Motor–Cognitive Treadmill Training With Virtual Reality in Parkinson’s Disease: The Effect of Training Duration

Treadmill training with virtual reality (TT + VR) has been shown to improve gait performance and to reduce fall risk in Parkinson’s disease (PD). However, there is no consensus on the optimal training duration. This study is a sub-study of the V-TIME randomized clinical trial (NCT01732653). In this study, we explored the effect of the duration of training based on the motor–cognitive interaction on motor and cognitive performance and on fall risk in subjects with PD. Patients in Hoehn and Yahr stages II–III, aged between 40 and 70 years, were included. In total, 96 patients with PD were assigned to 6 or 12 weeks of TT + VR intervention, and 77 patients completed the full protocol. Outcome measures for gait and cognitive performance were assessed at baseline, immediately after training, and at 1- and 6-month follow-up. The incident rate of falls in the 6-month pre-intervention was compared with that in the 6-month post-intervention. Dual-task gait performance (gait speed, gait speed variability and stride length under cognitive dual task and obstacle negotiation, and the leading foot clearance in obstacle negotiation) improved similarly in both groups with gains sustained at 6-month follow-up. A higher decrease in fall rate and fear of falling were observed in participants assigned to the 12-week intervention than the 6-week intervention. Improvements in cognitive functions (i.e., executive functions, visuospatial ability, and attention) were seen only in participants enrolled in 12-week training up to 1-month follow-up but vanished at the 6-month evaluation. Our results suggest that a longer TT + VR training leads to greater improvements in cognitive functions especially those directly addressed by the virtual environment.

A Two-Dimensional Polyimide-Graphene Heterostructure with Ultra-fast Interlayer Charge Transfer

Two-dimensional polymers (2DPs) are a class of atomically/molecularly thin crystalline organic 2D materials. They are intriguing candidates for the development of unprecedented organic-inorganic 2D van der Waals heterostructures (vdWHs) with exotic physicochemical properties. In this work, we demonstrate the on-water surface synthesis of large-area (cm2 ), monolayer 2D polyimide (2DPI) with 3.1-nm lattice. Such 2DPI comprises metal-free porphyrin and perylene units linked by imide bonds. We further achieve a scalable synthesis of 2DPI-graphene (2DPI-G) vdWHs via a face-to-face co-assembly of graphene and 2DPI on the water surface. Remarkably, femtosecond transient absorption spectroscopy reveals an ultra-fast interlayer charge transfer (ca. 60 fs) in the resultant 2DPI-G vdWH upon protonation by acid, which is equivalent to that of the fastest reports among inorganic 2D vdWHs. Such large interlayer electronic coupling is ascribed to the interlayer cation-π interaction between 2DP and graphene.

Chemical Evolution of Antivirals Against Enterovirus D68 through Protein-Templated Knoevenagel Reactions

The generation of bioactive molecules from inactive precursors is a crucial step in the chemical evolution of life, however, mechanistic insights into this aspect of abiogenesis are scarce. Here, we investigate the protein-catalyzed formation of antivirals by the 3C-protease of enterovirus D68. The enzyme induces aldol condensations yielding inhibitors with antiviral activity in cells. Kinetic and thermodynamic analyses reveal that the bioactivity emerges from a dynamic reaction system including inhibitor formation, alkylation of the protein target by the inhibitors, and competitive addition of non-protein nucleophiles to the inhibitors. The most active antivirals are slowly reversible inhibitors with elongated target residence times. The study reveals first examples for the chemical evolution of bio-actives through protein-catalyzed, non-enzymatic C-C couplings. The discovered mechanism works under physiological conditions and might constitute a native process of drug development.

MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances

In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

Heated rivalries: Phenological variation modifies competition for pollinators among arctic plants

When plant species compete for pollinators, climate warming may cause directional change in flowering overlap, thereby shifting the strength of pollinator-mediated plant-plant interactions. Such shifts are likely accentuated in the rapidly warming Arctic. Targeting a plant community in Northeast Greenland, we asked (a) whether the relative phenology of plants is shifting with spatial variation in temperature, (b) whether local plants compete for pollination, and (c) whether shifts in climatic conditions are likely to affect this competition. We first searched for climatic imprints on relative species phenology along an elevational gradient. We then tested for signs of competition with increasing flower densities: reduced pollinator visits, reduced representation of plant species in pollen loads, and reduced seed production. Finally, we evaluated how climate change may affect this competition. Compared to a dominant species, Dryas integrifolia × octopetala, the relative timing of other species shifted along the environmental gradient, with Silene acaulis and Papaver radicatum flowering earlier toward higher elevation. This shift resulted in larger niche overlap, allowing for an increased potential for competition for pollination. Meanwhile, Dryas emerged as a superior competitor by attracting 97.2% of flower visits. Higher Dryas density resulted in reduced insect visits and less pollen of S. acaulis being carried by pollinators, causing reduced seed set by S. acaulis. Our results show that current variation in climate shifts the timing and flowering overlap between dominant and less-competitive plant species. With climate warming, such shifts in phenology within trophic levels may ultimately affect interactions between them, changing the strength of competition among plants.

