Miracle-dz/UniFAR-FTUs huggingface dataset:
Humoral Immune Responses of Dengue Fever Patients Using Epitope-Specific Serotype-2 Virus-Like Particle Antigens

Dengue virus (DENV) is a serious mosquito-borne pathogen causing significant global disease burden, either as classic dengue fever (DF) or in its most severe manifestation dengue hemorrhagic fever (DHF). Nearly half of the world's population is at risk of dengue disease and there are estimated to be millions of infections annually; a situation which will continue to worsen with increasing expansion of the mosquito vectors and epidemic DF/DHF. Currently there are no available licensed vaccines or antivirals for dengue, although significant effort has been directed toward the development of safe and efficacious dengue vaccines for over 30 years. Promising vaccine candidates are in development and testing phases, but a better understanding of immune responses to DENV infection and vaccination is needed. Humoral immune responses to DENV infection are complex and may exacerbate pathogenicity, yet are essential for immune protection. In this report, we develop DENV-2 envelope (E) protein epitope-specific antigens and measure immunoglobulin responses to three distinct epitopes in DENV-2 infected human serum samples. Immunoglobulin responses to DENV-2 infection exhibited significant levels of individual variation. Antibody populations targeting broadly cross-reactive epitopes centered on the fusion peptide in structural domain II were large, highly variable, and greater in primary than in secondary DENV-2 infected sera. E protein domain III cross-reactive immunoglobulin populations were similarly variable and much larger in IgM than in IgG. DENV-2 specific domain III IgG formed a very small proportion of the antibody response yet was significantly correlated with DENV-2 neutralization, suggesting that the highly protective IgG recognizing this epitope in murine studies plays a role in humans as well. This report begins to tease apart complex humoral immune responses to DENV infection and is thus important for improving our understanding of dengue disease and immunological correlates of protection, relevant to DENV vaccine development and testing.
Impacts of Work on Family Life among Partnered Parents of Young Children

An increasing number of mothers with young children are in paid employment and the effect of this on family life is of increasing policy and scientific interest. Based on the first wave of the Longitudinal Study of Australian Children, this paper investigates potential sources of work-to-family strain for partnered parents of young children. The analysis identifies the importance of gender, job characteristics such as flexibility and autonomy, non-standard hours and long work hours, and the nature of the family environment - parental roles, supportive husband wife relationship - in exploring how working parents experience negative spillover between their work and family lives
Equivalence of Generative and Log-Linear Models

Conventional speech recognition systems are based on hidden Markov models (HMMs) with Gaussian mixture models (GHMMs). Discriminative log-linear models are an alternative modeling approach and have been investigated recently in speech recognition. GHMMs are directed models with constraints, e.g., positivity of variances and normalization of conditional probabilities, while log-linear models do not use such constraints. This paper compares the posterior form of typical generative models related to speech recognition with their log-linear model counterparts. The key result will be the derivation of the equivalence of these two different approaches under weak assumptions. In particular, we study Gaussian mixture models, part-of-speech bigram tagging models, and eventually, the GHMMs. This result unifies two important but fundamentally different modeling paradigms in speech recognition on the functional level. Furthermore, this paper will present comparative experimental results for various speech tasks of different complexity, including a digit string and large-vocabulary continuous speech recognition tasks.
Java Just-in-Time Compiler and Virtual Machine Improvements for Server and Middleware Applications

This paper describes optimization techniques recently applied to the Just-In-Time compilers that are part of the IBM® Developer Kit for JavaTM and the J9 Java virtual machine specification. It focusses primarily on those optimizations that improved server and middleware performance. Large server and middleware applications written in the Java programming language present a variety of performance challenges to virtual machines (VMs) and justin-time (JIT) compilers; we must address not only steady-state performance but also start-up time. In this paper, we describe 12 optimizations that have been implemented in IBM products because they improve the performance and scalability of these types of applications. These optimizations reduce, for example, the overhead of synchronization, object allocation, and some commonly used Java class library calls. We also describe techniques to address server start-up time, such as recompilation strategies. The experimental results show that the optimizations we discuss in this paper improve the performance of applications such as SPECjbb2000 and SPECjAppServer2002 by as much as 10-15%.
Hot-carrier reliability study and simulation methodology development for 65nm technology

In this work, we report degradation study in 65nm technology NMOS I/O transistors (Tox=55 Å) for different channel widths. Devices were stressed at maximum substrate current condition in order to stimulate HCI degradation. These measurements were validated using analytical equations and reliability models extracted to be compatible with Eldo simulation tool using User Defined Reliability Model (UDRM) approach.
A second generation force field for the simulation of proteins

