Monoclonal Light Chain–Mesangial Cell Interactions: Early Signaling Events and Subsequent Pathologic Effects

Glomerulopathic monoclonal light chains (G-LC) interact with mesangial cells (MC), resulting in alterations of mesangial homeostasis. Early signaling events control mitogenic activities and cytokine production, which in turn participate in the subsequent pathologic events. Mesangial homeostasis is affected in two very different ways, depending on whether the G-LC is from a patient with light chain deposition disease (LCDD) or light chain–related amyloidosis (AL-Am). In contrast, tubulopathic (T)-LC chains from patients with myeloma cast nephropathy do not significantly interact with MC and result in no alterations in mesangial homeostasis. Therefore, understanding early events in the monoclonal LC–MC interactions is fundamental. MC in culture were exposed to LC obtained and purified from the urine of patients with plasma cell dyscrasias and biopsy-proven renal disease, including LCDD, AL-Am, and myeloma cast nephropathy. Incubation of MC with G-LC, but not T-LC, resulted in cytoskeletal and cell shape changes, activation of platelet-derived growth factor-β (PDGF-β) and its corresponding receptor, cytoplasmic to nuclear migration of c-fos and NF-κβ signals, and production of monocyte chemoattractant protein-1 (MCP-1), as well as increased expression of Ki-67, a proliferation marker. Although NF-κβ activation was directly related to MCP-1 production, c-fos activation regulated proliferative signals and cytoskeletal changes in MC. Amyloidogenic LC were avidly internalized by the MC, whereas LCDD-LC effector targets were located at the MC surface. These cellular events are likely initiated as a result of interactions of the G-LC with yet-uncharacterized MC surface receptors. Dissecting the events taking place when G-LC interact with MC may define potential important targets for selective therapeutic manipulation to ameliorate or prevent the glomerular injury that ensues

Extended particle-in-cell schemes for physics in ultrastrong laser fields: Review and developments.

We review common extensions of particle-in-cell (PIC) schemes which account for strong field phenomena in laser-plasma interactions. After describing the physical processes of interest and their numerical implementation, we provide solutions for several associated methodological and algorithmic problems. We propose a modified event generator that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step. Based on this, we also develop an adaptive event generator that subdivides the time step for locally resolving QED events, allowing for efficient simulation of cascades. Further, we present a unified technical interface for including the processes of interest in different PIC implementations. Two PIC codes which support this interface, PICADOR and ELMIS, are also briefly reviewed

Data appendix for economic growth in the long run

This extended data appendix describes the sources and methods used to construct the data used in our paper "Economic Growth in the Long Run.

Completeness and Incompleteness of Synchronous Kleene Algebra

Synchronous Kleene algebra (SKA), an extension of Kleene algebra (KA), was proposed by Prisacariu as a tool for reasoning about programs that may execute synchronously, i.e., in lock-step. We provide a countermodel witnessing that the axioms of SKA are incomplete w.r.t. its language semantics, by exploiting a lack of interaction between the synchronous product operator and the Kleene star. We then propose an alternative set of axioms for SKA, based on Salomaa's axiomatisation of regular languages, and show that these provide a sound and complete characterisation w.r.t. the original language semantics.Comment: Accepted at MPC 201

Data appendix for economic growth in the long run

This extended data appendix describes the sources and methods used to construct the data used in our paper "Economic Growth in the Long Run.

Economic growth In the long run

We present new data on real output per worker, schooling per worker, human capital per worker, real physical capital per worker for 168 countries. The output data represent all available data from Maddison. The physical capital data represent all available data from Mitchell. One major contribution is a new set of human capital per worker, the foundation of which comes mostly from Mitchell. We provide original estimates of schooling per worker & per young worker. We find strong support for intergenerational accumulation of human capital with spillovers. With our preferred measure of human capital, over 90 percent of the variation in long run growth can be explained by variation in the growth of inputs per worker, and less than 10 percent from variation in TFP growth. Furthermore between 55% and 70% of the variation in log of output per worker can be explained by variation in the log input levels, and less than half of the log level output per worker variation is explained by variation in log TFP levels. These results are robust to different time periods, and different parameter values on the human capital accumulation technology. We find positive correlation with micro based cross country estimates of human capital, particularly those provided by Hendricks (2002) and Schoellman (2012)

Economic growth In the long run

We present new data on real output per worker, schooling per worker, human capital per worker, real physical capital per worker for 168 countries. The output data represent all available data from Maddison. The physical capital data represent all available data from Mitchell. One major contribution is a new set of human capital per worker, the foundation of which comes mostly from Mitchell. We provide original estimates of schooling per worker & per young worker. With our preferred measure of human capital, between 66 percent to 90 percent of all the variation in long run growth can be explained by variation in the growth of inputs per worker, and only 10-34 percent from variation in TFP growth! Furthermore between 66 percent and 80 percent of the variation in log levels can be explained by variation in the log input levels and only 20 percent to 34 percent is explained by variation in log TFP levels