Miracle-dz/UniFAR-FTUs huggingface dataset:
Adenovirus inhibition of cell translation facilitates release of virus particles and enhances degradation of the cytokeratin network

Infection of animal cells by a number of viruses generally results in an array of metabolic defects, including inhibition of host DNA, RNA, and protein synthesis, and morphological alterations known as cytopathic effects. For adenovirus infection there is a profound loss of cell structural integrity and a marked inhibition of host protein synthesis, the latter generally assumed necessary to enhance virus production. We examined the purpose of viral inhibition of cell translation and found that it was related in part to cytopathic wasting of infected cells. We show that viral shutoff of host translation promotes destruction of the intermediate filament network, particularly cytokeratins which are proteolysed at keratins K7 and K18 by the adenovirus late-acting L3 23-kDa proteinase. We found that if adenovirus is prevented from inhibiting cell translation, the intermediate filament network remains relatively intact, keratin proteins are still synthesized, and cells possess an almost normal morphological appearance and lyse poorly, reducing the release of nascent virus particles by several hundredfold. Remarkably, in tissue culture cells the accumulation of late viral structural proteins is only marginally reduced if host translation shutoff does not occur. Thus, a surprising major function for adenovirus inhibition of cellular protein synthesis is to enhance impairment of cellular structural integrity, facilitating cell lysis and release of progeny adenovirus particles.
SECONDARY POISONING OF STOATS AFTER AN AERIAL 1080 POISON OPERATION IN PUREORA FOREST , NEW ZEALAND

New Zealand Journal of Ecology (1999) 23(2): 175-182 ©New Zealand Ecological Society E.C. MURPHY, L. ROBBINS, J.B. Y UNG and J.E. DOWDING 1Science and Research Unit, Department of Conservation, Private Bag 4715, Christchurch, New Zealand. 2Department of Animal Ecology, Massey University, P.O. Box 84, Palmerston North, New Zealand. 3Department of Entomology and Animal Ecology, Lincoln University, P.O. Box 84, Canterbury, New Zealand. 4P.O. Box 36-274, Merivale, Christchurch, New Zealand.
First steps on Automatic Semantic Role Labeling for Basque Verbs

Semantic Role Labeling (SRL) is a research area on the rise in the eld of Natural Language Processing and is listed as a core NLP task by the Association for Computational Linguistics (ACL). As a matter of fact, having a large corpus with annotation of semantic roles is crucial for the development of applications and advanced systems for machine translation, language learning, text summarization and many others. This paper describes the process followed to develop a system for the automatic labeling of semantic roles for Basque verbs. EPEC-ROLSEM, which is a corpus labeled at predicate level following the VerbNet/PropBank model, has been used for this purpose. At rst, two di erent approaches are considered: The rst one consists of a system that will label semantic roles based on a set of linguistic rules; a second approach consisting of a system developed using Machine Learning (ML) techniques is considered. Afterwards, the nal tagger is implemented based on the results that were obtained from experiments, which had been performed on systems that used both approaches. First steps on SRL. 4/44
Value of monitoring Nrf2 activity for the detection of chemical and oxidative stress

Beyond specific limits of exposure, chemical entities can provoke deleterious effects in mammalian cells via direct interaction with critical macromolecules or by stimulating the accumulation of reactive oxygen species (ROS). In particular, these chemical and oxidative stresses can underpin adverse reactions to therapeutic drugs, which pose an unnecessary burden in the clinic and pharmaceutical industry. Novel pre-clinical testing strategies are required to identify, at an earlier stage in the development pathway, chemicals and drugs that are likely to provoke toxicity in humans. Mammalian cells can adapt to chemical and oxidative stress via the action of the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2), which up-regulates the expression of numerous cell defence genes and has been shown to protect against a variety of chemical toxicities. Here, we provide a brief overview of the Nrf2 pathway and summarize novel experimental models that can be used to monitor changes in Nrf2 pathway activity and thus understand the functional consequences of such perturbations in the context of chemical and drug toxicity. We also provide an outlook on the potential value of monitoring Nrf2 activity for improving the pre-clinical identification of chemicals and drugs with toxic liability in humans.
Estuarine Dissolved Organic Carbon Flux From Space: With Application to Chesapeake and Delaware Bays