We present the derivation of a new molecular mechanical force field for simulating the structures, conformational energies, and interaction energies of proteins, nucleic acids, and many related organic molecules in condensed phases. This effective two-body force field is the successor to the Weiner et al. force field and was developed with some of the same philosophies, such as the use of a simple diagonal potential function and electrostatic potential fit atom centered charges. The need for a 10-12 function for representing hydrogen bonds is no longer necessary due to the improved performance of the new charge model and new van der Waals parameters. These new charges are determined using a 6-31G* basis set and restrained electrostatic potential (RESP) fitting and have been shown to reproduce interaction energies, free energies of solvation, and conformational energies of simple small molecules to a good degree of accuracy. Furthermore, the new RESP charges exhibit less variability as a function of the molecular conformation used in the charge determination. The new van der Waals parameters have been derived from liquid simulations and include hydrogen parameters which take into account the effects of any geminal electronegative atoms. The bonded parameters developed by Weiner et al. were modified as necessary to reproduce experimental vibrational frequencies and structures. Most of the simple dihedral parameters have been retained from Weiner et al., but a complex set of 4 and yj parameters which do a good job of reproducing the energies of the low-energy conformations of glycyl and alanyl dipeptides has been developed for the peptide backbone.
Protein Fold Recognition and Comparative Modelling using HOMSTRAD , JOY and FUGUE

This article illustrates how tools exploiting the knowledge of protein three-dimensional structure can be used to identify homologues of known structure, generate sequencestructure alignments and assist model building. The tools described here include HOMSTRAD, a database of structure-based alignments for protein families of known structure, JOY, a program to annotate local environments in structure-based alignments, and FUGUE, a program to perform sequence-structure homology recognition. After a brief review of the whole process of homology recognition and comparative modeling, a specific example clarifies all the steps involved. This type of analysis will help obtain a better understanding of the function of many proteins whose sequences are known.
Recruitment of resting vesicles into recycling pools supports NMDA receptor-dependent synaptic potentiation in cultured hippocampal neurons

• Presynaptic terminals in hippocampal neurons are characterized by two functionally defined vesicle populations: a recycling pool, which supports activity‐evoked neurotransmission, and a resting pool. • Between individual synapses, the relative proportions of these two pools are highly variable, suggesting that this parameter might be specifically regulated to support changes in synaptic efficacy. • Using fluorescence imaging and correlative ultrastructural approaches we show here that a form of synaptic potentiation dependent on N‐methyl‐d‐aspartic acid (NMDA) receptor activity can lead to a rapid and sustained expansion of the recycling fraction at the expense of the resting pool. • This recruitment of vesicles depends on nitric oxide signalling and calcineurin activity, and is accompanied by an increase in synaptic release probability. • We suggest that vesicle exchange between these pools provides a rapid mechanism to support adjustments in synaptic strength associated with a form of Hebbian plasticity.
Predictor-based control of human emotions when reacting to a dynamic virtual 3D face stimulus

This paper introduces how predictor-based control principles are applied to the control of human emotion - excitement and frustration - signals. We use changing distance-between-eyes in a virtual 3D face as a control signal. A predictor-based control law is synthesized by minimizing control quality criterion in an admissible domain. Admissible domain is composed of input signal boundaries. Relatively high control quality of excitement and frustration signals is demonstrated by modelling results. Input signal boundaries allow decreasing variation of changes in a virtual 3D face.
Modélisation mathématique de la contagion de défaut

Cette these porte sur la modelisation mathematique de la contagion de defaut. Une premiere approche est donnee par les modeles a forme reduite, dans laquelles les occurrences de defaut son modelises par des instants d'arrivee d'un processus ponctuel marque. On propose une approche rigoureuse de la calibration de ces modeles a partir de prix de produits derives de credit, en utilisant des methodes de projection Markovienne et de controle d'intensite. Une deuxieme approche est celle des modeles structurels de risque de defaut: on modelis les liens economiques entre bilans des differentes entreprises comme un reseau de contreparties. Dans de tel reseau, un choc macroeconomique, qui induit des pertes initiales et le defaut de quelques institutions, peut etre amplifie par une contagion, via des cascades d'illiquidite ou d'insolvabilite, qui engendrent alors des defauts a grande echelle. Les principaux types de contagion sont l'illiquidite et l'insolvabilite. En modelisant le reseau financier par un graphe aleatoire pondere et oriente on obtient des resultats asymptotiques pour l'amplitude de la contagion dans un grand reseau financier. On aboutit en particulier a une expression analytique pour la fraction finale de defauts en fonction des caracteristiques du reseau. Ces resultats donnent un critere de robustesse d'un grand reseau financier et peuvent s'appliquer dans le cadre des stress tests effectues par les regulateurs. Enfin, on etudie la taille et la dynamique des cascades d'illiquidite dans les marches de gre a gre et l'impact, en terme de risque systemique, du a l'introduction d'une chambre de compensation pour les Credit default swaps (CDS).
Canine Mammary Mixed Tumours: A Review

