Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence diversity and large-scale typing of SNPs in the human apolipoprotein E gene.

A common strategy for genotyping large samples begins with the characterization of human single nucleotide polymorphisms (SNPs) by sequencing candidate regions in a small sample for SNP discovery. This is usually followed by typing in a large sample those sites observed to vary in a smaller sample. We present results from a systematic investigation of variation at the human apolipoprotein E locus (APOE), as well as the evaluation of the two-tiered sampling strategy based on these data. We sequenced 5.5 kb spanning the entire APOE genomic region in a core sample of 72 individuals, including 24 each of African-Americans from Jackson, Mississippi; European-Americans from Rochester, Minnesota; and Europeans from North Karelia, Finland. This sequence survey detected 21 SNPs and 1 multiallelic indel, 14 of which had not been previously reported. Alleles varied in relative frequency among the populations, and 10 sites were polymorphic in only a single population sample. Oligonucleotide ligation assays (OLA) were developed for 20 of these sites (omitting the indel and a closely-linked SNP). These were then scored in 2179 individuals sampled from the same three populations (n = 843, 884, and 452, respectively). Relative allele frequencies were generally consistent with estimates from the core sample, although variation was found in some populations in the larger sample at SNPs that were monomorphic in the corresponding smaller core sample. Site variation in the larger samples showed no systematic deviation from Hardy-Weinberg expectation. The large OLA sample clearly showed that variation in many, but not all, of OLA-typed SNPs is significantly correlated with the classical protein-coding variants, implying that there may be important substructure within the classical epsilon 2, epsilon 3, and epsilon 4 alleles. Comparison of the levels and patterns of polymorphism in the core samples with those estimated for the OLA-typed samples shows how nucleotide diversity is underestimated when only a subset of sites are typed and underscores the importance of adequate population sampling at the polymorphism discovery stage. [The sequence data described in this paper have been submitted to the GenBank data library under accession no. AF261279. ]
Essentials of financial risk management

Preface. Acknowledgments. 1. What Is Financial Risk Management? 2. Identifying Major Financial Risks. 3. Interest Rate Risk. 4. Foreign Exchange Risk. 5. Credit Risk. 6. Commodity Risk. 7. Operational Risk. 8. Risk Management Framework: Policy and Hedging. 9. Measuring Risk. 10. Global Initiatives in Financial Risk Management. Appendix. Index.
The role of liver biopsy in chronic hepatitis C

Hepatitis C virus (HCV) is a major cause of chronic liver disease worldwide, affecting 175 million people globally. Over 80% of acutely infected patients go on to develop chronicity, but only 20% to 25% will develop end‐stage liver disease and its complications. The sequelae of HCV‐induced chronic liver disease accounts for 8,000 to 10,000 deaths annually in the United States and is currently the leading indication for liver transplantation. To date, there are no accurate noninvasive markers of disease activity and fibrosis. Liver biopsy is indicated to exclude other forms of liver pathologies and to establish the stage of liver disease. In this study, the role of liver biopsy in chronic hepatitis C was evaluated. Additionally, we calculated a discriminant score to predict cirrhosis in chronic hepatitis C infection. Our results showed that additional diagnoses or unsuspected diagnoses are less frequent than clinicians' suspected. We confirmed that the discriminant score for predicting cirrhosis is inferior to liver biopsy. In conclusion, the majority of patients with chronic hepatitis C will require a liver biopsy, which has an important implication on staging of the liver disease, prognosis, and possibly further management options.
oct4‐EGFP reporter gene expression marks the stem cells in embryonic development and in adult gonads of transgenic medaka

Maintenance of pluripotency in stem cells is tightly regulated among vertebrates. One of the key genes in this process is oct4, also referred to as pou5f1 in mammals and pou2 in teleosts. Pou5f1 evolved by duplication of pou2 early in the tetrapod lineage, but only monotremes and marsupials retained both genes. Either pou2 or pou5f1 was lost from the genomes of the other tetrapods that have been analyzed to date. Consequently, these two homologous genes are often designated oct4 in functional studies. In most vertebrates oct4 is expressed in pluripotent cells of the early embryo until the blastula stage, and later persist in germline stem cells until adulthood. The isolation and analysis of stem cells from embryo or adult individuals is hampered by the need for reliable markers that can identify and define the cell populations. Here, we report the faithful expression of EGFP under the control of endogenous pou2/oct4 promoters in transgenic medaka (Oryzias latipes). In vivo imaging in oct4‐EGFP transgenic medaka reveals the temporal and spatial expression of pou2 in embryos and adults alike. We describe the temporal and spatial patterns of endogenous pou2 and oct4‐EGFP expression in medaka with respect to germline and adult stem cells, and discuss applications of oct4‐EGFP transgenic medaka in reproductive and stem cell biology. Mol. Reprod. Dev. 80: 48–58, 2013. © 2012 Wiley Periodicals, Inc.
Precision mapping of the vibrissa representation within murine primary somatosensory cortex

