VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Magnetic activated carbon as an adsorbent for extraction of DMMP from aqueous samples followed by GC-IMS analysis.

Micro-porous magnetic activated carbon was prepared under ultrasonic irradiation as an adsorbent for dispersed solid phase extraction of dimethyl methyl phosphonate from water samples, before analysis by gas chromatography-ion mobility spectrometry. The magnetic activated carbon was synthesized and characterized by using a vibrating sample magnetometer, Fourier transform infrared spectroscopy, scanning electron microscopy and X-ray diffraction techniques. Then, the effects of the amount of sorbent, extraction time and pH of the sample in the dispersive solid phase extraction method were investigated and optimized by the response surface method. The dispersion of 20 mg adsorbent powder in a 50 mL water sample for 5 minutes with chloroform as the desorption solvent showed an average recovery value of 95% for dimethyl methyl phosphonate. Afterward, the method was used successfully for the determination of dimethyl methyl phosphonate in river and spring water. The linear range was obtained to be 0.05-1 µg mL-1. The limit of detection and the limit of quantification were obtained to be 0.02 µg mL-1 and 0.05 µg mL-1 respectively. The analysis also showed good reproducibility with a relative standard deviation value of 3.1%. This method was shown to be easy, fast, reliable, and inexpensive.

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Poor adult nutrition impairs learning and memory in a parasitoid wasp.

Animals have evolved cognitive abilities whose impairment can incur dramatic fitness costs. While malnutrition is known to impact brain development and cognitive functions in vertebrates, little is known in insects whose small brain appears particularly vulnerable to environmental stressors. Here, we investigated the influence of diet quality on learning and memory in the parasitoid wasp Venturia canescens. Newly emerged adults were exposed for 24 h to either honey, 20% sucrose solution, 10% sucrose solution, or water, before being conditioned in an olfactory associative learning task in which an odor was associated to a host larvae (reward). Honey fed wasps showed 3.5 times higher learning performances and 1.5 times longer memory retention than wasps fed sucrose solutions or water. Poor diets also reduced longevity and fecundity. Our results demonstrate the importance of early adult nutrition for optimal cognitive function in these parasitoid wasps that must quickly develop long-term olfactory memories for searching suitable hosts for their progeny.

Wolbachia manipulate fitness benefits of olfactory associative learning in a parasitoid wasp.

Upon encountering a host, a female parasitoid wasp has to decide whether to learn positive or negative cues related to the host. The optimal female decision will depend on the fitness costs and benefits of learned stimuli. Reward quality is positively related to the rate of behavioral acquisition in processes such as associative learning. Wolbachia, an endosymbiotic bacterium, often plays an impressive role in the manipulation of its arthropod host's biology. Here, we studied the responses of two natural Wolbachia infected/uninfected Trichogramma brassicae wasp populations to theoretically high- and low-reward values during a conditioning process and the consequences of their responses in terms of memory duration. According to our results, uninfected wasps showed an attraction response to high-value rewards, but showed aversive learning in response to low-value rewards. The memory span of uninfected wasps after conditioning by low-value rewards was significantly shorter than that for high-value rewards. As our results revealed, responses to high-quality hosts will bring more benefits (bigger size, increased fecundity and enhanced survival) than those to low-quality hosts for uninfected wasps. Infected wasps were attracted to conditioned stimuli with the same memory duration after conditioning by both types of hosts. This was linked to the fact that parasitoids emerging from both types of hosts present the same life-history traits. Therefore, these hosts represent the same quality reward for infected wasps. According to the obtained results, it can be concluded that Wolbachia manipulates the learning ability of its host, resulting in the wasp responding to all reward values similarly.

Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion ('referral' test set), and on an externally stained set from outside institutions ('externally stained' test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

Wolbachia impairs post-eclosion host preference in a parasitoid wasp.

Host preference behavior can result in adaptive advantages with important consequences for the fitness of individuals. Hopkin's host-selection principle (HHSP) suggests that organisms at higher trophic levels demonstrate a preference for the host species on which they developed during their own larval stage. Although investigated in many herbivorous and predatory insects, the HHSP has, to our knowledge, never been tested in the context of insects hosting selfish endosymbiotic passengers. Here, we investigated the effect of infection with the facultative bacterial symbiont Wolbachia on post-eclosion host preference in the parasitoid wasp Trichogramma brassicae (Hymenoptera: Trichogrammatidae). We compared host preference in Wolbachia-infected individuals and uninfected adult female parasitoids after rearing them on two different Lepidopteran hosts, namely the flour moth Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) or the grain moth Sitotroga cerealella (Lepidoptera: Gelechiidae) in choice and no choice experimental design (n = 120 wasps per each choice/no choice experiments). We showed that in T. brassicae, Wolbachia affects the post-eclosion host preference of female wasps. Wolbachia-infected wasps did not show any host preference and more frequently switched hosts in the laboratory, while uninfected wasps significantly preferred to lay eggs on the host species they developed on. Additionally, Wolbachia significantly improved the emergence rate of infected wasps when reared on new hosts. Altogether, our results revealed that the wasp's infection with Wolbachia may lead to impairment of post-eclosion host preference and facilitates growing up on different host species. The impairment of host preference by Wolbachia may allow T. brassicae to shift between hosts, a behavior that might have important evolutionary consequences for the wasp and its symbiont.

