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In traditional observation schemes of stellar refraction navigation, the accuracy was limited due to unreasonable observation directions. In order to ameliorate this situation, a method of refracted starlight observation based on observability analysis is proposed. The function of this method is optimally generating an observation attitude sequence according to standard trajectories of spacecraft so that the selection of a refracted starlight observation sequence can be realized. Specifically, the improvement of Fisher information matrix calculation enables this method to be qualified for the navigation problem with unsteady measurement quantities as well as the non-fully observability which is defined as the capability of estimating the system state through measurements in finite time. Here, we construct a quantitative relationship between refracted starlight measurements and system observability by means of Fisher information index ( FII). Next, the observation scheme is retrieved by searching the maximum value of the optimized variable, which includes the ( FII). Finally, we resort to the extended Kalman filter to accomplish typical trajectory navigation simulations of the observation scheme. The results indicate that our method brings more accuracy than traditional ones in estimation of position and velocity of the optimal observation scheme.

Non-line-of-sight (NLoS) imaging reveals a hidden scene using indirect diffuse reflections. A common choice for analyzing the time-of-flight (ToF) data from a non-confocal system is an ellipsoid model whose operator is high-dimensional, leading to a computationally arduous task. In this Letter, the product-convolution expansions method is utilized to formulate the operator and its adjoint based on the observation of a shift-variant point spread function (PSF) in the ToF data. The operator and its adjoint are locally approximated as a convolution, which allows the forward and backward procedure to be computed efficiently through fast Fourier transform (FFT). Moreover, the low-rank approximation of the operator is obtained by matrix decompositions, further improving the computational efficiency. The proposed method is validated using publicly accessible datasets.

Motivation Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis. Results In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (named MINet and RVNet) that infer co-expression networks at different data scenarios. We demonstrate the superior prediction accuracy of Iso-Net over the existing methods for most simulation data, especially in two extreme cases: sample size is very small and exon numbers of two isoforms are quite different. Furthermore, by defining relevant quantitative measures (e.g., Jaccard correlation coefficient) and combining differential co-expression network analysis and GO functional enrichment analysis, a co-expression network analysis framework is developed to predict functions of isoforms and further, to discover their distinct functions within the same gene. We apply Iso-Net to study gene isoforms for several important transcription factors in human myeloid differentiation with the exon-level RNA-Seq data from three different cell lines. Availability and implementation Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net .

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements.

Special approaches are required to integrate low-cost MEMS-based inertial navigation system (MEMS-INS) with global navigation satellite system (GNSS). This article proposes a robust GNSS/MEMS-strapdown inertial navigation system (SINS), the tightly coupled navigation approach aided by non-holonomic constraint (NHC). The tight integration model is adopted to overcome the accuracy degradation caused by changing satellite geometry and frequent GNSS outages in urban areas. The innovation-based test statistic is devised to enhance the filtering with the resistance to GNSS outliers. To further improve the stand-alone accuracy during relatively long GNSS outages, the virtual measurement is also introduced using the NHC condition that land vehicle does not slip and always remain in contact with the ground. The field test results with continuous GNSS aiding indicate that the proposed algorithm can improve overall accuracy in position, velocity, and attitude by about 16, 43, and 19% with immunity from GNSS outliers, compared with the traditional loosely coupled (LC) method. Even during 60-s GNSS outages, the proposed algorithm can effectively compensate MEMS-SINS errors, accomplishing the overall accuracy improvements by about 46% (position), 35% (velocity), and 15% (attitude), compared with the traditional LC counterpart.

