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Background
Depression is a leading cause of disability worldwide and a significant contributor to the global burden of disease. Altered leptin levels are known to be associated with depressive symptoms, however discrepancies in the results of increased or decreased levels exist. Due to various limitations associated with commonly used antidepressant drugs, alternatives such as exercise therapy are gaining more importance. Therefore, the current study investigates whether depressed patients have higher leptin levels compared to healthy controls and if exercise is efficient to reduce these levels.
Methods
Leptin levels of 105 participants with major depressive disorder (MDD; 45.7% female, age mean ± SEM: 39.1 ± 1.0) and 34 healthy controls (HC; 61.8% female, age mean ± SEM: 36.0 ± 2.0) were measured before and after a bicycle ergometer test. Additionally, the MDD group was separated into three groups: two endurance exercise intervention groups (EX) differing in their intensities, and a waiting list control group (WL). Leptin levels were measured pre and post a 12-week exercise intervention or the waiting period.
Results
Baseline data showed no significant differences in leptin levels between the MDD and HC groups. As expected, correlation analyses displayed significant relations between leptin levels and body weight (HC: r = 0.474, p = 0.005; MDD: r = 0.198, p = 0.043) and even more with body fat content (HC: r = 0.755, p < 0.001; MDD: r = 0.675, p < 0.001). The acute effect of the bicycle ergometer test and the 12-week training intervention showed no significant changes in circulating leptin levels.
Conclusion
Leptin levels were not altered in patients with major depression compared to healthy controls and exercise, both the acute response and after 12 weeks of endurance training, had no effect on the change in leptin levels.
Trial registration
The study was registered at the German register for clinical studies (DRKS) and the International Clinical Trials Registry Platform of the World Health Organization https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00008869 on 28/07/2015.
How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.
Introduction
Elderly patients after hospitalisation for acute events on account of age-related diseases (eg, joint or heart valve replacement surgery) are often characterised by a remarkably reduced functional health. Multicomponent rehabilitation (MR) is considered an appropriate approach to restore the functioning of these patients. However, its efficacy in improving functioning-related outcomes such as care dependency, activities of daily living (ADL), physical function and health-related quality of life (HRQL) remains unclarified. We outline the research framework of a scoping review designed to map the available evidence of the effects of MR on the independence and functional capacity of elderly patients hospitalised for age-related diseases in four main medical specialties beyond geriatrics.
Methods and analysis
The biomedical databases (PubMed, Cochrane Library, ICTRP Search Platform, ClinicalTrials) and additionally Google Scholar will be systematically searched for studies comparing centre-based MR with usual care in patients ≥75 years of age, hospitalised for common acute events due to age-related diseases (eg, joint replacement, stroke) in one of the specialties of orthopaedics, oncology, cardiology or neurology. MR is defined as exercise training and at least one additional component (eg, nutritional counselling), starting within 3 months after hospital discharge. Randomised controlled trials as well as prospective and retrospective controlled cohort studies will be included from inception and without language restriction. Studies investigating patients <75 years, other specialties (eg, geriatrics), rehabilitation definition or differently designed will be excluded. Care dependency after at least a 6-month follow-up is set as the primary outcome. Physical function, HRQL, ADL, rehospitalisation and mortality will be additionally considered. Data for each outcome will be summarised, stratified by specialty, study design and type of assessment. Furthermore, quality assessment of the included studies will be performed.
Ethics and dissemination
Ethical approval is not required. Findings will be published in a peer-reviewed journal and presented at national and/or international congresses.
Step selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA‐based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analysing spatial data, and its importance is increasingly recognized in ecological models (e.g. species distribution models). Nonetheless, no framework yet exists to account for such correlation when analysing animal movement using SSA.
Here, we extend the popular method integrated step selection analysis (iSSA) by including a Gaussian field (GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian framework R‐INLA and the stochastic partial differential equations (SPDE) technique.
We show through a simulation study that our method provides accurate fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine (Gulo gulo) tracks.
Our method solves the problems of assuming spatially independent residuals in the SSA framework. In addition, it offers new possibilities for making long‐term predictions of habitat usage.
Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.
