miR-4286 is Involved in Connections Between IGF-1 and TGF-β Signaling for the Mesenchymal Transition and Invasion by Glioblastomas

Cellular and Molecular Neurobiology - The insulin-like growth factor (IGF)-1 and transforming growth factor (TGF)-β signal pathways are both recognized as important in regulating cancer...     

Modulation of α7nAchR by Melatonin Alleviates Ischemia and Reperfusion-Compromised Integrity of Blood–Brain Barrier Through Inhibiting HMGB1-Mediated Microglia Activation and CRTC1-Mediated Neuronal Loss

Cellular and Molecular Neurobiology - The only food and drug administration (FDA)-approved drug currently available for the treatment of acute ischemic stroke is tissue plasminogen activator (tPA),...     

Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model

Journal of Computational Neuroscience - In vitro studies have shown that hippocampal pyramidal neurons employ a mechanism similar to stochastic resonance (SR) to enhance the detection and...     

Coloration and phenology manifest nutrient variability in senesced leaves of 46 temperate deciduous woody species

颜色和物候表明46种温带落叶木本植物衰老叶片的养分变异 不同共生植物的叶片养分含量差异显著,反映了不同的叶片养分利用策略。然而,衰老叶片养分的种间变异及其驱动因素尚不清楚。本研究旨在探讨衰老叶片养分的种间变异及其驱动因素。我们在中国东北的帽儿山森林生态系统研究站测定了46种共存温带落叶木本植物新鲜落叶的碳、氮、磷浓度。 采用随机森林模型量化10个生物因素(菌根类型、固氮类型、生长形态、耐阴性、叶片质地、变色程度、变色类型、叶片变色峰期、落叶峰期和落叶末期)的相对重要性。研究结果表明,落叶氮浓度种间变化为4倍,磷浓度变化达9倍。较高的氮和磷平均浓度(15.38和1.24 mg g⁻¹)表明该森林氮和磷限制较弱。功能群仅对特定养分及其比值有显著影响。磷浓度、氮磷比与外生菌根树种的落叶高峰日和落叶结束日呈负相关。颜色鲜艳的叶片(红色＞棕色＞黄色＞黄绿色＞绿色)倾向于比绿色叶片氮和磷浓度更低而碳氮比和碳磷比较高。随机森林模型表明,秋季叶变色和落叶物候贡献了80%的种间变异解释量。这些结果增加了我们对温带森林木本植物营养策略之衰老叶片养分变异性的理解。     

The effects of immersive garden experience on the health care to elderly residents with mild-to-moderate cognitive impairment living in nursing homes after the COVID-19 pandemic

Elderly patients living in long-term care facilities have been restricted from leaving to comply with social distancing guidelines during the COVID-19 pandemic. This has led to a worsening of disorders, such as anxiety and depression. This study aims to understand the health benefits of an immersive garden experience to elderly nursing home residents with mild-to-moderate cognitive impairments. Virtual reality devices were used to provide immersive garden experiences for the residents who were unable to go outside. The heart rate and heart rate variability (HRV) data of the participants of the participants were collected using biofeedback instruments, and changes in the low frequency/high frequency (LF/HF) and the standard deviation of the NN interval (SDNN) values caused by immersive garden experiences were discussed. The results show that the immersive garden experiences were beneficial to these elderly residents. Within 6 min of completing the experiment, we found that the heart rates of participants had dropped slightly, while SDNN and HF values continued to rise. SDNN values before and after the experiment demonstrated a statistically significant improvement. Furthermore, participants expressed their satisfaction with the video intervention program. The results indicated that nursing homes can provide immersive landscape experiences to help increase HRV and SDNN of their elderly residents. This will not only help these residents recall beautiful memories of their past, but will also improve their quality of life.

     

How long should a kiss last between a kinase and its substrate?

In the presence of abscisic acid or environmental stress, activated SnRK2s transiently phosphorylate Raptor1B, a regulatory component of the TOR complex, to inhibit plant growth. To examine such transient interactions between a kinase and its substrate, comprehensive genetic or biochemistry evidence is more conclusive than a single negative co-immunoprecipitation test.     

