Dynamic incentive strategy for voluntary demand response based on TDP scheme

Abstract-The enhanced real-time metering and communication capabilities from smart meters and their associated advanced metering infrastructure make it possible for utility company to extend demand response (DR) to small customers through timedependent pricing (TDP). Considering the economic reason and infrastructure cost, the utility company has to design an incentive scheme to attract the traditional flat pricing (FP) users to be engaged in the TDP scheme. In this process, the utility company may share its revenue from the TDP scheme to those TDP users. It is found, with properly analyzing the energy procurement cost and user elasticity, a dynamic incentive strategy can be considered in dual-tariffs system when flat pricing (FP) and TDP pricing are co-existed. This dynamic incentive strategy gives appropriate stimulus to the users who are involved into the TDP program, and guarantee the utility company's profit at the same time

Performance evaluation of lossy quality compression algorithms for RNA-seq data

Background Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. Results In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. Conclusions Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results. Document type: Articl

The nuclear phosphatase SCP4 regulates FoxO transcription factors during muscle wasting in chronic kidney disease

Chronic kidney disease (CKD) and related inflammatory responses stimulate protein-energy wasting, a complication causing loss of muscle mass. Primarily, muscle wasting results from accelerated protein degradation via autophagic/lysosomal and proteasomal pathways, but mechanisms regulating these proteolysis pathways remain unclear. Since dephosphorylation of FoxOs regulates ubiquitin/proteasome protein metabolism, we tested whether a novel nuclear phosphatase, the small C-terminal domain phosphatase (SCP) 4, regulates FoxOs signaling and, in turn, muscle wasting. In cultured mouse myoblast cells, SCP4 overexpression stimulated proteolysis, while knockdown of SCP4 prevented the proteolysis stimulated by inflammatory cytokines. SCP4 overexpression led to nuclear accumulation of FoxO1/3a followed by increased expression of catabolic factors including myostatin, Atrogin-1, and MuRF-1, and induction of lysosomal-mediated proteolysis. Treatment of C2C12 myotubes with proinflammatory cytokines stimulated SCP4 expression in an NF-\u3baB-dependent manner. In skeletal muscle of mice with CKD, SCP4 expression was up-regulated. Similarly, in skeletal muscle of patients with CKD, SCP4 expression was significantly increased. Knockdown of SCP4 significantly suppressed FoxO1/3a-mediated expression of Atrogin-1 and MuRF-1 and prevented muscle wasting in mice with CKD. Thus, SCP4 is a novel regulator of FoxO transcription factors and promotes cellular proteolysis. Hence, targeting SCP4 may prevent muscle wasting in CKD and possibly other catabolic conditions

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems

Lossless audio compression - A scalable approach

Ph.DDOCTOR OF PHILOSOPH