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Structural regulation of Pd-based electrocatalytic hydrodechlorination (EHDC) catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challenging. Herein, a support electron inductive effect of Pd-Mn/Ni foam catalyst was proposed via in-situ Mn doping to optimize the electronic structure of the Ni foam (NF), which can inductive regulation of Pd for improving the EHDC performance. The mass activity and current efficiency of Pd-Mn/NF catalyst are 2.91 and 1.34 times superior to that of Pd/NF with 2,4-dichlorophenol as model compound, respectively. The Mn-doped interlayer optimized the electronic structure of Pd by bringing the d-state closer to the Fermi level than Pd on the NF surface, which optimizied the binding of EHDC intermediates. Additionally, the Mn-doped interlayer acted as a promoter for generating H* and accelerating the EHDC reaction. This work presents a simple and effective regulation strategy for constructing high-efficient cathode catalyst for the EHDC of chlorinated organic compounds.

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INTRODUCTION: Brain organoids are believed to be able to regenerate impaired neural circuits and reinstate brain functionality. The neuronal activity of organoids is considered a crucial factor for restoring host function after implantation. However, the optimal stage of brain organoid post-transplantation has not yet been established. External electrical signal plays a crucial role in the physiology and development of a majority of human tissues. However, whether electrical input modulates the development of brain organoids, making them ideal transplant donors, is elusive. METHODS: Bioelectricity was input into cortical organoids by electrical stimulation (ES) with a multi-electrode array (MEA) to obtain a better-transplanted candidate with better viability and maturity, realizing structural-functional integration with the host brain. RESULTS: We found that electrical stimulation facilitated the differentiation and maturation of organoids, displaying well-defined cortical plates and robust functional electrophysiology, which was probably mediated via the pathway of calcium-calmodulin (CaM) dependent protein kinase II (CAMK II)-protein kinase A (PKA)-cyclic-AMP response binding protein (pCREB). The ES-pretreated D40 organoids displayed superior cell viability and higher cell maturity, and were selected to transplant into the damaged primary sensory cortex (S1) of host. The enhanced maturation was exhibited within grafts after transplantation, including synapses and complex functional activities. Moreover, structural-functional integration between grafts and host was observed, conducive to strengthening functional connectivity and restoring the function of the host injury. CONCLUSION: Our findings supported that electrical stimulation could promote the development of cortical organoids. ES-pretreated organoids were better transplanted donors for strengthening connectivity between grafts and host. Our work presented a new physical approach to regulating organoids, potentially providing a novel translational strategy for functional recovery after brain injury. In the future, the development of 3D flexible electrodes is anticipated to overcome the drawbacks of 2D planar MEA, promisingly achieving multimodal stimulation and long-term recordings of brain organoids.

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Despite significant advances in phylogenetics over the past decades, the deep relationships within Bivalvia (phylum Mollusca) remain inconclusive. Previous efforts based on morphology or several genes have failed to resolve many key nodes in the phylogeny of Bivalvia. Advances have been made recently using transcriptome data, but the phylogenetic relationships within Bivalvia historically lacked consensus, especially within Pteriomorphia and Imparidentia. Here, we inferred the relationships of key lineages within Bivalvia using matrices generated from specifically designed ultraconserved elements (UCEs) with 16 available genomic resources and 85 newly sequenced specimens from 55 families. Our new probes (Bivalve UCE 2k v.1) for target sequencing captured an average of 849 UCEs with 1085-bp in mean length from in vitro experiments. Our results introduced novel schemes from six major clades (Protobranchina, Pteriomorphia, Palaeoheterodonta, Archiheterodonta, Anomalodesmata and Imparidentia), though some inner nodes were poorly resolved, such as paraphyletic Heterodonta in some topologies potentially due to insufficient taxon sampling. The resolution increased when analyzing specific matrices for Pteriomorphia and Imparidentia. We recovered three Pteriomorphia topologies different from previously published trees, with the strongest support for ((Ostreida + (Arcida + Mytilida)) + (Pectinida + (Limida + Pectinida))). Limida were nested within Pectinida, warranting further studies. For Imparidentia, our results strongly supported the new hypothesis of (Galeommatida + (Adapedonta + Cardiida)), while the possible non-monophyly of Lucinida was inferred but poorly supported. Overall, our results provide important insights into the phylogeny of Bivalvia and show that target enrichment sequencing of UCEs can be broadly applied to study both deep and shallow phylogenetic relationships.

