Hughes Horton (pepperpaint50)

25). The results within this paper could be used as a guide for the development and scalable manufacture of LNP systems using microfluidics.Coxsackie B (CVB) viruses have been associated with type 1 diabetes. We have recently observed that CVB1 was linked to the initiation of the autoimmune process leading to type 1 diabetes in Finnish children. Viral persistency in the pancreas is currently considered as one possible mechanism. In the current study persistent infection was established in pancreatic ductal and beta cell lines (PANC-1 and 1.1B4) using four different CVB1 strains, including the prototype strain and three clinical isolates. We sequenced 5' untranslated region (UTR) and regions coding for structural and non-structural proteins and the second single open reading frame (ORF) protein of all persisting CVB1 strains using next generation sequencing to identify mutations that are common for all of these strains. One mutation, K257R in VP1, was found from all persisting CVB1 strains. The mutations were mainly accumulated in viral structural proteins, especially at BC, DE, EF loops and C-terminus of viral capsid protein 1 (VP1), the puff region of VP2, the knob region of VP3 and infection-enhancing epitope of VP4. This showed that the capsid region of the viruses sustains various changes during persistency some of which could be hallmark(s) of persistency.In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load for creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.Spacecraft formation flying (SFF) in highly elliptical orbit (HEO) has attracted much attention since many applications in space explore, while precise guidance navigation and control (GNC) technology, especially precise ranging, conducted the basis of success for such SFF missions. In this paper, we introduced a novel K band microwave ranging (MWR) equipment that aimed for the on-orbit verification of submillimeter level precise ranging technology in future HEO SFF missions. The ranging technique is a synchronous dual one-way ranging (DOWR) microwave phase accumulation system, which achieved tens of microns of ranging accuracy in laboratory environment. Detailed design and development process of MWR equipment are provided, with ranging error sources analyzed, and relative orbit dynamic models for HEO formation scenes are given with real perturbations considered. Moreover, an adaptive Kalman filter algorithm is introduced for SFF relative navigation design, incorporating with process noise uncertainty. The performance of SFF relative navigation while using MWR are tested in a hardware in loop (HIL) simulation system within a high precision six degree of freedom (6-DOF) moving platform. The final range estimation errors from MWR using adaptive filter are less than 35 μm m and 8.5 μm/s for range rate, which demonstrated the promising accuracy for future HEO formation mission applica