Automatic bad‐pixel mask maker for X‐ray pixel detectors with application to serial crystallography

X‐ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X‐ray sources and enabled by employing high‐frame‐rate X‐ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad‐pixel masks for large‐area X‐ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X‐ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets.Attention is focused on perhaps the biggest bottleneck in data analysis for serial crystallography at X‐ray free‐electron lasers, which has not received serious enough examination to date. An effective and reliable way is presented to identify anomalies in detectors, using machine learning and recently developed mathematical methods in the field referred to as `robust statistics'. imag

Death-Associated Protein Kinase Activity Is Regulated by Coupled Calcium/Calmodulin Binding to Two Distinct Sites.

International audienceThe regulation of many protein kinases by binding to calcium/calmodulin connects two principal mechanisms in signaling processes: protein phosphorylation and responses to dose- and time-dependent calcium signals. We used the calcium/calmodulin-dependent members of the death-associated protein kinase (DAPK) family to investigate the role of a basic DAPK signature loop near the kinase active site. In DAPK2, this loop comprises a novel dimerization-regulated calcium/calmodulin-binding site, in addition to a well-established calcium/calmodulin site in the C-terminal autoregulatory domain. Unexpectedly, impairment of the basic loop interaction site completely abolishes calcium/calmodulin binding and DAPK2 activity is reduced to a residual level, indicative of coupled binding to the two sites. This contrasts with the generally accepted view that kinase calcium/calmodulin interactions are autonomous of the kinase catalytic domain. Our data establish an intricate model of multi-step kinase activation and expand our understanding of how calcium binding connects with other mechanisms involved in kinase activity regulation

Automatic bad pixel mask maker for X-ray pixel detectors with application to serial crystallography

X-ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X-ray sources and enabled by employing high-frame-rate X-ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad-pixel masks for large-area X-ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X-ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets

Data reduction for serial crystallography using a robust peak finder

A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of `robust statistics' has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (i.e. the background noise) and another group comprising outliers (i.e. Bragg peaks). Our robust statistics algorithm has two key advantages, which are demonstrated through testing using multiple SX data sets. First, it is relatively insensitive to the exact value of the input parameters and hence requires minimal optimization. This is critical for the algorithm to be able to run unsupervised, allowing for automated selection or `vetoing' of SX diffraction data. Secondly, the processing of individual diffraction patterns can be easily parallelized. This means that it can analyse data from multiple detector modules simultaneously, making it ideally suited to real-time data processing. These characteristics mean that the robust peak finder (RPF) algorithm will be particularly beneficial for the new class of MHz X-ray free-electron laser sources, which generate large amounts of data in a short period of time

Via placement optimization for a group of wires

Most PCB design CAD systems offer a limited number of “patterns” for the via placement on a bus (group of wires) which would be either a single- or a double-row placement. This article demonstrates the incorrectness of such limitations, because in this case the mounting space is used not in an optimal way. The paper presents the optimum solution for a certain type of problems on via placement when changing the layer of a bus. The solution suggests a regular (periodic) arrangement, but with a multi-row placement. The calculation of the parameters for optimal placement is narrowed, in general, to finding the number of via rows with which the area of a topological fragment is minimal