Miracle-dz/UniFAR-FTUs huggingface dataset:
In Silico Structural Homology Modelling and Docking for Assessment of Pandemic Potential of a Novel H7N9 Influenza Virus and Its Ability to Be Neutralized by Existing Anti-Hemagglutinin Antibodies

The unpredictable nature of pandemic influenza and difficulties in early prediction of pandemic potential of new isolates present a major challenge for health planners. Vaccine manufacturers, in particular, are reluctant to commit resources to development of a new vaccine until after a pandemic is declared. We hypothesized that a structural bioinformatics approach utilising homology-based molecular modelling and docking approaches would assist prediction of pandemic potential of new influenza strains alongside more traditional laboratory and sequence-based methods. The newly emerged Chinese A/Hangzhou/1/2013 (H7N9) influenza virus provided a real-life opportunity to test this hypothesis. We used sequence data and a homology-based approach to construct a 3D-structural model of H7-Hangzhou hemagglutinin (HA) protein. This model was then used to perform docking to human and avian sialic acid receptors to assess respective binding affinities. The model was also used to perform docking simulations with known neutralizing antibodies to assess their ability to neutralize the newly emerged virus. The model predicted H7N9 could bind to human sialic acid receptors thereby indicating pandemic potential. The model also confirmed that existing antibodies against the HA head region are unable to neutralise H7N9 whereas antibodies, e.g. Cr9114, targeting the HA stalk region should bind with high affinity to H7N9. This indicates that existing stalk antibodies initially raised against H5N1 or other influenza A viruses could be therapeutically beneficial in prevention and/or treatment of H7N9 infections. The subsequent publication of the H7N9 HA crystal structure confirmed the accuracy of our in-silico structural model. Antibody docking studies performed using the H7N9 HA crystal structure supported the model's prediction that existing stalk antibodies could cross-neutralise the H7N9 virus. This study demonstrates the value of using in-silico structural modelling approaches to complement physical studies in characterization of new influenza viruses.
Title of Document: Flame Spread through Wooden Dowles Flame Spread through Wooden Dowles

Title of Document: FLAME SPREAD THROUGH WOODEN DOWLES Zhao Zhao, Master of Science, 2014 Directed By: Assistant Professor, Michael J. Gollne r, Department of Fire Protection Engineering In practical applications, flammable materials are oft n arranged in arrays of discrete objects whose combustion properties may be differen t as that of a homogeneous material. In this study, the influence of spacing b etween arrays of wooden dowels on the rate of upward flame spread through arrays has been studied. This configuration in some ways modelled physics that similar with fla me spread through wildland fuels and cable trays. A single dowel was ignited at the base of an array of birch dowels with fixed spacings of 0.75, 0.875, 1.0 and 1.5 cm and allowed to spread upwards. Flame spread along the center columns, burning dura tion times and horizontal flame spread were plotted and compared with previous theo ry (Gollner et al., 2012 and Vogel and Williams, 1970). As a result of experimen tal results, it was shown that flame spread rates will decrease after a critical s p cing is reached, most likely due to limited availability of oxygen. Experiments on hori zontally-propagating flames through dowel arrays were performed to further show this effect. FLAME SPREAD THROUGH WOODEN DOWLES
Fusion-pore expansion during syncytium formation is restricted by an actin network

Cell-cell fusion in animal development and in pathophysiology involves expansion of nascent fusion pores formed by protein fusogens to yield an open lumen of cell-size diameter. Here we explored the enlargement of micron-scale pores in syncytium formation, which was initiated by a well-characterized fusogen baculovirus gp64. Radial expansion of a single or, more often, of multiple fusion pores proceeds without loss of membrane material in the tight contact zone. Pore growth requires cell metabolism and is accompanied by a local disassembly of the actin cortex under the pores. Effects of actin-modifying agents indicate that the actin cortex slows down pore expansion. We propose that the growth of the strongly bent fusion-pore rim is restricted by a dynamic resistance of the actin network and driven by membrane-bending proteins that are involved in the generation of highly curved intracellular membrane compartments.
An Efficient Event Matching Algorithm for Information Dissemination on Grid

In ubiquitous environment, wireless sensor networks are increasingly being deployed to collect information from real world. For sharing huge amount of sensor data efficiently with diverse users, an information dissemination (INFOD) system is very important. In our previous work, we proposed an INFOD system integrated with sensor network and data grid as a publish/subscribe system for a ubiquitous-healthcare application. To disseminate sensed data to diverse users, a matching algorithm called classed group index matching (CGIM) was proposed in our previous work to match published events with subscriptions efficiently. CGIM used statistical grouping method with equivalent intervals for each group which showed poor performance in case of non-uniform subscription distributions. We need to decide proper number of groups to improve the performance. In this paper, we propose efficiency based dynamic re-grouping (DRG) method for CGIM to improve its performance for our INFOD system. Experimental results clearly demonstrate the performance improvement of CGIM algorithm.
Forecasting for Policy

