SNX27:Retromer:ESCPE-1-mediated early endosomal tubulation impacts cytomegalovirus replication.

Introduction: Cytomegaloviruses (CMVs) extensively reorganize the membrane system of the cell and establish a new structure as large as the cell nucleus called the assembly compartment (AC). Our previous studies on murine CMV (MCMV)-infected fibroblasts indicated that the inner part of the AC contains rearranged early endosomes, recycling endosomes, endosomal recycling compartments and trans-Golgi membrane structures that are extensively tubulated, including the expansion and retention of tubular Rab10 elements. An essential process that initiates Rab10-associated tubulation is cargo sorting and retrieval mediated by SNX27, Retromer, and ESCPE-1 (endosomal SNX-BAR sorting complex for promoting exit 1) complexes. Objective: The aim of this study was to investigate the role of SNX27:Retromer:ESCPE-1 complexes in the biogenesis of pre-AC in MCMV-infected cells and subsequently their role in secondary envelopment and release of infectious virions. Results: Here we show that SNX27:Retromer:ESCPE1-mediated tubulation is essential for the establishment of a Rab10-decorated subset of membranes within the pre-AC, a function that requires an intact F3 subdomain of the SNX27 FERM domain. Suppression of SNX27-mediated functions resulted in an almost tenfold decrease in the release of infectious virions. However, these effects cannot be directly linked to the contribution of SNX27:Retromer:ESCPE-1-dependent tubulation to the secondary envelopment, as suppression of these components, including the F3-FERM domain, led to a decrease in MCMV protein expression and inhibited the progression of the replication cycle. Conclusion: This study demonstrates a novel and important function of membrane tubulation within the pre-AC associated with the control of viral protein expression.

Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy.

The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color, thereby reducing the prediction accuracy of fruit composition. In this study, we aimed to develop a deep learning model with color correction capability to predict oranges soluble solids content (SSC) based on multi-source data fusion. Initially, a machine vision and Vis/NIR spectroscopy online acquisition device was designed to collect and analyze color images and transmission spectra. Subsequently, data fusion methods were proposed for color features and spectral data. Finally, color-correction one-dimensional convolutional neural network (1D-CNN) models base on multi-source data were constructed. The results showed that, the RMSEP of optimal color-correction model was decreased by 36.4 % and 16.1 % compared to partial least squares model and conventional 1D-CNN model, respectively. The multi-source data fusion of machine vision and Vis/NIR spectroscopy has the potential to improve the accuracy of food composition prediction.

Differential analysis of sorting nexin 10 and sterol regulatory element-binding protein 2 expression in inflammatory bowel disease.

Sorting nexin 10 (SNX10) expression induces intestinal barrier dysfunction and inflammatory responses; in contrast, its inhibition promotes intestinal mucosal healing through sterol regulatory element-binding protein 2 (SREBP2)-mediated cholesterol synthesis. However, its regulatory mechanism for the pathogenesis of inflammatory bowel disease (IBD) remains unclear. In this study, we examined SNX10 and SREBP2 expression in ulcerative colitis (UC) and Crohn's disease (CD). A total of 30 and 28 patients with UC and CD, respectively, were recruited. The expression of SNX10 and SREBP2 in the colonic mucosa was measured by immunohistochemistry (IHC). We discovered that patients with CD had significantly higher expression levels of SNX10 and SREBP2 than patients with UC and healthy controls. In addition, the expression of SREBP2 in patients with UC was significantly higher than that in healthy controls. In our study, we indicated that SNX10 and SREBP2 may serve as biomarkers for identifying patients with UC and CD, thereby providing a clinical therapeutic strategy for the treatment of IBD by inhibiting SNX10.

Localization of neurons from extracellular footprints.

BACKGROUND: High density microelectrode arrays (HD-MEAs) are now widely used for both in-vitro and in-vivo recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices. NEW METHOD: The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions. RESULTS: The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than 10µm, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost. COMPARISON WITH EXISTING METHOD(S): The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before. CONCLUSION: The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.

Deep Recyclable Trash Sorting Using Integrated Parallel Attention.