Dark control: The default mode network as a reinforcement learning agent

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

Self-Assembled Supramolecular Hybrid Hydrogel Beads Loaded with Silver Nanoparticles for Antimicrobial Applications

This Full Paper reports the formation of silver (Ag) NPs within spatially resolved two-component hydrogel beads, which combine a low-molecular-weight gelator (LMWG) DBS-CONHNH2 and a polymer gelator (PG) calcium alginate. The AgNPs are formed through in situ reduction of AgI , with the resulting nanoparticle-loaded gels being characterised in detail. The antibacterial activity of the nanocomposite gel beads was tested against two drug-resistant bacterial strains, often associated with hospital-acquired infections: vancomycin-resistant Enterococcus faecium (VRE) and Pseudomonas aeruginosa (PA14), and the AgNP-loaded gels showed good antimicrobial properties against both types of bacteria. It is suggested that the gel bead format of these AgNP-loaded hybrid hydrogels makes them promising versatile materials for potential applications in orthopaedics or wound healing.

Biodiversity policy beyond economic growth

Increasing evidence-synthesized in this paper-shows that economic growth contributes to biodiversity loss via greater resource consumption and higher emissions. Nonetheless, a review of international biodiversity and sustainability policies shows that the majority advocate economic growth. Since improvements in resource use efficiency have so far not allowed for absolute global reductions in resource use and pollution, we question the support for economic growth in these policies, where inadequate attention is paid to the question of how growth can be decoupled from biodiversity loss. Drawing on the literature about alternatives to economic growth, we explore this contradiction and suggest ways forward to halt global biodiversity decline. These include policy proposals to move beyond the growth paradigm while enhancing overall prosperity, which can be implemented by combining top-down and bottom-up governance across scales. Finally, we call the attention of researchers and policy makers to two immediate steps: acknowledge the conflict between economic growth and biodiversity conservation in future policies; and explore socioeconomic trajectories beyond economic growth in the next generation of biodiversity scenarios.

Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods

This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three-dimensional variational (3D-Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D-Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.

mGluR5 receptor availability is associated with lower levels of negative symptoms and better cognition in male patients with chronic schizophrenia

Consistent findings postulate disturbed glutamatergic function (more specifically a hypofunction of the ionotropic NMDA receptors) as an important pathophysiologic mechanism in schizophrenia. However, the role of the metabotropic glutamatergic receptors type 5 (mGluR5) in this disease remains unclear. In this study, we investigated their significance (using [¹¹C]ABP688) for psychopathology and cognition in male patients with chronic schizophrenia and healthy controls. In the patient group, lower mGluR5 binding potential (BP_ND) values in the left temporal cortex and caudate were associated with higher general symptom levels (negative and depressive symptoms), lower levels of global functioning and worse cognitive performance. At the same time, in both groups, mGluR5 BP_ND were significantly lower in smokers (F_[27,1] = 15.500; p = .001), but without significant differences between the groups. Our findings provide support for the concept that the impaired function of mGluR5 underlies the symptoms of schizophrenia. They further supply a new perspective on the complex relationship between tobacco addiction and schizophrenia by identifying glutamatergic neurotransmission—in particularly mGluR5—as a possible connection to a shared vulnerability.

Sigma-1 receptor modulates neuroinflammation associated with mechanical hypersensitivity and opioid tolerance in a mouse model of osteoarthritis pain

BACKGROUND AND PURPOSE

Osteoarthritic pain is a chronic disabling condition lacking effective treatment. Continuous use of opioid drugs during osteoarthritic pain induces tolerance and may result in dose escalation and abuse. Sigma-1 (σ1) receptors, a chaperone expressed in key areas for pain control, modulates μ-opioid receptor activity and represents a promising target to tackle these problems. The present study investigates the efficacy of the σ1 receptor antagonist E-52862 to inhibit pain sensitization, morphine tolerance, and associated electrophysiological and molecular changes in a murine model of osteoarthritic pain.