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.Carbon (C) and nitrogen (N) metabolism are the basis for life on Earth. The production, balance, and tradeoffs of C and N metabolism are critical to all plant growth, yield, and local adaptation (Coruzzi and Bush, 2001; Coruzzi et al., 2007). In plants, there is a critical balance between the tissues that are producing energy (sources) and those using it (sinks), as the identities and locations of these vary through time and developmental stage (Smith et al., 2004). While a great deal of research has focused on the key genes and proteins involved in these processes (Wang et al., 1993; Kim et al., 2000; Takahashi et al., 2009), relatively little is known about the natural variation within a species that fine-tunes these processes in individual plants.In addition, a key aspect of core C metabolism involves the nature of plant photosynthesis. While the majority of plants use standard C₃ photosynthetic pathways, some, including maize (Zea mays) and many other grasses, use C₄ photosynthesis to concentrate CO₂ in bundle sheath cells to avoid wasteful photorespiration (Sage, 2004). Under some conditions (such as drought or high temperatures), C₄ photosynthesis is much more efficient than C₃ photosynthesis. Since these conditions are expected to become more prevalent in the near future due to climate change, various research groups are working to convert C₃ crop species to C₄ metabolism in order to boost crop production and food security (Sage and Zhu, 2011). Beyond this, better understanding of both C₃ and C₄ metabolic pathways will aid efforts to breed crops for superior yield, N-use efficiency, and other traits important for global food production.In the last two decades, quantitative trait locus (QTL) mapping, first with linkage analysis and later with association mapping, has been used to dissect C and N metabolism in several species, including Arabidopsis (Arabidopsis thaliana; Mitchell-Olds and Pedersen, 1998; Keurentjes et al., 2008; Lisec et al., 2008; Sulpice et al., 2009), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Hirel et al., 2001; Limami et al., 2002; Zhang et al., 2006, 2010a, 2010b). These studies identified key genetic regions underlying variation in core C and N metabolism, many of which include candidate genes known to be involved in these processes.Previous studies of genetic variation for C and N metabolism are limited by the fact that they identified trait loci only through linkage mapping in artificial families or through association mapping across populations of unrelated individuals. Linkage mapping benefits from high statistical power due to many individuals sharing the same genotype at any given location, but it suffers from low resolution due to the limited number of generations (and hence recombination events) since the initial founders. Association mapping, in turn, enjoys high resolution due to the long recombination histories of natural populations but suffers from low power, since most genotypes occur in only a few individuals. In addition, many of these studies focused on C and N in artificial settings (e.g. greenhouses or growth chambers) instead of field conditions, running the risk that important genetic loci could be missed if the conditions do not include important (and potentially unknown) natural environmental variables.To address these issues and improve our understanding of C and N metabolism in maize, we used a massive and diverse germplasm resource, the maize nested association mapping (NAM) population (Buckler et al., 2009; McMullen et al., 2009), to evaluate genetic variation underlying the accumulation of 12 targeted metabolites in maize leaf tissue under field conditions. This population was formed by mating 25 diverse maize lines to the reference line, B73, and creating a 200-member biparental family from each of these crosses. The entire 5,000-member NAM population thus combines the strengths of both linkage and association mapping (McMullen et al., 2009), and it has been used to identify QTLs for important traits such as flowering time (Buckler et al., 2009), disease resistance (Kump et al., 2011; Poland et al., 2011), and plant architecture (Tian et al., 2011; Peiffer et al., 2013). Most importantly, this combination of power and resolution frequently resolves associations down to the single-gene level, even when using field-based data.The metabolites we profiled are key indicators of photosynthesis, respiration, glycolysis, and protein and sugar metabolism in the plant (Sulpice et al., 2009). By taking advantage of a robotized metabolic phenotyping platform (Gibon et al., 2004), we performed more than 100,000 assays across 12,000 samples, with two independent samples per experimental plot. Raw data and the best linear unbiased predictors (BLUPs) of these data were included as part of a study of general functional variation in maize (Wallace et al., 2014), but, to our knowledge, this is the first in-depth analysis of these metabolic data. We find strong correlations among several of the metabolites, and we also find extensive pleiotropy among the different traits. Many of the top QTLs are also near or within candidate genes relating to C and N metabolism, thus identifying targets for future breeding and selection. These results provide a powerful resource for those working with core C and N metabolism in plants and for improving maize performance in particular.