Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Proteomics Analysis of Placenta from Pregnant Women with Intrahepatic Cholestasis of Pregnancy

Introduction Intrahepatic cholestasis of pregnancy (ICP) usually occurs in the third trimester and associated with increased risks in fetal complications. Currently, the exact cause of this disease is unknown. In this study we aim to investigate the potential proteins in placenta, which may participate in the molecular mechanisms of ICP-related fetal complications using iTRAQ-based proteomics approach. Methods The iTRAQ analysis combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to separate differentially expressed placental proteins from 4 pregnant women with ICP and 4 healthy pregnant women. Bioinformatics analysis was used to find the relative processes that these differentially expressed proteins were involved in. Three apoptosis related proteins ERp29, PRDX6 and MPO that resulted from iTRAQ-based proteomics were further verified in placenta by Western blotting and immunohistochemistry. Placental apoptosis was also detected by TUNEL assay. Results Proteomics results showed there were 38 differentially expressed proteins from pregnant women with ICP and healthy pregnant women, 29 were upregulated and 9 were downregulated in placenta from pregnant women with ICP. Bioinformatics analysis showed most of the identified proteins was functionally related to specific cell processes, including apoptosis, oxidative stress, lipid metabolism. The expression levels of ERp29, PRDX6 and MPO were consistent with the proteomics data. The apoptosis index in placenta from ICP patients was significantly increased. Conclusion This preliminary work provides a better understanding of the proteomic alterations of placenta from pregnant women with ICP and may provide us some new insights into the pathophysiology and potential novel treatment targets for ICP.
Prospects for Gene Therapy for CNS Disease

Substantial progress has been made over the last 20 years in understanding the basic biology and function of the normal nervous system, and in elucidating molecular and cellular mechanisms that underlie neurological disease. Together with these advances in understanding have come discoveries of novel genes and proteins, which collectively present an unprecedented opportunity to intervene in and treat a number of neurological disorders that heretofore have been untreatable.
A dual-light reporter system to determine the efficiency of protein–protein interactions in mammalian cells

Methods for determining protein–protein interactions in mammalian cells typically rely on single reporter functions and are susceptible to variations between samples particularly in regard to levels of transcription, processing and translation. A method has been developed for determining protein–protein interactions in mammalian cells, which bypasses these variables confounding single reporter assays. The approach utilizes two units of gene expression linked to reporter functions that are interposed by a deactivation–activation unit in such a way that the downstream expression unit is switched off. Hence upstream expression occurs regardless of protein–protein interaction, leading to the production of the upstream reporter. In the event of protein–protein interactions, the downstream expression unit is switched on leading to dual reporter read outs. Thus, the ratio of the two reporter activities provides a measure to determine the efficiency of protein–protein interactions. To access the system we screened a mutant of BMPR2 where the interaction between BMPR-II and LIMK is abrogated. BMPR-II is a type II receptor of the TGFβ superfamily and plays a key role in the pathogenesis of familial pulmonary arterial hypertension. This system has potential for high-throughput screening of libraries (peptide, chemical, cDNA, etc.) to isolate agents that are capable of interfering with highly selective protein–protein interaction.
Sparse multipath MIMO channels: Performance implications based on measurement data

The main focus of the present contribution is on the statistical assessment of Multiple-Input Multiple-Output (MIMO) systems from the viewpoint of underlying multipath sparsity. In contrast to the implicit assumption of rich multipath in the prevalent i.i. d. MIMO fading model, recent investigations and measurement campaigns have shown that physical channels exhibit a sparse multipath structure that in turn leads to spatial correlation. Using a set of measured data and a wellknown analytical model, the measured MIMO channel matrix is decomposed in terms of fixed virtual transmit and receive angles, thereby providing a simple geometric interpretation of the scattering environment. It is demonstrated that the presence of direct or obstructed Line-of-Sight (LoS) components strongly correlates with channel sparsity; consequently, the number of spatial degrees of freedom (DoF) is severely limited compared to that in the i.i. d. model. In the sequel, we investigate the relationship between the channel sparsity structure and the mean of propagation paths and quantify the impact of channel sparsity on two key MIMO channel metrics: ergodic capacity and diversity level. In all cases, the analysis of measured data reveals an approximate concave quadratic variation in capacity, diversity and mean number of paths as a function of the number of DoF, thereby suggesting a fundamental inter-dependency between all these features.
Semantic Malware Detection

