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Stramenopile algae contribute significantly to global primary productivity, and one class, Eustigmatophyceae, is increasingly studied for applications in high-value lipid production. Yet much about their basic biology remains unknown, including the nature of an enigmatic, pigmented globule found in vegetative cells. Here, we present an in-depth examination of this "red body," focusing on Nannochloropsis oceanica. During the cell cycle, the red body forms adjacent to the plastid, but unexpectedly it is secreted and released with the autosporangial wall following cell division. Shed red bodies contain antioxidant ketocarotenoids, and overexpression of a beta-carotene ketolase results in enlarged red bodies. Infrared spectroscopy indicates long-chain, aliphatic lipids in shed red bodies and cell walls, and UHPLC-HRMS detects a C32 alkyl diol, a potential precursor of algaenan, a recalcitrant cell wall polymer. We propose that the red body transports algaenan precursors from plastid to apoplast to be incorporated into daughter cell walls.

Abstract Stramenopile algae contribute significantly to global primary productivity, and one class, Eustigmatophyceae, is increasingly studied for applications in high-value lipid production. Yet much about their basic biology remains unknown, including the nature of an enigmatic, pigmented globule found in vegetative cells. Here, we present an in-depth examination of this “red body,” focusing on Nannochloropsis oceanica. During the cell cycle, the red body forms adjacent to the plastid, but unexpectedly it is secreted and released with the autosporangial wall following cell division. Shed red bodies contain antioxidant ketocarotenoids, and overexpression of a beta-carotene ketolase results in enlarged red bodies. Infrared spectroscopy indicates long-chain, aliphatic lipids in shed red bodies and cell walls, and UHPLC-HRMS detects a C32 alkyl diol, a potential precursor of algaenan, a recalcitrant cell wall polymer. We propose that the red body transports algaenan precursors from plastid to apoplast to be incorporated into daughter cell walls.

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

Abstract Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant discriminative power for all doses and time-points out to 90 days after exposure. Classification accuracy was maximized when testing 14 days after exposure (specificity > 0.9 with a sensitivity threshold of 0.9) and dropped by roughly 30% sensitivity at 90 days. Infrared frequencies point towards biological changes in DNA conformation, lipid oxidation and accumulation and shifts in protein secondary structure. Since only hundreds of samples were used to learn the highly discriminative signature, developing human-relevant diagnostic capabilities is likely feasible and this non-invasive procedure points toward rapid, non-invasive, and reagent-free biodosimetry applications at population scales.

Synchrotron infrared hyperspectral microscopy is a label-free and non-invasive technique well suited for imagingof chemical events in situ. It can track the spatial and temporal distributions of molecules of interests in a specimenin its native state by the molecule’s characteristic vibrational modes. Despite tremendous progress made in recentyears, IR hyperspectral imaging of chemical events in biomaterials in liquids remains challenging because of thedemanding requirements on environmental control and strong infrared absorption of water. Here we report amulti-phase capillary-driven membrane device for label-free and real-time investigation of enzymaticdeconstruction of algal cellulose purified from Cladophora aegagropila.

The execution and analysis of complex experiments are challenged by the vast dimensionality of the underlying parameter spaces. Although an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting owing to the sheer number of variables. New strategies for autonomous data acquisition are being developed, with one promising direction being the use of Gaussian process regression (GPR). GPR is a quick, non-parametric and robust approximation and uncertainty quantification method that can be applied directly to autonomous data acquisition. We review GPR-driven autonomous experimentation and illustrate its functionality using real-world examples from large experimental facilities in the USA and France. We introduce the basics of a GPR-driven autonomous loop with a focus on Gaussian processes, and then shift the focus to the infrastructure that needs to be built around GPR to create a closed loop. Finally, the case studies we discuss show that Gaussian-process-based autonomous data acquisition is a widely applicable method that can facilitate the optimal use of instruments and facilities by enabling the efficient acquisition of high-value datasets.

High-density microfluidics is becoming an important experimental platform for studying complex biological systems such as synthetic gene regulatory networks, molecular biocomputating of engineered cells, distributing rapid point-of-care diagnosis, and monitoring pathological environment. Imaging transient bio-chemical reactions happening in these systems at a single particle or cellular level requires precise time-dependent control of sample reaction and imaging conditions at the desired fluidic momentum. In this study, we showed our novel miniaturized and programmable electronic-based pneumatic system to meet the requirement. We demonstrated its capability to control reaction parameters such as concentrations and injection rates in a liposome production system.