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The pathological diagnosis and treatment of azoospermia depend on precise identification of spermatogenic cells. Traditional methods are time-consuming and highly subjective due to complexity of Johnsen score, posing challenges for accurately diagnosing azoospermia. Here, we introduce a novel SC-YOLO framework for automating the classification of spermatogenic cells that integrates S3Ghost module, CoordAtt module and DCNv2 module, effectively capturing texture and shape features of spermatogenic cells while reducing model parameters. Furthermore, we propose a simplified Johnsen score criteria to expedite the diagnostic process. Our SC-YOLO framework presents the higher efficiency and accuracy of deep learning technology in spermatogenic cell recognition. Future research endeavors will focus on optimizing the model's performance and exploring its potential for clinical applications.

YOLO framework was optimized as SC-YOLO and applied to shape detection for automatic classification of spermatogenic cells in testicular pathology images.The SC-YOLO framework has high accuracy in identifying results from binomial distributions, with or without haploid germ cells.The SC-YOLO framework also has good performance in the identification of other types of pathological sections.

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Manipulating the chirality at nanoscale has drawn great attention among scientists, considering its pivotal role in various applications of current interest, including nano-optics, biomedicine, and photocatalysis. In this work, we delve into this arena by fabricating chiral Swiss roll nanoarray (SRNA) continuous films employing colloidal lithography. The technique permits the dimension of Swiss roll metamaterials to reduce to nanoscale, thus achieving chiroptical response (circular dichroism (CD)) in the visible region. The interplay between the CD signals and plasmon resonance modes is revealed through theoretical simulations, enabling a deep understanding of chiral plasmonic metamaterials. The polarization-sensitive photocatalytic activity of chiral SRNAs is investigated, noting a marked increase in the reaction rate when the chirality of SRNAs matches with the handedness of circularly polarized light (CPL). Notably, the SRNA continuous films based on substrate possess integration and reusability without complex recycling process, enhancing their practicality in applications like sensing and plasmonic nanochemistry, particularly toward polarization-dependent photocatalysis.

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The Event-based Vision Sensor (EVS) is a bio-inspired sensor that captures detailed motions of objects, aiming to become the 'eyes' of machines like self-driving cars. Compared to conventional frame-based image sensors, the EVS has an extremely fast motion capture equivalent to 10,000-fps even with standard optical settings, plus high dynamic ranges for brightness and also lower consumption of memory and energy. Here, we developed 22 characteristic features for analysing the motions of aquatic particles from the EVS raw data and tested the applicability of the EVS in analysing plankton behaviour. Laboratory cultures of six species of zooplankton and phytoplankton were observed, confirming species-specific motion periodicities up to 41 Hz. We applied machine learning to automatically classify particles into four categories of zooplankton and passive particles, achieving an accuracy up to 86%. At the in situ deployment of the EVS at the bottom of Lake Biwa, several particles exhibiting distinct cumulative trajectory with periodicities in their motion (up to 16 Hz) were identified, suggesting that they were living organisms with rhythmic behaviour. We also used the EVS in the deep sea, observing particles with active motion and periodicities over 40 Hz. Our application of the EVS, especially focusing on its millisecond-scale temporal resolution and wide dynamic range, provides a new avenue to investigate organismal behaviour characterised by rapid and periodical motions. The EVS will likely be applicable in the near future for the automated monitoring of plankton behaviour by edge computing on autonomous floats, as well as quantifying rapid cellular-level activities under microscopy.