Although networks provide a powerful methodology to study a variety of real-world and engineered systems, their current formulation does not typically account for multiple interaction types that vary in space and time, or address interconnected systems such as networks of networks. Unavoidably, ignoring time-dependence and multiple interactions of topological structures might lead to important temporal and structural information loss and may obscure the actual organization. To achieve a deep understanding of the time-varying and interconnected complex networked systems, in this paper, we develop a more general mathematical model, referred to as a temporal multilayer network, which explicitly incorporates time-dependence and multiple relationships of topological structures into a system, and provides a natural and reasonable description for real-world complex systems. Furthermore, using a fifth-order tensorial framework to represent temporal multilayer networks, we generalize four important topological metrics, including overlapping degree, entropy, degree correlation and link overlap, to quantitatively evaluate the temporal multilayer networks. In particular, based on the tensorial framework, we propose two iterative refinement centralities for temporal multilayer networks, referred to as TM-eigenvector and TM-PageRank centralities, which are used to quantitatively evaluate the importance of nodes in real-world complex systems. Moreover, we use the theory of multilinear algebra and matrix analysis to strictly prove the convergence of these iterative algorithms. The proposed mathematical frameworks are validated using two real-world temporal multilayer networked systems related to complex diseases, i.e., influenza and heart diseases.

Appropriate cycle-slip and measurement-error models are essential for BeiDou carrier-phase-based integrity risk calculation. To establish the receiver’s measurement-error model, an accurate position reference of the GNSS antenna is fundamental for calculating the measurement error. However, it is still a challenge to acquire position references for dynamic BeiDou receivers, resulting in improper GNSS measurement-error models and unreliable integrity monitoring. This paper proposes an improved precise relative positioning scheme by adopting multi-antenna trajectory constraints for dynamic BeiDou receivers. The dynamic experiments show an obvious decline of 78.7%, at most, in the positioning failure rate of the proposed method, as compared with the traditional method. The position solutions obtained from the proposed approach are used as the reference to analyze the cycle-slip and measurement-error characteristics of the dynamic receiver. The field test results indicate that the cycle-slip rate decreases with the increase of signal-to-noise ratio (SNR), and cycle slipping obeys a positively skewed distribution that could be fitted by the Gaussian mixture model (GMM). On the other hand, the standard deviation of the carrier-phase measurement error is inversely proportional to SNR, and its distribution is characteristically fat-tailed, which could be fitted by the bi-normal model.

With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.

High-accuracy and reliable autonomous navigation is increasingly crucial for automated transfer vehicles (ATV). This paper proposes a novel strapdown inertial navigation system/celestial navigation system (SINS/CNS) tight integration scheme aided by dynamic model constraints for high-orbit ATV to realize accurate and autonomous navigation. In this scheme, the complete weightlessness constraint in orbit is used to address the divergence of position and velocity caused by inaccurate accelerometer bias estimation problem encountered in the traditional SINS/CNS integration method, and the image point position-based tight integration model is derived to handle the adverse influence of time-varying attitude measurement noise due to changes of star geometry observed by a large-view-filed star sensor. Moreover, an information filter is devised to fuse the multi-rate measurements. The proposed algorithm is evaluated by a representative high-orbit ATV trajectory simulation, which indicates significant improvements in navigation accuracy compared with its traditional counterparts. The proposed algorithm can realize navigation accuracy enhancements without introducing additional sensors, strengthening its potentials in engineering application.

The processing of cycle slip is one of the key techniques of carrier-phase-based high-precision positioning. As for the poor performance of the single-station inertial-aided cycle slip detection based on the tightly coupled integration system using pseudorange and Doppler measurements under inaccurate lever arm conditions, an improved real-time method based on ARIMA-GARCH (autoregressive integrated moving average-generalized autoregressive conditional heteroscedastic) model is proposed. The ARIMA (1, 1, 0) model is used to detrend the fundamental test statistics, and then, the GARCH (1,1) model is used to estimate the conditional variance of the residuals with the characteristics of volatility clustering. Considering the needs for real-time applications, two algorithms with model parameters varied at every epoch and periodically, respectively, are proposed. Two kinds of numerical experiments based on the real data collected from vehicular and flight tests are carried out. The results show that the proposed method can not only greatly reduce the false alarm rate but also can detect simulated 1-cycle slips for all frequencies of all satellites of BDS (BeiDou System) and the period-varied algorithm is recommended due to its good performance of cycle slip detection and low computation cost online.

