m5C and m6A modifications regulate the mobility of pumpkin CHOLINE KINASE 1 mRNA under chilling stress.

Mobile mRNAs serve as crucial long-distance signaling molecules, responding to environmental stimuli in plants. Although many mobile transcripts have been identified, only a limited subset has been characterized as functional long-distance signals within specific plant species, raising an intriguing question about whether the prevalence of species specificity in mobile transcripts implies a divergence in the mechanisms governing mRNA mobility across distinct plant species. Our study delved into the notable case of CHOLINE KINASE 1 (CK1), an extensively studied instance of mobile mRNAs regulated by a tRNA-like sequence (TLS) in Arabidopsis (Arabidopsis thaliana). We established an association between mRNA mobility and length, independent of TLS numbers. Notably, neither the mobile mRNAs nor the mechanisms underpinning their mobility proved to be conserved across different plant species. The exclusive mobility of pumpkin CK1 mRNA under chilling stress was pivotal in enhancing the chilling tolerance of cucumber/pumpkin heterografts. Distinct from the TLS-mediated mobility of AtCK1 mRNA, the mobility of CmoCK1 mRNA is orchestrated by both m5C and m6A modifications, adding dimensions to our understanding of mRNA transport mechanisms.

Developing and Verifying an Effective Diagnostic Model Linked to Immune Infiltration in Stanford Type A Aortic Dissection.

BACKGROUND: The deadly cardiovascular condition known as Stanford type A aortic dissection (TAAD) carries a high risk of morbidity and mortality. One important step in the pathophysiology of the condition is the influx of immune cells into the aorta media, which causes medial degeneration. The purpose of this work was to investigate the potential pathogenic significance of immune cell infiltration in TAAD and to test for associated biomarkers. METHODS: The National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database provided the RNA sequencing microarray data (GSE153434, GPL20795, GSE52093). Immune cell infiltration abundance was predicted using ImmuCellAI. GEO2R was used to select differentially expressed genes (DEGs), which were then processed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Additionally, hub genes linked to immune infiltration were found using functional and pathway enrichment, least absolute shrinkage and selection operator (LASSO), weighted gene co-expression network analysis (WGCNA), and differential expression analysis. Lastly, hub genes were validated and assessed using receiver operating characteristic (ROC) curves in the microarray dataset GSE52093. The hub gene expression and its connection to immune infiltration in TAAD were confirmed using both animal models and clinic data. RESULTS: We identified the most important connections between macrophages, T helper cell 17 (Th17), iTreg cells, B cells, natural killer cells and TAAD. And screened seven hub genes associated with immune cell infiltration: ABCG2, FAM20C, ELL2, MTHFD2, ANKRD6, GLRX, and CDCP1. The diagnostic model in TAAD diagnosis with the area under ROC (AUC) was 0.996, and the sensitivity was 99.21%, the specificity was 98.67%, which demonstrated a surprisingly strong diagnostic power of TAAD in the validation datasets. The expression pattern of four hub DEGs (ABCG2, FAM20C, MTHFD2, CDCP1) in clinic samples and animal models matched bioinformatics analysis, and ABCG2, FAM20C, MTHFD2 up-regulation, and the of CDCP1 down-regulation were also linked to poor cardiovascular function. CONCLUSIONS: This study developed and verified an effective diagnostic model linked to immune infiltration in TAAD, providing new approaches to studying the potential pathogenesis of TAAD and discovering new medication intervention targets.

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning.

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs.

Computationally-efficient linear scheme for overlap time-gating spatial frequency domain diffuse optical tomography using an analytical diffusion model.

