Microcrystal Delivery Using a Syringe and Syringe Pump Method for Serial Crystallography

The high peak brilliance and femtosecond pulse duration of X-ray free-electron lasers (XFELs) provide new scientific opportunities for experiments in physics, chemistry and biology. In structural biology, one of the major applications is serial femtosecond crystallography. The intense XFEL pulse results in the destruction of any exposed microcrystal, making serial data collection mandatory. This requires a high-throughput serial approach to sample delivery. To this end, a number of such sample-delivery techniques have been developed, some of which have been ported to synchrotron sources, where they allow convenient low-dose data collection at room temperature. Here, the current sample-delivery techniques used at XFEL and synchrotron sources are reviewed, with an emphasis on liquid injection and high-viscosity extrusion, including their application for time-resolved experiments. The challenges associated with sample delivery at megahertz repetition-rate XFELs are also outlined.

Structural information of the different conformational states of the two prototypical light-sensitive membrane proteins, bacteriorhodopsin and rhodopsin, has been obtained in the past by X-ray cryo-crystallography and cryo-electron microscopy. However, these methods do not allow for the structure determination of most intermediate conformations. Recently, the potential of X-Ray Free Electron Lasers (X-FELs) for tracking the dynamics of light-triggered processes by pump-probe serial femtosecond crystallography has been demonstrated using 3D-micron-sized crystals. In addition, X-FELs provide new opportunities for protein 2D-crystal diffraction, which would allow to observe the course of conformational changes of membrane proteins in a close-to-physiological lipid bilayer environment. Here, we describe the strategies towards structural dynamic studies of retinal proteins at room temperature, using injector or fixed-target based serial femtosecond crystallography at X-FELs. Thanks to recent progress especially in sample delivery methods, serial crystallography is now also feasible at synchrotron X-ray sources, thus expanding the possibilities for time-resolved structure determination.

Serial crystallography (SX) using X-ray free electron laser or synchrotron X-ray allows for the determination of structures, at room temperature, with reduced radiation damage. Moreover, it allows for the study of structural dynamics of macromolecules using a time-resolved pump-probe, as well as mix-and-inject experiments. Delivering a crystal sample using a viscous medium decreases sample consumption by lowering the flow rate while being extruded from the injector or syringe as compared to a liquid jet injector. Since the environment of crystal samples varies, continuous development of the delivery medium is important for extended SX applications. Herein, I report the preparation and characterization of a lard-based sample delivery medium for SX. This material was obtained using heat treatment, and then the soluble impurities were removed through phase separation. The lard injection medium was highly stable and could be injected via a syringe needle extruded at room temperature with a flow rate < 200 nL/min. Serial millisecond crystallography experiments were performed using lard, and the room temperature structures of lysozyme and glucose isomerase embedded in lard at 1.75 and 1.80 Å, respectively, were determined. The lard medium showed X-ray background scattering similar or relatively lower than shortenings and lipidic cubic phase; therefore, it can be used as sample delivery medium in SX experiments.

Serial synchrotron-based crystallography using intense microfocused X-ray beams, fast-framing detectors and protein microcrystals held at 300 K promises to expand the range of accessible structural targets and to increase overall structure-pipeline throughputs. To explore the nature and consequences of X-ray radiation damage under microbeam illumination, the time-, dose- and temperature-dependent evolution of crystal diffraction have been measured with maximum dose rates of 50 MGy s-1. At all temperatures and dose rates, the integrated diffraction intensity for a fixed crystal orientation shows non-exponential decays with dose. Non-exponential decays are a consequence of non-uniform illumination and the resulting spatial evolution of diffracted intensity within the illuminated crystal volume. To quantify radiation-damage lifetimes and the damage state of diffracting crystal regions, a revised diffraction-weighted dose (DWD) is defined and it is shown that for Gaussian beams the DWD becomes nearly independent of actual dose at large doses. An apparent delayed onset of radiation damage seen in some intensity-dose curves is in fact a consequence of damage. Intensity fluctuations at high dose rates may arise from the impulsive release of gaseous damage products. Accounting for these effects, data collection at the highest dose rates increases crystal radiation lifetimes near 300 K (but not at 100 K) by a factor of ∼1.5-2 compared with those observed at conventional dose rates. Improved quantification and modeling of the complex spatio-temporal evolution of protein microcrystal diffraction in intense microbeams will enable more efficient data collection, and will be essential in improving the accuracy of structure factors and structural models.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkSteiner TreeSensor ReadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the high flexibility and low cost of the deployment of UAVs, the application of UAV-assisted data collection has become widespread in the Internet of Things (IoT) systems. Meanwhile, the age of information (AoI) has been adopted as a key metric to evaluate the quality of the collected data. Most of the literature generally focuses on minimizing the age of all information. However, minimizing the overall AoI may lead to high costs and massive energy consumption. In addition, not all types of data need to be updated highly frequently. In this paper, we consider both the diversity of different tasks in terms of the data update period and the AoI of the collected sensing information. An efficient data collection method is proposed to maximize the system utility while ensuring the freshness of the collected information relative to their respective update periods. This problem is NP-hard. With the decomposition, we optimize the upload strategy of sensor nodes at each time slot, as well as the hovering positions and flight speeds of UAVs. Simulation results show that our method ensures the relative freshness of all information and reduces the time-averaged AoI by 96.5%, 44%, 90.4%, and 26% when the number of UAVs is 1 compared to the corresponding EMA, AOA, DROA, and DRL-eFresh, respectively.

