Miracle-dz/UniFAR-FTUs huggingface dataset:
"The FSGS factor:" enrichment and in vivo effect of activity from focal segmental glomerulosclerosis plasma.

A circulating causative factor has been postulated in focal segmental glomerulosclerosis (FSGS). It has been shown that serum or plasma from some FSGS increases glomerular albumin permeability (Palb) in vitro. Palb greater than 0.5 (i.e., FS activity) is associated with recurrence after transplantation. Specimens from 15 FSGS patients were studied to document the presence of a permeability factor, to isolate this factor, to characterize its biochemical properties, and to show its effect in vivo. Total lipids were extracted by chloroform/methanol (2: 1); FS activity was absent from total lipid extract. Chylomicrons and lipoproteins were removed from the plasma with dextran sulfate, followed by sequential precipitation of proteins at 50 and 70% ammonium sulfate saturation. FS activity was retained in the 70% ammonium sulfate supernatant and exhibited a 100-fold purification. FS activity was lost after heating at 100 degrees C for 10 min or after protease digestion. Under nondenaturing conditions, electrophoresis of the FSGS 70% supernatant showed a prominent low molecular weight band that was not evident in the 70% supernatant from normal plasma. Dialysis and centrifugation-based membrane ultrafiltration of the FSGS factor indicated a molecular size between 30 and 50 kD. Injection of the 70% FSGS supernatant into rats caused a threefold increase in urine protein in collections from 6 to 24 h after injection. No increase in proteinuria occurred in rats injected with 70% supernatant from normal individuals. It is concluded that the FSGS factor is a low molecular weight protein with the potential to increase Palb in vitro and to cause proteinuria in vivo.
Towards a hybrid approach of primitive cognitive network process and Bayesian network for evaluation

Evaluation is an essential step for decision making. This paper proposes a hybrid approach, named PCNP-BN, which combines the Primitive Cognitive Network Process (PCNP) and the Bayesian Network (BN) for evaluation activities. PCNP, which is an ideal alternative of the Analytic Hierarchy Process (AHP), helps to quantify the influence of factors, whilst Bayesian network is utilized to combine all the useful feedbacks. The proposed approach can support evaluation activities through quantifying complex factors into measurable values.
State-Dependent Pricing, Inflation, and Welfare in Search Economies

This paper investigates the welfare effects of inflation in economies with search frictions and menu costs. We first analyze an economy where there is no transaction demand for money balances: Money is a mere unit of account. We determine a condition under which price stability is optimal and a condition under which positive inflation is desirable. We relate these conditions to a standard efficiency condition for search economies. Second, we consider a related economy in which there is a transaction role for money. In the absence of menu costs, the Friedman rule is optimal. In the presence of menu costs, the optimal inflation rate is negative for all our numerical examples. A deviation from the Friedman rule can be optimal depending on the extent of the search externalities.
Improving Web clustering by cluster selection

Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
Analytical description of anomalous diffusion in living cells

We propose a stochastic model for intracellular transport processes associated with the activity of molecular motors. This out-of-equilibrium model, based on a generalized Langevin equation, considers a particle immersed in a viscoelastic environment and simultaneously driven by an external random force that models the motors activity. An analytical expression for the mean square displacement is derived, which exhibits a subdiffusive to superdiffusive transition. We show that the experimentally accessible statistical properties of the diffusive particle motion can be reproduced by this model.
Exploiting the feature vector model for learning linguistic representations of relational concepts

In this paper we focus our attention to the construction of one-to-many mappings between the coarse-grained relational concepts and the corresponding linguistic realisations with an eye on the problem of selecting the catalogue of the coarse-grained relational concepts. We here explore the extent and nature of the general semantic knowledge required for the task, and, consequently, the usability of general-purpose resources such as WordNet. We propose an original model, theverb semantic prints , for exploiting ambiguous semantic information within the feature vector
On the non-thermal high energy radiation of galaxy clusters