This study uses a neural network model trained with in situ data, combined with satellite data and hydrodynamic model products, to compute the daily estuarine export of dissolved organic carbon (DOC) at the mouths of Chesapeake Bay (CB) and Delaware Bay (DB) from 2007 to 2011. Both bays show large flux variability with highest fluxes in spring and lowest in fall as well as interannual flux variability (0.18 and 0.27 Tg C/year in 2008 and 2010 for CB; 0.04 and 0.09 Tg C/year in 2008 and 2011 for DB). Based on previous estimates of total organic carbon (TOCexp) exported by all Mid‐Atlantic Bight estuaries (1.2 Tg C/year), the DOC export (CB + DB) of 0.3 Tg C/year estimated here corresponds to 25% of the TOCexp. Spatial and temporal covariations of velocity and DOC concentration provide contributions to the flux, with larger spatial influence. Differences in the discharge of fresh water into the bays (74 billion m/year for CB and 21 billion m/year for DB) and their geomorphologies are major drivers of the differences in DOC fluxes for these two systems. Terrestrial DOC inputs are similar to the export of DOC at the bay mouths at annual and longer time scales but diverge significantly at shorter time scales (days to months). Future efforts will expand to the Mid‐Atlantic Bight and Gulf of Maine, and its major rivers and estuaries, in combination with coupled terrestrial‐estuarine‐ocean biogeochemical models that include effects of climate change, such as warming and CO2 increase. Plain Language Summary This study combines satellite data, field work observations, and statistical and numerical models to investigate the seasonal and interannual variability of dissolved organic carbon (DOC) export from two major East Coast estuaries, Chesapeake, and Delaware Bays. DOC is a food supplement, supporting growth of microorganisms and plays an important role in the global carbon cycle through the microbial loop, a marine pathway which incorporates DOC into the food chain. Using this novel methodology, we were able to better quantify the combined contribution of these estuaries to the East Coast carbon budget and contrast estuarine properties affecting the DOC export, such as riverine inputs, time scales of variability, and geomorphology. The combined DOC contribution of these two estuaries represents 25% of the total organic carbon exported by all Mid‐Atlantic Bight (the coastal region running from Massachusetts to North Carolina) estuaries, and 27% of the total atmospheric carbon dioxide uptake in the Mid‐Atlantic Bight.
Simulating episodic memory deficits in semantic dementia with the TraceLink model

Although semantic dementia is primarily characterised by deficits in semantic memory, episodic memory is also impaired. Patients show poor recall of old autobiographical and semantic memories, with better retrieval of recent experiences; they can form new memories, and normal performance on pictorial recognition memory has been demonstrated. As these abnormalities in episodic memory are virtually a mirror image of those seen in the amnesic syndromes, semantic dementia poses a challenge to extant models of remote memory and amnesia. Here, we show that one such model, TraceLink, can reproduce some of the principal findings on episodic memory in semantic dementia. A loss of nodes and connections within the trace system, which can be identified with the temporal neocortical memory storage sites implicated in semantic dementia, simulates without further assumptions the findings reported above.
Comparison of Static and Switching Characteristics of 1200 V 4H-SiC BJT and 1200 V Si-IGBT

In this paper, static and switching characteristics of a 1200 V 4H-silicon carbide (SiC) bipolar junction transistor (BJT) at a bus voltage of 600 V are reported for the first time. Comparison was made between the SiC BJT and a 1200 V Si insulated gate bipolar transistor (IGBT). The experimental data show that the SiC BJT has much smaller conduction and switching losses than the Si IGBT. The SiC BJT also shows an extremely large reverse bias safe operation area, and no second breakdown was observed. This removes one of the most unattractive aspects of the BJT. The results prove that, unlike Si BJTs, BJTs in 4H-SiC are good competitors for Si IGBTs.
Dynamic Load Balancing Method Based on Audience Ratings for Video Streaming Services

As broadband networks expand, the volume of video streaming content, such as sports and concerts, on the Internet rapidly increases. With the spread of high performance PCs, audiences can expect to watch more and more video streaming content. With the rapid spread of information shared through social network services, unanticipated surges in the volume of request rates have become problematic for many Internet websites. Increasing requests rates exerts a heavy load, causing servers to become temporarily unreachable. Streaming services are typically implemented by using a server farm provisioned with a large amount of network bandwidth to handle peak usage. Each request is handled independently as content by using unicast transmission or IP multicast transmission. However, these methods differ in terms of their domain of applicability. Therefore, a single method is not always applicable to the dynamically changing situations of a network. We focus on the dynamic load balancing method, which is an approach that unifies unicast transmission and IP multicast transmission depending on network traffic and audience ratings.
Views of hypertension among young African Americans who vary in their risk of developing hypertension.

