Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of the Resistance of SJL/J Mice to Pneumonia Virus of Mice, a Model for Infantile Bronchiolitis Due to a Respiratory Syncytial Virus

Respiratory syncytial virus (RSV), a prominent cause of airway morbidity in children, maintains an excessive hospitalization rate despite decades of research. Host factors are assumed to influence the disease severity. As a first step toward identifying the underlying resistance mechanisms, we recently showed that inbred mouse strains differ dramatically as regards their susceptibility to pneumonia virus of mice (PVM), the murine counterpart of RSV. PVM infection in mice has been shown to faithfully mimic the severe RSV disease in human infants. This study aimed at dissecting the remarkable PVM-resistance shown by the SJL/J strain. To characterize its genetic component, we assessed clinical, physiopathological, and virological resistance/susceptibility traits in large first (F1) and second (F2) generations obtained by crossing the SJL/J (resistant) and 129/Sv (susceptible) strains. Then, to acquire conclusive in vivo evidence in support of the hypothesis that certain radiosensitive hematopoietic cells might play a significant role in PVM-resistance, we monitored the same resistance/susceptibility traits in mock- and γ-irradiated SJL/J mice. Segregation analysis showed that (i) PVM-resistance is polygenic, (ii) the resistance alleles are recessive, and (iii) all resistance-encoding alleles are concentrated in SJL/J. Furthermore, there was no alteration of SJL/J PVM-resistance after immunosuppression by γ-irradiation, which suggests that adaptive immunity is not involved. We conclude that host resistance to pneumoviruses should be amenable to genetic dissection in this mouse model and that radioresistant lung epithelial cells and/or alveolar macrophages may control the clinical severity of pneumovirus-associated lung disease.
An in vitro evaluation of various irrigation techniques for the removal of double antibiotic paste from root canal surfaces

ABSTRACT Objective The aim of this study was to investigate the effectiveness of conventional syringe irrigations, passive ultrasonic irrigation (PUI), Vibringe, CanalBrush, XP-endo Finisher, and laser-activated irrigation (LAI) systems in removing double antibiotic paste (DAP) from root canals. Material and Methods One hundred five extracted single-rooted teeth were instrumented. The roots were split longitudinally. Three standard grooves were created and covered with DAP. The roots were distributed into seven groups: Group 1, beveled needle irrigation; Group 2, double side-vented needle irrigation; Group 3, CanalBrush; Group 4, XP-endo Finisher; Group 5, Vibringe; Group 6, PUI; Group 7, LAI. The amount of remaining DAP was scored under a stereomicroscope. Results Group 4, Group 6, and Group 7 removed significantly more DAP than the other protocols in the coronal region. Group 7 was more efficient in the middle region; however, no significant difference was found between Group 7 and Group 6. No differences were found between groups in the apical region either, except for the comparisons between groups 7 and 2, and groups 2 and 3. Conclusions None of the investigated protocols were able to completely remove the DAP from the grooves. The Vibringe and XP-endo Finisher systems showed results similar to those of conventional needle irrigation.
Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks

Recent advances in high-throughput molecular biology has motivated in the field of bioinformatics the use of network inference algorithms to predict causal models of molecular networks from correlational data. However, it is extremely difficult to evaluate the effectiveness of these algorithms because we possess neither the knowledge of the correct biological networks nor the ability to experimentally validate the hundreds of predicted gene interactions within a reasonable amount of time. Here, we apply a new approach developed by Smith, et al. (2002) that tests the ability of network inference algorithms to accurately and efficiently recover network structures based on gene expression data taken from a simulated biological pathway in which the structure is known a priori. We simulated a genetic regulatory network and used the resultant sampled data to test variations in the design of a Bayesian Network inference algorithm, as well as variations in total quantity of available data, length of sampling interval, method of data discretization, and presence of interpolated data between observed data points. We also advanced the inference algorithm by developing a heuristic influence score that infers the strength and sign of regulation (up or down) between genes. In these experiments, we found that our inference algorithm worked best when presented with data discretized into three categories, when using a greedy search algorithm with random restarts, and when evaluating networks using the BDe scoring metric. Under these conditions, the algorithm was both accurate and efficient in recovering the simulated molecular network when the sampled data sets were large. Under more biologically reasonable small amounts of sampled data, the algorithm worked best only when interpolated data was included, but had difficulty recovering relationships describing genes with more than one regulatory influence. These results suggest that network inference algorithms and sampling methods must be carefully designed and tested before they can be used to recover biological genetic pathways, especially in the context of highly limited quantities of data.
Factors Affecting Perceived Improvements in Environmental Quality from Precision Farming

