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Background: Hypophosphatasia (HPP) is a rare congenital disease caused by a mutation affecting tissue nonspecific alkaline phosphatase, an enzyme involved in phosphate metabolism. The clinical manifestation usually includes bone mineralization disorders, neurological symptoms, and persistent muscle pain. Case Report: This case involves a woman in her sixties of Central European descent who suffers from lifelong chronic pain and muscle weakness due to HPP and concomitant degenerative changes of the lumbar spine. The patient is physically impaired and limited in her ability to walk as a result. HPP-specific and guideline-based multimodal pain management including enzyme replacement therapy with asfotase alfa, opioids, invasive orthopedic and neurosurgical procedures, long-term physiotherapy, and psychotherapy did not yield sufficient treatment results. The average pain was given as 8.5 on a numerical rating scale (NRS, 0–10) for the last 3 years. Treatment with a cannabidiol-predominant, full-spectrum, prescription cannabis extract led to a clinically meaningful pain reduction to 2.5/10 NRS, a discontinuation of opioids, and a recent resumption of employment as a physician. Conclusion: A more widespread consideration of medical cannabinoids in the treatment of complex chronic pain is proposed. Cannabinoids may pose a particularly potent treatment option for HPP-related symptoms and inflammation due to their known anti-inflammatory properties.

Preliminary clinical data suggest that pain reduction through fasting may be effective for different diagnoses. This uncontrolled observational clinical study examined the effects of prolonged modified fasting on pain and functional parameters in hip and knee osteoarthritis. Patients admitted to the inpatient department of Internal Medicine and Nature-based Therapies of the Immanuel Hospital Berlin between February 2018 and December 2020 answered questionnaires at the beginning and end of inpatient treatment, as well as at 3, 6, and 12 months after discharge. Additionally, selected blood and anthropometric parameters, as well as subjective pain ratings, were routinely assessed during the inpatient stay. Fasting was the only common intervention for all patients, being performed as part of a multimodal integrative treatment program, with a daily caloric intake of

Preliminary clinical data suggest pain reduction through fasting in different diagnoses. This uncontrolled observational clinical study examined the effects of prolonged modified fasting on pain and functional parameters in hip and knee osteoarthritis. Patients admitted to the inpatient department of Internal Medicine and Nature-based Therapies of the Immanuel Hospital Berlin between February 2018 and March 2020, answered questionnaires at the beginning and end of inpatient treatment, as well as 3, 6 and 12 months after discharge. Additionally, selected blood and anthropometric parameters were routinely assessed during the inpatient stay. Fasting was performed as part of a multimodal integrative treatment program, with daily caloric intake of

Simulation tools are important to investigate and predict mobility and the destructive potential of gravitational mass flows (e.g. snow avalanches). AvaFrame – the open avalanche framework – offers well established computational modeling approaches, tools for data handling and analysis as well as ready to use modules for evaluation and testing. This paper presents the theoretical background, derivation and model verification for one of AvaFrame’s core modules, the thickness integrated computational model for dense flow avalanches, named com1DFA. Particular emphasis within the description of the utilized numerical particle grid method is given to the computation of spatial gradients and the accurate implementation of driving and resisting forces. The implemented method allows to provide a time-space criterion connecting the numerical particles, grid and time discretization. The convergence and robustness of the numerical implementation is checked with respect to the spatio-temporal evolution of the flow variables using tests with a known analytical solution. In addition we present a new test for verifying the accuracy of the numerical simulation in terms of runout (angle and distance). This test is derived from the total energy balance along the center of mass path of the avalanche. This manuscript, particularly in combination with the code availability (open-source code repository) and detailed online documentation provides a description of an extendable framework for modeling and verification of avalanche simulation tools.

