Cardiac Non-myocyte Cells Show Enhanced Pharmacological Function Suggestive of Contractile Maturity in Stem Cell Derived Cardiomyocyte Microtissues

The immature phenotype of stem cell derived cardiomyocytes is a significant barrier to their use in translational medicine and pre-clinical in vitro drug toxicity and pharmacological analysis. Here we have assessed the contribution of non-myocyte cells on the contractile function of co-cultured human embryonic stem cell derived cardiomyocytes (hESC-CMs) in spheroid microtissue format. Microtissues were formed using a scaffold free 96-well cell suspension method from hESC-CM cultured alone (CM microtissues) or in combination with human primary cardiac microvascular endothelial cells and cardiac fibroblasts (CMEF microtissues). Contractility was characterized with fluorescence and video-based edge detection. CMEF microtissues displayed greater Ca(2+ )transient amplitudes, enhanced spontaneous contraction rate and remarkably enhanced contractile function in response to both positive and negative inotropic drugs, suggesting a more mature contractile phenotype than CM microtissues. In addition, for several drugs the enhanced contractile response was not apparent when endothelial cell or fibroblasts from a non-cardiac tissue were used as the ancillary cells. Further evidence of maturity for CMEF microtissues was shown with increased expression of genes that encode proteins critical in cardiac Ca(2+ )handling (S100A1), sarcomere assembly (telethonin/TCAP) and β-adrenergic receptor signalling. Our data shows that compared with single cell-type cardiomyocyte in vitro models, CMEF microtissues are superior at predicting the inotropic effects of drugs, demonstrating the critical contribution of cardiac non-myocyte cells in mediating functional cardiotoxicity

Att-TasNet: attending to encodings in time-domain audio speech separation of noisy, reverberant speech mixtures

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures

The University of Sheffield CHiME-7 UDASE challenge speech enhancement system

The CHiME-7 unsupervised domain adaptation speech enhancement (UDASE) challenge targets domain adaptation to unlabelled speech data. This paper describes the University of Sheffield team’s system submitted to the challenge. A generative adversarial network (GAN) methodology based on a conformer-based metric GAN (CMGAN) is employed as opposed to the unsupervised RemixIT strategy used in the CHiME-7 baseline system. The discriminator of the GAN is trained to predict the output score of a Deep Noise Suppression Mean Opinion Score (DNSMOS) metric. Additional data augmentation strategies are employed which provide the discriminator with historical training data outputs as well as more diverse training examples from an additional pseudo-generator. The proposed approach, denoted as CMGAN+/+, achieves significant improvement in DNSMOS evaluation metrics with the best proposed system achieving 3.51 OVR-MOS, a 24% improvement over the baseline

Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI)

Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI)

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

Comprehensive genetic diagnosis of tandem repeat expansion disorders with programmable targeted nanopore sequencing

More than 50 neurological and neuromuscular diseases are caused by short tandem repeat (STR) expansions, with 37 different genes implicated to date. We describe the use of programmable targeted long-read sequencing with Oxford Nanopore's ReadUntil function for parallel genotyping of all known neuropathogenic STRs in a single assay. Our approach enables accurate, haplotype-resolved assembly and DNA methylation profiling of STR sites, from a list of predetermined candidates. This correctly diagnoses all individuals in a small cohort (n = 37) including patients with various neurogenetic diseases (n = 25). Targeted long-read sequencing solves large and complex STR expansions that confound established molecular tests and short-read sequencing and identifies noncanonical STR motif conformations and internal sequence interruptions. We observe a diversity of STR alleles of known and unknown pathogenicity, suggesting that long-read sequencing will redefine the genetic landscape of repeat disorders. Last, we show how the inclusion of pharmacogenomic genes as secondary ReadUntil targets can further inform patient care

CAV3 mutations causing exercise intolerance, myalgia and rhabdomyolysis: expanding the phenotypic spectrum of caveolinopathies

Rhabdomyolysis is often due to a combination of environmental trigger(s) and genetic predisposition; however, the underlying genetic cause remains elusive in many cases. Mutations in CAV3 lead to various neuromuscular phenotypes with partial overlap, including limb girdle muscular dystrophy type 1C (LGMD1C), rippling muscle disease, distal myopathy and isolated hyperCKemia. Here we present a series of eight patients from seven families presenting with exercise intolerance and rhabdomyolysis caused by mutations in CAV3 diagnosed by next generation sequencing (NGS) (n=6). Symptoms included myalgia (n=7), exercise intolerance (n=6) and episodes of rhabdomyolysis (n=2). Percussion-induced rapid muscle contractions (PIRCs) were seen in five out of six patients examined. A previously reported heterozygous mutation in CAV3 (p.T78M) and three novel variants (p.V14I, p.F41S, p.F54V) were identified. Caveolin-3 immunolabeling in muscle was normal in 3/4 patients however, immunoblotting showed more than 50% reduction of caveolin-3 in five patients compared with controls. This case series demonstrates that exercise intolerance, myalgia and rhabdomyolysis may be caused by CAV3 mutations and broadens the phenotypic spectrum of caveolinopathies. In our series immunoblotting was a more sensitive method to detect reduced caveolin-3 levels than immunohistochemistry in skeletal muscle. Patients presenting with muscle pain, exercise intolerance and rhabdomyolysis should be routinely tested for PIRCs as this may be an important clinical clue for caveolinopathies, even in the absence of other “typical” features. The use of NGS may expand current knowledge concerning inherited diseases, and unexpected/atypical phenotypes may be attributed to well-known human disease genes