Inertial/stellar refraction integrated navigation system can use the observation information of the refraction star sensor to correct the output of the inertial navigation system (INS), thereby effectively improving the positioning performance of the integrated navigation system. However, in practical applications, the errors of navigation system such as inertial navigation misalignment angle and star sensor installation error will affect the overall performance of the integrated navigation system to some extent, so it needs to be calibrated online. Since the carrier cannot achieve continuous observation of the refracted starlight, the system observability is weak. Therefore, a new online error calibration strategy is proposed, which is to construct observations by using the position information of the INS and the attitude and star spots information of the star sensor, and establish the relationship equations between the observations, the misalignment angle of the inertial navigation and installation error of star sensor. After linearization and decoupling, the inertial navigation misalignment angle and star sensor installation error are estimated by the least square method. Monte Carlo target practice simulation results show that, compared with the traditional filtering estimation method, the system error online calibration method proposed in this paper can more effectively estimate the inertial navigation misalignment angle and star sensor installation error, and improve the positioning and speed accuracy of the inertial/stellar refraction integrated navigation system.

Relative positioning with a moving base is required in many vehicle-to-vehicle applications. Due to unavoidable latency, conventional synchronous real-time kinematic (SRTK) positioning, which refers a fixed base, cannot fulfill the requirements of high-speed safety–critical situations since the movement of the base during the latency must be considered. We present an asynchronous RTK (ARTK)/time-differenced carrier phase (TDCP) integration method. We first introduce the ARTK method to directly obtain the asynchronous baseline between moving base and rover at their respective observation epochs. The position increments of the moving base during the latency are then calculated by the TDCP technique. Finally, the two positioning results are combined to achieve low-latency, high-rate, and high-precision relative positioning. With the proposed method, the size of transmitted data packets can be significantly reduced, which is beneficial for real-time implementation. It is also noted that the proposed method can be easily merged with global navigation satellite system/inertial navigation system (INS) integration to further improve the output rate of relative position. The performance is evaluated by a field test using two moving vehicles with one being the moving base and the other the rover. Results show that the baseline error is less than 1 cm compared to reliably post-processed SRTK results when latencies of raw observations remain below 1 s. Even when the latency is 15 s, centimeter-level accuracy can still be guaranteed. With ARTK/TDCP/INS integration, decimeter-to-centimeter-level higher-rate (> 100 Hz) relative positioning results can be obtained.

With the popularity and development of GNSS, the concept of integrity is proposed in order to ensure the safety of navigation and positioning. In some related fields such as civil aviation and lifesaving, the requirements for the integrity are becoming stricter, making the Integrity Monitoring a hot issue to be solved urgently. However, the existing methods for this problem are theoretically imperfect and the error model used can’t accurately describe the actual error distribution. In this article, we will first present a model for the observation error in single point positioning. Considering that the actual observation errors always have thick tails, a binormal error model has been raised. Next, based on the previous error model, we use the Robust Parameter Estimation (RPE) method based on predicted residual with single iteration to detect and exclude the fault observation values, and then calculate the positioning result. Finally, we derive a method for conservatively estimating the integrity risk in the position by segmenting and magnifying the test-passing domain. The experimental results show that compared with the single normal error model, our binormal error model can describe the actual error distribution better and is conservative in the tail. The RPE method based on predicted residual with single iteration has a good effect of detecting and excluding fault observations and has a small positioning error. When the theoretical risk is less than the risk threshold, the statistical integrity risk obtained from the data is also less than the threshold. In addition, in the case of a worse error distribution with larger fault probability and larger fault error variance, the integrity risk evaluation results are still credible, indicating our method has better robustness.