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Nanofibrous cellulose acetate/gelatin wound dressing endowed with antibacterial and healing efficacy using nanoemulsion of Zataria multiflora.

In this paper, a multifunctional nanofibrous cellulose acetate/gelatin/Zataria multiflora-nanoemulsion (CA/Gel/ZM-nano) wound dressing was fabricated, in which nanoemulsion of a natural active antibacterial plant, by the scientific name of Zataria multiflora (ZM) was loaded into the nanofibrous mat. To fabricate the wound dressing, different weight ratios of CA/Gel (100, 0, 75, 25, 50, 50 and 25, 75) were selected, and solutions with concentrations of 16, 15, 14 and 12% w/v were prepared for each ratio, respectively to achieve smooth and uniform fibers by electrospinning. In vitro and in vivo analysis was taken for the samples. Nanofibrous mats with a lower ratio of CA/Gel and incorporated with ZM-nano promoted the adhesion and proliferation of L929 fibroblast cells significantly. Also, by lowering the ratio of CA/Gel, nanoemulsion drug-loading into nanofibers increased considerably, as the amount of ZM-nano loaded into CA/Gel = 50:50 was found to be 2.4-fold higher than CA/Gel = 100:0. Moreover, the rat model experiment in our study revealed that the nanofibrous samples incorporated with nanoemulsion drug (ZM-nano) accelerated the wound healing process so that the relative wound area for the nanoemulsion-loaded dressings was much smaller than the other samples after 22 days. Therefore, this multifunctional CA/Gel/ZM-nano wound dressing could be a promising and potential candidate for wound healing applications.

clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers.

Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.

Why is there no impact of the host species on the cold tolerance of a generalist parasitoid?

For generalist parasitoids such as those belonging to the Genus Aphidius, the choice of host species can have profound implications for the emerging parasitoid. Host species is known to affect a variety of life history traits. However, the impact of the host on thermal tolerance has never been studied. Physiological thermal tolerance, enabling survival at unfavourable temperatures, is not a fixed trait and may be influenced by a number of external factors including characteristics of the stress, of the individual exposed to the stress, and of the biological and physical environment. As such, the choice of host species is likely to also have implications for the thermal tolerance of the emerging parasitoid. The current study aimed to investigate the effect of cereal aphid host species (Sitobion avenae, Rhopalosiphum padi and Metopolophium dirhodum) on adult thermal tolerance, in addition to sex and size, of the aphid parasitoids Aphidius avenae, Aphidius matricariae and Aphidius rhopalosiphi. Results revealed no effect of host species on the cold tolerance of the emerging parasitoid, as determined by CTmin and Chill Coma, for all parasitoid species. Host species significantly affected the size of the emerging parasitoid for A. rhopalosiphi only, with individuals emerging from R. padi being significantly larger than those emerging from S. avenae, although this did not correspond to a difference in thermal tolerance. Furthermore, a significant difference in the size of male and female parasitoids was observed for A. avenae and A. matricariae, although, once again this did not correspond to a difference in cold tolerance. It is suggested that potential behavioural thermoregulation via host manipulation may act to influence the thermal environment experienced by the wasp and thus wasp thermal tolerance and, in doing so, may negate physiological thermal tolerance or any impact of the aphid host.

Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer.

Characterization and quantification of tumour clonal populations over time via longitudinal sampling are essential components in understanding and predicting the response to therapeutic interventions. Computational methods for inferring tumour clonal composition from deep-targeted sequencing data are ubiquitous, however due to the lack of a ground truth biological data, evaluating their performance is difficult. In this work, we generate a benchmark data set that simulates tumour longitudinal growth and heterogeneity by in vitro mixing of cancer cell lines with known proportions. We apply four different algorithms to our ground truth data set and assess their performance in inferring clonal composition using different metrics. We also analyse the performance of these algorithms on breast tumour xenograft samples. We conclude that methods that can simultaneously analyse multiple samples while accounting for copy number alterations as a factor in allelic measurements exhibit the most accurate predictions. These results will inform future functional genomics oriented studies of model systems where time series measurements in the context of therapeutic interventions are becoming increasingly common. These studies will need computational models which accurately reflect the multi-factorial nature of allele measurement in cancer including, as we show here, segmental aneuploidies.