EXPERIMENTAL APPROACH

Mice received an intra-knee injection of monoiodoacetate followed by 14-day treatment with E-52862, morphine, or vehicle, and mechanical sensitivity was assessed before and after the daily doses.

KEY RESULTS

Monoiodoacetate-injected mice developed persistent mechanical hypersensitivity, which was dose-dependently inhibited by E-52862. Mechanical thresholds assessed before the daily E-52862 dose showed gradual recovery, reaching complete restoration by the end of the treatment. When repeated treatment started 15 days after knee injury, E-52862 produced enhanced short-term analgesia, but recovery to baseline threshold was slower. Both a σ1 receptor agonist and a μ receptor antagonist blocked the analgesic effects of E-52862. An acute, sub-effective dose of E-52862 restored morphine analgesia in opioid-tolerant mice. Moreover, E-52862 abolished spinal sensitization in osteoarthritic mice and inhibited pain-related molecular changes.

CONCLUSION AND IMPLICATIONS

These findings show dual effects of σ1 receptor antagonism alleviating both short- and long-lasting antinociception during chronic osteoarthritis pain. They identify E-52862 as a promising pharmacological agent to treat chronic pain and avoid opioid tolerance.

Negative feedback processes following drainage slow down permafrost degradation

The sustainability of the vast Arctic permafrost carbon pool under climate change is of paramount importance for global climate trajectories. Accurate climate change forecasts, therefore, depend on a reliable representation of mechanisms governing Arctic carbon cycle processes, but this task is complicated by the complex interaction of multiple controls on Arctic ecosystem changes, linked through both positive and negative feedbacks. As a primary example, predicted Arctic warming can be substantially influenced by shifts in hydrologic regimes, linked to, for example, altered precipitation patterns or changes in topography following permafrost degradation. This study presents observational evidence how severe drainage, a scenario that may affect large Arctic areas with ice-rich permafrost soils under future climate change, affects biogeochemical and biogeophysical processes within an Arctic floodplain. Our in situ data demonstrate reduced carbon losses and transfer of sensible heat to the atmosphere, and effects linked to drainage-induced long-term shifts in vegetation communities and soil thermal regimes largely counterbalanced the immediate drainage impact. Moreover, higher surface albedo in combination with low thermal conductivity cooled the permafrost soils. Accordingly, long-term drainage effects linked to warming-induced permafrost degradation hold the potential to alleviate positive feedbacks between permafrost carbon and Arctic warming, and to slow down permafrost degradation. Self-stabilizing effects associated with ecosystem disturbance such as these drainage impacts are a key factor for predicting future feedbacks between Arctic permafrost and climate change, and, thus, neglect of these mechanisms will exaggerate the impacts of Arctic change on future global climate projections.

Initiation and Flow Conditions of Contemporary Flows in Martian Gullies

Understanding the initial and flow conditions of contemporary flows in Martian gullies, generally believed to be triggered and fluidized by CO2 sublimation, is crucial for deciphering climate conditions needed to trigger and sustain them. We employ the RAMMS (RApid Mass Movement Simulation) debris flow and avalanche model to back calculate initial and flow conditions of recent flows in three gullies in Hale crater. We infer minimum release depths of 1.0-1.5 m and initial release volumes of 100-200 m3. Entrainment leads to final flow volumes that are ∼2.5-5.5 times larger than initially released, and entrainment is found necessary to match the observed flow deposits. Simulated mean cross-channel flow velocities decrease from 3-4 m/s to ∼1 m/s from release area to flow terminus, while flow depths generally decrease from 0.5-1 to 0.1-0.2 m. The mean cross-channel erosion depth and deposition thicknesses are ∼0.1-0.3 m. Back-calculated dry-Coulomb friction ranges from 0.1 to 0.25 and viscous-turbulent friction between 100 and 200 m/s2, which are values similar to those of granular debris flows on Earth. These results suggest that recent flows in gullies are fluidized to a similar degree as are granular debris flows on Earth. Using a novel model for mass flow fluidization by CO2 sublimation we are able to show that under Martian atmospheric conditions very small volumetric fractions of CO2 of ≪1% within mass flows may indeed yield sufficiently large gas fluxes to cause fluidization and enhance flow mobility.

Functional brain segmentation using inter-subject correlation in fMRI

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc.