Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Neuraminidase gene of Avian Influenza virus (H5N1) In Egypt from 2006-2009

Highly pathogenic avian influenza (AI) caused by the influenza A H5N1 virus, posses a significant threat to the poultry industry and human worldwide. Since 2006, the disease has become enzootic in poultry throughout Egypt and still circulates in the poultry population. The limitness of treatment options of avian influenza infection and the criticality of the antiviral drugs susceptibility highlighted the importance of the neuraminidase enzyme (NA) as a target for this study. The present study aimed to monitor genetic changes in the NA gene, specially the highly conserved active site to detect emerging possible strains of H5N1 that have antiviral resistant nature. Viral RNAs were isolated from thirty eight clinical samples, collected from infected different species of poultry at different governorates in Egypt during the period of 2006-2009. Real time RT-PCR was performed using specific primers for the matrix (M), Hemagglutinin (H5) and neuraminidase (N1) genes to confirm the viral subtype (H5N1). Sequencing analysis was used to monitor and detect the NA sequences in the isolated samples. Fifteen strains in the present study have mutations at the target of primers and probe without effect on the binding of the primer and probe in PCR reaction. The following mutations were recorded: Asp151His (NA active site mutation), Leu223Met (located between catalytic amino acid and framework one), Ser228Asp (located away from active site) and twenty amino acids deletion in the stalk motif of neuraminidase enzyme. [El Said T Awad, Eman M Gouda, Mona M Aly, Mohammed H El-Hussieny. Characterization of Neuraminidase gene of Avian Influenza virus (H5N1) In Egypt from 2006-2009. J Am Sci 2015;11(5):42-49]. (ISSN: 15451003). http://www.jofamericanscience. org. 6
The Role of the Working Alliance in Treatment for Alcohol Problems

Little research has been done on the role of the therapeutic working alliance in treatment for alcohol problems. This longitudinal study’s objectives were (a) to identify predictors of working alliance and (b) to investigate whether client and/or therapist reports of the working alliance predicted posttreatment motivation and then later treatment outcome. Client and therapist perceptions of the working alliance were assessed after the first treatment session using a short form of the Working Alliance Inventory (WAI) among 173 clients taking part in the United Kingdom Alcohol Treatment Trial (UKATT) and randomized to motivational enhancement therapy (MET) or social behavior and network therapy (SBNT) with complete data on all measures of interest. Structural equation models were fitted to identify predictors of WAI scores and investigate the relationships between WAI and measures of drinking during treatment, posttreatment motivation, and successful treatment outcome (abstinent or nonproblem drinker), and measures of drinks per drinking day and nondrinking days, assessed 9 months after the conclusion of treatment. Motivation to change drinking when treatment began was a strong predictor of client—adjusted coefficient = 2.21 (95% confidence interval [CI] [0.36, 4.06]—but not therapist WAI. Client WAI predicted successful treatment outcome—adjusted odds ratios (OR) = 1.09 (95% CI [1.02, 1.17])—and had effects on drinking during treatment, and on posttreatment motivation to change. There was evidence for effect modification by treatment, with strong associations between WAI and posttreatment motivation, and evidence of WAI prediction of treatment outcomes in the MET group, but no evidence of associations for SBNT. Therapist WAI was not strongly associated with treatment outcome (adjusted OR = 1.05; 95% CI [0.99, 1.10]). The working alliance is important to treatment outcomes for alcohol problems, with client evaluation of the alliance strongly related to motivation to change drinking throughout treatment for MET. It was also much more important than therapist-rated alliance in this study.
Head-Driven Generation with HPSG

As HPSG is head-driven, with clear semantic heads, semantic head-driven generation should be simple. We adapt van Noord's Prolog generator for use with an HPSG grammar in ProFIT. However, quantifiers and context factors are difficult to include in head-driven generation. We must adopt recent theoretical proposals for lexicalized scoping and context. With these revisions, head-driven generation with HPSG is not so simple, but it is possible.
On the fixed-interval smoothing problem †

After a review of the development of the Mayne-Fraser two-filter smoother, a first principle argument is used to rederive this smoother. Reversed-time Markov models play a key role in forming a state estimate from future observations. The built-in asymmetry of the Mayne-Fraser smoother is pointed out, and it is shown how this asymmetry may be removed. Additionally, a covariance analysis of the two-filter smoother is provided, and reduced-order smoothers are analyzed.
The intracellular transport of low density lipoprotein-derived cholesterol is defective in Niemann-Pick type C fibroblasts

