Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, with an estimated rising prevalence, in concert with the epidemics of obesity and type 2 diabetes. The pathogenesis ...of NAFLD is not fully elucidated. Besides weight gain and insulin resistance, many other factors seem to contribute, including adipokines, gut microbiota and genetic predisposition. The disease starts as hepatic steatosis, which may proceed to nonalcoholic steatohepatitis (NASH); if fibrosis is added, the risk of cirrhosis and/or hepatocellular carcinoma is augmented. Liver biopsy is considered the gold standard for the diagnosis and staging of NAFLD; the early use of reliable and easily applied diagnostic tools, such as noninvasive biomarkers, is needed to identify patients at different–preferably early–stages of disease however. Whilst lifestyle modification is the first step to manage NAFLD, there is poor compliance, leading to the need of drug therapy. Accordingly, a variety of medications is under investigation. Given the multifaceted pathophysiology of NAFLD, probably, a combination of approaches in an individualized basis may be a more appropriate management. This review summarizes evidence on the epidemiology, pathogenesis, diagnosis and treatment of NAFLD.
Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship ...between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (fesc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 = 0.4–0.6) of fesc to the seasonal dynamics of the photosynthetic light use efficiency (LUEP), while the estimated physiological SIF yield was almost entirely uncorrelated to LUEP both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and fesc, explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for fesc, improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIRV) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.
•A mechanistic decomposition of canopy SIF for three in situ crop datasets.•The canopy structure and radiation factor outperforms SIF for GPP estimation.•Canopy escape fraction of SIF correlates with photosynthetic light use efficiency.•Correcting SIF for canopy scattering improves the correlation to APAR but not GPP.•Estimates of physiological SIF yield show no clear seasonal patterns.
Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients ...extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface—and are precisely equidistant—from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.
Ethanol can be used as a platform molecule for synthesizing valuable chemicals and fuel precursors. Direct synthesis of C5+ ketones, building blocks for lubricants and hydrocarbon fuels, from ethanol ...was achieved over a stable Pd‐promoted ZnO‐ZrO2 catalyst. The sequence of reaction steps involved in the C5+ ketone formation from ethanol was determined. The key reaction steps were found to be the in situ generation of the acetone intermediate and the cross‐aldol condensation between the reaction intermediates acetaldehyde and acetone. The formation of a Pd–Zn alloy in situ was identified to be the critical factor in maintaining high yield to the C5+ ketones and the stability of the catalyst. A yield of >70 % to C5+ ketones was achieved over a 0.1 % Pd‐ZnO‐ZrO2 mixed oxide catalyst, and the catalyst was demonstrated to be stable beyond 2000 hours on stream without any catalyst deactivation.
The formation of Pd–Zn alloy on a Pd‐ZnO‐ZrO2 results in the modification of the Pd electronic structure and enables the highly selective formation of C5+ ketones from renewable ethanol (>70 %yield) for extended catalysts lifetimes above 2000 hours.
Cortical Gradients and Laminar Projections in Mammals Goulas, Alexandros; Zilles, Karl; Hilgetag, Claus C.
Trends in neurosciences (Regular ed.),
November 2018, 2018-11-00, 20181101, Letnik:
41, Številka:
11
Journal Article
Recenzirano
A key component of current theories of brain structure and function is the layer-specific origin of structural connections of the cerebral cortex. This fundamental connectional feature pertains to ...different mammalian cortices, and recent neuroimaging advancements have started to pave the way for its function-based mapping in humans. Here, we propose a framework that systematically explains the characteristic layer-specific origin of structural connections and its graded variation across the cortical sheet and across mammalian species. The framework unifies seemingly dispersed observations on multiple levels of cortical organization, including the cellular, connectional, and functional level. Moreover, the framework allows the prediction of the layer-specific origin of connections in a spectrum of mammals, from rodents to humans.
The laminar origin of cortico-cortical connections constitutes a key component of contemporary theories of brain structure and function.
Detailed mapping of the laminar origin of connections is experimentally feasible in non-human mammals using invasive methods.
Recent neuroimaging advancements pave the way for the functional mapping of cortico-cortical connections with laminar-specific resolution in humans.
A unifying framework for the graded laminar architecture of cortico-cortical connections across the cortical sheet and across mammalian species is lacking.
We highlight such a framework that exhibits explanatory and predictive power and links the different dimensions of cortical organization of mammals. This framework constitutes a basis for expanding our understanding of the multiple levels of architecture of the mammalian cerebral cortex.
Biological neuronal networks (BNNs) are a source of inspiration and analogy making for researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists increasingly use ANNs ...as a model for the brain. Despite certain similarities between these two types of networks, important differences can be discerned. First, biological neural networks are sculpted by evolution and the constraints that it entails, whereas artificial neural networks are engineered to solve particular tasks. Second, the network topology of these systems, apart from some analogies that can be drawn, exhibits pronounced differences. Here, we examine strategies to construct recurrent neural networks (RNNs) that instantiate the network topology of brains of different species. We refer to such RNNs as bio-instantiated. We investigate the performance of bio-instantiated RNNs in terms of: (i) the prediction performance itself, that is, the capacity of the network to minimize the cost function at hand in test data, and (ii) speed of training, that is, how fast during training the network reaches its optimal performance. We examine bio-instantiated RNNs in working memory tasks where task-relevant information must be tracked as a sequence of events unfolds in time. We highlight the strategies that can be used to construct RNNs with the network topology found in BNNs, without sacrificing performance. Despite that we observe no enhancement of performance when compared to randomly wired RNNs, our approach demonstrates how empirical neural network data can be used for constructing RNNs, thus, facilitating further experimentation with biologically realistic network topologies, in contexts where such aspect is desired.
•Constructing artificial neural networks with network topology of animal brains.•Network topology as a structural prior effecting performance of neural systems.•Framework for building neurobiologically realistic brain models.
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