Decoding the `Nature Encoded' Messages for Distributed Energy Generation Control in Microgrid

The communication for the control of distributed energy generation (DEG) in microgrid is discussed. Due to the requirement of realtime transmission, weak or no explicit channel coding is used for the message of system state. To protect the reliability of the uncoded or weakly encoded messages, the system dynamics are considered as a `nature encoding' similar to convolution code, due to its redundancy in time. For systems with or without explicit channel coding, two decoding procedures based on Kalman filtering and Pearl's Belief Propagation, in a similar manner to Turbo processing in traditional data communication systems, are proposed. Numerical simulations have demonstrated the validity of the schemes, using a linear model of electric generator dynamic system.Comment: It has been submitted to IEEE International Conference on Communications (ICC

Mitochondria-Localized Glutamic Acid-Rich Protein (MGARP) Gene Transcription Is Regulated by Sp1

Background: Mitochondria-localized glutamic acid-rich protein (MGARP) is a novel mitochondrial transmembrane protein expressed mainly in steroidogenic tissues and in the visual system. Previous studies showed that MGARP functions in hormone biosynthesis and its expression is modulated by the HPG axis. Methodology/principal findings: By bioinformatics, we identified two characteristic GC-rich motifs that are located proximal to the transcription start site (TSS) of MGARP, and each contains two Specificity protein 1 (Sp1) binding elements. We then determined that the −3 kb proximal MGARP promoter is activated in a Sp1-dependent manner using reporter assays and knockdown of Sp1 led to decreased expression of endogenous MGARP messages. We also demonstrated that one of the two GC-rich motifs, GC-Box1, harbors prominent promoter activity mediated by Sp1, and that it requires both GC boxes for full transcriptional activation. These findings suggest a dominant role for these GC boxes and Sp1 in activating the MGARP promoter through a synergistic mechanism. Consistently, the results of an Electrophoretic Mobility Gel Shift Assay (EMSA) and Chromatin Immunoprecipitation (ChIP) confirmed that Sp1 specifically interacts with the GC-rich region. We further found that estrogen receptor α (ERα), a known Sp1 co-activator, could potentiate GC-boxes containing MGARP promoter activity and this effect is mediated by Sp1. Knockdown of Sp1 significantly diminished the MGARP promoter transactivation and the expression of endogenous MGARP mediated by both Sp1 and ERα. Conclusions/significance: The present study identified a proximal core sequence in the MGARP promoter that is composed of two enriched Sp1 binding motifs and established Sp1 as one major MGARP transactivator whose functions are synergistic with ERα, providing a novel understanding of the mechanisms of MGARP gene transcriptional regulation

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

BACKGROUND: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics. RESULTS: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model. CONCLUSIONS: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques

High-Angular Resolution Dust Polarization Measurements: Shaped B-field Lines in the Massive Star Forming Region Orion BN/KL

We present observational results of the thermal dust continuum emission and its linear polarization in one of the nearest massive star-forming sites Orion BN/KL in Orion Molecular Cloud-1. The observations were carried out with the Submillimeter Array. With an angular resolution of 1" (~2 mpc; 480 AU), we have detected and resolved the densest cores near the BN/KL region. At a wavelength of ~870 micron, the polarized dust emission can be used to trace the structure of the magnetic field in this star-forming core. The dust continuum appears to arise from a V-shaped region, with a cavity nearly coincident with the center of the explosive outflows observed on larger scales. The position angles (P.A.s) of the observed polarization vary significantly by a total of about 90 degree but smoothly, i.e., curl-like, across the dust ridges. Such a polarization pattern can be explained with dust grains being magnetically aligned instead of mechanically with outflows, since the latter mechanism would cause the P.A.s to be parallel to the direction of the outflow, i.e., radial-like. The magnetic field projected in the plane of sky is therefore derived by rotating the P.A.s of the polarization by 90 degree. We find an azimuthally symmetric structure in the overall magnetic field morphology, with the field directions pointing toward 2.5" west to the center of the explosive outflows. We also find a preferred symmetry plane at a P.A. of 36 degree, which is perpendicular to the mean magnetic field direction (120 degree) of the 0.5 pc dust ridge. Two possible interpretations of the origin of the observed magnetic field structure are discussed.Comment: 27 pages, 7 figures; ApJ in pres

Algorithmic Financial Regulation: Limits of Computing Complex Adaptive Systems

This article examines the potential of and limits to the use of machine learning for financial regulation. Ideally, if we could fully understand the financial system and agree on long- and short-term regulatory goals, we would be able to write code that carries out the computation that extracts proper representations from the data and makes correct regulatory decisions. We cannot do this yet because of limited sources of data, the bias brought by human beings and algorithmic models, and the difficulty of improving uninterpretable models. Furthermore, since law is a combination of merits and facts, there are difficulties in establishing the ground truth, modeling complex financial systems, and attaining fair outcomes simply based on statistics. Statistics, as a method of inductive learning, can only recognize patterns from existing data. From a methodological perspective, this represents a paradigm shift from observational study (deduction) to data analytics (induction). However, in the financial field, there is a fundamental difference between measurable risks and unknowable uncertainty in the future, which significantly affects the reliability of models to determine regulation based on estimated risks. Therefore, algorithmic models cannot make reliable suggestions about unusual situations, nor deal with complex problems that lack sufficient training data. In cases of algorithmic regulation, despite the predetermined regulatory goals, specific standards should remain adaptive to new data collected from the regulated environment, so as to mitigate bias generated from historical data and the initial model setting