Long noncoding RNAs (lncRNAs) have emerged as significant players in almost every level of gene function and regulation. Thus, characterizing the structures and interactions of lncRNAs is essential ...for understanding their mechanistic roles in cells. Through a combination of (bio)chemical approaches and automated capillary and high-throughput sequencing (HTS), the complexity and diversity of RNA structures and interactions has been revealed in the transcriptomes of multiple species. These methods have uncovered important biological insights into the mechanistic and functional roles of lncRNA in gene expression and RNA metabolism, as well as in development and disease. In this review, we summarize the latest sequencing strategies to reveal RNA structure, RNA–RNA, RNA–DNA, and RNA–protein interactions, and highlight the recent applications of these approaches to map functional lncRNAs. We discuss the advantages and limitations of these strategies, and provide recommendations to further advance methodologies capable of mapping RNA structure and interactions in order to discover new biology of lncRNAs and decipher their molecular mechanisms and implication in diseases.
Higher-order structures and interactions of lncRNA are critical for its diverse roles in gene function and regulation.
Novel chemical and sequencing toolkits are being developed to decipher RNA structures and interactions in vitro and in vivo.
Application of these innovative methods to lncRNA has revealed new and important structural motifs and interaction groups.
The methods and results reviewed here can help to better understand and further investigate the lncRNA structure–function relationship.
Severe acute respiratory syndrome-coronavirus (SARS-CoV) and SARS-like coronavirus are a potential threat to global health. However, reviews of the long-term effects of clinical treatments in SARS ...patients are lacking. Here a total of 25 recovered SARS patients were recruited 12 years after infection. Clinical questionnaire responses and examination findings indicated that the patients had experienced various diseases, including lung susceptibility to infections, tumors, cardiovascular disorders, and abnormal glucose metabolism. As compared to healthy controls, metabolomic analyses identified significant differences in the serum metabolomes of SARS survivors. The most significant metabolic disruptions were the comprehensive increase of phosphatidylinositol and lysophospha tidylinositol levels in recovered SARS patients, which coincided with the effect of methylprednisolone administration investigated further in the steroid treated non-SARS patients with severe pneumonia. These results suggested that high-dose pulses of methylprednisolone might cause long-term systemic damage associated with serum metabolic alterations. The present study provided information for an improved understanding of coronavirus-associated pathologies, which might permit further optimization of clinical treatments.
Major depressive disorder (MDD) is a debilitating mental disease with a pronounced impact on the quality of life of many people; however, it is still difficult to diagnose MDD accurately. In this ...study, a nontargeted metabolomics approach based on ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to find the differential metabolites in plasma samples from patients with MDD and healthy controls. Furthermore, a validation analysis focusing on the differential metabolites was performed in another batch of samples using a targeted approach based on the dynamic multiple reactions monitoring method. Levels of acyl carnitines, ether lipids, and tryptophan pronouncedly decreased, whereas LPCs, LPEs, and PEs markedly increased in MDD subjects as compared with the healthy controls. Disturbed pathways, mainly located in acyl carnitine metabolism, lipid metabolism, and tryptophan metabolism, were clearly brought to light in MDD subjects. The binary logistic regression result showed that carnitine C10:1, PE-O 36:5, LPE 18:1 sn-2, and tryptophan can be used as a combinational biomarker to distinguish not only moderate but also severe MDD from healthy control with good sensitivity and specificity. Our findings, on one hand, provide critical insight into the pathological mechanism of MDD and, on the other hand, supply a combinational biomarker to aid the diagnosis of MDD in clinical usage.
The diffusion model has made progress in the field of image synthesis, especially in the area of conditional image synthesis. However, this improvement is highly dependent on large annotated ...datasets. To tackle this challenge, we present the Guided Diffusion model for Unlabeled Images (GDUI) framework in this article. It utilizes the inherent feature similarity and semantic differences in the data, as well as the downstream transferability of Contrastive Language-Image Pretraining (CLIP), to guide the diffusion model in generating high-quality images. We design two semantic-aware algorithms, namely, the pseudo-label-matching algorithm and label-matching refinement algorithm, to match the clustering results with the true semantic information and provide more accurate guidance for the diffusion model. First, GDUI encodes the image into a semantically meaningful latent vector through clustering. Then, pseudo-label matching is used to complete the matching of the true semantic information of the image. Finally, the label-matching refinement algorithm is used to adjust the irrelevant semantic information in the data, thereby improving the quality of the guided diffusion model image generation. Our experiments on labeled datasets show that GDUI outperforms diffusion models without any guidance and significantly reduces the gap between it and models guided by ground-truth labels.
