A Portable System with 0.1-ppm RMSE Resolution for 1-10 MHz Resonant MEMS Frequency Measurement

This article presents a portable and programmable frequency measurement system (PrO-FMS) with 0.1-ppm RMSE resolution over a measurement time interval of 33.2 s for bulk-acoustic resonator applications. PrO-FMS has a feature of choosing a frequency estimation method and accordingly the sampling frequency, which is programmable. Five frequency estimation methods, Candan, Djukanovic, Prony, M-Pisarenko, and zero crossing interpolation methods, are reviewed for high-resolution frequency measurement. PrO-FMS can also be reconfigured to select a particular resonance mode over a range of 1-10 MHz. Measurement results are provided for two different commercial quartz crystals and a microfabricated in-plane bulk acoustic resonator in two different modes of resonance. The results of these estimation methods are compared with a standard commercial tabletop frequency counter. Measurement results show that PrO-FMS can achieve a resolution of 0.1 ppm RMSE. For the same gate time, PrO-FMS provides better resolution than standard tabletop frequency counter. This article also provides a study on the behavior of the five different frequency estimation methods for short-and long-term measurements for quartz crystals and bulk acoustic resonator in two different modes

Mass spectrometry strategies to unveil modified aminophospholipids of biological interest

The biological functions of modified aminophospholipids (APL) have become a topic of interest during the last two decades, and distinct roles have been found for these biomolecules in both physiological and pathological contexts. Modifications of APL include oxidation, glycation, and adduction to electrophilic aldehydes, altogether contributing to a high structural variability of modified APL. An outstanding technique used in this challenging field is mass spectrometry (MS). MS has been widely used to unveil modified APL of biological interest, mainly when associated with soft ionization methods (electrospray and matrix-assisted laser desorption ionization) and coupled with separation techniques as liquid chromatography. This review summarizes the biological roles and the chemical mechanisms underlying APL modifications, and comprehensively reviews the current MS-based knowledge that has been gathered until now for their analysis. The interpretation of the MS data obtained by in vitro-identification studies is explained in detail. The perspective of an analytical detection of modified APL in clinical samples is explored, highlighting the fundamental role of MS in unveiling APL modifications and their relevance in pathophysiology.publishe

A new family of StART domain proteins at membrane contact sites has a role in ER-PM sterol transport

Sterol traffic between the endoplasmic reticulum (ER) and plasma membrane (PM) is a fundamental cellular process that occurs by a poorly understood non-vesicular mechanism. We identified a novel, evolutionarily diverse family of ER membrane proteins with StART-like lipid transfer domains and studied them in yeast. StART-like domains from Ysp2p and its paralog Lam4p specifically bind sterols, and Ysp2p, Lam4p and their homologs Ysp1p and Sip3p target punctate ER-PM contact sites distinct from those occupied by known ER-PM tethers. The activity of Ysp2p, reflected in amphotericin-sensitivity assays, requires its second StART-like domain to be positioned so that it can reach across ER-PM contacts. Absence of Ysp2p, Ysp1p or Sip3p reduces the rate at which exogenously supplied sterols traffic from the PM to the ER. Our data suggest that these StART-like proteins act in trans to mediate a step in sterol exchange between the PM and ER

TOR Complex 2-regulated protein kinase Ypk1 controls sterol distribution by inhibiting StARkin domain-containing proteins located at plasma membrane-endoplasmic reticulum contact sites

In our proteome-wide screen (Muir et al. 2014 Elife), Ysp2 (also known as Lam2/Ltc4) was identified as a likely physiologically relevant target of TORC2-dependent protein kinase Ypk1 in the yeast Saccharomyces cerevisiae. Ysp2 was subsequently shown to be one of a new family of sterol-binding proteins located at plasma membrane (PM)-endoplasmic reticulum (ER) contact sites (Gatta et al. 2015 Elife). Here we document that Ysp2 and its paralog Lam4/Ltc3 are authentic Ypk1 substrates in vivo and show using genetic and biochemical criteria that Ypk1-mediated phosphorylation inhibits the ability of these proteins to promote retrograde transport of sterols from the PM to the ER. Furthermore, we provide evidence that a change in PM sterol homeostasis promotes cell survival under membrane-perturbing conditions known to activate TORC2-Ypk1 signaling. These observations define the underlying molecular basis of a new regulatory mechanism for cellular response to plasma membrane stress

WTEN: An advanced coupled tensor factorization strategy for learning from imbalanced data

© Springer International Publishing AG 2016. Learning from imbalanced and sparse data in multi-mode and high-dimensional tensor formats efficiently is a significant problem in data mining research. On one hand,Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of heterogeneous sparse data generated from different sources. On the other hand,techniques such as sampling,cost-sensitive learning,etc. have been applied to many supervised learning models to handle imbalanced data. This research focuses on studying the effectiveness of combining advantages of both CTF and imbalanced data learning techniques for missing entry prediction,especially for entries with rare class labels. Importantly,we have also investigated the implication of joint analysis of the main tensor and extra information. One of our major goals is to design a robust weighting strategy for CTF to be able to not only effectively recover missing entries but also perform well when the entries are associated with imbalanced labels. Experiments on both real and synthetic datasets show that our approach outperforms existing CTF algorithms on imbalanced data

Popularity versus Similarity in Growing Networks

Popularity is attractive -- this is the formula underlying preferential attachment, a popular explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections that nodes have follows power laws observed in many real networks. Preferential attachment has been directly validated for some real networks, including the Internet. Preferential attachment can also be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks, or duplication. Here we show that popularity is just one dimension of attractiveness. Another dimension is similarity. We develop a framework where new connections, instead of preferring popular nodes, optimize certain trade-offs between popularity and similarity. The framework admits a geometric interpretation, in which popularity preference emerges from local optimization. As opposed to preferential attachment, the optimization framework accurately describes large-scale evolution of technological (Internet), social (web of trust), and biological (E.coli metabolic) networks, predicting the probability of new links in them with a remarkable precision. The developed framework can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon