PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.

Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after staining, which is laborious and hinders high-throughput screening. We developed an automatic detection tool (PollenDetect) to distinguish viable and nonviable pollen based on the YOLOv5 neural network, which is adjusted to adapt to the small target detection task. Compared with manual work, PollenDetect significantly reduced detection time (from approximately 3 min to 1 s for each image). Meanwhile, PollenDetect can maintain high detection accuracy. When PollenDetect was tested on cotton pollen viability, 99% accuracy was achieved. Furthermore, the results obtained using PollenDetect show that high temperature weakened cotton pollen viability, which is highly similar to the pollen viability results obtained using 2,3,5-triphenyltetrazolium formazan quantification. PollenDetect is an open-source software that can be further trained to count different types of pollen for research purposes. Thus, PollenDetect is a rapid and accurate system for recognizing pollen viability status, and is important for screening stress-resistant crop varieties for the identification of pollen viability and stress resistance genes during genetic breeding research.

Quantifying yeast colony morphologies with feature engineering from time-lapse photography.

Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The Local binary pattern (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability; it obtains the best representative colony image for any hierarchical cluster.

Fast anther dehiscence status recognition system established by deep learning to screen heat tolerant cotton.

BACKGROUND: From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly determines the probability of fertilization in cotton. Thus, rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. RESULT: The single-stage model based on YOLOv5 has higher recognition speed and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies are proposed for the Faster R-CNN model, where the improved model has higher detection accuracy than the YOLOv5 model. We have made three improvements to the Faster R-CNN model and after the ensemble of the three models and original Faster R-CNN model, R2 of "open" reaches to 0.8765, R2 of "close" reaches to 0.8539, R2 of "all" reaches to 0.8481, higher than the prediction results of either model alone, which are completely able to replace the manual counting results. We can use this model to quickly extract the dehiscence rate of cotton anthers under high temperature (HT) conditions. In addition, the percentage of dehiscent anthers of 30 randomly selected cotton varieties were observed from the cotton population under normal conditions and HT conditions through the ensemble of the Faster R-CNN model and manual counting. The results show that HT decreased the percentage of dehiscent anthers in different cotton lines, consistent with the manual method. CONCLUSIONS: Deep learning technology have been applied to cotton anther dehiscence status recognition instead of manual methods for the first time to quickly screen HT-tolerant cotton varieties. Deep learning can help to explore the key genetic improvement genes in the future, promoting cotton breeding and improvement.

Contrasting effects of exogenous and endogenous attention on size perception.

Although neuroimaging studies have shown that exogenous and endogenous attention are dissociable, only a few behavioural studies have explored their differential effects on visual sensitivity, and none have directly focused on visual appearance. Here, we show that exogenous and endogenous attention produces contrasting effects on apparent size. Participants performed a spatial pre-cueing comparative judgement task that had been frequently used to test the attentional effects on visual perception. The results showed that a smaller stimulus within the focus of exogenous attention was perceived to be equal in size as a larger unattended stimulus, whereas a larger stimulus within the focus of endogenous attention was perceived to be equal in size as a smaller unattended stimulus. In other words, exogenous attention increased the perceived size while endogenous attention decreased the perceived size. The contrasting effects may be attributed to the mechanism that exogenous attention favours parvocellular processing for which more neurons with smaller receptive fields (RFs) are activated for a given size, whereas endogenous attention favours magnocellular processing for which fewer neurons with larger RFs are activated. This finding shows that exogenous and endogenous attention acts differentially on size perception, and provides supportive evidence for the distinct mechanisms underlying the two types of attentional processing.

Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia.

Aim: The clinical utility of pharmacogenomics (PGx) has been gaining traction alongside growing evidence that adverse drug reactions (ADRs) have significant genetic associations. Nala PGx Core® is a multi-gene qPCR-based panel of 20 allele variants, comprising 18 SNPs and two CYP2D6 copy number markers across four pharmacogenes - CYP2C9, CYP2C19, CYP2D6 and SLCO1B1. Methods: In this study, we validated the performance of Nala PGx Core® against benchmark methods, on the Singaporean and Indonesian populations. Results & conclusion: Nala PGx Core® demonstrated robust and accurate genotyping when compared with other established benchmarks. Furthermore, the panel successfully characterized alleles of clinical relevance, such as CYP2D6*10 and CYP2D6*36, across major ethnic groups present of Singapore and Indonesia, suggesting its potential for adoption in clinical workflows regionally.

