Neuronal signal-regulatory protein alpha drives microglial phagocytosis by limiting microglial interaction with CD47 in the retina.

Microglia utilize their phagocytic activity to prune redundant synapses and refine neural circuits during precise developmental periods. However, the neuronal signals that control this phagocytic clockwork remain largely undefined. Here, we show that neuronal signal-regulatory protein alpha (SIRPα) is a permissive cue for microglial phagocytosis in the developing murine retina. Removal of neuronal, but not microglial, SIRPα reduced microglial phagocytosis, increased synpase numbers, and impaired circuit function. Conversely, prolonging neuronal SIRPα expression extended developmental microglial phagocytosis. These outcomes depended on the interaction of presynaptic SIRPα with postsynaptic CD47. Global CD47 deficiency modestly increased microglial phagocytosis, while CD47 overexpression reduced it. This effect was rescued by coexpression of neuronal SIRPα or codeletion of neuronal SIRPα and CD47. These data indicate that neuronal SIRPα regulates microglial phagocytosis by limiting microglial SIRPα access to neuronal CD47. This discovery may aid our understanding of synapse loss in neurological diseases.

Sculpting neural circuits by axon and dendrite pruning.

The assembly of functional neural circuits requires the combined action of progressive and regressive events. Regressive events encompass a variety of inhibitory developmental processes, including axon and dendrite pruning, which facilitate the removal of exuberant neuronal connections. Most axon pruning involves the removal of axons that had already made synaptic connections; thus, axon pruning is tightly associated with synapse elimination. In many instances, these developmental processes are regulated by the interplay between neurons and glial cells that act instructively during neural remodeling. Owing to the importance of axon and dendritic pruning, these remodeling events require precise spatial and temporal control, and this is achieved by a range of distinct molecular mechanisms. Disruption of these mechanisms results in abnormal pruning, which has been linked to brain dysfunction. Therefore, understanding the mechanisms of axon and dendritic pruning will be instrumental in advancing our knowledge of neural disease and mental disorders.

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.

Activity-induced MeCP2 phosphorylation regulates retinogeniculate synapse refinement.

Mutations in MECP2 give rise to Rett syndrome (RTT), an X-linked neurodevelopmental disorder that results in broad cognitive impairments in females. While the exact etiology of RTT symptoms remains unknown, one possible explanation for its clinical presentation is that loss of MECP2 causes miswiring of neural circuits due to defects in the brain's capacity to respond to changes in neuronal activity and sensory experience. Here, we show that MeCP2 is phosphorylated at four residues in the mouse brain (S86, S274, T308, and S421) in response to neuronal activity, and we generate a quadruple knock-in (QKI) mouse line in which all four activity-dependent sites are mutated to alanines to prevent phosphorylation. QKI mice do not display overt RTT phenotypes or detectable gene expression changes in two brain regions. However, electrophysiological recordings from the retinogeniculate synapse of QKI mice reveal that while synapse elimination is initially normal at P14, it is significantly compromised at P20. Notably, this phenotype is distinct from the synapse refinement defect previously reported for Mecp2 null mice, where synapses initially refine but then regress after the third postnatal week. We thus propose a model in which activity-induced phosphorylation of MeCP2 is critical for the proper timing of retinogeniculate synapse maturation specifically during the early postnatal period.

R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

NSRGRN: a network structure refinement method for gene regulatory network inference.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

Live imaging of retinotectal mapping reveals topographic map dynamics and a previously undescribed role for Contactin 2 in map sharpening.

Organization of neuronal connections into topographic maps is essential for processing information. Yet, our understanding of topographic mapping has remained limited by our inability to observe maps forming and refining directly in vivo. Here, we used Cre-mediated recombination of a new colorswitch reporter in zebrafish to generate the first transgenic model allowing the dynamic analysis of retinotectal mapping in vivo. We found that the antero-posterior retinotopic map forms early but remains dynamic, with nasal and temporal retinal axons expanding their projection domains over time. Nasal projections initially arborize in the anterior tectum but progressively refine their projection domain to the posterior tectum, leading to the sharpening of the retinotopic map along the antero-posterior axis. Finally, using a CRISPR-mediated mutagenesis approach, we demonstrate that the refinement of nasal retinal projections requires the adhesion molecule Contactin 2. Altogether, our study provides the first analysis of a topographic map maturing in real time in a live animal and opens new strategies for dissecting the molecular mechanisms underlying precise topographic mapping in vertebrates.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning.

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

Hirshfeld Atom Refinement (HAR) and Complementary Bonding Analysis of Lithium m-Terphenylhydridoborates Containing B-H···Li Linkages.

The synthesis and characterization of the lithium m-terphenylhydridoborates [Li(2,6-Mes2C6H3)BH3]2 (1), Li(2,6-Mes2C6H3)2BH2 (2) and Li(OEt2)(2,6-Mes2C6H3)2BH2 (3) are reported. Hirshfeld Atom Refinement (HAR) of the experimentally obtained molecular structures by single-crystal X-ray crystallography allowed the determination of the exact positions of the hydrogen atoms. The bond situations of the various B-H···Li linkages were investigated by a complementary bonding analysis using various methods including atoms in molecules (AIM), electron localizability indicator (ELI-D), non-covalent interaction (NCI) index and the compliance matrix.

