Depth Discrimination in Diffuse Optical Transmission Imaging by Planar Scanning Off-Axis Fibers: INITIAL Applications to Optical Mammography

We present a method for depth discrimination in parallel-plate, transmission mode, diffuse optical imaging. The method is based on scanning a set of detector pairs, where the two detectors in each pair are separated by a distance δD_i along direction δ D _i within the x-y scanning plane. A given optical inhomogeneity appears shifted by α_i δ D _i (with 0≤ α_i ≤1) in the images collected with the two detection fibers of the i-th pair. Such a spatial shift can be translated into a measurement of the depth z of the inhomogeneity, and the depth measurements based on each detector pair are combined into a specially designed weighted average. This depth assessment is demonstrated on tissue-like phantoms for simple inhomogeneities such as straight rods in single-rod or multiple-rod configurations, and for more complex curved structures which mimic blood vessels in the female breast. In these phantom tests, the method has recovered the depth of single inhomogeneities in the central position of the phantom to within 4 mm of their actual value, and within 7 mm for more superficial inhomogeneities, where the thickness of the phantom was 65 mm. The application of this method to more complex images, such as optical mammograms, requires a robust approach to identify corresponding structures in the images collected with the two detectors of a given pair. To this aim, we propose an approach based on the inner product of the skeleton images collected with the two detectors of each pair, and we present an application of this approach to optical in vivo images of the female breast. This depth discrimination method can enhance the spatial information content of 2D projection images of the breast by assessing the depth of detected structures, and by allowing for 3D localization of breast tumors.     

Visualization and Analysis of Microtubule Dynamics Using Dual Color-Coded Display of Plus-End Labels

Investigating spatial and temporal control of microtubule dynamics in live cells is critical to understanding cell morphogenesis in development and disease. Tracking fluorescently labeled plus-end-tracking proteins over time has become a widely used method to study microtubule assembly. Here, we report a complementary approach that uses only two images of these labels to visualize and analyze microtubule dynamics at any given time. Using a simple color-coding scheme, labeled plus-ends from two sequential images are pseudocolored with different colors and then merged to display color-coded ends. Based on object recognition algorithms, these colored ends can be identified and segregated into dynamic groups corresponding to four events, including growth, rescue, catastrophe, and pause. Further analysis yields not only their spatial distribution throughout the cell but also provides measurements such as growth rate and direction for each labeled end. We have validated the method by comparing our results with ground-truth data derived from manual analysis as well as with data obtained using the tracking method. In addition, we have confirmed color-coded representation of different dynamic events by analyzing their history and fate. Finally, we have demonstrated the use of the method to investigate microtubule assembly in cells and provided guidance in selecting optimal image acquisition conditions. Thus, this simple computer vision method offers a unique and quantitative approach to study spatial regulation of microtubule dynamics in cells.     

Reduction in required volume of imaging data and image reconstruction time for adaptive-illumination Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a promising super-resolution computational imaging technology. It stitches a series of low-resolution (LR) images in the Fourier domain by an iterative method. Thus, it obtains a large field of view and high-resolution quantitative phase images. Owing to its capability to perform high-spatial bandwidth product imaging, FPM is widely used in the reconstruction of conventional static samples. However, the influence of the FPM imaging mechanism limits its application in high-speed dynamic imaging. To solve this problem, an adaptive-illumination FPM scheme using regional energy estimation is proposed. Starting with several captured real LR images, the energy distribution of all LR images is estimated, and select the measurement images with large information to perform FPM reconstruction. Simulation and experimental results show that the method produces efficient imaging performance and reduces the required volume of data to more than 65% while ensuring the quality of FPM reconstruction.     

Identification of Genomic Regions and the Isoamylase Gene for Reduced Grain Chalkiness in Rice