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.

Sediment-hosted CO2-rich aquifers deep below the Colorado Plateau (USA) contain a remarkable diversity of uncultivated microorganisms, including Candidate Phyla Radiation (CPR) bacteria that are putative symbionts unable to synthesize membrane lipids. The origin of organic carbon in these ecosystems is unknown and the source of CPR membrane lipids remains elusive. We collected cells from deep groundwater brought to the surface by eruptions of Crystal Geyser, sequenced the community, and analyzed the whole community lipidome over time. Characteristic stable carbon isotopic compositions of microbial lipids suggest that bacterial and archaeal CO2 fixation ongoing in the deep subsurface provides organic carbon for the complex communities that reside there. Coupled lipidomic-metagenomic analysis indicates that CPR bacteria lack complete lipid biosynthesis pathways but still possess regular lipid membranes. These lipids may therefore originate from other community members, which also adapt to high in situ pressure by increasing fatty acid unsaturation. An unusually high abundance of lysolipids attributed to CPR bacteria may represent an adaptation to membrane curvature stress induced by their small cell sizes. Our findings provide new insights into the carbon cycle in the deep subsurface and suggest the redistribution of lipids into putative symbionts within this community.

OBJECTIVE:This study assesses the performance of the North American Association of Central Cancer Registries Asian/Pacific Islander Identification Algorithm (NAPIIA) to infer Hmong ethnicity. DESIGN AND SETTING:Analyses of electronic health records (EHRs) from 1 January 2011 to 1 October 2015. The NAPIIA was applied to the EHR data, and self-reported Hmong ethnicity from a questionnaire was used as the gold standard. Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire. PARTICIPANTS:EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed. RESULTS:The NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. The prevalence of Hmong population in the sample was 3.9%. CONCLUSION:The high sensitivity of the NAPIIA indicates its effectiveness in detecting Hmong ethnicity. The applicability of the NAPIIA to a multitude of Asian subgroups can advance Asian health disparity research by enabling researchers to disaggregate Asian data and unmask health challenges of different Asian subgroups.

The idea that original soft tissue structures and the native structural proteins comprising them can persist across geological time is controversial, in part because rigorous and testable mechanisms that can occur under natural conditions, resulting in such preservation, have not been well defined. Here, we evaluate two non-enzymatic structural protein crosslinking mechanisms, Fenton chemistry and glycation, for their possible contribution to the preservation of blood vessel structures recovered from the cortical bone of a Tyrannosaurus rex (USNM 555000 [formerly, MOR 555]). We demonstrate the endogeneity of the fossil vessel tissues, as well as the presence of type I collagen in the outermost vessel layers, using imaging, diffraction, spectroscopy, and immunohistochemistry. Then, we use data derived from synchrotron FTIR studies of the T. rex vessels to analyse their crosslink character, with comparison against two non-enzymatic Fenton chemistry- and glycation-treated extant chicken samples. We also provide supporting X-ray microprobe analyses of the chemical state of these fossil tissues to support our conclusion that non-enzymatic crosslinking pathways likely contributed to stabilizing, and thus preserving, these T. rex vessels. Finally, we propose that these stabilizing crosslinks could play a crucial role in the preservation of other microvascular tissues in skeletal elements from the Mesozoic.

The human skin microbiome acts as an important barrier protecting our body from pathogens and other environmental influences. Recent investigations have provided evidence that Archaea are a constant but highly variable component of the human skin microbiome, yet factors that determine their abundance changes are unknown. Here, we tested the hypothesis that the abundance of archaea on human skin is influenced by human age and skin physiology by quantitative PCR of 51 different skin samples taken from human subjects of various age. Our results reveal that archaea are more abundant in human subjects either older than 60 years or younger than 12 years as compared to middle-aged human subjects. These results, together with results obtained from spectroscopy analysis, allowed us gain first insights into a potential link of lower sebum levels and lipid content and thus reduced skin moisture with an increase in archaeal signatures. Amplicon sequencing of selected samples revealed the prevalence of specific eury- and mainly thaumarchaeal taxa, represented by a core archaeome of the human skin.

