Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

An intelligent system for estimating frog community calling activity and species richness

Over the past decade, dramatic declines in frog populations have been noticed worldwide. To examine this decline, monitoring frogs is becoming increasingly important. Compared to traditional field survey methods, recent advances in acoustic sensor technology have greatly extended spatial and temporal scales for monitoring animal populations. In this paper, we examine the problem of monitoring frog populations by analysing acoustic sensor data, where the population is reflected by community calling activity and species richness. Specifically, a novel acoustic event detection (AED) algorithm is first proposed to filter out those recordings without frog calls. Then, multi-label learning is used to classify each individual recording with six acoustic features: linear predictive coding coefficients, Mel-frequency cepstral coefficients, linear-frequency cepstral coefficients, acoustic complexity index, acoustic diversity index, and acoustic evenness index. Next, frog community calling activity and species richness are estimated by accumulating the results of AED and multi-label learning, respectively. Finally, ordinary least squares regression (OLS) is conducted to reveal the relationship between frog populations (frog calling activity and species richness) and weather variables (maximum temperature and rainfall). Experimental results demonstrate that our proposed intelligent system can significantly facilitate the effort to estimate frog community calling activity and species richness with comparable accuracies. The statistical results of OLS indicate that rainfall pattern has a lagged impact on frog community calling activity (significant in the first day after rainy day) and species richness (significant in the fourth day after rainy day). Temperature is shown to affect species richness but is less likely to change calling activity.     

Tree-ring dating of “brush structures” in Kluane National Park and Reserve,Yukon, Canada

“Brush structures” are temporary wooden structures built with unmodified local materials and used as shelters by First Nation Peoples in the forests of the Yukon prior to European contact. This paper reports a preliminary attempt to date these structures using dendrochronology. Investigations were carried out of four njel (“teepee like”) structures and eight män-ku (low 2–3 sided wall structures) at four main sites. The primary material cored was poles (dead spruce trunks), often only 10–20 cm diameter, with narrow, sometimes extremely suppressed ring sequences. These structures are dated between 1865 and 1887, based on the latest (outermost) ring in the sampled material. The limited sampling and use of old wood in these structures (whether fire-kill, standing dead or reused from previous features) makes it difficult to give precise dates for the initial evidence of First Nation activity at these sites: more extensive sampling could provide further insight into the settlement history and construction techniques used. The sites investigated date from the latter half of the nineteenth century shortly before the first European gold rush to this region.     

Measurement of 7-dehydrocholesterol and cholesterol in hair can be used in the diagnosis of Smith-Lemli-Opitz syndrome

7-dehydrocholesterol (7-DHC) and cholesterol (CHOL) are biomarkers of Smith-Lemli-Opitz Syndrome (SLOS), a congenital autosomal recessive disorder characterized by elevated 7-DHC level in patients. Hair samples have been shown to have great diagnostic and research value, which has long been neglected in the SLOS field. In this study, we sought to investigate the feasibility of using hair for SLOS diagnosis. In the presence of antioxidants (2,6-ditert-butyl-4-methylphenol and triphenylphosphine), hair samples were completely pulverized and extracted by micro-pulverized extraction in alkaline solution or in n-hexane. After microwave-assisted derivatization with N,O-Bis(trimethylsilyl)trifluoroacetamide, the analytes were measured by GC-MS. We found that the limits of determination for 7-DHC and CHOL were 10 ng/mg and 8 ng/mg, respectively. In addition, good linearity was obtained in the range of 50–4000 ng/mg and 30–6000 ng/mg for 7-DHC and CHOL, respectively, which fully meets the requirement for SLOS diagnosis and related research. Finally, by applying the proposed method to real hair samples collected from 14 healthy infants and two suspected SLOS patients, we confirmed the feasibility of hair analysis as a diagnostic tool for SLOS. In conclusion, we present an optimized and validated analytical method for the simultaneous determination of two SLOS biomarkers using human hair.     

Isolation and PCR detection of <Emphasis Type="Italic">Enterobacter sakazakii</Emphasis> in South African food products,specifically infant formula milks

Enterobacter sakazakii has recently been identified as an opportunistic pathogen. The current culture-dependent detection methods for these bacteria are time-consuming and in this study a PCR method for the detection of E. sakazakii in South African food products, including an internal amplification control (IAC) was developed. DNA was isolated and amplified from the products and they were plated on selective growth media after pre-enrichment and enrichment in Enterobacteriaceae enrichment broth. Four of the 22 products tested positive for the presence of E. sakazakii, confirmed by PCR detection and growth on selective media. The PCR method proved effective in detecting E. sakazakii in South African products after three days and could serve as an alternative for traditional microbiological techniques.     

The importance of chemical environmental cues for juvenile Lethrinus nebulosus Forsskål (Lethrinidae, Teleostei) when settling into their first benthic habitat

A commercially important coral-reef fish, the spangled emperor Lethrinus nebulosus, settles into seagrass beds at the end of its pelagic larval phase, but the mechanism for locating these beds is unknown. To investigate this mechanism we first used a wide-choice, ex situ setup to examine the ability of captivity-reared naïve L. nebulosus settlers to select their first benthic habitat by reference to chemical cues. Second, we examined the morphology and ultrastructure of the nasal olfactory organ in settling L. nebulosus juveniles. We obtained the first evidence of a tropical seagrass-settling coral reef fish that can use chemical environmental cues in selecting its first benthic habitat at ranges up to at least 2 m. The L. nebulosus settlers exhibited a well developed pair of nasal olfactory organs, positioned in nares on the dorsal side of the head. These organs were elliptical radial rosettes, one in each of the olfactory chambers, and each comprised 12 lamellae, six on each side of a midline raphe, which were totally covered with sensory and non-sensory cilia, except for the margins. This type of cilia distribution is thought to indicate an acute sense of smell. The olfactory epithelium contained mature and immature ciliated receptor neurons bearing three to five cilia, and a second type of receptor neuron bearing six to eight microvilli.     

Biosensors based on peptide exposure show single molecule conformations in live cells

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Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

Barred owl occupancy surveys within the range of the northern spotted owl

The range expansion by barred owls (Strix varia) into western North America has raised considerable concern regarding their potential effects on declining northern spotted owl (Strix occidentalis caurina) populations, yet most information on the occurrence of barred owls in the region is limited to incidental detections during surveys for spotted owls. To address this shortcoming we investigated response behavior, detection probabilities, and landscape occupancy patterns of barred owls in western Oregon, USA, during conspecific versus spotted owl call-broadcast surveys. Subtle differences in barred owl response behavior to conspecific versus spotted owl vocalizations combined with minor procedural differences between species-specific survey protocols led to a sizeable difference in estimated detection probabilities during conspecific (0.66, 95% CI = 0.61–0.71) versus spotted owl (0.48, 95% CI = 0.39–0.56) surveys. We identified 61 territorial pairs of barred owls during repeated surveys of a multi-ownership study area with the probability of occupancy being highest in the structurally diverse mixture of mature and old forests that occurred almost entirely on public lands. Our findings suggest that research and management strategies to address potential competitive interactions between spotted owls and barred owls will require carefully designed, species-specific survey methods that account for erratic response behaviors and imperfect detection of both species. Our sampling methods can be used by forest managers to determine the occurrence and distribution of barred owls with high confidence. © 2011 The Wildlife Society.