Any complete account of “forecasting for policy” could be formidably long. There are an enormous number of economic policy issues that require a forecast. For example, the effects of changes in taxes and tariffs upon output and employment, the impact of legislation relating to retirement incomes upon savings behaviour, questions relating to variations in regional growth rates and even the setting of greenhouse emission targets. This diversity is naturally associated with a corresponding diversity in what is forecast, the interval of time between which successive forecasts must be made, the nature of the association between those making forecasts and the policy makers, and the forecasting methods that are deemed to be most suitable. Contemplating such a list, it is clear that we need to narrow the focus considerably in order to make the topic manageable. Perhaps the one thing about which there may be agreement is that forecasts, whether explicit or implicit, are at the heart of policy – Budd (1999) – while the methods for making a forecast often engender much less agreement.
Spatiotemporal receptive field structures in retinogeniculate connections of cat

The spatial structure of the receptive field (RF) of cat lateral geniculate nucleus (LGN) neurons is significantly elliptical, which may provide a basis for the orientation tuning of LGN neurons, especially at high spatial frequency stimuli. However, the input mechanisms generating this elliptical RF structure are poorly defined. We therefore compared the spatiotemporal RF structures of pairs of retinal ganglion cells (RGCs) and LGN neurons that form monosynaptic connections based on the cross-correlation analysis of their firing activities. We found that the spatial RF structure of both RGCs and LGN neurons were comparably elliptical and oriented in a direction toward the area centralis. Additionally, the spatial RF structures of pairs with the same response sign were often overlapped and similarly oriented. We also found there was a small population of pairs with RF structures that had the opposite response sign and were spatially displaced and independently oriented. Finally, the temporal RF structure of an RGC was tightly correlated with that of its target LGN neuron, though the response duration of the LGN neuron was significantly longer. Our results suggest that the elliptical RF structure of an LGN neuron is mainly inherited from the primary projecting RGC and is affected by convergent inputs from multiple RGCs. We discuss how the convergent inputs may enhance the stimulus feature sensitivity of LGN neurons.
Software Architecture and Task Definition of a Multiple Humanoid Cooperative Control System

This paper presents a cooperative control software architecture that coordinates a team of multiple humanoid to complete a mission by collaborating with each other. The mission of the humanoid team is decomposed into tasks and distributed to each humanoid to be executed. Each task is described by the proposed humanoid action primitives, which are designed to abstract broad classes of humanoid tasks appropriately. In particular, missions and tasks for the humanoid team are designed by using a finite state machine with a developed user interface. The multiple humanoid cooperative control software consists of 3 layers: the mission layer, task layer, and action layer. The software architecture has scalability to the number of humanoids and the number of assigned missions with its framework based on the CORBA middleware, which integrates many different functionalities of the humanoid. The feasibility and robustness of the implemented software architecture are verified through successful completion of the mission assigned to the humanoid team while each humanoid performs its given task sequentially.
Research on approximated Log-MAP Algorithm

This paper proposes an approximate calculation for correction function of Log-MAP Algorithm and obtains a well turbo decoding in AWGN channel. The algorithm mainly uses the method of segmenting and fitting for correction function by spline function. The proposed method has solved the difficulties and obstacles resulting from inaccessible calculation of correction function with exponential and logarithm computations. It shows that error correction performance of turbo decoder with the interpolation function to calculate the correction function is the same as that with Log-MAP algorithm and the former algorithm complexity is much obviously lower than the latter.
Lidar-based gait analysis in people tracking and 4D visualization

In this paper we introduce a new approach on gait analysis based on data streams of a Rotating Multi Beam (RMB) Lidar sensor. The gait descriptors for training and recognition are observed and extracted in realistic outdoor surveillance scenarios, where multiple pedestrians walk concurrently in the field of interest, while occlusions or background noise may affects the observation. The proposed algorithms are embedded into an integrated 4D vision and visualization system. Gait features are exploited in two different components of the workflow. First, in the tracking step the collected characteristic gait parameters support as biometric descriptors the re-identification of people, who temporarily leave the field of interest, and re-appear later. Second, in the visualization module, we display moving avatar models which follow in real time the trajectories of the observed pedestrians with synchronized leg movements. The proposed approach is experimentally demonstrated in eight multi-target scenes.
A Parallel SystemC Environment: ArchSC