A long-standing desire in biological and biomedical sciences is to be able to probe cellular chemistry as biological processes are happening inside living cells. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectral microscopy is a label-free and nondestructive analytical technique that can provide spatiotemporal distributions and relative abundances of biomolecules of a specimen by their characteristic vibrational modes. Despite great progress in recent years, SR-FTIR imaging of living biological systems remains challenging because of the demanding requirements on environmental control and strong infrared absorption of water. To meet this challenge, microfluidic devices have emerged as a method to control the water thickness while providing a hospitable environment to measure cellular processes and responses over many hours or days. This paper will provide an overview of microfluidic device development for SR-FTIR imaging of living biological systems, provide contrast between the various techniques including closed and open-channel designs, and discuss future directions of development within this area. Even as the fundamental science and technological demonstrations develop, other ongoing issues must be addressed; for example, choosing applications whose experimental requirements closely match device capabilities, and developing strategies to efficiently complete the cycle of development. These will require imagination, ingenuity and collaboration.

Bacteria from phyla lacking cultivated representatives are widespread in natural systems and some have very small genomes. Here we test the hypothesis that these cells are small and thus might be enriched by filtration for coupled genomic and ultrastructural characterization. Metagenomic analysis of groundwater that passed through a ~0.2-μm filter reveals a wide diversity of bacteria from the WWE3, OP11 and OD1 candidate phyla. Cryogenic transmission electron microscopy demonstrates that, despite morphological variation, cells consistently have small cell size (0.009±0.002 μm(3)). Ultrastructural features potentially related to cell and genome size minimization include tightly packed spirals inferred to be DNA, few densely packed ribosomes and a variety of pili-like structures that might enable inter-organism interactions that compensate for biosynthetic capacities inferred to be missing from genomic data. The results suggest that extremely small cell size is associated with these relatively common, yet little known organisms.

The CELLULOSE SYNTHASE-LIKE F6 (CslF6) gene was previously shown to mediate the biosynthesis of mixed-linkage glucan (MLG), a cell wall polysaccharide that is hypothesized to be tightly associated with cellulose and also have a role in cell expansion in the primary cell wall of young seedlings in grass species. We have recently shown that loss-of-function cslf6 rice mutants do not accumulate MLG in most vegetative tissues. Despite the absence of a structurally important polymer, MLG, these mutants are unexpectedly viable and only show a moderate growth compromise compared to wild type. Therefore these mutants are ideal biological systems to test the current grass cell wall model. In order to gain a better understanding of the role of MLG in the primary wall, we performed in-depth compositional and structural analyses of the cell walls of 3 day-old rice seedlings using various biochemical and novel microspectroscopic approaches. We found that cellulose content as well as matrix polysaccharide composition was not significantly altered in the MLG deficient mutant. However, we observed a significant change in cellulose microfibril bundle organization in mesophyll cell walls of the cslf6 mutant. Using synchrotron source Fourier Transform Mid-Infrared (FTM-IR) Spectromicroscopy for high-resolution imaging, we determined that the bonds associated with cellulose and arabinoxylan, another major component of the primary cell walls of grasses, were in a lower energy configuration compared to wild type, suggesting a slightly weaker primary wall in MLG deficient mesophyll cells. Taken together, these results suggest that MLG may influence cellulose deposition in mesophyll cell walls without significantly affecting anisotropic growth thus challenging MLG importance in cell wall expansion.

Determining the transient chemical properties of the intracellular environment can elucidate the paths through which a biological system adapts to changes in its environment, for example, the mechanisms which enable some obligate anaerobic bacteria to survive a sudden exposure to oxygen. Here we used high-resolution Fourier Transform Infrared (FTIR) spectromicroscopy to continuously follow cellular chemistry within living obligate anaerobes by monitoring hydrogen bonding in their cellular water. We observed a sequence of well orchestrated molecular events that correspond to changes in cellular processes in those cells that survive, but only accumulation of radicals in those that do not. We thereby can interpret the adaptive response in terms of transient intracellular chemistry and link it to oxygen stress and survival. This ability to monitor chemical changes at the molecular level can yield important insights into a wide range of adaptive responses.

