Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image ...processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.
We present a learning-based single image super-resolution (SISR) method to obtain a high resolution (HR) image from a single given low resolution (LR) image. Our method gives more accurate results ...while also testing (runs) and training faster with a smaller number of training samples compared to other methods. We posed SISR as a problem of estimating a function to predict the pixels of an HR patch using its corresponding LR pixels and their spatial neighborhood. We studied the impact of varying the input LR and output HR patch sizes and gained the following insights: reconstruction accuracy for a given output HR patch size improves when input LR patch size is increased, but the improvement saturates after including a few extra layers of LR pixels. Moreover, HR reconstruction accuracy is the highest when the output HR patch is restricted to only that which corresponds to one LR pixel. We used zero component analysis as a pre-processing step to enhance the estimation optimization energy on perceptually salient features such as edges. We tapped into the ability of polynomial neural networks to hierarchically learn refinements of a function that maps LR to HR patches. Accurate HR reconstruction with small input and output patch sizes not only makes learning more efficient, it also indicates that SISR is a highly local problem. In contrast, a recently proposed and related technique using convolutional neural networks needs much larger training set and longer training time because of larger input-output patch sizes and a computationally expensive learning algorithm.
Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain ...vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.
Abstract
The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly ...iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.
RNA interference (RNAi) has become a widely used reverse genetic tool to study gene function in eukaryotic organisms and is being developed as a technology for insect pest management. The efficiency ...of RNAi varies among organisms. Insects from different orders also display differential efficiency of RNAi, ranging from highly efficient (coleopterans) to very low efficient (lepidopterans). We investigated the reasons for varying RNAi efficiency between lepidopteran and coleopteran cell lines and also between the Colorado potato beetle, Leptinotarsa decemlineata and tobacco budworm, Heliothis virescens. The dsRNA either injected or fed was degraded faster in H. virescens than in L. decemlineata. Both lepidopteran and coleopteran cell lines and tissues efficiently took up the dsRNA. Interestingly, the dsRNA administered to coleopteran cell lines and tissues was taken up and processed to siRNA whereas the dsRNA was taken up by lepidopteran cell lines and tissues but no siRNA was detected in the total RNA isolated from these cell lines and tissues. The data included in this paper showed that the degradation and intracellular transport of dsRNA are the major factors responsible for reduced RNAi efficiency in lepidopteran insects.
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the ...tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.
Characterization of C3 in C3 glomerulopathy Sethi, Sanjeev; Vrana, Julie A; Fervenza, Fernando C ...
Nephrology, dialysis, transplantation,
03/2017, Letnik:
32, Številka:
3
Journal Article
Recenzirano
Odprti dostop
C3 glomerulopathy (C3G) is caused by overactivity of the alternative pathway of complement that results in bright glomerular C3 staining with minimal or no deposition of immunoglobulins on ...immunofluorescence microscopy. Laser microdissection and mass spectrometry of the two subtypes, C3 glomerulonephritis (C3GN) and dense deposit disease (DDD), have identified C3 as the predominant glomerular complement protein, although lesser amounts of C9, C5, C6, C7 and C8 are detectable. C3 plays a central role in complement activity, with its proteolytic cleavage first generating C3a and C3b, followed by inactivation of C3b generating iC3b (which includes C3α and C3β), which undergoes further breakdown yielding C3c and terminal breakdown fragment C3dg. The composition of C3 breakdown products in C3G is not known.
In this study, we chose six cases each of C3GN and DDD to analyze the composition of C3 deposits. We analyzed the amino acid sequence of C3 spectra detected by mass spectrometry to determine the relative abundance of C3 fragments in C3G. Thus we were able to determine the amino acid sequences mapping to the various C3 activation products including C3dg, C3α (C3α1 and α2), and C3β that are part of C3b/iC3b/C3c.
C3dg is the predominant cleavage product detected with the highest amino acid coverage. The remaining amino acids map to C3α (C3α1 and α2) and C3β. Amino acids mapping to C3a and C3f are absent. Taken together, the C3α and C3β amino acids represent iC3b prior to or after C3c cleavage of C3dg. The C3 spectra for both C3GN and DDD are surprisingly similar.
The finding of large amounts of C3dg suggests that C3b deposition in the glomerulus is an active process triggered by thioester binding of C3b to the glycocalyx overlying the glomerular endothelial cells and glomerular basement membrane. Regulatory protein-mediated inactivation of C3b results in the generation of iC3b. After additional cleavages, mostly C3dg remains.
