Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydroxychloroquine reduces risk of incident diabetes mellitus in lupus patients in a dose-dependent manner: a population-based cohort study.

OBJECTIVE SLE is associated with increased risk of diabetes mellitus. Treatment for SLE requires high-dose glucocorticoids that may worsen glucose homoeostasis. HCQ can reduce diabetes risk in RA. This study aimed to investigate the association of HCQ use and diabetes mellitus risk in SLE patients. METHODS This nationwide, population-based cohort study was conducted using the Taiwan National Health Insurance Research Database. In the period 2001-10, 8628 newly diagnosed SLE patients were identified after excluding those with a previous diagnosis of RA, psoriasis or diabetes mellitus. Incidence of diabetes mellitus was identified as a new diagnostic code using a diabetes mellitus-specific medication. RESULTS Two hundred and twenty-one newly diagnosed diabetes mellitus patients were identified among SLE patients (6795 had taken HCQ and 1833 had never taken HCQ), with an average follow-up period of 5.6 years. Compared with patients without HCQ treatment, the hazard ratio (HR) of diabetes mellitus in patients taking HCQ at a cumulative dose ≥129 g was reduced [HR 0.26 (95% CI 0.18, 0.37), P < 0.001]. Daily glucocorticoid ≥10 mg prednisolone-equivalent dose was associated with increased risk of developing diabetes mellitus [HR 2.47 (95% CI 1.44, 4.23), P = 0.001], which was minimized by concomitant HCQ use at a cumulative dose ≥129 g. CONCLUSION In SLE patients, the use of HCQ is associated with reduced risk of incident diabetes mellitus in a dose-dependent manner. High-dose glucocorticoids increase the risk of diabetes, which can be decreased by concomitant HCQ use.
How to kill tumor cells with inhibitors of poly(ADP‐ribosyl)ation

Poly(ADP‐ribosyl)ation is a post‐translational modification catalyzed by the enzyme family of poly(ADP‐ribose) polymerases (PARPs). PARPs exhibit pleiotropic cellular functions ranging from maintenance of genomic stability and chromatin remodeling to regulation of cell death, thereby rendering PARP homologues promising targets in cancer therapy. Depending on the molecular status of a cancer cell, low‐molecular weight PARP inhibitors can (i) either be used as monotherapeutic agents following the concept of synthetic lethality or (ii) to support classical chemotherapy or radiotherapy. The rationales are the following: (i) in cancers with selective defects in homologous recombination repair, inactivation of PARPs directly causes cell death. In cancer treatment, this phenomenon can be employed to specifically target tumor cells while sparing nonmalignant tissue. (ii) PARP inhibitors can also be used to sensitize cells to cytotoxic DNA‐damaging treatments, as some PARPs actively participate in genomic maintenance. Apart from that, PARP inhibitors possess antiangiogenic functions, thus opening up a further option to inhibit tumor growth. In view of the above, a number of high‐potency PARP inhibitors have been developed during the last decade and are currently evaluated as cancer therapeutics in clinical trials by several leading pharmaceutical companies.
H∞ filtering for uncertain bilinear stochastic systems

This paper is concerned with the problem of H ∞ filtering for continuous-time uncertain stochastic systems. The model under consideration contains both state-dependent stochastic noises and deterministic parameter uncertainties residing in a polytope. According to the online availability of the information on the uncertain parameters, we propose two approaches, namely robust stochastic H ∞ filtering and parameter-dependent stochastic H ∞ filtering. Both approaches solve the filtering problems based on a modified (improved) bounded real lemma for continuous-time stochastic systems, which decouples the product terms between the Lyapunov matrix and systems matrices and enables us to exploit parameter-dependent stability idea in the filter designs. Sufficient conditions for the existence of admissible robust stochastic H ∞ filters and parameter-dependent stochastic H ∞ filters are obtained in terms of linear matrix inequalities, upon which the filter designs are cast into convex optimization problems. Since the filter designs make full use of the parameter-dependent stability idea, the obtained results are less conservative than the existing one in the quadratic framework. A numerical example is provided to illustrate the effectiveness and advantage of the filter design methods proposed in this paper.
A high performance low complexity noise suppression algorithm

