Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector raw data allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information, by taking advantage of the compressibility of sparse data typical for LCLS-II applications. We investigated if recent developments in machine learning are useful in data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets between computer vision and radiation imaging. However, we redesigned in Tensorflow mathematically equivalent versions of the state-of-the-art, "classical" algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning architectures for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.Comment: 9 pages, 9 figure

A measurement of the decay rate for the process kaon(L) going to positive muon negative muon

A sample of 87 events of the GIM suppressed decay K\sb{\rm L} \to \mu\sp+\mu\sp- were observed in an experiment performed in 1988 at the Brookhaven National Laboratory. Concurrently, 8,887 examples of the CP-violating decay K\sb{\rm L} \to \pi\sp+\pi\sp- were also seen. The apparatus consisted of a double-magnet spectrometer as well as electromagnetic and muon detector systems. From the previously measured branching ratio for K\sb{\rm L} \to \pi\sp+\pi\sp- and the different instrumental acceptances of the detector for the two decays, the data sample was normalized to the effective number of K\sb{\rm L} decays observed. A value for the ratio (K\sb{\rm L} \to \mu\sp+\mu\sp-)/(K\sb{\rm L} \to anything) of (5.7 $\pm$ 0.6(stat.) $\pm$ 0.3(syst.)) $\times$ 10\sp{-9} was obtained

Virtual Engineering Approach to Developing Selective Harvest Technologies

Agricultural crop residues (e.g., straw and stover) are a current focus for bioenergy feedstocks, with new technologies being developed to improve the economics of bioenergy production. Among the emerging technologies focused on feedstock engineering is the selective harvest concept. Due to the complexity of the biomass separations required for addressing the challenges and requirements of selective harvest, high fidelity models and advanced experimental methods that allow observation and measurement of the physical system are needed. These models and methods were developed and include computational fluid dynamics (CFD) modeling to simulate the cleaning shoe of a grain combine and a particle image velocimetry (PIV) technique to quantitatively and qualitatively characterize the cleaning shoe performance. While these techniques alone can be sufficient engineering and analysis tools for developing selective harvest technologies, this paper presents a new methodology, Virtual Engineering (VE), that integrates the CFD and PIV data into a virtual environment, where the data is coupled with the geometric model of a grain combine to provide a virtual representation of the cleaning shoe performance. Using VE visualization capabilities, the CFD and PIV data can be viewed in the context of the physical system for an interactive evaluation of characteristics and performance. This paper also discusses the concepts of additional VE tools that are being developed to provide necessary visualization, simulation and integration functionality

BDNF stimulation of protein synthesis in cortical neurons requires the map kinase-interacting kinase MNK1

Although the MAP kinase-interacting kinases (MNKs) have been known for >15 years, their roles in the regulation of protein synthesis have remained obscure. Here, we explore the involvement of the MNKs in brain-derived neurotrophic factor (BDNF)-stimulated protein synthesis in cortical neurons from mice. Using a combination of pharmacological and genetic approaches, we show that BDNF-induced upregulation of protein synthesis requires MEK/ERK signaling and the downstream kinase, MNK1, which phosphorylates eukaryotic initiation factor (eIF) 4E. Translation initiation is mediated by the interaction of eIF4E with the m7GTP cap of mRNA and with eIF4G. The latter interaction is inhibited by the interactions of eIF4E with partner proteins, such as CYFIP1, which acts as a translational repressor. We find that BDNF induces the release of CYFIP1 from eIF4E, and that this depends on MNK1. Finally, using a novel combination of BONCAT and SILAC, we identify a subset of proteins whose synthesis is upregulated by BDNF signaling via MNK1 in neurons. Interestingly, this subset of MNK1-sensitive proteins is enriched for functions involved in neurotransmission and synaptic plasticity. Additionally, we find significant overlap between our subset of proteins whose synthesis is regulated by MNK1 and those encoded by known FMRP-binding mRNAs. Together, our data implicate MNK1 as a key component of BDNF-mediated translational regulation in neurons

User's Guide for Flight Simulation Data Visualization Workstation

Today's modern flight simulation research produces vast amounts of time sensitive data. The meaning of this data can be difficult to assess while in its raw format. Therefore, a method of breaking the data down and presenting it to the user in a graphical format is necessary. Simulation Graphics (SimGraph) is intended as a data visualization software package that will incorporate simulation data into a variety of animated graphical displays for easy interpretation by the simulation researcher. This document is intended as an end user's guide

Quantitative non-canonical amino acid tagging based proteomics identifies distinct patterns of protein synthesis rapidly induced by hypertrophic agents in cardiomyocytes, revealing new aspects of metabolic remodeling

Cardiomyocytes undergo growth and remodeling in response to specific pathological or physiological conditions. Pathological myocardial growth is a risk factor for cardiac failure to which faster protein synthesis is a major driving element. We aimed to quantify the rapid effects of different pro-hypertrophic stimuli on the synthesis of specific proteins in ARVC and to determine whether such effects are due to alterations on mRNA abundance or the translation of specific mRNAs. Cardiomyocytes have very low rates of protein synthesis, posing a challenging problem in terms of studying changes in the synthesis of specific proteins, which also applies to other non-dividing primary cells. To address this, an optimized QuaNCAT LC/MS method was used to selectively quantify newly synthesized proteins in such cells. The study showed both classical (phenylephrine; PE) and more recent (insulin) pathological cardiac hypertrophic agents increased the synthesis of proteins involved in glycolysis, the Krebs cycle / beta-oxidation, and sarcomeric components. However, insulin increased synthesis of many metabolic enzymes to a greater extent than PE. Using a novel validation method, we confirmed that synthesis of selected candidates is indeed up-regulated by PE and insulin. Synthesis of all proteins studied was upregulated by signaling through mTORC1 without changes in their mRNA levels, showing the key importance of translational control in the rapid effects of hypertrophic stimuli. Expression of PKM2 was upregulated in rat hearts following TAC. This isoform possesses specific regulatory properties that may be involved in metabolic remodeling and as a novel candidate biomarker. Levels of translation factor eEF1 also increased during TAC, likely contributing to faster cell mass accumulation. Interestingly, PKM2 and eEF1 were not up-regulated in pregnancy or exercise induced CH, suggesting them as pathological CH specific markers. The study methods may be of utility to the examination of protein synthesis in primary cells

Not Just Fun and Games: A Review of College Drinking Games Research From 2004 to 2013

Drinking games are a high-risk social drinking activity consisting of rules and guidelines that determine when and how much to drink (Polizzotto et al., 2007). Borsari\u27s (2004) seminal review paper on drinking games in the college environment succinctly captured the published literature as of February 2004. However, research on college drinking games has grown exponentially during the last decade, necessitating an updated review of the literature. This review provides an in-depth summary and synthesis of current drinking games research (e.g., characteristics of drinking games, and behavioral, demographic, social, and psychological influences on participation) and suggests several promising areas for future drinking games research. This review is intended to foster a better understanding of drinking game behaviors among college students and improve efforts to reduce the negative impact of this practice on college campuses