Gapless Genome Assembly of ZH8015 and Preliminary Multi-Omics Analysis to Investigate ZH8015's Responses Against Brown Planthopper Infestation

Accurate genomic information is essential for advancing genetic breeding research in specific rice varieties.This study presented a gapless genome assembly of the indica rice cultivar Zhonghui 8015(ZH8015)using Pac Bio HiFi,Hi-C,and ONT(Oxford Nanopore Technologies)ultra-long sequencing technologies,annotating 43037 gene structures.Subsequently,utilizing this genome along with transcriptomic and metabolomic techniques,we explored ZH8015's response to brown planthopper(BPH)infestation.Continuous transcriptomic sampling indicated significant changes in gene expression levels around 48 h after BPH feeding.Enrichment analysis revealed particularly significant alterations in genes related to reactive oxygen species scavenging and cell wall formation.Metabolomic results demonstrated marked increases in levels of several monosaccharides,which are components of the cell wall and dramatic changes in flavonoid contents.Omics association analysis identified differentially expressed genes associated with key metabolites,shedding light on ZH8015's response to BPH infestation.In summary,this study constructed a reliable genome sequence resource for ZH8015,and the preliminary multi-omics results will guide future insect-resistant breeding research.

BABA-induced pathogen resistance: a multi-omics analysis of the tomato response reveals a hyper-receptive status involving ethylene

Prior exposure to microbial-associated molecular patterns or specific chemical compounds can promote plants into a primed state with stronger defence responses.β-aminobutyric acid(BABA)is an endogenous stress metabolite that induces resistance protecting various plants towards diverse stresses.In this study,by integrating BABA-induced changes in selected metabolites with transcriptome and proteome data,we generated a global map of the molecular processes operating in BABA-induced resistance(BABA-IR)in tomato.BABA significantly restricts the growth of the pathogens Oidium neolycopersici and Phytophthora parasitica but not Botrytis cinerea.A cluster analysis of the upregulated processes showed that BABA acts mainly as a stress factor in tomato.The main factor distinguishing BABA-IR from other stress conditions was the extensive induction of signaling and perception machinery playing a key role in effective resistance against pathogens.Interestingly,the signalling processes and immune response activated during BABA-IR in tomato differed from those in Arabidopsis with substantial enrichment of genes associated with jasmonic acid(JA)and ethylene(ET)signalling and no change in Asp levels.Our results revealed key differences between the effect of BABA on tomato and other model plants studied until now.Surprisingly,salicylic acid(SA)is not involved in BABA downstream signalization whereas ET and JA play a crucial role.

Integrated multi-omics analysis of developing‘Newhall’orange and its glossy mutant provide insights into citrus fragrance formation

Sesquiterpene valencene is dominant in flavedo tissues of sweet oranges and imparts a unique woody aroma.However,the interaction between the biosynthetic pathways of valencene and other nutritional compounds is less studied.Sesquiterpenoids were significantly accumulated in a previously reported glossy mutant of orange(MT)than the wild type(WT),especially valencene and caryophyllene.In addition,we identified several other pathways with variations at both the transcriptional and metabolic levels in MT.It’s interesting to found those upregulated metabolites in MT,such as eukaryotic lipids,kaempferol and proline also showed strong positive correlation with valencene along with fruit maturation while those down-regulated metabolites,such as phenylpropanoid coumarins and most of the modified flavonoids exhibited negative correlation.We then categorized these shifted pathways into the‘sesquitepenoid-identical shunt’and the sesquitepenoid-opposite shunt’and confirmed the classification result at transcriptional level.Our results provide important insights into the connections between various fruit quality-related properties.