This study identified the factors that influenced whether farmers in the Southeastern United States perceived an improvement in environmental quality from adopting precision farming technologies (PFTs). Farmers with larger farms or higher yields were more likely to believe that they observed positive externalities associated with PFTs. Farmers who found PFTs profitable or who believed input reduction was important had higher probabilities whereas those with higher incomes or who were more dependent on farm income were less likely to perceive such benefits. Interestingly, the importance of environmental quality and length of time using PFTs were not found to affect the probability of perceiving an improvement in environmental quality.
Frobenius und Maschke type theorems for Doi-Hopf modules and entwined modules revisited: a unified approach

We study when induction functors (and their adjoints) between categories of Doi-Hopf modules and, more generally, entwined modules are separable, resp. Frobenius. We present a unified approach, leading to new proofs of old results by the authors, as well as to some new ones. Also our methods provide a categorical explanation for the relationship between separability and Frobenius properties.
Low decoding complexity STBC design for turbo coded broadcast transmission

In this paper, we study the Space Time Block Code (STBC) called Matrix D (MD) that enjoys low-complexity detection for broadcast transmission. Such a transmission uses a powerful channel code like a turbo code. To produce Log-Likelihood Ratio (LLR) for the channel decoder, we propose a soft Maximum Likelihood (ML) detector for MD STBC while keeping the same low-complexity order as the original one. Then, we optimize the MD STBC for low Signal to Noise Ratio (SNR) under the trace criterion. The performance of the concatenation of a turbo code with optimized MD is given. A gain of almost 0.2 dB is obtained between optimized MD and original MD. However, the proposed code loses its full-diversity as its determinant is equal to 0. Then, a tradeoff angle is chosen and a low-complexity detection full-rate full-diversity STBC is designed for turbo coded broadcast transmission which performs well for both low and high SNR.
Car Following Markov Regime Classification and Calibration

The car following behavior has recently gained much attention due to its wide variety of applications. This includes accident analysis, driver assessment, support systems, and road design. In this paper, we present a model that leverages Markov regime switching models to classify various car following regimes. The detected car following regimes are then mined to calibrate the parameters of drivers to be dependent on the driver's current driving regime. A two stage Markov regime switching model is utilized to detect different car following regimes. The first stage discriminates normal car following regimes from abnormal ones, while the second stage classifies normal car following regimes to their fine-grained regimes like braking, accelerating, standing, free-flowing, and normal following. A genetic algorithm is then employed to the observed driver data in each car following regime to optimize car following model parameter values of the driver in each regime. Experimental evaluation of the proposed model using a real dataset shows that it can detect up-normal (rare and short time) events. In addition, it can infer the switching process dynamics such as the expected duration, the probability of moving from one regime to another and the switching parameters of each regime. Finally, the model is able to accurately calibrate the parameters of drivers according to their driving regimes, so we can achieve a better understanding of drivers behavior and better simulation of driving situation.
A comparison between shallow and deep architecture classifiers on small dataset

Many machine learning algorithms have been introduced to solve different types of problem. Recently, many of these algorithms have been applied to deep architecture model and showed very impressive performance. In general, deep architecture model suffers from over-fitting problem when there is a small number of training data. In this paper, we attempted to remedy this problem in deep architecture with regularization techniques including overlap pooling, flipped-image augmentation and dropout, and we also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. However, a deep model augmented with a regularization technique-CNN with dropout technique-was competitive to the shallow models.
The Unconscious Eye Opener