Increased rates of glacier retreat and thinning need accurate local estimates of glacier elevation change to predict future changes in glacier runoff and their contribution to sea level rise. Glacier elevation change is typically derived from digital elevation models (DEMs) tied to surface change analysis from satellite imagery. Yet, the rugged topography in mountain regions can cast shadows onto glacier surfaces, making it difficult to detect local glacier elevation changes in remote areas. A rather untapped resource comprises precise, time-stamped metadata on the solar position and angle in satellite images. These data are useful for simulating shadows from a given DEM. Accordingly, any differences in shadow length between simulated and mapped shadows in satellite images could indicate a change in glacier elevation relative to the acquisition date of the DEM. We tested this hypothesis at five selected glaciers with long-term monitoring programmes. For each glacier, we projected cast shadows onto the glacier surface from freely available DEMs and compared simulated shadows to cast shadows mapped from ∼40 years of Landsat images. W validated the relative differences with geodetic measurements of glacier elevation change where these shadows occurred. We find that shadow-derived glacier elevation changes are consistent with independent photogrammetric and geodetic surveys in shaded areas. Accordingly, a shadow cast on Baltoro Glacier (the Karakoram, Pakistan) suggests no changes in elevation between 1987 and 2020, while shadows on Great Aletsch Glacier (Switzerland) point to negative thinning rates of about 1 m yr−1 in our sample. Our estimates of glacier elevation change are tied to occurrence of mountain shadows and may help complement field campaigns in regions that are difficult to access. This information can be vital to quantify possibly varying elevation-dependent changes in the accumulation or ablation zone of a given glacier. Shadow-based retrieval of glacier elevation changes hinges on the precision of the DEM as the geometry of ridges and peaks constrains the shadow that we cast on the glacier surface. Future generations of DEMs with higher resolution and accuracy will improve our method, enriching the toolbox for tracking historical glacier mass balances from satellite and aerial images.
We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-dependent memory exponent α(t ) in a changing environment. In MMFBM the built-in, long-range memory is continuously modulated by α(t ). We derive the essential statistical properties of MMFBM such as its response function, mean-squared displacement (MSD), autocovariance function, and Gaussian distribution. In contrast to existing forms of FBM with time-varying memory exponents but a reset memory structure, the instantaneous dynamic of MMFBM is influenced by the process history, e.g., we show that after a steplike change of α(t ) the scaling exponent of the MSD after the α step may be determined by the value of α(t ) before the change. MMFBM is a versatile and useful process for correlated physical systems with nonequilibrium initial conditions in a changing environment.
Functional near-infrared spectroscopy (fNIRS) allows for a reliable assessment of oxygenated blood flow in relevant brain regions. Recent advancements in immersive virtual reality (VR)-based technology have generated many new possibilities for its application, such as in stroke rehabilitation. In this study, we asked whether there is a difference in oxygenated hemoglobin (HbO2) within brain motor areas during hand/arm movements between immersive and non-immersive VR settings. Ten healthy young participants (24.3 ± 3.7, three females) were tested using a specially developed VR paradigm, called “bus riding”, whereby participants used their hand to steer a moving bus. Both immersive and non-immersive conditions stimulated brain regions controlling hand movements, namely motor cortex, but no significant differences in HbO2 could be found between the two conditions in any of the relevant brain regions. These results are to be interpreted with caution, as only ten participants were included in the study.
In the Gasht-Masuleh area in the Alborz Mountains, gabbroic magma intruded Palaeozoic metasediments and Mesozoic sediments and crystallised as isotropic and cumulate gabbros. LREE enrichment points to relatively low degrees of mantle melting and depletion of Ti, Nb and Ta relative to primitive mantle points to an arc related component in the magma. Clinopyroxene compositions indicate MORB to arc signatures. U–Pb zircon crystallisation ages of 99.5 ± 0.6 Ma and 99.4 ± 0.6 Ma and phlogopite 40Ar/39Ar ages of 97.1 ± 0.4 Ma, 97.5 ± 0.4 Ma, 97.1 ± 0.1 Ma, within 2σ error, indicate that gabbro intrusion occurred in the (Albian-)Cenomanian (mid-Cretaceous). As active subduction did not take place in the Cretaceous in North Iran, the small volume mafic magmatism in the Gasht-Masuleh area must be due to local, extension-related mantle melting. Melting was most likely caused by far field effects triggered by roll-back of the Neo-Tethys subducting slab. As subduction took place at a distance of ~ 400 km (present distance) from the Alborz Mountains, the observed arc geochemical signatures must be inherited from a previous subduction event and concomitant mantle metasomatism, possibly in combination with contamination of the magma by crustal material.
The pathogenesis of influenza A viruses (IAVs) is influenced by several factors, including IAV strain origin and reassortment, tissue tropism and host type. While such factors were mostly investigated in the context of virus entry, fusion and replication, little is known about the viral-induced changes to the host lipid membranes which might be relevant in the context of virion assembly. In this work, we applied several biophysical fluorescence microscope techniques (i.e., Förster energy resonance transfer, generalized polarization imaging and scanning fluorescence correlation spectroscopy) to quantify the effect of infection by two IAV strains of different origin on the plasma membrane (PM) of avian and human cell lines. We found that IAV infection affects the membrane charge of the inner leaflet of the PM. Moreover, we showed that IAV infection impacts lipid–lipid interactions by decreasing membrane fluidity and increasing lipid packing. Because of such alterations, diffusive dynamics of membrane-associated proteins are hindered. Taken together, our results indicate that the infection of avian and human cell lines with IAV strains of different origins had similar effects on the biophysical properties of the PM.