Niemann-Pick disease type C (NPC) is characterized by substantial intracellular accumulation of unesterified cholesterol. The accumulation of unesterified cholesterol in NPC fibroblasts cultured with low density lipoprotein (LDL) appears to result from the inability of LDL to stimulate cholesterol esterification in addition to impaired LDL-mediated downregulation of LDL receptor activity and cellular cholesterol synthesis. Although a defect in cholesterol transport in NPC cells has been inferred from previous studies, no experiments have been reported that measure the intracellular movement of LDL- cholesterol specifically. We have used four approaches to assess intracellular cholesterol transport in normal and NPC cells and have determined the following: (a) mevinolin-inhibited NPC cells are defective in using LDL-cholesterol for growth. However, exogenously added mevalonate restores cell growth equally in normal and NPC cells; (b) the transport of LDL-derived [3H]cholesterol to the plasma membrane is slower in NPC cells, while the rate of appearance of [3H]acetate- derived, endogenously synthesized [3H]cholesterol at the plasma membrane is the same for normal and NPC cells; (c) in NPC cells, LDL- derived [3H]cholesterol accumulates in lysosomes to higher levels than normal, resulting in defective movement to other cell membranes; and (d) incubation of cells with LDL causes an increase in cholesterol content of NPC lysosomes that is threefold greater than that observed in normal lysosomes. Our results indicate that a cholesterol transport defect exists in NPC that is specific for LDL-derived cholesterol.
Hexa-μ2-acetato-triaqua-μ3-oxido-triiron(III) nitrate acetic acid solvate

The asymmetric unit of the title compound, [Fe3(CH3COO)6O(H2O)3]NO3·CH3COOH, consists of a hexa-μ2-acetato-triaqua-μ3-oxo-triiron(III) macrocation, a nitrate ion and an acetic acid solvent molecule. In the cation, each Fe3+ ion is coordinated by four carboxylate O atoms, one central bridged O atom and one water molecule, resulting in distorted FeO6 octahedra. A network of O—H⋯O hydrogen bonds helps to establish the packing.
A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks

This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications

We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a dierence between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step. We empirically and theoretically show that the per-iteration cost of our algorithms is much less than [30], and our algorithms can be used to efficiently minimize a dierence between submodular functions under various combinatorial constraints, a problem not previously addressed. We provide computational bounds and a hardness result on the multiplicative inapproximability of minimizing the dierence between submodular functions. We show, however, that it is possible to give worst-case additive bounds by providing a polynomial time computable lower-bound on the minima. Finally we show how a number of machine learning problems can be modeled as minimizing the dierence between submodular functions. We experimentally show the validity of our algorithms by testing them on the problem of feature selection with submodular cost features.
Title Flashing color on the performance of SSVEP-based brain-computer Interfaces

— A critical problem in using steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) for clinical and commercial use is the visual fatigue the user may suffer when staring at flashing stimuli. Aiming at the design of user-friendly BCIs with satisfactory performance, this work is to preliminarily investigate how different colors influence the SSVEP (i.e. frequency or phase) and system performance. The results show that white stimuli can lead to the highest performance, followed by gray, red, green and blue stimuli.
When is import competition detrimental to workers? An application of TAR model

1 January 2010 was one major milestone in Indonesia's trade liberalization process. On that date, Indonesia simultaneously put into practice several trade agreements which Indonesia had signed under ASEAN framework. They are ASEAN free trade agreements with Japan, India, China, Korea, Australia, and New Zealand. These agreements will enable Indonesia to gain many benefits from free trade. Nonetheless, these agreements also expose Indonesia's economy to harsher foreign competition. In the beginning of 2010, workers of the manufacturing sector urged the government of Indonesia to renegotiate these free trade agreements, particularly ASEAN-China Free Trade Agreement. These workers, particularly those who work in the textile and apparel industries, felt threatened that cheap products, especially from China, would make them lose their job. The government of Indonesia needs to find the middle ground which allows Indonesia to acquire benefits of free trade without causing excessive detriment to workers. This study set forth to assist the government of Indonesia in discovering the limit of expanding import competition. So as to fulfill that intention, this study employed threshold autoregressive (TAR) model.
Negotiating the Semantics of Agent Communication Languages

This article presents a formal framework and outlines a method that autonomous agents can use to negotiate the semantics of their communication language at run–time. Such an ability is needed in open multi–agent systems so that agents can ensure they understand the implications of the utterances that are being made and so that they can tailor the meaning of the primitives to best fit their prevailing circumstances. To this end, the semantic space framework provides a systematic means of classifying the primitives along multiple relevant dimensions. This classification can then be used by the agents to structure their negotiation (or semantic fixing) process so that they converge to the mutually agreeable semantics that are necessary for coherent social interactions.
Habitual action video game playing is associated with caudate nucleus-dependent navigational strategies

