Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R <sup>2</sup> = 0.87), fatty infiltration (R <sup>2</sup> = 0.91), and overall muscle degeneration (R <sup>2</sup> = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R <sup>2</sup> = 0.61) than human raters (R <sup>2</sup> = 0.87). Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters

Tracking icebergs with time-lapse photography and sparse optical flow, LeConte Bay, Alaska, 2016–2017

We present a workflow to track icebergs in proglacial fjords using oblique time-lapse photos and the Lucas-Kanade optical flow algorithm. We employ the workflow at LeConte Bay, Alaska, where we ran five time-lapse cameras between April 2016 and September 2017, capturing more than 400 000 photos at frame rates of 0.5–4.0 min−1. Hourly to daily average velocity fields in map coordinates illustrate dynamic currents in the bay, with dominant downfjord velocities (exceeding 0.5 m s−1 intermittently) and several eddies. Comparisons with simultaneous Acoustic Doppler Current Profiler (ADCP) measurements yield best agreement for the uppermost ADCP levels (∼ 12 m and above), in line with prevalent small icebergs that trace near-surface currents. Tracking results from multiple cameras compare favorably, although cameras with lower frame rates (0.5 min−1) tend to underestimate high flow speeds. Tests to determine requisite temporal and spatial image resolution confirm the importance of high image frame rates, while spatial resolution is of secondary importance. Application of our procedure to other fjords will be successful if iceberg concentrations are high enough and if the camera frame rates are sufficiently rapid (at least 1 min−1 for conditions similar to LeConte Bay).This work was funded by the U.S. National Science Foundation (OPP-1503910, OPP-1504288, OPP-1504521 and OPP-1504191).Ye

An automated microreactor for semi-continuous biosensor measurements.

Living bacteria or yeast cells are frequently used as bioreporters for the detection of specific chemical analytes or conditions of sample toxicity. In particular, bacteria or yeast equipped with synthetic gene circuitry that allows the production of a reliable non-cognate signal (e.g., fluorescent protein or bioluminescence) in response to a defined target make robust and flexible analytical platforms. We report here how bacterial cells expressing a fluorescence reporter ("bactosensors"), which are mostly used for batch sample analysis, can be deployed for automated semi-continuous target analysis in a single concise biochip. Escherichia coli-based bactosensor cells were continuously grown in a 13 or 50 nanoliter-volume reactor on a two-layered polydimethylsiloxane-on-glass microfluidic chip. Physiologically active cells were directed from the nl-reactor to a dedicated sample exposure area, where they were concentrated and reacted in 40 minutes with the target chemical by localized emission of the fluorescent reporter signal. We demonstrate the functioning of the bactosensor-chip by the automated detection of 50 μgarsenite-As l(-1) in water on consecutive days and after a one-week constant operation. Best induction of the bactosensors of 6-9-fold to 50 μg l(-1) was found at an apparent dilution rate of 0.12 h(-1) in the 50 nl microreactor. The bactosensor chip principle could be widely applicable to construct automated monitoring devices for a variety of targets in different environments

Thermostable designed ankyrin repeat proteins (DARPins) as building blocks for innovative drugs

Designed ankyrin repeat proteins (DARPins) are antibody mimetics with high and mostly unexplored potential in drug development. By using in silico analysis and a rationally guided Ala scanning, we identified position 17 of the N-terminal capping repeat to play a key role in overall protein thermostability. The melting temperature of a DARPin domain with a single full-consensus internal repeat was increased by 8 °C to 10 °C when Asp17 was replaced by Leu, Val, Ile, Met, Ala, or Thr. We then transferred the Asp17Leu mutation to various backgrounds, including clinically validated DARPin domains, such as the vascular endothelial growth factor-binding domain of the DARPin abicipar pegol. In all cases, these proteins showed improvements in the thermostability on the order of 8 °C to 16 °C, suggesting the replacement of Asp17 could be generically applicable to this drug class. Molecular dynamics simulations showed that the Asp17Leu mutation reduces electrostatic repulsion and improves van-der-Waals packing, rendering the DARPin domain less flexible and more stable. Interestingly, this beneficial Asp17Leu mutation is present in the N-terminal caps of three of the five DARPin domains of ensovibep, a SARS-CoV-2 entry inhibitor currently in clinical development, indicating this mutation could be partly responsible for the very high melting temperature (>90 °C) of this promising anti-COVID-19 drug. Overall, such N-terminal capping repeats with increased thermostability seem to be beneficial for the development of innovative drugs based on DARPins. Keywords: DARPin; N-terminal capping repeat; abicipar pegol; designed ankyrin repeat protein; drug development; drug engineering; ensovibep; molecular dynamics; thermostabilit

Buildings behaving badly:A behavioral experiment on how different motivational frames influence residential energy label adoption in the Netherlands

Heating buildings contributes to approximately 36% of Europe’s energy demand and several EU member states have adopted mandatory energy labels to improve energy efficiency by promoting home weatherization investments. This paper focuses on the perception of the energy label for residential buildings in the Netherlands and the role of different frames (egoistic, biospheric and social norms and neutral frames) in motivating adoption of energy labels for housing. We used a behavioral email experiment and an online survey to investigate these motivational factors. We find that biospheric frames are weaker than the other three motivational frames in terms of engaging interest in the energy label, but that the biospheric frame results in higher willingness to pay (WTP) for the energy label. We also find that age (rather than income) correlates with higher willingness to pay for home energy labels

