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A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app-SVCurator-to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.

The properties of laser signals are affected by deformation of the optical fibre through which they are transmitted. While this deformation dependence is undesirable in telecommunication, it can be exploited for the construction of novel seismic sensors that fill a niche in data acquisition where traditional seismometer arrays would be difficult to deploy. This includes densely populated urban centers, the oceans, volcanoes and the Earth’s polar regions. These notes complement a presentation on recent methodological developments and applications in fibre-optic seismology. The first part is focused on the use of distributed fibre-optic sensing in cryosphere research, and specifically the investigation of the internal structure and seismicity of glaciers and ice sheets. The second part is dedicated to recent advances in integrated fibre-optic sensing, with emphasis on novel measurement principles and sensitivity.

Abstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

We present a distributed acoustic sensing (DAS) experiment at Grímsvötn, Iceland. This is intended to investigate volcano-microseismicity at Grímsvötn specifically, and to assess the suitability of DAS as a subglacial volcano monitoring tool in general. In spring 2021, we trenched a 12 km long fiber-optic cable into the ice sheet around and within the caldera, followed by nearly one month of continuous recording. An image processing algorithm that exploits spatial coherence in DAS data detects on average ~100 events per day, almost 2 orders of magnitude more than in the regional earthquake catalog. A nonlinear Bayesian inversion reveals the presence of pronounced seismicity clusters, containing events with magnitudes between −3.4 and 1.7. Their close proximity to surface volcanic features suggests a geothermal origin. In addition to painting a fine-scale picture of seismic activity at Grímsvötn, this work confirms the potential of DAS in subglacial volcano monitoring.

Entirely covered by the Vatnajökull ice cap, Grímsvötn is among Iceland’s largest and most hazardous volcanoes. Here we demonstrate that fiber-optic sensing technology deployed on a natural floating ice resonator can detect volcanic tremor at the level of few nanostrain/s, thereby enabling a new mode of subglacial volcano monitoring under harsh conditions. A 12.5 km long fiber-optic cable deployed on Grímsvötn in May 2021 revealed a high level of local earthquake activity, superimposed onto nearly monochromatic oscillations of the caldera. High data quality combined with dense spatial sampling identify these oscillations as flexural gravity wave resonance of the ice sheet that floats atop a subglacial lake. Although being affected by the ambient wavefield, the time–frequency characteristics of observed caldera resonance require the presence of an additional persistent driving force with temporal variations over several days, that is most plausibly explained in terms of low-frequency volcanic tremor. In addition to demonstrating the logistical feasibility of installing a large, high-quality fiber-optic sensing network in a subarctic environment, our experiment shows that ice sheet resonance may act as a natural amplifier of otherwise undetectable (volcanic) signals. This suggests that similar resonators might be used in a targeted fashion to improve monitoring of ice-covered volcanic systems.

Aging involves gradual loss of tissue function, and transcription factor (TF) expression can ameliorate this in progeroid mice. Here the authors show that transient TF expression reverses age-associated epigenetic marks, inflammatory profiles and restores regenerative potential in naturally aged human cells.

Volumetric muscle loss leads to functional muscle impairment, and current stem cell-based treatments show limited efficacy. Here, the authors generate a stem cell scaffold, implant it in mice, and show that an exercise regimen enhances innervation and restoration of muscle function in mice.

Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self-noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors.

Observations of glacier melt and runoff are of fundamental interest in the study of glaciers and their interactions with their environment. Considerable recent interest has developed around distributed acoustic sensing (DAS), a sensing technique which utilizes Rayleigh backscatter in fiber optic cables to measure the seismo-acoustic wavefield in high spatial and temporal resolution. Here, we present data from a month-long, 9 km DAS deployment extending through the ablation and accumulation zones on Rhonegletscher, Switzerland, during the 2020 melt season. While testing several types of machine learning (ML) models, we establish a regression problem, using the DAS data as the dependent variable, to infer the glacier discharge observed at a proglacial stream gauge. We also compare two predictive models that only depend on meteorological station data. We find that the seismo-acoustic wavefield recorded by DAS can be utilized to infer proglacial discharge. Models using DAS data outperform the two models trained on meteorological data with mean absolute errors of 0.64, 2.25 and 2.72 m3 s−1, respectively. This study demonstrates the ability of in situ glacier DAS to be used for quantifying proglacial discharge and points the way to a new approach to measuring glacier runoff.