Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Nedd4 and Ubiquitination of Rous Sarcoma Virus Gag in Budding of Virus-Like Particles from Cells

ABSTRACT Rous sarcoma virus (RSV) budding requires an interaction of the L domain within the p2b region of Gag with cellular Nedd4-family E3 ubiquitin protein ligases. Members of our laboratories previously demonstrated that overexpression of a fragment of the chicken Nedd4-like protein (LDI-1 WW) inhibits Gag release in a dominant-negative manner (A. Kikonyogo, F. Bouamr, M. L. Vana, Y. Xiang, A. Aiyar, C. Carter, and J. Leis, Proc. Natl. Acad. Sci. USA 98:11199-11204, 2001). We have now identified the complete 3′ end of LDI-1 and determined that it has a C-terminal ubiquitin ligase HECT domain, similar to other Nedd4 family members. While overexpression of the full-length LDI-1 clone (LDI-1 FL) had little effect on Gag budding, an LDI-1 FL mutant with a substitution in the HECT domain catalytic site blocked Gag release, similar to LDI-1 WW. The coexpression of Gag and hemagglutinin-tagged ubiquitin (HA-Ub) resulted in the detection of mono- and polyubiquitinated forms of Gag in cells and mostly monoubiquitinated Gag in virus-like particles (VLPs). When the Nedd4-binding site (L domain) was deleted, ubiquitinated Gag was not detected. Interestingly, the release of Gag with ubiquitin covalently linked to the C terminus (Gag-Ub) was still blocked by LDI-1 WW. To understand the mechanism of this inhibition, we examined cells expressing Gag and LDI-1 WW by electron microscopy. In the presence of LDI-1 WW, VLPs were found in electron-dense inclusion bodies in the cytoplasm of transfected cells. In contrast, when cells that coexpressed Gag-Ub and LDI-1 WW were examined, inclusion bodies were detected but did not contain VLPs. These results indicate that the ubiquitination of Gag is dependent upon Nedd4 binding to the L domain and suggest that Nedd4 has additional functions during RSV release besides the ubiquitination of Gag.
Ultima Ratio-a visual language for argumentation

In the third act of Shakespeare's Hamlet, the hero is unsure whether to kill Claudius-the assassin of Hamlet's father-or not. He argues that if he does kill him, Claudius who is praying at that very moment goes to heaven and if he does not kill him Hamlet's father is not revenged. A contradiction. Ultima Ratio aims at formalization and visualization of argumentation for agents. An agent is constituted by a set of arguments and assumptions. Facing a particular world, the agent's believes may be inconsistent triggering a rational monologue to deal with the situation. Formally, we define a framework for argumentation based on extended logic programming under well-founded semantics. The system serves as decision support and is capable of detecting and removing contradictions and deriving conclusions of the agent's arguments. To demonstrate the structure and dynamics of the agent's argumentation, we visualise the process as dynamic construction of proof trees. The paper includes screenshots of the logical engine and the visualisation unit as exhibited at the computer arts exhibition Ars Electronica 98.
Towards test suite reduction using maximal frequent data mining concept

All the industries are going through a revolution where software has become an essential component. Software testing is important to produce more reliable systems. Over the years this testing activity has evolved into a major resource consumer and cost driver for the software industries. This invariably leads to the addressing of key issues for efficient test case selection and managing test suite size. In this paper, a data mining-based algorithm has been proposed to address the issue of test suite optimisation. This algorithm focuses on selecting maximal frequent test sets for dataflow testing with Define Use DU pairs as requirements. Experimentation has been carried out to compare the relative performance and effectiveness of the proposed test suite reduction algorithm with the state-of-the-art algorithms: Harrold Gupta and Soffa HGS and Bi-Objective Greedy BOG using test metrics.
A variable sea surface temperature threshold for tropical convection.