Polymorphic and metamorphic malware use code obfuscation techniques to construct new variants which preserve the semantics of the original but change the code syntax, evading current compiled code based detection methods. Dynamic slicing is a technique that, given a variable of interest within a program, isolates a relevant subset of executed program code that influences that variable. Using dynamic slicing to condition semantic traces identifies ‘core’ behaviours that, as part of an overall semantics based approach, has the potential to play a significant rˆole in detecting difficult malware variants. We preface this by a discussion of the motivation and the contextual role for this form of slicing in semantics based matching. A brief outline of the semantic trace mapping algorithm is presented with an example. We complete the report with presentation of our test data generation technique using backward domain reduction with some examples as a stand-alone step in the process of genearting data inputs for producing unique semantic program traces.
High-Dimensional Feature Descriptors to Characterize Volumetric Data

Volumetric data offer considerably more low-level information than the density and gradient values traditionally employed in transfer function-guided volume visualization. We propose the use of a richer set of low-level feature descriptors to allow a more sensitive and refined characterization of the data. The ensemble of these feature descriptors then forms a distinct data signature that can describe, classify, and categorize a dataset at levels of detail.
Bittersweet ambivalence: Austin’s street artists speak of gentrification

ABSTRACT Both gentrification and street art are concerned with the conquest of urban space. Although historically, graffiti and street art have functioned to challenge the status quo, a growing appreciation for urban art unveils a far more collaborative attitude between some street artists and the elite. A familiar esthetic of gentrified terrains involves repurposing spaces that capture the urban experience. In some cities, urban redevelopment preserves original materials that capture that “urban feel” by highlighting exposed brick structures, rustic furnishings, industrial lofts, and urban art. In other cities, these styles are recreated consciously. This paper draws from in-depth interviews with street artists from Austin, Texas, one of the fastest growing urban landscapes in the U.S., to discuss street artists’ attitudes towards gentrification. Its examination of stories and personal narratives about gentrification shows the complexities of rapid urban expansion as perceived by Austin street artists, and concludes that street artists remain ambivalent towards gentrification. While street artists experience some negative effects resulting from gentrification, urban redevelopment also has another clear benefit for them: an expansion of their urban canvas. The growth of city space extends street artists’ creative playground, which advances the artists’ opportunities for paid work and exposure.
The concept maps method as a tool to evaluate the usability of APIs

Application programming interfaces (APIs) are the interfaces to existing code structures, such as widgets, frameworks, or toolkits. Therefore, they very much do have an impact on the quality of the resulting system. So, ensuring that developers can make the most out of them is an important challenge. However standard usability evaluation methods as known from HCI have limitations in grasping the interaction between developer and API as most IDEs (essentially the GUI) capture only part of it. In this paper we present the Concept Map method to study the usability of an API over time. This allows us to elicit the mental model of a programmer when using an API and thereby identify usability issues and learning barriers and their development over time.
Analysis and approximation of a strain-limiting nonlinear elastic model

Elastic solids with strain-limiting response to external loading represent an interesting class of material models, capable of describing stress concentration at strains with small magnitude. A theoretical justification of this class of models comes naturally from implicit constitutive theory. We investigate mathematical properties of static deformations for such strain-limiting nonlinear models. Focusing on the spatially periodic setting, we obtain results concerning existence, uniqueness and regularity of weak solutions, and existence of renormalized solutions for the full range of the positive scalar parameter featuring in the model. These solutions are constructed via a Fourier spectral method. We formulate a sufficient condition for ensuring that a renormalized solution is in fact a weak solution, and we comment on the extension of the analysis to nonperiodic boundary-value problems.
Generic Uniqueness and Continuity of Rationalizable Strategies

For a finite set of actions and a rich set of fundamentals, consider the rationalizable actions on a universal type space, endowed with the usual product topology. (1) Generically, there exists a unique rationalizable action profile. (2) Every model can be approximately embedded in a dominance-solvable model. (3) A rationalizable strategy is continuous at a finite type if and only if there is a unique rationalizable action for that type.
WAVELET PACKET TRANSFORM FEATURES WITH APPLICATION TOSPEAKER

This study proposes a new set of feature parameters based on wavelet packet transform analysis of the speech signal. The new speech features are named subband based cepstral parameters (SBC) and wavelet packet parameters (WPP). The ability of each parameter set to capture speaker identity conveyed in the speech signal is compared to the widely used Mel-frequency cepstral coee-cents (MFCC). The proposed parametrization methods are shown to achieve 48% and 67% reduction in relative error over MFCC for 630 and 168 speakers, respectively using the TIMIT (downsampled to 8 kHz) database.
Large-Scale Performance Evaluation of the IETF Internet of Things Protocol Suite for Smart City Solutions