Sorting recyclable trash is critical to reducing energy consumption and mitigating environmental pollution. Currently, trash sorting heavily relies on manpower. Computer vision technology enables automated trash sorting. However, existing trash image classification datasets contain a large number of images without backgrounds. Moreover, the models are vulnerable to background interference when categorizing images with complex backgrounds. In this work, we provide a recyclable trash dataset that supports model training and design a model specifically for trash sorting. Firstly, we introduce the TrashIVL dataset, an image dataset for recyclable trash sorting encompassing five classes (TrashIVL-5). All images are collected from public trash datasets, and the original images were captured by RGB imaging sensors, containing trash items with real-life backgrounds. To achieve refined recycling and improve sorting efficiency, the TrashIVL dataset can be further categorized into 12 classes (TrashIVL-12). Secondly, we propose the integrated parallel attention module (IPAM). Considering the susceptibility of sensor-based systems to background interference in real-world trash sorting scenarios, our IPAM is specifically designed to focus on the essential features of trash images from both channel and spatial perspectives. It can be inserted into convolutional neural networks (CNNs) as a plug-and-play module. We have constructed a recyclable trash sorting network building upon the IPAM, which produces an acuracy of 97.42% on TrashIVL-5 and 94.08% on TrashIVL-12. Our work is an effective attempt of computer vision in recyclable trash sorting. It makes a positive contribution to environmental protection and sustainable development.

Microcavity-assisted cloning (MAC) of hard-to-clone HepG2 cell lines: cloning made easy.

Cloning is a key molecular biology procedure for obtaining a genetically homogenous population of organisms or cell lines. It requires the expansion of new cell populations starting from single genetically modified cells. Despite the technical progress, cloning of many cell lines remains difficult. Cloning often fails either due to the strenuous conditions associated with manipulating cells or because many cells don't tolerate a single-cell state. Here we describe a new cloning method utilizing low adhesion microcavity plates. This new technique, named microcavity-assisted cloning (MAC) was developed to clone difficult-to-clone HepG2 cells. The clones were produced following CRISPR/Cas9 knockout of the GSTA1 gene by a random distribution of 200, 400, and 800 cells into 550 microcavities of a 24-well low adhesion plate originally designed for the culture of spheroids. The knockout of GSTA1 was verified at the protein level using Western blotting. The advantages of the MAC method are its low cost, ease of the procedure, and the possibility of scaling up the throughput and automatization.

Networked Multicomponent Ensemble as AND Gate with FRET Output.

A networked supramolecular logic AND gate system is accomplished using precise chemical communication within a multicomponent ensemble via metal ion-driven self-sorting processes. The cybernetic AND gate is composed of a copper(I)-loaded nanoswitch, an aza-crown ether and a rhodamine receptor. The modus operandi of the AND gate, from state (0,0), was induced with stoichiometric amounts of two inputs (IN-1 = Hg2+, IN-2 = Li+) generating copper(I) ions as output only in state (1,1). Generation of state (1,1) from state (0,0) involves selective Cu+ ion translocation from the nanoswitch to the aza-crown ether in the first step (IN-1) and then from the aza-crown ether to the rhodamine receptor in the second step (IN-2). The released copper(I) output acts as a messenger that binds to the rhodamine receptor, triggering it's spiro-lactam ring opening, which leads to a diagnostic FRET emission from the copper(I)-loaded Rhodamine scaffold accompanied by a remarkable fluorescence and colour change from pale yellow to pink.

Nuclear sorting of short RNA polymerase II transcripts.

Mammalian genomes produce an abundance of short RNA. This is, to a large extent, due to the genome-wide and spurious activity of RNA polymerase II (RNAPII). However, it is also because the vast majority of initiating RNAPII, regardless of the transcribed DNA unit, terminates within a ∼3-kb early "pausing zone." Given that the resultant RNAs constitute both functional and non-functional species, their proper sorting is critical. One way to think about such quality control (QC) is that transcripts, from their first emergence, are relentlessly targeted by decay factors, which may only be avoided by engaging protective processing pathways. In a molecular materialization of this concept, recent progress has found that both "destructive" and "productive" RNA effectors assemble at the 5' end of capped RNA, orchestrated by the essential arsenite resistance protein 2 (ARS2) protein. Based on this principle, we here discuss early QC mechanisms and how these might sort short RNAs to their final fates.

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Kinetic Control over Social and Narcissistic Self-Sorting from Multicomponent Mixtures in Seed-Initiated Supramolecular Polymerization by Fine-Tuning of Steric Effects.