PAM50 gene profiling assigns each cancer to a single intrinsic subtype. However, individual cancers vary in their adherence to a prototype, and due to bulk tissue sampling, some may exhibit ...expression patterns that indicate intra-tumor admixture of multiple subtypes. Our objective was to develop admixture metrics from PAM50 gene expression profiles in order to stratify Luminal A (LumA) cases according to their degree of subtype admixture, and then relate such admixture to clinical and molecular variables.
We re-constructed scaled, normalized PAM50 profiles for 1980 cases (674 LumA) in the METABRIC cohort and for each case computed its Mahalanobis (M-) distance from its assigned centroid and M-distance from all other centroids. We used t-SNE plots to visualize overlaps in subtype clustering. With Normal-like cases excluded, we developed two metrics: Median Distance Criteria (MDC) classified pure cases as those located within the 50th percentile of the LumA centroid and > =50th percentile from any other centroid. Distance Ratio Criteria (DRC) was computed as the ratio of M-distances from the LumA centroid to the nearest non-assigned centroid. Pure and admixed LumA cases were compared on clinical/molecular traits. TCGA LumA cases (n = 509) provided independent validation.
Compared to pure cases in METABRIC, admixed ones had older age at diagnosis, larger tumor size, and higher grade and stage. These associations were stronger for the DRC metric compared to MDC. Admixed cases were associated with HER2 gain, high proliferation, higher PAM50 recurrence scores, more frequent TP53 mutation, and less frequent PIK3CA mutation. Similar results were observed in the TCGA validation cohort, which also showed a positive association between admixture and number of clonal populations estimated by PyClone. LumA-LumB confusion predominated, but other combinations were also present. Degree of admixture was associated with overall survival in both cohorts, as was disease-free survival in TCGA, independent of age, grade and stage (HR = 2.85, Tertile 3 vs.1).
Luminal A breast cancers subgrouped based on PAM50 subtype purity support the hypothesis that admixed cases have worse clinical features and survival. Future analyses will explore more extensive genomic metrics for admixture and their spatial significance within a single tumor.
Purpose
Online customer communities have become a strategic tool for business-to-business (B2B) firms to drive collaboration among customers around the company’s products and services. This paper ...aims to argue that the three social capital dimensions, that is, structural, relational and cognitive, themselves driven by brand community trust, can affect brand loyalty for the organization.
Design/methodology/approach
The authors use a survey to collect data and structural equation modeling to test the conceptual framework by collecting data from 214 participants across three online B2B communities operated by three technology firms in India.
Findings
Brand community trust is found to have a strong association with social network ties, identification and norm of reciprocity and shared vision. These three have concomitant effects on the quality of customer-to-customer (C2C) interactions. Such communication generates functional, emotional and social benefits, which, in turn, curate brand loyalty.
Practical implications
The authors’ findings guide community managers in leveraging such conversations in shaping customer loyalty for the corporate brand.
Originality/value
This work provides an integrated framework to explain the important role of C2C interactions in B2B online brand communities.
RNA interference (RNAi)-based technology shows great potential for use in agriculture, particularly for management of costly insect pests. In the decade since the insecticidal effects of ...environmentally-introduced RNA were first reported, this treatment has been applied to several types of insect pests. Through the course of those efforts, it has become apparent that different insects exhibit a range of sensitivity to environmentally-introduced RNAs. The variation in responses across insect is not well-understood, with differences in the underlying RNAi mechanisms being one explanation. This study evaluates eight proteins among three agricultural pests whose responses to environmental RNAi are known to differ: western corn rootworm (Diabrotica virgifera virgifera), fall armyworm (Spodoptera frugiperda), and southern green stink bug (Nezara viridula). These proteins have been identified in various organisms as centrally involved in facilitating the microRNA- and small interfering-RNA-mediated interference responses. Various bioinformatics tools, as well as gene expression profiling, were used to identify and evaluate putative homologues for characteristics that may contribute to the differing responses of these insects, such as the absence of critical functional domains within expressed sequences, the absence of entire gene sequences, or unusually low or undetectable expression of critical genes. Though many similarities were observed, the number of isoforms and expression levels of double-stranded RNA-binding and argonaute proteins varied across insect. Differences among key RNAi machinery genes of these three pests may impact the function of their RNAi pathways, and therefore, their respective responses to exogenous RNAs.
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