Various facets of global changes and related problems and challenges are asking for sound impact assessments and corresponding coping strategies. The human impact on nature is a major driver of biodiversity loss and restricted ecosystem functioning and services. Assessing such global changes is often done by using biomes as benchmarks. However, even if the wording and terminology seem common sense (‘tropical rain forest’, ‘steppe’, ‘boreal forest’) global biome units and maps deviate in many ways. This is well justified by their individual intention, expert opinions, disciplinary background, and methodology of creation. A closer look reveals linkages between spatial accordance and common origin in climate classifications and maps. Their original influence, however, is rarely evident. In consequence, it is difficult if not impossible for users to realize and understand differences in these global maps. Furthermore, it is difficult to accept the fact that there is no common standard for global biomes. Even more surprising is the fact that some approaches are uncritically taken for common sense and are perpetuated over decades. This study aims to review established global biome concepts. Regions that are consistently assigned to comparable types of biomes shall be detected and also regions where ambiguity exists. For this purpose, we shortly review the history of existing concepts and the generic relations between them. Biomes, ecozones and climate classifications are considered. We digitized the most prominent biome classifications. Spatial match and mismatch between concepts were analyzed globally. We detect areas of spatial agreement and regions with ambiguous classifications. A clustering approach including 287 individual biomes originating from 12 established global biome concepts and their classifications/units revealed 12 terrestrial biome clusters among which 8 can be assigned to terrestrial ecological units. One cluster on ice caps adds to this. And finally, 3 clusters represent rather transition zones (ecotones), high mountain plateaus or are of minor areal extent. The spatial arrangement of these emerging clusters is displayed on a global map. Additionally, regions of uncertainty related to class assignment were identified. Those primarily occur in the vicinity of mountainous regions. The findings of this study should be seen as a work in progress and as a basis for further optimization of global biome concepts.

Distress is a growing public health concern. In this three-armed randomized controlled trial, n = 102 adults with elevated stress levels and stress-related symptoms were randomly assigned to (1) “integrative” yoga classes which combined physical exercises, mindfulness training, and ethical/philosophical aspects of traditional yoga; to (2) Iyengar yoga classes which entailed primarily physical exercises; or to (3) mindfulness training without physical training. We hypothesized the synergistic effects of physical yoga exercises, mindfulness, and ethical/philosophical aspects. The primary outcome was the group difference on Cohen’s Perceived Stress Scale (PSS) after 12 weeks. Secondary outcomes included burnout, quality of life, physical complaints, depression, anxiety, mindfulness, interoceptive awareness, self-regulation, spirituality, mysticism, and posttraumatic stress. All outcomes were evaluated at baseline (V0), after 12 weeks (V1), and after 24 weeks (V2). A subset of participants took part in qualitative interviews. A lasting and clinically relevant stress reduction was observed within all groups (PSS ΔV0–V1Integrative Yoga = −6.69 ± 6.19; ΔV0–V1Iyengar Yoga = −6.00 ± 7.37; ΔV0–V1Mindfulness = −9.74 ± 7.80; all p < 0.00). Effect sizes were also statistically large at the end of the follow-up period (Cohen’s d Integrative Yoga = 1.41; d Iyengar Yoga = 1.37; d Mindfulness = 1.23). There were no significant group differences or evidence of relevant synergistic effects from combining mindfulness and physical yoga exercises. All three interventions were found to be equally effective methods of stress reduction. Their use in practice should be based on availability and patient preference.

Introduction Phenylketonuria (PKU) is a rare, treatable inborn error of metabolism with frequent neurological and neuropsychiatric complications, especially in undiagnosed or insufficiently treated individuals. Given the wide range of clinical presentations and the importance of treatment implications, we here delineate the neurological and neuropsychiatric symptom spectrum in a large cohort of previously unreported adults with late-treated PKU. Methods We consecutively evaluated late-treated PKU cases and pooled clinical and paraclinical data, including video-material, from three centers with expertise in complex movement disorders, inborn errors of metabolism and pediatrics. Results 26 individuals were included (10 females, median age 52 years). Developmental delay and intellectual disability were omnipresent with severe impairment of expressive communication noted in 50% of cases. Movement disorders were prevalent (77%), including tremor (38%, mostly postural), stereotypies (38%), and tics (19%). One case had neurodegenerative levodopa-responsive parkinsonism. Mild ataxia was noted in 54% of cases and 31% had a history of seizures. Neuropsychiatric characteristics included obsessive-compulsive (35%) and self-injurious behaviors (31%), anxiety (27%), depression (19%) and features compatible with those observed in individuals with autism spectrum disorder (19%). Neuroimaging revealed mild white matter changes. Adherence to dietary treatment was inconsistent in the majority of cases, particularly throughout adolescence. Conclusion A history of movement disorders, particularly tremor, stereotypies and tics, in the presence of developmental delay, intellectual disability and neuropsychiatric features, such as obsessive-compulsive and self-injurious behaviors in adults should prompt the diagnostic consideration of PKU. Initiation and adherence to (dietary) treatment can ameliorate the severity of these symptoms.

A structured Reynolds-averaged Navier–Stokes solver is directly coupled to a linear stability theory (LST) solver to include the effect of laminar–turbulent transition in the flow simulations. The ...