Time-domain (TD) spatial frequency domain (SFD) diffuse optical tomography (DOT) potentially enables laminar tomography of both the absorption and scattering coefficients. Its full time-resolved-data scheme is expected to enhance performances of the image reconstruction but poses heavy computational costs and also susceptible signal-to-noise ratio (SNR) limits, as compared to the featured-data one. We herein propose a computationally-efficient linear scheme of TD-SFD-DOT, where an analytical solution to the TD phasor diffusion equation for semi-infinite geometry is derived and used to formulate the Jacobian matrices with regard to overlap time-gating data of the time-resolved measurement for improved SNR and reduced redundancy. For better contrasting the absorption and scattering and widely adapted to practically-available resources, we develop an algebraic-reconstruction-technique-based two-step linear inversion procedure with support of a balanced memory-speed strategy and multi-core parallel computation. Both simulations and phantom experiments are performed to validate the effectiveness of the proposed TD-SFD-DOT method and show an achieved tomographic reconstruction at a relative depth resolution of ∼4 mm.

Effect of Composite Probiotics on Antioxidant Capacity, Gut Barrier Functions, and Fecal Microbiome of Weaned Piglets and Sows.

This study investigated the efficacy of a composite probiotics composed of lactobacillus plantarum, lactobacillus reuteri, and bifidobacterium longum in alleviating oxidative stress in weaned piglets and pregnant sows. Evaluations of growth, oxidative stress, inflammation, intestinal barrier, and fecal microbiota were conducted. Results showed that the composite probiotic significantly promoted average daily gain in piglets (p < 0.05). It effectively attenuated inflammatory responses (p < 0.05) and oxidative stress (p < 0.05) while enhancing intestinal barrier function in piglets (p < 0.01). Fecal microbiota analysis revealed an increase in the abundance of beneficial bacteria such as faecalibacterium, parabacteroides, clostridium, blautia, and phascolarctobacterium in piglet feces and lactobacillus, parabacteroides, fibrobacter, and phascolarctobacterium in sow feces, with a decrease in harmful bacteria such as bacteroides and desulfovibrio in sow feces upon probiotic supplementation. Correlation analysis indicated significant negative associations of blautia with inflammation and oxidative stress in piglet feces, while treponema and coprococcus showed significant positive associations. In sow feces, lactobacillus, prevotella, treponema, and CF231 exhibited significant negative associations, while turicibacter showed a significant positive association. Therefore, the composite probiotic alleviated oxidative stress in weaned piglets and pregnant sows by modulating fecal microbiota composition.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Wide-field illumination diffuse optical tomography within a framework of single-pixel time-domain spatial frequency domain imaging.

We present a wide-field illumination time-domain (TD) diffusion optical tomography (DOT) for three-dimensional (3-D) reconstruction within a shallow region under the illuminated surface of the turbid medium. The methodological foundation is laid on the single-pixel spatial frequency domain (SFD) imaging that facilitates the adoption of the well-established time-correlated single-photon counting (TCSPC)-based TD detection and generalized pulse spectrum techniques (GPST)-based reconstruction. To ameliorate the defects of the conventional diffusion equation (DE) in the forward modeling of TD-SFD-DOT, mainly the low accuracy in the near-field region and in profiling early-photon migration, we propose a modified model employing the time-dependent δ-P1 approximation and verify its improved accuracy in comparison with both the Monte Carlo and DE-based ones. For a simplified inversion process, a modified GPST approach is extended to TD-SFD-DOT that enables the effective separation of the absorption and scattering coefficients using a steady-state equivalent strategy. Furthermore, we set up a single-pixel TD-SFD-DOT system that employs the TCSPC-based TD detection in the SFD imaging framework. For assessments of the reconstruction approach and the system performance, phantom experiments are performed for a series of scenarios. The results show the effectiveness of the proposed methodology for rapid 3-D reconstruction of the absorption and scattering coefficients within a depth range of about 5 mean free pathlengths.

Multiregion WES of metastatic pancreatic neuroendocrine tumors revealed heterogeneity in genomic alterations, immune microenvironment and evolutionary patterns.