OBJECTIVES To describe beliefs about hypertension and health education of young African American adults varying in their hypertension risk status. DESIGN Fifty-eight African American young adults (17-20 years) were selected based on low and high risk criteria for hypertension assessed in earlier investigations of hemodynamic responses to stress. The sample included 15 high risk males, 13 low risk males, 14 high risk females, and 16 low risk females who were interviewed indepth about their hypertension beliefs and health education experiences. Variable-based matrices identified participants with similar responses. RESULTS Overall, participants had a limited understanding of hypertension. For example, they linked the condition to eating a diet high in pork and fat and having a positive family history of hypertension with little acknowledgement of weight, smoking, race and sex as key contributors to hypertension onset. Distinctions were found between risk categories and along sex lines. Most high risk participants believed stress could cause hypertension. High risk females believed that they were at risk for developing hypertension but lacked prevention strategies. High risk males were generally uninformed about risk or prevention. More low risk females believed in prevention compared to few low risk males. Participants reported little formal health education about hypertension and recommended that hypertension education be improved through more effective and relevant approaches in high school health education classes. CONCLUSIONS These findings support the need for comprehensive and revitalized hypertension prevention programs for young African Americans. Participant views varied with hypertension risk status and gender suggesting that targeted educational efforts should reflect these differences.
Interleaving Natural Language Parsing and Generation through Uniform Processing

We present a new model of natural language processing in which natural language parsing and generation are strongly interleaved tasks. Interleaving of parsing and generation is important if we assume that natural language understanding and production are not only performed in isolation but also can work together to obtain subsentential interactions in text revision or dialog systems. The core of the model is a new uniform agenda-driven tabular algorithm, called UTA. Although uniformly deened, UTA is able to conngure itself dynamically for either parsing or generation, because it is fully driven by the structure of the actual input|a string for parsing and a semantic expression for generation. EEcient interleaving of parsing and generation is obtained through item sharing between parsing and generation. This novel processing strategy facilitates exchanging items (i.e., partial results) computed in one direction automatically to the other direction as well. The advantage of UTA in combination with the item sharing method is that we are able to extend the use of memoization techniques even to the case of an inter-leaved approach. In order to demonstrate UTA's utility for developing high-level performance methods, we present a new algorithm for incremental self-monitoring during natural language production.
p21 rs3176352 G>C and p73 rs1801173 C>T Polymorphisms Are Associated with an Increased Risk of Esophageal Cancer in a Chinese Population

Objective Esophageal cancer was the fifth most commonly diagnosed cancer and the fourth leading cause of cancer-related death in China in 2009. Genetic factors might play an important role in esophageal squamous cell carcinoma (ESCC) carcinogenesis. Designs and Methods To evaluate the effect p21, p53, TP53BP1 and p73 single nucleotide polymorphisms (SNPs) on the risk of ESCC, we conducted a hospital based case–control study. A total of 629 ESCC cases and 686 controls were recruited. Their genotypes were determined using ligation detection reaction (LDR) method. Results When the p21 rs3176352 GG homozygote genotype was used as the reference group, the CC genotype was associated with a significantly increased risk of ESCC. When the p73 rs1801173 CC homozygote genotype was used as the reference group, the CT genotype was associated with a significantly increased risk of ESCC. After Bonferroni correction, for p21 rs3176352 G>C, the p correct was still significant. For the other six SNPs, in all comparison models, no association between the polymorphisms and ESCC risk was observed. Conclusions p21 rs3176352 G>C and p73 rs1801173 C>T SNPs are associated with increased risk of ESCC. To confirm the current findings, additional, larger studies and tissue-specific biological characterization are required.
Evaluation of particle swarm optimization algorithm in photovoltaic applications

Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of Particle Swarm Optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems.
Rank Aggregation: Together We're Strong

We consider the problem of finding a ranking of a set of elements that is "closest to" a given set of input rankings of the elements; more precisely, we want to find a permutation that minimizes the Kendall-tau distance to the input rankings, where the Kendall-tau distance is defined as the sum over all input rankings of the number of pairs of elements that are in a different order in the input ranking than in the output ranking. If the input rankings are permutations, this problem is known as the Kemeny rank aggregation problem. This problem arises for example in building meta-search engines for Web search, aggregating viewers' rankings of movies, or giving recommendations to a user based on several different criteria, where we can think of having one ranking of the alternatives for each criterion. Many of the approximation algorithms and heuristics that have been proposed in the literature are either positional, comparison sort or local search algorithms. The rank aggregation problem is a special case of the (weighted) feedback arc set problem, but in the feedback arc set problem we use only information about the preferred relative ordering of pairs of elements to find a ranking of the elements, whereas in the case of the rank aggregation problem, we have additional information in the form of the complete input rankings. The positional methods are the only algorithms that use this additional information. Since the rank aggregation problem is NP-hard, none of these algorithms is guaranteed to find the optimal solution, and different algorithms will provide different solutions. We give theoretical and practical evidence that a combination of these different approaches gives algorithms that are superior to the individual algorithms. Theoretically, we give lower bounds on the performance for many of the "pure" methods. Practically, we perform an extensive evaluation of the "pure" algorithms and combinations of different approaches. We give three recommendations for which (combination of) methods to use based on whether a user wants to have a very fast, fast or reasonably fast algorithm.
Learning-Facilitated Synaptic Plasticity at CA3 Mossy Fiber and Commissural–Associational Synapses Reveals Different Roles in Information Processing