Guanine-rich sequences are able to form complex RNA structures termed RNA G-quadruplexes in vitro. Because of their high stability, RNA G-quadruplexes are proposed to exist in vivo and are suggested ...to be associated with important biological relevance. However, there is a lack of direct evidence for RNA G-quadruplex formation in living eukaryotic cells. Therefore, it is unclear whether any purported functions are associated with the specific sequence content or the formation of an RNA G-quadruplex structure.
Using rG4-seq, we profile the landscape of those guanine-rich regions with the in vitro folding potential in the Arabidopsis transcriptome. We find a global enrichment of RNA G-quadruplexes with two G-quartets whereby the folding potential is strongly influenced by RNA secondary structures. Using in vitro and in vivo RNA chemical structure profiling, we determine that hundreds of RNA G-quadruplex structures are strongly folded in both Arabidopsis and rice, providing direct evidence of RNA G-quadruplex formation in living eukaryotic cells. Subsequent genetic and biochemical analyses show that RNA G-quadruplex folding is able to regulate translation and modulate plant growth.
Our study reveals the existence of RNA G-quadruplex in vivo and indicates that RNA G-quadruplex structures act as important regulators of plant development and growth.
Plasmodium falciparum, a protozoan parasite and causative agent of human malaria, has one of the most A/T-biased genomes sequenced to date. This may give the genome and the transcriptome unusual ...structural features. Recent progress in sequencing techniques has made it possible to study the secondary structures of RNA molecules at the transcriptomic level. Thus, in this study we produced the in vivo RNA structurome of a protozoan parasite with a highly A/U-biased transcriptome. We showed that it is possible to probe the secondary structures of P. falciparum RNA molecules in vivo using two different chemical probes, and obtained structures for more than half of all transcripts in the transcriptome. These showed greater stability (lower free energy) than the same structures modelled in silico, and structural features appeared to influence translation efficiency and RNA decay. Finally, we compared the P. falciparum RNA structurome with the predicted RNA structurome of an A/U-balanced species, P. knowlesi, finding a bias towards lower overall transcript stability and more hairpins and multi-stem loops in P. falciparum. This unusual protozoan RNA structurome will provide a basis for similar studies in other protozoans and also in other unusual genomes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Spherical signals exist in many applications such as planetary data, lidar scanning and digitization of 3D objects, so we need models that can effectively process spherical data. When the spherical ...data is simply projected onto a two-dimensional plane and then convolutional neural networks (CNNs) are used, the performance of the previous algorithms that exist in the literature is poor due to the distortion caused by the projection and the invalid translational equivariance. We propose a spherical vector network with rotation-equivariant self-attention mechanism for part-whole relationships learning to avoid a certain degree of distortion in this paper. Specifically, we take first the spherical convolutional network as the front-end network to obtain primary vectors, then we achieve the part-whole relationships between vectors through proposed rotation-equivariant self-attention mechanism to obtain advanced vectors which can represent the existence probability of the entity and orientations. Experimental results show that the proposed method combined with the front-end network improves the 3D mesh classification accuracy of the front-end network by 9% when the training set is not rotated and the test set is rotated arbitrarily under the rigid ModelNet40 dataset. Similarly, the 3D mesh classification accuracy of the front-end network improves by 12.2% under the non-rigid SHREC15 dataset. In addition, our method is compared with the recent method in the spherical image semantic segmentation task, achieving an improvement of 2.2% in mean pixel accuracy and 1.3% in mean intersection over union.
Learning the relationship between the part and whole of an object, such as humans recognizing objects, is a challenging task. In this paper, we specifically design a novel neural network to explore ...the local-to-global cognition of 3D models and the aggregation of structural contextual features in 3D space, inspired by the recent success of Transformer in natural language processing (NLP) and impressive strides in image analysis tasks such as image classification and object detection. We build a 3D shape Transformer based on local shape representation, which provides relation learning between local patches on 3D mesh models. Similar to token (word) states in NLP, we propose local shape tokens to encode local geometric information. On this basis, we design a shape-Transformer-based capsule routing algorithm. By applying an iterative capsule routing algorithm, local shape information can be further aggregated into high-level capsules containing deeper contextual information so as to realize the cognition from the local to the whole. We performed classification tasks on the deformable 3D object data sets SHREC10 and SHREC15 and the large data set ModelNet40, and obtained profound results, which shows that our model has excellent performance in complex 3D model recognition and big data feature learning.