During the past few years, serial crystallography methods have undergone continuous development and serial data collection has become well established at high-intensity synchrotron-radiation beamlines and XFEL radiation sources. However, the application of experimental phasing to serial crystallography data has remained a challenging task owing to the inherent inaccuracy of the diffraction data. Here, a particularly gentle method for incorporating heavy atoms into micrometre-sized crystals utilizing lipidic cubic phase (LCP) as a carrier medium is reported. Soaking in LCP prior to data collection offers a new, efficient and gentle approach for preparing heavy-atom-derivative crystals directly before diffraction data collection using serial crystallography methods. This approach supports effective phasing by utilizing a reasonably low number of diffraction patterns. Using synchrotron radiation and exploiting the anomalous scattering signal of mercury for single isomorphous replacement with anomalous scattering (SIRAS) phasing resulted in high-quality electron-density maps that were sufficient for building a complete structural model of proteinase K at 1.9 Å resolution using automatic model-building tools.

The past years have witnessed an increasing interest and concern regarding the development of security monitoring and management mechanisms for Critical Infrastructures, due to their vital role in ensuring the availability of many essential services. This task is not easy due to the specific characteristics of such systems, and the natural resistance of Critical Infrastructures operators against actions implying downtime. Digital Twins, as accurate virtual models of physical objects or processes, can provide a faithful environment for security analysis or evaluation of potential mitigation strategies to be deployed in face of specific situations. Nonetheless, their on-premises deployment can be expensive, implying a significant CAPEX whose return will depend on the ability to plan and deploy a suitable support infrastructure, as well as implementing efficient and scalable data collection and processing mechanisms capable of taking advantage of the acquired resources. This paper presents an off-premises approach to design and deploy Digital Twins to secure critical infrastructures, developed in the scope of the ELEGANT project. Such Digital Twins are built using real-time, high fidelity replicas of Programming Logic Controllers, coupled with scalable and efficient data collection processes, supporting the development and validation of Machine Learning models to mitigate security threats like Denial of Service attacks. The validation approach of ELEGANT, which leveraged from the capabilities of the Fed4Fire federated testbeds evaluated the feasibility of using cloudified Digital Twins, thus converting a significant part of the projected CAPEX for the in-premises model into on-demand, pay-as-you-go OPEX, eventually paving the way for the establishment of a DTaaS (Digital Twin as a Service) paradigm. The achieved results demonstrate that the data pipelines providing support for the ELEGANT Digital Twins have low impact in terms of resource usage in Denial of Service and Distributed Denial of Service attack scenarios, when higher volumes of data are generated.

A key aspect of collaboration systems, and therefore collaboration technologies is the ability to access concerned information in timely manner. The Internet of things (IoT) is being envisioned as the architecture of the future to assist in this regard. It will be created by combining sensing and communicating devices, which will provide data that can be analyzed and used to initiate automated actions. This structure of IoT has now given a new recognition to Wireless Sensor etworks (WS ) such that WS can be identified as the datacollecting component of the IoT. Efficient data collection from a large scale WS , with limited power supply, bandwidth and packet sizes, is a critical issue. One of the methods of data collection in a WS is through forming multiple clusters of the sensor nodes with one cluster head (CH) in each cluster. The objective of this work is to develop an energy efficient data collection environment for a large scale, randomly deployed cluster based wireless sensor networks by using a virtual grid based mechanism to localize the clusters and stabilize the cluster sizes in the network. This has been done as a precondition to implement the proposed differential data aggregation scheme for the spatially correlated data in a cluster.