Environmental Impact Assessment and Strategic Environmental Assessment in the UK after leaving the European Union

The United Kingdom has voted to leave the European Union and, until the terms of the ‘Brexit’ are negotiated, this has led to considerable uncertainty over the future practice of Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) in the UK. Here we show that multiple obligations exist outside the scope of the EU which mean that EIA and SEA will continue to be required in the long-term, but that their future compliance with the Directives remains unclear. We consider three scenarios for Brexit and present the implications of each; these are: signing up to the European Economic Area (EEA) Agreement; membership of the European Free Trade Association (EFTA), but not EEA, or negotiate a separate agreement. The implications of no longer being subjected to the obligations of the Directives under some scenarios are discussed and include opening the door for increasing diversity of application across the regions of the UK, and the probability of raised screening thresholds so as to reduce the burden of assessment on developers

Non-linear glacier response to calving events, Jakobshavn Isbræ, Greenland

Jakobshavn Isbræ, a tidewater glacier that produces some of Greenland’s largest icebergs and highest speeds, reached record-high flow rates in 2012 (Joughin and others, 2014). We use terrestrial radar interferometric observations from August 2012 to characterize the events that led to record-high flow.Jakobshavn Isbræ, a tidewater glacier that produces some of Greenland’s largest icebergs and highest speeds, reached record-high flow rates in 2012 (Joughin and others, 2014). We use terrestrial radar interferometric observations from August 2012 to characterize the events that led to record-high flow. We find that the highest speeds occurred in response to a small calving retreat, while several larger calving events produced negligible changes in glacier speed. This non-linear response to calving events suggests the terminus was close to flotation and therefore highly sensitive to terminus position. Our observations indicate that a glacier’s response to calving is a consequence of two competing feedbacks: (1) an increase in strain rates that leads to dynamic thinning and faster flow, thereby promoting desta- bilization, and (2) an increase in flow rates that advects thick ice toward the terminus and promotes restabilization. The competition between these feedbacks depends on temporal and spatial variations in the glacier’s proximity to flotation. This study highlights the importance of dynamic thinning and advective processes on tidewater glacier stability, and further suggests the latter may be limiting the current retreat due to the thick ice that occupies Jakobshavn Isbræ’s retrograde bed.We are grateful to many people and organizations that sup- ported this project. TRIs were purchased with funds from the Gordon and Betty Moore Foundation (GBMF2627). Field work was completed through NASA (NNX08AN74G). Cassotto was supported by the New Hampshire Space Grant Consortium (NNX10AL97H) and later by a NASA Earth and Space Science Fellowship Program (NNX14AL29H). We thank CH2 M HILL Polar Services and Air Greenland for logistics support, and Judy McIlrath and Denis Voytenko for assistance in the field. Landsat imagery courtesy of the US Geological Survey. Joe Licciardi, Tim Bartholomaus and an anonymous reviewer provided valu- able insight that improved this manuscript. Data are available upon request by contacting the primary author.Ye

Exposure-age record of Holocene ice sheet and ice shelf change in the northeast Antarctic Peninsula

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Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry

Melt from supraglacial ice cliffs is an important contributor to the mass loss of debris-covered glaciers. However, ice cliff contribution is difficult to quantify as they are highly dynamic features, and the paucity of observations of melt rates and their variability leads to large modelling uncertainties. We quantify monsoon season melt and 3D evolution of four ice cliffs over two debris-covered glaciers in High Mountain Asia (Langtang Glacier, Nepal, and 24K Glacier, China) at very high resolution using terrestrial photogrammetry applied to imagery captured from time-lapse cameras installed on lateral moraines. We derive weekly flow-corrected digital elevation models (DEMs) of the glacier surface with a maximum vertical bias of ±0.2 m for Langtang Glacier and ±0.05 m for 24K Glacier and use change detection to determine distributed melt rates at the surfaces of the ice cliffs throughout the study period. We compare the measured melt patterns with those derived from a 3D energy balance model to derive the contribution of the main energy fluxes. We find that ice cliff melt varies considerably throughout the melt season, with maximum melt rates of 5 to 8 cm d−1, and their average melt rates are 11–14 (Langtang) and 4.5 (24K) times higher than the surrounding debris-covered ice. Our results highlight the influence of redistributed supraglacial debris on cliff melt. At both sites, ice cliff albedo is influenced by the presence of thin debris at the ice cliff surface, which is largely controlled on 24K Glacier by liquid precipitation events that wash away this debris. Slightly thicker or patchy debris reduces melt by 1–3 cm d−1 at all sites. Ultimately, our observations show a strong spatio-temporal variability in cliff area at each site, which is controlled by supraglacial streams and ponds and englacial cavities that promote debris slope destabilisation and the lateral expansion of the cliffs. These findings highlight the need to better represent processes of debris redistribution in ice cliff models, to in turn improve estimates of ice cliff contribution to glacier melt and the long-term geomorphological evolution of debris-covered glacier surfaces