First approaches to analyze the propagation of hazardous, extremely rapid, landslides go back at least to the 1930s, when data-driven approaches such as angles of reach were used to estimate runout distances. Various much more sophisticated tools for the simulation of flow-type landsides including debris flows or rock avalanches, or cascading effects involving more than one type of phenomenon, have been developed since then, particularly throughout the last two decades. They build on increasingly complex physical-mathematical models, starting from comparatively simple and straightforward, depth-averaged Voellmy-type mixture models, moving to still depth-averaged two- and three-phase models able to simulate the interaction of landslides with water bodies, and currently proceeding to highly complex and highly flexible full 3D models. Phenomena such as erosion, entrainment, deposition, phase separation, or non-hydrostatic effects are increasingly well understood and incorporated into operational mass flow simulation tools.However, this trend of increasing model complexity, supported by increasing physical process understanding and enhanced computational capacities, is not necessarily in line with the demand by natural hazard practitioners, who need capable but easy-to-handle simulation tools for their work. Besides the computational costs, it is mainly the multitude of often unknown, rather conceptual model parameters representing a barrier for practitioners towards using the more or even the most advanced approaches. Further, more complex models do not necessarily provide more accurate results than simpler ones, this always depends on the scope, purpose, and phenomenon. If only estimates of runout distances or impact areas are needed, very simple data-driven models may do the work. Even when it comes to flow thicknesses, velocities, or impact pressures, ordinary debris flows may still be better represented by comparatively simple mixture models than by parameter-hungry two- or three-phase models. Yet, more complex models are needed for more complex processes such as landslide-lake interactions or other types of process chains with dynamically changing material composition. Therefore, intelligent approaches have to be found to find an appropriate balance between simplicity and complexity.Mainly based on seven years of experience with the depth-averaged multi-phase mass flow simulation framework r.avaflow, we will discuss the main challenges and ideas to find a useful balance between simplicity and complexity in the simulation of landslide runout.

With the aim to achieve long-term sustainability of the growing aviation sector, a significant amount of research and development is invested in improving the efficiency of future airframe architectures. This requires advancements in all engineering disciplines, from lightweight materials to beyond state-of-the-art aerodynamic technologies. In light of these developments, a key technology concerned with the reduction of airframe drag has been identified: The extension of the laminar flow regime around aircraft surfaces offers large potential for friction drag reduction. The objective of this work therefore is to advance the current analysis and design capabilities to include the effect of laminar-turbulent transition. The goal is to build a framework for laminar wing design based on the extension of Natural Laminar Flow (NLF) and developed from existing first principle methods. First, a literature survey on laminar wing design has been conducted. Information on the physics of laminar-turbulent transition includes the effect of transition on the aircraft level and the different paths to transition in the considered flows. Additionally, different methods for transition prediction in combination with a Reynolds-Averaged Navier-Stokes (RANS) solver are discussed. Finally, optimization methods for the delay of laminar-turbulent transition are summarized. Second, a RANS solver is coupled to a Linear Stability Theory (LST) solver to include a prediction capability for laminar-turbulent transition in the flow simulations. The instabilities taken into account using this approach are of the Tollmien-Schlichting (TS) and the cross-flow (CF) type. A critical amplification factor curve for CF and TS instabilities is used to determine the onset of transition. The boundary layer data used in the LST analyses is directly obtained from the RANS solution. Transition is imposed onto the flow using an intermittency function controlling the applied eddy viscosity predicted by the turbulence model. Third, the implemented coupling is validated by reproducing the results of a variety of significant experimental test cases. These are TU Braunschweig's Sickle Wing, NASA's NLF version of the Common Research Model (CRM-NLF), and the NLF(1)-0416 airfoil. This covers a wide range of test conditions in the sub- and transonic flow regime, featuring separation-induced as well as TS and CF dominated transition for varying levels of geometric complexity. The comparison of numerical and experimental results shows that the methodology is capable to accurately predict transition for a wide range of flow regimes under industrial interest. Furthermore, the results reveal interesting information on the transition mechanisms on three-dimensional wings. Finally, the continuous adjoint formulation for the employed RANS solver was extended by a flow-matching functional. The aim is to drive the geometry to a shape that satisfies a specified target flow field that will reduce the amplification rates of the instabilities. By means of the sensitivities of the transition location predicted by the LST solver with respect to the flow field variables, a target flow is defined. It is shown that using this approach, the amplification rates for an airfoil can be reduced. It is ultimately proposed to use this approach to optimize profiles and wings with extended laminar flow.