MV2Net: Multi-Variate Multi-View Brain Network Comparison over Uncertain Data.

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

UrbanMotion: Visual Analysis of Metropolitan-Scale Sparse Trajectories.

Visualizing massive scale human movement in cities plays an important role in solving many of the problems that modern cities face (e.g., traffic optimization, business site configuration). In this article, we study a big mobile location dataset that covers millions of city residents, but is temporally sparse on the trajectory of individual user. Mapping sparse trajectories to illustrate population movement poses several challenges from both analysis and visualization perspectives. In the literature, there are a few techniques designed for sparse trajectory visualization; yet they do not consider trajectories collected from mobile apps that possess long-tailed sparsity with record intervals as long as hours. This article introduces UrbanMotion, a visual analytics system that extends the original wind map design by supporting map-matched local movements, multi-directional population flows, and population distributions. Effective methods are proposed to extract and aggregate population movements from dense parts of the trajectories leveraging their long-tailed sparsity. Both characteristic and anomalous patterns are discovered and visualized. We conducted three case studies, one comparative experiment, and collected expert feedback in the application domains of commuting analysis, event detection, and business site configuration. The study result demonstrates the significance and effectiveness of our system in helping to complete key analytics tasks for urban users.

Environmental stresses induce karyotypic instability in colorectal cancer cells.

Understanding how cells acquire genetic mutations is a fundamental biological question with implications for many different areas of biomedical research, ranging from tumor evolution to drug resistance. While karyotypic heterogeneity is a hallmark of cancer cells, few mutations causing chromosome instability have been identified in cancer genomes, suggesting a nongenetic origin of this phenomenon. We found that in vitro exposure of karyotypically stable human colorectal cancer cell lines to environmental stress conditions triggered a wide variety of chromosomal changes and karyotypic heterogeneity. At the molecular level, hyperthermia induced polyploidization by perturbing centrosome function, preventing chromosome segregation, and attenuating the spindle assembly checkpoint. The combination of these effects resulted in mitotic exit without chromosome segregation. Finally, heat-induced tetraploid cells were on the average more resistant to chemotherapeutic agents. Our studies suggest that environmental perturbations promote karyotypic heterogeneity and could contribute to the emergence of drug resistance.

Mammalian Cells Undergo Endoreduplication in Response to Lactic Acidosis.

Polyploidization, a common event during the evolution of different tumours, has been proposed to confer selective advantages to tumour cells by increasing the occurrence of mutations promoting cancer progression and by conferring chemotherapy resistance. While conditions leading to polyploidy in cancer cells have been described, a general mechanism explaining the incidence of this karyotypic change in tumours is still missing. In this study, we tested whether a widespread tumour microenvironmental condition, low pH, could induce polyploidization in mammalian cells. We found that an acidic microenvironment, in the range of what is commonly observed in tumours, together with the addition of lactic acid, induced polyploidization in transformed and non-transformed human cell lines in vitro. In addition, we provide evidence that polyploidization was mainly driven through the process of endoreduplication, i.e. the complete skipping of mitosis in-between two S-phases. These findings suggest that acidic environments, which characterize solid tumours, are a plausible path leading to polyploidization of cancer cells.

Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae.

Biofilm formation by microorganisms is a major cause of recurring infections and removal of biofilms has proven to be extremely difficult given their inherent drug resistance . Understanding the biological processes that underlie biofilm formation is thus extremely important and could lead to the development of more effective drug therapies, resulting in better infection outcomes. Using the yeast Saccharomyces cerevisiae as a biofilm model, overexpression screens identified DIG1, SFL1, HEK2, TOS8, SAN1, and ROF1/YHR177W as regulators of biofilm formation. Subsequent RNA-seq analysis of biofilm and nonbiofilm-forming strains revealed that all of the overexpression strains, other than DIG1 and TOS8, were adopting a single differential expression profile, although induced to varying degrees. TOS8 adopted a separate profile, while the expression profile of DIG1 reflected the common pattern seen in most of the strains, plus substantial DIG1-specific expression changes. We interpret the existence of the common transcriptional pattern seen across multiple, unrelated overexpression strains as reflecting a transcriptional state, that the yeast cell can access through regulatory signaling mechanisms, allowing an adaptive morphological change between biofilm-forming and nonbiofilm states.

