Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear and breakdown properties and atomistic modeling

Introduction to Volume II * Characterization of Surface Morphology * Nonlinear Conductivity and Dielectric Constant: the Continuum Approach * Nonlinear Conductivity, Dielectric Constant and Optical Properties: The Discrete Approach * Nonlinear Rigidity and Elastic Moduli: The Continuum Approach * Electrical and Dielectric Breakdown: The Discrete Approach * Fracture: Basic Concepts and Experimental Techniques * Brittle Fracture: The Continuum Approach * Brittle Fracture: The Discrete Approach * Atomistic Modeling of Materials * Multiscale Modeling of Materials: Joining Atomistic Models with Continuum Mechanics * References * Index
Low-Frequency Noise Spectroscopy at Nanoscale: Carbon Nanotube Materials and Devices

This section presents brief description of peculiarities of carbon materials and advantages of noise spectsroscopy for the study of unique carbon nanotubes (CNT) materials and devices. In general, carbon is truly an extraordinary material with physical structures spanning three dimensional (3D) graphite, two-dimensional (2D) graphene and zero-dimensional (0D) buckyballs or buckminster fullerine spheres. It is not surprising that the structural characteristics of carbon thus yield band diagrams displaying a diverse array of physical properties. When a 2D graphene sheet is rolled into a cylinder, a one-dimensional (1D) or quasi-1D form of carbon results, namely CNTs, which have been one of the most extensively studied materials since their discovery. A single rolled-up sheet of graphene results in a single-walled nanotube (SWNT) with a typical diameter of 1 – 2 nm. The rolling direction is characterized by a chiral index ( , ) n m . Achiral zigzag ( ,0) n and armchair ( , ) n n CNTs are distinguished from the rest (chiral CNTs). Armchair tubes are always metallic, while zigzag tubes can be semiconducting or metallic (Reich et al., 2004). Multi-walled nanotubes (MWNTs) consist of concentric cylinders with an interlayer spacing of 0.3 – 0.4 nm, and diameters that are at least an order of magnitude larger than SWNTs, between 10 – 30 nm. CNTs have high elastic modulus, strength, show efficient conductivity of heat, exhibit high thermal and chemical stability, flexibility, low mass density, and unique electrical properties (metallic conductivity and semiconductivity) which makes them excellent candidates for nano-devices and polymer composite materials (Sanchez-Pomales et al., 2010; Service, 1998). At the same time the CNTs are extremely difficult to manage due to their low solubility in both aqueous and organic solvents, which restrict the extent of their applications. Therefore one of the main challenges is the need for the development of new functionalization chemistries that can increase the solubility of CNTs without altering their CNTs properties. Transport in the CNTs of molecular SWand MWNT-field-effect transistors (FETs) is dominated by holes and, at room temperature, it appears to be diffusive (Martel et al., 1998). Using the gate electrode, the conductance of a SWNT-FET is modulated
Compressive Millimeter-Wave Phased Array Imaging

In this paper, we introduce a 3-D compressive phased array imaging method. The derived system model is general and can also be used in other array configurations. With its compressive sampling capability, the proposed method can recover images with far fewer samples than traditionally required in Fourier transform-based methods. This measurement strategy greatly reduces the data acquisition time at the expense of higher computational costs. Imaging results show increased resolving power in both range and cross-range directions. Moreover, the use of beam steered data enables better reconstruction quality in the presence of noise than its counterpart in switched array scheme. Sensing configurations, like array length and number of angles, which affect the reconstruction performance, are also analyzed.
Robust Maximum-likelihood On-line LiDAR-to-Camera Calibration Monitoring and Refinement

In this paper, we present a novel method for online LiDAR–Camera system calibration tracking and refinement. The method is correspondencefree, formulated as a maximum-likelihood learning task. It is based on a consistency of projected LiDAR point cloud corners and optical image edges. The likelihood function is robustified using a model in which the inlier/outlier label for the image edge pixel is marginalized out. The learning is performed by a stochastic on-line algorithm that includes a delayed learning mechanism improving its stability. Ground-truth experimental results are shown on KITTI sequences with known reference calibration. Assuming motion-compensated LiDAR data the method is able to track synthetic rotation calibration drift with about 0.06 degree accuracy in yaw and roll angles and 0.1 degree accuracy in the pitch angle. The basin of attraction of the optimization is about ±1.2 degree. The method is able to track rotation calibration parameter drift of 0.02 degree per measurement mini-batch. Full convergence occurs after about 50 mini-batches. We conclude the method is suitable for real-scene driving scenarios.
Respiratory Syncytial Virus Infections Enhance Cigarette Smoke Induced COPD in Mice