In domains of VLSI and the rising SoC, the system design exceedingly depends on the simulation and modeling. Conventional HDLs have some weakness including the extravagant precision and the slow speed. On the other hand, the system-level modeling, such as SystemC, has been widely used on all kinds of projects and achieved favorable results. When the target system scales excessively up, the simulation speed also drops to the most extern. So, it is very important to speed up by parallelizing the system-level modeling over HPC such as cluster. This paper introduces a novel parallel SystemC environment named ArchSC which is constructed on a largescale system-level parallel simulation platform, ArchSim. The test results demonstrate that ArchSC has some advantages of high scalability, good generality and preferable acceleration performance. Keywords-ArchSC, parallel SystemC, ArchSim, parallel engine, parallel channel
COMPUTING MARGINALS USING LOCAL COMPUTATION

This paper describes an abstract framework called valuation network for computation of marginals using local computation. In valuation networks, we represent knowledge using functions called valuations. Making inferences involves using two operators called marginalization and combination. Marginalization tells us how to coarsen a valuation by eliminating some variables. Combination tells us how to combine valuations. Making inferences from a valuation network can be simply described as finding the marginal of the joint valuation for each variable of interest. The joint valuation is the combination of all valuations. We state some simple axioms that marginalization and combination need to satisfy to enable us to compute marginals using local computation. We describe a fusion algorithm for computing a marginal using local computation. We describe the fusion algorithm as a message passing in join tree algorithm. Next we describe how the messagepassing algorithm can be adapted to compute multiple marginals efficiently. Finally, we describe how Bayesian probability theory, Dempster-Shafer's belief function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory, constraint satisfaction problems, and various other problems fit in the VN framework.
Multi-GPU-accelerated FDTD method for PhC fibers characterization

In the work, we have studied the PhC fiber dispersion characteristics by means of the FDTD technique. The method has been accelerated using CUDA-compatible GPU cards and its stability was compared with classical CPU computing. In contrast to other numerical methods, the FDTD allows studying the beam propagation along the fiber taking into account material dispersion and the nonlinearity. To improve the method performance, we have concentrated attention on the method details that allowed reduce dramatically the computation time and achieve the accuracy close to the one provided by the finite elements method.
Is C-reactive protein an innocent bystander or proatherogenic culprit?: The Verdict Is Still Out

Presence at the scene of a crime is not itself necessarily compelling evidence of guilt. — Mark B. Pepys The recognition of inflammation as a central contributor to atherothrombosis has engendered a sustained effort to characterize the specific participants and pathways and to identify noninvasive markers that enable detection of underlying inflammatory activation for the purpose of assessing cardiovascular risk. C-reactive protein (CRP), an acute-phase reactant, has been investigated in the pursuit of both of these objectives. Epidemiological studies have demonstrated an increased risk of cardiovascular events in patients with elevated levels of CRP.2–5 When considered together with experimental evidence placing CRP within arterial atheroma6–8 and clinical data revealing lowering of CRP with some preventive therapies, this strong base of epidemiological evidence has led to the hypothesis that CRP is both a marker of and a causal agent in the development of atherosclerosis.9,10 In other words, CRP may be both a “marker” and a “maker” of atherothrombosis.11 This hypothesis carries substantial clinical implications in that it forms the basis for both development of potential therapeutic agents that directly target CRP and consideration of CRP itself as a modifiable cardiovascular risk factor. This unifying theory regarding CRP, while appealing, is not yet established by the available evidence.1,11 We will review the in vitro and in vivo data that support the assertion that CRP is itself pathogenic and the conflicting findings that render this conclusion premature. To understand the basis for the intense investigation of CRP as a proatherogenic agent, it is worthwhile to trace the epidemiological evidence available to date. In 1944, Lofstrom12 described concordant rises in body temperature and white blood cell count, along with an increase in the concentration of serum capsular swelling protein (later named CRP), in a patient with acute myocardial …
Aspects of multiscale modelling in a process algebra for biological systems

We propose a variant of the CCS process algebra with new features aiming at allowing multiscale modelling of biological systems. In the usual semantics of process algebras for modelling biological systems actions are instantaneous. When different scale levels of biological systems are considered in a single model, one should take into account that actions at a level may take much more time than actions at a lower level. Moreover, it might happen that while a component is involved in one long lasting high level action, it is involved also in several faster lower level actions. Hence, we propose a process algebra with operations and with a semantics aimed at dealing with these aspects of multiscale modelling. We study behavioural equivalences for such an algebra and give some examples.
USH1G with unique retinal findings caused by a novel truncating mutation identified by genome-wide linkage analysis