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Antiferromagnets are promising for nano-scale oscillator in a wide frequency range from gigahertz up to terahertz. Experimentally realizing antiferromagnetic moment oscillation via spin-orbit torque, however, remains elusive. Here, we demonstrate that the optical spin-orbit torque induced by circularly polarized laser can be used to drive free decaying oscillations with a frequency of 2 THz in metallic antiferromagnetic Mn2Au thin films. Due to the local inversion symmetry breaking of Mn2Au, ultrafast a.c. current is generated via spin-to-charge conversion, which can be detected through free-space terahertz emission. Both antiferromagnetic moments switching experiments and dynamics analyses unravel the antiferromagnetic moments, driven by optical spin-orbit torque, deviate from its equilibrium position, and oscillate back in 5 ps once optical spin-orbit torque is removed. Besides the fundamental significance, our finding opens a new route towards low-dissipation and controllable antiferromagnet-based spin-torque oscillators.

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Neuroblastoma (NB) is a significant pediatric cancer associated with high mortality rates, demanding innovative and appropriate approaches for its accurate detection. This paper described the design of a dual-target electrochemical aptasensor capable of simultaneously detecting neuroblastoma-associated microRNAs (miRNA-181 and miRNA-184) with exceptional sensitivity. Screen-printed carbon electrodes (SPCEs) were utilized with gold nanorods (AuNRs), and aptamers functionalized gold nanoparticles (AuNPs) to improve sensitivity, specificity, and portable detection ability. The detection method employed in this study includes differential pulse voltammetry (DPV) and cyclic voltammetry (CV). Our aptasensor exhibited remarkable limits of detections (LODs) of 5.10 aM for miRNA-181 and 9.39 aM for miRNA-184, respectively, along with a broad linear range spanning from 0.1 fM to 100 pM for both miRNAs. The practical significance of neuroblastoma diagnosis was shown through the validation of serum samples and comparison with quantitative polymerase chain reaction (qPCR). Our electrochemical aptasensor is user-friendly, easy to engineer, and offers a promising approach for accurately and selectively detecting important miRNA biomarkers in cancer screening and diagnosis, showing potential application in various clinical scenarios.

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Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

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Mitochondrial DNA (mtDNA) is an important genetic marker for degraded biological sample identification, maternal pedigree tracing, and population genetic structure study owing to its characteristics of high copy number, anti-degradable ring structure, and maternal inheritance. Whole mtDNA genome sequencing is an optimal method for the analysis of mtDNA polymorphism and heterogeneity because it allows for the comprehensive use of maternal genetic information. However, because of lacking quantitative evaluations for sequencing data, the scientific interpretation standards for mtDNA sequencing results of the previously used sequencing systems are often different, and false positive or false negative results are prone to occur when faced with the interference of nuclear genomic DNA, or the heterogeneities of mtDNA sequence and structure. In this study, we evaluated a novel mtDNA whole genome sequencing system using long fragment amplification strategy on the DNA nanoball (DNB) sequencing platform. This system demonstrated high sequencing quality and specific mtDNA sequencing efficiencies on positive control DNA and FTA bloodstain samples, as the average Q20 and Q30 values of the corresponding samples were 97.17 % and 91.93 %; 97.37 % and 92.48 %, respectively. The mean mapping percentages for the reference sequences of whole genome DNA (wgDNA), mtDNA, and nuclear genomic DNA (ngDNA) in the corresponding samples were 99.98 %, 99.97 %, 0.03 %, and 99.91 %, 99.40 %, 0.60 %; respectively. The average error calling rates for the bases A, C, G, and T of the whole mtDNA genome were 0.2519 %, 0.2550 %, 0.2906 %; and 0.2392 %, respectively. The efficacy of heteroplasmy identification was assessed using a set of theoretical sites with predetermined rates. These sites were created by combining the samples with known mtDNA haplotypes in certain proportions. The absolute errors between observed and theoretical heteroplasmy values were 89.59 %, 74.68 %, 50.20 %, 12.65 %, 8.31 %, and 4.85 %, while the theoretical heteroplasmy values were 5 %, 10 %, 20 %, 80 %, 90 %, and 95 %, respectively. The absolute error exhibited relative stability when the mtDNA sequencing depth exceeded 500×. Furthermore, the system sequencing efficiency was also confirmed among different kinds of samples, and these samples included natural samples (e.g., peripheral blood samples preserved on FTA cards for 2 and 11 years, and on filter paper for 6 and 9 years), degraded samples, sensitivity samples, samples derived from various bodily fluids, and maternal pedigree samples. In summary, the whole mtDNA genome sequencing system used for forensic identification demonstrated high performance in analyzing mtDNA sequence information, and showed significant prospects for forensic application and maternal genetic research.