Grain chalkiness is an important grain quality related to starch granules in the endosperm. A high percentage of grain chalkiness is a major problem because it diminishes grain quality in rice. Here, we report quantitative trait loci identification for grain chalkiness using high-throughput single nucleotide polymorphism genotyping of a chromosomal segment substitution line population in which each line carried one or a few introduced japonica cultivar Nipponbare segments in the genetic background of the indica cultivar ZS97. Ten quantitative trait loci regions were commonly identified for the percentage of grain chalkiness and the degree of endosperm chalkiness. The allelic effects at nine of these quantitative trait loci reduced grain chalkiness. Furthermore, a quantitative trait locus (qPGC8-2) on chromosome 8 was validated in a chromosomal segment substitution line–derived segregation population, and had a stable effect on chalkiness in a multiple-environment evaluation of the near-isogenic lines. Residing on the qPGC8-2 region, the isoamylase gene (ISA1) was preferentially expressed in the endosperm and revealed some nucleotide polymorphisms between two varieties, Nipponbare and ZS97. Transgenic lines with suppression of ISA1 by RNA interference produced grains with 20% more chalkiness than the control. The results support that the gene may underlie qPGC8-2 for grain chalkiness. The multiple-environment trials of the near-isogenic lines also show that combination of the favorable alleles such as the ISA1 gene for low chalkiness and the GS3 gene for long grains considerably improved grain quality of ZS97, which proves useful for grain quality improvement in rice breeding programs.     

Automatic identification of anterior segment eye abnormality

The eyes are complex sensory organs and are designed to optimize vision under conditions of varying light. There are a number of eye disorders that can influence vision. Eye disorders among the elderly are a major health problem. With advancing age, the normal function of eye tissues decreases and there is an increased incidence of ocular pathology. The most common symptoms elicited from ocular diseases are few in number and non-specific in nature: blurred vision, pain, and redness. Cataracts occur most frequently in older people and have significant impact on an individual's quality of life. There are effective therapies and visual aids for these potential vision-limiting conditions. Corneal haze a complication of refractive surgery is characterized by the cloudiness of the normally clear cornea. Iridocyclitis is the inflammation of the Iris and ciliary body. In corneal arcus are white circles in the cornea of the eye caused by fatty deposits. So, there is a need to diagnose to the normal eye from the abnormal one. This paper presents an identification of normal eye image and abnormal (consists of five kinds of eye images) classes using radial basis function (RBF) classifier. The features are extracted from the raw images using the image processing techniques and fuzzy K-means algorithm. Our system uses 150 subjects, consisting of five different kinds of eye disease conditions. We demonstrated a sensitivity of 90%, for the classifier with the specificity of 100%. Our systems are ready clinically to run on large amount of data sets.     

Structure of yeast Ape1 and its role in autophagic vesicle formation

In Saccharomyces cerevisiae, a constitutive biosynthetic transport pathway, termed the cytoplasm-to-vacuole targeting (Cvt) pathway, sequesters precursor aminopeptidase I (prApe1) dodecamers in the form of a large complex into a Cvt vesicle using autophagic machinery, targeting it into the vacuole (the yeast lysosome) where it is proteolytically processed into its mature form, Ape1, by removal of an amino-terminal 45-amino acid propeptide. prApe1 is thought to serve as a scaffolding cargo critical for the assembly of the Cvt vesicle by presenting the propeptide to mediate higher-ordered complex formation and autophagic receptor recognition. Here we report the X-ray crystal structure of Ape1 at 2.5 Å resolution and reveal its dodecameric architecture consisting of dimeric and trimeric units, which associate to form a large tetrahedron. The propeptide of prApe1 exhibits concentration-dependent oligomerization and forms a stable tetramer. Structure-based mutagenesis demonstrates that disruption of the inter-subunit interface prevents dodecameric assembly and vacuolar targeting in vivo despite the presence of the propeptide. Furthermore, by examining the vacuolar import of propeptide-fused exogenous protein assemblies with different quaternary structures, we found that 3-dimensional spatial distribution of propeptides presented by a scaffolding cargo is essential for the assembly of the Cvt vesicle for vacuolar delivery. This study describes a molecular framework for understanding the mechanism of Cvt or autophagosomal biogenesis in selective macroautophagy.     