Purpose Usher syndrome (USH) is an autosomal recessive disorder divided into three distinct clinical subtypes based on the severity of the hearing loss, manifestation of vestibular dysfunction, and the age of onset of retinitis pigmentosa and visual symptoms. To date, mutations in seven different genes have been reported to cause USH type 1 (USH1), the most severe form. Patients diagnosed with USH1 are known to be ideal candidates to benefit from cochlear implantation. Methods Genome-wide linkage analysis using Affymetrix GeneChip Human Mapping 10K arrays were performed in three cochlear implanted Saudi siblings born from a consanguineous marriage, clinically diagnosed with USH1 by comprehensive clinical, audiological, and ophthalmological examinations. From the linkage results, the USH1G gene was screened for mutations by direct sequencing of the coding exons. Results We report the identification of a novel p.S243X truncating mutation in USH1G that segregated with the disease phenotype and was not present in 300 ethnically matched normal controls. We also report on the novel retinal findings and the outcome of cochlear implantation in the affected individuals. Conclusions In addition to reporting a novel truncating mutation, this report expands the retinal phenotype in USH1G and presents the first report of successful cochlear implants in this disease.
Are Globular Clusters the Remnant Nuclei of Progenitor Disk Galaxies?

The globular cluster system of a typical spheroidal galaxy makes up about 0.25% of the total galaxy mass. This is roughly the same mass fraction as contained in the nuclear star cluster (or stellar nucleus) present in most nearby low-mass galaxies. Motivated by this “coincidence,” this Letter discusses a scenario in which globular clusters of present-day galaxies are the surviving nuclei of the dwarf galaxies that—according to the hierarchical merging paradigm of galaxy formation—constitute the “building blocks” of present-day massive galaxies. This scenario, which was first suggested by Freeman, has become more attractive recently in light of studies that demonstrate a complex star formation history in a number of massive globular clusters.
Adaptive nulling in time-modulated antenna arrays

A flexible approach based on a hybrid differential evolution strategy (HDES) is developed for adaptive pattern nulling with time modulated linear antenna arrays. The HDES adjusts the least significant bits of the digital phase shifters and the on-off time sequence to minimize the total output power, deep nulls can then be placed in the direction of the interferences. Thanks to the new design freedom introduced in time modulated antenna arrays, this adaptive nulling approach exhibits better performance over the traditional phase-only, amplitude-only, and phase-amplitude adaptive nulling methods, in terms of the practical implementation, nulling depth, and pattern deterioration. Several representative examples are presented to show the effectiveness of the proposed adaptive nulling approach.
Block Splitting for Large-Scale Distributed Learning

Machine learning and statistics with very large datasets is now a topic of widespread interest, both in academia and industry. Many such tasks can be posed as convex optimization problems, so algorithms for distributed convex optimization serve as a powerful, general-purpose mechanism for training a wide class of models on datasets too large to process on a single machine. In previous work, it has been shown how to solve such problems in such a way that each machine only looks at either a subset of training examples or a subset of features. In this paper, we extend these algorithms by showing how to split problems by both examples and features simultaneously, which is necessary to deal with datasets that are very large in both dimensions. We present some experiments with these algorithms run on Amazon’s Elastic Compute Cloud.
The Role of Internal Processes in the Sustainability Performance and Disclosure Relationship

The present study investigates the role of internal processes and specific performance measures (flaring and CO2 emissions) in corporate sustainability reporting in the Canadian petroleum industry. It also contributes to the debate between socio-political and economic perspectives for explaining the level of sustainability reporting. By limiting our study to just one industry, the Canadian oil and gas industry, we are able to eliminate potentially confounding variables that have been present in other cross-sectional studies. We introduce Environmental Management System (process), which we argue is a proxy for strategy, into the equation. We also emphasize the contextual importance of institutional investors and consumer presence to sustainability reporting. We find that these variables offer considerable explanatory power to our equation. The behavior of the economic and sustainability performance variables is a mix of the predictions of various socio-political and economic theories. As a result, we call for more firm level contextual research before closing the debate among researchers subscribing to alternative perspectives of theorizing.
Exploiting Wikipedia Categorization for Predicting Age and Gender of Blog Authors

For privacy reasons, personally identifiable information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the fields of advertising, forensics and business intelligence. Existing methods for this problem have focused on classifier learning using content based features like word n-grams and style based features like Part of Speech (POS) n-grams. Two major drawbacks of previous approaches are: (1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. We propose a novel method to address these drawbacks by representing the document using Wikipedia concepts and category information. Experimental results show that classifiers learned using such features along with previously used features help us achieve significantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features.