Institute de Physique Theorique, Universite de Lausanne, BSP 1015 Lausanne, SwitzerlandReceived / AcceptedAbstract. The origin of the nonthermal EUV and hard X-ray emission “excess” reported from some galaxyclusters has been intensively debated over last several years. The most favored models which refer this excessto relativistic electrons upscattering the 2.7 K CMBR generally requires very low magnetic ﬁeld, signiﬁcantlybelow the estimates derived from the Faraday Rotation Measurements, unless one invokes rather nonstandardassumptions concerning the energy and pitch angle distributions of nonthermal electrons. In this paper we suggesta new model assuming that the “nonthermal” excess is due to synchrotron radiation of ultrarelativistic (multi-TeV) electrons of “photonic” origin. These electrons are continuously introduced throughout the entire intraclustermedium by very high energy (hypothetical) γ-rays through interactions with the diﬀuse extragalactic radiationﬁelds. We present numerical calculations for the Coma cluster, and brieﬂy discuss implications of the model forother galaxy clusters both in the X- and γ-ray energy domains.Key words. X-rays: galaxies: clusters – Galaxies: clusters: individual: Coma – Gamma rays: theory
Self-adaptive cluster segmentation aircraft objects in aerial images

We present a self-adaptive cluster segmentation method for the problem of automatically detecting the aircraft location from complex aerial images. Image segmentation that partitions a given image into meaningful regions is an important task of image analysis for recognition. We introduce knowledge about the location of the object of interest and knowledge about the behavior of edges in scale space, in order to enhance edge information. We present a new region potential term based on the classical iterative method when it is applied to the segmentation of aerial images. Following an edge-linking procedure, the regions of objects can be bounded by closed boundaries. The method is applied to a number of aerial images, each one of which contains one or more objects. Experimental results are provided to illustrate the correction of this object detection method in a lot of domain, regardless of the complexity of background in images.
A Model of Computation for Real-Time Applications on Embedded Manycores

This paper presents a first work to bring a consistent Model of Computation (MoC) for real-time applications on embedded many-core systems. Embedded manycores have lots of constraints (power, heterogeneity, memory, etc.) but also lots of promises with regard to harness able applications, i.e. New kinds of applications that could fit embedded systems. Such a MoC would encompass both data movements in Manycore's memories, determinism of execution, composability but also real-time requirements. We build our MoC on a well known base - CycloStatic Data Flow (CSDF) - for its desirable properties (i.e. Determinism and composability), and thank to several case-studies, we extend it to a superset, adding two special data processing filters which preserve these good properties and provide useful features for embedded computing.
An improvement in the restoring division algorithm (Needy Restoring Division Algorithm)

An improvement in the current restoring computer division algorithm which minimizes the number of restorations performed by the computer. Currently, restoration is checked after each shifting of the register but Needy Restoring Division Algorithm checks restoration only when it is needed. The needless restorations are removed by the use of an extra register. This removal decreases the time for the division than taken in the current restoring division algorithm considerably.
Effective ranked conceptual retrieval

We describe the design and implementation of the SMARTER prototype system for collecting, structuring and searching information derived from full text ebooks available on the World Wide Web [4]. Standard methods of parsing and extended Boolean schemes for weighting are used to build an index of individual pages. Ranked pages are retrieved from the system by means of a correlated and combined p-norm and vector query mechanisms. Furthermore, we present a complete evaluation of the relative effectiveness of the SMARTER system versus the Google Desktop [7].
Steps toward the Power Spectrum of Matter. III. The Primordial Spectrum

We compare the observed power spectrum of matter found in our earlier papers with analytical power spectra. We extrapolate the observed power spectra on small scales to find the linear power spectrum of matter. We consider spatially flat cold and mixed dark matter models with the cosmological constant as well as open models. We fix the Hubble constant and the baryon density in the middle of the allowed range and vary the density parameter and the cosmological constant. We determine the primordial power spectrum of matter using the power spectrum of matter and the transfer functions of analytical models. We take two different spectra suggested by observations: one with a sharp maximum at 120 h-1 Mpc and a second one with a broader maximum, as found for regions with rich and medium-rich superclusters of galaxies, respectively. For both models, the primordial power spectra have a break in amplitude; in the case of the spectrum with a sharp maximum the break is sharp. We conclude that a scale-free primordial power spectrum is excluded if presently available data on the distribution of clusters and galaxies represent the true mass distribution of the universe.
Self-report versus care provider registration of healthcare utilization: Impact on cost and cost-utility