X-ray diffraction and its emerging applications in the food industry.

X-ray diffraction (XRD) is an analytical technique that has found several applications focusing on the identification of crystal structure, space groups, plane, and orientation, in addition to qualitative and quantitative phase identification, and polymorphism behavior. An XRD diffractogram pattern/Bragg's peak can also provide valuable information that can be used for various food applications. While this review details the fundamental principles of XRD, the types of XRD systems, instrumentation, and the components thereof, the focus is to serve as a structured resource on explored applications of XRD in food, majorly revolving around food quality and safety. While recent studies relevant to the field are highlighted, leads for futuristic prospects are presented. With its unique approach, the XRD analysis can prove to be a rapid, robust, and sensitive nondestructive approach to food quality evaluation. Recent reports indicate its scope for nonconventional applications such as the assessment of 3D printability of foods, ice crystal formation, and screening food adulterants. Studies also highlight its scope to complement or replace conventional food quality testing approaches that involve the usage of chemicals, extensive sample preparation procedures, derivatization steps and demand long testing times.

Endeavours made by trade associations, pharmaceutical companies and regulators in the replacement, reduction and refinement of animal experimentation in safety testing of pharmaceuticals.

Following the European Commission decision to develop a roadmap to phase out animal testing and the signing of the US Modernisation Act, there is additional pressure on regulators and the pharmaceutical industry to abandon animal experimentation in safety testing. Often, endeavours already made by governments, regulators, trade associations, and industry to replace, reduce and refine animal experimentation (3Rs) are unnoticed. Herein, we review such endeavours to promote wider application and acceptance of 3Rs. ICH guidelines have stated 3Rs objectives and have enjoyed many successes driven by global consensus. Initiatives driven by US and European regulators such as the removal of the Abnormal Toxicity Test are neutralised by reticent regional regulators. Stream-lined testing requirements have been proposed for new modalities, oncology, impurity management and animal pharmacokinetics/metabolism. Use of virtual controls, value of the second toxicity species, information sharing and expectations for life-threatening diseases, human specific or well-characterised targets are currently being scrutinised. Despite much effort, progress falls short of the ambitious intent of decisionmakers. From a clinical safety and litigation perspective pharmaceutical companies and regulators are reluctant to step away from current paradigms unless replacement approaches are validated and globally accepted. Such consensus has historically been best achieved through ICH initiatives.

New supporting data to guide the use of evident toxicity in acute oral toxicity studies (OECD TG 420).

Currently there are three test guidelines (TG) for acute oral toxicity studies of substances or mixtures from the Organisation for Economic Co-operation and Development (OECD). TG 423 and TG 425 use lethality as an endpoint, while TG 420 replaces death with 'evident toxicity', defined as clear signs that exposure to a higher dose would result in death. However, the perceived subjectivity of 'evident toxicity' may be preventing wider use of TG 420. To address this, the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) and the European Partnership for Alternative Approaches to Animal Testing (EPAA) collaborated to provide recommendations on the recognition of 'evident toxicity'. Historical data from acute oral toxicity studies were analysed for clinical signs at the lower dose that could have predicted death at the higher dose. Several signs including ataxia, laboured respiration, and eyes partially closed, alone or in combination, are highly predictive. Others such as lethargy, decreased respiration, and loose faeces have lower but still appreciable positive predictive value (PPV). The data has been used to develop recommendations to promote use of TG 420 and thus reduce the suffering and numbers of animals used in acute oral toxicity studies.

Development of a novel primate welfare assessment tool for research macaques.

Primates are important species for biomedical research and ensuring their good welfare is critical for research translatability and ethical responsibility. Systematic animal welfare assessments can support continuous programme improvements and build institutional awareness of areas requiring more attention. A multi-facility, collaborative project aimed to develop and implement a novel primate welfare assessment tool (PWAT) for use with research macaques. PWAT development involved: establishing an internal focus group of primate subject matter experts, identifying animal welfare categories and descriptors based on literature review, developing a preliminary tool, beta-testing the tool to ensure practicality and final consensus on descriptors, finalising the tool in a database with semi-automated data analysis, and delivering the tool to 13 sites across four countries. The tool uses input- and outcome-based measures from six categories: physical, behavioural, training, environmental, procedural, and culture of care. The final tool has 133 descriptors weighted based upon welfare impact, and is split into three forms for ease of use (room level, site level, and personnel interviews). The PWAT was trialled across facilities in March and September 2022 for benchmarking current macaque behavioural management programmes. The tool successfully distinguished strengths and challenges at the facility level and across sites. Following this benchmarking, the tool is being applied semi-annually to assess and monitor progress in behavioural management programmes. The development process of the PWAT demonstrates that evidence-based assessment tools can be developed through collaboration and consensus building, which are important for uptake and applicability, and ultimately for promoting global improvements in research macaque welfare.

A systematic review of the development and application of home cage monitoring in laboratory mice and rats.