The Internet of Things (IoT) intends to interconnect massive amount of heterogeneous, smart devices, with the goal of interweaving the virtual world with the physical world. Smart Cities are typical IoT application domains, comprising networks with large number of sensors that survey environmental data in order to provide different services, such as City Mobility solutions that facilitate on-street parking and traffic flow monitoring. Standards and specifications defined by the Internet Engineering Task Force (IETF), like IPv6, the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) adaptation layer, and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) are cornerstones of the IoT. In this paper we carry out large-scale experimental evaluations in an IoT testbed of 251 nodes to analyze the performance of the IETF IoT protocol suite in such a large-scale network. We define a City Mobility Solution application, using traces from a commercial Smart City deployment and the commonly employed Contiki implementation of IETF IoT protocol suite. The results show that the out of the box Contiki IoT protocol stack is not capable of delivering a satisfying performance. However, after a thorough analysis of the initial results, a set of improved parameter configurations is derived that allows the network to achieve much higher performance. Among others, improvements of 60.39% in PDR and 63.67% in delay are achieved. Furthermore, the paper presents and discusses the technical solutions and best-practice guidelines for a specific City Mobility solution being developed by Urbiotica, a company with ample expertise in Smart City deployments.
Small-scale structural reengineering of software

Programmers often face the difficulty of having to make small-scale structural design decisions early in the software process, well before the consequences of those decisions can be understood. For example, what is the right balance between efficiency and modularity for a particular bit of code? Should an integrity check be located at the callee site or at all the caller sites? When should copying be done to assure the structural integrity of a private data structure in an encapsulation? In practice, once structural commitments are made, they can be very difficult to revise. But this brittleness is not a necessary attribute of software, and we suggest that semantics-based program analysis and manipulation techniques can offer a way for programmers to retain structural flexibility. This short paper illustrates several cases where structural manipulations support reengineering and enable deferal or revision of structural decisions. These include a year-2000 scenario and a summary of a larger derivation of the two Java base classes for mutable and immutable strings from simple C “strings. ” A principal open issue is whether analysis and manipulation techniques such as these, which operate on small-scale architectural features, can effectively scale up to larger aggregations and major software architectural decisions.
Genetic Operators with Dynamic Biases that Operate on Attribute Grammar Representations of Neural Networks

Grammar-based representations of neural networks have shown promise in advancing the study of the evolutionary optimization of neural networks (Yao, 1993; Gruau, 1995; Hussain and Browse, 1998). Our research on the Network Generating Attribute Grammar Encoding (NGAGE) technique has demonstrated that attribute grammars may be used successfully in representing and exploring a space of neural networks (Browse, Hussain and Smillie, 1999). In addition to offering the capability of representing a wide variety of neural network models, NGAGE also offers the potential of designing meaningful dynamic genetic operators. In this paper, we present two reproduction operators that perform a biased offspring creation, and use knowledge of the grammar representation to adapt those biases in response to fitness measurements.
First Workshop on Language Resources and Technologies for Turkic Languages Workshop Programme 14 : 00 – 14 : 10 Welcome 14

In this paper we report on the preliminary findings of our ongoing study on Turkish referring expressions used in situated dialogs. Situated dialogs of pairs of Turkish speakers were collected while they were engaged with a collaborative Tangram puzzle solving task, which was designed by Spanger et al (2011) in an effort to build a corpus of referring expressions in Japanese and English. The paper provides our preliminary results on the Turkish corpus and compares them with the findings of comparable studies conducted on Japanese and English referring expressions.
Implication of creatinine and gut flora in the uremic syndrome: induction of "creatininase" in colon contents of the rat by dietary creatinine.

"Creatininase" activity was induced in the colon flora of rats by feeding creatinine. Sarcosine and methylamine were tentatively identified as the major products of the enzymic action. A third (unidentified) compound was observed. These compounds and methylguanidine were labeled in the urine of rats given 14CH3-creatinine orally. The results suggest that creatinine should not be considered biologically inert in animals, especially in those with decreased renal function.
Towards Optimal Learning from Very Small Data Sets

The problem of generalization in neural networks, that is, how well a network will perform on unseen data, is has received much attention recently. In this paper we present an approach to generating learning algorithms which have the potential to generalize from very small training sets. We believe that in this paper outlines a new, although potentially computationally expensive, approach to optimal learning in Neural Networks.
Learning from Examples and Side Information

