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Register a new userConsiderations for Using the Vasculature as a Coordinate System to Map All the Cells in the Human Body
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Several ongoing international efforts are developing methods of localizing single cells within organs or mapping the entire human body at the single cell level, including the Chan Zuckerberg Initiatives Human Cell Atlas (HCA), and the Knut and Allice Wallenberg Foundations Human Protein Atlas (HPA), and the National Institutes of Healths Human BioMolecular Atlas Program (HuBMAP). Their goals are to understand cell specialization, interactions, spatial organization in their natural context, and ultimately the function of every cell within the body. In the same way that the Human Genome Project had to assemble sequence data from different people to construct a complete sequence, multiple centers around the world are collecting tissue specimens from a diverse population that varies in age, race, sex, and body size. A challenge will be combining these heterogeneous tissue samples into a 3D reference map that will enable multiscale, multidimensional Google Maps-like exploration of the human body. Key to making alignment of tissue samples work is identifying and using a coordinate system called a Common Coordinate Framework (CCF), which defines the positions, or addresses, in a reference body, from whole organs down to functional tissue units and individual cells. In this perspective, we examine the concept of a CCF based on the vasculature and describe why it would be an attractive choice for mapping the human body.
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AMP activated protein kinase (AMPK) is a critical energy sensor, regulating signaling networks involved in pathology including metabolic diseases and cancer. This increasingly recognized role of AMPK has prompted tremendous research efforts to develop new pharmacological AMPK activators. To precisely study the role of AMPK, and the specificity and activity of AMPK activators in cellular models, genetic AMPK inactivating tools are required. We report here methods for genetic inactivation of AMPK $alpha1/ alpha2$ catalytic subunits in human cell lines by the CRISPR/Cas9 technology, a recent breakthrough technique for genome editing.
We propose a new embedding method which is particularly well-suited for settings where the sample size greatly exceeds the ambient dimension. Our technique consists of partitioning the space into simplices and then embedding the data points into features corresponding to the simplices barycentric coordinates. We then train a linear classifier in the rich feature space obtained from the simplices. The decision boundary may be highly non-linear, though it is linear within each simplex (and hence piecewise-linear overall). Further, our method can approximate any convex body. We give generalization bounds based on empirical margin and a novel hybrid sample compression technique. An extensive empirical evaluation shows that our method consistently outperforms a range of popular kernel embedding methods.
The paper represents three supplements to the source paper, q-bio/0610044 [q-bio.OT], with three new series of harmonic structures of the genetic code, determined by Gauss arithmetical algorithm; by Table of Minimal Adding, as in (Rakocevic, 2011a: Table 4; 2011b: Table 4); all structures in relation to Binary-code tree (Rakocevic, 1998). The determination itself is realized through atom and nucleon number balancing and nuancing of molekular polarity. In the first supplement the word is about some additional harmonic structures in relation to a previous our paper (Rakocevic, 2004); in the second one about the relation that structures with the polarity of protein amino acids. In the third supplement we give new ideas about the genetic code by an inclusion of the notions cipher of the genetic code and the key of that cipher.
It is shown that there is a sense in splitting Genetic Code Table (GCT) into three parts using the harmonic mean, calculated by the formula H (a, b) = 2ab / (a + b), where a = 63 and b = 31.5. Within these three parts, the amino acids (AAs) are positioned on the basis of the validity of the evident regularities of key parameters, such as polarity, hydrophobicity and enzyme-mediated amino acid classification. In addition, there are obvious balances of the number of atoms in the nucleotide triplets and corresponding amino acid groups and/or classes.
A quantum model on the chemically and physically induced pluripotency in stem cells is proposed. Based on the conformational Hamiltonian and the idea of slow variables (molecular torsions) slaving fast ones the conversion from the differentiate state to pluripotent state is defined as the quantum transition between conformational states. The transitional rate is calculated and an analytical form for the rate formulas is deduced. Then the dependence of the rate on the number of torsion angles of the gene and the magnitude of the rate can be estimated by comparison with protein folding. The reaction equations of the conformational change of the pluripotency genes in chemical reprogramming are given. The characteristic time of the chemical reprogramming is calculated and the result is consistent with experiments. The dependence of the transition rate on physical factors such as temperature, PH value and the volume and shape of the coherent domain is analyzed from the rate equation. It is suggested that by decreasing the coherence degree of some pluripotency genes a more effective approach to the physically induced pluripotency can be made.
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