Decrease of memory retention in a parasitic wasp: an effect of host manipulation by Wolbachia?

Several factors, such as cold exposure, aging, the number of experiences and viral infection, have been shown to affect learning ability in different organisms. Wolbachia has been found worldwide as an arthropod parasite/mutualist symbiont in a wide range of species, including insects. Differing effects have been identified on physiology and behavior by Wolbachia. However, the effect of Wolbachia infection on the learning ability of their host had never previously been studied. The current study carried out to compare learning ability and memory duration in 2 strains of the parasitoid Trichogramma brassicae: 1 uninfected and 1 infected by Wolbachia. Both strains were able to associate the novel odors with the reward of an oviposition into a host egg. However, the percentage of females that responded to the experimental design and displayed an ability to learn in these conditions was higher in the uninfected strain. Memory duration was longer in uninfected wasps (23.8 and 21.4 h after conditioning with peppermint and lemon, respectively) than in infected wasps (18.9 and 16.2 h after conditioning with peppermint and lemon, respectively). Memory retention increased in response to the number of conditioning sessions in both strains, but memory retention was always shorter in the infected wasps than in the uninfected ones. Wolbachia infection may select for reduced memory retention because shorter memory induces infected wasps to disperse in new environments and avoid competition with uninfected wasps by forgetting cues related to previously visited environments, thus increasing transmission of Wolbachia in new environments.

Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer.

Circulating tumour DNA analysis can be used to track tumour burden and analyse cancer genomes non-invasively but the extent to which it represents metastatic heterogeneity is unknown. Here we follow a patient with metastatic ER-positive and HER2-positive breast cancer receiving two lines of targeted therapy over 3 years. We characterize genomic architecture and infer clonal evolution in eight tumour biopsies and nine plasma samples collected over 1,193 days of clinical follow-up using exome and targeted amplicon sequencing. Mutation levels in the plasma samples reflect the clonal hierarchy inferred from sequencing of tumour biopsies. Serial changes in circulating levels of sub-clonal private mutations correlate with different treatment responses between metastatic sites. This comparison of biopsy and plasma samples in a single patient with metastatic breast cancer shows that circulating tumour DNA can allow real-time sampling of multifocal clonal evolution.

Multifocal endometriotic lesions associated with cancer are clonal and carry a high mutation burden.

Endometriosis is a significant risk factor for clear cell and endometrioid ovarian cancers and is often found contiguous with these cancers. Using whole-genome shotgun sequencing of seven clear cell ovarian carcinomas (CCC) and targeted sequencing in synchronous endometriosis, we have investigated how this carcinoma may evolve from endometriosis. In every case we observed multiple tumour-associated somatic mutations in at least one concurrent endometriotic lesion. ARID1A and PIK3CA mutations appeared consistently in concurrent endometriosis when present in the primary CCC. In several cases, one or more endometriotic lesions carried the near-complete complement of somatic mutations present in the index CCC tumour. Ancestral mutations were detected in both tumour-adjacent and -distant endometriotic lesions, regardless of any cytological atypia. These findings provide objective evidence that multifocal benign endometriotic lesions are clonally related and that CCCs arising in these patients progress from endometriotic lesions that may already carry sufficient cancer-associated mutations to be considered neoplasms themselves, albeit with low malignant potential. We speculate that genomically distinct classes of endometriosis exist and that ovarian endometriosis with high mutational burden represents one class at high risk for malignant transformation.

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.

Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution, underpinning important emergent features such as drug resistance and metastasis. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

Study of photosynthetic pigments changes of maize (Zea mays L.) under nano Tio2 spraying at various growth stages.

Tests were done on the effects of treatments of titanium dioxide spray on corn (Zea mays L.). The study was conducted as a factorial experiment in a randomized complete block design with four replications. Treatments consisted of two factors; the first factor was stage of plant growth that spraying was applied (vegetative stage, appearance of male flowers and female flowers); and the second factor was that of different concentrations of titanium dioxide nanoparticles (Tio2) that consisted of spray with water (control), titanium dioxide or bulk, nano titanium dioxide at concentrations of 0.01% and 0.03%. Results showed that effect of nano Tio2 was significant on chlorophyll content (a and b), total chlorophyll (a + b), chlorophyll a/b, carotenoids and anthocyanins. The maximum amount of pigment was recorded from the treatment of nano Tio2 spray at the reproductive stage (appearance of male and female flowers) in comparison with control. Thus, an application of nanoparticles (nanao Tio2) can facilitate an increase in crop yield, especially corn yield.

Learning oncogenetic networks by reducing to mixed integer linear programming.

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.