MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP

Satellite infrared emission instruments require efficient systems that can separate and flag observations which are affected by clouds and aerosols. This paper investigates the identification of cloud and aerosols from infrared, limb sounding spectra that were recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), a high spectral resolution Fourier transform spectrometer on the European Space Agency's (ESA) ENVISAT (Now inoperative since April 2012 due to loss of contact). Specifically, the performance of an existing cloud and aerosol particle detection method is simulated with a radiative transfer model in order to establish, for the first time, confident detection limits for particle presence in the atmosphere from MIPAS data. The newly established thresholds improve confidence in the ability to detect particle injection events, plume transport in the upper troposphere and lower stratosphere (UTLS) and better characterise cloud distributions utilising MIPAS spectra. The method also provides a fast front-end detection system for the MIPClouds processor; a processor designed for the retrieval of macro- and microphysical cloud properties from the MIPAS data. <br><br> It is shown that across much of the stratosphere, the threshold for the standard cloud index in band A is 5.0 although threshold values of over 6.0 occur in restricted regimes. Polar regions show a surprising degree of uncertainty at altitudes above 20 km, potentially due to changing stratospheric trace gas concentrations in polar vortex conditions and poor signal-to-noise due to cold atmospheric temperatures. The optimised thresholds of this study can be used for much of the time, but time/composition-dependent thresholds are recommended for MIPAS data for the strongly perturbed polar stratosphere. In the UT, a threshold of 5.0 applies at 12 km and above but decreases rapidly at lower altitudes. The new thresholds are shown to allow much more sensitive detection of particle distributions in the UTLS, with extinction detection limits above 13 km often better than 10<sup>−4</sup> km<sup>−1</sup>, with values approaching 10<sup>−5</sup> km<sup>−1</sup> in some cases. <br><br> Comparisons of the new MIPAS results with cloud data from HIRDLS and CALIOP, outside of the poles, establish a good agreement in distributions (cloud and aerosol top heights and occurrence frequencies) with an offset between MIPAS and the other instruments of 0.5 km to 1 km between 12 km and 20 km, consistent with vertical oversampling of extended cloud layers within the MIPAS field of view. We conclude that infrared limb sounders provide a very consistent picture of particles in the UTLS, allowing detection limits which are consistent with the lidar observations. Investigations of MIPAS data for the Mount Kasatochi volcanic eruption on the Aleutian Islands and the Black Saturday fires in Australia are used to exemplify how useful MIPAS limb sounding data were for monitoring aerosol injections into the UTLS. It is shown that the new thresholds allowed such events to be much more effectively derived from MIPAS with detection limits for these case studies of 1 × 10<sup>−5</sup> km<sup>−1</sup> at a wavelength of 12 μm

Nucleus subtype classification using inter-modality learning

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology

Transcriptional co-activators YAP1-TAZ of Hippo signalling in doxorubicin-induced cardiomyopathy

Aims Hippo signalling is an evolutionarily conserved pathway that controls organ size by regulating apoptosis, cell proliferation, and stem cell self-renewal. Recently, the pathway has been shown to exert powerful growth regulatory activity in cardiomyocytes. However, the functional role of this stress-related and cell death-related pathway in the human heart and cardiomyocytes is not known. In this study, we investigated the role of the transcriptional co-activators of Hippo signalling, YAP and TAZ, in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in response to cardiotoxic agents and investigated the effects of modulating the pathway on cardiomyocyte function and survival. Methods and results RNA-sequencing analysis of human heart samples with doxorubicin-induced end-stage heart failure and healthy controls showed that YAP and ERBB2 (HER2) as upstream regulators of differentially expressed genes correlated with doxorubicin treatment. Thus, we tested the effects of doxorubicin on hiPSC-CMs in vitro. Using an automated high-content screen of 96 clinically relevant antineoplastic and cardiotherapeutic drugs, we showed that doxorubicin induced the highest activation of YAP/TAZ nuclear translocation in both hiPSC-CMs and control MCF7 breast cancer cells. The overexpression of YAP rescued doxorubicin-induced cell loss in hiPSC-CMs by inhibiting apoptosis and inducing proliferation. In contrast, silencing of YAP and TAZ by siRNAs resulted in elevated mitochondrial membrane potential loss in response to doxorubicin. hiPSC-CM calcium transients did not change in response to YAP/TAZ silencing. Conclusions Our results suggest that Hippo signalling is involved in clinical anthracycline-induced cardiomyopathy. Modelling with hiPSC-CMs in vitro showed similar responses to doxorubicin as adult cardiomyocytes and revealed a potential cardioprotective effect of YAP in doxorubicin-induced cardiotoxicity