Objectives: This study aims to compare the impact of two different sources of resource use, self-report versus care provider registrations, on cost and cost utility. Methods: Data were gathered for a cost-effectiveness study performed alongside a 2-year randomized controlled trial evaluating the effect of an INTERdisciplinary COMmunity-based management program (INTERCOM) for patients with chronic obstructive pulmonary disease (COPD). The program was offered by physiotherapists, dieticians and respiratory nurses. During the 2-year period, patients reported all resource use in a cost booklet. In addition, data on hospital admissions and outpatient visits, visits to the physiotherapist, dietician or respiratory nurse, diet nutrition, and outpatient medication were obtained from administrative records. The cost per quality-adjusted life-year (QALY) was calculated in two ways, using data from the cost booklet or registrations. Results: In total, 175 patients were included in the study. Agreement between self-report and registrations was almost perfect for hospitalizations (rho = 0.93) and physiotherapist visits (rho = 0.86), but above 0.55, moderate, for all other types of care. The total cost difference between the registrations and the cost booklet was 464 euros with the highest difference for hospitalizations 386 euro. Based on the cost booklet the cost difference between the treatment group and usual care was 2,444 euros (95 percent confidence interval [CI], −819 to 5,950), which resulted in a cost-utility of 29,100 euro/QALY. For the registrations, the results were 2,498 euros (95 percent CI, −88 to 6,084) and 29,390 euro/QALY, respectively. Conclusions: This study showed that the use of self-reported data or data from registrations effected within-group costs, but not between-group costs or the cost utility.
Multiple laser polar scan matching with application to SLAM

Polar Scan Matching is one of the methods of point to point scan matching for Simultaneous Localization and Mapping Application. It works in the original laser polar coordinate system and therefore eliminates the need for an expensive correspondence search as in other scan matching methods by using the matching bearing association rule. However, most of the low-cost laser range finders available on the market have limited field of view and therefore the use of more than one laser range finder is necessary to cover 360 degree field of view and hence to provide a better Scan-Matching SLAM performance. This paper presents extensions of Polar Scan Matching to allow for matching with multiple laser scans simultaneously. Also a method for updating reference scans while mapping the environment is introduced. This paper also illustrates the advantages of using multiple laser range finders for SLAM. The results are illustrated with results from SLAM with a mobile robot equipped with two laser rangefinders.
Efficiently Identifying Interesting Time Points in Text Archives

Large scale text archives are increasingly becoming available on the Web. Exploring their evolving contents along both text and temporal dimensions enables us to realize their full potential. Standard keyword queries facilitate exploration along the text dimension only. Recently proposed time-travel keyword queries enable query processing along both dimensions, but require the user to be aware of the exact time point of interest. This may be impractical if the user does not know the history of the query within the collection or is not familiar with the topic. In this work, our aim is to efficiently identify interesting time points in Web archives with an assumption that we receive a result list for a given query in standard relevance-order from an existing retrieval system. We consider two forms of Web archives: (i) one where documents have a publication time-stamp and never change (such as news archives), and (ii) the archives where documents undergo revisions, and are thus versioned. In both settings, we define interestingness as the change in top-k result set of two consecutive time-points. The key step in our solution is the maintenance of top-k results valid at each time-point of the archive, which can then be used to compute the interestingness scores for the time-points. We propose two techniques to realize efficient identification of interesting time points: (i) For the case when documents once published never change, we have a simple but effective technique. (ii) For the more general case with versioned documents, we develop an extension to the segment tree which makes it rank-aware and dynamic. To further improve efficiency, we propose an early termination technique which is proven to be very effective. Our methods are shown to be effective in efficiently finding interesting time points in a set of experiments using the New York Times news archive and the Wikipedia versioned archive.
Improving paper books: searchable books

Much of today’s information is digitised. Representation of information is increasingly becoming digital. Yet, paper books remain popular, as many readers prefer the reading experience that paper books provide, which digital interfaces cannot. In this paper, our aim is to improve users’ reading experience by enhancing books with digital functionalities. We conducted a user survey study to identify features that users desire. The study highlights one speciﬁc feature – content searching within books. From this result, we discuss three design choices that can incorporate digital searching into paper books.
Virtual Active Touch: Perception of Virtual Gratings Wavelength through Pointing-Stick Interface

Tactile feedback enhances the usability and enjoyment of human-computer interfaces. Many feedback techniques have been devised to present tactile stimuli corresponding to a user's hand movements taking account of the concept of active touch. However, hand movements may not necessarily be required for achieving natural tactile feedback. Here, we propose a virtual-active-touch method that achieves haptic perception without actual/direct hand movements. In this method, a cursor manipulated by a force-input device is regarded as a virtual finger of the operator on the screen. Tactile feedback is provided to the operator in accordance with cursor movements. To validate the translation of virtual roughness gratings, we compare the virtual-active-touch interface with an interface that involves actual hand movements. By using the appropriate force-to-velocity gain for the pointing-stick interface, we show that the virtual-active-touch method presents the surface wavelengths of the gratings, which is a fundamental property for texture roughness, and that the gain significantly influences the textures experienced by the operators. Furthermore, we find that the perceived wavelengths of objects scaled and viewed on a small screen are skewed. We conclude that although some unique problems remain to be solved, we may be able to perceive the surface wavelengths solely with the intentions of active touch through virtual-active-touch interfaces.
Task-Based Language Teaching: A Lesson Plan Proposal For Indonesian ELT Context (A Consideration Before Integrating ICT in ELT)