BACKGROUND Medical cannabinoids are controversial. Their use is comparatively rare, but it is rising. Since 2017, cannabinoids can be prescribed in Germany for a broader range of indications. Patient surveys on these drugs are hampered by the stigmatization of cannabinoids and their (still) low prevalence in medical contexts. Against this background, patients’ willingness to provide information is limited. Moreover, it is logistically challenging to reach them with a survey. A thorough knowledge of currently ongoing therapies and their effects and side effects, however, is important for a more appropriate and effective use of cannabinoids in the future. OBJECTIVE This study is an exploratory data collection using a representative sample. The main goal is to provide a detailed picture of the current use of medical cannabinoids in Germany. It is intended to identify subgroups that may benefit particularly well or poorly. METHODS We are conducting a representative, anonymous, cross-sectional, one-time, web-based survey based on mixed methods in 3 German federal states. Health conditions under cannabinoid therapy and before are documented with validated, symptom-specific questionnaires. This allows an estimation of the effect sizes of these therapies. The selection of parameters and questionnaires was based on the results of independent qualitative interviews in advance. Representative samples of the hard-to-reach study population are obtained by cluster sampling via contracted physicians of the statutory health insurance companies. RESULTS Recruitment was ongoing until the end of June 2022, with 256 enrolled participants. Validated questionnaires on pain, spasticity, anorexia or wasting, multiple sclerosis, nausea or vomiting, depression, and attention deficit hyperactivity disorder (ADHD) were selected. Symptom scores are being assessed for both current conditions under cannabinoid therapy and conditions prior to this therapy (in retrospect). Validated questionnaires are also used for treatment satisfaction and general quality of life. These are supplemented by existing diagnoses, a detailed medication history, any previous experiences with cannabis or illegal substances, experiences with the prescription process, and sociodemographic data. Based on the results of the previous qualitative interviews, questions were added regarding prior experience with relaxation methods and psychotherapy, personal opinions about cannabinoids, pre-existing or symptom-related psychological trauma, and different experiences with different cannabis-based therapies. CONCLUSIONS The exploratory mixed methods approach of this project is expected to provide valid and relevant data as a basis for future clinical research. The study design may be representative for a large proportion of outpatients treated with cannabinoids in the German federal states studied. It may have less bias toward social desirability and may provide valuable information in addition to existing studies. Due to the observational and cross-sectional nature of this study, various limitations apply. Causal relations cannot be drawn. CLINICALTRIAL German Clinical Trials Register DRKS00023344; https://drks.de/search/en/trial/DRKS00023344 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/38814

At the core of many avalanche simulation tools, numerical kernels are utilized to solve flow model equations. Aside from trying to fit the models as best as possible to the current understanding of actual flow mechanisms, these kernels have to fulfill general mathematical requirements, such as convergence, stability and consistency. The precision of numerical solutions is limited and needs to be determined by appropriate uncertainty quantification approaches. It is also necessary to assess the impact of input variability propagating through the numerical kernel.To allow kernel testing and uncertainty quantification, the AvaFrame framework provides a suite of test cases as well as analysis tools. This includes tests with known solutions usable to determine the kernel errors (ana1Tests) and idealized/real world topographies to estimate effects of varying simulation setups. By changing numerical settings, flow model setup or input data it is possible to show their effects on simulation results in a quantitative manner. It therefore allows us to relate input variations to the uncertainty in simulation results. Error and uncertainty quantification is done using modules for computing statistical measures (ana4Stats), indicators along an avalanche path (ana3AIMEC) and various visualization routines.We showcase this for our com1DFA dynamical dense flow avalanche (DFA) module. The kernel of com1DFA is based on depth integrated governing equations (shallow water) and solved numerically using the smoothed particle hydrodynamics (SPH) method. Applying our analysis tools, we evaluate the convergence of the DFA kernel with regard to the numerical parameters time step, SPH kernel size and particles size. We investigate the accuracy and precision of the numerical solution using the similarity solution test, a test with a semi-analytic solution for depth integrated equations. It allows us to establish a suitable relation between time step, SPH kernel size and particles size for the com1DFA kernel.Using the same approach for an avalanche setup, we can also vary selected input parameters like friction coefficients and/or release thickness and quantify the resulting uncertainties on simulation results, e.g. runout and peak flow variables.

