A survey of thermodynamic properties of the compounds of the elements chnops progress report, 1 feb. - 30 jun. 1965

Heat capacities, entropies, enthalpies, and free energies of organic and inorganic compounds of carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulfu

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Expanded encyclopaedias of DNA elements in the human and mouse genomes

All data are available on the ENCODE data portal: www.encodeproject. org. All code is available on GitHub from the links provided in the methods section. Code related to the Registry of cCREs can be found at https:// github.com/weng-lab/ENCODE-cCREs. Code related to SCREEN can be found at https://github.com/weng-lab/SCREEN.© The Author(s) 2020. The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.This work was supported by grants from the NIH under U01HG007019, U01HG007033, U01HG007036, U01HG007037, U41HG006992, U41HG006993, U41HG006994, U41HG006995, U41HG006996, U41HG006997, U41HG006998, U41HG006999, U41HG007000, U41HG007001, U41HG007002, U41HG007003, U54HG006991, U54HG006997, U54HG006998, U54HG007004, U54HG007005, U54HG007010 and UM1HG009442

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Emergent Behavior in a Low-Order Fluidized-Bed Bubble Model

Low-order models are becoming increasingly useful for simulating the complex collective behavior arising in large dynamical systems. Modeling of traffic flow and the behavior of biological systems such as ant colonies and bird flocks are commonly referenced examples of this new approach. We have explored the application of this type of model to describe the dynamics of voids in bubbling fluidized beds. The model considers vertical interactions between neighboring bubbles in fluidized beds. Emergent collective behavior is shown to arise in a manner consistent with observed experimental behaviors of fluidized beds. One important example is the tendency for larger beds to form a central channel of high void fraction. This effect is shown to occur without invoking the usual assumption that lateral bubble motion is induced by solids convection. Another behavior captured by this model is the tendency to form multiple preferred bubble migration paths (channels) much like the “rat-holing ” observed in cohesive powder beds. Visualizations of example emergent behavior in the bubble patterns of both 2-D and 3-D fluidized beds are presented. Background Since the publication of Davidson and Harrison’s classic book(1) on fluidization in 1963, the two-phase model has been the most widely used concepts to describe the behavior of bubbling fluidized beds. Th

Symbol-Sequence Statistics for Monitoring Fluidization

We propose that data-symbolization methods derived from nonlinear dynamics and chaos theory can be useful for characterizing and monitoring patterns in fluidized-bed measurement signals. Data symbolization involves the discretization of a measurement signal into a limited set of values. In this discretized form, the measurements can be processed very efficiently to detect dynamic patterns that signify various types of physical phenomena, including bubbling, slugging, and transitions between fluidization states. Besides computational efficiency, symbolic methods are also robust when noise is present. Using various types of measurements from experimental beds, we illustrate specific examples of how symbolization can be applied to fluidization diagnostics. We also suggest directions for future research. NOMENCLATURE HS Modified Shannon entropy N obs Number of non-zero-frequency sequences p i Observed relative frequency (probability) of sequence i T Euclidean norm T irr Time irreversib..