Triple-Pomeron Matrix Model for Dispersive Corrections to Nucleon-Nucleus Total Cross Section

Dispersive corrections to the total cross section for high-energy scattering from a heavy nucleus are calculated using a matrix model, based on the triple-Pomeron behavior of diffractive scattering from a single nucleon, for the cross section operator connecting different states of the projectile nucleon . Energy-dependent effects due to the decrease in longitudinal momentum transfers and the opening of more channels with increasing energy are included. The three leading terms in an expansion in the number of inelastic transitions are evaluated and compared to exact results for the model in the uniform nuclear density approximation for the the scattering of nucleons from Pb^{208} for laboratory momenta ranging from 50 to 200 GeV/c.Comment: 16 pages, 2 figures, RevTex

Identification of a cytotoxic form of dimeric interleukin-2 in murine tissues

10.1371/journal.pone.0102191PLoS ONE97e10219

Identification of a Chromogranin A Domain That Mediates Binding to Secretogranin III and Targeting to Secretory Granules in Pituitary Cells and Pancreatic β-Cells

Chromogranin A (CgA) is transported restrictedly to secretory granules in neuroendocrine cells. In addition to pH- and Ca(2+)-dependent aggregation, CgA is known to bind to a number of vesicle matrix proteins. Because the binding-prone property of CgA with secretory proteins may be essential for its targeting to secretory granules, we screened its binding partner proteins using a yeast two-hybrid system. We found that CgA bound to secretogranin III (SgIII) by specific interaction both in vitro and in endocrine cells. Localization analysis showed that CgA and SgIII were coexpressed in pituitary and pancreatic endocrine cell lines, whereas SgIII was not expressed in the adrenal glands and PC12 cells. Immunoelectron microscopy demonstrated that CgA and SgIII were specifically colocalized in large secretory granules in male rat gonadotropes, which possess large-type and small-type granules. An immunocytochemical analysis revealed that deletion of the binding domain (CgA 48–111) for SgIII missorted CgA to the constitutive pathway, whereas deletion of the binding domain (SgIII 214–373) for CgA did not affect the sorting of SgIII to the secretory granules in AtT-20 cells. These findings suggest that CgA localizes with SgIII by specific binding in secretory granules in SgIII-expressing pituitary and pancreatic endocrine cells, whereas other mechanisms are likely to be responsible for CgA localization in secretory granules of SgIII-lacking adrenal chromaffin cells and PC12 cells

Mutant Proinsulin That Cannot Be Converted Is Secreted Efficiently from Primary Rat β-Cells via the Regulated Pathway

Prohormones are directed from the trans-Golgi network to secretory granules of the regulated secretory pathway. It has further been proposed that prohormone conversion by endoproteolysis may be necessary for subsequent retention of peptides in granules and to prevent their release by the so-called “constitutive-like” pathway. To address this directly, mutant human proinsulin (Arg/Gly(32):Lys/Thr(64)), which cannot be cleaved by conversion endoproteases, was expressed in primary rat islet cells by recombinant adenovirus. The handling of the mutant proinsulin was compared with that of wild-type human proinsulin. Infected islet cells were pulse labeled and both basal and stimulated secretion of radiolabeled products followed during a chase. Labeled products were quantified by high-performance liquid chromatography. As expected, the mutant proinsulin was not converted at any time. Basal (constitutive and constitutive-like) secretion was higher for the mutant proinsulin than for wild-type proinsulin/insulin, but amounted to <1% even during a prolonged (6-h) period of basal chase. There was no difference in stimulated (regulated) secretion of mutant and wild-type proinsulin/insulin at any time. Thus, in primary islet cells, unprocessed (mutant) proinsulin is sorted to the regulated pathway and then retained in secretory granules as efficiently as fully processed insulin

Dependency on suppliers as a peril in the acquisition of innovations? The role of buyer attractiveness in mitigating potential negative dependency effects in buyer–supplier relations

New product development occurs nowadays mostly in joint buyer–supplier projects, which require closer ties between the partners in order to mobilize their resources. One issue arising from this collaborative model is that the buyer tends to become more dependent on the supplier. Multiple cases of supplier obstructionism have been reported. To mitigate this dilemma, this paper analyzes the relevance of customer attractiveness as an enabler of collaboration. Testing this hypothesis on a sample of 218 buyer–supplier relationships, we show that dependency as such is not the issue in the presence of close ties. Buyers who are a preferred customer of their suppliers can accept the risk of becoming dependent on them. The managerial implications of this finding is that firms should apply a reverse marketing approach and thus attempt to become the preferred customers of their important suppliers. From a conceptual perspective, our findings indicate the need to consider dependency not as an isolated variable, but in conjunction with attractiveness

A guide to the BRAIN initiative cell census network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.Horizon 2020 (H2020)R01 NS096720Radiolog