•Multi-agent negotiation techniques demonstrated for a transactive energy market.•Microgrid agent behavior differs by instantaneous load, solar, and storage level.•Trading in a 3-node and 9-node ...network gives 3.6% and 5.4% energy cost reduction.•Nodes with storage were self-sufficient and traded less often with other consumers.
Distributed energy resources are becoming increasingly common and forcing change in conventional energy markets with growing attention given to transactive energy networks that allow power trading between neighboring microgrids or distributed energy resources customers to supplement transactions with an electric utility. This study develops and evaluates a generalizable method for managing energy trading between microgrids in a grid-connected network through multi-agent techniques. The approach is demonstrated for a 3-node network and a 9-node network for a simulated year with hourly load and solar data for each unique microgrid agent. Results are compared against baseline networks without trading enabled to quantify a 3.6% and 5.4% reduction in the levelized cost of energy, respectively, with trading enabled for the 3-node and 9-node cases. Local energy storage capacities are varied to examine impact on the levelized cost of energy and trading behaviors. Results indicate that trading between microgrids reduces the levelized cost of energy for each individual node and the whole network, and that certain trends emerge between agents that allow some microgrids to operate at a lower cost than others.
Summary Non-alcoholic fatty liver disease (NAFLD) is a frequent accompaniment of obesity and insulin resistance. With the prevalence approaching 85% in obese populations, new therapeutic approaches ...to manage NAFLD are warranted. A systematic search of the literature was conducted for studies pertaining to the effect of omega-3 polyunsaturated fatty acid (PUFA) supplementation on NAFLD in humans. Primary outcome measures were liver fat and liver function tests: alanine aminotransferase (ALT) and aspartate aminotransferase 1 . Data were pooled and meta-analyses conducted using a random effects model. Nine eligible studies, involving 355 individuals given either omega-3 PUFA or control treatment were included. Beneficial changes in liver fat favoured PUFA treatment (effect size = −0.97, 95% CI: −0.58 to −1.35, p < 0.001). A benefit of PUFA vs . control was also observed for AST (effect size = −0.97, 95% CI: −0.13 to −1.82, p = 0.02). There was a trend towards favouring PUFA treatment on ALT but this was not significant (effect size = −0.56, 95% CI: −1.16 to 0.03, p = 0.06). Sub-analyses of only randomised control trials (RCTs) showed a significant benefit for PUFA vs . control on liver fat (effect size = −0.96, 95% CI: −0.43 to −1.48, p < 0.001), but not for ALT ( p = 0.74) or AST ( p = 0.28). There was significant heterogeneity between studies. The pooled data suggest that omega-3 PUFA supplementation may decrease liver fat, however, the optimal dose is currently not known. Well designed RCTs which quantify the magnitude of effect of omega-3 PUFA supplementation on liver fat are needed.
White matter hyperintensities (WMHs) are among the most commonly observed marker of cerebrovascular disease. Age is a key risk factor for WMH development. Cardiorespiratory fitness (CRF) is ...associated with increased vessel compliance, but it remains unknown if high CRF affects WMH volume. This study explored the effects of CRF on WMH volume in community-dwelling older adults. We further tested the possibility of an interaction between CRF and age on WMH volume. Participants were 76 adults between the ages of 59 and 77 (mean age = 65.36 years, SD = 3.92) who underwent a maximal graded exercise test and structural brain imaging. Results indicated that age was a predictor of WMH volume (beta = .32, p = .015). However, an age-by-CRF interaction was observed such that higher CRF was associated with lower WMH volume in older participants (beta = -.25, p = .040). Our findings suggest that higher levels of aerobic fitness may protect cerebrovascular health in older adults.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background & Aims Non-alcoholic fatty liver disease (NAFLD) affects up to 30% of the population and signifies increased risk of liver fibrosis and cirrhosis, type 2 diabetes, and cardiovascular ...disease. Therapies are limited. Weight loss is of benefit but is difficult to maintain. We aimed at examining the effect of the Mediterranean diet (MD), a diet high in monounsaturated fatty acids, on steatosis and insulin sensitivity, using gold standard techniques. Methods Twelve non-diabetic subjects (6 Females/6 Males) with biopsy-proven NAFLD were recruited for a randomised, cross-over 6-week dietary intervention study. All subjects undertook both the MD and a control diet, a low fat-high carbohydrate diet (LF/HCD), in random order with a 6-week wash-out period in- between. Insulin sensitivity was determined with a 3-h hyperinsulinemic–euglycemic clamp study and hepatic steatosis was assessed with localized magnetic resonance1 H spectroscopy (1 H-MRS). Results At baseline, subjects were abdominally obese with elevated fasting concentrations of glucose, insulin, triglycerides, ALT, and GGT. Insulin sensitivity at baseline was low (M = 2.7 ± 1.0 mg/kg/min−1 ). Mean weight loss was not different between the two diets ( p = 0.22). There was a significant relative reduction in hepatic steatosis after the MD compared with the LF/HCD: 39 ± 4% versus 7 ± 3%, as measured by1 H-MRS ( p = 0.012). Insulin sensitivity improved with the MD, whereas after the LF/HCD there was no change ( p = 0.03 between diets). Conclusions Even without weight loss, MD reduces liver steatosis and improves insulin sensitivity in an insulin-resistant population with NAFLD, compared to current dietary advice. This diet should be further investigated in subjects with NAFLD.