Mammary mixed tumours are the most frequent neoplasias in female dogs. In humans, mixed tumours are frequently found in the salivary glands and are known as pleomorphic adenomas. In addition to their histomorphologic similarities, mixed tumours and pleomorphic adenomas have the potential to become malignant and give rise to carcinomas in mixed tumours and carcinomas ex-pleomorphic adenoma, respectively. The factors associated with malignant transformation are still poorly known in the case of canine mixed tumours. However, this form of neoplasia tends to be associated with a better prognosis than other malignant histological types. This paper discusses the main features associated with female canine mammary mixed tumours.
Monopolistic Competition in Switzerland and Mark-up Pricing Over the Business Cycle

We investigate how firms' market power affects the price level. In our small macromodel we show, that firms - in addition to hypothesised structural mark-up pricing power - may take advantage of favourable business cycle fluctuations. The paper provides empirical evidence for both these propositions to hold. To show this, we estimate the model in a multivariate time series framework with double integrated variables. We can derive a model based business cycle indicator which compares very well with exogenous survey data information.
CodeGenie:: a tool for test-driven source code search

We present CodeGenie, a tool that implements a test-driven approach to search and reuse of code available on largescale code repositories. With CodeGenie, developers designtest cases for a desired feature first, similar to Test-driven Development (TDD). However, instead of implementing the feature from scratch, CodeGenie automatically searches foran existing implementation based on information available in the tests. To check the suitability of the candidate results in the local context, each result is automatically woven into the developer's project and tested using the original tests. The developer can then reuse the most suitable result. Later, reused code can also be unwoven from the project as wished. For the code searching and wrapping facilities, CodeGenie relies on Sourcerer, an Internet-scale source code infrastructure that we have developed.
Water Allocation, Climate Change, and Sustainable Peace The Israeli Proposal

Abstract Israeli-Palestinian joint water management strategies fail to account for increased water demand caused by increasing populations and potential decreases in water availability due to climate change. This study examines the impacts of population growth and climate change on the water supplies of Israelis and Palestinians under “business-as-usual” conditions as well as under the Israeli-proposed water resources division discussed in meetings parallel to those at Camp David in 2000. The analysis establishes renewable water resources by source in Israel/Palestine and presents the current sectoral trends in water consumption. From this baseline, eight scenarios are developed that describe conditions in 2000 and 2025. Several indicators are used to measure the positive and negative effects of these conditions. The indicators show that population growth and climate change will negate many of the benefits of the water resources division proposed in 2000. Furthermore, the indicators reveal extreme water resources stress among Palestinians as well as potential environmental degradation as climate change depletes natural water supplies.
[ Draft : Work In Progress ] Deep Challenges of Natural Vision for AI

Much research on vision assumes that the functions of vision are obvious and attempts to investigate, or build, mechanisms that those functions require. However, there are many functions of biological vision that go unnoticed, or are mis-described, and that holds back both science and engineering. Some generally unrecognized or poorly characterised functions of vision are described, including uses of vision to provide information about what is not the case, the variety of requirements for integrating visual input across time and space, and the roles of vision in mathematical discoveries leading up to Euclid’s Elements, for instance reasoning about what is and is not possible in the environment and why. Some visual functions are important for “online intelligence”, e.g. in visual servo-control, others for “offline intelligence”, e.g. in planning, designing or explaining. Vision can be important in “social intelligence” including requesting or providing shareable information, and acquiring and using information about other individuals (e.g. what they can or cannot reach, or do, or see, or their states of mind). Exhaustively specifying functions of vision is impossible, since they can change. However, we can start with a set of unnoticed requirements relevant to research on vision in intelligent animals and machines.
High performance control of active camera head using PaLMtree

In this paper, we propose a new feedback-error-learning controller enhanced by the PaLM tree that is an easy-to-use function approximator developed by our research group. We investigate the ability of our feedback-error-learning controller by applying it to controlling an active camera head to pursuit a moving target with high accuracy and high response. The PaLM-tree learns the inverse model in the feedback-error-learning scheme correctly. Although our active camera head has unknown mechanical friction and our closed-loop control system has a relatively large latency, our active camera head can successfully pursuit eye movement by our feedback-error-learning controller based on the PaLM-tree. Through experiments, we confirmed that our method could achieve high-performance control over the tuned feedback control
SLINC : A FRAMEWORK FOR VOLUNTEER COMPUTING