Component Partitioning Under Demand and Capacity Uncertainty in Printed Circuit Board Assembly

This paper considers the problem of configuring a printed circuit board (PCB) assembly line experiencing uncertainty in demand and capacity. The PCB assembly process involves a single line of automatic placement machines, a variety of board types, and a number of component types. The line is set up only once, at the beginning of a production cycle, to eliminate setups between board types. Using this strategy, the line therefore can assemble all different types of PCBs without feeder changes. The problem then becomes to partition component types to the different machines in the hope of processing all boards quickly with a good workload balance. In this paper, the board demands and machine breakdowns are random but follow some probability distribution, which can be predicted from past observations of the system. We formulate this problem as a stochastic mixed-integer programming formulation with the objective of minimizing the expected makespan for assembling all PCBs during a production cycle. The results obtained indicate significant improvement over the existing methods. We hope that this research will provide more PCB assembly facilities with models and techniques to hedge against variable forecasts and capacity plans     

Design of single assembly line for the delayed differentiation of product variants

Delayed Product Differentiation (DPD) can reduce the manufacturing complexities arising due to the proliferation of products variety. A new optimization model constructs the optimum layout of delayed differentiation assembly lines for a mix of products to be manufactured by the same system and optimizes the position of the differentiation points. This model employs a classification tool (Cladistics) used in biological analysis and modifies it for use in planning DPD assembly lines configurations in order to incorporate the assembly precedence constraints, required production rates of different product variants and existing production capacity of work stations. The optimum layout configuration ensures that the quantities required of different products are produced on the same line; while achieving balance, minimizing duplication of stations and maximizing the overall system utilization. The developed model has been applied to a group of automobile engine accessories normally assembled on different lines. The use of Cladistics to analyze product variants that are candidates for delayed assembly is an original approach for designing the assembly line layout and identifying the best differentiation points. It also helps rationalize the design of product variants and their features to further delay their assembly differentiation and achieve economy of scale without affecting their functionality.     

YSL3-mediated copper distribution is required for fertility,seed size and protein accumulation in Brachypodium

Addressing the looming global food security crisis requires the development of high-yielding crops. In agricultural soils, deficiency in the micronutrient copper significantly decreases grain yield in wheat (Triticum aestivum), a globally important crop. In cereals, grain yield is determined by inflorescence architecture, flower fertility, grain size, and weight. Whether copper is involved in these processes, and how it is delivered to the reproductive organs is not well understood. We show that copper deficiency alters not only the grain set but also flower development in both wheat and its recognized model, Brachypodium distachyon. We then show that the Brachypodium yellow stripe-like 3 (YSL3) transporter localizes to the phloem, transports copper in frog (Xenopus laevis) oocytes, and facilitates copper delivery to reproductive organs and grains. Failure to deliver copper, but not iron, zinc, or manganese to these structures in the ysl3 CRISPR-Cas9 mutant results in delayed flowering, altered inflorescence architecture, reduced floret fertility, grain size, weight, and protein accumulation. These defects are rescued by copper supplementation and are complemented by YSL3 cDNA. This knowledge will help to devise sustainable approaches for improving grain yield in regions where soil quality is a major obstacle for crop production. Copper distribution by a phloem-localized transporter is essential for the transition to flowering, inflorescence architecture, floret fertility, size, weight, and protein accumulation in seeds.

Copper distribution by a phloem-localized transporter is essential for the transition to flowering, inflorescence architecture, floret fertility, size, weight and protein accumulation in seeds.     

Near-infrared spectral imaging for quality assurance of pharmaceutical products: analysis of tablets to assess powder blend homogeneity

The objective of this study was to evaluate near-infrared (NIR) spectroscopic imaging as a tool to assess a pharmaceutical quality assurance problem—blend uniformity in the final dosage product. A system based on array detector technology was used to rapidly collect high-contrast NIR images of furosemide tablets. By varying the mixing, 5 grades of experimental tablets containing the same amount of furosemide and microcrystalline cellulose were produced, ranging from well blended to unblended. For comparison, these tablets were also analyzed by traditional NIR spectroscopy, and both approaches were used to evaluate drug product homogeneity. NIR spectral imaging was capable of clearly differentiating between each grade of blending, both qualitatively and quantitatively. The spatial distribution of the components was based on the variation or contrast in pixel intensity, which is due to the NIR spectral contribution to each pixel. The chemical nature of each pixel could be identified by the localized spectrum associated with each pixel. Both univariate and partial least squares (PLS) images were evaluated. In the suboptimal blends, the regions of heterogeneity were obvious by visual inspection of the images. A quantitative measure of blending was determined by calculating the standard deviation of the distribution of pixel intensities in the PLS score images. The percent standard deviation increased progressively from 11% to 240% from well blended to unblended tablets. The NIR spectral imaging system provides a rapid approach for acquiring spatial and spectral information on pharmaceuticals. The technique has potential for a variety of applications in product quality assurance and could affect the control of manufacturing processes.     