Comprehensive multi-omics analysis identified core molecular processes in esophageal cancer and revealed GNGT2 as a potential prognostic marker

BACKGROUND Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world.Although a series of studies on esophageal cancer have been reported,the molecular pathogenesis of the disease remains elusive.AIM To investigate comprehensively the molecular process of esophageal cancer.METHODS Differential expression analysis was performed to identify differentially expressed genes(DEGs)in different stages of esophageal cancer from The Cancer Genome Atlas data.Exacting gene interaction modules were generated,and hub genes in the module interaction network were found.Further,through survival analysis,methylation analysis,pivot analysis,and enrichment analysis,some important molecules and related functions/pathways were identified to elucidate potential mechanisms in esophageal cancer.RESULTS A total of 7457 DEGs and 14 gene interaction modules were identified.These module genes were significantly involved in the positive regulation of protein transport,gastric acid secretion,insulin-like growth factor receptor binding,and other biological processes as well as p53 signaling pathway,epidermal growth factor signaling pathway,and epidermal growth factor receptor signaling pathway.Transcription factors(including hypoxia inducible factor 1A)and noncoding RNAs(including colorectal differentially expressed and hsa-miR-330-3p)that significantly regulate dysfunction modules were identified.Survival analysis showed that G protein subunit gamma transducin 2(GNGT2)was closely related to survival of esophageal cancer.DEGs with strong methylation regulation ability were identified,including SST and SH3GL2.Furthermore,the expression of GNGT2 was evaluated by quantitative real time polymerase chain reaction,and the results showed that GNGT2 expression was significantly upregulated in esophageal cancer patient samples and cell lines.Moreover,cell counting kit-8 assay revealed that GNGT2 could promote the proliferation of esophageal cancer cell lines.CONCLUSION This study not only revealed the potential regulatory factors involved in the development of esophageal cancer but also deepens our understanding of its underlying mechanism.

Multi-omics analysis of attenuated variant reveals potential evaluation marker of host damaging for SARS-CoV-2 variants

SARS-CoV-2 continues to threaten human society by generating novel variants via mutation and recombination.The high number of mutations that appeared in emerging variants not only enhanced their immune-escaping ability but also made it difficult to predict the pathogenicity and virulence based on viral nucleotide sequences.Molecular markers for evaluating the pathogenicity of new variants are therefore needed.By comparing host responses to wild-type and variants with attenuated pathogenicity at proteome and metabolome levels,six key molecules on the polyamine biosynthesis pathway including putrescine,SAM,dc-SAM,ODC1,SAMS,and SAMDC were found to be differentially upregulated and associated with pathogenicity of variants.To validate our discovery,human airway organoids were subsequently used which recapitulates SARS-CoV-2 replication in the airway epithelial cells of COVID-19 patients.Using ODC1 as a proof-ofconcept,differential activation of polyamine biosynthesis was found to be modulated by the renin-angiotensin system(RAS)and positively associated with ACE2 activity.Further experiments demonstrated that ODC1 expression could be differentially activated upon a panel of SARS-CoV-2 variants of concern(VOCs)and was found to be correlated with each VOCs’pathogenic properties.Particularly,the presented study revealed the discriminative ability of key molecules on polyamine biosynthesis as a predictive marker for virulence evaluation and assessment of SARS-CoV-2 variants in cell or organoid models.Our work,therefore,presented a practical strategy that could be potentially applied as an evaluation tool for the pathogenicity of current and emerging SARS-CoV-2 variants.

Dynamic immune recovery process after liver transplantation revealed by single-cell multi-omics analysis

Elucidating the temporal process of immune remodeling under immunosuppressive treatment after liver transplantation(LT)is critical for precise clinical management strategies.Here,we performed a single-cell multi-omics analysis of peripheral blood mononuclear cells(PBMCs)collected from LT patients(with and without acute cellular rejection[ACR])at 13 time points.Validation was performed in two independent cohorts with additional LT patients and healthy controls.Our study revealed a four-phase recovery process after LT and delineated changes in immune cell composition,expression programs,and interactions along this process.The intensity of the immune response differs between the ACR and non-ACR patients.Notably,the newly identified inflamed NK cells,CD14+RNASE2+monocytes,and FOS-expressing monocytes emerged as predictive indicators of ACR.This study illuminates the longitudinal evolution of the immune cell landscape under tacrolimus-based immunosuppressive treatment during LT recovery,providing a four-phase framework that aids the clinical management of LT patients.