High-throughput sequencing of small RNAs (sRNA-seq) is a popular method used to discover and annotate microRNAs (miRNAs), endogenous short interfering RNAs (siRNAs), and Piwi-associated RNAs ...(piRNAs). One of the key steps in sRNA-seq data analysis is alignment to a reference genome. sRNA-seq libraries often have a high proportion of reads that align to multiple genomic locations, which makes determining their true origins difficult. Commonly used sRNA-seq alignment methods result in either very low precision (choosing an alignment at random), or sensitivity (ignoring multi-mapping reads). Here, we describe and test an sRNA-seq alignment strategy that uses local genomic context to guide decisions on proper placements of multi-mapped sRNA-seq reads. Tests using simulated sRNA-seq data demonstrated that this local-weighting method outperforms other alignment strategies using three different plant genomes. Experimental analyses with real sRNA-seq data also indicate superior performance of local-weighting methods for both plant miRNAs and heterochromatic siRNAs. The local-weighting methods we have developed are implemented as part of the sRNA-seq analysis program ShortStack, which is freely available under a general public license. Improved genome alignments of sRNA-seq data should increase the quality of downstream analyses and genome annotation efforts.
A line loss approximation via parametrization is developed to improve performance of the simplified Baran and Wu DistFlow method, while maintaining a linear set of equations. The approach is ...evaluated on thousands of training feeders that are created to determine a numerically optimal setting for the parameterization. Feeders are generated using recent advances in synthetic network test case generation. The problem is formulated with the same structure as the simplified DistFlow, yet is more accurate given that line losses are explicitly expressed and quantified. The single-phase methodology is extended to multiphase systems by formulating matrix-vector equations that maintain an analogy to their single-phase counterpart. Results with approximated line losses are shown to also improve the accuracy of multiphase distribution system calculations.
Optically active amines represent highly valuable building blocks for the synthesis of advanced pharmaceutical intermediates, drug molecules, and biologically active natural products. Hemoproteins ...have recently emerged as promising biocatalysts for the formation of C–N bonds via carbene transfer, but asymmetric N–H carbene insertion reactions using these or other enzymes have so far been elusive. Here, we report the successful development of a biocatalytic strategy for the asymmetric N–H carbene insertion of aromatic amines with 2-diazopropanoate esters using engineered variants of myoglobin. High activity and stereoinduction in this reaction could be achieved by tuning the chiral environment around the heme cofactor in the metalloprotein in combination with catalyst-matching and tailoring of the diazo reagent. Using this approach, an efficient biocatalytic protocol for the synthesis of a broad range of substituted aryl amines with up to 82% ee was obtained. In addition, a stereocomplementary catalyst useful to access the mirror-image form of the N–H insertion products was identified. This work paves the way to asymmetric amine synthesis via biocatalytic carbene transfer, and the present strategy based on the synergistic combination of protein and diazo reagent engineering is expected to prove useful in the context of these as well as other challenging asymmetric carbene transfer reactions.
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA ...microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.
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