Single-cell phenomics in budding yeast

The demand for phenomics, a high-dimensional and high-throughput phenotyping method, has been increasing in many fields of biology. The budding yeast Saccharomyces cerevisiae, a unicellular model organism, provides an invaluable system for dissecting complex cellular processes using high-resolution phenotyping. Moreover, the addition of spatial and temporal attributes to subcellular structures based on microscopic images has rendered this cell phenotyping system more reliable and amenable to analysis. A well-designed experiment followed by appropriate multivariate analysis can yield a wealth of biological knowledge. Here we review recent advances in cell imaging and illustrate their broad applicability to eukaryotic cells by showing how these techniques have advanced our understanding of budding yeast.     

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Genome-wide protein–protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.     

Imaging techniques for the detection of stored product pests

Stored grains are subject to deterioration and losses through various factors, but mainly insects and fungi. Various techniques are employed to detect stored product pests; however, there is an urgent need for an industrial-scale on-line detection technique. Near-infrared hyperspectroscopic imaging and soft X-rays have shown the potential for real-time application. These techniques are particularly effective for detecting internal infestations of stored grains. The digital images of the scanned objects are analyzed for various spectral and image features using statistical techniques such as complex multivariate tools. Classification accuracies as high as 80–100 % have been achieved for various pest and grain combinations. Dual-energy X-rays have been shown to detect the concealed eggs of stored product insect pests. The main threats to stored cereals come from Aspergillus spp., Penicillium spp., and Fusarium spp., which may produce mycotoxins. These imaging techniques have shown good results in the detection of fungal infections of stored grain.     

qc3C: Reference-free quality control for Hi-C sequencing data

Hi-C is a sample preparation method that enables high-throughput sequencing to capture genome-wide spatial interactions between DNA molecules. The technique has been successfully applied to solve challenging problems such as 3D structural analysis of chromatin, scaffolding of large genome assemblies and more recently the accurate resolution of metagenome-assembled genomes (MAGs). Despite continued refinements, however, preparing a Hi-C library remains a complex laboratory protocol. To avoid costly failures and maximise the odds of successful outcomes, diligent quality management is recommended. Current wet-lab methods provide only a crude assay of Hi-C library quality, while key post-sequencing quality indicators used have—thus far—relied upon reference-based read-mapping. When a reference is accessible, this reliance introduces a concern for quality, where an incomplete or inexact reference skews the resulting quality indicators. We propose a new, reference-free approach that infers the total fraction of read-pairs that are a product of proximity ligation. This quantification of Hi-C library quality requires only a modest amount of sequencing data and is independent of other application-specific criteria. The algorithm builds upon the observation that proximity ligation events are likely to create k-mers that would not naturally occur in the sample. Our software tool (qc3C) is to our knowledge the first to implement a reference-free Hi-C QC tool, and also provides reference-based QC, enabling Hi-C to be more easily applied to non-model organisms and environmental samples. We characterise the accuracy of the new algorithm on simulated and real datasets and compare it to reference-based methods.     

Local contagion and regional compression: habitat selection drives spatially explicit,multiscale dynamics of colonisation in experimental metacommunities

Habitat selection, including oviposition site choice, is an important driver of community assembly in freshwater systems. Factors determining patch quality are assessed by many colonising organisms and affect colonisation rates, spatial distribution and community structure. For many species, the presence/absence of predators is the most important factor affecting female oviposition decisions. However, individual habitat patches exist in complex landscapes linked by processes of dispersal and colonisation, and spatial distribution of factors such as predators has potential effects beyond individual patches. Perceived patch quality and resulting colonisation rates depend both on risk conditions within a given patch and on spatial context. Here we experimentally confirm the role of one context‐dependent processes, spatial contagion, functioning at the local scale, and provide the first example of another context‐dependent process, habitat compression, functioning at the regional scale. Both processes affect colonisation rates and patterns of spatial distribution in naturally colonised experimental metacommunities.     

