Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotavirus vaccination for prevention of serious acute gastroenteritis and the importance of postlicensure safety monitoring.

(See the major article by Yen et al, on pages 41–8.) With the ability to save millions of lives each year in both the developed and developing nations, vaccination against childhood infectious diseases is a priority area for global health. An essential aspect of the success of any vaccination program is the careful monitoring following implementation to ensure that the benefits of the program outweigh any risks to the recipients or the community. Infant rotavirus vaccination programs are an important example of the collaborative expertise required for effective and timely monitoring and reporting of any adverse events following implementation. Rotavirus vaccines have been instrumental in reducing morbidity from rotavirus infection. Prior to the introduction of rotavirus vaccine, it is estimated that >500 000 rotavirus-related child deaths occurred globally each year [1]. Studies assessing the impact of rotavirus vaccine on incidence of rotavirus hospitalizations have occurred in numerous countries, including Australia and the United States, with dramatic reductions in the incidence of rotavirus hospitalizations (up to 80% reduction) shown following implementation of the rotavirus vaccination programs and suggestions of herd immunity benefits for older, unvaccinated populations [2–6]. The development of vaccines to prevent serious infectious diseases has been a global triumph, but large-scale postlicensure studies are essential to ensure that vaccination programs deliver the anticipated benefits. In response to the overwhelming global burden of rotavirus infections, particularly in children aged <5 years, a live, attenuated tetravalent rotavirus vaccine (RotaShield) was licensed for routine use in infants in 1998, before being withdrawn in 1999 due to concerns about an increased risk of intussusception in vaccine recipients [7]. RotaShield was estimated to
The Dirt on Worms: Knowledge, Attitudes and Behaviors Concerning Invasive Earthworms in the Town of Webb, NY

The invasion of exotic earthworms in previously earthworm-free areas eliminates the forest litter layer, alters the carbon and nitrogen cycles and reduces native plant cover. Humans are the principal agents of invasive earthworm dispersal, spreading worms both inadvertently through the horticulture industry, logging and road travel, and voluntarily through composting and the disposal of earthworm bait on land. This is the first study to focus on the human dimensions of earthworm invasion. Using a knowledge-attitudesbehavior (KAB) framework, this study explores the correlates of voluntary earthworm dispersal in the Town of Webb, NY, and attempts to determine which groups of people are spreading earthworms and through bait use and composting and how environmental knowledge and attitudes contribute to this behavior. The results uphold the knowledgeattitudes-behavior model for general invasive species eradication efforts, but not for voluntary earthworm dispersal. While general invasive species knowledge is high, only 17% are aware that earthworms are invasive. The most important factors predicting earthworm disposal in forests are infrequent participation in fishing and efforts to eradicate invasive species, while the factors predicting composting participation are the belief that earthworms have a positive effect on plant life and self-reported environmental knowledge.
An Architecture for Selling XML Content

Content provides a way for business to increase revenues on the Internet. However, the key to increasing revenues through content is efficient billing. Therefore this paper introduces an architecture for selling XML content. The architecture is based on a family of models that provides a guide to the requirements for a content-based billing system in XML environments incorporating various pricing strategy options. XML gives a content-based billing system device, platform and media independence.
The Hausdorff dimension of the boundary of the Mandelbrot set and Julia sets

It is shown that the boundary of the Mandelbrot set M has Hausdorff dimension two and that for a generic c E AM, the Julia set of z I > Z2 + C also has Hausdorff dimension two. The proof is based on the study of the bifurcation of parabolic periodic points.
A Hypothesis Concerning the Biphasic Dose-response of Tumors to Angiostatin and Endostatin

This manuscript proposes a hypothesis to explain the U-shaped dose-response observed for angiostatin and other high-molecular-weight drugs in various anti-cancer bio-assays. The dose-response curves for angiostatin and endostatin (measured as suppression of tumor growth) go through an optimum (i.e., minimum tumor growth) and then becomes less effective at higher doses. The literature suggests that at lower doses the primary action of these high-molecular-weight drugs is to counteract the angiogenic effects of vascular endothelial growth factor (VEGF). To do this, the drugs must pass out of the blood vessel and enter the extra-cellular matrix (ECM) where VEGF induces the growth and fusion of tip cells. Ironically, VEGF actually facilitates access of the drugs to the ECM by making the vascular endothelium leaky. At higher doses, the high-molecular-weight drugs seem to reverse VEGF-induced permeability of the endothelium. Thus, at high dose rates, it is hypothesized that the drugs are not able to enter the ECM and block the angiogenic effects of VEGF there. As a result, high doses of the drugs do not suppress vascularization of the tumor or tumor growth. Moreover, if the permeability of the vessels is suppressed, the VEGF released by the stroma is concentrated in the ECM where it amplifies the angiogenic activity around the tumor.
Parameter Estimation in a Cell Cycle Model for Frog Egg Extracts

