Miracle-dz/UniFAR-FTUs huggingface dataset:
HoBi-like viruses – the typical ‘atypical bovine pestivirus’

Abstract HoBi-like viruses, also referred to as bovine viral diarrhea virus 3 (BVDV-3) and atypical pestivirus, have been proposed as a new putative bovine pestivirus species. These viruses were first identified in the last decade and are currently distributed in at least three continents. Published findings suggest that these viruses may be endemic at least in parts of South America and Asia. The clinical presentations in cattle, described in field outbreaks and controlled studies, are similar to those associated with BVDV and range from subclinical to mild clinical signs, sporadically associated with reproductive losses, respiratory illness and hemorrhagic syndrome. The complete host range of HoBi-like virus is unknown, but data suggest higher adaptation of HoBi-like viruses to ruminants than swine. Acute infections, characterized by mild clinical signs, such as low-grade pyrexia and leukopenia, have been observed in both cattle and sheep. Virus has been isolated from nasal swabs indicating that virus was being shed. While seroconversion has been observed in pigs, no clinical presentation or viral shedding was evident following inoculation. While some commercial BVDV diagnostic tests may detect HoBi-like viruses, these tests do not differentiate between BVDV and HoBi-like viruses. The differentiation of BVDV and HoBi-like viruses is critical to the design of surveillance programs for these viruses.
Common processing of in vivo MR spectra

This introductory article addresses approaches currently in use to process in vivo spectra. First, a brief overview is given of the information content represented by the parameters of MR signals. Subsequently, common steps in the processing of MR spectra such as pre‐processing, normalisation and quantification and the use of prior knowledge are described. Finally, some prospects for more advanced processing are given. Copyright © 2001 John Wiley & Sons, Ltd.
Towards Ecological Validity in Evaluating Uncertainty

Uncertainty is an inherent part of making decisions in a broad spectrum of situations, from wildfire management [14] and hurricane evacuation [3], to water use policy [4]. Studying the effects of visualizing uncertainty in these decision making contexts, however, remains a challenge due to the influence of expertise and individual differences in how experiential knowledge influences decision making [15]. This makes controlled studies of the effects of uncertainty visualization on decision making in real situations difficult, if not impossible. While there is a growing recognition that evaluation in visualization could benefit from a greater variety of empirical methodologies [2, 7], the field remains dominated by two primary modes of evaluation: quantitatively-focused user-studies and the more qualitative case studies found in design studies. These two modes of evaluation sit on opposite ends of the experimental spectrum, with user-studies prioritizing precision over realism and case studies prioritizing the inverse. This spectrum involves tradeoffs between generalizability, precision, and realism [11], with experimental control positively correlated to the precision of measurements and results, and the amount of realism closely tied to the concept of ecological validity. Ecological validity specifically refers to how closely the experimental setting matches the setting in which the results might be applied [2]. Psychologists have long noted that there is an explicit trade-off between experimental control and ecological validity [8]. Existing user-studies dealing with uncertainty visualization have largely dealt with simplified low-level detailed tasks [6]. These simplifications, however, often force expert users to make judgments outside of their usual decision-making contexts, making it unclear how applicable the results of these studies are in the real-world. The qualitative case-study feedback traditionally found in design studies, on the other hand, presents its own set of challenges. Case studies can show that an effect exists, but it is extremely difficult to link the cause to a particular design decision. While additional feedback from activities like contextual interviews and think-aloud protocols can provide some insight into why a case study was successful, there remains a risk that the qualitative feedback conflates users’ preferences or demand characteristics with performance [1, 5, 12]. In this workshop paper we will discuss why ecological validity is critical in evaluating the effects of uncertainty visualization in weather forecasting specifically, as well as describe our initial attempt at designing and running an ecologically valid user-study in this domain. Our hope is to spark a more general discussion about the need for increased realism in uncertainty visualization user-studies, the difficulties and uncertainties involved in designing such a study, and the open questions we face as a community.
Terrain perception for DEMO III