Pancreatic neuroendocrine tumors (PanNETs), though uncommon, have a high likelihood of spreading to other body parts. Previously, the genetic diversity and evolutionary patterns in metastatic PanNETs were not well understood. To investigate this, we performed multiregion sampling whole-exome sequencing (MRS-WES) on samples from 10 patients who had not received prior treatment for metastatic PanNETs. This included 29 primary tumor samples, 31 lymph node metastases, and 15 liver metastases. We used the MSK-MET dataset for survival analysis and validation of our findings. Our research indicates that mutations in the MEN1/DAXX genes might trigger the early stages of PanNET development. We categorized the patients based on the presence (MEN1/DAXXmut, n = 7) or absence (MEN1/DAXXwild, n = 3) of these mutations. Notable differences were observed between the two groups in terms of genetic alterations and clinically relevant mutations, confirmed using the MSK-MET dataset. Notably, patients with mutations in MEN1/DAXX/ATRX genes had a significantly longer median overall survival compared to those without these mutations (median not reached vs. 43.63 months, p = 0.047). Multiplex immunohistochemistry (mIHC) analysis showed a more prominent immunosuppressive environment in metastatic tumors, especially in patients with MEN1/DAXX mutations. These findings imply that MEN1/DAXX mutations lead PanNETs through a unique evolutionary path. The disease's progression pattern indicates that PanNETs can spread early, even before clinical detection, highlighting the importance of identifying biomarkers related to metastasis to guide personalized treatment strategies.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

Significance: The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption (µa) and reduced scattering (µs') coefficients at various depths. Aim: To achieve the stratified reconstruction of OPs and the separation between µa and µs', using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Approach: Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (E, continuous-wave component, ⟨t⟩, mean time of flight). We compared their performance using a series of simulation and phantom validations. Results: Compared to the LUT approach, utilizing full TR, E and ⟨t⟩ datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Conclusions: Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of µa and µs' at different depths within 5 mm.

Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy.

BACKGROUND: Computed diffusion-weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed. PURPOSE: To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT. STUDY TYPE: Retrospective. POPULATION: Eighty-seven consecutive patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DWI. ASSESSMENT: All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000 ) and another with b = 0 and 700 s/mm2 on a 3.0-T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000 ). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000 . STATISTICAL TESTS: Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used. RESULTS: The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0-Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists. DATA CONCLUSION: Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning.

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

Functional analogs of mammalian 4E-BPs reveal a role for TOR in global plant translation.

Mammalian/mechanistic target of rapamycin (mTOR) regulates global protein synthesis through inactivation of eIF4E-binding proteins (m4E-BPs) in response to nutrient and energy availability. Until now, 4E-BPs have been considered as metazoan inventions, and how target of rapamycin (TOR) controls cap-dependent translation initiation in plants remains obscure. Here, we present short unstructured 4E-BP-like Arabidopsis proteins (4EBP1/4EBP2) that are non-homologous to m4E-BPs except for the eIF4E-binding motif and TOR phosphorylation sites. Unphosphorylated 4EBPs exhibit strong affinity toward eIF4Es and can inhibit formation of the cap-binding complex. Upon TOR activation, 4EBPs are phosphorylated, probably when bound directly to TOR, and likely relocated to ribosomes. 4EBPs can suppress a distinct set of mRNAs; 4EBP2 predominantly inhibits translation of core cell-cycle regulators CycB1;1 and CycD1;1, whereas 4EBP1 interferes with chlorophyll biosynthesis. Accordingly, 4EBP2 overexpression halts early seedling development, which is overcome by induction of Glc/Suc-TOR signaling. Thus, TOR regulates cap-dependent translation initiation by inactivating atypical 4EBPs in plants.

Ligand and solvent effects on the absorption spectra of CdS magic-sized clusters.