Volunteer computing is an innovative approach to high performance computing that relies on volunteers who donate their personal computers’ unused resources to a computationally intensive research project. Prominent volunteer computing projects include SETI@home, Folding@Home, and The Great Internet Mersenne Prime Search (GIMPS). Many volunteer computing projects are built upon a volunteer computing framework that abstracts functionality that is common to all volunteer computing projects, such as network communications, database access, and project management. These volunteer computing frameworks tend to be complex, limiting, and difficult to use. We have designed and implemented a new volunteer computing framework called the Simple Light-weight Infrastructure for Network Computing (SLINC) that addresses the disadvantages we identified with existing frameworks. SLINC is a flexible and extensible volunteer computing framework that will enable researchers to more easily build volunteer computing projects.
Automatic Interpretation of Outdoor Scenes

This paper describes recent work on a neural network approach to outdoor scene interpretation. The results of evaluating a range of automatic region-based segmentation techniques based on a new segmentation quality metric are presented. The optimal technique is used to segment images of natural outdoor scenes. A powerful set of features designed for outdoor scene analysis is extracted from regions in the segmented images and used to train a neural network to recognise eleven different classes of objects, including sky, road, building and vegetation. The system is tested on a large number of images which have been hand-labelled to provide ground-truth segmentations and interpretations. This fully automated system achieves an impressive mean classification accuracy of 81.4% per image on unseen data, and runs in approximately 1 CPU minute on a Sun SPARCstation 20.
A Novel Method for The Spatial-Temporal Spectral Estimation Without Eigendecomposition

In this paper, a novel method for the spatial-temporal spectral estimation of multiple narrowband plane waves is developed based on the multistage Wiener filter (MSWF). The proposed method does not apply the estimation of covariance matrix and its eigendecomposition, therefore, its computational complexity is low. The numerical examples are used to demonstrate the performance of proposed method
The Structure of Molecular Clouds: I - All Sky Near Infrared Extinction Maps

We are studying the column density distribution of all nearby giant molecular clouds. As part of this project we generated several all sky extinction maps. They are calculated using the median near infrared colour excess technique applied to data from the Two Micron All-Sky Survey (2MASS). Our large scale approach allows us to fit spline functions to extinction free regions in order to accurately determine the colour excess values. Two types of maps are presented: i) Maps with a constant noise and variable spatial resolution; ii) Maps with a constant spatial resolution and variable noise. Our standard Av map uses the nearest 49 stars to the centre of each pixel for the determination of the extinction. The one sigma variance is constant at 0.28mag Av in the entire map. The distance to the 49th nearest star varies from below 1arcmin near the Galactic Plane to about 10arcmin at the poles, but is below 5arcmin for all giant molecular clouds (|b|<30degr). A comparison with existing large scale maps shows that our extinction values are systematically larger by 20% compared to Dobashi et al. and 40% smaller compared to Schlegel et al. This is most likely caused by the applied star counting technique in Dobashi et al. and systematic uncertainties in the dust temperature and emissivity in Schlegel et al. Our superior resolution allows us to detect more small scale high extinction cores compared to the other two maps.
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold

Accurately measuring the aerosol optical depth (AOD) is essential for our understanding of the climate. Currently, AOD can be measured by (i) satellite instruments, which operate on a global scale but have limited accuracies; and (ii) ground-based instruments, which are more accurate but not widely available. Recent approaches focus on integrating measurements from these two sources to complement each other. In this paper, we further improve the prediction accuracy by using the observation that the AOD varies slowly in the spatial domain. Using a probabilistic approach, we impose this smoothness constraint by a Gaussian random field on the Earth's surface, which can be considered as a two-dimensional manifold. The proposed integration approach is computationally simple, and experimental results on both synthetic and real-world data sets show that it significantly outperforms the state-of-the-art.
Kobe University Repository : Kernel Tit le Rim 2 alpha determines docking and priming states ininsulin granule exocytosis

Insulin secretion is essential for maintenance of glucose homeostasis, but the mechanism of insulin granule exocytosis, the final step of insulin secretion, is largely unknown. Here we investigated the role of Rim2in insulin granule exocytosis including the docking, priming, and fusion steps. We found that interaction of Rim2 and Rab3A is required for docking, which is considered a brake on fusion events, and that docking is necessary for K +-induced exocytosis but not for glucose-induced exocytosis. Furthermore, we found that dissociation of the Rim2/Munc13-1 complex by glucose stimulation activates Syntaxin1 by Munc13-1, indicating that Rim2 primes insulin granules for fusion. Thus, Rim2 determines docking and priming states in insulin granule exocytosis depending on its interacting partner, Rab3A or Munc13-1, respectively. Since Rim2-/-mice exhibit impaired secretion of various hormones stored as dense-core granules, including glucose-dependent insulinotropic polypeptide, growth hormone, and epinephrine, Rim2 plays a critical role in exocytosis of dense-core granules.