Sea surface temperatures (SSTs) contribute to modulation of deep convection over tropical oceans. Using a threshold value of outgoing long-wave radiation (OLR) as a proxy for deep tropical convection, regional and temporal relationships between SST and deep convection and its variability are examined. Based on this approach, an SST threshold of 27.5°C for deep convection across the global tropics is identified; this is in agreement with the critical SST of around 27°C for the onset of tropical deep convection identified in previous studies. Monthly and regional variations in the critical SST threshold (SSTMON-CRIT) required for the onset of deep tropical convection are also identified. SSTMON-CRIT exhibits interannual and seasonal variations for each of the monsoon regions analysed here. It varies between regions consistent with the mean SST of that region, so is higher in the Pacific than the Atlantic. Further, within a region, the critical SST threshold for convection is cooler in the wet season than the dry. The threshold SST in each region is generally a good proxy for convective activity in the wet season, but this connection breaks down in the dry season. The impact of atmospheric divergence in suppressing convection is much more evident in the dry season. Thus, the applicability of SST as a predictor of convection varies with atmospheric forcing, by region and by season and so we conclude that an absolute SST threshold is not a robust metric for tropical deep convection. Theses results have implications for understanding the distribution of tropical cyclogenesis in varied climate regimes.
Comparison of mathematical and artificial neural network models for inhibition of fuel oil ash under high temperature corrosion

This research reports the results of literature data of mass loss tests of high temperature corrosion inhibition of steel in different concentration ratios of MgO, Al2O3 and SiO2 to corrosive fuel ash of V2O5 in the temperature range of 550-590°C and time range of 8-100 h. Analysis focused on determining optimum mathematical equation and artificial neural network (ANN) architecture in order to gain good prediction properties. Three mathematical equations and five ANN architectures were suggested. A computer aided program was used for developing these models. Results show that polynomial mathematical equation and multilayer perceptron are able to accurately predict selected data with high correlation coefficients.
Online Sequential Learning based on Enhanced Extreme Learning Machine using Left or Right Pseudo-inverse

The class imbalance problem has been reported as an importan t ch llenge in various fields such as Pattern Recognition, Data Mining and Machine Learning. A less explo red research area is related to how to evaluate classifiers on imbalanced data sets. This work analyzes the b ehaviour of performance measures widely used on imbalanced problems, as well as other metrics recently prop osed in the literature. We perform two theoretical analysis based on Pearson correlation and operations for a 2 ×2 confusion matrix with the aim to show the strengths and weaknesses of those performance metrics in th e presence of skewed distributions.
Application of rapid prototyping tools for a hierarchical microgrid control implementation

This paper presents the application of rapid prototyping tools for the implementation of hierarchical microgrid automation and control algorithms in Real-Time/FPGA platforms based on a Triphase 3PExpress and a National Instruments CompactRIO platforms (NI cRIO). A three layer hierarchical control structure has been used, where the primary layer implements the power electronics controls, the secondary layer implements the microgrid controls and automation strategy, and the tertiary layer implements the energy management algorithms. The rapid prototyping tools offer an environment that allows the implementation of concepts related to the coordination, control and management of a Commercial and Industrial (C&I) building microgrid to provide high quality power to both the local loads (e.g. harmonic distortion) and the grid (e.g. ancillary services). The microgrid is composed of a three-phase diesel generator, a Li-ion battery, a motor-generator test rig for wind turbine emulation, a load bank with resistive, inductive and capacitive passive load emulation capabilities, a resistive load bank, a fault emulator and a back to back converter to emulate loads or distributed resources.
On community detection in real-world networks and the importance of degree assortativity

Graph clustering, often addressed as community detection, is a prominent task in the domain of graph data mining with dozens of algorithms proposed in recent years. In this paper, we focus on several popular community detection algorithms with low computational complexity and with decent performance on the artificial benchmarks, and we study their behaviour on real-world networks. Motivated by the observation that there is a class of networks for which the community detection methods fail to deliver good community structure, we examine the assortativity coefficient of ground-truth communities and show that assortativity of a community structure can be very different from the assortativity of the original network. We then examine the possibility of exploiting the latter by weighting edges of a network with the aim to improve the community detection outputs for networks with assortative community structure. The evaluation shows that the proposed weighting can significantly improve the results of community detection methods on networks with assortative community structure.
Using Brain Oscillations and Corticospinal Excitability to Understand and Predict Post-Stroke Motor Function

