Roles of neuronal lysosomes in the etiology of Parkinson’s disease

Therapeutic progress in neurodegenerative conditions such as Parkinson’s disease has been hampered by a lack of detailed knowledge of its molecular etiology.The advancements in genetics and genomics have provided fundamental insights into specific protein players and the cellular processes involved in the onset of disease.In this respect,the autophagy-lysosome system has emerged in recent years as a strong point of convergence for genetics,genomics,and pathologic indications,spanning both familial and idiopathic Parkinson’s disease.Most,if not all,genes linked to familial disease are involved,in a regulatory capacity,in lysosome function(e.g.,LRRK2,alpha-synuclein,VPS35,Parkin,and PINK1).Moreover,the majority of genomic loci associated with increased risk of idiopathic Parkinson’s cluster in lysosome biology and regulation(GBA as the prime example).Lastly,neuropathologic evidence showed alterations in lysosome markers in autoptic material that,coupled to the alpha-synuclein proteinopathy that defines the disease,strongly indicate an alteration in functionality.In this Brief Review article,I present a personal perspective on the molecular and cellular involvement of lysosome biology in Parkinson’s pathogenesis,aiming at a larger vision on the events underlying the onset of the disease.The attempts at targeting autophagy for therapeutic purposes in Parkinson’s have been mostly aimed at“indiscriminately”enhancing its activity to promote the degradation and elimination of aggregate protein accumulations,such as alpha-synuclein Lewy bodies.However,this approach is based on the assumption that protein pathology is the root cause of disease,while pre-pathology and pre-degeneration dysfunctions have been largely observed in clinical and pre-clinical settings.In addition,it has been reported that unspecific boosting of autophagy can be detrimental.Thus,it is important to understand the mechanisms of specific autophagy forms and,even more,the adjustment of specific lysosome functionalities.Indeed,lysosomes exert fine signaling capacities in addition to their catabolic roles and might participate in the regulation of neuronal and glial cell functions.Here,I discuss hypotheses on these possible mechanisms,their links with etiologic and risk factors for Parkinson’s disease,and how they could be targeted for disease-modifying purposes.

Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility

In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.

Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp

This study aimed to produce a high-quality landslide susceptibility map for Teziutlán municipality, a landslide-prone region in Mexico, which is characterised by a depositional pyroclastic ramp. The heterogeneous quality of available topographic information(i.e. higher resolution digital elevation model only for a sub-region) encouraged to confront modelling results based on two different study area delineations and two raster resolutions. Input data was based on the larger modelling region L15(163 km2) and smaller S(70 km2; located inside L15) with an associated raster cell size of 15 m(region L15 and S15) and 5 m(region S5). The resulting three data sets(L15, S15 and S5) were included into three differently flexible modelling techniques(Generalized Linear Model-GLM, General Additive Model-GAM, Support Vector Machine-SVM) to produce nine landslide susceptibility models. Preceding variable selection was performed heuristically and supported by an exploratory data analysis. The final models were based on the explanatory variables slope angle, slope aspect, lithology, relative slope position, elevation, convergence index, distance to streams, distance to springs and topographic wetness index. The ability of the models to classify independent test data was elaborated using a k-fold cross validation procedure and the AUROC(Area Under the Receiver Operating Characteristic) metric. In general, all produced landslide susceptibility maps depicted the hillslopes of the ravines, which cut the pyroclastic ramp, as prone to landsliding. The modelling results showed that predictive performances(i.e. AUROC values) slightly increased with an increasing flexibility of the applied modelling technique. Thus, SVM performed best, while the GAM outperformed the GLM. This tendency was most distinctive when modelling with the largest landslide sample size(i.e. data set L15; n = 662 landslides). Non-linear classifiers(GAMs, SVMs) performed slightly better when trained on the basis of lower raster resolution(data set S15) compared to the 5 m counterparts(data set S5). Highest predictive performance was obtained for the model based on data set L15 and the SVM classifier(median AUROC: 0.82). However, SVMs also indicated the highest degree of model overfitting. This study indicates that the decision to delineate a study area, the selection of a raster resolution as well as the chosen classification technique can affect varying aspects of subsequent modelling results. The results do not support the assumption that a higher raster resolution(i.e. a more detailed digital representation of the terrain) inevitably leads to better performing or geomorphically more plausible landslide susceptibility maps.

SLP-2: a potential new target for improving mitochondrial function in Parkinson's disease

Parkinson＇s disease （PD） is a progressive neurodegenerative disease, which is generally considered a multifactorial disorder that arises owing to a combination of genes and environmental factors. While most cases are idiopathic, in about 10% of the patients a genetic cause can be detected, ascribable to mutations in more than a dozen genes. PD is characterized clinically by tremor, rigidity, reduced mo- tor activity （bradykinesia）, and postural instability and pathological- ly by loss of dopaminergic （DA） neurons in the substantia nigra pars compacta, loss of DA innervation in the striatum, and the presence of a-synuclein positive aggregates in the form of Lewy bodies. The symptomatic treatment of PD with levodopa, which aims at replac- ing dopamine, remains the gold standard, and no neuroprotective or disease-modifying therapy is available. During treatment, the disease continues to progress, and long-term use of levodopa has import- ant limitations including motor complications termed dyskinesias. Therefore, a pharmacological therapy able to prevent or halt the neu- rodegenerative process is urgently required.