The Demo III program has as its primary focus the development of autonomous mobility for a small rugged cross country vehicle. Enabling vision based terrain perception technology for classification of scene geometry and material is currently under development at JPL. In this paper we report recent progress on both stereo-based obstacle detection and terrain cover color-based classification. Our experiments show that the integration of geometric description and terrain cover characterization may be the key to enabling successful autonomous navigation in cross-country vegetated terrain.
Action video game play facilitates the development of better perceptual templates

Significance Recent advances in the field of learning have identified improvement of perceptual templates as a key mechanism underlying training-induced performance enhancements. Here, using a combination of psychophysics and neural modeling, we demonstrate that this mechanism—improved learning of perceptual templates—is also engaged after action video game play. Habitual action gamers or individuals trained to play action games demonstrate perceptual templates better tuned to the task and stimulus at hand than control groups, a difference shown to emerge as learning proceeds. This work further illustrates the importance of the development of improved perceptual templates as a mechanism mediating training and transfer effects and provides a novel account for the surprisingly broad transfer of performance enhancements noted after action game play. The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.
Expert Elicitation of the Value per Statistical Life in an Air Pollution Context

The monetized value of avoided premature mortality typically dominates the calculated benefits of air pollution regulations; therefore, characterization of the uncertainty surrounding these estimates is key to good policymaking. Formal expert judgment elicitation methods are one means of characterizing this uncertainty. They have been applied to characterize uncertainty in the mortality concentration‐response function, but have yet to be used to characterize uncertainty in the economic values placed on avoided mortality. We report the findings of a pilot expert judgment study for Health Canada designed to elicit quantitative probabilistic judgments of uncertainties in Value‐per‐Statistical‐Life (VSL) estimates for use in an air pollution context. The two‐stage elicitation addressed uncertainties in both a base case VSL for a reduction in mortality risk from traumatic accidents and in benefits transfer‐related adjustments to the base case for an air quality application (e.g., adjustments for age, income, and health status). Results for each expert were integrated to develop example quantitative probabilistic uncertainty distributions for VSL that could be incorporated into air quality models.
Personalizing Web Directories with the Aid of Web Usage Data

This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. In contrast to most of the work on Web usage mining, the usage data that are analyzed here correspond to user navigation throughout the Web, rather than a particular Web site, exhibiting as a result a high degree of thematic diversity. For modeling the user communities, we introduce a novel methodology that combines the users' browsing behavior with thematic information from the Web directories. Following this methodology, we enhance the clustering and probabilistic approaches presented in previous work and also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. The experiments also assess the effectiveness of the different machine learning techniques on the task.
Wearable ECG Monitoring and Alerting System Associated with Smartphone: iHeart

Heart disease has the second high mortality rate behind cancer in Japan, and requires quick treatment. To take a part in emerging mHealth, the authors developed a wearable electrocardiographic ECG monitoring and alerting system "iHeart". iHeart continuously monitors patient's ECG in his/her daily activities and issues an alert to the patient as well as surrounding people if it detects abnormal heart behaviour. iHeart consists of a wireless ECG sensor and a smartphone to achieve light-weighted, low-cost system that does not degrade the patient's Quality of Life. In parallel, the authors developed ECG analysis algorithm to detect R-wave as well as arrhythmia, and implemented these algorithms in wireless ECG sensor rather than in smartphone to save power consumption of ECG sensor caused by radio communication. The authors proof the practicality and usefulness of our system in clinical experiment. This paper describes the implementation of iHeart, evaluation experiment, and future requirements of the system.
Synergistic Effect of COX-2 Inhibitor on Paclitaxel-Induced Apoptosis in the Human Ovarian Cancer Cell Line OVCAR-3