In this paper, an enhanced tightly coupled navigation approach using pseudorange, Doppler and carrier phase measurements is proposed. Firstly, the PDPI vector is obtained by differencing time-differenced carrier phase measurements between satellites. Then, the difference between PDPI and position increment derived from SINS recursion is determined. Finally, navigation system states are adjusted by the precise difference of position increment. Compared with time-differenced carrier phase measurements, the introduction of precise PDPI measurements, derived from time-differenced carrier phase, can increase computation efficiency without iteration calculations. A field vehicular test is performed to verify the proposed approach. In comparison with the method using pseudorange and Doppler measurements, the PDPI-aided method improves performance of navigation system significantly. On one hand, since high-precise PDPI measurement is added in the new approach, accuracy of the integrated navigation system is improved. The results show horizontal position error is reduced by 56.9% and horizontal velocity error is reduced by 39.2%. Vertical velocity error is reduced by 81.0% and the heading error is reduced by 19.3%. On the other hand, the PDPI measurement in this new approach establishes the relationship between two successive epochs, which leads to a smoothing effect.

One challenging issue in information science, biological systems and many other field is to determine the most central agents in multilayer networked systems characterized by different types of interrelationships. In this paper, using a fourth-order tensor to represent multilayer networks, we propose a new centrality measure, referred to as the Singular Vector of Tensor (SVT) centrality, which is used to quantitatively evaluate the importance of nodes connected by different types of links in multilayer networks. First, we present a novel iterative method to obtain four alternative metrics that can quantify the hub and authority scores of nodes and layers in multilayer networked systems. Moreover, we use the theory of multilinear algebra to prove that the four metrics converge to four singular vectors of the adjacency tensor of the multilayer network under reasonable conditions. Furthermore, a novel SVT centrality measure is obtained by integrating these four metrics. The experimental results demonstrate that the proposed method is a new centrality measure that significantly outperforms six other published centrality methods on two real-world multilayer networks related to complex diseases, i.e., gastric and colon cancers.

The control of nonlinear networked systems is one of the challenging problems in the network science and engineering fields. Based on physical and realistic considerations, it is a critical goal to...

This paper develops a novel approach to obtain the fixed ambiguity solution with orthonormal constraints for Beidou attitude determination. The approach extends the traditional attitude model by taking the attitude matrix elements as unknown parameters directly. The orthonormal property of the attitude matrix is used as constraints to assist in calculating the float ambiguity solution and its corresponding variance–covariance matrix. An iteration algorithm with the linearization of orthonormal constraints is developed to solve the nonlinear conditional extremum problem and a sequential filtering multi-epoch ambiguity resolution method is derived for real-time applications. Long-time static experiments with three antennas tracing Beidou signals are employed to demonstrate the reliability and effectiveness of the proposed approach. The results indicate that, compared to unconstrained ambiguity resolution, the proposed approach can improve the reliability of the integer ambiguity vectors for single epochs and can also decrease the time to fix for multiple epochs.

The major challenge of current Strapdown Inertial Naviagtion System/Celestial Navigation System (SINS/CNS) integrated systems is navigation accuracy degradation due to the failure to accurately estimate the accelerometer bias even when using stellar refraction information (e.g. the number of refraction stars is less than three). To solve this problem, this paper presents a new method for improving the accuracy of the traditional inertial-based integrated systems installed on ballistic vehicles. In an analogy with nonholonomic constraints in land navigation, this algorithm exploits the constraints that govern the motion of a ballistic vehicle in the free flight phase to obtain accelerometer bias observations. Improvements in dynamic equations are used to reduce the propagation of navigation errors, and high-rate virtual constraints are used to reduce the impact of bias errors. An information filter is devised to fuse the multi-rate observations from multiple sources, i.e. SINS, CNS and model constraints. The proposed method is also evaluated by long-range ballistic vehicle navigation simulations. The results indicate that the proposed constrained algorithm can address the degradation problem with remarkable accuracy improvements without adding extra sensors, enhancing the SINS-based navigation performance for ballistic applications.

In order to enhance the capability of autonomous operation for small unmanned aerial vehicles (UAV), a MEMS-based inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation method is proposed. An augmented Cubature Kalman filter is derived to fulfil the data fusion of precise GNSS real-time kinematic (RTK) solution and noisy inertial measurements. In the filter, Cubature Kalman filtering is adopted to handle the strong INS model nonlinearity caused by sudden and large UAV maneuvers, and the technique of state-augmentation is used to capture meaningful odd-order moment information and reduce the adverse impacts of non-additive noise in inertial measurements. It is analyzed that the basic difference between the augmented and non-augmented CKFs generally favors the augmented CKF, which is supported by a representative example and flight test. The results of flight test have also shown that the proposed augmented Cubature Kalman filtering method can complete more accurate navigation compared with the conventional EKF/UKF-based approaches.