The dynamics of snow avalanches and in particular their rheology is of big importance to develop improved avalanche models and thus increase safety in mountainous areas. Existing measurement systems only allow a limited in situ view of the dynamics of snow avalanches and therefore demand the development of innovative measurement systems. Furthermore, due to the limited measurement capability of existing systems, comprehensible motion reconstruction is currently not possible. Therefore, the aim of this work is to present a measurement system that enables accurate in flow observations of snow avalanches and has the mechanical properties of a typical snow granule. A main objective of the measurement system is to allow a full motion reconstruction regarding translations and rotations with a high sampling rate and without exceeding sensor ranges.The newly developed system, denoted as AvaNode, has the shape of a concave cube with a variable density to fit typical snow granules in flowing avalanches and their deposits. The AvaNode contains a strapdown inertial navigation sensor capable of measuring accelerations, angular velocities, and magnetic flux densities with up to 400Hz and allows for an estimation of the orientations, velocities, and positions of the AvaNode using state of the art motion reconstruction algorithms. The reconstruction is significantly improved due to precise calibration of all sensors using reference measurements with a 6R robot and onsite magnetic field calibration. In order to get a refined motion trajectory, the AvaNodes are also equipped with radio ranging modules. These modules allow performing time of flight (TOF) measurements, determining the distance between several nodes. A Global Navigation Satellite System (GNSS) module determines longitude, latitude, and altitude, as well as world time, however, with low frequency resolution and larger errors due to snow coverage. To measure the temperature evolution in avalanches, an infrared temperature sensor is attached. Multiple recovery systems like Recco rescue reflector (passive), Pieps TX600 (active), and Lambda4 Smilla (active) are integrated to allow fast retrieval of the sensors.As first results, we present the employed sensor calibration approaches for the inertial navigation with corresponding laboratory data signatures. The sensor calibration allows in-depth analysis of motion data, identifying typical data signatures observed in avalanches. Furthermore, we show first data acquired from in-flow snow avalanche measurements, which prove the functionality of the system and allow the first insights into trajectories of snow granules, regarding accelerations, angular velocities, rotations, and position.

Forests cover large parts of mountain areas. It is therefore necessary to include their effects in simulations at the regional scale to understand the key role forests have for risk mitigation. Process-based physical models can be used for such simulations, but they often require larger computational resources than statistical models. Flow-Py is a customizable, open-source simulation tool to predict the runout and intensity of gravitational mass flows (GMF). Flow-Py is based on data-driven empirical modeling ideas with automated path identification to solve the routing and stopping of GMFs in three-dimensional terrain, requiring fewer parameters than physical GMF runout models. Here we present the custom-built forest plug-in to the Flow-Py simulation tool which accounts for forest effects in the transit and runout zones of snow avalanches. Flow-Py employs the well-known runout angle (α) concept to determine the stopping of a GMF, and routing algorithm consisting of a terrain contribution and persistence contribution. The interaction between forest and avalanches, which can reduce their runout and decrease their intensity can be broken down into two main processes, 1) adding friction and 2) reducing flowing mass or the detrainment of snow. The forest plug-in has the capability to mimic these physical interactions by increasing the runout angle and adjusting the routing flux in forested areas. We present the framework of the forest plug-in for a test case and the results of a sensitivity study on parameters controlling the forest-avalanche interaction. The forest plug-in requires the spatial extent of the forest and an estimate of the kinetic energy of the avalanche to compute the forest’s effect on the avalanche movement. Additional information on the structure of the forest (e.g., forest type, stem density, canopy cover, basal area) can be used to amplify or dampen these effects. The forest information is summarized in the forest structure index (FSI), which indicates how developed a forest is with regards to its optimal protective effect against snow avalanches and ranges between 0 (no protection) and 1 (optimal protection), considering, e.g., dominant forest type, elevation band, or the forest development stage. Forests located in the starting zones of avalanches have long been used as an efficient mitigation measure to reduce avalanche risk. However, forests located in the transit and runout zones of avalanches also have mitigating properties, but the degree of protection is difficult to quantify without simulation tools and their integrated models. Including forest-avalanche interactions in regional-scale simulations with Flow-Py and its forest plug-in allows to estimate the degree to which forest protects human activity and infrastructure against potential avalanches. That is, by combining simulation results with and without forest it is possible to estimate the forest impact, i.e., how much the forest reduces the magnitude (runout and intensity) of the avalanche. Such regional overviews can be calculated fast with large-scale input data, which is important to, e.g., quantify changes in the protective effect of a forest area caused by disturbance agents such as wind, bark beetles or fire.

