Size of Cell-Surface Kv2.1 Domains is Governed by Growth Fluctuations

AbstractThe Kv2.1 voltage-gated potassium channel forms stable clusters on the surface of different mammalian cells. Even though these cell-surface structures have been observed for almost a decade, little is known about the mechanism by which cells maintain them. We measure the distribution of domain sizes to study the kinetics of their growth. Using a Fokker-Planck formalism, we find no evidence for a feedback mechanism present to maintain specific domain radii. Instead, the size of Kv2.1 clusters is consistent with a model where domain size is established by fluctuations in the trafficking machinery. These results are further validated using likelihood and Akaike weights to select the best model for the kinetics of domain growth consistent with our experimental data

Radio Astronomy

Contains reports on eight research projects.National Science Foundation (Grant AST77-06052)Joint Services Electronics Program (Contract DAAG29-78-C-0020)National Aeronautics and Space Administration (Contract NAS5-21980)U. S. Department of Commerce - National Oceanic and Atmospheric Administration (Grant 04-8-M01-1)National Aeronautics and Space Administration (Contract NAS5-22929)National Aeronautics and Space Administration (Contract NAS5-23677)National Science Foundation (Grant AST77-12960)National Science Foundation (Grant AST77-26896

Radio Astronomy

Contains reports on twelve research projects.National Science Foundation (Grant AST77-06052)Joint Services Electronics Program (Contract DAAB07-76-C-1400)National Aeronautics and Space Administration (Contract NAS5-21980)U.S. Air Force - Electronic Systems Division (Contract F19628-75-C-0122)U.S. Department of Commerce - National Oceanic Atmospheric Administration (Grant 04-8-M01-1)National Aeronautics and Space Administration (Contract NAS5-22929)National Aeronautics and Space Administration (Contract NAS5-23677)National Science Foundation (Grant AST73-05042-A03)National Science Foundation (Grant AST76-20376

Mercury in Hair Is Inversely Related to Disease Associated Damage in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease, and environmental factors are proposed to exacerbate existing symptoms. One such environmental factor is mercury. The aim of this study was to investigate the relationship between exposure to mercury (Hg) and disease activity and disease associated damage in Total Hg concentrations in hair and urine were measured in 52 SLE patients. Dental amalgams were quantified. Disease activity was assessed using three indexes including the British Isles Lupus Assessment Group Index (BILAG). Disease associated damage was measured using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology SLICC/ACR Damage Index. Pearson’s correlation identified a significant negative correlation between hair Hg and BILAG (r = −0.323, p = 0.029) and SLICC/ACR (r = −0.377, p = 0.038). Multiple regression analysis identified hair Hg as a significant predictor of disease associated damage as determined by SLICC/ACR (β = −0.366, 95% confidence interval (CI): −1.769, −0.155 p = 0.019). Urinary Hg was not related to disease activity or damage. Fish consumption is the primary route of MeHg exposure in humans and the inverse association of hair Hg with disease activity observed here might be explained by the anti-inflammatory effects of n-3 long chain polyunsaturated fatty acids also found in fish

Lees and Moonshine: Remembering Richard III, 1485-1635

Published version of article deposited in accordance with Sherpa Romeo guidelines. © University of Chicago Press, 2010publication-status: AcceptedNot long after Shakespeare’s birth (1564) the last witnesses to the reign of Richard III (1483-85) would have reached the end of their lives. Richard III (c. 1592) occupies a distinctive historical moment in relation to its subject – a period after the extinction of living memory, but still within the horizon of communicative memory, the period in which stories and recollections may be transmitted across multiple generations. This essay explores how memories and “postmemories” of Richard’s reign were preserved, transmitted and transformed over the course of the sixteenth century and into the seventeenth. Whilst reflecting the powerful influence of emerging contexts including the Reformation and, ultimately, Shakespeare’s play, these memories remained distinct from and sometimes at odds with textual history. They survived because they offered their bearers a resource for interpreting and resisting the predicaments of the present, from the problem of tyranny to the legacies of the Reformation

Centrosome defects cause microcephaly by activating the 53BP1-USP28-TP53 mitotic surveillance pathway

Mutations in centrosome genes deplete neural progenitor cells (NPCs) during brain development, causing microcephaly. While NPC attrition is linked to TP53-mediated cell death in several microcephaly models, how TP53 is activated remains unclear. In cultured cells, mitotic delays resulting from centrosome loss prevent the growth of unfit daughter cells by activating a pathway involving 53BP1, USP28, and TP53, termed the mitotic surveillance pathway. Whether this pathway is active in the developing brain is unknown. Here, we show that the depletion of centrosome proteins in NPCs prolongs mitosis and increases TP53-mediated apoptosis. Cell death after a delayed mitosis was rescued by inactivation of the mitotic surveillance pathway. Moreover, 53BP1 or USP28 deletion restored NPC proliferation and brain size without correcting the upstream centrosome defects or extended mitosis. By contrast, microcephaly caused by the loss of the non-centrosomal protein SMC5 is also TP53-dependent but is not rescued by loss of 53BP1 or USP28. Thus, we propose that mutations in centrosome genes cause microcephaly by delaying mitosis and pathologically activating the mitotic surveillance pathway in the developing brain

Facial expression recognition in dynamic sequences: An integrated approach

Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or attempt to identify the particular individual frames in which the expected behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage of the information contained in the dynamic signature from facial movements for the task of expression recognition. In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences but does not rely on any subjective preprocessing or additional user-supplied information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of AUs. The proposed framework explores a parametric space of over 300 dimensions and is tested with six state-of-the-art machine learning techniques. Such robust and extensive experimentation provides an important foundation for the assessment of the performance for future work. A further contribution of the paper is offered in the form of a user study. This was conducted in order to investigate the correlation between human cognitive systems and the proposed framework for the understanding of human emotion classification and the reliability of public databases

Radio Astronomy

Contains research objectives and summary of research on seven research projects.M.I.T. Sloan Fund for Basic ResearchNational Aeronautics and Space Administration (Contract NAS5-21980)National Aeronautics and Space Administration (Contract NAS5-22485)National Aeronautics and Space Administration (Contract NAS5-23677)National Aeronautics and Space Administration (Contract NAS5-22929)U. S. Air Force - Electronic Systems Division (Contract F19628-75-C-0122)National Science Foundation (Grant AST73-05043-A02)National Science Foundation (Grant AST73-05042-A03