Cellulose, a sustainable source of fermentable glucose and nanomaterials, exists as highly-ordered, solvent-inaccessible fibrils held by intra- and intermolecular hydrogen bonds. Cellulose hydrolysis rates and extents are limited by the availability and accessibility of productive cellulase binding sites at the water–cellulose interface. There is a need to understand how spatial heterogeneity of celluloses impacts hydrolysis kinetics. We report a real-time, in situ infrared spectromicroscopy study of enzymatic cellulose hydrolysis in buffer with micrometer-scale spatial mapping. Algal cellulose depletion by a purified cellobiohydrolase Cel7A was tracked as time-resolved decreases in the absorption peak intensities of the glycosidic bond (1161 cm−1), C2–O2 (1112 cm−1), C3–O3 (1059 cm−1), and C6–O6 rotamers (1034 cm−1, 1013 cm−1, and 997 cm−1). Depletion kinetics varied spatially, with peak intensities decreasing to zero in some areas and plateauing in others. Hydration impacted cellulose ordering where C6–O6 and C3–O3 peaks were sharp, narrow, and centered at higher frequencies when hydrated, but broader, less well-defined and centered at lower frequencies when dried. Temporal trends of the Lateral Order Index (LOI), Total Crystallinity Index (TCI), and Hydrogen Bonding Index (HBI) indicated that hydrolysis by Cel7A preferentially removed less extensively hydrogen bonded cellulose, without significantly affecting the overall crystallinity of highly ordered cellulose. [ABSTRACT FROM AUTHOR]

Surface enhanced infrared absorption (SEIRA) spectroscopy is a powerful tool in which plasmonically enhanced electromagnetic fields provide high‐sensitivity molecular detection. Most SEIRA platforms operate at a single resonant frequency, which must be tuned to match that of the target molecule, and commonly rely on time‐consuming lithographic techniques. This study presents a high‐throughput and cost‐effective plasmonic metasurface for broadband, tunable, and strong infrared signal enhancement. The platform is built upon the principle of dispersion‐engineered plasmonic Fabry–Pérot (FP) nanocavity arrays. It offers 1) tight squeezing of infrared (IR) photons into deep sub‐wavelength nano‐volumes and 2) spectrally tunable near‐field enhancements of up to ≈106, two to three orders of magnitude higher than most optical metasurface systems. By coupling multilayer nano‐thin film deposition and nanoskiving fabrication techniques, the dispersive FP metasurfaces can be rapidly and reproducibly constructed in a scalable and lithography‐free manner. Using IR spectroscopy, the selective and sensitive label‐free detection of a molecular monolayer is achieved at a range of frequencies. An enhancement factor of nearly 105 is measured at the carbonyl (C = O) vibrational marker band of the molecule. The confluence of high field enhancement, broadband plasmonic response, and facile fabrication makes this metasurface a promising platform for SEIRA spectroscopy. [ABSTRACT FROM AUTHOR]

In biomedical research with longitudinal designs, missing values due to intermittent nonresponse or premature withdrawal are usually ’nonignorable’ in the sense that un- observed values are related to the patterns of missingness. When missing values are simply ignored, analyses based on observed-data likelihood may yield biased estimates or invalid inferences. By drawing the framework of a shared-parameter mechanism, the process yielding the repeated count measures and the process yielding missing val- ues can be modelled separately, conditionally on a group of shared parameters. For chronic diseases, Markov transition models can be used to study the transitional fea- tures of the pathologic processes. In this paper, Markov chain Monte Carlo (MCMC) algorithms are developed to fit a random-effects Markov transition model (REMTM) for incomplete count repeated measures, within which random effects are shared by the counting process and the missing-data mechanism. Assuming a Poisson distribu- tion for the count measures, the transition probabilities are estimated using a Poisson linear regression model. The missingness mechanism is modeled with a multinomial- logit regression to calculate the transition probabilities of the missingness indicators. The method is demonstrated using both simulated data sets and a practical data set from a smoking cessation clinical trial

ChIP-chip (or ChIP-on-chip) is a technology for isolation and identification of genomic sites occupied by specific DNA binding proteins in living cells. The ChIP-chip data can be obtained over the whole genome by tiling arrays, where a peak in the signal is generally observed at a protein binding site. In this paper, we describe and present a probability theory for modelling ChIP-chip data. We then propose a model-based computa- tional method for locating and testing peaks for the purpose of identifying potential protein binding sites.