A high performance low complexity algorithm for noise suppression in hearing aids, using spatial information for estimating the required noise and speech power spectral densities (PSDs), is proposed. The main assumption of the scheme is that the target is directly in front of the hearing aid user while the noise comes from the back hemisphere. Furthermore, the goal is to develop a scheme that can be implemented in real time on a comercially available hearing aid. With the proposed approach, no statistical models are needed to estimate the speech and noise PSDs, which results in a robust and high performing noise suppression scheme. In a first step, the noise suppression filter is implemented in the FFT domain using a weighted overlap add scheme (WOLA), as is common for digital hearing aids. In a second step, to further reduce the computational complexity, a carefully selected wavelet decomposition is used instead of a WOLA. Hearing tests as well as objective performance measures show the excellent performance of the low complexity algorithm in the FFT as well as in the wavelet domain.
Skeletonization by blocks for large 3D datasets: application to brain microcirculation

Skeletons are compact representations that allow mathematical analysis of objects. A skeleton must be homotopic, thin and medial in relation to the object it represents. Numerous approaches already exist which focus on computational efficiency. However, when dealing with data too large to be loaded into the main memory of a personal computer, such approaches can no longer be used. We present in this article a skeletonization algorithm that processes the data locally (in sub-images) while preserving global properties (medial localization). Our privileged application is the study of the cerebral micro-vascularisation, and we show some results obtained on a mosaic of 3-D images acquired by confocal microscopy.
FunPi: An Interactive Learning Experience Using Story Narration

Learning via story-telling is an old-concept which helps children learn about things and widen the scope of their imagination and creative thinking. FunPi, a learning device for children of age group of 6-12 years, based on Raspberry Pi which provides an interactive user interface to play and read audio-stories. In this paper an innovative pedagogical method to achieve constructivist learning and better understanding of what they learn is targeted using digital story telling technique. A smart device, FunPi, is targeted which provides users with stories presented to them as per their choice. Along with that, dictionary meanings of difficult words in different languages, related URLs of the difficult words with choice of voice and pace. Initial prototype is tested with 11 users (children) of different ages and the results are analyzed and discussed based on the findings and Fitt's Law of Human Computer Interaction (HCI). System is targeted for young children but can be used by any age-group.
Thymoglobulin induction in heart transplantation: patient selection and implications for maintenance immunosuppression

Clinical data relating to rabbit antithymocyte globulin (rATG) induction in heart transplantation are far less extensive than for other immunosuppressants, or indeed for rATG in other indications. This was highlighted by the low grade of evidence and the lack of detailed recommendations for prescribing rATG in the International Society for Heart and Lung Transplantation (ISHLT) guidelines. The heart transplant population includes an increasing frequency of patients on mechanical circulatory support (MCS), often with ongoing infection and/or presensitization, who are at high immunological risk but also vulnerable to infectious complications. The number of patients with renal impairment is also growing due to lengthening waiting times, intensifying the need for strategies that minimize calcineurin inhibitor (CNI) toxicity. Additionally, the importance of donor‐specific antibodies (DSA) in predicting graft failure is influencing immunosuppressive regimens. In light of these developments, and in view of the lack of evidence‐based prescribing criteria, experts from Germany, Austria, and Switzerland convened to identify indications for rATG induction in heart transplantation and to develop an algorithm for its use based on patient characteristics.
The Nature of Belief

Neo-Cartesian approaches to belief place greater evidential weight on a subject’s introspective judgments than do neo-behaviorist accounts. As a result, the two views differ on whether our absent-minded and weak-willed actions are guided by belief. I argue that simulationist accounts of the concept of belief are committed to neo-Cartesianism, and, though the conceptual and empirical issues that arise are inextricably intertwined, I discuss experimental results that should point theory-theorists in that direction as well. Belief is even less closely connected to behavior than most contemporary functionalists allow. 1. The Concept of Belief ‘Belief’ cannot be defined. That is, barring trivial examples, the concept of belief is not equivalent to the content of any definite description. As with ‘knows’ we begin to doubt the possibility of a fruitful analysis by looking at the bevy of unsuccessful attempts. The steady stream of counter-examples casts doubt on our supplying anything substantive that is even extensionally equivalent to ‘belief’; and as the analyses not subject to obvious refutation gain complexity, the claim of synonymy or even some looser form of cognitive equivalence grows ever more implausible. Can we nevertheless give an account of belief? Yes. We can say what belief is by identifying its properties and the relations in which it is involved. Some of these properties may * I would like to thank Daniel Dennett, Tamar Szabo Gendler, Carl Ginet, Eric Schwitzgebel, Sydney Shoemaker, Robert Stalnaker and audiences at the University of Calgary, Johns Hopkins, the University of Memphis, and the University of Vermont for helpful discussions of earlier incarnations of this work. Address for correspondence: Aaron Z. Zimmerman, Department of Philosophy, University of California, Santa Barbara, CA 93106 Email: azimmerman@philosophy.ucsb. edu 1 Jerry Fodor (1998) argues for an ‘atomistic’ conception of almost every concept expressed by a simple predicate. Cf. Timothy Williamson’s (2000) case against analysis of the concept expressed by ‘knows’ when arguing for a ‘knowledge first’ account of epistemology.
Model based code generation approach for fast-deployment wireless security applications