Cell cycle models used in biology can be very complex, involving many parameters with initially unknown values. The values of the parameters vastly affect the accuracy of a model in representing real biological cells. Typically people search for the best parameters of a model using the computer only as a tool to run simulations. In this paper methods and results are described for a computer program that searches for parameters to a specific model using well tested algorithms. The code for this program uses ODRPACK for parameter estimation and LSODAR to solve the differential equations that comprise the model.
Malicious virtual machines detection through a clustering approach

Cloud computing aims to provide enormous resources and services, parallel processing and reliable access for users on the networks. The flexible resources of clouds could be used by malicious actors to attack other infrastructures. Cloud can be used as a platform to perform these attacks, a virtual machine(VM) in the Cloud can play the role of a malicious VM belonging to a Botnet and sends a heavy traffic to the victim. For cloud service providers, preventing their infrastructure from being turned into an attack platform is very challenging since it requires detecting attacks at the source, in a highly dynamic and heterogeneous environment. In this paper, an approach to detect these malicious behaviors in the Cloud based on the analysis of network parameters is proposed. This approach is a source-based attack detection, which applies both Entropy and clustering methods on network parameters. The environment of Cloud is simulated on Cloudsim. The data clustering allows achieving high performance, with a high percentage of correctly clustered VMs.
Methylation as an epigenetic source of random genetic effects in the classical twin design

The epigenetic effects of cytosine methylation on gene expression are an acknowledged source of phenotypic variance. The discordant monozygotic (MZ) twin design has been used to demonstrate the role of methylation in disease. Application of the classical twin design, featuring both monozygotic and dizygotic twins, has demonstrated that individual differences in methylation levels are attributable to genetic and environmental (including stochastic) factors, with the latter explaining most of the variance. What implications epigenetic sources of variance have for the twin modeling of (non-epigenetic) phenotypes such as height and IQ is an open question. One possibility is that epigenetic effects are absorbed by the variance component attributable to unshared environmental. Another possibility is that such effects form an independent source of variance distinguishable in principle from standard genetic and environmental sources. In the present paper, we conceptualized epigenetic processes as giving rise to randomness in the effects of polygenetic influences. This means that the regression coefficient in the regression of the phenotype on the polygenic factor, as specified in the twin model, varies over individuals. We investigate the consequences of ignoring this randomness in the standard twin model.
BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection.
On the Automatic Analysis of Learner Language: Introduction to the Special Issue

Natural language processing (NLP) has long been used to automatically analyze language produced by language learners, typically aimed at providing individualized feedback and learner modeling in intelligent computer-assisted language learning systems (see Heift & Schulze, 2007). While much interesting research has been reported, it is difficult to determine the state of the art for the automatic analysis of learner language. Which error types and other learner language properties can be detected and diagnosed automatically? How reliably can this be done, for which kind of learner language, resulting from which types of tasks? For sustainable progress on the automatic analysis of learner language it is arguably crucial to answer these questions, to discuss and compare the performance of different analysis methods on real-life learner data sets.
Plaque Neovascularization Is Increased in Ruptured Atherosclerotic Lesions of Human Aorta: Implications for Plaque Vulnerability