The ability to form an accurate map of sensory input to the brain is an essential aspect of interpreting functional brain signals. Here, we consider the somatotopic map of vibrissa-based touch in the primary somatosensory (vS1) cortex of mice. The vibrissae are represented by a Manhattan-like grid of columnar structures that are separated by inter-digitating septa. The development, dynamics and plasticity of this organization is widely used as a model system. Yet, the exact anatomical position of this organization within the vS1 cortex varies between individual mice. Targeting of a particular column in vivo therefore requires prior mapping of the activated cortical region, for instance by imaging the evoked intrinsic optical signal (eIOS) during vibrissa stimulation. Here, we describe a procedure for constructing a complete somatotopic map of the vibrissa representation in the vS1 cortex using eIOS. This enables precise targeting of individual cortical columns. We found, using C57BL/6 mice, that although the precise location of the columnar field varies between animals, the relative spatial arrangement of the columns is highly preserved. This finding enables us to construct a canonical somatotopic map of the vibrissae in the vS1 cortex. In particular, the position of any column, in absolute anatomical coordinates, can be established with near certainty when the functional representations in the vS1 cortex for as few as two vibrissae have been mapped with eIOS. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.
Object-oriented software metrics - a practical guide

I. INTRODUCTION. II. OO SOFTWARE METRICS. Project Metrics. Average number of support classes per key class.Average man-days per application class.Average number of classes per developer.Number of major iterations.Number of subsystems.Number of contracts completed.Design Metrics. Average method size.Average number of methods per class.Average number of instance variables per class.Class hierarchy nesting level.Method complexity.Number of methods overridden, inherited, and added by a subclass.Global usage.Instance variable usage.Average number of comment lines per class/method.Number of problem reports per class or contract.Number of times a class is reused.Number of classes/methods thrown away. III. APPENDICES. Project Experience Database. SmallTalk Projects.C++ Projects.Metrics Form. Metrics Tools.
Modelling uncertainty in touch interaction

Touch interaction is an increasingly ubiquitous input modality on modern devices. It appears on devices including phones, tablets, smartwatches and even some recent laptops. Despite its popularity, touch as an input technology suffers from a high level of measurement uncertainty. This stems from issues such as the ‘fat finger problem’, where the soft pad of the finger creates an ambiguous contact region with the screen that must be approximated by a single touch point. In addition to these physical uncertainties, there are issues of uncertainty of intent when the user is unsure of the goal of a touch. Perhaps the most common example is when typing a word, the user may be unsure of the spelling leading to touches on the wrong keys. The uncertainty of touch leads to an offset between the user’s intended target and the touch position recorded by the device. While numerous models have been proposed to model and correct for these offsets, existing techniques in general have assumed that the offset is a deterministic function of the input. We observe that this is not the case — touch also exhibits a random component. We propose in this dissertation that this property makes touch an excellent target for analysis using probabilistic techniques from machine learning. These techniques allow us to quantify the uncertainty expressed by a given touch, and the core assertion of our work is that this allows useful improvements to touch interaction to be obtained. We show this through a number of studies. In Chapter 4, we apply Gaussian Process regression to the touch offset problem, producing models which allow very accurate selection of small targets. In the process, we observe that offsets are both highly non-linear and highly user-specific. In Chapter 5, we make use of the predictive uncertainty of the GP model when applied to a soft keyboard — this allows us to obtain key press probabilities which we combine with a language model to perform autocorrection. In Chapter 6, we introduce an extension to this framework in which users are given direct control over the level of uncertainty they express. We show that not only can users control such a system succesfully, they can use it to improve their performance when typing words not known to the language model. Finally, in Chapter 7 we show that users’ touch behaviour is significantly different across different tasks, particularly for typing compared to pointing tasks. We use this to motivate an investigation of the use of a sparse regression algorithm, the Relevance Vector Machine, to train offset models using small amounts of data.
Metadata Extraction and Management in Data LakesWith GEMMS