Multi-omics Analysis of Young Portulaca oleracea L.Plants’Responses to High NaCl Doses Reveals Insights into Pathways and Genes Responsive to Salinity Stress in this Halophyte Species

Soil salinity is among the abiotic stressors that threaten agriculture the most,and purslane(Portulaca oleracea L.)is a dicot species adapted to inland salt desert and saline habitats that hyper accumulates salt and has high phytoremediation potential.Many researchers consider purslane a suitable model species to study the mechanisms of plant tolerance to drought and salt stresses.Here,a robust salinity stress protocol was developed and used to characterize the morphophysiological responses of young purslane plants to salinity stress;then,leaf tissue underwent characterization by distinct omics platforms to gain further insights into its response to very high salinity stress.The salinity stress protocol did generate different levels of stress by gradients of electrical conductivity at field capacity and water potential in the saturation extract of the substrate,and the morphological parameters indicated three distinct stress levels.As expected from a halophyte species,these plants remained alive under very high levels of salinity stress,showing salt crystal-like structures constituted mainly by Na^(+),Cl^(−),and K^(+)on and around closed stomata.A comprehensive and large-scale metabolome and transcriptome single and integrated analyses were then employed using leaf samples.The multi-omics integration(MOI)system analysis led to a data-set of 51 metabolic pathways with at least one enzyme and one metabolite differentially expressed due to salinity stress.These data sets(of genes and metabolites)are valuable for future studies aimed to deepen our knowledge on the mechanisms behind the high tolerance of this species to salinity stress.In conclusion,besides showing that this species applies salt exclusion already in young plants to support very high levels of salinity stress,the initial analysis of metabolites and transcripts data sets already give some insights into other salt tolerance mechanisms used by this species to support high levels of salinity stress.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

Lagrangian coherent structure analysis on transport of Acetes chinensis along coast of Lianyungang,China

Spatial heterogeneity or“patchiness”of plankton distributions in the ocean has always been an attractive and challenging scientific issue to oceanographers.We focused on the accumulation and dynamic mechanism of the Acetes chinensis in the Lianyungang nearshore licensed fishing area.The Lagrangian frame approaches including the Lagrangian coherent structures theory,Lagrangian residual current,and Lagrangian particle-tracking model were applied to find the transport pathways and aggregation characteristics of Acetes chinensis.There exist some material transport pathways for Acetes chinensis passing through the licensed fishing area,and Acetes chinensis is easy to accumulate in the licensed fishing area.The main mechanism forming this distribution pattern is the local circulation induced by the nonlinear interaction of topography and tidal flow.Both the Lagrangian coherent structure analysis and the particle trajectory tracking indicate that Acetes chinensis in the licensed fishing area come from the nearshore estuary.This work contributed to the adjustment of licensed fishing area and the efficient utilization of fishery resources.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

Gears are pivotal in mechanical drives,and gear contact analysis is a typically difficult problem to solve.Emerging isogeometric analysis(IGA)methods have developed new ideas to solve this problem.In this paper,a threedimensional body parametric gear model of IGA is established,and a theoretical formula is derived to realize single-tooth contact analysis.Results were benchmarked against those obtained from commercial software utilizing the finite element analysis(FEA)method to validate the accuracy of our approach.Our findings indicate that the IGA-based contact algorithmsuccessfullymet theHertz contact test.When juxtaposed with the FEA approach,the IGAmethod demonstrated fewer node degrees of freedomand reduced computational units,all whilemaintaining comparable accuracy.Notably,the IGA method appeared to exhibit consistency in analysis accuracy irrespective of computational unit density,and also significantlymitigated non-physical oscillations in contact stress across the tooth width.This underscores the prowess of IGA in contact analysis.In conclusion,IGA emerges as a potent tool for addressing contact analysis challenges and holds significant promise for 3D gear modeling,simulation,and optimization of various mechanical components.