Supramolecular polymers offer an intriguing possibility to transfer molecular properties from the nano- to the mesoscale. Towards this achievement, seed-initiated supramolecular polymerization has emerged as a powerful tool, as it prevents unlimited growth and enables size control of the assembly outcome. However, the potential application of the seeding method in the context of complex supramolecular systems is hitherto unclear. Herein we demonstrate that minute differences in molecular design in direct proximity to intermolecular recognition sites govern the molecular packing and in turn dictate the efficacy of seeded polymerization processes. We introduce a stepwise increase in steric demand in the central amino acid residue of a diamide system, which gradually increases the rotational displacement within the aggregated state. This fine-tuning of the molecular packing directly affects the propensity of the different aggregates to act as seeds for the other supramolecular synthons. In turn this allows us to selectively target specific trapped monomer states in binary mixtures for social or narcissistic seeded polymerization.

Reproducible 3D culture of multicellular tumor spheroids in supramolecular hydrogel from cancer stem cells sorted by sedimentation field-flow fractionation.

Three-dimensional (3D) cancer models, such as multicellular tumor spheroids (MCTS), are biological supports used for research in oncology, drug development and nanotoxicity assays. However, due to various analytical and biological challenges, the main recurring problem faced when developing this type of 3D model is the lack of reproducibility. When using a 3D support to assess the effect of biologics, small molecules or nanoparticles, it is essential that the support remains constant over time and multiples productions. This constancy ensures that any effect observed following molecule exposure can be attributed to the molecule itself and not to the heterogeneous properties of the 3D support. In this study, we address these analytical challenges by evaluating for the first time the 3D culture of a sub-population of cancer stem cells (CSCs) from a glioblastoma cancer cell line (U87-MG), produced by a SdFFF (sedimentation field-flow fractionation) cell sorting, in a supramolecular hydrogel composed of single, well-defined molecule (bis-amide bola amphiphile 0.25% w/v) with a stiffness of 0.4 kPa. CSCs were chosen for their ability of self-renewal and multipotency that allow them to generate fully-grown tumors from a small number of cells. The results demonstrate that CSCs cultured in the hydrogel formed spheroids with a mean diameter of 336.67 ± 38.70 µm by Day 35, indicating reproducible growth kinetics. This uniformity is in contrast with spheroids derived from unsorted cells, which displayed a more heterogeneous growth pattern, with a mean diameter of 203.20 ± 102.93 µm by Day 35. Statistical analysis using an unpaired t-test with unequal variances confirmed that this difference in spheroid size is significant, with a p-value of 0.0417 (p < 0.05). These findings demonstrate that CSC-derived spheroids, when cultured in a well-defined hydrogel, exhibit highly reproducible growth patterns compared to spheroids derived from unsorted cells, making them a more reliable 3D model for biological research and drug testing applications.

Natural sorting of sediments in the wave run-up zone works for microplastics as well.

The distribution of plastic pollution in the marine environment is highly variable in time and space, making it difficult to assess pollution levels. This study shows that mixing and natural sorting of material in the wave run-up zone of a sandy beach results in a relatively stable abundance of microplastics in the size range 0.5-2 mm (S-MPs). Based on 175 samples collected over 14 months during 42 monitoring surveys at 6 stations along the shore of the Vistula Spit (Baltic Sea), the mean abundance of S-MPs was found to be 64 ± 36 items/kg DW (98.6 % fibers), with a coefficient of variation of only 56 % over more than one year. Statistical tests confirmed its independence from current wind speed, significant wave height, mean sediment grain size, sediment sorting, percentage of certain sand fractions, month, season, or location along the shore. It can therefore be used as a suitable indicator for long-term monitoring of increasing plastic pollution in the marine environment.

Essential Fluidics for a Flow Cytometer.

