Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors.

A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.

Cryo-Electron Microscopy Reveals Cardiac Myosin Binding Protein-C M-Domain Interactions with the Thin Filament.

Cardiac myosin binding protein C (cMyBP-C) modulates cardiac contraction via direct interactions with cardiac thick (myosin) and thin (actin) filaments (cTFs). While its C-terminal domains (e.g. C8-C10) anchor cMyBP-C to the backbone of the thick filament, its N-terminal domains (NTDs) (e.g. C0, C1, M, and C2) bind to both myosin and actin to accomplish its dual roles of inhibiting thick filaments and activating cTFs. While the positions of C0, C1 and C2 on cTF have been reported, the binding site of the M-domain on the surface of the cTF is unknown. Here, we used cryo-EM to reveal that the M-domain interacts with actin via helix 3 of its ordered tri-helix bundle region, while the unstructured part of the M-domain does not maintain extensive interactions with actin. We combined the recently obtained structure of the cTF with the positions of all the four NTDs on its surface to propose a complete model of the NTD binding to the cTF. The model predicts that the interactions of the NTDs with the cTF depend on the activation state of the cTF. At the peak of systole, when bound to the extensively activated cTF, NTDs would inhibit actomyosin interactions. In contrast, at falling Ca2+ levels, NTDs would not compete with the myosin heads for binding to the cTF, but would rather promote formation of active cross-bridges at the adjacent regulatory units located at the opposite cTF strand. Our structural data provides a testable model of the cTF regulation by the cMyBP-C.

Modeling associations between genetic markers using Bayesian networks.

MOTIVATION: Understanding the patterns of association between polymorphisms at different loci in a population (linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. RESULTS: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D'. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers. AVAILABILITY: The implementation of the method is available upon request by email.