Preliminary electromagnetic analysis of the COOL blanket for CFETR

The supercritical CO_(2)cOoled Lithium-Lead(COOL)blanket has been designed as one advanced blanket candidate for the Chinese Fusion Engineering Test Reactor(CFETR).This work focuses on the electromagnetic(EM)loads(Maxwell force and Lorentz force)acting on the COOL blanket,which are important mechanical loads in further structural analysis of the COOL blanket.A 3D electromagnetic analysis is performed using the ANSYS finite element method to obtain EM loads on the COOL blanket in this study.At first,the magnetic scalar potential(MSP)method is used to obtain the magnetic field and the Maxwell force on the COOL blanket.Then,the magnetic vector potential(MVP)method is performed during a plasma disruption event to get the eddy current distribution.At last,a multi-step method is adopted for the calculation of the Lorentz force and the torque.The maximum Lorentz forces of inboard and outboard blanket structural components are 5624 kN and 2360 kN respectively.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Erratum to:Vibration analysis of a gear-rotor-bearing system with outer-ring spalling and misalignment

Because of an unfortunate mistake during the production of this article,Figure 13 was wrongly inserted.The corrected Figure 13 is shown as follows.

Advances in microfluidic-based DNA methylation analysis

DNA methylation has been extensively investigated in recent years,not least because of its known relationship with various diseases.Progress in analytical methods can greatly increase the relevance of DNA methylation studies to both clinical medicine and scientific research.Microflu-idic chips are excellent carriers for molecular analysis,and their use can provide improvements from multiple aspects.On-chip molecular analysis has received extensive attention owing to its advantages of portability,high throughput,low cost,and high efficiency.In recent years,the use of novel microfluidic chips for DNA methylation analysis has been widely reported and has shown obvious superiority to conventional methods.In this review,wefirst focus on DNA methylation and its applications.Then,we discuss advanced microfluidic-based methods for DNA methylation analysis and describe the great progress that has been made in recent years.Finally,we summarize the advantages that microfluidic technology brings to DNA methylation analysis and describe several challenges and perspectives for on-chip DNA methylation analysis.This review should help researchers improve their understanding and make progress in developing microfluidic-based methods for DNA methylation analysis.

Stability Analysis of Inverse Lax-Wendroff Procedure for a High order Compact Finite Difference Schemes

This paper considers the finite difference(FD)approximations of diffusion operators and the boundary treatments for different boundary conditions.The proposed schemes have the compact form and could achieve arbitrary even order of accuracy.The main idea is to make use of the lower order compact schemes recursively,so as to obtain the high order compact schemes formally.Moreover,the schemes can be implemented efficiently by solving a series of tridiagonal systems recursively or the fast Fourier transform(FFT).With mathematical induction,the eigenvalues of the proposed differencing operators are shown to be bounded away from zero,which indicates the positive definiteness of the operators.To obtain numerical boundary conditions for the high order schemes,the simplified inverse Lax-Wendroff(SILW)procedure is adopted and the stability analysis is performed by the Godunov-Ryabenkii method and the eigenvalue spectrum visualization method.Various numerical experiments are provided to demonstrate the effectiveness and robustness of our algorithms.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Privacy Protection in COVID Data Tracking: Textual Analysis of the Literature

The literary review presented in the following paper aims to analyze the tracking tools used in different countries during the period of the COVID-19 pandemic. Tracking apps that have been adopted in many countries to collect data in a homogeneous and immediate way have made up for the difficulty of collecting data and standardizing evaluation criteria. However, the regulation on the protection of personal data in the health sector and the adoption of the new General Data Protection Regulation in European countries has placed a strong limitation on their use. This has not been the case in non-European countries, where monitoring methodologies have become widespread. The textual analysis presented is based on co-occurrence and multiple correspondence analysis to show the contact tracing methods adopted in different countries in the pandemic period by relating them to the issue of privacy. It also analyzed the possibility of applying Blockchain technology in applications for tracking contagions from COVID-19 and managing health data to provide a high level of security and transparency, including through anonymization, thus increasing user trust in using the apps.