A diverse set of sensors and actuators are key components of integrated security systems, which provide protection against various types of attacks and threats. Based on the type of the protected objects and environment the sensor/actuator components can be completely different, and the control logic, which makes decision based on the sensor readings, must be configured to the actual scenario. Short-lifetime security systems require fast and cost effective deployment but the safety requirements are still high. In this paper an architecture and a corresponding model-based code generation scheme is proposed, which provides easy and fast deployment for security applications with various sensory needs.
System Resource Utilization Analysis Based on Model Checking Method

Model checking method is a widely used formal method for proving whether or not a given model satisfies properties, and for producing counter examples if the model does not satisfy properties. In this paper, we show model checking methods can be used for resource utilization analysis of systems. We specify system utilization properties using temporal logic called LTL, and find a bottleneck of system performance using model checking.
Central banking intervention under a floating exchange rate regime: ten years of Mexican experience

From December 1994 to date, the Mexican peso/US dollar rate has been determined by market forces under a floating regime. During this time span the exchange rate has suffered severe shocks, coming both from the domestic economy (the 1995 crisis) and from the rest of the world (Asian, Russian and Brazilian crises, 11 September 2001 and the Iraqi war). The flexibility of the exchange rate has helped to soften the effects of these shocks. However, the main support of the exchange rate stems from the perceived soundness of Mexico’s macroeconomic fundamentals and from the financial authorities’ commitment to avoid discretionary interventions. In this regard, as will be shown in this document, in implementing foreign exchange policy Banco de Mexico has adhered to two main principles: (1) not to interfere with the normal functioning of the market and (2) to foster the development of the market through the creation of new instruments and by encouraging the entrance of new participants.
Term Weighting in Information Retrieval Using the Term Precision Model

At3STRACT It iS known that the use of weighted, as opposed to binary, content identifiers attached to the records of an information file improves the effectiveness of the retrieval operations Under well-defined conditions the term precision offers the best possible term weighting system A mathematscal model is used in the present study to relate the term precision weights to the frequency of occurrence of the terms in a given document collecuon and to the number of relevant documents a user wishes to retrieve in response to a query This provides for the assignment of user-dependent weights to the content identifiers and relates the term precision weights to other well-known term weighting systems
Comprehensive Proteomic Profiling of the Membrane Constituents of a Mycobacterium tuberculosis Strain*S

Mycobacterium tuberculosis is an infectious microorganism that causes human tuberculosis. The cell membranes of pathogens are known to be rich in possible diagnostic and therapeutic protein targets. To compliment the M. tuberculosis genome, we have profiled the membrane protein fraction of the M. tuberculosis H37Rv strain using an analytical platform that couples one-dimensional SDS gels to a microcapillary liquid chromatography-nanospray-tandem mass spectrometer. As a result, 739 proteins have been identified by two or more distinct peptide sequences and have been characterized. Interestingly, ∼450 proteins represent novel identifications, 79 of which are membrane proteins and more than 100 of which are membrane-associated proteins. The physicochemical properties of the identified proteins were studied in detail, and then biological functions were obtained by sorting them according to Sanger Institute gene function category. Many membrane proteins were found to be involved in the cell envelope, and those proteins with energy metabolic functions were also identified in this study.
A Collaborative Biomedical Image-Mining Framework: Application on the Image Analysis of Microscopic Kidney Biopsies

The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, such as data acquisition, preprocessing, segmentation, feature extraction, and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image dataset and experiment type. They may thus necessitate advanced image processing and classification knowledge and skills from the side of the biomedical expert. In this study, an application, exploiting web services and applying ontological modeling, is presented, to enable the intelligent creation of image-mining workflows. The described tool can be directly integrated to the RapidMiner, Taverna or similar workflow management platforms. A case study dealing with the creation of a sample workflow for the analysis of kidney biopsy microscopy images is presented to demonstrate the functionality of the proposed framework.
Areas of Grey: Tensions in EU External Relations Competences. EIPAScope 2006(1):pp. 20-26