Flow cytometry is an inherently fluidic process that flows particles on a one-by-one basis through a sensing region to discretely measure their optical and physical properties. It can be used to analyze particles ranging in size from nanoparticles to whole organisms (e.g., zebrafish). It has particular value for blood analysis, and thus most instruments are fluidically optimized for particles that are comparable in size to a typical blood cell. The principles of fluid dynamics allow for particles of such size to be precisely positioned in flow as they pass through sensing regions that are tens of microns in length at linear velocities of meters per second. Such fluidic systems enable discrete analysis of cell-sized particles at rates approaching 100 kHz. For larger particles, the principles of fluidics greatly reduce the achievable rates, but such high rates of data acquisition for cell-sized particles allow rapid collection of information on many thousands to millions of cells and provides for research and clinical measurements of both rare and common cell populations with a high degree of statistical confidence. Additionally, flow cytometers can accurately count particles via the use of volumetric sample delivery and can be coupled with high-throughput sampling technologies to greatly increase the rate at which independent samples can be delivered to the system. Due to the combination of high analysis rates, sensitive multiparameter measurements, high-throughput sampling, and accurate counting, flow cytometry analysis is the gold standard for many critical applications in clinical, research, pharmaceutical, and environmental areas. Beyond the power of flow cytometry as an analytical technique, the fluidic pathway can be coupled with a sorting mechanism to collect particles based on desired properties. We present an overview of fluidic systems that enable flow cytometry-based analysis and sorting. We introduce historical approaches, explanations of commonly implemented fluidics, and brief discussions of potential future fluidics where appropriate. © 2024 Wiley Periodicals LLC.

Deep learning approaches for classification of copper-containing metal scrap in recycling processes.

Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an image classification model to separate copper from iron scrap has been performed. Twenty of the most common and most easily available CNN architectures were trained on 2200 metal scrap specimens and evaluated inline on a sensor-based sorting rig for their prediction accuracy and their inference latency to mimic real circumstances in an industrial setting. Out of these evaluated architectures, DenseNet-201 with 98% accuracy in inline tests is recommended if potent hardware is available. Otherwise AlexNet with 92% accuracy or MobileNet-V2 with 90% accuracy are recommended for further investigation and model creation if hardware restrictions apply. Based on the presented results in this article, the initial cumbersome surveyance of the most suitable network architecture can be substantially reduced and the creation of a sorting model can be streamlined. This article thus provides the basis for creating an inline applicable sorting method for scrap metal that uses low cost sensorics equipment and can provide reasonably high accuracy in its prediction.

Deep learning-based spike sorting: a survey.

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.Main Results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.

Differential Neural Mechanisms of Feedback Processing in Children with Developmental Language Disorder: An Examination of Midfrontal Theta Connectivity.

BACKGROUND/OBJECTIVES: Previous research indicates that children with Developmental Language Disorder (DLD) face challenges learning from feedback, resulting in suboptimal performance and learning outcomes. Feedback processing, a key developing executive function, involves cognitive processes critical for goal-directed behavior. This study examined the neural mechanisms underlying feedback processing in school-age children with DLD compared to typically developing (TD) peers, focusing on midfrontal theta band (4-8 Hz) oscillations as an index of cognitive control and error monitoring. METHODS: We measured midfrontal theta inter-trial coherence (ITC) and inter-site coherence (ISC) at midfrontal (FCz), lateral prefrontal (F3/F4), and lateral central (C3/C4) sites in children with and without DLD (n = 33, age 8-13 years) in response to feedback provision within a Wisconsin Card Sorting Test (WCST) in two time windows (200-400 ms, which is associated with the Feedback-Related Negativity, or FRN, and 400-600 ms, which is associated with the P3a). RESULTS: Children with and without DLD showed elevated midfrontal theta oscillations in response to negative feedback that was followed by successful behavioral adjustments in the FRN time window. Activation in the P3a time window was only found in the TD group. Group differences were also noted in the inter-site coherence (ISC) associated with the effective processing of negative feedback. While in the TD group, effective processing of negative feedback was linked to high connectivity between midfrontal and right sensorimotor regions, in the DLD group, effective processing of negative feedback was associated with high connectivity between midfrontal and left sensorimotor sites. CONCLUSIONS: Differential ISC patterns in children with DLD may indicate that they employ alternative or compensatory neural strategies, possibly due to atypical right sensorimotor engagement.

Expression of TLR7/8 in canine sperm and evaluation of the effect of ligand R848 on the sorting of canine X/Y sperm.