Subregion-dependent differences in the role of the hippocampus in information processing exist. Recently, it has emerged that a special relationship exists between the expression of persistent forms of synaptic plasticity in hippocampal subregions and the encoding of different types of spatial information. Little is known about this type of information processing at CA3 synapses. We report that in freely behaving rats, long-term potentiation (LTP) is facilitated at both mossy fiber (mf)–CA3 and commissural–associational (AC)–CA3 synapses by exploration of a novel (empty) environment. Exploration of large spatial landmarks facilitates long-term depression (LTD) at mf-CA3 synapses and impairs synaptic depression at AC-CA3 synapses. Novel exploration of small environmental features does not facilitate LTD at mf synapses but facilitates persistent LTD at AC synapses. Thus, depending on the quality of the information synaptic plasticity at AC-CA3 and mf-CA3 synapses is differentially modulated. These data suggest that expression of LTP as a result of environmental change is a common property of hippocampal synapses. However, LTD at mf synapses or AC synapses may subserve distinct and separate functions within the CA3 region.
Model-driven functional test engineering for service centric systems

Functional Testing of service centric systems poses new challenges to traditional test design approaches. As a mean for achieving network and service convergence, such systems integrate various different notations and technologies into a common framework. Assuming that test artifacts targeting each of the technologies involved exist, designing functional tests at the service level, by integrating existing test artifacts is still a very complex engineering task. We propose to address those challenges with a model-driven test engineering approach based on test patterns. The approach allows tests to be designed at a higher level of abstraction as abstract test models, using a test specific, but platform independent modeling notation called UTML(Unified Test Modeling Language). We argue that our approach can be helpful in speeding up the production of new test cases through reuse of existing test artifacts and test infrastructure, as well as automated generation of executable test scripts from the abstract test models.
Spectral entropy feature in full-combination multi-stream for robust ASR

In a recent paper, we reported promising automatic speech recognition results obtained by appending spectral entropy features to PLP features. In the present paper, spectral entropy features are used along with PLP features in the framework of multi-stream combination. In a full-combination multi-stream hidden Markov model/artificial neural network (HMM/ANN) hybrid system, we train a separate multi-layered perceptron (MLP) for PLP features, for spectral entropy features and for both combined by concatenation. The output posteriors from these three MLPs are combined with weights inversely proportional to the entropies of their respective posterior distributions. We show that on the Numbers95 database, this approach yields a significant improvement under both clean and noisy conditions as compared to simply appending the features. Further, in the framework of a Tandem HMM/ANN system, we apply the same inverse entropy weighting to combine the outputs of the MLPs before the softmax non-linearity. Feeding the combined and decorrelated MLP outputs to the HMM gives a 9.2% relative error reduction as compared to the baseline.
Rapid Implementation of Floating-point Computations Using Phase-Coherent Dynamically Configurable Pipelines

The Phase-Coherent Dynamically Configurable Pipeline is a concept for the rapid implementation of pipelined computational algorithms in configurable hardware. The approach allows a high level of sharing of floating-point resources among multiple computations. The concept features a simple tag-based control scheme and a sparse-pipeline allocation approach that enables all the stages of an arithmetic pipeline to be processing simultaneously, with multiple computations allocated to the same pipeline. Thus the approach increases hardware resource utilization and reduces power consumption. A framework is presented that implements the concept. The current framework targets a Field-Programmable Gate Array (FPGA), and simplifies the coding phase of the algorithm and troubleshooting. The framework is demonstrated on a technology currently under development by NASA to provide automatic hazard detection and avoidance for spacecraft landing systems.
Representation of the player action in sport videos

In a sport video, the complex and dynamic background and the complexity of player actions bring considerable difficulty to analyze the player's action. In this paper we propose a method to represent the player's action on the panoramic background in a whole. It is helpful for watching and analyzing the player's action. We first build the panoramic background by the inter-frame relationship. Then object segmentation is implemented by subtraction between the panoramic background and each warped frame. Finally, the player's action in each frame is represented on the panoramic background. And the trajectory of the player action is presented on the background. The experimental results show that the proposed method is effective for the sports video like diving, jumps, which the player performs his or her action in a large arena, and the camera motion mainly includes horizontal and vertical direction.