Dissecting Gene Expression Changes Accompanying a Ploidy-Based Phenotypic Switch.

Aneuploidy, a state in which the chromosome number deviates from a multiple of the haploid count, significantly impacts human health. The phenotypic consequences of aneuploidy are believed to arise from gene expression changes associated with the altered copy number of genes on the aneuploid chromosomes. To dissect the mechanisms underlying altered gene expression in aneuploids, we used RNA-seq to measure transcript abundance in colonies of the haploid Saccharomyces cerevisiae strain F45 and two aneuploid derivatives harboring disomies of chromosomes XV and XVI. F45 colonies display complex "fluffy" morphologies, while the disomic colonies are smooth, resembling laboratory strains. Our two disomes displayed similar transcriptional profiles, a phenomenon not driven by their shared smooth colony morphology nor simply by their karyotype. Surprisingly, the environmental stress response (ESR) was induced in F45, relative to the two disomes. We also identified genes whose expression reflected a nonlinear interaction between the copy number of a transcriptional regulatory gene on chromosome XVI, DIG1, and the copy number of other chromosome XVI genes. DIG1 and the remaining chromosome XVI genes also demonstrated distinct contributions to the effect of the chromosome XVI disome on ESR gene expression. Expression changes in aneuploids appear to reflect a mixture of effects shared between different aneuploidies and effects unique to perturbing the copy number of particular chromosomes, including nonlinear copy number interactions between genes. The balance between these two phenomena is likely to be genotype- and environment-specific.

Quantitative analysis of colony morphology in yeast.

Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism's virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.

Aneuploidy underlies a multicellular phenotypic switch.

Although microorganisms are traditionally used to investigate unicellular processes, the yeast Saccharomyces cerevisiae has the ability to form colonies with highly complex, multicellular structures. Colonies with the "fluffy" morphology have properties reminiscent of bacterial biofilms and are easily distinguished from the "smooth" colonies typically formed by laboratory strains. We have identified strains that are able to reversibly toggle between the fluffy and smooth colony-forming states. Using a combination of flow cytometry and high-throughput restriction-site associated DNA tag sequencing, we show that this switch is correlated with a change in chromosomal copy number. Furthermore, the gain of a single chromosome is sufficient to switch a strain from the fluffy to the smooth state, and its subsequent loss to revert the strain back to the fluffy state. Because copy number imbalance of six of the 16 S. cerevisiae chromosomes and even a single gene can modulate the switch, our results support the hypothesis that the state switch is produced by dosage-sensitive genes, rather than a general response to altered DNA content. These findings add a complex, multicellular phenotype to the list of molecular and cellular traits known to be altered by aneuploidy and suggest that chromosome missegregation can provide a quick, heritable, and reversible mechanism by which organisms can toggle between phenotypes.

A new panel of NS1 antibodies for easy detection and titration of influenza A virus.

The non-structural protein NS1 of the influenza A virus is a good target for the development of diagnostic assays. In this study, three NS1 monoclonal antibodies (mAbs) were generated by using recombinant NS1 protein of H5N1 virus and found to bind both the native and denatured forms of NS1. Two of the mAbs, 6A4 and 2H6, bind NS1 of three different strains of influenza A virus, namely H1N1, H3N2, and H5N1. Epitope mapping revealed that residues 42-53 of H5N1 NS1 are essential for the interaction with both mAbs. Between the three strains, there is only one amino acid difference in this domain, which is consistent with the observed cross-reactivities. On the other hand, mAb 1G1 binds to residues 206-215 of H5N1 NS1 and does not bind NS1 of H1N1 or H3N2. Furthermore, all three mAbs detected NS1 proteins expressed in virus infected MDCK cells and indirect immunofluorescence staining with mAbs 6A4 and 2H6 provided an alternative method for viral titer determination. Quantifying the numbers of fluorescent foci units yielded viral titers for three different isolates of H5N1 virus that are highly comparable to that obtained by observing cytopathic effect induced by virus infection. Importantly, this alternative method yields results at 1 day post-infection while the conventional method using cytopathic effect yields results at 3 days post-infection. The results showed that this new panel of NS1 antibodies can detect NS1 protein expressed during viral infection and can be used for fast and easy titration of influenza A virus. J. Med. Virol. 82:467-475, 2010. (c) 2010 Wiley-Liss, Inc.