Recent research suggests that reward cues, in the absence of awareness, can enhance people’s investment of physical resources (Aarts, Custers, & Marien, 2008; Pessiglione et al., 2007). Pessiglione et al., for example, showed that participants spent more physical effort in a demanding force task when they could gain a high-value coin (a pound) than when they could gain a low-value coin (a penny), even when the coins were presented subliminally (i.e., below the threshold of awareness). One explanation for this intriguing finding is that subliminal reward information is processed strategically—that the costs (i.e., the required effort) and benefits (i.e., the value of the reward) of an action are weighed against each other. However, such a weighing process would require higher control functions (Cohen, Heller, & Ranganath, 2005) that are typically thought to operate only on information available to consciousness (Baars, 2002). Another explanation is that the prime directly boosts resources. From this perspective, the effects of subliminal rewards can be explained in terms of low-level, reflex-like responses to primes (Bargh, 2006). Here, we challenge the latter perspective by examining the interaction of reward value and task demands. We aim to show that resources are not directly recruited in reaction to high-reward cues, but instead are recruited strategically—only when the task requires it, and regardless of whether or not the cues enter conscious awareness. In a computerized experiment, we employed an on-line, physiological index: pupil dilation. Because the pupil dilates with sympathetic activity and constricts with parasympathetic activity (Steinhauer, Siegle, Condray, & Pless, 2004), pupil size is an accurate and unobtrusive measure of the resources invested in a task. Ruling out potential alternative explanations, such as anxiety, research demonstrates that pupil dilation increases when tasks require more resources, either because of variations within or between tasks (Kahneman, 1973) or because of individual differences in, for example, cognitive ability (Ahern & Beatty, 1979). These findings demonstrate that the amount of resources individuals need to mobilize for a task can be reliably indexed by changes in their pupil size. If subliminal reward cues input into the strategic processes involved in resource recruitment, the effects of rewards on pupil dilation should occur when the task is demanding (here, recall of five digits), but not when the task is undemanding (recall of three digits), as undemanding tasks can be completed routinely and do not require many resources. It is important to note that this interactive effect of reward and demand on recruitment of resources is expected to occur regardless of whether the reward is processed consciously or nonconsciously.
A pilot study of positive expectations and focused attention via a new protocol for optimizing therapeutic hypnosis and psychotherapy assessed with DNA microarrays: the creative psychosocial genomic healing experience

1 We extend the use of DNA microarrays to explore a new psychotherapeutic therapeutic protocol, The Creative Psychosocial Genomic Healing Experience, an easy-to-learn approach to facilitating therapeutic hypnosis, psychotherapy, rehabilitation, meditation, and pastoral counseling. This pilot study assessed the hypothesis that a top-down creatively oriented positive human experience can modulate gene expression on the molecular level. A DNA microarray data analysis of the white blood cells of three human subjects was performed immediately before, one hour after, and 24 hours after The Creative Psychosocial Genomic Healing Experience. We documented changes in the expression of 15 early response genes within one hour that apparently initiated a further cascade of 77 genes 24 hours later. This could provide the mind/molecular genomic foundation of new therapeutic models for optimizing human consciousness, health, and well being via therapeutic hypnosis, psychotherapy, pastoral counseling, and psychiatry. This proof-of- principle pilot study now requires cross validation with more subjects with a variety of diagnostic classifications to document the validity and reliability of using DNA microarrays to assess our new creative psychosocial genomic therapeutic protocol in a variety of cultures. (Sleep and Hypnosis 2008;10(2):39-44)
BUILDING MORPHOLOGY, TRANSPARENCE, AND ENERGY PERFORMANCE

Certain energy-related building standards make use of simple numeric indicators to describe a building's geometric compactness. Typically, such indicators make use of the relation between the volume of a built form and its surface area. The indicators are then used along with information on the thermal transmittance of the building enclosure elements to evaluate the degree to which a building design meets the relevant thermal insulation criteria. Using extensive parametric thermal simulations, this paper examines the reliability of such simple compactness indicators for energy-related evaluative assessments given that buildings with the same compactness attribute could differ in enclosure transparence, orientation, and morphology.
Democracy, Market Economics, and Environmental Policy in Chile

hile's return to democratic rule with sustained economic growth provided the opportunity for some much-welcomed political space that, it was hoped, would permit the country to attend, at long last, to a number of pressing issues which had been long deferred, if not ignored, by the previous military government. Some of those issues, such as the consolidation of democracy, poverty, and human rights, had been the subject of considerable study by scholars. Much less studied, however, although also on the agenda, was the promise to address Chile's environmental problems, which had become much exacerbated under the laissez-faire economic model favored by the military regime. Because the new democratic administrations have followed through on that promise, the Chilean example lends support to that hypothesis which holds that environmental concerns in developing countries can be addressed more effectively under economically stable democratic regimes than by authoritarian political systems. While Chile's recent environmental policy clearly offers hope for the future, it also presents a challenge. Policy debates on environmental questions have been surprisingly sharp and bruising. The force and outcome of these debates give rise to two questions. First, can developing countries with neoliberal policy orientations consider a sufficiently Eduardo Silva is Assistant Professor of Political Science and a Fellow of the
Resonant oscillations of a granular cluster