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956)

A supergene determines highly divergent male reproductive morphs in the ruff

Three strikingly different alternative male mating morphs (aggressive 'independents', semicooperative 'satellites' and female-mimic 'faeders') coexist as a balanced polymorphism in the ruff, Philomachus pugnax, a lek-breeding wading bird1, 2, 3. Major differences in body size, ornamentation, and aggressive and mating behaviors are inherited as an autosomal polymorphism4, 5. We show that development into satellites and faeders is determined by a supergene6, 7, 8 consisting of divergent alternative, dominant and non-recombining haplotypes of an inversion on chromosome 11, which contains 125 predicted genes. Independents are homozygous for the ancestral sequence. One breakpoint of the inversion disrupts the essential CENP-N gene (encoding centromere protein N), and pedigree analysis confirms the lethality of homozygosity for the inversion. We describe new differences in behavior, testis size and steroid metabolism among morphs and identify polymorphic genes within the inversion that are likely to contribute to the differences among morphs in reproductive traits

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL

Adult telomere length is positively correlated with survival and lifetime reproductive success in a wild passerine

Explaining variation in individual fitness is a key goal in evolutionary biology. Recently, telomeres, repeating DNA sequences capping chromosome ends, have gained attention as a biomarker for body state, physiological costs, and senescence. Existing research has provided mixed evidence for whether telomere length correlates with fitness, including survival and reproductive output. Moreover, few studies have examined how the rate of change in telomere length correlates with fitness in wild populations. Here, we intensively monitored an insular population of house sparrows, and collected longitudinal telomere and life history data (16 years, 1225 individuals). We tested whether telomere length and its rate of change predict fitness measures, namely survival, lifespan and annual and lifetime reproductive effort and success. Telomere length positively predicted short-term survival, independent of age, but did not predict lifespan, suggesting either a diminishing telomere length—survival correlation with age or other extrinsic factors of mortality. The positive association of telomere length with survival translated into reproductive benefits, as birds with longer telomeres produced more genetic recruits, hatchlings and reared more fledglings over their lifetime. In contrast, there was no association between telomere dynamics and annual reproductive output, suggesting telomere dynamics might not reflect the costs of reproduction in this population, potentially masked by variation in individual quality. The rate of change of telomere length did not correlate with neither lifespan nor lifetime reproductive success. Our results provide further evidence that telomere length correlates with fitness, and contribute to our understanding of the selection on, and evolution of, telomere dynamics

A multiplex microsatellite set for non-invasive genotyping and sexing of the osprey (Pandion haliaetus)

During the 1950s and 1970s the osprey (Pandion haliaetus) experienced a dramatic population crash and remains of conservation concern in several parts of the world. We isolated 37 microsatellite loci and assessed these in ospreys sampled in the UK and Norway (using mouth swabs/feathers). From 26 loci variable in four ospreys, we selected 13, combined these into two multiplex-PCR sets and included a sex-typing marker. Additional markers confirmed sexes. In 17 ospreys, feather-sampled in central Norway, we found 3–10 alleles per locus. The 13 loci are autosomal (heterozygotes were present in both sexes) and observed heterozygosities ranged from 0.24 to 0.94. The combined probability of identity for the 13 loci was 8.0 × 10−12. These microsatellite loci will be useful for genetic monitoring, parentage analysis and population genetic studies of the osprey

Measurements of stratospheric constituents by ISAMS

ISAMS is a limb sounding radiometer flying on the UARS, and designed to measure temperature, pressure, O3, CO, NO, NO2, N2O5, HNO3, CH4, H2O, N2O, and aerosol. Its capabilities are described, together with the present status of validation of its data products, and plans for future improvement