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Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious, and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys, but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g., for silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual point-count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above-ground biomass – a metric often used to quantify habitat quality across forest degradation gradients. Synthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets.

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Timeseries CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147,000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

Purpose To systematically study somatic variants arising during development in the human brain across a spectrum of neurodegenerative disorders. Methods In this study we developed a pipeline to identify somatic variants from exome sequencing data in 1461 diseased and control human brains. Eighty-eight percent of the DNA samples were extracted from the cerebellum. Identified somatic variants were validated by targeted amplicon sequencing and/or PyroMark® Q24. Results We observed somatic coding variants present in >10% of sampled cells in at least 1% of brains. The mutational signature of the detected variants showed a predominance of C>T variants most consistent with arising from DNA mismatch repair, occurred frequently in genes that are highly expressed within the central nervous system, and with a minimum somatic mutation rate of 4.25 × 10−10 per base pair per individual. Conclusion These findings provide proof-of-principle that deleterious somatic variants can affect sizeable brain regions in at least 1% of the population, and thus have the potential to contribute to the pathogenesis of common neurodegenerative diseases.

Mitochondrial dysfunction is involved in a wide array of devastating diseases, but the heterogeneity and complexity of the symptoms of these diseases challenges theoretical understanding of their causation. With the explosion of omics data, we have the unprecedented opportunity to gain deep understanding of the biochemical mechanisms of mitochondrial dysfunction. This goal raises the outstanding need to make these complex datasets interpretable. Quantitative modelling allows us to translate such datasets into intuition and suggest rational biomedical treatments. Taking an interdisciplinary approach, we use a recently published large-scale dataset and develop a descriptive and predictive mathematical model of progressive increase in mutant load of the MELAS 3243A>G mtDNA mutation. The experimentally observed behaviour is surprisingly rich, but we find that our simple, biophysically motivated model intuitively accounts for this heterogeneity and yields a wealth of biological predictions. Our findings suggest that cells attempt to maintain wild-type mtDNA density through cell volume reduction, and thus power demand reduction, until a minimum cell volume is reached. Thereafter, cells toggle from demand reduction to supply increase, up-regulating energy production pathways. Our analysis provides further evidence for the physiological significance of mtDNA density and emphasizes the need for performing single-cell volume measurements jointly with mtDNA quantification. We propose novel experiments to verify the hypotheses made here to further develop our understanding of the threshold effect and connect with rational choices for mtDNA disease therapies.

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.

Heterogeneity in mitochondrial content has been previously suggested as a major contributor to cellular noise, with multiple studies indicating its direct involvement in biomedically important cellular phenomena. A recently published dataset explored the connection between mitochondrial functionality and cell physiology, where a non-linearity between mitochondrial functionality and cell size was found. Using mathematical models, we suggest that a combination of metabolic scaling and a simple model of cell death may account for these observations. However, our findings also suggest the existence of alternative competing hypotheses, such as a non-linearity between cell death and cell size. While we find that the proposed non-linear coupling between mitochondrial functionality and cell size provides a compelling alternative to previous attempts to link mitochondrial heterogeneity and cell physiology, we emphasise the need to account for alternative causal variables, including cell cycle, size, mitochondrial density and death, in future studies of mitochondrial physiology.

There is one subject in recent publications on pediatric sinusitis on which most authors agree, and that is that the public cannot continue to receive antibiotics on demand solely because of purulent nasal discharge, and that clinicians cannot continue to prescribe broad-spectrum and expensive antibiotics for minimal indications. Two publications show that the prevalence of various anatomical variations is no greater in children with rhinosinusitis than in a control group. This mirrors recent work in adults and implies that immunity and the way the mucosa reacts to pathogens is likely to be of primary importance, rather than the anatomy. Several good reviews on the management of rhinosinusitis in children all emphasize the need for medical and not surgical management of these patients except in cases in which the complications of sinusitis develop. The fact that computed tomography is poor at diagnosing sinusitis in children is also a recurring theme.

Background: Several texts detail the possible complications of nasal reconstruction but few critically describe a series. We present an audit of the complications of 95 major nasal reconstructions. Methods: This is a retrospective analysis of 95 consecutive major nasal reconstructions (49 male, 46 female, aged between 4 and 92 years) over a 16-year period treated in a tertiary referral centre. Results: Fifty-eight patients in our series required nasal reconstruction after Moh's Micrographic surgery for a morpheiform basal cell or a squamous cell carcinoma. Eight patients had further removal using frozen section to determine the margins, as the disease was very extensive. Minor procedures with local random flaps or full thickness skin grafts were excluded. Ninety-five patients underwent major reconstructive procedures and had an average of 2.8 operations. Summary: The main problem was the suboptimal contouring of the alar margin in 6 of 54 patients who had a full thickness defect repaired. Other complications included telangectasia or hair growth requiring laser ablation (13), stenosis of the nasal valve area (2), ectropion after a cheek advancement flap, donor site haematoma of the pinna, and neuropathic pain.

What should a rheumatologist know about nasal disorders? In a significant minority of patients with rheumatological disorders nasal symptoms and signs are part of the picture and can help in establishing a diagnosis. To illustrate this point, over 50% of patients with Wegener’s granulomatosis (WG) have nasal symptoms and signs at presentation and these have a positive predictive value of 63%, while a positive nasal biopsy has a positive predictive factor of 100%.1 The most common nasal symptoms in these patients are nasal obstruction and discharge but it is the unusual symptom of crusting that should alert the clinician and helps differentiate these patients from those with other more common rhinological conditions. (This is important as approximately 19% of most populations have nasal symptoms and nasal obstruction as part of their rhinosinusitis.2 The majority have seasonal allergic rhinitis (16%) while the remainder have perennial allergic rhinitis, chronic infective rhinosinusitis, idiopathic rhinitis or systemic disease that affects the nose.3) Apart from nasal crusting or progressive bilateral nasal obstruction, other symptoms of WG include a minor epistaxis, whistling if there is a septal perforation, a reduction in sense of smell, and through this taste. You do not need any sophisticated equipment to inspect the nasal mucosa. An otoscope with a large speculum allows a good view of the nasal mucosa as long as the patient is asked to breathe through their mouth to avoid condensation on the lens. Alternatively elevate the tip of the nose upwards with your thumb and inspect the anterior third of the nose with the light from a pen torch. …