Land Surface Displacement Geohazards Monitoring Using Multi-temporal InSAR Techniques

China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various geohazards.Therefore,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination studies.Suspicious movements detected in the different study areas were cross validated with different SAR sensors and truth data.

Helicobacter pylori in ancient human remains

The bacterium Helicobacter pylori(H.pylori)infects the stomachs of approximately 50%of all humans.With its universal occurrence,high infectivity and virulence properties it is considered as one of the most severe global burdens of modern humankind.It has accompanied humans for many thousands of years,and due to its high genetic variability and vertical transmission,its population genetics reflects the history of human migrations.However,especially complex demographic events such as the colonisation of Europe cannot be resolved with population genetic analysis of modern H.pylori strains alone.This is best exemplified with the reconstruction of the 5300-year-old H.pylori genome of the Iceman,a European Copper Age mummy.Our analysis provided precious insights into the ancestry and evolution of the pathogen and underlined the high complexity of ancient European population history.In this review we will provide an overview on the molecular analysis of H.pylori in mummified human remains that were done so far and we will outline methodological advancements in the field of ancient DNA research that support the reconstruction and authentication of ancient H.pylori genome sequences.

Adopting the margin of stability for space–time landslide prediction–A data-driven approach for generating spatial dynamic thresholds

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge.Notably,the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions.The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context.In the currently evolving field of space–time landslide modelling,we recommend focusing on data error handling,model interpretability,and geomorphic plausibility,rather than allocating excessive resources to algorithm and case study comparisons.

Water budgets in an arid and alpine permafrost basin:Observations from the High Mountain Asia

Ground freeze-thaw processes have significant impacts on infiltration,runoff and evapotranspiration.However,there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions,especially of the interactions among permafrost,ecology,and hydrology.In this study,an alpine permafrost basin on the northeastern Qinghai-Tibet Plateau was selected to conduct hydrological and meteorological observations.We analyzed the annual variations in runoff,precipitation,evapotranspiration,and changes in water storage,as well as the mechanisms for runoff gen-eration in the basin from May 2014 to December 2015.The annual flow curve in the basin exhibited peaks both in spring and autumn floods.The high ratio of evapotranspiration to annual precipitation(>1.O)in the investigated wetland is mainly due to the considerably underestimated‘observed'precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation.The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands.This study can provide data support and validation for hydrological model simulation and prediction,as well as water resource assessment,in the upper Yellow River Basin,especially for the headwater area.The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Changes in hydrological processes in the headwater area of Yellow River,China during 1956-2019 under the influences of climate change,permafrost thaw and dam

Discharge characteristics are crucial for detecting changes in hydrological processes.Recently,the river hydrology)in the Headwater Area of the Yellow River(HAYR)has exhibited erratic regimes(e.g.,monotonously declining/low/high hydrograph,even with normal precipitation)under the effects of climate change,permafrost thaw and changes in dam operation.This study integrates hydroclimatic variables(air temperature,precipitation,and potential evapotranspiration)with anthropogenic dam operation and permafrost degradation impact data to systematically examine the mechanisms of these hydrological process changes during 1956–2019.The results show the following:1)compared with the pre-dammed gauged flow,dam construction(January 1998–January 2000)and removal of dam(September 2018–August 2019)induced monotonously low(−17.2 m^(3) s^(−1);−61%)and high(+54.6 m^(3) s^(−1);+138%)hydrographs,respectively;2)hydroclimatic variables mainly controlled the summer–autumn hydrological processes in the HAYR;3)the monotonous decline of the hydrograph of Yellow River in the HAYR in some hydrological years(e.g.,1977,1979,1990 and 1995)was closely related with unusually high atmospheric demands of evaporation and low-intense rainfall during summer–autumn seasons;and 4)the lengthening of subsurface hydrological pathways and residence time,permafrost degradation reduced the recession coefficient(−0.002 per year)of winter flow and altered the hydrological regimes of seasonal rivers,which resulted in flattened hydrographs that reduced and delayed the peak flow(of 0.05 mm per year and 1.65 d per year,respectively)as well as boosted the winter baseflow(0.01 mm per year).This study can provide updated and systematic understanding of changing hydrological processes in typical alpine catchments on northeastern Qinghai–Tibet Plateau,China under a warming climate.

Beyond the Expected-Residual Risk and Cases of Overload in the Context of Managing Alpine Natural Hazards

Structural protection measures are designed to protect the population and infrastructure against natural hazards up to a specific predefined protection goal.Extreme events with intensities that exceed the capacity of these protection structures are called“cases of overload”and are associated with“residual risks”that remain after the implementation of protection measures.In order to address residual risks and to reduce the damages from overload events,a combination of structural protection measures with additional,nonstructural measures is required.Based on data collected through a literature review,a questionnaire survey,expert interviews,and an expert workshop we highlight the status quo as well as key challenges of dealing with residual risks and cases of overload in Alpine countries in the context of geohydrological hazards and gravitational mass movements.We present a holistic conceptual framework that describes the relationships of residual risks,cases of overload,and protection goals in the context of both risk governance and integrated risk management.This framework is valuable for decision makers aiming at an improved management of natural hazards that takes adequate account of residual risk and cases of overload in Alpine countries and mountain areas worldwide.