while improving classification accuracy.
Presynaptic GABAB Receptors Regulate Hippocampal Synapses during Associative Learning in Behaving Mice

GABAB receptors are the G-protein-coupled receptors for GABA, the main inhibitory neurotransmitter in the central nervous system. Pharmacological activation of GABAB receptors regulates neurotransmission and neuronal excitability at pre- and postsynaptic sites. Electrophysiological activation of GABAB receptors in brain slices generally requires strong stimulus intensities. This raises the question as to whether behavioral stimuli are strong enough to activate GABAB receptors. Here we show that GABAB1a-/- mice, which constitutively lack presynaptic GABAB receptors at glutamatergic synapses, are impaired in their ability to acquire an operant learning task. In vivo recordings during the operant conditioning reveal a deficit in learning-dependent increases in synaptic strength at CA3-CA1 synapses. Moreover, GABAB1a-/- mice fail to synchronize neuronal activity in the CA1 area during the acquisition process. Our results support that activation of presynaptic hippocampal GABAB receptors is important for acquisition of a learning task and for learning-associated synaptic changes and network dynamics.
Frame Synchronization for Variable-Length Packets

A cognitive radio can sense its environment and adapt some of its features, such as carrier frequency, transmission bandwidth, transmission power, and modulation, thus allowing dynamic reuse of the available spectrum. Due to their high degree of adaptability to environmental variations, cognitive radios are expected to utilize packet-based transmission with variable-length frames. Packet-based transmission requires the receiver to perform frame synchronization, an important enabling step that allows adaptation in cognitive radios. However, proper metrics to characterize the performance of frame synchronization for transmission of variable-length frames are currently unavailable. To address this issue, we put forth two performance metrics, namely the expected duration to complete frame synchronization and the probability of correct acquisition within a given duration. We then develop analytical expressions for these important metrics. This paper advances our understanding of frame synchronization for the continuous transmission of variable-length frames and for bursty transmission.
Development of monotonic neuronal tuning in the monkey inferotemporal cortex through long-term learning of fine shape discrimination

Visual expertise in discriminating fine differences among a group of similar objects can be obtained through extensive long‐term training. Here we investigated the neural bases of this superior capability. The inferotemporal cortex, located at the final stage along the ventral visual pathway, was a candidate site in monkeys because cells there respond to various complex features of objects. To identify the changes that underlie the development of visual expertise in fine discrimination, we created a set of parametrically designed object stimuli and compared the stimulus selectivity of inferotemporal cells between two different training histories. One group of recordings was conducted after the monkeys had been extensively trained for fine discrimination (fine‐discrimination period) and the other after the monkeys had been exposed only for coarse discrimination (coarse‐discrimination period). We found that the tuning of responses recorded in the fine‐discrimination period was more monotonic in the stimulus parameter space. The stimuli located at the extreme in the parameter space evoked the maximum responses in a larger proportion of cells and the direction of response decrease in the parameter space was more consistent. Moreover, the stimulus arrangement reconstructed from the responses recorded during the fine‐discrimination period was more similar to the original stimulus arrangement. These results suggest that visual expertise could be based on the development, in the inferotemporal cortex, of neuronal selectivity monotonically tuned over the parameter space of the object images.
Are Accounting Standards Diversifiable ? Evidence of the Aggregate Effects of Standards

Prior accounting research documents that individual stock’s returns respond to earnings differently under new accounting standards, regulations, or changes in enforcement. We take a macro perspective and study the properties of aggregate earnings, aggregate cash flows, aggregate returns on the market portfolio. First, we document that aggregate earnings and operating income have remained relatively stable after 1951, a period with changes in accounting standards and the only discernable patterns are from SFAS 142. Second, we document that the relation between market returns and aggregate earnings appears stable over long time periods. Third, we show that the relation between aggregate accounting earnings and GDP has remained stable over time as well. Finally, we show that common earnings attributes (such as asymmetric timeliness, asymmetric earnings persistence, and predictability of cash flows), while present in firm-level analyses, disappear in the aggregate. Overall, our results lend support to accounting standards having limited effect in the aggregate from a valuation perspective. Γ We would like to thank Marc Giannoni, Amit Khandelwal, Nellie Kim, Efraim Sadka and the workshop participants of the four-school conference and University of Colorado at Boulder for valuable comments and suggestions. Any errors are our own. ∗ Bjorn is from University of Colorado at Boulder and Jing and Gil are from Columbia University. The authors’ emails are bjorn.jorgensen@colorado.edu, jl2491@columbia.edu, and gs2235@columbia.edu.
Towards Automatic Interpolation for Real and Distant Image Pairs

Image-based rendering offers the advantage of being able to provide realistic output and at the same time to avoid the difficult problem of a complete geometric and photometric modeling of the real world. The method described here is able to deal with non rigid scenes and large camera motions. We present in this report a three step algorithm for the interpolation of two views of a scene, from which we can for instance simulated a camera motion withing the given scene. The first step establishes pixel correspondenc- es between the images and is the most difficult part. We justify the choice of region-growing based dense matching methods and we summarize their principle. Secondly, a robust algorithm converts these pixel correspondences to an adequate structure for the last step: image interpolation. This structure encompasses the transformation between the images using constrained and dependent triangulations in both of them, and handles the half-occluded areas. The implementation of the whole process is outlined and the process is demonstrated on real images.
Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression

The development of codon bias indices (CBIs) remains an active field of research due to their myriad applications in computational biology. Recently, the relative codon usage bias (RCBS) was introduced as a novel CBI able to estimate codon bias without using a reference set. The results of this new index when applied to Escherichia coli and Saccharomyces cerevisiae led the authors of the original publications to conclude that natural selection favours higher expression and enhanced codon usage optimization in short genes. Here, we show that this conclusion was flawed and based on the systematic oversight of an intrinsic bias for short sequences in the RCBS index and of biases in the small data sets used for validation in E. coli. Furthermore, we reveal that how the RCBS can be corrected to produce useful results and how its underlying principle, which we here term relative codon adaptation (RCA), can be made into a powerful reference-set-based index that directly takes into account the genomic base composition. Finally, we show that RCA outperforms the codon adaptation index (CAI) as a predictor of gene expression when operating on the CAI reference set and that this improvement is significantly larger when analysing genomes with high mutational bias.
Retention and mortality outcomes from a community-supported public–private HIV treatment programme in Myanmar

There is a growing interest in the potential contribution the private sector can make towards increasing access to antiretroviral therapy (ART) in low‐ and middle‐income settings. This article describes a public–private partnership that was developed to expand HIV care capacity in Yangon, Myanmar. The partnership was between private sector general practitioners (GPs) and a community‐based non‐governmental organization (International HIV/AIDS Alliance).
Knowledge Representation and Inductive Learning with XML

This paper presents a novel knowledge representation method and learning system for XML documents. The traditional machine learning methods which use attribute-value languages are not suitable for representing XML documents due to their complex structures. In this paper, we propose a decision-tree algorithm for XML learning, which is based on a rich representation language for structured data and driven by precision/recall heuristic.
Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.