The aim of this study was to analyze the expression pattern of Toll receptor 7/8 (TLR7/8) in canine sperm, and explore the feasibility of using TLR7/8 ligand resiquimod(R848)to separate canine X and Y sperm. In this study, cellular immunofluorescence was used to analyze the expression of TLR7/8 in canine sperm, real-time fluorescence quantitative PCR was used to calculate the proportion of X sperm in the lower layer of the incubation solution with R848 to evaluate the sorting effect of R848 on canine X/Y sperm, and sperm quality detection system was used to analyze the effect of R848 on the motility of canine sperm. The mechanism of effect of R848 on canine sperm motility was analyzed by Western blot. The results showed that TLR8 was not expressed in all canine sperm, while TLR7 was expressed in all canine sperm and was localized in the head and tail of sperm. When 0.4 µM R848 was incubated with canine sperm for 1 h, the total motility, average path velocity (VAP), average straight-line velocity (VSL), and average curved-line velocity (VCL) of canine sperm were significantly decreased(P < 0.05). There was no significant difference between the lower and upper layers of the R848 treatment group and the control group(P > 0.05), and the proportion of X sperm was nearly half. The levels of NF-κB and GSK3α/ß phosphorylation of sperm in R848 treatment group were significantly increased compared with control group(P < 0.05). The above results showed that TLR7/8 was not differentially expressed in canine X and Y sperm. R848 could decrease the motility of canine spermatozoa and inhibit sperm motility by the GSK3α/ß-hexokinase pathway through the phosphorylation of NFκB and GSK3α/ß, while could not separate X and Y spermatozoa. The method of sorting X/Y sperm based on TLR7/8 is not feasible for dogs.

A Phylogenomic Backbone for Acoelomorpha Inferred from Transcriptomic Data.

Xenacoelomorpha are mostly microscopic, morphologically simple worms, lacking many structures typical of other bilaterians. Xenacoelomorphs -which include three main groups: Acoela, Nemertodermatida, and Xenoturbella- have been proposed to be an early diverging Bilateria, sister to protostomes and deuterostomes, but other phylogenomic analyses have recovered this clade nested within the deuterostomes, as sister to Ambulacraria. The position of Xenacoelomorpha within the metazoan tree has understandably attracted a lot of attention, overshadowing the study of phylogenetic relationships within this group. Given that Xenoturbella includes only six species whose relationships are well understood, we decided to focus on the most speciose Acoelomorpha (Acoela + Nemertodermatida). Here, we have sequenced 29 transcriptomes, doubling the number of sequenced species, to infer a backbone tree for Acoelomorpha based on genomic data. The recovered topology is mostly congruent with previous studies. The most important difference is the recovery of Paratomella as the first off-shoot within Acoela, dramatically changing the reconstruction of the ancestral acoel. Besides, we have detected incongruence between the gene trees and the species tree, likely linked to incomplete lineage sorting, and some signal of introgression between the families Dakuidae and Mecynostomidae, which hampers inferring the correct placement of this family and, particularly, of the genus Notocelis. We have also used this dataset to infer for the first time diversification times within Acoelomorpha, which coincide with known bilaterian diversification and extinction events. Given the importance of morphological data in acoelomorph phylogenetics, we tested several partitions and models. Although morphological data failed to recover a robust phylogeny, phylogenetic placement has proven to be a suitable alternative when a reference phylogeny is available.

Dual modes of ZFC3H1 confer selectivity in nuclear RNA sorting.

The export and degradation pathways compete to sort nuclear RNAs, yet the default pathway remains unclear. Sorting of mature RNAs to degradation, facilitated by the exosome co-factor poly(A) exosome targeting (PAXT), is particularly challenging for their resemblance to mRNAs intended for translation. Here, we unveil that ZFC3H1, a core PAXT component, is co-transcriptionally loaded onto the first exon/intron of RNA precursors (pre-RNAs). Interestingly, this initial loading does not lead to pre-RNA degradation, as ZFC3H1 adopts a "closed" conformation, effectively blocking exosome recruitment. As processing progresses, RNA fate can be reshaped. Longer RNAs with more exons are allowed for nuclear export. By contrast, short RNAs with fewer exons preferentially recruit transient PAXT components ZC3H3 and RBM26/27 to the 3' end, triggering ZFC3H1 "opening" and subsequent exosomal degradation. Together, the decoupled loading and activation of ZFC3H1 pre-configures RNA fate for decay while still allowing a switch to nuclear export, depending on mature RNA features.

Research on efficient matching method of coal gangue recognition image and sorting image.

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot's grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint's ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.