Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Comparative genomics on chloroplasts of Rubus (Rosaceae).

Rubus, the largest genus in Rosaceae, contains over 1400 species that distributed in multiple habitats across the world, with high species diversity in the temperate regions of Northern Hemisphere. Multiple Rubus species are cultivated for their valuable fruits. However, the intrageneric classification and phylogenetic relationships are still poorly understood. In this study, we sequenced, assembled, and characterized 17 plastomes of Rubus, and conducted comparative genomics integrating with 47 previously issued plastomes of this genus. The 64 plastomes of Rubus exhibited typical quadripartite structure with sizes ranging from 155,144 to 156,700 bp, and contained 132 genes including 87 protein-coding genes, 37 tRNA genes and eight rRNA genes. All plastomes are conservative in the gene order, the frequency of different types of long repeats and simple sequence repeats (SSRs), the codon usage, and the selection pressure of protein-coding genes. However, there are also some differences in the Rubus plastomes, including slight contraction and expansion of the IRs, a variation in the numbers of SSRs and long repeats, and some genes in certain clades undergoing intensified or relaxed purifying selection. Phylogenetic analysis based on whole plastomes showed that the monophyly of Rubus was strongly supported and resolved it into six clades corresponding to six subgenera. Moreover, we identified 12 highly variable regions that could be potential molecular markers for phylogenetic, population genetic, and barcoding studies. Overall, our study provided insight into plastomic structure and sequence diversification of Rubus, which could be beneficial for future studies on identification, evolution, and phylogeny in this genus.

Enhancing the spatial resolution of time-of-flight based non-line-of-sight imaging via instrument response function deconvolution.

Non-line-of-sight (NLOS) imaging retrieves the hidden scenes by utilizing the signals indirectly reflected by the relay wall. Benefiting from the picosecond-level timing accuracy, time-correlated single photon counting (TCSPC) based NLOS imaging can achieve theoretical spatial resolutions up to millimeter level. However, in practical applications, the total temporal resolution (also known as total time jitter, TTJ) of most current TCSPC systems exceeds hundreds of picoseconds due to the combined effects of multiple electronic devices, which restricts the underlying spatial resolution of NLOS imaging. In this paper, an instrument response function deconvolution (IRF-DC) method is proposed to overcome the constraints of a TCSPC system's TTJ on the spatial resolution of NLOS imaging. Specifically, we model the transient measurements as Poisson convolution process with the normalized IRF as convolution kernel, and solve the inverse problem with iterative deconvolution algorithm, which significantly improves the spatial resolution of NLOS imaging after reconstruction. Numerical simulations show that the IRF-DC facilitates light-cone transform and frequency-wavenumber migration solver to achieve successful reconstruction even when the system's TTJ reaches 1200 ps, which is equivalent to what was previously possible when TTJ was about 200 ps. In addition, the IRF-DC produces satisfactory reconstruction outcomes when the signal-to-noise ratio (SNR) is low. Furthermore, the effectiveness of the proposed method has also been experimentally verified. The proposed IRF-DC method is highly applicable and efficient, which may promote the development of high-resolution NLOS imaging.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Self-Assembling Peptide RADA16 Nanofiber Scaffold Hydrogel-Wrapped Concentrated Growth Factors in Osteogenesis of MC3T3.

Concentrated growth factors (CGFs) are widely used in surgery with bone grafting, but the release of growth factors from CGFs is rapid. RADA16, a self-assembling peptide, can form a scaffold that is similar to the extracellular matrix. Based on the properties of RADA16 and CGF, we hypothesized that the RADA16 nanofiber scaffold hydrogel could enhance the function of CGFs and that the RADA16 nanofiber scaffold hydrogel-wrapped CGFs (RADA16-CGFs) would perform a good osteoinductive function. This study aimed to investigate the osteoinductive function of RADA16-CGFs. Scanning electron microscopy, rheometry, and ELISA were performed, and MC3T3-E1 cells were used to test cell adhesion, cytotoxicity, and mineralization after administration with RADA16-CGFs. We found that RADA16 endowed with the sustained release of growth factors from CGFs, which can help maximize the function of CGFs in osteoinduction. The application of the atoxic RADA16 nanofiber scaffold hydrogel with CGFs can be a new therapeutic strategy for the treatment of alveolar bone loss and other problems that require bone regeneration.

Adhesive Property of 3D-Printed PEEK Abutments: Effects of Surface Treatment and Temporary Crown Material on Shear Bond Strength.

Three-dimensionally printed polyetheretherketone (PEEK) materials are promising for fabricating customized dental abutments. This study aimed to investigate the adhesive property of a 3D-printed PEEK material. The effects of surface treatment and temporary crown materials on shear bond strength were evaluated. A total of 108 PEEK discs were 3D printed by fused-filament fabrication. Surface treatments, including sandblasting, abrasive paper grinding, and CO2 laser ablation, were applied to the PEEK discs, with the untreated specimens set as the control. Afterward, the surface topographies of each group were investigated by scanning electron microscopy (SEM, n = 1) and roughness measurements (n = 7). After preparing the bonding specimens with three temporary crown materials (Artificial teeth resin (ATR), 3M™ Filtek™ Supreme Flowable Restorative (FR), and Cool Temp NATURAL (CTN)), the shear bond strength was measured (n = 6), and the failure modes were analyzed by microscopy and SEM. The results showed that ATR exhibited a significantly higher shear bond strength compared to FR and CTN (p < 0.01), and the PEEK surfaces treated by sandblasting and abrasive paper grinding showed a statistically higher shear bond strength compared to the control (p < 0.05). For clinical application, the ATR material and subtractive surface treatments are recommended for 3D-printed PEEK abutments.