What determines motor recovery in stroke is still unknown and finding markers that could predict and improve stroke recovery is a challenge. In this study, we aimed at understanding the neural mechanisms of motor function recovery after stroke using neurophysiological markers by means of cortical excitability (transcranial magnetic stimulation—TMS) and brain oscillations (electroencephalography—EEG). In this cross-sectional study, 55 subjects with chronic stroke (62 ± 14 yo, 17 women, 32 ± 42 months post-stroke) were recruited in two sites. We analyzed TMS measures (i.e., motor threshold—MT—of the affected and unaffected sides) and EEG variables (i.e., power spectrum in different frequency bands and different brain regions of the affected and unaffected hemispheres) and their correlation with motor impairment as measured by Fugl-Meyer. Multiple univariate and multivariate linear regression analyses were performed to identify the predictors of good motor function. A significant interaction effect of MT in the affected hemisphere and power in beta bandwidth over the central region for both affected and unaffected hemispheres was found. We identified that motor function positively correlates with beta rhythm over the central region of the unaffected hemisphere, while it negatively correlates with beta rhythm in the affected hemisphere. Our results suggest that cortical activity in the affected and unaffected hemisphere measured by EEG provides new insights on the association between high-frequency rhythms and motor impairment, highlighting the role of an excess of beta in the affected central cortical region in poor motor function in stroke recovery.
Pre-ictal phase detection with SVMs

Over 50 million persons worldwide are affected by epilepsy. Epilepsy is a brain disorder known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologist diagnose and determine the patient's treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that may correspond to epilepsy. The objective of this paper is to implement a novel detection technique of pre-ictal state that announces epileptic seizures from the online EEG data analysis. Unlike most published methods, that are aimed to distinguish only the normal from the epilepsy state, in this work the pre-ictal state is introduced as a new patient status, thus differentiating three possible states: normal (healthy), pre-ictal and epileptic seizure. In this manner, the patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.
Learning Discrete Representations via Information Maximizing Self-Augmented Training

Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invari-ance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning.
Abduction Engine Argument Generator Argument Analyzer Argument Strategist Goal

In order to argue effectively one must have a grasp of both the normative strength of the inferences that come into play and the effect that the proposed inferences will have on the audience. In this paper we describe a program, NAG (Nice Argument Generator), that attempts to generate arguments that are both persuasive and correct. To do so NAG incorporates two models: a normative model, for judging the normative correctness of an argument, and a user model, for judging the persuasive effect of the same argument upon the user. The user model incorporates some of the common errors humans make when reasoning. In order to limit the scope of its reasoning during argument evaluation and generation NAG explicitly simulates attentional processes in both the user and the normative models.
Fiber optical sampling of NADH concentration in guinea pig hearts during ischemia

A new method to detect and quantify ischemic states of tissue is presented. One important indicator of the metabolic and ischemic status of a cell is the concentration ratio of NADH : NAD+. The use of time-resolved laser- induced fluorescence techniques (LIF) in combination with previous biochemical measurements of NADH (Nicotineamide Adenine Dinucleotide) concentrations in tissue offer the possibility of quantitative NADH detection. The essential of the new method is a double-rescaling procedure of the fluorescence signal taking into account both the optical limitations of the device and the individually and locally varying optical tissue properties which are the scattering coefficient (mu) s, the absorption coefficient (mu) a and the anisotropy factor g. All the data presented in this paper were acquired from experiments with in vivo guinea pig hearts.
Generating example data for dataflow programs

While developing data-centric programs, users often run (portions of) their programs over real data, to see how they behave and what the output looks like. Doing so makes it easier to formulate, understand and compose programs correctly, compared with examination of program logic alone. For large input data sets, these experimental runs can be time-consuming and inefficient. Unfortunately, sampling the input data does not always work well, because selective operations such as filter and join can lead to empty results over sampled inputs, and unless certain indexes are present there is no way to generate biased samples efficiently. Consequently new methods are needed for generating example input data for data-centric programs. We focus on an important category of data-centric programs, dataflow programs, which are best illustrated by displaying the series of intermediate data tables that occur between each pair of operations. We introduce and study the problem of generating example intermediate data for dataflow programs, in a manner that illustrates the semantics of the operators while keeping the example data small. We identify two major obstacles that impede naive approaches, namely (1) highly selective operators and (2) noninvertible operators, and offer techniques for dealing with these obstacles. Our techniques perform well on real dataflow programs used at Yahoo! for web analytics.
Dynamic Chromatin Environment of Key Lytic Cycle Regulatory Regions of the Epstein-Barr Virus Genome