We set up a theoretical framework for learning from examples and side information which enables us to compute the tradeoo between the sample complexity and information complexity for learning a target function in a Sobolev functional class F. We use the notion of the n th minimal radius of information of Traub et. al. 23] and combine it with VC-theory to deene a new quantity I n;d (F) which measures the minimal approximation error of a target g 2 F by the family of function classes with pseudo-dimension d under a given side information which consists of any n measurements on the target function g constrained to being linear operators. By obtaining almost tight upper and lower bounds on I n;d (F) we nd an information operator ^ Nn which yields a worst-case error no larger than a logarithmic factor in n and d than the lower bound on I n;d (F). Hence to within a logarithmic factor it is the most eecient way of providing side information about a target g under the constraint that the information operator must be linear and that the approximating class has pseudo-dimension d.
Hybrid OWL-S Service Retrieval with OWLS-MX: Benefits and Pitfalls

The OWLS-MX matchmaker selects OWL-S 1.1 services that are relevant to a given service request by means of combined logic based and approximative matching filters [5]. In this paper, we build upon this work and analyse its retrieval performance in terms of false positive and false negatives to reveal the benefits and pitfalls of both logic based and hybrid matching with OWLS-MX. The analysis results have been exploited in an improved version OWLS-MX+.
Toxic Affect: Are Anger, Anxiety, and Depression Independent Risk Factors for Cardiovascular Disease?

Three negative affective dispositions—anger, anxiety, and depression—are hypothesized to increase physical disease risk and have been the subject of epidemiological studies. However, the overlap among the major negative affective dispositions, and the superordinate construct of trait negative affectivity (NA) are only beginning to be tested. Presented here is a narrative review of recent prospective studies that simultaneously tested anger, anxiety, depression, and trait NA as risk factors for cardiac outcomes. Anxiety and depression emerged as independent risk factors for premature heart disease in population studies of persons nominally healthy at baseline, and for recurrence/mortality among patients with existing heart disease. General trait NA also was a cardiac risk factor in population samples.
Fixed Income Pricing

This chapter surveys the literature on fixed-income pricing models, including dynamic term-structure models, and interest-rate sensitive, derivative pricing models. Our overview of conceptual approaches highlights the tradeoffs that have emerged between the complexity of the probability model for the "risk factors[equal, rising dots], data availability, the pricing objective, and the tractability of the resulting pricing model. Initially, we examine term-structure models that price both bonds (default-free and defaultable) and fixed-income derivatives with payoffs in terms of prices or yields on these bonds. These include affine, quadratic-Gaussian, and various stochastic volatility models of the term structure. Then we turn to models designed to price fixed-income derivatives, taking the current yield curve as an input into the pricing framework. These include models based on forward rates and the LIBOR and Swaption Market models.
Differential item functioning detection with logistic regression

La regression logistique a ete utilisee comme une methode d’identification du DIF dans differents contextes. Certaines etudes ont montre que cette procedure peut etre affectee par des variables comme le ratio des tailles entre groupes, la taille de l’echantillon, et qu’elle semble liee avec les gammes de difficulte et la discrimination des items [Herrera, 2005 ; Santana 2009]. Nous avons fait une etude de simulation avec quatre variables independantes partiellement traversees qui ont abouti a 270 conditions et simule 200 repliques pour chacune d’elles. La difference des McFadden R2 (R2∆) entre modeles a ete utilisee comme une mesure de la taille de l’effet et comme variable dependante afin de minimiser les taux de faux positifs et negatifs que le test statistique n’aurait pas ete en mesure de controler. Nous avons utilise des modeles lineaires pour definir les variables qui affectent les mesures de la taille de l’effet : R2∆ pour la detection des items avec du DIF uniforme (DRU) et R2∆ pour detecter les items avec du DIF non uniforme (DRN). Les resultats montrent que les variables manipulees et leurs interactions affectent de differentes manieres le DRU et le DRN. Nous avons egalement obtenu des seuils pour les variables dependantes, aussi bien pour DRU que pour DRN, pour plusieurs niveaux des variables en jeu.
Privacy-Preserving Bayesian Network for Horizontally Partitioned Data

Construction of learning structures for Bayesian Networks is considered in this work when data is securely maintained by different parties, not willing to reveal their individual private data to each other. We propose a privacy-preserving protocol for Bayesian Network from data which ishomogeneously partitioned among two or more parties by usingK2 algorithm, a heuristic algorithm typically used to construct Bayesian Network. Three secure building blocks are also presented to use inside the main protocol; Secure Exponentiation, Secure Multi-party Factorial and Secure Product Comparison. We have also modified two existing building blocks which are used in this paper, Secure Multi-Party Addition and Multiplication, to improve their resistance against colluding attack. These protocols have the added advantage that they can even be used over public channels. That is, channels over which any party is able to see any messages exchanged between any two or more parties.