A detailed knowledge of avalanche dynamics is crucial to optimize flow models that allow avalanche simulation tools to be effectively used for dimensioning mitigation measures or identifying endangered terrain. There are different ways to observe the dynamics in an avalanche during the flow. It can be achieved with remote sensing approaches or fixed sensor systems that interact with the flow. In this Abstract we introduce an inflow sensor system, the so called AvaNodes that are equipped with a variety of sensors, investigating the potential of Global Navigation Satellite System (GNSS) modules.The AvaNode is a cube with 16 cm side length. It is designed to flow in the avalanche and obtain GNSS position and velocity, inertial measurement unit (IMU) based accelerations, angular velocities and the magnetic flux densities, and temperature by means of an infrared thermometer.The utilized GNSS modules are from the ublox CAM-M8 series, that have a position accuracy of 2 m and velocity accuracy of 0.05 m/s, according to the datasheet.To estimate the position accuracy of the AvaNode while covered with snow, experiments were performed with the AvaNode buried in snow at different depths at a known location. Results show that the position accuracy is highly dependent on the number of satellites that the module currently tracks, ranging between 2 and 10 meters. To estimate the GNSS velocity accuracy while the AvaNode is covered with snow, a dynamic experiment with moving sensors was performed. The AvaNode was transported on a sledge while it was buried in 10 and 20 cm of snow. An accuracy in the range of 0.5 m/s was observed, allowing to potentially investigate the dynamics in real avalanches. The influence of burial or snow cover depth did not show conclusive influence on the results and requires further investigation. In 2021 this inflow sensor system was used in two avalanche experiments, on March 15 and 16, obtaining start and end positions, as well as promising GNSS velocities. On March 15 one AvaNode was transported by an avalanche, where the GNSS velocity shows a maximum of 15 m/s and a duration of 50 seconds of the avalanche. On March 16 two AvaNodes were picked up by an avalanche, both showing similar velocity distributions, with a maximum velocity of 17 and 13 m/s.

GIS-based open-source simulation tools for extremely rapid mass flow processes such as snow avalanches, rock avalanches, or debris flows are readily available, covering a broad range of complexity levels – e.g., from single-phase to multi-phase. However, these tools are not suitable for slower types of mass flows characterized by high viscosities. The conventionally used momentum balance equations for rapid flows often appear numerically unstable for high viscosities, leading to the immediate reversion of flow direction or stopping, without appropriate numerical treatment. GIS-based simulation efforts of slow geomorphic flows are reported in the literature, and open source tools are available for specific phenomena such as glaciers, but no comprehensive and readily usable simulation tools have been proposed yet.We present a simple depth-averaged model implementation for the simulation of slow geomorphic flows, including glaciers, rock glaciers, highly viscous lava flows, and those flow-type landslides not classified as extremely or very rapid. Thereby, we use an equilibrium-of-motion concept. For each time step, flow momentum and velocity are computed as the equilibrium between accelerating gravitational forces and decelerating viscous forces, also including a simple law for basal sliding. Momentum balances are not carried over from one time step to the next, meaning that inertial forces, which are not important for slow-moving mass flows, are neglected. Whereas these basic principles are applied to all relevant processes, there is flexibility with regard to the details of model formulation and parameterization: e.g., the well-established shallow-ice approximation can be used to simulate glacier flow.The model is implemented with the GRASS GIS-based open-source mass flow simulation framework r.avaflow and demonstrated on four case studies: an earth flow, the growth of a lava dome, a rock glacier, and a glacier (considering accumulation and ablation). All four processes were reproduced in a plausible way. However, parameterization remains a challenge due to spatio-temporal changes and temperature dependency of viscosity and basal sliding. Our model and its implementation open up new possibilities for climate change impact studies, natural hazard analysis, and environmental education.

We propose our quarterly earnings prediction (QEPSVR) model, which is based on epsilon support vector regression (ε-SVR), as a new univariate model for quarterly earnings forecasting. This follows the recommendations of Lorek (Adv Account 30:315–321, 2014. 10.1016/j.adiac.2014.09.008), who notes that although the model developed by Brown and Rozeff (J Account Res 17:179–189, 1979) (BR ARIMA) is advocated as still being the premier univariate model, it may no longer be suitable for describing recent quarterly earnings series. We conduct empirical studies on recent data to compare the predictive accuracy of the QEPSVR model to that of the BR ARIMA model under a multitude of conditions. Our results show that the predictive accuracy of the QEPSVR model significantly exceeds that of the BR ARIMA model under 24 out of the 28 tested experiment conditions. Furthermore, significance is achieved under all conditions considering short forecast horizons or limited availability of historic data. We therefore advocate the use of the QEPSVR model for firms performing short-term operational planning, for recently founded companies and for firms that have restructured their business model.

