Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathophysiological Defects and Transcriptional Profiling in the RBM20-/- Rat Model

Our recent study indicated that RNA binding motif 20 (Rbm20) alters splicing of titin and other genes. The current goals were to understand how the Rbm20-/- rat is related to physiological, structural, and molecular changes leading to heart failure. We quantitatively and qualitatively compared the expression of titin isoforms between Rbm20-/- and wild type rats by real time RT-PCR and SDS agarose electrophoresis. Isoform changes were linked to alterations in transcription as opposed to translation of titin messages. Reduced time to exhaustion with running in knockout rats also suggested a lower maximal cardiac output or decreased skeletal muscle performance. Electron microscopic observations of the left ventricle from knockout animals showed abnormal myofibril arrangement, Z line streaming, and lipofuscin deposits. Mutant skeletal muscle ultrastructure appeared normal. The results suggest that splicing alterations in Rbm20-/- rats resulted in pathogenic changes in physiology and cardiac ultrastructure. Secondary changes were observed in message levels for many genes whose splicing was not directly affected. Gene and protein expression data indicated the activation of pathophysiological and muscle stress-activated pathways. These data provide new insights on Rbm20 function and how its malfunction leads to cardiomyopathy.
A Review on Multilevel wrApper Verification System with maintenance Model Enhancement

The online data sources have prompted to an expanded utilization of wrappers for extract data from Web sources. We present a unique idea, to explain the expressed problems and formally demonstrate its accuracy. Conventional research techniques have concentrated on snappy and effective era of wrappers; the advancement of devices for wrapper support has gotten less consideration and no arrangement to self upkeep. This empowers us to learn wrappers in a totally unsupervised way from consequently and inexpensively preparing information, e.g., utilizing word references and standard expressions. This turns into a research issue since Web sources frequently change progressively in ways that keep the wrappers from removing data accurately. We will probably help programming engineers develop wrapping operators that translate questions written in abnormal state organized language. Work introduces a proficient idea for auxiliary data about information from positive cases alone. Framework utilizes this data for wrapper upkeep applications: utilizing wrapper check and enlistment component planning a support show. The wrapper verification framework identifies when a wrapper is not extricating right information, for the most part on the grounds that the Web source has changed its organization. Sites are constantly advancing, upgrading and basic changes happen with no cautioning, which for the most part results in wrappers working mistakenly. Tragically, wrappers may flop in the undertaking of separating information from a Web page, if its structure changes, once in a while even marginally, in this way requiring the abusing of new procedures to be naturally held to adjust the wrapper to the new structure of the page, in the event of disappointment.
The mediating impact of money motives in the association between materialism and well-being

5 CHAPTER 1. Literature Review: Consumer culture, materialism and well-being What is consumer culture 6 Measuring happiness 8 Materialism 10 Materialism as a personality trait 11 Materialism as a value 12 Materialism as a goal 14 Materialism and Subjective Well-Being 15 Is materialism inherently bad? 16 Self-Determination Theory 18 Value conflict 19 Motives 20 Conclusion 22 CHAPTER 2. Brief report: Qualifying the link between financial goals and subjective well-being: The role of happiness and identity money motives Abstract 24 Introduction 25 Why money motives 28 Happiness as motive 28 Identity as motive 29 Aims and hypotheses of the current study 30 Method 31 Participants and procedure 31
Auto‐Align – Multi‐Modality Fluorescence Microscopy Image Co‐registration

Multi‐modality microscopes incorporate multiple microscopy techniques into one module, imaging through a common objective lens. Simultaneous or consecutive image acquisition of a single specimen, using multiple techniques, increases the amount of measurable information available. In order to benefit from each modality, it is necessary to accurately co‐register data sets. Intrinsic differences in the image formation process employed by each modality result in images which possess different characteristics. In addition, as a result of using different measurement devices, images often differ in size and can suffer relative geometrical deformations including rotation, scale and translation, making registration a complex problem. Current methods generally rely on manual input and are therefore subject to human error. Here, we present an automated image registration tool for fluorescence microscopy. We show that it successfully registers images obtained via total internal reflection fluorescence (TIRF), or epi‐fluorescence, and confocal microscopy. Furthermore, we provide several other applications including channel merging following image acquisition through an emission beam splitter, and lateral stage drift correction. We also discuss areas of membrane trafficking which could benefit from application of Auto‐Align. Auto‐Align is an essential item in the advanced microscopist's toolbox which can create a synergy of single or multi‐modality image data.
A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups

We deal with the problem of variable selection when variables must be selected group-wise, with possibly overlapping groups defined a priori. In particular we propose a new optimization procedure for solving the regularized algorithm presented in [12], where the group lasso penalty is generalized to overlapping groups of variables. While in [12] the proposed implementation requires explicit replication of the variables belonging to more than one group, our iterative procedure is based on a combination of proximal methods in the primal space and projected Newton method in a reduced dual space, corresponding to the active groups. This procedure provides a scalable alternative with no need for data duplication, and allows to deal with high dimensional problems without pre-processing for dimensionality reduction. The computational advantages of our scheme with respect to state-of-the-art algorithms using data duplication are shown empirically with numerical simulations.
Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images

Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetation's biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coefficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related applications.
A cross-layer approach to multi-hop networking with cognitive radios

Cognitive radio (CR) is an enabling technology for efficient use of available spectrum and promises unprecedented flexibility in multi-hop wireless networking. This paper explores networking related issues associated with CRs. Specifically, we consider how to maximize the rates of a set of user communication sessions in a multi-hop CR-based wireless network. Due to potential interference at the physical layer, we find that it is essential to follow a cross-layer approach, with joint optimization at physical (power control), link (frequency band scheduling), and network (flow routing) layers. We give a mathematical characterization of this cross-layer optimization problem. We develop a centralized solution procedure based on the branch-and-bound framework. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the joint optimization at different layers.
CONTROL OF GRANULOCYTE PRODUCTION: SEPARATION AND CHEMICAL IDENTIFICATION OF A SPECIFIC INHIBITOR (CHALONE)

Abstract. It has previously been shown by others that blood serum contains inhibitors of blood cell production acting on the proliferation of granulocy tic and erythrocytic precursor cells in the bone marrow. It is now shown that the active extract from calf blood serum can be further subfractionated into six different components, all of them exhibiting inhibitory effects on the proliferation of rat bone marrow cells in vitro. Ascitic fluid from rats treated intraperitoneally with polyvinylpyrrolidone contains inhibitors which apparently are the same as those found in calf serum.
CAMEO: Enabling social networks for Massively Multiplayer Online Games through Continuous Analytics and cloud computing

Millions of people play Massively Multiplayer Online Games (MMOGs) and participate in the social networks built around MMOGs daily. These players turn into a collaborative community to exchange game news, advice, and expertise, but in return expect support such as player reports and clan statistics. Thus, the MMOG social networks need to collect and analyze MMOG data, in a process of continuous MMOG analytics. With the appearance of cloud computing, it has become attractive to use on-demand resources to run automated MMOG data analytics tools. In this work we present CAMEO, an architecture for Continuous Analytics for Massively multiplayEr Online games on cloud resources. Our architecture provides various mechanisms for MMOG data collection and continuous analytics of a pre-determined accuracy in real settings. We implement and deploy CAMEO to perform continuous analytics on data from RuneScape, a popular MMOG. Using resources from various real clouds, including the commercial cloud of Amazon, CAMEO can analyze the characteristics of a community of over 3,000,000 active players, and follow the progress of 500,000 of these players for over a week. Thus, we show evidence that CAMEO can support the social networks built around MMOGs.
Evaluation of the effect of high-dose folic acid on endothelial dysfunction in pre-eclamptic patients: A randomized clinical trial