Respiratory syncytial viral (RSV) infections are a frequent cause of chronic obstructive pulmonary disease (COPD) exacerbations, which are a major factor in disease progression and mortality. RSV is able to evade antiviral defenses to persist in the lungs of COPD patients. Though RSV infection has been identified in COPD, its contribution to cigarette smoke-induced airway inflammation and lung tissue destruction has not been established. Here we examine the long-term effects of cigarette smoke exposure, in combination with monthly RSV infections, on pulmonary inflammation, protease production and remodeling in mice. RSV exposures enhanced the influx of macrophages, neutrophils and lymphocytes to the airways of cigarette smoke exposed C57BL/6J mice. This infiltration of cells was most pronounced around the vasculature and bronchial airways. By itself, RSV caused significant airspace enlargement and fibrosis in mice and these effects were accentuated with concomitant smoke exposure. Combined stimulation with both smoke and RSV synergistically induced cytokine (IL-1α, IL-17, IFN-γ, KC, IL-13, CXCL9, RANTES, MIF and GM-CSF) and protease (MMP-2, -8, -12, -13, -16 and cathepsins E, S, W and Z) expression. In addition, RSV exposure caused marked apoptosis within the airways of infected mice, which was augmented by cigarette smoke exposure. RSV and smoke exposure also reduced protein phosphatase 2A (PP2A) and protein tyrosine phosphates (PTP1B) expression and activity. This is significant as these phosphatases counter smoke-induced inflammation and protease expression. Together, these findings show for the first time that recurrent RSV infection markedly enhances inflammation, apoptosis and tissue destruction in smoke-exposed mice. Indeed, these results indicate that preventing RSV transmission and infection has the potential to significantly impact on COPD severity and progression.
ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed up data analytics. However, caches in data analytics systems cannot rely on simple caching policies and a fixed data layout to achieve good performance. Different datasets and workloads require different layouts and policies to achieve optimal performance. This paper presents ReCache, a cache-based performance accelerator that is reactive to the cost and heterogeneity of diverse raw data formats. Using timing measurements of caching operations and selection operators in a query plan, ReCache accounts for the widely varying costs of reading, parsing, and caching data in nested and tabular formats. Combining these measurements with information about frequently accessed data fields in the workload, ReCache automatically decides whether a nested or relational column-oriented layout would lead to better query performance. Furthermore, ReCache keeps track of commonly utilized operators to make informed cache admission and eviction decisions. Experiments on synthetic and real-world datasets show that our caching techniques decrease caching overhead for individual queries by an average of 59%. Furthermore, over the entire workload, ReCache reduces execution time by 19-75% compared to existing techniques.
Circulating myomiRs: a new class of biomarkers to monitor skeletal muscle in physiology and medicine

MicroRNAs (miRNA) are small non‐coding RNAs that target mRNAs and are consequently involved in the post‐transcriptional regulation of gene expression. Some miRNAs are ubiquitously expressed in tissue, while others are tissue‐specific or tissue‐enriched. miRNAs can be released by cells and are found in various biofluids, including serum and plasma. Thus, measuring miRNAs in the circulation may provide information on the originating tissue or cells. MyomiRs are described as striated muscle‐specific or muscle‐enriched miRNAs. Their circulating levels can be measured and have been proposed to be new biomarkers of physiological and pathological muscle processes. The aims of this review are to summarize the current knowledge of circulating myomiRs, to identify the types of information they can provide about skeletal muscle, and to determine how to apply that information in the fields of research and medicine.
Properties of implied cost of capital using analysts’ forecasts

We evaluate the influence of measurement error in analysts’ forecasts on the accuracy of implied cost of capital estimates from various implementations of the ‘implied cost of capital’ approach, and develop corrections for the measurement error. The implied cost of capital approach relies on analysts’ short- and long-term earnings forecasts as proxies for the market’s expectation of future earnings, and solves for the implied discount rate that equates the present value of the expected future payoffs to the current stock price. We document predictable error in the implied cost of capital estimates resulting from analysts’ forecasts that are sluggish with respect to information in past stock returns. We propose two methods to mitigate the influence of sluggish forecasts on the implied cost of capital estimates. These methods substantially improve the ability of the implied cost of capital estimates to explain cross-sectional variation in future stock returns, which is consistent with the corrections being effective in mitigating the error in the estimates due to analysts’ sluggishness.
A Tool Selection Framework for Cross Platform Mobile App Development