Following Marr’s insight, we propose a generative image representation called primal sketch, which integrates two modeling components. The first component explains the structural part of an image, such as ob ject boundaries, by a hidden layer of image primitives. The second component models the remaining textural part without distinguishable elements by Markov random fields that interpolate the structural part of the image. We adopt an artist’s notion by calling the two components “sketchable” and “non-sketchable” parts respectively. A dictionary of image primitives are used for modeling structures in natural images, and each primitive is specified by variables for its photometric, geometric, and topological attributes. The primitives in the image representation are not independent but organized in an sketch graph. This sketch graph is modeled by a spatial Markov model that enforces Gestalt organizations. The inference of the sketch graph consists of two phases. Phase I sequentially adds the most prominent image primitives in a procedure similar to matching pursuit. Phase II edits the sketch graph by a number of graph operators to achieve good Gestalt organizations. Experiments show that the primal sketch model produces satisfactory results for a large number of generic images. The primal sketch model is not only a parsimonious image representation for lossy image coding, but also provides a meaningful mid-level generic representation for other vision tasks.

Vision can be posed as a statistical learning and inference problem. As an over-simplified account, let W be a description of the outside scene in terms of ìwhat is where,î let I be the retina image, and let p(W, I) be the joint distribution of W and I. 1 Then visual learning is to learn p(W, I) from training data, and visual perception is to infer W from I based on p(W|I).There are two major schools on visual learning and perception. One school is operation-oriented and learns the inferential process defined by p(W|I) directly, often in the form of an explicit transformation W ? F(I). This scheme is mostly used in supervised learning, where W is object category, and is given in training data. The other school is representation-oriented and learns the generative process p(W) and p(I|W) explicitly, then perception is to invert the generative process by maximizing or sampling p(W|I) ? p(W)p(I|W). In this scheme, p(W) may also be accounted for by a regularization term such as smoothness or sparsity. This scheme is often used in unsupervised learning where W is not available in training data.

Computer vision can be considered a highly specialized data collection and data analysis problem. We need to understand the special properties of image data in order to construct statistical models for representing the wide variety of image patterns. One special property of vision that distinguishes itself from other sensory data such as speech data is that distance or scale plays a profound role in image data. More specifically, visual objects and patterns can appear at a wide range of distances or scales, and the same visual pattern appearing at different distances or scales produces different image data with different statistical properties, thus entails different regimes of statistical models. In particular, we show that the entropy rate of the image data changes over the viewing distance (as well as the camera resolution). Moreover, the inferential uncertainty changes with viewing distance too. We call these changes information scaling. From this perspective, we examine both empirically and theoretically two prominent and yet largely isolated research themes in image modeling literature, namely, wavelet sparse coding and Markov random fields. Our results indicate that the two models are appropriate on two different entropy regimes: sparse coding targets the low entropy regime, whereas the random fields are suitable for the high entropy regime. Because of information scaling, both models are necessary for representing and interpreting image intensity patterns in the whole entropy range, and information scaling triggers transitions between these two regimes of models. This motivates us to propose a full-zoom primal sketch model that integrates both sparse coding and Markov random fields. In this model, local image intensity patterns are classified into “sketchable regime” and “non-sketchable regime” by a sketchability criterion. In the sketchable regime, the image data are represented deterministically by highly parametrized sketch primitives. In the non-sketchable regime, the image data are characterized by Markov random fields whose sufficient statistics summarize computational results from failed attempts of sparse coding. The contribution of our work is two folded. First, information scaling provides a dimension to chart the space of natural images. Second, the full-zoom modeling scheme provides a natural integration of sparse coding and Markov random fields, thus enables us to develop a new and richer class of statistical models.

Synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectromicroscopy is a newly emerging bioanalytical and imaging tool. This unique technique provides mid-infrared (IR) spectra, hence chemical information, with high signal-to-noise at spatial resolutions as fine as 3 to 10 microns. Thus it enables researchers to locate, identify, and track specific chemical events within an individual living mammalian cell. Mid-IR photons are too low in energy (0.05 - 0.5 eV) to either break bonds or to cause ionization. In this review, we show that the synchrotron IR beam has no detectable effects on the short- and long-term viability, reproductive integrity, cell-cycle progression, and mitochondrial metabolism in living human cells, and produces only minimal sample heating (< 0.5 degrees C). These studies have established an important foundation for SR-FTIR spectromicroscopy in biological and biomedical research.

Synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectromicroscopy is a newly emerging bioanalytical and imaging tool. This unique technique provides mid-infrared (IR) spectra, hence chemical information, with high signal-to-noise at spatial resolutions as fine as 3 to 10 microns. Thus it enables researchers to locate, identify, and track specific chemical events within an individual living mammalian cell. Mid-IR photons are too low in energy (0.05 - 0.5 eV) to either break bonds or to cause ionization. In this review, we show that the synchrotron IR beam has no detectable effects on the short- and long-term viability, reproductive integrity, cell-cycle progression, and mitochondrial metabolism in living human cells, and produces only minimal sample heating (< 0.5 degrees C). We will then present several examples demonstrating the application potentials of SR-FTIR spectromicroscopy in biomedical research. These will include monitoring living cells progressing through the cell cycle, including death, and cells reacting to dilute concentrations of toxins.