Background—Growth of atherosclerotic plaques is accompanied by neovascularization from vasa vasorum microvessels extending through the tunica media into the base of the plaque and by lumen-derived microvessels through the fibrous cap. Microvessels are associated with plaque hemorrhage and may play a role in plaque rupture. Accordingly, we tested this hypothesis by investigating whether microvessels in the tunica media, the base of the plaque, and the fibrous cap are increased in ruptured atherosclerotic plaques in human aorta. Methods and Results—Microvessels, defined as CD34-positive tubuloluminal capillaries recognized in cross-sectional and longitudinal profiles, were quantified in 269 advanced human plaques by bicolor immunohistochemistry. Macrophages/T lymphocytes and smooth muscle cells were defined as CD68/CD3-positive and &agr;-actin–positive cells. Total microvessel density was increased in ruptured plaques when compared with nonruptured plaques (P=0.0001). Furthermore, microvessel density was increased in lesions with severe macrophage infiltration at the fibrous cap (P=0.0001) and at the shoulders of the plaque (P=0.0001). In addition, microvessel density was also increased in lesions with intraplaque hemorrhage (P=0.04) and in thin-cap fibroatheromas (P=0.038). Logistic regression analysis identified plaque base microvessel density (P=0.003) as an independent correlate to plaque rupture. Conclusions—Thus, neovascularization as manifested by the localized appearance of microvessels is increased in ruptured plaques in the human aorta. Furthermore, microvessel density is increased in lesions with inflammation, with intraplaque hemorrhage, and in thin-cap fibroatheromas. Microvessels at the base of the plaque are independently correlated with plaque rupture, suggesting a contributory role for neovascularization in the process of plaque rupture.
Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18-24.

OBJECTIVE This report estimates the numbers of 18-24 year old United States college students who annually experience alcohol-related deaths, injuries and other health problems. METHOD We examined traffic and unintentional injury deaths in 1998 reported by the National Highway Traffic Safety Administration and the Centers for Disease Control (CDC). We also examined results of national coroner studies, Department of Education college enrollment data, the National Household Survey on Drug Abuse (NHSDA), the CDC National College Health Risk Behavior Survey and the Harvard School of Public Health College Alcohol Survey (CAS). All survey participants were ages 18-24: 6,930 college and 12,394 noncollege respondents in the NHSDA survey; 3,077 college students in the CDC survey; and 12,217 full-time 4-year college students in the CAS. Based on the number and proportion of 18-24 year olds enrolled in college, data on alcohol involvement in injury deaths among 18-24 year olds and survey responses, we calculated the numbers of 18-24 year old alcohol-related injury deaths and other health problems. RESULTS We estimate that over 1,400 students aged 18-24 and enrolled in 2- and 4-year colleges died in 1998 from alcohol-related unintentional injuries, including motor vehicle crashes. According to surveys conducted in 1999, in the preceding year, over 2 million of the 8 million college students in the United States drove under the influence of alcohol and over 3 million rode with a drinking driver. Over 500,000 full-time 4-year college students were unintentionally injured under the influence of alcohol and over 600,000 were hit or assaulted by another student who had been drinking. CONCLUSIONS There is an urgent need for expanding prevention and treatment programs, to reduce alcohol-related harm among U.S. college students and other young adults.
An Eecient Representation for Sparse Sets

Sets are a fundamental abstraction widely used in programming. Many representations are possible, each ooering diierent advantages. We describe a representation that supports constant-time implementations of clear-set, add-member, and delete-member. Additionally, it supports an eecient forall iterator, allowing enumeration of all the members of a set in time proportional to the cardinality of the set. We present detailed comparisons of the costs of operations on our representation and on a bit-vector representation. Additionally, we give experimental results showing the eeectiveness of our representation in a practical application: construction of an interference graph for use during graph coloring register allocation. While this representation was developed to solve a speciic problem arising in register allocation, we have found it useful throughout our work, especially when implementing eecient analysis techniques for large programs. However, the new representation is not a panacea. The operations required for a particular set should be carefully considered before this representation, or any other representation, is chosen.
Multirate Asynchronous State and Parameter Estimation for Centrifugal Compressors

A number of strategies for state and parameter estimation for a centrifugal compressor system are investigated using a nonlinear dynamical model. It is found that high frequency measurements can be combined with lower frequency measurements to achieve high fidelity state estimation for stable equilibria, limit cycles, as well as transients using either a multirate discrete extended Kalman filter or a data fusion based algorithm with one independent estimator per measurement. When there is a change in one of the parameters of the system only a filter designed to adapt to such changes produces estimates with acceptable errors for a locally stable equilibrium.
Communicative efficiency in language production and learning: Optional plural marking

