Analysis of rotational coupling in collisions of Li+ with Ne leading to double excitation of Ne \ud

Electron angular distributions due to autoionization of Ne, doubly excited to the (2p43s2)1D state in collisions with Li+ in the energy range 1.2-2.2 keV, are measured in coincidence with Li+ scattered into a well defined direction ( Phi =0 degrees , Theta cm=10.8 degrees ). The experimental findings are analysed with the help of a collision model proposed earlier. In this model the initial excitation occurs by radial diabatic coupling to a molecular Sigma -state at small distances, followed by rotational coupling to Pi - and Delta -states at intermediate distances in the second half of the collision. The energy splitting between the Sigma -, Pi - and Delta -states is described by a model function. By adapting two parameters of this model function, the experimental findings can be reproduced within the experimental error in numerical calculations involving the relevant set of coupled differential equations. \u

Functional profiles of orphan membrane transporters in the life cycle of the malaria parasite

Assigning function to orphan membrane transport proteins and prioritizing candidates for detailed biochemical characterization remain fundamental challenges and are particularly important for medically relevant pathogens, such as malaria parasites. Here we present a comprehensive genetic analysis of 35 orphan transport proteins of Plasmodium berghei during its life cycle in mice and Anopheles mosquitoes. Six genes, including four candidate aminophospholipid transporters, are refractory to gene deletion, indicative of essential functions. We generate and phenotypically characterize 29 mutant strains with deletions of individual transporter genes. Whereas seven genes appear to be dispensable under the experimental conditions tested, deletion of any of the 22 other genes leads to specific defects in life cycle progression in vivo and/or host transition. Our study provides growing support for a potential link between heavy metal homeostasis and host switching and reveals potential targets for rational design of new intervention strategies against malaria

Copper-transporting ATPase is important for malaria parasite fertility

Homeostasis of the trace element copper is essential to all eukaryotic life. Copper serves as a cofactor in metalloenzymes and catalyses electron transfer reactions as well as the generation of potentially toxic reactive oxygen species. Here, we describe the functional characterization of an evolutionarily highly conserved, predicted copper-transporting P-type ATPase (CuTP) in the murine malaria model parasite Plasmodium berghei. Live imaging of a parasite line expressing a fluorescently tagged CuTP demonstrated that CuTP is predominantly located in vesicular bodies of the parasite. A P. berghei loss-of-function mutant line was readily obtained and showed no apparent defect in in vivo blood stage growth. Parasite transmission through the mosquito vector was severely affected, but not entirely abolished. We show that male and female gametocytes are abundant in cutp− parasites, but activation of male microgametes and exflagellation were strongly impaired. This specific defect could be mimicked by addition of the copper chelator neocuproine to wild-type gametocytes. A cross-fertilization assay demonstrated that female fertility was also severely abrogated. In conclusion, we provide experimental genetic and pharmacological evidence that a healthy copper homeostasis is critical to malaria parasite fertility of both genders of gametocyte and, hence, to transmission to the mosquito vector

lassopack: Model selection and prediction with regularized regression in Stata

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more information see https://statalasso.github.io

VIENA2: A Driving Anticipation Dataset

Action anticipation is critical in scenarios where one needs to react before the action is finalized. This is, for instance, the case in automated driving, where a car needs to, e.g., avoid hitting pedestrians and respect traffic lights. While solutions have been proposed to tackle subsets of the driving anticipation tasks, by making use of diverse, task-specific sensors, there is no single dataset or framework that addresses them all in a consistent manner. In this paper, we therefore introduce a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. We discuss our data acquisition strategy and the statistics of our dataset, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201