Bronchial asthma (asthma) is a chronic inflammatory disease of the airways, involving a variety of cells and cellular components, that manifests clinically as recurrent episodes of wheezing, ...shortness of breath, with or without chest tightness or cough, airway hyperresponsiveness, and variable airflow limitation. The number of people with asthma has reached 358 million worldwide and asthma causes huge economic loss. However, there is a subset of patients who are not sensitive to existing drugs and the existing drugs have many adverse effects. Therefore, it's important to find new drugs for asthma patients.
Publications related to biologics in asthma published from 2000 to 2022 were retrieved from Web of Science Core Collection. The search strategies were as follows: topic: TS=(biologic* OR "biologic* product*" OR "biologic* therap*" OR biotherapy* OR "biologic* agent*" OR Benralizumab OR "MEDI-563" OR Fasenra OR "BIW-8405" OR Dupilumab OR SAR231893 OR "SAR-231893" OR Dupixent OR REGN668 OR "REGN-668" OR Mepolizumab OR Bosatria OR "SB-240563" OR SB240563 OR Nucala OR Omalizumab OR Xolair OR Reslizumab OR "SCH-55700" OR SCH55700 OR "CEP-38072" OR CEP38072 OR Cinqair OR "DCP-835" OR DCP835 OR Tezspire OR "tezepelumab-ekko" OR "AMG-157" OR tezspire OR "MEDI-9929" OR "MEDI-19929" OR MEDI9929 OR Itepekimab OR "REGN-3500"OR REGN3500 OR "SAR-440340"OR SAR440340 OR Tralokinumab OR "CAT-354" OR Anrukinzumab OR "IMA-638" OR Lebrikizumab OR "RO-5490255"OR "RG-3637"OR "TNX-650"OR "MILR1444A"OR "MILR-1444A"OR"PRO301444"OR "PRO-301444"OR Pitrakinra OR altrakincept OR "AMG-317"OR"AMG317" OR Etokimab OR Pascolizumab OR "IMA-026"OR Enokizumab OR "MEDI-528"OR "7F3COM-2H2" OR 7F3COM2H2 OR Brodalumab OR "KHK-4827" OR "KHK4827"OR "AMG-827"OR Siliq OR Ligelizumab OR "QGE-031" OR QGE031 OR Quilizumab OR Talizumab OR "TNX-901" OR TNX901 OR Infliximab OR Etanercept OR "PRS-060") AND TS=asthma*. The document type was set to articles and review articles and the language restriction was set to English. Three different analysis tools including one online platform, VOS viewer1.6.18, and CiteSpace V 6.1.R1 software were used to conduct this bibliometric study.
This bibliometric study included 1,267 English papers published in 244 journals from 2,012 institutions in 69 countries/regions. Omalizumab, benralizumab, mepolizumab, and tezepelumab in relation to asthma were the research hotspots in the field.
This study systematically uncovers a holistic picture of existing literature related to the biologic treatment of asthma over the past 20 years. We consulted scholars in order to understand key information in this field from the perspective of bibliometrics, which we believe may greatly facilitate future research in this field.
3D mesh as a complex data structure can provide effective shape representation for 3D objects, but due to the irregularity and disorder of the mesh data, it is difficult for convolutional neural ...networks to be directly applied to 3D mesh data processing. At the same time, the extensive use of convolutional kernels and pooling layers focusing on local features can cause the loss of spatial information and dependencies of low-level features. In this paper, we propose a self-attentive convolutional network MixFormer applied to 3D mesh models. By defining 3D convolutional kernels and vector self-attention mechanisms applicable to 3D mesh models, our neural network is able to learn 3D mesh model features. Combining the features of convolutional networks and transformer networks, the network can focus on both local detail features and long-range dependencies between features, thus achieving good learning results without stacking multiple layers and saving arithmetic overhead compared to pure transformer architectures. We conduct classification and semantic segmentation experiments on SHREC15, SCAPE, FAUST, MIT, and Adobe Fuse datasets. Experimental results show that the network can achieve 96.7% classification and better segmentation results by using fewer parameters and network layers.
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