The habitual playing of video games is associated with increased grey matter and activity in the striatum. Studies in humans and rodents have shown an inverse relationship between grey matter in the striatum and hippocampus. We investigated whether action video game playing is also associated with increased use of response learning strategies during navigation, known to be dependent on the caudate nucleus of the striatum, when presented in a dual solution task. We tested 26 action video game players (actionVGPs) and 33 non-action video game players (nonVGPs) on the 4-on-8 virtual maze and a visual attention event-related potential (ERP) task, which elicits a robust N-2-posterior-controlateral (N2pc) component. We found that actionVGPs had a significantly higher likelihood of using a response learning strategy (80.76%) compared to nonVGPs (42.42%). Consistent with previous evidence, actionVGPs and nonVGPs differed in the way they deployed visual attention to central and peripheral targets as observed in the elicited N2pc component during an ERP visual attention task. Increased use of the response strategy in actionVGPs is consistent with previously observed increases in striatal volume in video game players (VGPs). Using response strategies is associated with decreased grey matter in the hippocampus. Previous studies have shown that decreased volume in the hippocampus precedes the onset of many neurological and psychiatric disorders. If actionVGPs have lower grey matter in the hippocampus, as response learners normally do, then these individuals could be at increased risk of developing neurological and psychiatric disorders during their lifetime.
An Energy Efficiency Channel Binding Mechanism for Multi-user MIMO in 802.11ac

In 802.11ac protocol, MU-MIMO technical is widely used for its excellent performance. In order to effectively save channel resources and decrease power consumption of the entire network, an Energy efficiency channel binding mechanism is proposed. The mechanism uses channel binding technology, binding two or more channels together, used to transmit the same data. Analysis and simulation results shown that the proposed method can get better power and channel resource utilities compared with other methods. It will be helpful to introduce a green network.
Early hominid evolution and ecological change through the African Plio-Pleistocene.

The habitats in which extinct hominids existed has been a key issue in addressing the origin and extinction of early hominids, as well as in understanding various morphological and behavioral adaptations. Many researchers postulated that early hominids lived in an open savanna (Dart, 1925; Robinson, 1963; Howell, 1978). However, Vrba (1985, 1988) has noted that a major global climatic and environmental shift from mesic, closed to xeric, open habitats occurred in the late African Pliocene (approximately 2.5 m.y.a.), thus implying that the earliest hominids existed in these mesic, wooded environs. This climatic shift is also suggested to have contributed to a pulse in speciation events with turnovers of many bovid and possibly hominid species. Previous environmental reconstructions of hominid localities have concentrated on taxonomic identities and taxonomic uniformitarianism to provide habitat reconstructions (e.g., Vrba, 1975; Shipman & Harris, 1988). In addition, relative abundances of species are often used to reconstruct a particular environment, when in fact taphonomic factors could be affecting the proportions of taxa. This study uses the morphological adaptations of mammalian assemblages found with early hominids to reconstruct the habitat based on each species' ecological adaptations, thus minimizing problems introduced by taxonomy and taphonomy. Research presented here compares east and south African Plio-Pleistocene mammalian fossil assemblages with 31 extant mammalian communities from eight different habitat types. All communities are analyzed through ecological diversity methods, that is, each species trophic and locomotor adaptations are used to reconstruct an ecological community and derive its vegetative habitat. Reconstructed habitats show that Australopithecus species existed in fairly wooded, well-watered regions. Paranthropus species lived in similar environs and also in more open regions, but always in habitats that include wetlands. Homo is the first hominid to exist in areas of fairly open, arid grassland. This change from closed to open habitats occurs gradually from about 4 m.y. a. until about 2 m.y. a. when there is a major increase in arid and grazing adapted mammals. Therefore, the appearance of open savannas do not appear to have influenced the origination or adaptations of the earliest hominids, but could have contributed to their demise. As Stanley (1992) hypothesized, Homo species appear the first to be adapted to open, arid environments.
National lung screening trial: variability in nodule detection rates in chest CT studies.