The EU’s competences in external relations are shared between the European Community and the more intergovernmental ‘pillars’ on which the Union rests. This is most obvious in the case of the Common Foreign and Security Policy (CFSP – the so-called ‘second pillar’). Moreover, the demise of the cold war and the rapid growth of CFSP and its subset, the European Security and Defence Policy (ESDP), have led to some tensions in the ‘grey areas’ that fall in-between the Community and CFSP. The purpose of this contribution is to examine the nature and extent of these tensions and to consider various approaches to resolving, or at least diminishing, them.
Growth and Advancements in Neural Control of Limb

Centuries of study has unfolded our understanding regarding different bodily movement routinely performed. It has been observed that all these movements require intricate communication between the brain and associated muscles. Our sensory systems help in guiding this communication by providing information about the external environment and surroundings, thereby helping the motor system plan the different movements leading to controlled action by the muscles. Billions of neuron with quadrillion connections between them and muscles are responsible for coordinated movements that humans perform routinely. Though our knowledge and understanding about motor neuron diseases and neuro-degeneration disorders are limited, yet efforts have been made to overcome or improve the present state of these disorders either by drugs, artificial prosthetic devices, robotics, stimulation or stem cell therapy. These treatments are attempts to help relieve symptoms, improve functionality, provide support and effectively slow down the disease's progression. Furthermore, disabled individuals were aided with walking stick, wheelchair or stroller till recently; however, significant technological advancements in the past few decades have brought in more of man-machine interactive devices such as deployment of artificial prosthetics, improved brain-computer interactions and advanced neuroprosthetics for supporting activities of daily living in these patients. Additionally, new tools like computer simulations, medical imaging and computational models are being used to simulate simple movement tasks and compare the outcomes with real limb control and neural elements, thereby testing how brain signals are processed to achieve sophisticated motor control. Researchers are regularly improving existing devices for ease of use and efficiency, and new ones are being developed such that it can mimic the maneuverability of the natural limb.
Mining frequent serial episodes over uncertain sequence data

Data uncertainty has posed many unique challenges to nearly all types of data mining tasks, creating a need for uncertain data mining. In this paper, we focus on the particular task of mining probabilistic frequent serial episodes (P-FSEs) from uncertain sequence data, which applies to many real applications including sensor readings as well as customer purchase sequences. We first define the notion of P-FSEs, based on the frequentness probabilities of serial episodes under possible world semantics. To discover P-FSEs over an uncertain sequence, we propose: 1) an exact approach that computes the accurate frequentness probabilities of episodes; 2) an approximate approach that approximates the frequency of episodes using probability models; 3) an optimized approach that efficiently prunes a candidate episode by estimating an upper bound of its frequentness probability using approximation techniques. We conduct extensive experiments to evaluate the performance of the developed data mining algorithms. Our experimental results show that: 1) while existing research demonstrates that approximate approaches are orders of magnitudes faster than exact approaches, for P-FSE mining, the efficiency improvement of the approximate approach over the exact approach is marginal; 2) although it has been recognized that the normal distribution based approximation approach is fairly accurate when the data set is large enough, for P-FSE mining, the binomial distribution based approximation achieves higher accuracy when the the number of episode occurrences is limited; 3) the optimized approach clearly outperforms the other two approaches in terms of the runtime, and achieves very high accuracy.
Matrix Completion From any Given Set of Observations

In the matrix completion problem the aim is to recover an unknown real matrix from a subset of its entries. This problem comes up in many application areas, and has received a great deal of attention in the context of the netflix prize. A central approach to this problem is to output a matrix of lowest possible complexity (e.g. rank or trace norm) that agrees with the partially specified matrix. The performance of this approach under the assumption that the revealed entries are sampled randomly has received considerable attention (e.g. [1, 2, 3, 4, 5, 6, 7, 8]). In practice, often the set of revealed entries is not chosen at random and these results do not apply. We are therefore left with no guarantees on the performance of the algorithm we are using. We present a means to obtain performance guarantees with respect to any set of initial observations. The first step remains the same: find a matrix of lowest possible complexity that agrees with the partially specified matrix. We give a new way to interpret the output of this algorithm by next finding a probability distribution over the non-revealed entries with respect to which a bound on the generalization error can be proven. The more complex the set of revealed entries according to a certain measure, the better the bound on the generalization error.
Drop Caps Decomposition for Indexing a New Letter Extraction Method

This paper present a new method to extract shapes in drop caps and particularly the most important shape: Letter itself. This method relies on a combination of a Aujol and Chambolle algorithm first, and a Segmentation using a Zipf Law in a second step. This method can be enhanced as a three-step process: 1)Decomposition in layers 2)Segmentation using a Zipf Law 3)Selection of connected components to only emphasize the required information - letter itself.