Purpose Celecoxib, a highly selective cyclooxygenase-2 inhibitor, regulates apoptosis of several types of human cancer cells. The purpose of this study was to investigate whether celecoxib in combination with paclitaxel modulates apoptosis of ovarian cancer cells, and to identify the signal pathway by which celecoxib mediates apoptosis. Materials and Methods OVCAR-3 cells were exposed to paclitaxel (20 µM) in the absence or presence of celecoxib (10 µM). Cell viability was evaluated using a Cell Counting Kit-8 assay. Apoptosis was evaluated using Annexin-V/7-aminoactinomycin D staining and a cellular DNA fragmentation enzyme-linked immunosorbent assay. Caspase-3, -9, and cleavage of poly ADP-ribose polymerase (PARP) were determined by western blotting. Expression of nuclear factor-κB (NF-κB) and vascular endothelial growth factor (VEGF) and Akt activation were assessed using reverse transcriptase-polymerase chain reaction and western blotting. Results Celecoxib enhanced paclitaxel-induced growth inhibition of OVCAR-3 cells. Celecoxib significantly increased paclitaxel-induced apoptosis of OVCAR-3 cells. Pretreatment with celecoxib also increased activation of caspase-9, -3 and cleaved PARP following paclitaxel-treatment. Exposure of OVCAR-3 cells to celecoxib in combination with paclitaxel resulted in downregulation of NF-κB activation and VEGF expression. Furthermore, combining celecoxib and paclitaxel inhibited phosphorylation of Akt. Conclusion OVCAR-3 cells were sensitized to paclitaxel-induced apoptosis by celecoxib through downregulation of NF-κB and Akt activation, suggesting that celecoxib may work synergistically with paclitaxel to inhibit different targets and ultimately produce anticancer effects. Combining celecoxib with paclitaxel may prove beneficial in the clinical treatment of ovarian cancer.
Virtual machine power metering and provisioning

Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.
Modification of gastric mucin oligosaccharide expression in rhesus macaques after infection with Helicobacter pylori.

BACKGROUND & AIMS Helicobacter pylori attaches to mucin oligosaccharides that are expressed on host gastric epithelium. We used the rhesus macaque model to characterize the effect of H. pylori infection on gastric mucin oligosaccharides during acute and chronic infection. METHODS Specific pathogen (H. pylori)-free rhesus macaques were inoculated with H. pylori J166. Biopsy specimens of the gastric antrum were obtained 2 and 4 weeks before and 2, 8, and 24 weeks after infection with H. pylori. O-linked mucin oligosaccharides were released from gastric biopsy samples by beta-elimination and profiled by matrix-assisted laser desorption/ionization mass spectrometry. Similar studies were performed on gastric biopsy samples from H. pylori-infected and uninfected humans. Formalin-fixed, paraffin-embedded sections of rhesus antrum biopsy samples were stained with H&E, periodic acid-Schiff stain, and antibody to MUC5AC, the predominant mucin expressed in the stomach. RESULTS H. pylori-induced gastritis was accompanied by an acute and dramatic decrease in diversity and relative abundance of O-linked mucin oligosaccharides in the rhesus stomach, which largely recovered during the 24-week observation period. These variations in oligosaccharide abundance detected by mass spectrometry were reflected by changes in periodic acid-Schiff-positive material and expression of MUC5AC over time. Relatively few differences were seen in gastric mucin oligosaccharide composition between H. pylori-infected and uninfected patients, which is consistent with the results in rhesus macaques because infection occurs in childhood. CONCLUSIONS Acute H. pylori infection is accompanied by a dramatic but transient loss in mucin oligosaccharides that may promote colonization and persistence.
Adaptive Sequential Monte Carlo approach for real-time applications

This paper presents an adaptive Sequential Monte Carlo approach for real-time applications. Sequential Monte Carlo method is employed to estimate the states of dynamic systems using weighted particles. The proposed approach reduces the run-time computation complexity by adapting the size of the particle set. Multiple processing elements on FPGAs are dynamically allocated for improved energy efficiency without violating real-time constraints. A robot localisation application is developed based on the proposed approach. Compared to a non-adaptive implementation, the dynamic energy consumption is reduced by up to 70% without affecting the quality of solutions.
μ-Opioid Receptor Antibody Reveals Tissue-Dependent Specific Staining and Increased Neuronal μ-Receptor Immunoreactivity at the Injured Nerve Trunk in Mice