PURPOSE To characterize the variability in radiologists' interpretations of computed tomography (CT) studies in the National Lung Screening Trial (NLST) (including assessment of false-positive rates [FPRs] and sensitivity), to examine factors that contribute to variability, and to evaluate trade-offs between FPRs and sensitivity among different groups of radiologists. MATERIALS AND METHODS The HIPAA-compliant NLST was approved by the institutional review board at each screening center; all participants provided informed consent. NLST radiologists reported overall screening results, nodule-specific findings, and recommendations for diagnostic follow-up. A noncalcified nodule of 4 mm or larger constituted a positive screening result. The FPR was defined as the rate of positive screening examinations in participants without a cancer diagnosis within 1 year. Descriptive analyses and mixed-effects models were utilized. The average odds ratio (OR) for a false-positive result across all pairs of radiologists was used as a measure of variability. RESULTS One hundred twelve radiologists at 32 screening centers each interpreted 100 or more NLST CT studies, interpreting 72 160 of 75 126 total NLST CT studies in aggregate. The mean FPR for radiologists was 28.7% ± 13.7 (standard deviation), with a range of 3.8%-69.0%. The model yielded an average OR of 2.49 across all pairs of radiologists and an OR of 1.83 for pairs within the same screening center. Mean FPRs were similar for academic versus nonacademic centers (27.9% and 26.7%, respectively) and for centers inside (25.0%) versus outside (28.7%) the U.S. "histoplasmosis belt. " Aggregate sensitivity was 96.5% for radiologists with FPRs higher than the median (27.1%), compared with 91.9% for those with FPRs lower than the median (P = .02). CONCLUSION There was substantial variability in radiologists' FPRs. Higher FPRs were associated with modestly higher sensitivity.
Face Attributes Recognition via Deep Multi-Task Cascade

Predicting face attributes in images is challenging due to complex face variations. We observe that the signals of some attributes are strong in roughly fixed facial area. In this paper, we propose a deep multi-task cascaded network for face attributes recognition, which is trained in an end-to-end way by the combination of attribute-associated region discovery and attribute prediction together. Associated region is mapped by the first sub-network, which is a fully convolutional network (FCN). Then the second sub-network takes the arbitrary-sized associated regions as input. We use RoI pooling to transform them into a uniform-sized region, and train the whole model by stochastic gradient descent (SGD). We also introduce several useful training strategies, including the batch sampling and the early stopping. We adopt sample augmentation for the balance of positive and negative training data in batch sampling. We involve the early stopping to terminate the iterations flexibly for each attribute in case of the model deviation caused by the overfitting of easy attributes. We evaluate our model on two widely used attributes recognition databases, and the experimental results demonstrate that the performance of our approach is better than other state-of-the-art methods on the most of face attributes.
MIG-10, an Adapter Protein, Interacts with ABI-1, a Component of Actin Polymerization Machinery

MIG-10 is a protein known to be involved in axon guidance and neuronal migration in early development in C. elegans. In an effort to better understand the protein's function, this project used the yeast two hybrid system to screen a cDNA library (representing the entire C. elegans genome) for proteins with which MIG-10 interacts. The idea is if we can associate it with proteins of known function, we'll have a better idea as to what MIG-10 itself does. Our research revealed that MIG-10 interacts with (among other proteins) ABI-1, which is a component of actin polymerization machinery.
Using Mean Shift for Video Image Segmentation

The goal of this project was to develop a fast video image segmentation routine which could be used as a preprocessing step for motion tracking. We chose mean shift [1] as the primary algorithm. Our implementation includes several enhancements including dynamically adjusting the kernel bandwidth based on the overall level of image noise, and keeping a cache of past moves to avoid repeated computations. The frame rate of the resulting implementation is still slow due to the quadratic complextity and iterative nature of the underlying algorithm, but provides a reasonable starting point for refinements. We conclude with a discussion of several limitations of the mean shift as a procedure for image segmentation and present a couple options for improvement. GENERAL PROBLEM FORMULATION Image segmentation is an important low-level task in computer vision. As we saw in the homework the k-means algorithm can be used to compress an image into a reduced color space that approximately represents the original image. A robust segmentation could be the first stage in a object tracking pipeline, and in some ways represents a dual approach to the more common approach of tracking corner features. Informally, we define the video image segmentation problem as follows.
Two Approaches for Building an Unsupervised Dependency Parser and Their Other Applications

Much work has been done on building a parser for natural languages, but most of this work has concentrated on supervised parsing. Unsupervised parsing is a less explored area, and unsupervised dependency parser has hardly been tried. In this paper we present two approaches for building an unsupervised dependency parser. One approach is based on learning dependency relations and the other on learning subtrees. We also propose some other applications of these approaches.