Driven granular media is a fascinating example of intrinsically non-equilibrium system. I consider an ensemble of inelastically colliding hard disks in a channel driven by two opposite thermal walls with different temperatures. When the inelasticity of collisions is large enough, a dense cluster, located closer to the colder wall, is formed. The steady state profiles are computed from the equations of granular hydrodynamics, and a good agreement with the results of molecular dynamics simulations is found. When the temperature of one of the thermal walls is varied periodically, a dense cluster develops oscillations. The oscillations amplitude reaches its maximum in a resonance, when the frequency of driving is roughly equal to the hydrodynamic frequency of oscillatory acoustic modes in the system. 2008 Wiley Periodicals, Inc. Complexity 00: 00–00, 2008
A Framework for the Conceptualization of Approaches to "Create-by-Reuse" of Learning Design Solutions

IMS Learning Design (IMS LD) is an interoperable and standardized language that enables the computational representation of Units of Learning (UoLs). However, its adoption and extensive use in real practice largely depends on the extent to which teachers can design and author their own UoLs according to the requirements of their educational situations. Many of the proposed design processes for facilitating the creation of UoLs are based on the reuse of complete or non-complete learning design solutions at different levels of granularity. This paper introduces a comparison framework that conceptually analyzes and classifies reusable learning design solutions and processes that drive the creation of ready-to-run UoLs. The framework provides a comprehensible representation of such processes and units of reuse over two dimensions, namely granularity and completeness. It also offers a frame for discussing issues, such as the proper level of reuse, of existing and forthcoming proposals. Finally, it opens the path to other strands for future research such as providing language independence of learning designs or proposing approaches for the selection of the reusable solutions.
DTNBP1 (dysbindin) gene variants modulate prefrontal brain function in schizophrenic patients – support for the glutamate hypothesis of schizophrenias

Dysbindin (DTNBP1) is a recently characterized protein that seems to be involved in the modulation of glutamatergic neurotransmission in the human brain, thereby influencing prefrontal cortex function and associated cognitive processes. While association, neuroanatomical and cellular studies indicate that DTNBP1 might be one of several susceptibility genes for schizophrenia, the effect of dysbindin on prefrontal brain function at an underlying neurophysiological level has not yet been explored for these patients. The NoGo‐anteriorization (NGA) is a topographical event‐related potential measure, which has been established as a valid neurophysiological marker of prefrontal brain function. In the present study, we investigated the influence of seven dysbindin gene variants on the NGA in a group of 44 schizophrenic patients. In line with our a priori hypothesis, one DTNBP1 polymorphism previously linked to schizophrenia (rs2619528) was found to be associated with changes in the NGA; however, the direction of this association directly contrasts with our previous findings in a healthy control sample. This differential impact of DTNBP1 gene variation on prefrontal functioning in schizophrenic patients vs. healthy controls is discussed in terms of abnormal glutamatergic baseline levels in patients suffering from schizophrenic illnesses. This is the first report on a role of DTNBP1 gene variation for prefrontal functioning at a basic neurophysiological level in schizophrenic patients. An impact on fundamental processes of cognitive response control may be one mechanism by which DTNBP1 gene variants via glutamatergic transmission contribute to the pathophysiology underlying schizophrenic illnesses.
Conciliation biology: the eco-evolutionary management of permanently invaded biotic systems

Biotic invaders and similar anthropogenic novelties such as domesticates, transgenics, and cancers can alter ecology and evolution in environmental, agricultural, natural resource, public health, and medical systems. The resulting biological changes may either hinder or serve management objectives. For example, biological control and eradication programs are often defeated by unanticipated resistance evolution and by irreversibility of invader impacts. Moreover, eradication may be ill‐advised when nonnatives introduce beneficial functions. Thus, contexts that appear to call for eradication may instead demand managed coexistence of natives with nonnatives, and yet applied biologists have not generally considered the need to manage the eco‐evolutionary dynamics that commonly result from interactions of natives with nonnatives. Here, I advocate a conciliatory approach to managing systems where novel organisms cannot or should not be eradicated. Conciliatory strategies incorporate benefits of nonnatives to address many practical needs including slowing rates of resistance evolution, promoting evolution of indigenous biological control, cultivating replacement services and novel functions, and managing native–nonnative coevolution. Evolutionary links across disciplines foster cohesion essential for managing the broad impacts of novel biotic systems. Rather than signaling defeat, conciliation biology thus utilizes the predictive power of evolutionary theory to offer diverse and flexible pathways to more sustainable outcomes.