Brain lesions are a rare cause of tic disorders. However, they can provide uniquely causal insights into tic pathophysiology and can also inform on possible neuromodulatory therapeutic targets. Based on a systematic literature review, we identified 22 cases of tics causally attributed to brain lesions and employed ‘lesion network mapping’ to interrogate whether tic-inducing lesions would be associated with a common network in the average human brain. We probed this using a normative functional connectome acquired in 1000 healthy participants. We then examined the specificity of the identified network by contrasting tic-lesion connectivity maps to those seeding from 717 lesions associated with a wide array of neurological and/or psychiatric symptoms within the Harvard Lesion Repository. Finally, we determined the predictive utility of the tic-inducing lesion network as a therapeutic target for neuromodulation. Specifically, we collected retrospective data of 30 individuals with Tourette disorder, who underwent either thalamic (n = 15; centromedian/ventrooralis internus) or pallidal (n = 15; anterior segment of globus pallidus internus) deep brain stimulation and calculated whether connectivity between deep brain stimulation sites and the lesion network map could predict clinical improvements. Despite spatial heterogeneity, tic-inducing lesions mapped to a common network map, which comprised the insular cortices, cingulate gyrus, striatum, globus pallidus internus, thalami and cerebellum. Connectivity to a region within the anterior striatum (putamen) was specific to tic-inducing lesions when compared with control lesions. Connectivity between deep brain stimulation electrodes and the lesion network map was predictive of tic improvement, regardless of the deep brain stimulation target. Taken together, our results reveal a common brain network involved in tic generation, which shows potential as a therapeutic target for neuromodulation.

Distress is a growing public health concern. In this three-armed randomized controlled trial, n = 102 adults with elevated stress levels and stress-related symptoms were randomly assigned to (1) "integrative" yoga classes which combined physical exercises, mindfulness training, and ethical/philosophical aspects of traditional yoga; to (2) Iyengar yoga classes which entailed primarily physical exercises; or to (3) mindfulness training without physical training. We hypothesized the synergistic effects of physical yoga exercises, mindfulness, and ethical/philosophical aspects. The primary outcome was the group difference on Cohen's Perceived Stress Scale (PSS) after 12 weeks. Secondary outcomes included burnout, quality of life, physical complaints, depression, anxiety, mindfulness, interoceptive awareness, self-regulation, spirituality, mysticism, and posttraumatic stress. All outcomes were evaluated at baseline (V0), after 12 weeks (V1), and after 24 weeks (V2). A subset of participants took part in qualitative interviews. A lasting and clinically relevant stress reduction was observed within all groups (PSS Delta V0-V1(Integrative Yoga) = -6.69 +/- 6.19; Delta V0-V1(Iyengar Yoga) = -6.00 +/- 7.37; Delta V0-V1(Mindfulness) = -9.74 +/- 7.80; all p < 0.00). Effect sizes were also statistically large at the end of the follow-up period (Cohen's d (Integrative Yoga) = 1.41; d (Iyengar Yoga) = 1.37; d (Mindfulness) = 1.23). There were no significant group differences or evidence of relevant synergistic effects from combining mindfulness and physical yoga exercises. All three interventions were found to be equally effective methods of stress reduction. Their use in practice should be based on availability and patient preference.

Satellite-based radar measurement are increasingly important for the detection of avalanche activity and the characterization of the snow cover properties on a regional scale. However, they operate...

Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows.

Antimicrobial peptides play a critical role in the barrier function of human skin. They offer a fast response to invading microorganisms and protect from external microbial infection. Here we show the isolation of the kallikrein-related peptidase inhibitor SPINK9 as a major antibacterial factor from healthy stratum corneum. In total, six N-terminal SPINK9 variants were identified in the stratum corneum. Whereas all variants exhibited similar inhibition activities against kallikrein-related peptidase, only three variants with either lysine or glutamine as their first N-terminal residues were able to kill various Escherichia coli strains, but not other bacteria or fungi. The killing activity also depended on the sequence essential for kallikrein-related peptidase inhibition. Ultrastructural electron microscopy analyses suggested that SPINK9 entered the cell and killed growing bacteria. A bacterial chaperone, SKP, was identified as the major SPINK9 interacting partner in E. coli cells. The Skp-deleted mutant was more sensitive to SPINK9 than the wild-type control, suggesting that the bactericidal activity of SPINK9 should first overcome the resistance from the bacterial chaperone SKP. Thus, SPINK9 is a member of epidermal antimicrobial peptides for selective killing of E. coli, which might contribute to the innate barrier function of human skin.