Background: Pre-eclampsia as a hypertensive disorder of pregnancy complicates up to 5–10% of pregnancies worldwide. Endothelial dysfunction plays an important role in the pathogenesis of pre-eclampsia. In this study, we aim to evaluate the effect of high-dose folic acid on endothelial dysfunction in pre-eclamptic patients. Materials and Methods: In this triple-blinded randomized clinical trial, the enrolled patients were divided randomly into two groups. Folic acid 5.0 mg or placebo was taken daily by oral administration from the initiation of diagnosis until 2 months after delivery by the participants. Every patient's flow-mediated dilation (FMD) was evaluated at the beginning of the study and 2 months after delivery with the same experienced operator at the same period of time (3–5 p.m.) by high-resolution B-mode ultrasonography. Potential confounding variables were included in the independent samples t-test. t-test or Mann–Whitney U-test was used in the comparison of means between the intervention and placebo groups. To compare FMD in each group, before and after the intervention, paired t-test was used. Results: Mean value of FMD in intervention (9.64 ± 5.57) and control group (9.30 ± 4.25) has no significant difference before the consumption of drugs (P > 0.05). FMD in intervention group (13.72 ± 7.89) significantly increases after daily consumption of 5 mg folic acid in comparison with control group (10.02 ± 4.81) after daily consumption of placebo (P = 0.002). Conclusion: Increased mean of FMD in intervention group shows that this supplement can improve endothelial function and can be significantly affected by maternal blood pressure during pregnancy and some endothelium-dependent disease such as pre-eclampsia and its associated adverse outcomes.
Diagnosabilities of regular networks

In this paper, we study diagnosabilities of multiprocessor systems under two diagnosis models: the PMC model and the comparison model. In each model, we further consider two different diagnosis strategies: the precise diagnosis strategy proposed by Preparata et al. and the pessimistic diagnosis strategy proposed by Friedman. The main result of this paper is to determine diagnosabilities of regular networks with certain conditions, which include several widely used multiprocessor systems such as variants of hypercubes and many others.
Quality-adaptive scheduling for live streaming over multiple access networks

Video streaming ranks among the most popular services offered through the Internet today. At the same time, accessing the Internet over public WiFi and 3G networks has become part of our everyday lives. However, streaming video in wireless environments is often subject to frequent periods of rebuffering and characterized by low picture quality. In particular, achieving smooth and quality-adaptive streaming of live video poses a big challenge in mobile scenarios. Building on the observation that the subjective video experience on mobile devices decreases when quality changes are more frequent than every 1 to 2 seconds, we present a client-side scheduler that retrieves segments of several video encodings over heterogeneous network interfaces simultaneously. By extending the DAVVI streaming platform with support for multiple interfaces, the proposed scheduler's performance is experimentally evaluated. The results show that our scheduler reduces the video interruptions and achieves a higher and more stable average quality over multiple, truly heterogeneous wireless interfaces.
Systematic top-down approach to clinical chemistry

This paper introduces a systematic approach to organizing the discipline of clinical chemistry. The approach is called a top-down, systems approach because it starts at the top with the most general concepts and works down through less general concepts to the most specific details and techniques. The hypothesis is that the discipline can be organized into hierarchical levels of functional processes and operational approaches to those processes. The functional processes represent what clinical scientists do; the operatinal approaches represent how they do it. Because functional processes change little, if at all, with time, they are used to develop a stable infrastructure or framework for the discipline. That infrastructure is then used to organize and understand operational approaches that tend to change rapidly with time in response to technological advances. The paper begins with the most general functional processes and then uses selected examples of the more general functions to illustrate lower hierarchical levels or functional processes and operational approaches.
Assimilation of global carbon cycle observations into an Earth system model to estimate uncertain terrestrial carbon cycle parameters

