Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response. Please read the corrigendum first before accessing the articl

Drift-dependent changes in iceberg size-frequency distributions

Although the size-frequency distributions of icebergs can provide insight into how they disintegrate, our understanding of this process is incomplete. Fundamentally, there is a discrepancy between iceberg power-law size-frequency distributions observed at glacial calving fronts and lognormal size-frequency distributions observed globally within open waters that remains unexplained. Here we use passive seismic monitoring to examine mechanisms of iceberg disintegration as a function of drift. Our results indicate that the shift in the size-frequency distribution of iceberg sizes observed is a product of fracture-driven iceberg disintegration and dimensional reductions through melting. We suggest that changes in the characteristic size-frequency scaling of icebergs can be explained by the emergence of a dominant set of driving processes of iceberg degradation towards the open ocean. Consequently, the size-frequency distribution required to model iceberg distributions accurately must vary according to distance from the calving front

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response

Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal

Coseismic landslides represent the first stage of a broader cascading sequence of geohazards associated with high-magnitude continental earthquakes, with the subsequent remobilisation of coseismic landslide debris posing a long-term post-seismic legacy in mountain regions. Here, we quantify the controls on the hazard posed by landslide remobilisation and debris runout, and compare the overlap between areas at risk of runout and the pattern of post-seismic landslides and debris flows that actually occurred. Focusing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we show that the extent of the area that could be affected by debris runout remained elevated above coseismic levels 4.5 years after the event. While 150 km2 (0.6% of the study area) was directly impacted by landslides in the earthquake, an additional 614 km2 (2.5%) was left at risk from debris runout, increasing to 777 km2 (3.2%) after the 2019 monsoon. We evaluate how this area evolved by comparing modelled predictions of runout from coseismic landslides to multi-temporal post-seismic landslide inventories, and find that 14% (85 km2) of the total modelled potential runout area experienced landslide activity within 4.5 years after the earthquake. This value increases to 32% when modelled runout probability is thresholded, equivalent to 10 km2 of realised runout from a remaining modelled area of 32 km2. Although the proportion of the modelled runout area from coseismic landslides that remains a hazard has decreased through time, the overall runout susceptibility for the study area remains high. This indicates that runout potential is changing both spatially and temporally as a result of changes to the landslide distribution after the earthquake. These findings are particularly important for understanding evolving patterns of cascading hazards following large earthquakes, which is crucial for guiding decision-making associated with post-seismic recovery and reconstruction

Targeting Huntingtin expression in patients with Huntington's disease

Background Huntington’s disease is an autosomal-dominant neurodegenerative disease caused by CAG trinucleotide repeat expansion in HTT, resulting in a mutant huntingtin protein. IONIS-HTTRx (hereafter, HTTRx) is an antisense oligonucleotide designed to inhibit HTT messenger RNA and thereby reduce concentrations of mutant huntingtin. Methods We conducted a randomized, double-blind, multiple-ascending-dose, phase 1–2a trial involving adults with early Huntington’s disease. Patients were randomly assigned in a 3:1 ratio to receive HTTRx or placebo as a bolus intrathecal administration every 4 weeks for four doses. Dose selection was guided by a preclinical model in mice and nonhuman primates that related dose level to reduction in the concentration of huntingtin. The primary end point was safety. The secondary end point was HTTRx pharmacokinetics in cerebrospinal fluid (CSF). Prespecified exploratory end points included the concentration of mutant huntingtin in CSF. Results Of the 46 patients who were enrolled in the trial, 34 were randomly assigned to receive HTTRx (at ascending dose levels of 10 to 120 mg) and 12 were randomly assigned to receive placebo. Each patient received all four doses and completed the trial. Adverse events, all of grade 1 or 2, were reported in 98% of the patients. No serious adverse events were seen in HTTRx-treated patients. There were no clinically relevant adverse changes in laboratory variables. Predose (trough) concentrations of HTTRx in CSF showed dose dependence up to doses of 60 mg. HTTRx treatment resulted in a dose-dependent reduction in the concentration of mutant huntingtin in CSF (mean percentage change from baseline, 10% in the placebo group and −20%, −25%, −28%, −42%, and −38% in the HTTRx 10-mg, 30-mg, 60-mg, 90-mg, and 120-mg dose groups, respectively). Conclusions Intrathecal administration of HTTRx to patients with early Huntington’s disease was not accompanied by serious adverse events. We observed dose-dependent reductions in concentrations of mutant huntingtin. (Funded by Ionis Pharmaceuticals and F. Hoffmann–La Roche; ClinicalTrials.gov number, NCT02519036.