There is an increasing demand for simple and efficient sample delivery technology to match the rapid development of serial crystallography and its wide application in analyzing the structural dynamics of biological macromolecules. Here, a microfluidic rotating-target device is presented, capable of three-degrees-of-freedom motion, including two rotational degrees of freedom and one translational degree of freedom, for sample delivery. Lysozyme crystals were used as a test model with this device to collect serial synchrotron crystallography data and the device was found to be convenient and useful. This device enables in situ diffraction from crystals in a microfluidic channel without the need for crystal harvesting. The circular motion ensures that the delivery speed can be adjusted over a wide range, showing its good compatibility with different light sources. Moreover, the three-degrees-of-freedom motion guarantees the full utilization of crystals. Hence, sample consumption is greatly reduced, and only 0.1 mg of protein is consumed in collecting a complete dataset.

Field data collection is a foundation of ecological research, but it can be challenging and error‐prone. Researchers planning and undertaking fieldwork for the first time are especially vulnerable to logistical issues that can be avoided with more guidance. We offer guidelines to enhance the accuracy and efficiency of field data collection. Our guidance takes the prospective field ecologist through five steps: (1) identifying which variables should be recorded (focusing on your scientific question, but considering opportunities for additional data collection); (2) designing the data structure (understanding data structure, assigning useful IDs and avoiding confusing characters); (3) preparing a data collection protocol (building in redundancy, choosing a data collection technology, clean data recording and taking additional notes); (4) undertaking a pilot test (including data collection and digitising, communicating with the team); (5) following the protocol (assigning a decision‐maker, noting any changes, following routines and backing up data). This article is motivated by our own trial‐and‐error experiences and those of our colleagues and students, and a desire to help future researchers to avoid them. Our intention is to streamline the planning and field stages of data collection to avoid common pitfalls, thereby saving money, time and resources and supporting the whole data life cycle. Field data collection occurs across disciplines looking to understand the natural world, and each field excursion poses its own unique challenges. However, these guidelines can be useful as a starter for researchers to support open, reproducible, efficient and effective field data collection.

With its recent upgrade to the fourth-generation synchrotron called "Extremely Brilliant Source" (EBS), the ESRF is developing a new sub-microfocus beamline for the time-resolved study of serial synchrotron crystallography (TR-SSX) at room temperature with extremely high photon flux (up to 10 16 ph/sec).The combination of two choppers generates a pulsed beam allowing the data collection from tens of microsecond to millisecond time delays; the double multilayer mirror monochromator (DMM) allows tuning ID29 beam on a wide energy range (from 10 to 20 keV) with variable bandwidth.A new developed diffractometer for fixed-target [1][2][3][4] experiments is adapted to accommodate different other sample delivery devices such as high viscosity injector 5-7 , microfluidic 8 , and tape-drive 9-11 .This high versatility will make possible to perform mixing experiments but also pump-probe experiments with the support of a high repetition rate nanosecond laser, which runs synchronously with the pulsed beam.In addition to the beamline, users have access to a laboratory in order to prepare their sample and characterise them offline before their beamtime.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

Data collection is a fundamental task in wireless sensor networks. In many applications of wireless sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with prespecified error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform global approximate data collection on the sink node according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of sensor nodes in subsets, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and propose a greedy heuristic algorithm to find an approximate solution. We further propose a monitoring algorithm to adaptively adjust the composition of node subsets according to changes of sensor readings. Our trace-driven simulations demonstrate that ADC remarkably reduces communication cost of data collection with guaranteed error bounds.

Serial crystallography experiments have been continuously performed and developed at X-ray free-electron lasers (XFELs) and synchrotron beamlines over the last decade.In this method, a complete diffraction dataset is collected from a large number of microcrystals serially delivered and exposed to an X-ray beam in random orientations at roomtemperature.Short X-ray exposure minimizes radiation damage, and data collection at room-temperature provides more biologically reliable data on structural dynamics in proteins compared to the data collection at cryo-temperatures.Serial crystallography experiments at XFELs have inspired novel sample delivery techniques, which are now also being used at synchrotrons.The XFEL Hub at Diamond is developing several techniques to enable time-resolved serial crystallography experiments at synchrotrons and XFELs.This development relies on a growing variety of sample presentation methods each with unique requirements, including different fixed target supports (1), injection methods using high-viscosity extrusion injectors, and on-demand acoustic droplet ejection or piezoelectric injection of nanolitre to picolitre (2,3) droplets either into the X-ray beam or onto a tape drive.Some of the drop-on-demand methods are also compatible with complementary X-ray emission spectroscopy measurements from the same sample and X-ray pulse.This allows to follow the oxidation/spin states changes of biologically relevant metals during catalysis.Our goals include promoting the efficient use of samples and accessibility of serial crystallography experiments for academic and industry users, in part, by transferring/ adapting appropriate technology between different facilities.