This paper discusses a lesson plan proposal for a task based language teaching approach supplemented by its critical rationale and evaluation. It reflects the English Language Teaching (ELT) context in Indonesia based on the personal background knowledge of the author and a long list of relevant references. The lesson plan was created accordingly to Willis‟s (1996) task-based framework by integrating some principles proposed by Nunan (2004). Furthermore, the four principle o f learning conditions suggested by Willis (1996) are well considered in order to ensure successful learning. It also provides sufficient modelling and substantial amount of opportunity for a recycling process. Thus, expected objectives of the lesson are projected to be achieved where students are able to understand the intended language form with meaning and context incorporated as well as automaticity in language production. Nonetheless, the author also reviews its weaknesses critically.
Lognormal Property of Weak-Lensing Fields

The statistical properties of weak-lensing fields are studied quantitatively using ray-tracing simulations. Motivated by an empirical lognormal model that excellently characterizes the probability distribution function of a three-dimensional mass distribution, we critically investigate the validity of the lognormal model in weak-lensing statistics. Assuming that the convergence field κ is approximately described by the lognormal distribution, we present analytic formulae of convergence for the one-point probability distribution function (PDF) and the Minkowski functionals. The validity of the lognormal models is checked in detail by comparing those predictions with ray-tracing simulations in various cold dark matter models. We find that the one-point lognormal PDF can accurately describe the non-Gaussian tails of convergence fields up to ν ~ 10, where ν is the level threshold given by ν ≡ κ/⟨κ2⟩1/2, although the systematic deviation from the lognormal prediction becomes manifest at higher source redshift and larger smoothing scales. The lognormal formulae for Minkowski functionals also fit the simulation results when the source redshift is low, zs = 1. Accuracy of the lognormal fit remains good even at small angular scales 2′ ≲ θ ≲ 4′, where the perturbation formulae by the Edgeworth expansion break down. On the other hand, the lognormal model enables us to predict higher order moments, i.e., skewness S3,κ and kurtosis S4,κ, and we thus discuss the consistency by comparing the predictions with the simulation results. Since these statistics are very sensitive to the high- and low-convergence tails, the lognormal prediction does not provide a successful quantitative fit. We therefore conclude that the empirical lognormal model of the convergence field is safely applicable as a useful cosmological tool, as long as we are concerned with the non-Gaussianity of ν ≲ 5 for low-zs samples.
Bmc Medical Informatics and Decision Making a Controlled Trial of Automated Classification of Negation from Clinical Notes

Background: Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e.
Modified Fuzzy Relation and Its Applications

In this paper we used difference operator to obtain diagnostic conclusion about Diabetes by giving membership grades to symptoms of a disease from 0 to 1 with the help of fuzzy relations which depend upon Ro (an occurrence relation obtained by a sufficient number of patients), Rc (a conformability relation confirmed by doctors) and Rs (a matrix which contains degree of symptoms seen by doctor or given by himself).
Cytokines in Bipolar Disorder: Paving the Way for Neuroprogression

Bipolar disorder (BD) is a severe, chronic, and recurrent psychiatric illness. It has been associated with high prevalence of medical comorbidities and cognitive impairment. Its neurobiology is not completely understood, but recent evidence has shown a wide range of immune changes. Cytokines are proteins involved in the regulation and the orchestration of the immune response. We performed a review on the involvement of cytokines in BD. We also discuss the cytokines involvement in the neuroprogression of BD. It has been demonstrated that increased expression of cytokines in the central nervous system in postmortem studies is in line with the elevated circulating levels of proinflammatory cytokines in BD patients. The proinflammatory profile and the immune imbalance in BD might be regarded as potential targets to the development of new therapeutic strategies.
Cognitive and physiological effects of Omega‐3 polyunsaturated fatty acid supplementation in healthy subjects

Background It has been reported that Omega‐3 fatty acids may play a role in nervous system activity and that they improve cognitive development and reference memory‐related learning, increase neuroplasticity of nerve membranes, contribute to synaptogenesis and are involved in synaptic transmission. The aim of this study was to examine the effects of Omega‐3 supplementation on some cognitive and physiological parameters in healthy subjects.