The absorption spectra of congenetic wurtzite (WZ) and zincblende (ZB) CdS magic-sized clusters are investigated. We demonstrate that the exciton peak positions can be tuned by up to 500 meV by varying the strong coupling between X-type ligands and the semiconductor cores, while the addition of L-type ligands primarily affects cluster midgap states. When Z-type ligands are displaced by L-type ligands, red shifts in the absorption spectra are observed, despite the fact there is a small decrease in cluster size. Density functional theory calculations are used to explain these findings and they reveal the importance of Cd and S dangling bonds on the midgap states during the Z- to L-type ligand exchange process. Overall, ZB CdS clusters show higher chemical stability than WZ clusters but their optical properties exhibit greater sensitivity to the solvent. Conversely, WZ CdS clusters are not stable in a Lewis base-rich environment, resulting in various changes in their spectra. Our findings enable researchers to select capping ligands that modulate the optical properties of semiconductor clusters while maintaining precise control over their solvent interactions.

Nuclear envelope dynamics in connection to chromatin remodeling.

The nucleus is a central organelle of eukaryotic cells undergoing dynamic structural changes during cellular fundamental processes such as proliferation and differentiation. These changes rely on the integration of developmental and stress signals at the nuclear envelope (NE), orchestrating responses at the nucleo-cytoplasmic interface for efficient genomic functions such as DNA transcription, replication and repair. While in animals, correlation has already been established between NE dynamics and chromatin remodeling using last-generation tools and cutting-edge technologies, this topic is just emerging in plants, especially in response to mechanical cues. This review summarizes recent data obtained in this field with more emphasis on the mechanical stress response. It also highlights similarities/differences between animal and plant cells at multiples scales, from the structural organization of the nucleo-cytoplasmic continuum to the functional impacts of NE dynamics.

Ischemic stroke protected by ISO-1 inhibition of apoptosis via mitochondrial pathway.

Macrophage migration inhibitory factor (MIF) is an immune mediator associated with inflammation, which is upregulated after ischemia in brain tissue. ISO-1 is a potent inhibitor of MIF tautomerase and can protect neurons by reducing the permeability of blood brain barrier (BBB). In this study, we investigated the role of ISO-1 in cerebral ischemia/reperfusion injury by establishing a model of middle cerebral artery occlusion/reperfusion in rats. Rats were randomly divided into four groups: the sham operation group, the ISO-1group, the cerebral I/R group, and the ISO-1 + I/R group. We assessed the degree of neurological deficit in each group and measured the volume of cerebral infarction. We detected the expression of MIF in the core necrotic area and penumbra. We detected the expression of apoptosis-related proteins, apoptosis-inducing factor (AIF), endonuclease G (EndoG) and cytochrome c oxidase-IV (COX-IV) in the ischemic penumbra region. The results showed that MIF was expressed in the ischemic penumbra, while the injection of ISO-1 was able to alleviate neurological damage and reduce the infarction volume. In the cerebral ischemic penumbra region, ISO-1 could reduce the expression of Bax and Caspase3 and inhibit the displacement of AIF and EndoG to the nucleus simultaneously. Besides, ISO-1 also exhibited the ability to reduce apoptosis. In summary, ISO-1 may inhibit neuronal apoptosis through the endogenous mitochondrial pathway and reduce the injury of brain I/R after ischemic stroke.

TOR represses stress responses through global regulation of H3K27 trimethylation in plants.

Target of rapamycin (TOR) functions as a central sensory hub linking a wide range of external stimuli to gene expression. The mechanisms underlying stimulus-specific transcriptional reprogramming by TOR remain elusive. Here, we describe an in silico analysis in Arabidopsis demonstrating that TOR-repressed genes are associated with either bistable or silent chromatin states. Both states regulated by the TOR signaling pathway are associated with a high level of histone H3K27 trimethylation (H3K27me3) deposited by CURLY LEAF in a specific context with LIKE HETEROCHROMATIN PROTEIN1. The combination of the two epigenetic histone modifications H3K4me3 and H3K27me3 implicates a bistable feature that alternates between an 'on' and an 'off' state, allowing rapid transcriptional changes upon external stimuli. The chromatin remodeler SWI2/SNF2 ATPase BRAHMA activates TOR-repressed genes only at bistable chromatin domains to rapidly induce biotic stress responses. Here, we demonstrate both in silico and in vivo that TOR represses transcriptional stress responses through global maintenance of H3K27me3.