Does IR-loss promote plastome structural variation and sequence evolution?

Plastids are one of the main distinguishing characteristics of the plant cell. The plastid genome (plastome) of most autotrophic seed plants possesses a highly conserved quadripartite structure containing a large single-copy (LSC) and a small single-copy (SSC) region separated by two copies of the inverted repeat (termed as IRA and IRB). The IRs have been inferred to stabilize the plastid genome via homologous recombination-induced repair mechanisms. IR loss has been documented in seven autotrophic flowering plant lineages and two autotrophic gymnosperm lineages, and the plastomes of these species (with a few exceptions) are rearranged to a great extent. However, some plastomes containing normal IRs also show high structural variation. Therefore, the role of IRs in maintaining plastome stability is still controversial. In this study, we first integrated and compared genome structure and sequence evolution of representative plastomes of all nine reported IR-lacking lineages and those of their closest relative(s) with canonical inverted repeats (CRCIRs for short) to explore the role of the IR in maintaining plastome structural stability and sequence evolution. We found the plastomes of most IR-lacking lineages have experienced significant structural rearrangement, gene loss and duplication, accumulation of novel small repeats, and acceleration of synonymous substitution compared with those of their CRCIRs. However, the IR-lacking plastomes show similar structural variation and sequence evolution rate, and even less rearrangement distance, dispersed repeat number, tandem repeat number, indels frequency and GC3 content than those of IR-present plastomes with variation in Geraniaceae. We argue that IR loss is not a driver of these changes but is instead itself a consequence of other processes that more broadly shape both structural and sequence-level plastome evolution.

Fast non-line-of-sight imaging based on product-convolution expansions.

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.

Using Multi-Antenna Trajectory Constraint to Analyze BeiDou Carrier-Phase Observation Error of Dynamic Receivers.

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.

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

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.

Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation.

With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation.

Control of multilayer biological networks and applied to target identification of complex diseases.

BACKGROUND: Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes. RESULT: In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature. CONCLUSIONS: The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.

Corrigendum: Diffraction data of core-shell nanoparticles from an X-ray free electron laser.

This corrects the article DOI: 10.1038/sdata.2017.48.

Identifying key nodes in multilayer networks based on tensor decomposition.

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

Corrigendum: Double-flow focused liquid injector for efficient serial femtosecond crystallography.

This corrects the article DOI: 10.1038/srep44628.

Diffraction data of core-shell nanoparticles from an X-ray free electron laser.

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.

Double-flow focused liquid injector for efficient serial femtosecond crystallography.

Serial femtosecond crystallography requires reliable and efficient delivery of fresh crystals across the beam of an X-ray free-electron laser over the course of an experiment. We introduce a double-flow focusing nozzle to meet this challenge, with significantly reduced sample consumption, while improving jet stability over previous generations of nozzles. We demonstrate its use to determine the first room-temperature structure of RNA polymerase II at high resolution, revealing new structural details. Moreover, the double-flow focusing nozzles were successfully tested with three other protein samples and the first room temperature structure of an extradiol ring-cleaving dioxygenase was solved by utilizing the improved operation and characteristics of these devices [corrected].

Atomic structure of granulin determined from native nanocrystalline granulovirus using an X-ray free-electron laser.

To understand how molecules function in biological systems, new methods are required to obtain atomic resolution structures from biological material under physiological conditions. Intense femtosecond-duration pulses from X-ray free-electron lasers (XFELs) can outrun most damage processes, vastly increasing the tolerable dose before the specimen is destroyed. This in turn allows structure determination from crystals much smaller and more radiation sensitive than previously considered possible, allowing data collection from room temperature structures and avoiding structural changes due to cooling. Regardless, high-resolution structures obtained from XFEL data mostly use crystals far larger than 1 µm3 in volume, whereas the X-ray beam is often attenuated to protect the detector from damage caused by intense Bragg spots. Here, we describe the 2 Å resolution structure of native nanocrystalline granulovirus occlusion bodies (OBs) that are less than 0.016 µm3 in volume using the full power of the Linac Coherent Light Source (LCLS) and a dose up to 1.3 GGy per crystal. The crystalline shell of granulovirus OBs consists, on average, of about 9,000 unit cells, representing the smallest protein crystals to yield a high-resolution structure by X-ray crystallography to date. The XFEL structure shows little to no evidence of radiation damage and is more complete than a model determined using synchrotron data from recombinantly produced, much larger, cryocooled granulovirus granulin microcrystals. Our measurements suggest that it should be possible, under ideal experimental conditions, to obtain data from protein crystals with only 100 unit cells in volume using currently available XFELs and suggest that single-molecule imaging of individual biomolecules could almost be within reach.

Serial femtosecond crystallography datasets from G protein-coupled receptors.

We describe the deposition of four datasets consisting of X-ray diffraction images acquired using serial femtosecond crystallography experiments on microcrystals of human G protein-coupled receptors, grown and delivered in lipidic cubic phase, at the Linac Coherent Light Source. The receptors are: the human serotonin receptor 2B in complex with an agonist ergotamine, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2, the human smoothened receptor in complex with an antagonist cyclopamine, and finally the human angiotensin II type 1 receptor in complex with the selective antagonist ZD7155. All four datasets have been deposited, with minimal processing, in an HDF5-based file format, which can be used directly for crystallographic processing with CrystFEL or other software. We have provided processing scripts and supporting files for recent versions of CrystFEL, which can be used to validate the data.