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Graphene oxide (GO) is a two-dimensional metastable nanomaterial. Interestingly, GO formed oxygen clusterings in addition to oxidized and graphitic phases during the low-temperature thermal annealing process, which could be further used for biomolecule bonding. By harnessing this property of GO, we created a bio-interface with patterned structures with a common laboratory hot plate that could tune cellular behavior by physical contact. Due to the regional distribution of oxygen clustering at the interface, we refer to it as patterned annealed graphene oxide (paGO). In addition, since the paGO was a heterogeneous interface and bonded biomolecules to varying degrees, arginine-glycine-aspartic acid (RGD) was modified on it and successfully regulated cellular-directed growth and migration. Finally, we investigated the FRET phenomenon of this heterogeneous interface and found that it has potential as a biosensor. The paGO interface has the advantages of easy regulation and fabrication, and the one-step thermal reduction method is suitable for biological applications. We believe that this low-temperature thermal annealing method would make GO interfaces more accessible, especially for the development of nano-interfacial modifications for biological applications, revealing its potential for biomedical applications.

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Gastrointestinal tumors, the second leading cause of human mortality, are characterized by their association with inflammation. Currently, progress in the early diagnosis and effective treatment of gastrointestinal tumors is limited. Recent whole-genome analyses have underscored their profound heterogeneity and extensive genetic and epigenetic reprogramming. Epigenetic reprogramming pertains to dynamic and hereditable alterations in epigenetic patterns, devoid of concurrent modifications in the underlying DNA sequence. Common epigenetic modifications encompass DNA methylation, histone modifications, noncoding RNA, RNA modifications, and chromatin remodeling. These modifications possess the potential to invoke or suppress a multitude of genes associated with cancer, thereby governing the establishment of chromatin configurations characterized by diverse levels of accessibility. This intricate interplay assumes a pivotal and indispensable role in governing the commencement and advancement of gastrointestinal cancer. This article focuses on the impact of epigenetic reprogramming in the initiation and progression of gastric cancer, esophageal cancer, and colorectal cancer, as well as other uncommon gastrointestinal tumors. We elucidate the epigenetic landscape of gastrointestinal tumors, encompassing DNA methylation, histone modifications, chromatin remodeling, and their interrelationships. Besides, this review summarizes the potential diagnostic, therapeutic, and prognostic targets in epigenetic reprogramming, with the aim of assisting clinical treatment strategies.

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Compensated synthetic antiferromagnets (SAFs) stand out as promising candidates to explore various spintronic applications, benefitting from high precession frequency and negligible stray field. High-frequency antiferromagnetic resonance in SAFs, especially the optic mode (OM), is highly desired to attain fast operation speed in antiferromagnetic spintronic devices. SAFs exhibit ferromagnetic configurations above saturation field; however in that case, the intensity of OM is theoretically zero and hard to be detected in well-established microwave resonance experiments. To expose the hidden OM, the exchange symmetry between magnetic layers must be broken, inevitably introducing remanent magnetization. Here, we experimentally demonstrate a feasible method to break the symmetry via surface acoustic waves with the maintenance of compensated SAF structure. By introducing an out-of-plane strain gradient inside the Ir-mediated SAFs, we successfully reveal the hidden OM. Remarkably, the OM intensity can be effectively modulated by controlling strain gradients in SAFs with different thicknesses, confirmed by finite-element simulations. Our findings provide a feasible scheme for detecting the concealed OM, which would trigger future discoveries in magnon-phonon coupling and hybrid quasiparticle systems.