In addition to volume and velocity, Big data is also characterized by its variety. Variety in structure and semantics requires new integration approaches which can resolve the integration challenges also for large volumes of data. Data lakes should reduce the upfront integration costs and provide a more flexible way for data integration and analysis, as source data is loaded in its original structure to the data lake repository. Some syntactic transformation might be applied to enable access to the data in one common repository; however, a deep semantic integration is done only after the initial loading of the data into the data lake. Thereby, data is easily made available and can be restructured, aggregated, and transformed as required by later applications. Metadata management is a crucial component in a data lake, as the source data needs to be described by metadata to capture its semantics. We developed a Generic and Extensible Metadata Management System for data lakes (called GEMMS) that aims at the automatic extraction of metadata from a wide variety of data sources. Furthermore, the metadata is managed in an extensible metamodel that distinguishes structural and semantical metadata. The use case applied for evaluation is from the life science domain where the data is often stored only in files which hinders data access and efficient querying. The GEMMS framework has been proven to be useful in this domain. Especially, the extensibility and flexibility of the framework are important, as data and metadata structures in scientific experiments cannot be defined a priori .
How Does Adoption of the Outlet Channel Impact Customers' Spending in the Retail Stores: Conflict or Synergy?

We investigate how adoption of a retailer’s factory outlet channel impacts customers’ spending in the retailer’s traditional retail store channel. In recent years, many retailers have added exclusively sourced factory outlet stores into their channel mix to achieve market expansion and customer segmentation. However, the impact of adoption of this lower-quality, lower-price alternative channel on spending at the retailer’s higher-quality, higher-price retail store channel is not clear. Customers adopting the outlet channel might increase their spending in the retail store channel because of the opportunity to become familiar with the brand at a lower price point or transfer of positive associations formed through patronage of the outlet channel to the store channel. Customers adopting the lower-quality channel might also decrease their spending in the retail store channel because of brand dilution or cannibalization. To investigate this issue, we use a unique individual-level panel data set from a specialty apparel retailer from a period during which the retailer opened many factory outlet stores. This allows us to study how purchase behavior changes after customers adopt the outlet channel while carefully controlling for alternative explanations including customer heterogeneity and selection effects. We find that although customers who adopt the outlet channel spend less with the retailer compared to store-only customers, the difference cannot be attributed to the impact of adoption of the outlet channel. After controlling for heterogeneity and selection effects, we uncover a positive spillover to the retail store channel from adoption of the outlet channel. Customers who adopt the outlet channel not only make incremental purchases at the outlet channel, but also increase their spending in the retail store channel after adoption. The increase in spending is due to more frequent retail store purchases and not to larger per-purchase expenditure. This paper was accepted by J. Miguel Villas-Boas, marketing .
Predicting incorrect mappings: a data-driven approach applied to DBpedia

DBpedia releases consist of more than 70 multilingual datasets that cover data extracted from different language-specific Wikipedia instances. The data extracted from those Wikipedia instances are transformed into RDF using mappings created by the DBpedia community. Nevertheless, not all the mappings are correct and consistent across all the distinct language-specific DBpedia datasets. As these incorrect mappings are spread in a large number of mappings, it is not feasible to inspect all such mappings manually to ensure their correctness. Thus, the goal of this work is to propose a data-driven method to detect incorrect mappings automatically by analyzing the information from both instance data as well as ontological axioms. We propose a machine learning based approach to building a predictive model which can detect incorrect mappings. We have evaluated different supervised classification algorithms for this task and our best model achieves 93% accuracy. These results help us to detect incorrect mappings and achieve a high-quality DBpedia.
TrafficShaper: Shaping Inter-Datacenter Traffic to Reduce the Transmission Cost

The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet service providers with the widely adopted <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula><italic>th percentile charging model</italic>. In such a charging model, the time slots with top (<inline-formula> <tex-math notation="LaTeX">$100-q$ </tex-math></inline-formula>) percent of data transmission do not affect the total transmission cost and can be viewed as <italic>“free. ”</italic> This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, a very little work has been done to exploit those <italic>“free”</italic> time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to <italic>“free”</italic> time slots, or inevitably relies on prior knowledge of future traffic arrival patterns. In this paper, we present <italic>TrafficShaper</italic>, a new scheduler that shapes the inter-DC traffic to exploit the <italic>“free”</italic> time slots involved in the <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula><italic>th percentile charging model</italic>, so as to reduce or even minimize the transmission cost. When shaping traffic, <italic>TrafficShaper</italic> advocates a simple principle: <italic>more traffic peaks</italic> should be scheduled in <italic>“free”</italic> time slots, while <italic>less traffic differentiation</italic> should be maintained among the remaining time slots. To this end, <italic>TrafficShaper</italic> designs a pricing-aware control framework, which makes online decisions for inter-DC transfers without requiring a prior knowledge of traffic arrivals. To verify the performance of <italic>TrafficShaper</italic>, we conduct rigorous theoretical analysis based on <italic>Lyapunov optimization</italic> techniques, large-scale trace-driven simulations, and small-scale testbed implementation. Results from rigorous mathematical analyses demonstrate that <italic>TrafficShaper</italic> can make the transmission cost arbitrarily close to the optimum value. Extensive trace-driven simulation results show that <italic>TrafficShaper</italic> can reduce the transmission cost by up to 40.23%, compared with the state-of-the-art solutions. The testbed experiments further verify that <italic>TrafficShaper</italic> can realistically reduce the transmission cost by up to 19.38%.
Differentiation-Dependent Regulation of Human Endogenous Retrovirus K Sequences and Neighboring Genes in Germ Cell Tumor Cells