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/ by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.This study was supported in the Netherlands by two Memorabel grants from Deltaplan Dementie (The Netherlands Organisation for Health Research and Development and Alzheimer Nederland; grant numbers 733050813,733050103 and 733050513), the Bluefield Project to Cure Frontotemporal Dementia, the Dioraphte foundation (grant number 1402 1300), the European Joint Programme—Neurodegenerative Disease Research and the Netherlands Organisation for Health Research and Development (PreFrontALS: 733051042, RiMod-FTD: 733051024); V.V. and S.K. have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 666992 (EuroPOND). E.B. was supported by the Hartstichting (PPP Allowance, 2018B011); in Belgium by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie; in the UK by the MRC UK GENFI grant (MR/M023664/1); J.D.R. is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH); I.J.S. is supported by the Alzheimer’s Association; J.B.R. is supported by the Wellcome Trust (103838); in Spain by the Fundació Marató de TV3 (20143810 to R.S.V.); in Germany by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198) and by grant 779357 ‘Solve-RD’ from the Horizon 2020 Research and Innovation Programme (to MS); in Sweden by grants from the Swedish FTD Initiative funded by the Schörling Foundation, grants from JPND PreFrontALS Swedish Research Council (VR) 529–2014-7504, Swedish Research Council (VR) 2015–02926, Swedish Research Council (VR) 2018–02754, Swedish Brain Foundation, Swedish Alzheimer Foundation, Stockholm County Council ALF, Swedish Demensfonden, Stohnes foundation, Gamla Tjänarinnor, Karolinska Institutet Doctoral Funding and StratNeuro. H.Z. is a Wallenberg Scholar.info:eu-repo/semantics/publishedVersio

Altered plasma protein profiles in genetic FTD – a GENFI study

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Plasma biomarkers reflecting the pathology of frontotemporal dementia would add significant value to clinical practice, to the design and implementation of treatment trials as well as our understanding of disease mechanisms. The aim of this study was to explore the levels of multiple plasma proteins in individuals from families with genetic frontotemporal dementia. Methods: Blood samples from 693 participants in the GENetic Frontotemporal Dementia Initiative study were analysed using a multiplexed antibody array targeting 158 proteins. Results: We found 13 elevated proteins in symptomatic mutation carriers, when comparing plasma levels from people diagnosed with genetic FTD to healthy non-mutation controls and 10 proteins that were elevated compared to presymptomatic mutation carriers. Conclusion: We identified plasma proteins with altered levels in symptomatic mutation carriers compared to non-carrier controls as well as to presymptomatic mutation carriers. Further investigations are needed to elucidate their potential as fluid biomarkers of the disease process.Open access funding provided by Karolinska Institute. C.G. received funding from EU Joint Programme—Neurodegenerative Disease Research -Prefrontals Vetenskapsrådet Dnr 529–2014-7504, Vetenskapsrådet 2015–02926, Vetenskapsrådet 2018–02754, the Swedish FTD Inititative-Schörling Foundation, Alzheimer Foundation, Brain Foundation, Dementia Foundation and Region Stockholm ALF-project. PN received funding from KTH Center for Applied Precision Medicine (KCAP) funded by the Erling-Persson Family Foundation, the Swedish FTD Inititative-Schörling Foundation and Åhlén foundation. D.G. received support from the EU Joint Programme—Neurodegenerative Disease Research and the Italian Ministry of Health (PreFrontALS) grant 733051042. E.F. has received funding from a Canadian Institute of Health Research grant #327387. F.M. received funding from the Tau Consortium and the Center for Networked Biomedical Research on Neurodegenerative Disease. J.B.R. has received funding from the Welcome Trust (103838) and is supported by the Cambridge University Centre for Frontotemporal Dementia, the Medical Research Council (SUAG/051 G101400) and the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215–20014). J.C.V.S. was supported by the Dioraphte Foundation grant 09–02-03–00, Association for Frontotemporal Dementias Research Grant 2009, Netherlands Organization for Scientific Research grant HCMI 056–13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland and the Bluefield Project. J.D.R. is supported by the Bluefield Project and the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and a Miriam Marks Brain Research UK Senior Fellowship. M.M. has received funding from a Canadian Institute of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. M.O. has received funding from Germany’s Federal Ministry of Education and Research (BMBF). R.S-V. is supported by Alzheimer’s Research UK Clinical Research Training Fellowship (ARUK-CRF2017B-2) and has received funding from Fundació Marató de TV3, Spain (grant no. 20143810). R.V. has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. This work was also supported by the EU Joint Programme—Neurodegenerative Disease Research GENFI-PROX grant [2019–02248; to J.D.R., M.O., B.B., C.G., J.C.V.S. and M.S.info:eu-repo/semantics/publishedVersio