Textons refer to fundamental micro-structures in generic natural images and thus constitute the basic elements in early (pre-attentive) visual perception. However, the word "textong" remains a vague concept in the literature of computer vision and visual perception, and a precist mathematical definition has yet to be found. In this article, we argue that the definition of texton should be governed by a sound mathematical model of images, and the set of textons must be learned from, or best tuned to, an image ensemble. We adopt a generative image model that an image is a superposition of bases from an over-complete dictionary, then a texton is defined as a mini-template that consists of a varying number of image bases with some geometric and photometric configurations. By analogy to physics, if image bases are like protons, neutrons and electrons, then textons are like atoms. Then a small number of textons can be learned from training images as repeating micro-structures. We report four experiments for comparison. The first experiment computes clusters in feature space of filter responses. The second transformed component analysis in both feature space and image patches. The third adopts a two-layer generative model where an image is generated by image bases and image bases are generated by textons. The fourth experiment shows textons from motion image sequences, which we call movetons.

Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the images bases, and the sketch can be visually illustrated by depicting each image base by a linelet of the same length and orientation. Built on the insight of sparse and independent component analysis, we propose a two-level generative model for textures. At the bottom-level, the texture image is represented by a linear superposition of image bases. At the top-level, a Markov model is assumed for the placement of the image bases or the sketch, and the model is characterized by a set of simple geometrical feature statistics.

This paper presents a class of statistical models that integrate two statistical modeling paradigms in the literature: I) Descriptive methods, such as Markov random fields and minimax entropy learning [41] and II) Generative methods, such as principal component analysis, independent component analysis [2] transformed component analysis [11], wavelet coding [27, 5], and sparse coding [30, 24]. In this paper, we demonstrate the integrated framwork by constructing a class of hierarchical models for texton patterns ) the term "texton" was coined by psychologist Julez in the early 80's). At the bottom level of the model, we assume that an observed texture image is generated by multiple hidden "texton maps", and textons on each map are translated, scaled, stretched, and oriented versions of a window function, like mini-templates or wavelet bases. The texton maps generate the observed image by occlusion or linear superposition. this bottom level of the model is generative in nature. At the top level of the model, the spatial arrangements of the textons in the texton maps are characterized by minimax entropy principle, which leads to embellished versions of Gibbs point rocess [34]. The top level of the model is descriptive in nature. We demonstrate the integrated model by a set of experiments.

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the “essence” of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of second-order stationary processes, we identify the model sub-optimally in closed-form. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computa- tional cost. We present experimental evidence that, within our framework, even low-dimensional models can capture very complex visual phenomena.

Independent component analysis (ICA) is an important method for blind source separation and unsupervised learning. Recently, the method has been extended to the overcomplete situation where the number of sources is greater than the number of receivers. Comparing complete ICA and overcomplete ICA in existing literature, one can notice that complete ICA does not assume noise in observations, and the sources can be ex- plicitly solved from the receivers, whereas the overcomplete ICA in gen- eral assumes noise in observations and the sources are implicitly solved by gradient type algorithms. In this paper, we present an explicit null- space representation for overcomplete ICA in the noiseless situation based on singular value decomposition (SVD), and develop an algorithm for estimating mixing matrix and recovering the sources. The null-space representation makes the connection between complete ICA and over- complete ICA more apparent, and leads to a mathematical explanation of lateral inhibition in the context of overcomplete linear model. It also appears to work well on the experimental examples. Moreover, it can be extended to the situation where there is noise in observations, and may lead to more efficient algorithms in this situation.

Abstract The human skin microbiome acts as an important barrier protecting our body from pathogens and other environmental influences. Recent investigations have provided evidence that Archaea are a constant but highly variable component of the human skin microbiome, yet factors that determine their abundance changes are unknown. Here, we tested the hypothesis that the abundance of archaea on human skin is influenced by human age and skin physiology by quantitative PCR of 51 different skin samples taken from human subjects of various age. Our results reveal that archaea are more abundant in human subjects either older than 60 years or younger than 12 years as compared to middle-aged human subjects. These results, together with results obtained from spectroscopy analysis, allowed us gain first insights into a potential link of lower sebum levels and lipid content and thus reduced skin moisture with an increase in archaeal signatures. Amplicon sequencing of selected samples revealed the prevalence of specific eury- and mainly thaumarchaeal taxa, represented by a core archaeome of the human skin.

Synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectromicroscopy is a newly emerging analytical tool capable of monitoring the biochemistry within an individual living mammalian cell in real time. This unique technique provides infrared (IR)spectra, hence chemical information, with high signal-to-noise at spatial resolutions as fine as 3 to 10 microns. Mid-IR photons are too low in energy (0.05-0.5 eV) to either break bonds or to cause ionization, and the synchrotron IR beam has been shown to produce minimal sample heating. However, an important question remains, "Does the intense synchrotron beam induce any cytotoxic effects in living cells?" In this work, we present the results from a series of standard biological assays to evaluate any short-and/or long-term effects on cells exposed to the synchrotron radiation-based infrared (SR-IR) beam. Cell viability was tested using alcian blue dye-exclusion and colony formation assays. Cell-cycle progression was tested with bromodeoxyuridine (BrdU) uptake during DNA synthesis. Cell metabolism was tested using an 3-[4,5-Dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay. All control, 5-, 10-, and 20-minute SR-IR exposure tests (267 total and over 1000 controls) show no evidence of cytotoxic effects. Concurrent infrared spectra obtained with each experiment confirm no detectable chemistry changes between control and exposed cells.

The role of humic acid (HA) in the biodegradation of toxic polycyclic aromatic hydrocarbons (PAHs) has been the subject of controversy, particularly in unsaturated environments. By utilizing an infrared spectromicroscope and a very bright, nondestructive synchrotron photon source, we monitored in situ and, over time, the influence of HA on the progression of degradation of pyrene (a model PAH) by a bacterial colony on a magnetite surface. Our results indicate that HA dramatically shortens the onset time for PAH biodegradation from 168 to 2 h. In the absence of HA, it takes the bacteria about 168 h to produce sufficient glycolipids to solubilize pyrene and make it bioavailable for biodegradation. These results will have large implications for the bioremediation of contaminated soils.

We have learned a lot from studying the sequence of artful works of the two authors on EM/data augmentation. In this note, we will discuss some of our thoughts (or rather speculations) on the problem of vision from the perspective of missing data modeling and data augmentation.

This paper presents a mathematical framework for visual learning that integrates two popular statistical learning paradigms in the literature: I) Descriptive learning, such as Markov random fields and minimax entropy learning, and II) Generative learning, such as PCA, ICA, TCA, and HMM. We apply the integrated learning framework to texture modeling, and we assume that an observed texture image is generated by multiple layers of hidden stochastic processes with various texture elements called "textons". Each texton is expressed as a window function like a mini-template or a wavelet, and each hidden stochastic process is a spatial pattern with a number of textons subject to affine transfomrations. The hidden layers are characterized by minimax entropy models, and they generate images by occlusion or linear addition. Thus given a raw input image, the learning framework achieves four goals: i) Computing the appearance of textons. ii) Inferring the hidden stochastic processes iii) Learning Gibbs models for each hidden stochastic process and iv) Verifying the learnt textons and models through random sampling. The integrated framework subsumes the minimax entropy learning paradigm and creates a richer class of probability models for visual patterns. Furthermore we show that the integration of descriptive and generative methods is a natural path of visual learning. We demonstrate the proposed framework and algorithms on many real images.

A new method is being developed to quickly screen for the human exposure potential to polycyclic aromatic hydrocarbons (PAHs) and organochlorines (OCs). The development involves two key elements: identifying suitable signals that represent intracellular changes that are specific to PAH and OC exposure, and constructing a device to guide the biological cell growth so that signals from individual cells are consistent and reproducible. We are completing the identification of suitable signals by using synchrotron radiation-based (SR) Fourier-transform infrared (FTIR) spectromicroscopy in the mid-infrared region (4000-400 cm-1). Distinct changes have been observed in the IR spectra after treatment of human cells in culture medium with PAHs and OCs. The potential use of this method for detecting exposure to PAHs and OCs has been tested and compared to a reverse transcription polymerase chain reaction (RT-PCR) assay that quantifies increased expression of the CYP1A1 gene in response to exposure to PAHs or OCs.