Adjacency matrix formulation of energy flow in dendrimeric polymers

Dendrimers are synthetic, highly branched polymers with an unusually high density of chromophores. As a result of their extremely high absorption cross-sections for visible light, they represent some of the most promising new materials for energy harvesting. Although the signature of the bonding structure in dendrimers is an essentially fractal geometry, the three-dimensional molecular folding of most higher generation materials results in a chromophore layout that is more obviously akin to concentric spherical shells. The number of chromophores in each shell is a simple function of the distance from the central core. The energy of throughput optical radiation, on capture by any of the chromophores, passes by a multi-step but highly efficient process to the photoactive core. Modeling this crucial migration process presents a number of challenges. It is far from a simple diffusive random walk; each step is subject to an intricate interplay of geometric and spectroscopic features. In this report, the first results of a new approach to the theory is described, developed and adapted from an adjacency matrix formulation. It is shown how this method offers not only kinetic information but also insights into the typical number of steps and the patterns of internal energy flow

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance

A new variance ratio metric to detect the timescale of compensatory dynamics

Understanding the mechanisms governing ecological stability—why a property such as primary productivity is stable in some communities and variable in others—has long been a focus of ecology. Compensatory dynamics, in which anti-synchronous fluctuations between populations buffer against fluctuations at the community level, are a key theoretical mechanism of stability. Classically, compensatory dynamics have been quantified using a variance ratio approach that compares the ratio between community variance and aggregate population variance, such that a lower ratio indicates compensation and a higher ratio indicates synchrony among species fluctuations. However, population dynamics may be influenced by different drivers that operate on different timescales, and evidence from aquatic systems indicates that communities can be compensatory on some timescales and synchronous on others. The variance ratio and related metrics cannot reflect this timescale specificity, yet have remained popular, especially in terrestrial systems. Here, we develop a timescale-specific variance ratio approach that formally decomposes the classical variance ratio according to the timescales of distinct contributions. The approach is implemented in a new R package, called tsvr, that accompanies this paper. We apply our approach to a long-term, multisite grassland community dataset. Our approach demonstrates that the degree of compensation vs. synchrony in community dynamics can vary by timescale. Across sites, population variability was typically greater over longer compared to shorter timescales. At some sites, minimal timescale specificity in compensatory dynamics translated this pattern of population variability into a similar pattern of greater community variability on longer compared to shorter timescales. But at other sites, differentially stronger compensatory dynamics at longer compared to shorter timescales produced lower-than-expected community variability on longer timescales. Within every site, there were plots that exhibited shifts in the strength of compensation between timescales. Our results highlight that compensatory vs. synchronous dynamics are intrinsically timescale-dependent concepts, and our timescale-specific variance ratio provides a metric to quantify timescale specificity and relate it back to the classic variance ratio

Biodiversity promotes ecosystem functioning despite environmental change

Three decades of research have demonstrated that biodiversity can promote the functioning of ecosystems. Yet, it is unclear whether the positive effects of biodiversity on ecosystem functioning will persist under various types of global environmental change drivers. We conducted a meta-analysis of 46 factorial experiments manipulating both species richness and the environment to test how global change drivers (i.e. warming, drought, nutrient addition or CO2 enrichment) modulated the effect of biodiversity on multiple ecosystem functions across three taxonomic groups (microbes, phytoplankton and plants). We found that biodiversity increased ecosystem functioning in both ambient and manipulated environments, but often not to the same degree. In particular, biodiversity effects on ecosystem functioning were larger in stressful environments induced by global change drivers, indicating that high-diversity communities were more resistant to environmental change. Using a subset of studies, we also found that the positive effects of biodiversity were mainly driven by interspecific complementarity and that these effects increased over time in both ambient and manipulated environments. Our findings support biodiversity conservation as a key strategy for sustainable ecosystem management in the face of global environmental change

Chamber-specific transcriptional responses in atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia, yet the molecular signature of the vulnerable atrial substrate is not well understood. Here, we delineated a distinct transcriptional signature in right versus left atrial cardiomyocytes (CMs) at baseline and identified chamber-specific gene expression changes in patients with a history of AF in the setting of end-stage heart failure (AF+HF) that are not present in heart failure alone (HF). We observed that human left atrial (LA) CMs exhibited Notch pathway activation and increased ploidy in AF+HF but not in HF alone. Transient activation of Notch signaling within adult CMs in a murine genetic model is sufficient to increase ploidy in both atrial chambers. Notch activation within LA CMs generated a transcriptomic fingerprint resembling AF, with dysregulation of transcription factor and ion channel genes, including Pitx2, Tbx5, Kcnh2, Kcnq1, and Kcnip2. Notch activation also produced distinct cellular electrophysiologic responses in LA versus right atrial CMs, prolonging the action potential duration (APD) without altering the upstroke velocity in the left atrium and reducing the maximal upstroke velocity without altering the APD in the right atrium. Our results support a shared human/murine model of increased Notch pathway activity predisposing to AF

Tissue Expression Difference between mRNAs and lncRNAs

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups

Environmental heterogeneity modulates the effect of plant diversity on the spatial variability of grassland biomass

Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.Fil: Daleo, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Chaneton, Enrique Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Iribarne, Oscar Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Tognetti, Pedro Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Borer, Elizabeth. University of Minnesota; Estados UnidosFil: Bruschetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: MacDougall, Andrew S.. University Of Guelph. Department Of Integrative Biology.; CanadáFil: Pascual, Jesus Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Sankaran, Mahesh. University of Leeds; Reino Unido. Tata Institute of Fundamental Research; IndiaFil: Seabloom, Eric. University of Minnesota; Estados UnidosFil: Wang, Shaopeng. Peking University; ChinaFil: Bagchi, Sumanta. Indian Institute of Science; IndiaFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Catford, Jane A.. University of Melbourne; Australia. Kings College London (kcl);Fil: Dickman, Chris R.. The University Of Sydney; AustraliaFil: Dickson, Tymothy L.. University of Nebraska; Estados UnidosFil: Donohue, Ian. Trinity College Dublin; Reino UnidoFil: Eisenhauer, Nico. Universitat Leipzig; Alemania. German Centre for Integrative Biodiversity Research; AlemaniaFil: Gruner, Daniel S.. University of Maryland; Estados UnidosFil: Haider, Sylvia. German Centre for Integrative Biodiversity Research; Alemania. Martin Luther University Halle-Wittenberg; Alemania. Leuphana University of Lüneburg; AlemaniaFil: Jentsch, Anke. University of Bayreuth; AlemaniaFil: Knops, Johannes M. H.. Xi’an Jiaotong-Liverpool University; ChinaFil: Lekberg, Ylva. University of Montana; Estados UnidosFil: McCulley, Rebecca L.. University of Kentucky; Estados UnidosFil: Moore, Joslin L.. University of Melbourne; Australia. Monash University; Australia. Arthur Rylah Institute for Environmental Research; AustraliaFil: Mortensen, Brent. Benedictine College; Estados UnidosFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de la Patagonia Austral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rocca, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentin

Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale

Energy flow in dendrimers: An adjacency matrix representation

The distinctive light harvesting characteristics of dendrimers owe their origin to the efficiency and highly directed nature of a multi-step process that delivers captured light energy to the core. Each step involves the resonance transfer of energy between chromophores, a process subject to an interplay of geometric and spectroscopic features. This Letter presents the first results of a new approach to modelling the multi-step flow, developing an adjacency matrix formulation of the bond connectivity informed by spectroscopic principles. It is shown that this method has the capacity to reveal the character and patterns of internal energy flow in dendrimeric materials