Detecting temporal and spatial effects of epithelial cancers with Raman spectroscopy.

PublishedJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tThis is the final version of the article. Available from Hindawi Publishing Corporation via the DOI in this record.Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.The authors acknowledge the financial support of the NCI/NIH (R01-CA95405 and R21-CA95995), as well as the Howard Hughes Medical Institute (pre-doctoral fellowship for MK). We would also like to thank the doctors and staff at Vanderbilt University Medical Center and Tri-state Women’s Health for all their assistance

Fire effects on soils: the human dimension

Soils are among the most valuable non-renewable resources on the Earth. They support natural vegetation and human agro-ecosystems, represent the largest terrestrial organic carbon stock, and act as stores and filters for water. Mankind has impacted on soils from its early days in many different ways, with burning being the first human perturbation at landscape scales. Fire has long been used as a tool to fertilize soils and control plant growth, but it can also substantially change vegetation, enhance soil erosion and even cause desertification of previously productive areas. Indeed fire is now regarded by some as the seventh soil-forming factor. Here we explore the effects of fire on soils as influenced by human interference. Human-induced fires have shaped our landscape for thousands of years and they are currently the most common fires in many parts of the world. We first give an overview of fire effect on soils and then focus specifically on (i) how traditional land-use practices involving fire, such as slash-and-burn or vegetation clearing, have affected and still are affecting soils; (ii) the effects of more modern uses of fire, such as fuel reduction or ecological burns, on soils; and (iii) the ongoing and potential future effects on soils of the complex interactions between human-induced land cover changes, climate warming and fire dynamics. This article is part of the themed issue ‘The interaction of fire and mankind’

Parton Fragmentation within an Identified Jet at NNLL

The fragmentation of a light parton i to a jet containing a light energetic hadron h, where the momentum fraction of this hadron as well as the invariant mass of the jet is measured, is described by "fragmenting jet functions". We calculate the one-loop matching coefficients J_{ij} that relate the fragmenting jet functions G_i^h to the standard, unpolarized fragmentation functions D_j^h for quark and gluon jets. We perform this calculation using various IR regulators and show explicitly how the IR divergences cancel in the matching. We derive the relationship between the coefficients J_{ij} and the quark and gluon jet functions. This provides a cross-check of our results. As an application we study the process e+ e- to X pi+ on the Upsilon(4S) resonance where we measure the momentum fraction of the pi+ and restrict to the dijet limit by imposing a cut on thrust T. In our analysis we sum the logarithms of tau=1-T in the cross section to next-to-next-to-leading-logarithmic accuracy (NNLL). We find that including contributions up to NNLL (or NLO) can have a large impact on extracting fragmentation functions from e+ e- to dijet + h.Comment: expanded introduction, typos fixed, journal versio

Analysis of the nonlinear Kerr effects in optical transmission systems that deploy optical phase conjugation

In this work, we will derive, validate, and analyze the theoretical description of nonlinear Kerr effects resulted in various transmission system that deploy single or multiple optical phase conjugators (OPC). We will show that the nonlinear Kerr compensation can be achieved, with various efficiencies, in both lumped and distributed Raman transmission systems. The results show that first order distributed Raman systems are superior to the discretely amplified systems in terms of the nonlinear Kerr compensation efficiency that a mid-link OPC can achieve. Also, we will show that the multi-OPC approach will diminish the nonlinearity compensation efficiency in any system as it will act as periodic dispersion compensators

Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles

The lack of well-structured annotations in a growing amount of RNA expression data complicates data interoperability and reusability. Commonly - used text mining methods extract annotations from existing unstructured data descriptions and often provide inaccurate output that requires manual curation. Automatic data-based augmentation (generation of annotations on the base of expression data) can considerably improve the annotation quality and has not been well-studied. We formulate an automatic augmentation of small RNA-seq expression data as a classification problem and investigate deep learning (DL) and random forest (RF) approaches to solve it. We generate tissue and sex annotations from small RNA-seq expression data for tissues and cell lines of homo sapiens. We validate our approach on 4243 annotated small RNA-seq samples from the Small RNA Expression Atlas (SEA) database. The average prediction accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex - 77% (DL). The "one dataset out" average accuracy for tissue group prediction is 83% (DL) and 59% (RF). On average, DL provides better results as compared to RF, and considerably improves classification performance for 'unseen' datasets