Figure S1. In Pathways in cancer, the above genes are clustered near the PI3K-AKT signaling pathway. Figure S2. In Colorectal cancer pathway, the above genes are clustered near the PI3K-AKT signaling pathway and MAPK signaling pathway. Figure S3. In Apoptosis pathway, the above genes are clustered near the PI3K-AKT signaling pathway and NF-κB signaling pathway. Figure S4. In B cell receptor signaling pathway, the above genes are clustered near the PI3K-AKT signaling pathway, MAPK signaling pathway and NF-κB signaling pathway. Figure S5. In T cell receptor signaling pathway, the above genes are clustered near the MAPK signaling pathway and NF-κB signaling pathway. Figure S6. The enrichment analysis of genes from MsigDB database. Table S1. Biological functions of nodes in MFVS for three multilayer networks. Table S2. The names of drugs that AKT, P21, BCATENIN, IFNG, JAK, JUN, NFKB, IKB, PI3K, RAF, SMAD, P53 and IAP can combine with. Table S3. The drugs which ENV, GAG, NEF and GAG-POL can combine with. Table S4. Enrichment analysis of genes in MFVS of the HHMG network. Table S5. The biological information of cytokines and cell types related to cancer immune system. (PDF 626 kb)

Nowadays, the navigation systems, such as phone-based and vehicle navigation, are developing, and techniques of GNSS/INS loosely and tightly coupled navigation are under widely used. Compared with loosely coupled navigation, the tightly coupled navigation shows more and more superiorities in urban canyons, because it can provide continuous integrated navigation solution even with less than four satellites. Conventional tightly coupled navigation only use pseudo range and Doppler observations, while the time-differenced carrier phase (TDCP)/Inertial navigation system (INS) tightly coupled navigation uses an extra TDCP observation; this can provides higher attitude accuracy than the conventional tightly coupled navigation. However, the mathematical model of the conventional TDCP/INS tightly coupled navigation makes difference between the reference epoch and current epoch, this creates poor real-time. To improve the accuracy and reduce the computation burden, this paper presents a modified TDCP/INS tightly coupled navigation method, infers a modified mathematical model of TDCP, which can make difference between the current 0.1 s epoch. The experimental results show that, compared with the loosely coupled navigation, the modified TDCP/INS tightly coupled navigation method provides higher accuracy of position, velocity and attitude. As expected, compared with the conventional tightly coupled navigation, the attitude accuracy is significantly improved, meanwhile the accuracy of position and velocity are slightly improved. In the face of few observable satellites and turning, the modified TDCP/INS tightly coupled navigation method shows a clear advantage.

MEMS-based integrated system of a global navigation satellite system (GNSS) and an inertial navigation system (INS) has been widely used in various navigation applications. However, such integration encounters some major limitations. On the one hand, the noisy MEMS-based INS undermines the accuracy with time during the frequently occurring GNSS outages caused by signal blockage or attenuation in certain situations such as urban canyon, tunnels, and high trees. On the other hand, the model mismatch between actual GNSS error and the assumed one would also degrade the obtained accuracy even with continuous GNSS aiding. To improve the overall performance for GNSS/MEMS-INS, better error models can be obtained using Allan variance (AV) analysis technique for modeling inertial sensor errors instead of the commonly recommended auto-regressive processes, and on the other hand, the measurement update in Kalman filter is improved using innovation filtering and AV calculation. The performance of each method and the combined algorithm is evaluated by a field test with either differential GNSS (DGNSS) or single-point positioning (SPP) as external aid. In addition to the considerable navigation enhancement brought by each method, the experimental results show the combined algorithm accomplishes overall accuracy improvements by about 18% (position), 8% (velocity), and 38% (attitude) for integration with DGNSS, and by about 15% (position), 75% (velocity), and 77% (attitude) for that with SPP, compared with corresponding traditional counterparts.