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Background: Discoveries of new species often depend on one or a few specimens, leading to delays as researchers wait for additional context, sometimes for decades. There is currently little professional incentive for a single expert to publish a stand-alone species description. Additionally, while many journals accept taxonomic descriptions, even specialist journals expect insights beyond the descriptive work itself. The combination of these factors exacerbates the issue that only a small fraction of marine species are known and new discoveries are described at a slow pace, while they face increasing threats from accelerating global change. To tackle this challenge, this first compilation of Ocean Species Discoveries (OSD) presents a new collaborative framework to accelerate the description and naming of marine invertebrate taxa that can be extended across all phyla. Through a mode of publication that can be speedy, taxonomy-focused and generate higher citation rates, OSD aims to create an attractive home for single species descriptions. This Senckenberg Ocean Species Alliance (SOSA) approach emphasises thorough, but compact species descriptions and diagnoses, with supporting illustrations and with molecular data when available. Even basic species descriptions carry key data for distributions and ecological interactions (e.g., host-parasite relationships) besides universally valid species names; these are essential for downstream uses, such as conservation assessments and communicating biodiversity to the broader public. New information: This paper presents thirteen marine invertebrate taxa, comprising one new genus, eleven new species and one re-description and reinstatement, covering wide taxonomic, geographic, bathymetric and ecological ranges. The taxa addressed herein span three phyla (Mollusca, Arthropoda, Echinodermata), five classes, eight orders and twelve families. Apart from the new genus, an updated generic diagnosis is provided for four other genera. The newly-described species of the phylum Mollusca are Placiphorellamethanophila Voncina, sp. nov. (Polyplacophora, Mopaliidae), Lepetodrilusmarianae Chen, Watanabe & Tsuda, sp. nov. (Gastropoda, Lepetodrilidae), Shinkailepasgigas Chen, Watanabe & Tsuda, sp. nov. (Gastropoda, Phenacolepadidae) and Lyonsiellaillaesa Machado & Sigwart, sp. nov. (Bivalvia, Lyonsiellidae). The new taxa of the phylum Arthropoda are all members of the subphylum Crustacea: Lepechinellanaces Lörz & Engel, sp. nov. (Amphipoda, Lepechinellidae), Cuniculomaeragrata Tandberg & Jazdzewska, gen. et sp. nov. (Amphipoda, Maeridae), Pseudionellapumulaensis Williams & Landschoff, sp. nov. (Isopoda, Bopyridae), Mastigoniscusminimus Wenz, Knauber & Riehl, sp. nov. (Isopoda, Haploniscidae), Macrostylispapandreas Jonannsen, Riehl & Brandt, sp. nov. (Isopoda, Macrostylidae), Austroniscusindobathyasellus Kaiser, Kniesz & Kihara, sp. nov. (Isopoda, Nannoniscidae) and Apseudopsisdaria Esquete & Tato, sp. nov. (Tanaidacea, Apseudidae). In the phylum Echinodermata, the reinstated species is Psychropotesbuglossa E. Perrier, 1886 (Holothuroidea, Psychropotidae).The study areas span the North and Central Atlantic Ocean, the Indian Ocean and the North, East and West Pacific Ocean and depths from 5.2 m to 7081 m. Specimens of eleven free-living and one parasite species were collected from habitats ranging from an estuary to deep-sea trenches. The species were illustrated with photographs, line drawings, micro-computed tomography, confocal laser scanning microscopy and scanning electron microscopy images. Molecular data are included for nine species and four species include a molecular diagnosis in addition to their morphological diagnosis.The five new geographic and bathymetric distribution records comprise Lepechinellanaces Lörz & Engel, sp. nov., Cuniculomaeragrata Tandberg & Jazdzewska, sp. nov., Pseudionellapumulaensis Williams & Landschoff, sp. nov., Austroniscusindobathyasellus Kaiser, Kniesz & Kihara, sp. nov. and Psychropotesbuglossa E. Perrier, 1886, with the novelty spanning from the species to the family level. The new parasite record is Pseudionellapumulaensis Williams & Landschoff, sp. nov., found in association with the hermit crab Pagurusfraserorum Landschoff & Komai, 2018.

