The Economic Impact of Implementing Nondestructive Testing of Reinforced Concrete Bridge Decks in Indiana

The deck is among the most expensive components of a bridge over its lifetime because of the frequent and costly maintenance and rehabilitation required. Currently, the Indiana Department of Transportation (INDOT) performs visual inspections of a bridge deck as the principal means of determining its condition, which enables the inspector to definitively document the surface condition while the unseen condition below the deck surface is left to the inspector’s expert judgement. To compensate for this lack of data, INDOT supplements visual inspections with programmatic scheduling for major work actions, which is very effective for INDOT but costly. In this continuing era of funding shortfalls, INDOT commissioned this study to investigate nondestructive testing (NDT) methods to fill their data gap to inform its work action decision. The NDT methods have been shown to accurately locate corrosion and delamination and are a cost-effective alternative. A project level comparison between the NDT methods was performed to show which method, as well as which combination of methods, were the best choices from a cost perspective. A project level analysis of 30 bridge decks was performed, and those costs were compared to the costs of the current INDOT programmatic schedules. Finally, the analysis was expanded to the network level, which included the entire bridge inventory in Indiana. The results of this study indicate that implementing the NDT methods is cost-effective for INDOT at both the project and network levels

Toll-like receptor 3 activation is required for normal skin barrier repair following UV damage.

UV damage to the skin leads to the release of noncoding RNA (ncRNA) from necrotic keratinocytes that activates Toll-like receptor 3 (TLR3). This release of ncRNA triggers inflammation in the skin following UV damage. Recently, TLR3 activation was also shown to aid wound repair and increase the expression of genes associated with permeability barrier repair. Here, we sought to test whether skin barrier repair after UVB damage is dependent on the activation of TLR3. We observed that multiple ncRNAs induced expression of skin barrier repair genes, that the TLR3 ligand Poly (I:C) also induced expression and function of tight junctions, and that the ncRNA U1 acts in a TLR3-dependent manner to induce expression of skin barrier repair genes. These observations were shown to have functional relevance as Tlr3-/- mice displayed a delay in skin barrier repair following UVB damage. Combined, these data further validate the conclusion that recognition of endogenous RNA by TLR3 is an important step in the program of skin barrier repair

Discreteness-Induced Oscillatory Instabilities of Dark Solitons

We reveal that even weak inherent discreteness of a nonlinear model can lead to instabilities of the localized modes it supports. We present the first example of an oscillatory instability of dark solitons, and analyse how it may occur for dark solitons of the discrete nonlinear Schrodinger and generalized Ablowitz-Ladik equations.Comment: 11 pages, 4 figures, to be published in Physical Review Letter

The Post-Apoptotic Fate of RNAs Identified Through High-Throughput Sequencing of Human Hair

The hair of all mammals consists of terminally differentiated cells that undergo a specialized form of apoptosis called cornification. While DNA is destroyed during cornification, the extent to which RNA is lost is unknown. Here we find that multiple types of RNA are incompletely degraded after hair shaft formation in both mouse and human. Notably, mRNAs and short regulatory microRNAs (miRNAs) are stable in the hair as far as 10 cm from the scalp. To better characterize the post-apoptotic RNAs that escape degradation in the hair, we performed sequencing (RNA-seq) on RNA isolated from hair shafts pooled from several individuals. This hair shaft RNA library, which encompasses different hair types, genders, and populations, revealed 7,193 mRNAs, 449 miRNAs and thousands of unannotated transcripts that remain in the post-apoptotic hair. A comparison of the hair shaft RNA library to that of viable keratinocytes revealed surprisingly similar patterns of gene coverage and indicates that degradation of RNA is highly inefficient during apoptosis of hair lineages. The generation of a hair shaft RNA library could be used as months of accumulated transcriptional history useful for retrospective detection of disease, drug response and environmental exposure

Airway expression of Transient Receptor Potential (TRP) Vanniloid-1 and Ankyrin-1 channels is not increased in patients with Idiopathic Pulmonary Fibrosis

Dry cough is a common symptom described in patients with Idiopathic Pulmonary Fibrosis (IPF) and impairs quality of life. The exact mechanisms causing cough in IPF remain unclear, however evidence suggests altered cough neurophysiology and sensitisation plays a role; IPF patients have an enhanced cough reflex sensitivity to inhaled capsaicin. The Transient Receptor Potential Vanniloid-1 channel (TRPV-1) has a role in the cough reflex and airway expression is increased in patients with chronic cough. The Ankyrin-1 receptor (TRPA-1) is often coexpressed. It was hypothesised that, like chronic cough patients, IPF patients have increased airway TRP receptor expression. Bronchial biopsies were obtained from 16 patients with IPF, 11 patients with idiopathic chronic cough and 8 controls without cough. All other causes of cough were rigorously excluded. Real-time quantitative Polymerase Chain Reaction was used to detect TRPV-1 and TRPA-1 mRNA expression with Immunohistochemistry demonstrating protein expression. Mean TRPV-1 and TRPA-1 gene expression was higher in IPF patients compared with controls, but the difference did not reach statistical significance. Immunostaining supported these findings. This study suggests that structural up-regulation of central airway TRP receptors is not the key mechanism for cough in IPF patients. It is probable that IPF cough results from altered neuronal sensitivity at multiple levels of the cough pathway

On the Spiral Structure of the Milky Way Galaxy

We consider the possible pattern of the overall spiral structure of the Galaxy, using data on the distribution of neutral (atomic), molecular, and ionized hydrogen, on the base of the hypothesis of the spiral structure being symmetric, i.e. the assumption that spiral arms are translated into each other for a rotation around the galactic center by 180{\deg} (a two-arm pattern) or by 90{\deg} (a four-arm pattern). We demonstrate that, for the inner region, the observations are best represented with a four-arm scheme of the spiral pattern, associated with all-Galaxy spiral density waves. The basic position is that of the Carina arm, reliably determined from distances to HII regions and from HI and H2 radial velocities. This pattern is continued in the quadrants III and IV with weak outer HI arms; from their morphology, the Galaxy should be considered an asymmetric multi-arm spiral. The kneed shape of the outer arms that consist of straight segments can indicate that these arms are transient formations that appeared due to a gravitational instability in the gas disk. The distances between HI superclouds in the two arms that are the brightest in neutral hydrogen, the Carina arm and the Cygnus (Outer) arm, concentrate to two values, permitting to assume the presence of a regular magnetic field in these arms.Comment: 21 pages, 14 fugures; accepted for publication in Astronomichesky Journal (Astron. Rep.

Geospatial framework to assess fireline effectiveness for large wildfires in the western USA, A

Includes bibliographical references (pages 16-19).Quantifying fireline effectiveness (FLE) is essential to evaluate the efficiency of large wildfire management strategies to foster institutional learning and improvement in fire management organizations. FLE performance metrics for incident-level evaluation have been developed and applied to a small set of wildfires, but there is a need to understand how widely they vary across incidents to progress towards targets or standards for performance evaluation. Recent efforts to archive spatially explicit fireline records from large wildfires facilitate the application of these metrics to a broad sample of wildfires in different environments. We evaluated fireline outcomes (burned over, held, not engaged) and analyzed incident-scale FLE for 33 large wildfires in the western USA from the 2017 and 2018 fire seasons. FLE performance metrics varied widely across wildfires and often aligned with factors that influence suppression strategy. We propose a performance evaluation framework based on both the held to engaged fireline ratio and the total fireline to perimeter ratio. These two metrics capture whether fireline was placed in locations with high probability of engaging with the wildfire and holding and the relative level of investment in containment compared to wildfire growth. We also identify future research directions to improve understanding of decision quality in a risk-based framework

White-Box Transformers via Sparse Rate Reduction

In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dimensional Gaussian distributions supported on incoherent subspaces. The quality of the final representation can be measured by a unified objective function called sparse rate reduction. From this perspective, popular deep networks such as transformers can be naturally viewed as realizing iterative schemes to optimize this objective incrementally. Particularly, we show that the standard transformer block can be derived from alternating optimization on complementary parts of this objective: the multi-head self-attention operator can be viewed as a gradient descent step to compress the token sets by minimizing their lossy coding rate, and the subsequent multi-layer perceptron can be viewed as attempting to sparsify the representation of the tokens. This leads to a family of white-box transformer-like deep network architectures which are mathematically fully interpretable. Despite their simplicity, experiments show that these networks indeed learn to optimize the designed objective: they compress and sparsify representations of large-scale real-world vision datasets such as ImageNet, and achieve performance very close to thoroughly engineered transformers such as ViT. Code is at \url{https://github.com/Ma-Lab-Berkeley/CRATE}.Comment: 33 pages, 11 figure