Cross platform Mobile application development is currently enjoying some good riddance in the Mobile App Development Community. Nonetheless, developers are somehow confronted with a tool selection dilemma since development for multiple platforms are still at the inceptive stage. In this paper we propose a framework for choosing the appropriate Cross Platform Development Tool for a given project. The framework defines the set of criteria to be considered for evaluation and makes pair-wise comparison to compute the priority for the criteria as well as priority of the alternative tools compared. The implementation of the framework on the Tools: PhoneGap, Titanium and Xamarin recommend PhoneGap as the preferred tool for Cross Platform Development. The result from the implementation also considers the capability criteria as the most important in Mobile Cross Platform Development.
Salient Object Detection Via Nonconvex Structured Matrix Decomposition

In the salient object detection, the feature matrix of an image can be represented as a low-rank matrix plus a sparse matrix, corresponding to the background and the salient regions, respectively. Generally, the rank function is approximated by the nuclear norm. However, solving the nuclear norm minimization problem usually leads to a suboptimal solution. To address this problem, we propose a novel nonconvex structure matrix decomposition model, where using a nonconvex surrogate (i.e., the l1 norm of logistic function) on the singular values of a matrix to approximate the rank function. In addition, our model contains two structural regularizations: a group sparsity induced norm regularization to explore the relationship between each superpixel, making salient object highlighted consistently, and a Laplacian regularization to increase the distance between salient regions and non-salient regions in feature space. Finally, high-level priors are integrated to our model. Experimental results show that our model can achieve better performance compared with the state-of-the-art methods.
Rat1p maintains RNA polymerase II CTD phosphorylation balance

The S. cerevisiae 5′-3′ exonuclease Rat1p is supposed to partake in transcription termination via decay of downstream RNA after 3′-end cleavage. This study reveals that rat1-1 mutant displays increased phosphorylation of RNA polymerase II C-terminal domain and transcription elongation rates and that rat1-1 phenotypes can be suppressed by overexpression of the phosphatase Fcp1p. Thus, Rat1p plays complex roles in controlling transcription that have to be considered when analyzing rat1-1 mutant phenotypes.
Benchmarking workflow discovery: a case study from bioinformatics

Automation in science is increasingly marked by the use of workflow technology. The sharing of workflows through repositories supports the verifiability, reproducibility and extensibility of computational experiments. However, the subsequent discovery of workflows remains a challenge, both from a sociological and technological viewpoint. Based on a survey with participants from 19 laboratories, we investigate the current practices in workflow sharing, re‐use and discovery among life scientists chiefly using the Taverna workflow management system. To address their perceived lack of effective workflow discovery tools, we go on to develop benchmarks for the evaluation of discovery tools, drawing on a series of practical exercises. We demonstrate the value of the benchmarks on two tools: one using graph matching and the other relying on text clustering. Copyright © 2009 John Wiley & Sons, Ltd.
Capacity to adapt to environmental change: evidence from a network of organizations concerned with increasing wildfire risk

The National Science Foundation's (NSF) Coupled Human and Natural Systems Program (NSF Grant CNH-1013296) and the U.S. Forest Service PNW Research Station provided the funding for this research. Support was also provided by the National Socio-Environmental Synthesis Center (SESYNC; DBI-1052875). The authors would like to acknowledge all the interview informants who generously gave their time to participate in the study. The authors would also like to acknowledge Orjan Bodin for reviewing the study plan for the research; Susan Charnley, Emily Platt, and Kerry Grimm for assisting with data collection; Maribel Vidrio for assisting with data management; and Ken Vance-Borland for assisting with preliminary data analysis.
Effect of Dietary Betaine on Metabolic Syndrome Risk Factors in Asian

Objective: Betaine (trimethylglycine) is a naturally-occurring osmolyte and methyl donor with reported effects on liver metabolism. The aim of this study was to evaluate the safety and the effects of betaine administration to metabolic risk factors in Asian males. Methodology: Twenty Japanese males (30-59 years) with mild fatty liver were enrolled in this blinded, placebocontrolled, randomized, parallel trial to assess the effects of 3 g of daily betaine for 12 weeks on safety and metabolic biomarkers. Selection criteria included fatty liver disease, as assessed by an abdominal ultrasonogram (US), the practice of moderate alcohol consumption, liver performance status and body mass index (≤27). Abdominal computed tomography (CT) as well as blood and urine sampling were performed before and after the intervention period. Results: No differences in the degree of hepatic steatosis or in the occurrence of adverse events were observed between the groups. Compared to the placebo group, the betaine group showed a significant increase in plasma betaine (P<0.0001) and high-density lipoprotein (HDL)-cholesterol (P<0.05) concentrations. Conclusion: Low-dose betaine supplementation to subjects with mild fatty liver disease is safe and well tolerated. Betaine increased plasma HDL which may reflect the favorable effects of betaine on liver function-related metabolic metabolism in Asian population.
Lithium treatment and hippocampal subfields and amygdala volumes in bipolar disorder