Correcting for finite spatial scales of self-similarity when calculating fractal dimensions of real-world structures

Fractal geometry is a potentially valuable tool for quantitatively characterizing complex structures. The fractal dimension (D) can be used as a simple, single index for summarizing properties of real and abstract structures in space and time. Applications in the fields of biology and ecology range from neurobiology to plant architecture, landscape structure, taxonomy and species diversity. However, methods to estimate the D have often been applied in an uncritical manner, violating assumptions about the nature of fractal structures. The most common error involves ignoring the fact that ideal, i.e. infinitely nested, fractal structures exhibit self-similarity over any range of scales. Unlike ideal fractals, real-world structures exhibit self-similarity only over a finite range of scales.Here we present a new technique for quantitatively determining the scales over which real-world structures show statistical self-similarity. The new technique uses a combination of curve-fitting and tests of curvilinearity of residuals to identify the largest range of contiguous scales that exhibit statistical self-similarity. Consequently, we estimate D only over the statistically identified region of self-similarity and introduce the finite scale- corrected dimension (FSCD). We demonstrate the use of this method in two steps. First, using mathematical fractal curves with known but variable spatial scales of self-similarity (achieved by varying the iteration level used for creating the curves), we demonstrate that our method can reliably quantify the spatial scales of self-similarity. This technique therefore allows accurate empirical quantification of theoretical Ds. Secondly, we apply the technique to digital images of the rhizome systems of goldenrod (Solidago altissima). The technique significantly reduced variations in estimated fractal dimensions arising from variations in the method of preparing digital images. Overall, the revised method has the potential to significantly improve repeatability and reliability for deriving fractal dimensions of real-world branching structures.     

Differences of Starch Granule Distribution in Grains from Different Spikelet Positions in Winter Wheat

Wheat starch development is a complex process and is markedly difference by changes in spikelet spatial position. The present study deals with endosperm starch granule distribution and spatial position during filling development. The study was conducted with pure starch isolated from wheat (Triticum aestivum L.), Jimai20 and Shannong1391, at 7–35 days after anthesis (DAA). The results showed that grain number, spikelet weight and grain weight per spikelet in different spatial position showed parabolic changes. Upper spikelets had highest starch and amylose content followed by basal spikelets, then middle spikelets. The paper also suggested the volume percents of B-type and A-type granule in grain of middle spikelets were remarkably higher and lower than those of basal and upper spikelets, respectively. However, no significant difference occurred in the number percents of the two type granule. The ratio of amylase to amylopectin was positively correlated with the volume proportion of 22.8–42.8 µm, but was negatively related to the volume proportion of <9.9 µm. The results indicated that the formation and distribution of starch granules were affected significantly by spikelet position, and grains at upper and basal spikelet had the potential of increasing grain weight through increasing the volume of B-type granules.     

Systematic spatial mapping of proteins at exocytic and endocytic structures

Vesicular secretion (exocytosis) involves the release and then compensatory recycling of vesicle components through endocytosis. This fundamental cellular process is controlled by the coordinated assembly and interactions of dozens of proteins at the plasma membrane. Understanding the molecular composition of individual exocytic and endocytic structures and their organization across the plasma membrane is critical to understanding the behavior and regulation of these two cellular processes. Here we develop a high-resolution and high-throughput fluorescence imaging–based approach for the unbiased mapping of 78 proteins at single exocytic vesicles and endocytic structures in neuroendocrine PC12 cells. This analysis uses two-color single-frame images to provide a systems-level map of the steady-state distributions of proteins at individual exocytic and endocytic structures in the cell. Along with this quantitative map, we find that both calcium-regulated exocytic vesicles (dense core vesicles) and endocytic structures (clathrin-coated structures) and the proteins associated with these structures exhibit a random spatial distribution in unstimulated neuroendocrine PC12 cells. This approach is broadly applicable for quantitatively mapping the molecular composition and spatial organization of discrete cellular processes with central molecular hubs.     

A production policy considering reworking of imperfect items and trade credit

Making production decisions that will reduce total cost is a goal that most manufacturers pursue actively. However, the traditional production model development assumed that all products are perfect quality, which is far from reality. Since trade credit is a popular payment method in today??s business environment, this paper analyzes the production problem under trade credit and imperfect product reworking conditions. This work extends the traditional production model by considering reworking imperfect items and trade credit to cope with realistic situations. The objective of this study is to determine the optimal production lot size while minimizing the total cost. This paper develops an easy-to-use algorithm to solve the problem described, provides numerical examples to illustrate the proposed solution procedure, and discusses the impact of various system parameters.