Scientific Data 4:170048 doi: 10.1038/sdata201748 (2017); Published 11 April 2017; Updated 24 October 2017. The Data Descriptor incorrectly states the number of normal incidences used to generate the plot in Fig. 4b as 209. This plot was generated from 32 normal incidence cases.

Viscous sample delivery that decreases the net protein consumed in serial femtosecond crystallography is described. The agarose stream has a low background, is compatible with membrane proteins and can be used at a wide range of temperatures., Serial femtosecond crystallography (SFX) has opened a new era in crystallo­graphy by permitting nearly damage-free, room-temperature structure determination of challenging proteins such as membrane proteins. In SFX, femtosecond X-ray free-electron laser pulses produce diffraction snapshots from nanocrystals and microcrystals delivered in a liquid jet, which leads to high protein consumption. A slow-moving stream of agarose has been developed as a new crystal delivery medium for SFX. It has low background scattering, is compatible with both soluble and membrane proteins, and can deliver the protein crystals at a wide range of temperatures down to 4°C. Using this crystal-laden agarose stream, the structure of a multi-subunit complex, phycocyanin, was solved to 2.5 Å resolution using 300 µg of microcrystals embedded into the agarose medium post-crystallization. The agarose delivery method reduces protein consumption by at least 100-fold and has the potential to be used for a diverse population of proteins, including membrane protein complexes.

Angiotensin II type 1 receptor (AT(1)R) is a G protein-coupled receptor that serves as a primary regulator for blood pressure maintenance. Although several anti-hypertensive drugs have been developed as AT(1)R blockers (ARBs), the structural basis for AT(1)R ligand-binding and regulation has remained elusive, mostly due to the difficulties of growing high-quality crystals for structure determination using synchrotron radiation. By applying the recently developed method of serial femtosecond crystallography at an X-ray free-electron laser, we successfully determined the room-temperature crystal structure of the human AT(1)R in complex with its selective antagonist ZD7155 at 2.9-angstrom resolution. The AT(1)R-ZD7155 complex structure revealed key structural features of AT(1)R and critical interactions for ZD7155 binding. Docking simulations of the clinically used ARBs into the AT(1)R structure further elucidated both the common and distinct binding modes for these antihypertensive drugs. Our results thereby provide fundamental insights into AT(1)R structure-function relationship and structure-based drug design.

Lipidic cubic phases (LCPs) have emerged as successful matrixes for the crystallization of membrane proteins. Moreover, the viscous LCP also provides a highly effective delivery medium for serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs). Here, the adaptation of this technology to perform serial millisecond crystallography (SMX) at more widely available synchrotron microfocus beamlines is described. Compared with conventional microcrystallography, LCP-SMX eliminates the need for difficult handling of individual crystals and allows for data collection at room temperature. The technology is demonstrated by solving a structure of the light-driven proton-pump bacteriorhodopsin (bR) at a resolution of 2.4 Å. The room-temperature structure of bR is very similar to previous cryogenic structures but shows small yet distinct differences in the retinal ligand and proton-transfer pathway., IUCrJ, 2 (2), ISSN:2052-2525

Scientific data 4, 170048 (2017). doi:10.1038/sdata.2017.48, X-ray free-electron lasers provide novel opportunities to conduct single particle analysis on nanoscale particles. Coherent diffractive imaging experiments were performed at the Linac Coherent Light Source (LCLS), SLAC National Laboratory, exposing single inorganic core-shell nanoparticles to femtosecond hard-X-ray pulses. Each facetted nanoparticle consisted of a crystalline gold core and a differently shaped palladium shell. Scattered intensities were observed up to about 7 nm resolution. Analysis of the scattering patterns revealed the size distribution of the samples, which is consistent with that obtained from direct real-space imaging by electron microscopy. Scattering patterns resulting from single particles were selected and compiled into a dataset which can be valuable for algorithm developments in single particle scattering research., Published by Springer Nature, London