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Malnutrition is widespread in older people and represents a major geriatric syndrome with multifactorial etiology and severe consequences for health outcomes and quality of life. The aim of the present paper is to describe current approaches and evidence regarding malnutrition treatment and to highlight relevant knowledge gaps that need to be addressed. Recently published guidelines of the European Society for Clinical Nutrition and Metabolism (ESPEN) provide a summary of the available evidence and highlight the wide range of different measures that can be taken—from the identification and elimination of potential causes to enteral and parenteral nutrition—depending on the patient’s abilities and needs. However, more than half of the recommendations therein are based on expert consensus because of a lack of evidence, and only three are concern patient-centred outcomes. Future research should further clarify the etiology of malnutrition and identify the most relevant causes in order to prevent malnutrition. Based on limited and partly conflicting evidence and the limitations of existing studies, it remains unclear which interventions are most effective in which patient groups, and if specific situations, diseases or etiologies of malnutrition require specific approaches. Patient-relevant outcomes such as functionality and quality of life need more attention, and research methodology should be harmonised to allow for the comparability of studies.
The primary function of leaves is to provide an interface between plants and their environment for gas exchange, light exposure and thermoregulation. Leaves have, therefore a central contribution to plant fitness by allowing an efficient absorption of sunlight energy through photosynthesis to ensure an optimal growth. Their final geometry will result from a balance between the need to maximize energy uptake while minimizing the damage caused by environmental stresses. This intimate relationship between leaf and its surroundings has led to an enormous diversification in leaf forms. Leaf shape varies between species, populations, individuals or even within identical genotypes when those are subjected to different environmental conditions. For instance, the extent of leaf margin dissection has, for long, been found to inversely correlate with the mean annual temperature, such that Paleobotanists have used models based on leaf shape to predict the paleoclimate from fossil flora. Leaf growth is not only dependent on temperature but is also regulated by many other environmental factors such as light quality and intensity or ambient humidity. This raises the question of how the different signals can be integrated at the molecular level and converted into clear developmental decisions. Several recent studies have started to shed the light on the molecular mechanisms that connect the environmental sensing with organ-growth and patterning. In this review, we discuss the current knowledge on the influence of different environmental signals on leaf size and shape, their integration as well as their importance for plant adaptation.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
The simultaneous detection of gravitational waves and light from the binary neutron star merger GW170817 led to independent measurements of distance and redshift, providing a direct estimate of the Hubble constant H-0 that does not rely on a cosmic distance ladder, nor assumes a specific cosmological model.
By using gravitational waves as "standard sirens", this approach holds promise to arbitrate the existing tension between the H-0 value inferred from the cosmic microwave background and those obtained from local measurements.
However, the known degeneracy in the gravitational-wave analysis between distance and inclination of the source led to a H-0 value from GW170817 that was not precise enough to resolve the existing tension.
In this review, we summarize recent works exploiting the viewing-angle dependence of the electromagnetic signal, namely the associated short gamma-ray burst and kilonova, to constrain the system inclination and improve on H-0.
We outline the key ingredients of the different methods, summarize the results obtained in the aftermath of GW170817 and discuss the possible systematics introduced by each of these methods.
As a tumor suppressor and the most frequently mutated gene in cancer, p53 is among the best-described molecules in medical research. As cancer is in most cases an age-related disease, it seems paradoxical that p53 is so strongly conserved from early multicellular organisms to humans. A function not directly related to tumor suppression, such as the regulation of metabolism in nontransformed cells, could explain this selective pressure. While this role of p53 in cellular metabolism is gradually emerging, it is imperative to dissect the tissue-and cell-specific actions of p53 and its downstream signaling pathways. In this review, we focus on studies reporting p53's impact on adipocyte development, function, and maintenance, as well as the causes and consequences of altered p53 levels in white and brown adipose tissue (AT) with respect to systemic energy homeostasis. While whole body p53 knockout mice gain less weight and fat mass under a high-fat diet owing to increased energy expenditure, modifying p53 expression specifically in adipocytes yields more refined insights: (1) p53 is a negative regulator of in vitro adipogenesis; (2) p53 levels in white AT are increased in diet-induced and genetic obesity mouse models and in obese humans; (3) functionally, elevated p53 in white AT increases senescence and chronic inflammation, aggravating systemic insulin resistance; (4) p53 is not required for normal development of brown AT; and (5) when p53 is activated in brown AT in mice fed a high-fat diet, it increases brown AT temperature and brown AT marker gene expression, thereby contributing to reduced fat mass accumulation. In addition, p53 is increasingly being recognized as crucial player in nutrient sensing pathways. Hence, despite existence of contradictory findings and a varying density of evidence, several functions of p53 in adipocytes and ATs have been emerging, positioning p53 as an essential regulatory hub in ATs. Future studies need to make use of more sophisticated in vivo model systems and should identify an AT-specific set of p53 target genes and downstream pathways upon different (nutrient) challenges to identify novel therapeutic targets to curb metabolic diseases