Stationary phase analysis of ambient noise cross-correlations: Focusing on non-ballistic arrivals

Stacked cross-correlation functions have become ubiquitous in the ambient seismic imaging and monitoring community as approximations to the Green's function between two receivers. While theoretical understanding of this approximation to the ballistic arrivals is well established, the equivalent analysis for the non-ballistic arrivals is alarmingly inadequate compared to the exponential growth of its applications. To provide a fundamental understanding of the cross-correlation functions beyond the ballistic arrivals, we derive analytical stationary phase solutions for ambient noise cross-correlations with a focus on non-ballistic arrivals. We establish the mathematical and corresponding physical conditions that drastically differentiate the non-ballistic arrivals in the stacked cross-correlation and the actual Green's functions. In ambient noise environments, the coda waves due to random medium scatterings of an impulsive source cannot be distinguished from the cross-talk artifacts due to overlapping random noise sources. Therefore, changes in the non-ballistic arrivals cannot be uniquely attributed to changes in the medium or changes in the noise source environment without additional constraints. The theoretical results demand that interpreting large-elapse-time arrivals in the stacked cross-correlation functions as coda waves for deterministic information about the propagation medium should be conducted only after the source influence is sufficiently ruled out. Once the source influence is eliminated, the stationary phase solutions for scattering waves provide a solid basis for extracting reliable scattering information from the noise correlation functions for higher-resolution imaging and monitoring.Comment: 22 pages, 11 figures, 1 tabl

Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference

<p>Abstract</p> <p>Background</p> <p>Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality.</p> <p>Results</p> <p>Microarray experiments were performed in a γ-proteobacterium <it>Shewanella oneidensis</it>. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing <it>Shewanella </it>genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.</p> <p>Conclusion</p> <p>These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.</p

Biostatistical Considerations of the Use of Genomic DNA Reference in Microarrays

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories (2, 3, 5, 7, 9, 20, 24-26). While some reported that experimental results of microarrays using genomic DNA reference conformed nicely to those obtained by cDNA: cDNA co-hybridization method, others acquired poor results. We hypothesized that these conflicting reports could be resolved by biostatistical analyses. To test it, microarray experiments were performed in a 4 proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either by direct cDNA: cDNA co-hybridization, or by indirect calculation through a Shewanella genomic DNA reference. Several major biostatistical techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses significantly improved the data quality. These findings could potentially serve as guidelines for microarray data analysis using genomic DNA as reference

Cross-domain Human Parsing via Adversarial Feature and Label Adaptation

Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduce the discrepancy between feature distributions of two domains. Besides, our model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the cross-domain human parsing problem.Comment: Accepted by AAAI 201

Peptides/Proteins Encoded by Non-coding RNA: A Novel Resource Bank for Drug Targets and Biomarkers

Non-coding RNAs (ncRNAs) are defined as RNA molecules that do not encode proteins, but recent evidence has proven that peptides/proteins encoded by ncRNAs do indeed exist and usually contain less than 100 amino acids. These peptides/proteins play an important role in regulating tumor energy metabolism, epithelial to mesenchymal transition of cancer cells, the stability of the c-Myc oncoprotein, and the ubiquitination and degradation of proliferating cell nuclear antigen (PCNA). These peptides/proteins represent promising drug targets for fighting against tumor growth or biomarkers for predicting the prognosis of cancer patients. In this review, we summarize the characteristics of peptides/proteins that have recently been identified as putative ncRNA translation products and their outlook for small molecule peptide drugs, drug targets, and biomarkers

Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters.

The ecological concept of the r-K life history strategy is widely applied in macro-ecology to characterize functional traits of taxa. However, its adoption in microbial communities is limited, owing to the lack of a measureable, convenient functional trait for classification. In this study, we performed an experiment of stepwise organic amendments in triplicate anaerobic digesters. We found that high resource availability significantly favored microbial r-strategists such as Bacillus spp. Incremental resource availability heightened average rRNA operon copy number of microbial community, resulting in a strong, positive correlation (r&gt;0.74, P&lt;0.008). This study quantifies how resource availability manipulations influence microbial community composition and supports the idea that rRNA operon copy number is an ecologically meaningful trait which reflects resource availability

Low-energy properties and magnetization plateaus in a 2-leg mixed spin ladder

Using the density matrix renormalization group technique we investigate the low-energy properties and the magnetization plateau behavior in a 2-leg mixed spin ladder consisting of a spin-1/2 chain coupled with a spin-1 chain. The calculated results show that the system is in the same universality class as the spin-3/2 chain when the interchain coupling is strongly ferromagnetic, but the similarity between the two systems is less clear under other coupling conditions. We have identified two types of magnetization plateau phases. The calculation of the magnetization distribution on the spin-1/2 and the spin-1 chains on the ladder shows that one plateau phase is related to the partially magnetized valence-bond-solid state, and the other plateau state contains strongly coupled S=1 and s=1/2 spins on the rung.Comment: 6 pages with 8 eps figure

Reducing the Uncertainty in Estimating Soil Microbial-Derived Carbon Storage

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios

Structural Basis and Kinetics of Force-Induced Conformational Changes of an αA Domain-Containing Integrin

Integrin α(L)β₂ (lymphocyte function-associated antigen, LFA-1) bears force upon binding to its ligand intercellular adhesion molecule 1 (ICAM-1) when a leukocyte adheres to vascular endothelium or an antigen presenting cell (APC) during immune responses. The ligand binding propensity of LFA-1 is related to its conformations, which can be regulated by force. Three conformations of the LFA-1 αA domain, determined by the position of its α₇-helix, have been suggested to correspond to three different affinity states for ligand binding.The kinetics of the force-driven transitions between these conformations has not been defined and dynamically coupled to the force-dependent dissociation from ligand. Here we show, by steered molecular dynamics (SMD) simulations, that the αA domain was successively transitioned through three distinct conformations upon pulling the C-terminus of its α₇-helix. Based on these sequential transitions, we have constructed a mathematical model to describe the coupling between the αA domain conformational changes of LFA-1 and its dissociation from ICAM-1 under force. Using this model to analyze the published data on the force-induced dissociation of single LFA-1/ICAM-1 bonds, we estimated the force-dependent kinetic rates of interstate transition from the short-lived to intermediate-lived and from intermediate-lived to long-lived states. Interestingly, force increased these transition rates; hence activation of LFA-1 was accelerated by pulling it via an engaged ICAM-1.Our study defines the structural basis for mechanical regulation of the kinetics of LFA-1 αA domain conformational changes and relates these simulation results to experimental data of force-induced dissociation of single LFA-1/ICAM-1 bonds by a new mathematical model, thus provided detailed structural and kinetic characterizations for force-stabilization of LFA-1/ICAM-1 interaction