BACKGROUND: Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside of their home cages at selected time points. However, the outcome of such tests can be influenced by various factors and valuable information may be missed when the animals are only monitored for short periods. These issues can be overcome by longitudinally monitoring mice and rats in their home cages. To shed light on the development of home cage monitoring (HCM) and the current state-of-the-art, a systematic review was carried out on 521 publications retrieved through PubMed and Web of Science. RESULTS: Both the absolute (~ × 26) and relative (~ × 7) number of HCM-related publications increased from 1974 to 2020. There was a clear bias towards males and individually housed animals, but during the past decade (2011-2020), an increasing number of studies used both sexes and group housing. In most studies, animals were kept for short (up to 4 weeks) time periods in the HCM systems; intermediate time periods (4-12 weeks) increased in frequency in the years between 2011 and 2020. Before the 2000s, HCM techniques were predominantly applied for less than 12 h, while 24-h measurements have been more frequent since the 2000s. The systematic review demonstrated that manual monitoring is decreasing in relation to automatic techniques but still relevant. Until (and including) the 1990s, most techniques were applied manually but have been progressively replaced by automation since the 2000s. Independent of the year of publication, the main behavioral parameters measured were locomotor activity, feeding, and social behaviors; the main physiological parameters were heart rate and electrocardiography. External appearance-related parameters were rarely examined in the home cages. Due to technological progress and application of artificial intelligence, more refined and detailed behavioral parameters have been investigated in the home cage more recently. CONCLUSIONS: Over the period covered in this study, techniques for HCM of mice and rats have improved considerably. This development is ongoing and further progress as well as validation of HCM systems will extend the applications to allow for continuous, longitudinal, non-invasive monitoring of an increasing range of parameters in group-housed small rodents in their home cages.

Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform paths characterizes this error. In response to this challenge, we propose a linear, parameter-free model that uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established by the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation, with substantial pose differences and efficient pose optimization in larger circuits.

LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection.

The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.

A Music-Driven Dance Generation Method Based on a Spatial-Temporal Refinement Model to Optimize Abnormal Frames.

Since existing music-driven dance generation methods have abnormal motion when generating dance sequences which leads to unnatural overall dance movements, a music-driven dance generation method based on a spatial-temporal refinement model is proposed to optimize the abnormal frames. Firstly, the cross-modal alignment model is used to learn the correspondence between the two modalities of audio and dance video and based on the learned correspondence, the corresponding dance segments are matched with the input music segments. Secondly, an abnormal frame optimization algorithm is proposed to carry out the optimization of the abnormal frames in the dance sequence. Finally, a temporal refinement model is used to constrain the music beats and dance rhythms in the temporal perspective to further strengthen the consistency between the music and the dance movements. The experimental results show that the proposed method can generate realistic and natural dance video sequences, with the FID index reduced by 1.2 and the diversity index improved by 1.7.

Information-based group sequential design for post-market safety monitoring of medical products using real world data.

Real world healthcare data are commonly used in post-market safety monitoring studies to address potential safety issues related to newly approved medical products. Such studies typically involve repeated evaluations of accumulating safety data with respect to pre-defined hypotheses, for which the group sequential design provides a rigorous and flexible statistical framework. A major challenge in designing a group sequential safety monitoring study is the uncertainty associated with product uptake, which makes it difficult to specify the final sample size or maximum duration of the study. To deal with this challenge, we propose an information-based group sequential design which specifies a target amount of information that would produce adequate power for detecting a clinically significant effect size. At each interim analysis, the variance estimate for the treatment effect of interest is used to compute the current information time, and a pre-specified alpha spending function is used to determine the stopping boundary. The proposed design can be applied to regression models that adjust for potential confounders and/or heterogeneous treatment exposure. Simulation results demonstrate that the proposed design performs reasonably well in realistic settings.

Adaptive Point-Line Fusion: A Targetless LiDAR-Camera Calibration Method with Scheme Selection for Autonomous Driving.

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR-camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

Promotion of the Nucleation of Ultrafine Ni-Rich Layered Oxide Primary Particles by an Atomic Layer-Deposited Thin Film for Enhanced Mechanical Stability.

Understanding the atomistic mechanisms of non-equilibrium processes during solid-state synthesis, such as nucleation and grain structure formation of a layered oxide phase, is a critical challenge for developing promising cathode materials such as Ni-rich layered oxide for Li-ion batteries. In this study, we found that the aluminum oxide coating layer transforms into lithium aluminate as an intermediate, which has favorable low interfacial energies with the layered oxide to promote the nucleation of the latter. The fast and uniform nucleation and formation of the layered oxide phase at relatively low temperatures were evidenced using solid-state nuclear magnetic resonance and in situ synchrotron X-ray diffraction. The resulting Ni-rich layered oxide cathode has fine primary particles, as visualized by three-dimensional tomography constructed using a focused-ion beam and scanning electron microscopy. The densely packed fine primary particles enable the excellent mechanical strength of the secondary particles, as demonstrated by in situ compression tests. This strategy provides a new approach for developing next-generation, high-strength battery materials.