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The preferable antigen delivery profile accompanied by sufficient adjuvants favors vaccine efficiency. Mitochondria, which feature prokaryotic characteristics and contain various damage-associated molecular patterns (DAMPs), are easily taken up by phagocytes and simultaneously activate innate immunity. In the current study, we established a mitochondria engineering platform for generating antigen-enriched mitochondria as cancer vaccine. Ovalbumin (OVA) and tyrosinase-related protein 2 (TRP2) were used as model antigens to synthesize fusion proteins with mitochondria-localized signal peptides. The lentiviral infection system was then employed to produce mitochondrial vaccines containing either OVA or TRP2. Engineered OVA- and TRP2-containing mitochondria (OVA-MITO and TRP2-MITO) were extracted and evaluated as potential cancer vaccines. Impressively, the engineered mitochondria vaccine demonstrated efficient antitumor effects when used as both prophylactic and therapeutic vaccines in murine tumor models. Mechanistically, OVA-MITO and TRP2-MITO potently recruited and activated dendritic cells (DCs) and induced a tumor-specific cell-mediated immunity. Moreover, DC activation by mitochondria vaccine critically involves TLR2 pathway and its lipid agonist, namely, cardiolipin derived from the mitochondrial membrane. The results demonstrated that engineered mitochondria are natively well-orchestrated carriers full of immune stimulants for antigen delivery, which could preferably target local dendritic cells and exert strong adaptive cellular immunity. This proof-of-concept study established a universal platform for vaccine construction with engineered mitochondria bearing alterable antigens for cancers as well as other diseases.

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Diabetic peripheral neuropathy (DPN) is a significant and frequent complication of diabetes. Bu-Yang-Huan-Wu Decoction (BHD) is a classic traditional Chinese herbal prescription that is commonly used in modern clinical practice for the effective treatment of DPN, but the underlying mechanism is not yet clearly defined. The chemical constituents of BHD were characterized by UPLC-Q-Orbitrap HR MS/MS, and a total of 101 chemical components were identified, including 30 components absorbed into blood. An interaction network of "compound-target-disease" interactions was constructed based on the compounds detected absorbed in blood and their corresponding targets of diabetic neuropathy acquired from disease gene databases, and the possible biological targets and potential signalling pathways of BHD were predicted via network pharmacology analysis. Subsequently, methylglyoxal-induced (MGO-induced) Schwann cells (SCs) were used to identify the active ingredients in blood components of BHD and verify the molecular mechanisms of BHD. Through network topological analysis, 30 shared targets strongly implicated in the anti-DPN effects of BHD were identifed. Combined network pharmacology and in vitro cellular analysis, we found that the active ingredient of BHD may treat DPN by modulating the AGEs/RAGE pathway. This study provides valuable evidence for future mechanistic studies and potential therapeutic applications for patients with DPN.

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Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data are always limited or scarce. To address this issue, we focus on the semi-supervised graph classification task, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. We introduce a novel framework termed in this article, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factorwise graph representations for both supervised and unsupervised models. Then, we train two models via supervised objective and mutual information (MI)-based constraints, respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experiments conducted on various publicly available datasets demonstrate the effectiveness of our .

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Background: Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma. Methods: We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs). Results: We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration. Conclusions: The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.

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Chilling stress is a major environmental factor that significantly reduces crop production. To adapt to chilling stress, plants activate a series of cellular responses and accumulate an array of metabolites, particularly proline. Here, we report that the transcription factor SlWRKY51 increases proline contents in tomato (Solanum lycopersicum) under chilling stress. SlWRKY51 expression is induced under chilling stress. Knockdown or knockout of SlWRKY51 led to chilling-sensitive phenotypes, with lower photosynthetic capacity and more reactive oxygen species (ROS) accumulation than the wild type (WT). The proline contents were significantly reduced in SlWRKY51 knockdown and knockout lines under chilling stress, perhaps explaining the phenotypes of these lines. D-1-pyrroline-5-carboxylate synthetase (P5CS), which catalyses the rate-limiting step of proline biosynthesis, is encoded by two closely related P5CS genes (P5CS1 and P5CS2). We demonstrate that SlWRKY51 directly activates the expression of P5CS1 under chilling stress. In addition, the VQ (a class of plant-specific proteins containing the conserved motif FxxhVQxhTG) family member SlVQ10 physically interacts with SlWRKY51 to enhance its activation of P5CS1. Our study reveals that the chilling-induced transcription factor SlWRKY51 enhances chilling tolerance in tomato by promoting proline accumulation.