Under physiological conditions, most human endogenous retroviruses (HERVs) are transcriptionally silent. However, re-activation of HERVs is observed under pathological conditions like inflammation or cancer. In addition to expression of HERV sequences, an impact of HERV-loci on expression of adjacent genes has been suggested as probably important patho-physiological mechanism. A candidate for such a gene is PRODH (proline dehydrogenase 1), which is located on chromosome 22 adjacent to HERVK-24. Germ cell tumors (GCTs) are known to express high level of HERVK sequences. In addition, non-seminomatous GCT are useful models to study HERV expression in the context of differentiation since they reflect aspects of cellular development during embryogenesis and usually contain different cell types. This is due to the embryonal carcinoma (EC) cells, which are the stem cell component of GCT. They are pluripotent, show high expression of pluripotency markers like OCT4 and LIN28A and can differentiate into either somatic derivatives (teratoma cells) or choriocarcinoma or yolk-sac tumor cells reflecting extra-embryonal differentiation. OCT4 is lost upon differentiation. We used GCT derived cell lines of varying differentiation stages to analyze expression of HERVK and PRODH. Differentiation status and cellular relationship of GCT cells was determined using microarray analysis and western blotting of the embryonic pluripotency markers OCT4 and LIN28A. The highest expression of HERVK was found in undifferentiated EC cells, which retain a stem cell phenotype and express both OCT4 and LIN28. In contrast, the lowest expression of HERVK was observed in somatic differentiated GCT cells which also lack OCT4 and LIN28A whereas GCT cells with differentiation characteristics of yolk-sac tumor expressed LIN28A but not OCT4 and showed intermediate level of HERVK. A similar pattern was found for PRODH. Differentiation of EC cells by siRNA mediated knock-down of OCT4 or treatment with differentiation inducing medium decreased expression of HERVK and PRODH. Treatment of differentiated GCT cells with 5′-azacytidine and trichostatin A increased expression of HERVK and PRODH, indicating that epigenetic mechanisms are responsible for altered expression of these genes. Our data suggest that HERVK expression is dependent on cellular differentiation stages regulated by epigenetic mechanisms, which can also affect expression of neighboring genes.
CONTEXTUAL VARIABILITY MANAGEMENT OF IOT APPLICATION WITH XML-BASED FEATURE MODELLING

Internet of Things (IoT) can be used anywhere, anytime and with any device. IoT devices are used broadly in recent industry such as health care, transport, education and communication channels. IoT applications make enable the use of IoT systems in different environment such as heat sensor functionality varies for indoor and outdoor, water sensors in automobile for screen viper movement (auto working during rain and stop if there is no raining), heartbeat sensors for old and young patients. Moreover, IoT devices are same but the operating functionality is different according to environment and end user’s perspective. Therefore, this kind of variability management is very important for reusability of resources in multiple environments. However, variability management is difficult task due to the dependency of resources, relationships between them and compatibility issues. Feature Modeling of Software Product Line (SPL) is extensively used to manage the variability management for family of software that share common resources and varies in different points. In this paper we have proposed simple and effective approach for variability management of IoT-based applications by using feature modeling. We have proposed XML-based feature modeling for IoT-based applications for variability management and increase the reusability of existing resources. Our approach defines the complete package from domain modeling to application development by using XML-schema and XSLT files to select and deselect the features for final application development. Our final architecture of IoT-based feature model shows the complete relationships and include exclude associations between them without any constraint violations.
Mathematics Education and Language: Interpreting Hermeneutics and Post-Structuralism

Acknowledgements. About the Author. Introduction. Short Review of Recent Research. 1. Hermeneutics and Mathematics Education. 2. The Production of Mathematical Meaning: A Post-Structuralist Perspective. 3. Sharing Mathematical Perspectives. 4. Some Lessons. 5. The Phenomenology of the Mathematics Classroom. 6. Teacher-Student Interactions. 7. Narratives of Learning Mathematics. 8. Mathematics and Language. 9. Developing Teacher Practice. 10. Mathematical Cultures. 11. The Social Constitution of Mathematics. References. Name Index. Subject Index.