ABSTRACT The ability of Epstein-Barr virus (EBV) to establish latency allows it to evade the immune system and to persist for the lifetime of its host; one distinguishing characteristic is the lack of transcription of the majority of viral genes. Entry into the lytic cycle is coordinated by the viral transcription factor, Zta (BZLF1, ZEBRA, and EB1), and downstream effectors, while viral genome replication requires the concerted action of Zta and six other viral proteins at the origins of lytic replication. We explored the chromatin context at key EBV lytic cycle promoters (BZLF1, BRLF1, BMRF1, and BALF5) and the origins of lytic replication during latency and lytic replication. We show that a repressive heterochromatin-like environment (trimethylation of histone H3 at lysine 9 [H3K9me3] and lysine 27 [H3K27me3]), which blocks the interaction of some transcription factors with DNA, encompasses the key early lytic regulatory regions. Epigenetic silencing of the EBV genome is also imposed by DNA methylation during latency. The chromatin environment changes during the lytic cycle with activation of histones H3, H4, and H2AX occurring at both the origins of replication and at the key lytic regulatory elements. We propose that Zta is able to reverse the effects of latency-associated repressive chromatin at EBV early lytic promoters by interacting with Zta response elements within the H3K9me3-associated chromatin and demonstrate that these interactions occur in vivo. Since the interaction of Zta with DNA is not inhibited by DNA methylation, it is clear that Zta uses two routes to overcome epigenetic silencing of its genome.
Formation of Intermetallic Phases in AlSi7Fe1 Alloy Processed Onboard the ISS

This paper provides an analysis of the formation of intermetallic phases in AlSi7Fe1 alloy in samples processed onboard the ISS. Based on axial 2D cross-sections obtained from regions of pure diffusive growth and also solidified with forced melt flow, the sizes and distribution of intermetallic β-Al5FeSi phases were determined for different solidification velocities. In diffusive case the phases are larger and more homogeneously distributed than in case of induced melt flow. Additionally, especially for lower solidification velocity, the enrichment of Si and Fe in the centre part of the sample results in a few but rather large β-Al5FeSi particles.
Numerical Investigation of Turbulent Flow around a Rotating Stepped Cylinder for Corrosion Study

Direct numerical simulation has been carried out for turbulent flow set up by a rotating cylinder with two backward-facing steps axisymmetrically mounted in the circumferential direction. This flow geometry creates a qualitatively similar flow pattern as observed near, a sudden, pipe expansion or a plane backward-facing step, characterized by flow separation and reattachment. A region of intense turbulence intensity and high wall-shear-stress fluctuations is formed in,the recirculating I region downstream of the step, where high mass-transfer capacity was also experimentally observed. Since, corrosion is frequently mass-transfer. , controlled, our findings, put forward this apparatus as useful tool for future corrosion research.
A preliminary investigation on a wearable sensory system for lower leg swelling detection

In this paper, a wearable sensory system is proposed for lower leg swelling detection during sitting, standing and walking. This sensor potentially can be used to trigger controllable compression modalities that are used for vanous insufficiency disorders in the lower leg particularly, edema and orthostatic intolerance. The proposed sensory system includes two medical strain gauges and an accelerometer integrated into a leg brace. An experimental protocol is used to examine the sensor for 6 different body situations. The protocol is tested on a human subject with simulated leg swelling. The experimental data is processed and analyzed using four different machine learning algorithms. The results show an average accuracy of 96% for the Linear Discriminant Analysis (LDA) algorithm.
A comparison of Flashcache with IQ-Twemcached

Person-to-person cloud service providers such as Facebook use Host-side (HsC) and Application-side (AsC) caches to enhance performance. Using Facebook's Flashcache as the representative of HsC and IQ-Twemcached as the representative of AsC, this study quantifies their tradeoffs using both a read-heavy and a write-heavy workload. Obtained results show Flashcache provides significant benefit for I/O intensive workloads, e.g., read-heavy workloads when the application working set does not fit in memory. IQ-Twemcached is most suitable for those workloads that fully utilize the CPU of the server hosting the database management system. For some workloads, the performance enhancement observed with the two caches deployed together is several folds higher than the sum of the performance benefit realized by each cache individually.
ICPS: an integrative cancer profiler system

UNLABELLED Founded upon the database of 570 public signatures, ICPS is a web-based application to obtain biomarker profiles among 11 common cancers by integrating genomic alterations with transcription signatures on the basis of a previously developed integrative pipeline. ICPS supports both public data and user's in-house data, and performs meta-analysis at a cancer subtype level by combining heterogeneous datasets. Finally, ICPS returns the robust gene signature containing potential cancer biomarkers that may be useful to carcinogenesis study and clinical cancer diagnosis. AVAILABILITY http://server.bioicps. org CONTACT zhxy@mail.tsinghua. edu.cn; zxy-dcs@mail.tsinghua. edu.cn