Results from magnetic resonance imaging (MRI) studies are heterogeneous with regard to hippocampal and amygdala volume alterations in bipolar disorder (BD). Lithium treatment may influence both structures. It is unknown if lithium treatment has distinct effects on hippocampal subfield volumes and if subfield volumes change over the course of illness in BD.
Distributed Expectation-Maximization Algorithm for Speaker Localization in Reverberant Environments

Localization of acoustic sources has attracted a considerable amount of research attention in recent years. A major obstacle to achieving high localization accuracy is the presence of reverberation, the influence of which obviously increases with the number of active speakers in the room. Human hearing is capable of localizing acoustic sources even in extreme conditions. In this study, we propose to combine a method based on human hearing mechanisms and a modified incremental distributed expectation-maximization (IDEM) algorithm. Rather than using phase difference measurements that are modeled by a mixture of complex-valued Gaussians, as proposed in the original IDEM framework, we propose to use time difference of arrival measurements in multiple subbands and model them by a mixture of real-valued truncated Gaussians. Moreover, we propose to first filter the measurements in order to reduce the effect of the multipath conditions. The proposed method is evaluated using both simulated data and real-life recordings.
Population Pharmacokinetics of High-Dose Oxazepam in Alcohol-Dependent Patients: Is There a Risk of Accumulation?

Background: According to the guidelines, benzodiazepines with a short half-life are the reference medication to treat alcohol withdrawal syndrome. The doses of oxazepam used in this population may reach up to 300 mg per day, significantly higher than usual doses. Its use in these patients deserves further information to confirm that the half-life remains constant and that no accumulation appears. The objective of this study was to investigate the pharmacokinetics of high doses of oxazepam in alcohol-dependent patients treated for alcohol withdrawal syndrome. Methods: Overall, 63 outpatients [weight, 71.1 kg (45.0–118.0); age, 47.6 years (31–67)] followed in the addictology unit, were studied. Total mean dose of 96.0 mg per day (range, 20–300 mg/d) was administered by oral route. Therapeutic drug monitoring allowed the measurement of 96 plasma concentrations. The following covariates were evaluated: demographic data (age, body weight, height, gender) and biological data (creatinine, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, gamma-glutamyl transferase). Pharmacokinetic analysis was performed using a nonlinear mixed-effect population model. Results: Data were modeled with a 1-compartment pharmacokinetic model. The population typical mean 90% confidence interval values for clearance, apparent volume of distribution (V), and duration of absorption (D1) were 6.8 L/h (range, 3.9–8.0 L/h), 159 L (range, 98.0–282 L), and 2 hours (fixed), respectively. The interindividual variability of clearance and V, and residual variability (90% confidence interval) were 74% (44%–96%), 69% (40%–89%), and 32% (20%–41%), respectively. The elimination half-life was 16 hours (range, 3–42 hours). Conclusions: Oxazepam exhibited a linear pharmacokinetics with a proportional relationship from 20 to 300 mg per day, the dose range currently used in alcohol-dependent patients treated for alcohol withdrawal syndrome. We did not find any evidence of drug accumulation with these doses.
The long and winding road to IgA deficiency: causes and consequences

ABSTRACT Introduction: The most common humoral immunodeficiency is IgA deficiency. One of the first papers addressing the cellular and molecular mechanisms underlying IgA deficiency indicated that immature IgA-positive B-lymphocytes are present in these patients. This suggests that the genetic background for IgA is still intact and that class switching can take place. At this moment, it cannot be ruled out that genetic as well as environmental factors are involved. Areas covered: A clinical presentation, the biological functions of IgA, and the management of IgA deficiency are reviewed. In some IgA deficient patients, a relationship with a loss-of-function mutation in the TACI (transmembrane activator and calcium-modulating cyclophilin ligand interaction) gene has been found. Many other genes also have been associated. Gut microbiota are an important environmental trigger for IgA synthesis. Expert commentary: Expression of IgA deficiency is due to both genetic and environmental factors and a role for gut microbiota cannot be excluded.
The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks

Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.
Heat Injury Prevention—A Military Perspective

Abstract Epstein, Y, Druyan, A, and Heled, Y. Heat injury prevention—A military perspective. J Strength Cond Res 26(7): S82–S86, 2012—Heat-related injuries, and specifically exertional heat stroke, are a significant occupational risk in the armed forces, especially for those soldiers who are rapidly deployed from a temperate climate region to hot climate regions. Traditionally, adaptation to heat was considered as a matter of physiological adaptation. It is clear today that these injuries are mostly avoidable when applying proper education and behavioral adaptations. Education on behavioral adaptation for the prevention of heat injuries should be targeted at the individual and the organization level. This article summarizes the issue of proper preventive measures that should be taken to avoid, or at least minimize, the risk of exertional heat related injuries during military operations and training.