Neuropathic pain is a debilitating chronic disease often resulting from damage to peripheral nerves. Activation of opioid receptors on peripheral sensory neurons can attenuate pain without central nervous system side effects. Here we aimed to analyze the distribution of neuronal μ-opioid receptors, the most relevant opioid receptors in the control of clinical pain, along the peripheral neuronal pathways in neuropathy. Hence, following a chronic constriction injury of the sciatic nerve in mice, we used immunohistochemistry to quantify the μ-receptor protein expression in the dorsal root ganglia (DRG), directly at the injured nerve trunk, and at its peripheral endings in the hind paw skin. We also thoroughly examined the μ-receptor antibody staining specificity. We found that the antibody specifically labeled μ-receptors in human embryonic kidney 293 cells as well as in neuronal processes of the sciatic nerve and hind paw skin dermis, but surprisingly not in the DRG, as judged by the use of μ/δ/κ-opioid receptor knockout mice. Therefore, a reliable quantitative analysis of μ-receptor expression in the DRG was not possible. However, we demonstrate that the μ-receptor immunoreactivity was strongly enhanced proximally to the injury at the nerve trunk, but was unaltered in paws, on days 2 and 14 following injury. Thus, μ-opioid receptors at the site of axonal damage might be a promising target for the control of painful neuropathies. Furthermore, our findings suggest a rigorous tissue-dependent characterization of antibodies' specificity, preferably using knockout animals.
An Efficient Sampling Algorithm for Influence Diagrams

We describe an efficient sampling algorithm for solving influence diagrams that achieves its efficiency by reusing samples for each of the decision strategies. Our algorithm is exhaustive in the sense of computing the expected utility of each of the possible decision strategies. We show how by a parallel evaluation of all strategies we not only save a significant amount of computation but also produce better quality anytime behavior.
The Construct Validity of Assertion: Contributions of Four Assessment Procedures and Norman's Personality Factors

The construct validity of assertion was analyzed in three independent correlational studies The convergence of self-reported, laboratory roleplayed, self-observed, and peer-rated measures was examined, as well as their relationship to Norman's peer-rated five personality factors of extraversion, agreeableness, conscientiousness, emotional stability, and culture Twenty-three and 29 undergraduate students participated in Studies 1 and 2, respectively In general, results showed poor convergence among assertiveness measures even when increasing the representativeness and reliability of the role-played laboratory and self-observed measures, as in Study 2 In Study 3, the role-playing test was abandoned and 45 subjects served as peers for one another The results of Study 3 supported the prediction that assertiveness measures showed at least moderate convergence, in addition there were significant relationships between assertiveness and extraversion and between peer-rated assertiveness and agreeableness, conscientiousness, and culture The relative contribution of each assessment procedure to the construct validity of assertion is discussed
The changing effects of Alaska's boreal forests on the climate system

In the boreal forests of Alaska, recent changes in climate have influenced the exchange of trace gases, water, and energy between these forests and the atmosphere. These changes in the structure and function of boreal forests can then feed back to impact regional and global climates. In this manuscript, we examine the type and magnitude of the climate feedbacks from boreal forests in Alaska. Research generally suggests that the net effect of a warming climate is a positive regional feedback to warming. Currently, the primary positive climate feedbacks are likely related to decreases in surface albedo due to decreases in snow cover. Fewer negative feedbacks have been identified, and they may not be large enough to counterbalance the large positive feedbacks. These positive feedbacks are most pronounced at the regional scale and reduce the resilience of the boreal vegetation – climate system by amplifying the rate of regional warming. Given the recent warming in this region, the large variety of associated .