Objectives This review aims to describe the role of the school nurse in protecting children and young people from maltreatment by examining the international literature. Child maltreatment is a prevalent issue in global society today and includes physical, emotional and sexual abuse, neglect and exploitation. School nurses are ideally placed to identify and work with children and young people who are at risk of maltreatment through their regular contact with the school community. Design Integrative literature review incorporating thematic analysis. Data Sources Electronic databases British Nursing Database, Cumulative Index of Nursing and Allied Health Literature, Medline, PsycInfo, Cochrane Library Database for Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL). Search Terms Role, School Nurse, Child Maltreatment. Review Methods Initial title and abstract review of 444 studies resulted in 78 studies for full text review. Additional search strategies identified one relevant study. Inclusion and exclusion criteria were employed as follows; (1) study published in the English language, (2) peer-reviewed, (3) primary research, (4) inclusion of school nurses (or equivalent role internationally) in the study sample, and (5) a focus on the role of school nurses in preventing child maltreatment. Studies were appraised using the Critical Appraisal Skills Programme tool for qualitative studies and the Strengthening the Reporting of Observational Studies in Epidemiology checklist for mixed-methods and quantitative studies. Findings from the studies were identified, summarised and organised into a summary table, before being analysed thematically. Results 21 studies met inclusion and quality criteria and were included in the review. Key themes that emerged from the analysis were; supporting the child and family, detective work, working with other professionals, training and supervision, barriers to protecting children and young people from maltreatment and trust. Conclusion International literature highlights the variety of activities that school nurses may undertake in daily practice to protect children and young people from maltreatment. Several challenges to this role are identified, including time management and building relationships with children and young people. Recommendations for practice and further research are made.

Bitcoin is built on a blockchain, an immutable decentralised ledger that allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins are associated with alpha-numeric addresses and are transferred via transactions. Each transaction is composed of a set of input addresses (associated with unspent outputs received from previous transactions) and a set of output addresses (to which Bitcoins are transferred). Despite Bitcoin was designed with anonymity in mind, different heuristic approaches exist to detect which addresses in a specific transaction belong to the same entity. By applying these heuristics, we build an Address Correspondence Network: in this representation, addresses are nodes are connected with edges if at least one heuristic detects them as belonging to the same entity. %addresses are nodes and edges are drawn between addresses detected as belonging to the same entity by at least one heuristic. %nodes represent addresses and edges model the likelihood that two nodes belong to the same entity %In this network, connected components represent sets of addresses controlled by the same entity. In this paper, we analyse for the first time the Address Correspondence Network and show it is characterised by a complex topology, signalled by a broad, skewed degree distribution and a power-law component size distribution. Using a large-scale dataset of addresses for which the controlling entities are known, we show that a combination of external data coupled with standard community detection algorithms can reliably identify entities. The complex nature of the Address Correspondence Network reveals that usage patterns of individual entities create statistical regularities; and that these regularities can be leveraged to more accurately identify entities and gain a deeper understanding of the Bitcoin economy as a whole., 21 pages, 13 figures

Several powerful physics-based computational landslide run-out models have been developed and validated throughout the last years. The geohazards community applies these forward models in simulation tools to predict potential landslide run-out outcomes including their uncertainties, and uses inverse approaches to conduct reanalyses and to infer on model parameters for calibration purposes. Yet it remains challenging to turn these computational frameworks into robust, transparent and transferrable simulation-based decision support tools for geohazard mitigation. In particular, the landscape of uncertainties – such as those resulting from the idealised model description itself, input data (e.g., material parameters or topographic data), and numerical scheme related hyperparameters – is still not systematically managed when conducting landslide simulations. Probabilistic hazard maps that take these uncertainties into account imply a large number of model evaluations, which constitutes a computational bottle neck. This issue can be overcome by using High Performance Computing (HPC) resources along with the existing software and resources. Alternatively, physics-informed machine learning strategies use simulation results of the original process model, i.e., the simulator, to train a statistically valid representation, the so-called emulator. Once being trained, the emulator significantly reduces computational costs, while at the same time it grants access to an estimation of the introduced error. A software framework has recently been set up to integrate Gaussian process emulation and the landslide run-out model r.avaflow, an open-source mass flow simulation tool. Emulation-based sensitivity analysis was of comparable quality to conventional studies, and the computational costs were cut significantly. The emulator allowed for the first time to conduct a global sensitivity analyses at every location simultaneously for a complete landslide impact area. A joint effort across different institutes in Europe has been made in this contribution to test the potential and limitation of the emulation techniques by revisiting a number of published case studies. Selection of test cases has been made according to data availability, failure type and computational demand. Preliminary findings suggest that the emulator is capable of reducing the computational effort of modelling various flow-like landslides substantially. Future work will focus on curating a well-defined database of test scenarios across multiple institutes with cases ranging from small to medium-sized debris flows to large rock avalanches.