Uncertainties about potential feedbacks in the terrestrial carbon cycle are a key driver of the uncertainty in carbon cycle and climate projections. Here we analyze how oceanic, atmospheric, and ice core carbon cycle observations improve key biogeochemical parameter estimates. We calibrate the University of Victoria Earth System Climate Model using atmospheric CO 2 concentration obtained from atmospheric and ice core measurements as well as estimates of decadal-scale oceanic carbon fluxes. We estimate the joint probability density function (PDF) of two key terrestrial feedback parameters quantifying carbon fertilization (Kc 25) and the respiration-temperature sensitivity (Q 10). To maximize computational efficiency, we use a relatively small ensemble of 48 model runs combined with a statistical emulation technique using principal component analysis. In the joint posterior PDF, there is a positive correlation between the two estimated parameters at low values of Kc 25 which reverses to a negative correlation at high values of Kc 25. This is a result of the nonlinearity of terrestrial carbon cycle feedbacks. CO 2 concentrations from Manua Loa are the most powerful constraint, and using a single CO 2 observation from the year 1999 can reproduce the general shape of the posterior PDF.
Neural-based Context Representation Learning for Dialog Act Classification

We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurrent neural network architectures and attention mechanisms (AMs) at different context levels. Our experimental results on two benchmark datasets show consistent improvements compared to the models without contextual information and reveal that the most suitable AM in the architecture depends on the nature of the dataset.
Genesys: A Virtual Reality scene builder

Virtual Reality (VR) is a computer technology which simulates real world environments and a user's interactions within it. Current trends in VR, however, are focused on the gaming industry with limited work on VR creation tools. Thus, we propose a VR application that allows users to create 3-dimensional scenes in virtual reality named Genesys. Scenes created are viewed with the use of VR head mounted displays which provide the user with a more immersive work environment compared to traditional 2D monitors. Interactions within this workspace are done through motion controllers that allow users to manipulate with objects and spawn more from an online repository. Scenes created can be shared to the Genesys community of artists, creators and users through an online website. This feature widens the project's applications in education, business, and entertainment as a world builder and presentation tool. To measure the effectiveness of the application, a scene composition test was conducted at the end of the experiment. The results showed that Genesys is generally easy to use for people with limited experience in VR. However, improvements can still be made on the object manipulation controls such as rotation and scaling.
Low-sidelobe weighted-coupled integrated acoustooptic tunable filter using focused surface acoustic waves

A single-stage low-sidelobe weighted-coupled integrated acoustooptic tunable filter (IAOTF) operating at the optical wavelength of 1.31 mu m has been realized on a Y-cut X-propagating LiNbO/sub 3/ substrate for the first time. The weighted-coupling, which enabled the achievement of low-sidelobe levels, was facilitated by focused surface acoustic waves generated by a curved interdigital transducer. First sidelobe levels as low as -17.6 dB have been obtained. Optical bandwidth of 4 nm and tuning range of 170 nm for an interaction length of 6 mm were also accomplished. <<ETX>>
Intellect and Somatic Health: Associations with Hypochondriacal Concerns, Perceived Threat and Fainting

Background: Compared to the other domains of the Big 5, intellect has been relatively underresearched with regard to somatic health outcomes. It is argued that this, in part, is because specific hypotheses concerning intellect and somatic health have not been formulated. It is argued that intellect is related to both alexithymia and hypnotic ability. As such the High Risk Model of Threat Perception forms one useful theoretical framework for generating hypotheses about intellect and somatic health. On this basis it is hypothesised that intellect should show a U-shaped relationship to hypochondriacal concerns, a positive association with appraisals of threat and loss, and a negative association with levels of self-reported vasovagal syncope (feelings of faintness). Methods: Two cross-sectional studies, using student samples (n = 205, 179), examined the relationships between intellect and hypochondriacal concerns as well as intellect and appraisals of threat and loss. A third prospective study, using a sample of blood donors (n = 373), examined the relationship between levels of self-reported vasovagal syncope and intellect. Results: Compared to the other domains of the Big 5, intellect demonstrated a U-shaped association with hypochondriacal concerns, a positive association with perceived threat and loss and a negative association with fainting. The negative association with fainting remained once incidence of previous fainting, the number of previous blood donations and emotional stability were held as covariates. Conclusions: Intellect is associated in a number of important ways with a variety of psychosomatic variables.