HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data.

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicating the matter further is the fact that not all zeros are created equal: some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros); others are indeed due to insufficient sequencing depth (sampling zeros or dropouts), especially for loci that interact infrequently. Differentiating between structural zeros and dropouts is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchical model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data have led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions.

High-throughput assays have been developed to measure DNA methylation, among which bisulfite-based sequencing (BS-seq) and microarray technologies are the most popular for genome-wide profiling. A major goal in DNA methylation analysis is the detection of differentially methylated genomic regions under two different conditions. To accomplish this, many state-of-the-art methods have been proposed in the past few years; only a handful of these methods are capable of analyzing both types of data (BS-seq and microarray), though. On the other hand, covariates, such as sex and age, are known to be potentially influential on DNA methylation; and thus, it would be important to adjust for their effects on differential methylation analysis. In this chapter, we describe a Bayesian curve credible bands approach and the accompanying software, BCurve, for detecting differentially methylated regions for data generated from either microarray or BS-Seq. The unified theme underlying the analysis of these two different types of data is the model that accounts for correlation between DNA methylation in nearby sites, covariates, and between-sample variability. The BCurve R software package also provides tools for simulating both microarray and BS-seq data, which can be useful for facilitating comparisons of methods given the known "gold standard" in the simulated data. We provide detailed description of the main functions in BCurve and demonstrate the utility of the package for analyzing data from both platforms using simulated data from the functions provided in the package. Analyses of two real datasets, one from BS-seq and one from microarray, are also furnished to further illustrate the capability of BCurve.

Enhanced Thermoelectric Performance by Strong Phonon Scattering at the Heterogeneous Interfaces of the Mg2Sn/Mg3Sb2 High-Content Nanocomposite.

Nano approaches are practical strategies to boost the thermoelectric figure of merit due to the strong phonon scattering from the grain boundaries and nanoinclusions. Here, we have reported a strong phonon scattering at the heterogeneous interfaces of Mg2Sn/Mg3Sb2 high-content nanocomposites (HCnCs). As a result, a significantly reduced lattice thermal conductivity of 1.09 W m-1 K-1 was observed in the equimolar Mg2Sn/Mg3Sb2 HCnC, 80% lower than pure Mg2Sn and 25% lower than pure Mg3Sb2. As a result, a high ZT ∼ 1.13 at 773 K was achieved in the Mg2Sn/Mg3Sb2 HCnC. Furthermore, various defects, including solid solutions, nanoinclusions, and misfit dislocations, were observed in both the Mg3Sb2 phase and the Mg2Sn phase through the microstructure characterization.

Group dynamics analysis and the correction of coal miners' unsafe behaviors.

The rate of coal mine accidents in China is still high and most coal mine accidents are caused by human unsafe behavior, and the correction of the behavior is, therefore, paramount. In this article, a group dynamics field model and a hierarchical index system of the group dynamics factors of the unsafe behavior of coal miners are established. The internal and external dynamics of groups are analyzed and the importance of each factor is calculated and determined. On this basis, suggested correction measures are put forward. Then, in combination with a questionnaire, the corrective measures of unsafe behaviors are determined and simulated. The results show that, while the correction of unsafe behaviors both in progress and after implementation can achieve good results, the former is more effective than the latter. Via the present research, both unsafe behaviors and the occurrence of coal mine accidents can be effectively prevented, and the safety of coal mine production can be ensured.

Are dropout imputation methods for scRNA-seq effective for scHi-C data?

The prevalence of dropout events is a serious problem for single-cell Hi-C (scHiC) data due to insufficient sequencing depth and data coverage, which brings difficulties in downstream studies such as clustering and structural analysis. Complicating things further is the fact that dropouts are confounded with structural zeros due to underlying properties, leading to observed zeros being a mixture of both types of events. Although a great deal of progress has been made in imputing dropout events for single cell RNA-sequencing (RNA-seq) data, little has been done in identifying structural zeros and imputing dropouts for scHiC data. In this paper, we adapted several methods from the single-cell RNA-seq literature for inference on observed zeros in scHiC data and evaluated their effectiveness. Through an extensive simulation study and real data analysis, we have shown that a couple of the adapted single-cell RNA-seq algorithms can be powerful for correctly identifying structural zeros and accurately imputing dropout values. Downstream analysis using the imputed values showed considerable improvement for clustering cells of the same types together over clustering results before imputation.

Giant thermopower of ionic gelatin near room temperature.

Harvesting heat from the environment into electricity has the potential to power Internet-of-things (IoT) sensors, freeing them from cables or batteries and thus making them especially useful for wearable devices. We demonstrate a giant positive thermopower of 17.0 millivolts per degree Kelvin in a flexible, quasi-solid-state, ionic thermoelectric material using synergistic thermodiffusion and thermogalvanic effects. The ionic thermoelectric material is a gelatin matrix modulated with ion providers (KCl, NaCl, and KNO3) for thermodiffusion effect and a redox couple [Fe(CN)6 4-/Fe(CN)6 3-] for thermogalvanic effect. A proof-of-concept wearable device consisting of 25 unipolar elements generated more than 2 volts and a peak power of 5 microwatts using body heat. This ionic gelatin shows promise for environmental heat-to-electric energy conversion using ions as energy carriers.