Integrating plus energy buildings and districts with the eu energy community framework:Regulatory opportunities, barriers and technological solutions

The aim of this paper is to assess opportunities the Clean Energy Package provides for Plus Energy Buildings (PEBs) and Plus Energy Districts (PEDs) regarding their economic optimization and market integration, possibly leading to new use cases and revenue streams. At the same time, insights into regulatory limitations at the national level in transposing the set of EU Clean Energy Package provisions are shown. The paper illustrates that the concepts of PEBs and PEDs are in principle compatible with the EU energy community concepts, as they relate to technical characteristics while energy communities provide a legal and regulatory framework for the organization and governance of a community, at the same time providing new regulatory space for specific activities and market integration. To realize new use cases, innovative ICT approaches are needed for a range of actors actively involved in creating and operating energy communities as presented in the paper. The paper discusses a range of different options to realize PEBs and PEDs as energy communities based on the H2020 EXCESS project. It concludes, however, that currently the transposition of the Clean Energy Package by the EU Member States is incomplete and limiting and as a consequence, in the short term, the full potential of PEBs and PEDs cannot be exploited

Exploiting the Temporal Logic Hierarchy and the Non-Confluence Property for Efficient LTL Synthesis

The classic approaches to synthesize a reactive system from a linear temporal logic (LTL) specification first translate the given LTL formula to an equivalent omega-automaton and then compute a winning strategy for the corresponding omega-regular game. To this end, the obtained omega-automata have to be (pseudo)-determinized where typically a variant of Safra's determinization procedure is used. In this paper, we show that this determinization step can be significantly improved for tool implementations by replacing Safra's determinization by simpler determinization procedures. In particular, we exploit (1) the temporal logic hierarchy that corresponds to the well-known automata hierarchy consisting of safety, liveness, Buechi, and co-Buechi automata as well as their boolean closures, (2) the non-confluence property of omega-automata that result from certain translations of LTL formulas, and (3) symbolic implementations of determinization procedures for the Rabin-Scott and the Miyano-Hayashi breakpoint construction. In particular, we present convincing experimental results that demonstrate the practical applicability of our new synthesis procedure

The 1st Verified Software Competition, Extended Experience Report

We, the organizers and participants, report our experiences from the 1st Veried Software Competition, held in August 2010 in Edinburgh at the VSTTE 2010 conferenc

The 1st Verified Software Competition, Extended Experience Report

We, the organizers and participants, report our experiences from the 1st Veried Software Competition, held in August 2010 in Edinburgh at the VSTTE 2010 conferenc

TFIIB aptamers inhibit transcription by perturbing PIC formation at distinct stages

Transcription in eukaryotes is a multistep process involving the assembly and disassembly of numerous inter- and intramolecular interactions between transcription factors and nucleic acids. The roles of each of these interactions and the regions responsible for them have been identified and studied primarily by the use of mutants, which destroy the inherent properties of the interacting surface. A less intrusive but potentially effective way to study the interactions as well as the surfaces responsible for them is the use of RNA aptamers that bind to the interacting factors. Here, we report the isolation and characterization of high-affinity RNA aptamers that bind to the yeast general transcription factor TFIIB. These aptamers fall into two classes that interfere with TFIIB's interactions with either TBP or RNA polymerase II, both of which are crucial for transcription in yeast. We demonstrate the high affinity and specificity of these reagents, their effect on transcription and preinitiation complex formation and discuss their potential use to address mechanistic questions in vitro as well as in vivo

An RNA-based transcription activator derived from an inhibitory aptamer

According to the recruitment model of transcriptional activation, an activator helps initiate transcription by bringing the RNA polymerase to a specific location on the DNA through interaction with components of the transcriptional machinery. However, it is difficult to isolate and define the activities of specific activator–target pairs experimentally through rearranging existing protein parts. Here we designed and constructed an RNA-based transcriptional activator to study specificity from both sides of the activator–target interface. Utilizing a well-characterized site-specific RNA aptamer for TFIIB, we were able to delineate some key features of this process. By rationally converting an inhibitory aptamer into the activation domain of the activator, we also introduced a new source of submolecular building blocks to synthetic biology

Statistical Modeling of Transcription Factor Binding Affinities Predicts Regulatory Interactions

Recent experimental and theoretical efforts have highlighted the fact that binding of transcription factors to DNA can be more accurately described by continuous measures of their binding affinities, rather than a discrete description in terms of binding sites. While the binding affinities can be predicted from a physical model, it is often desirable to know the distribution of binding affinities for specific sequence backgrounds. In this paper, we present a statistical approach to derive the exact distribution for sequence models with fixed GC content. We demonstrate that the affinity distribution of almost all known transcription factors can be effectively parametrized by a class of generalized extreme value distributions. Moreover, this parameterization also describes the affinity distribution for sequence backgrounds with variable GC content, such as human promoter sequences. Our approach is applicable to arbitrary sequences and all transcription factors with known binding preferences that can be described in terms of a motif matrix. The statistical treatment also provides a proper framework to directly compare transcription factors with very different affinity distributions. This is illustrated by our analysis of human promoters with known binding sites, for many of which we could identify the known regulators as those with the highest affinity. The combination of physical model and statistical normalization provides a quantitative measure which ranks transcription factors for a given sequence, and which can be compared directly with large-scale binding data. Its successful application to human promoter sequences serves as an encouraging example of how the method can be applied to other sequences

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Prospective identification of parasitic sequences in phage display screens

Phage display empowered the development of proteins with new function and ligands for clinically relevant targets. In this report, we use next-generation sequencing to analyze phage-displayed libraries and uncover a strong bias induced by amplification preferences of phage in bacteria. This bias favors fast-growing sequences that collectively constitute <0.01% of the available diversity. Specifically, a library of 10[superscript 9] random 7-mer peptides (Ph.D.-7) includes a few thousand sequences that grow quickly (the ‘parasites’), which are the sequences that are typically identified in phage display screens published to date. A similar collapse was observed in other libraries. Using Illumina and Ion Torrent sequencing and multiple biological replicates of amplification of Ph.D.-7 library, we identified a focused population of 770 ‘parasites’. In all, 197 sequences from this population have been identified in literature reports that used Ph.D.-7 library. Many of these enriched sequences have confirmed function (e.g. target binding capacity). The bias in the literature, thus, can be viewed as a selection with two different selection pressures: (i) target-binding selection, and (ii) amplification-induced selection. Enrichment of parasitic sequences could be minimized if amplification bias is removed. Here, we demonstrate that emulsion amplification in libraries of ~10[superscript 6] diverse clones prevents the biased selection of parasitic clones