Recent work suggests that language production exhibits a bias towards efficient information transmission. Speakers tend to provide more linguistic signal for meaning elements that are difficult to recover while reducing contextually inferrable (more frequent, probable, or expected) elements. This tradeoff has been hypothesized to shape grammatical systems over generations, contributing to cross-linguistic patterns. We put this idea to an empirical test using miniature artificial language learning over variable input. Two experiments were conducted to demonstrate that the inferrability of plurality information inversely predicts the likelihood of overt plural marking, as would be expected if learners prefer communicatively efficient systems. The results were obtained even with input frequency counts of the plural marker counteracting the bias, and thus provide strong support for a critical role of inferrability of meaning in language learning, production, as well as in typologically attested variations.
Overview of Cognitive Function in ALS, with Special Attention to the Temporal Lobe: Semantic Fluency and Rating the Approachability of Faces

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that affects upper and lower motor neurons and is more recently known to be associated with declines in cognitive and behavioral functions for a subset of patients (Strong, et al., 1996; Strong et al., 2009). The cognitive changes associated with ALS can vary from mild impairments that may or may not affect the individual’s daily functioning to more severe cognitive and behavioral changes that meet criteria for a diagnosis of frontotemporal dementia (ALS-FTD). There are at least mild cognitive changes in 40 to 60% of sporadic and familial ALS patients (Massman et al., 1996; Phukan et al., 1996; Ringholz et al., 2005; Wheaton et al., 2007). The cognitive and behavioral changes associated with ALS follow a pattern consistent with involvement of the frontal and temporal lobes (Strong et al., 2009, Hodges et al., 2004), presenting as difficulties with attention, working memory, verbal fluency, and semantic abilities (Abe et al., 1997; Ringholz et al., 2005; Rippon et al., 2006, Schmolck et al., 2007; Strong et al, 1999; Strong et al., 2009). Furthermore, 15% of ALS patients demonstrate more severe cognitive and behavioral changes consistent with ALS-FTD (Lomen-Hoeth et al., 2002; Ringholz et al., 2005; Wheaton et al., 2007), presenting as declines in judgment, problem solving, and reasoning, which are more frontally mediated cognitive functions. In addition to cognitive changes, up to 25% of patients with ALS may also experience significant changes in their behavior, mood, and personality characteristics (Hodges et al., 2004; Kertesz et al., 2005), including a loss of empathy, problems with organization and planning, changes in social behavior and personality, difficulties with impulse control, and apathy. The temporal lobes are purported to be involved in auditory perception, language comprehension, naming, processing of semantic knowledge and long-term memory storage, high-level visual processing of complex stimuli such as faces and scenes, and episodic memory (Lezak, 1995). In addition, they contain the amygdala and associated limbic areas, which are key structures for processing emotional stimuli and detecting threat from the environment, and are of particular interest for ALS patients. ALS patients show a lack of
Behavioral Methods for Investigating Morphological and Syntactic Processing in Children

While most first language acquisition research to date has focused on the development of children’s linguistic competence, a number of research teams have also investigated the mechanisms children employ to process sentence-level and word-level information in real time, by applying experimental techniques familiar from the adult processing literature to children. This chapter presents an overview of different kinds of behavioral tasks for investigating both morphological and syntactic processing in children focusing on three techniques that we have explored in our own research on children’s on-line language processing: self-paced listening, cross-modal priming, and speeded production.
Green Provisioning for Optical WDM Networks

Since the Internet consumes a large (and increasing) amount of energy, “green” strategies are desirable to help service providers (SP) operate their networks and provision services more energy efficiently. We focus on green provisioning strategies for optical wavelength-division multiplexing networks. A number of approaches from component layer to network layer are discussed, which should help improve the energy efficiency of the networks. Then, we consider a typical optical backbone network architecture, and minimize the operational power for provisioning. Typically, operational power depends on strategy (e.g., optical bypass versus traffic grooming), operations (e.g., electronic domain versus optical domain), and route. We analyze the constituents of operational power in various scenarios, and discuss the opportunities for energy savings. We propose a novel auxiliary graph, which can capture the power consumption of each provisioning operation. Based on the auxiliary graph, we develop a power-aware provisioning scheme to minimize the total operational power. Performance evaluation shows that our scheme always needs the least operational power, with comparison to a direct-lightpath approach and a traffic-grooming approach. The result also suggests proportional power consumption by operations (network equipment) and end-node traffic grooming to fully exploit the power-saving potential of optical networks.