Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.

Ovarian endometriosis is characterized by the growth of endometrial tissue within the ovary, causing infertility and chronic pain. However, its pathophysiology remains unclear. Utilizing high-precision single-cell RNA sequencing, we profile the normal, eutopic, and ectopic endometrium from 34 individuals across proliferative and secretory phases. We observe an increased proportion of ciliated cells in both eutopic and ectopic endometrium, characterized by a diminished expression of estrogen sulfotransferase, which likely confers apoptosis resistance. After translocating to ectopic lesions, endometrial epithelium upregulates nicotinamide N-methyltransferase expression that inhibits apoptosis by promoting deacetylation and subsequent nuclear exclusion of transcription factor forkhead box protein O1, thereby leading to the downregulation of the apoptotic gene BIM. Moreover, epithelial cells in ectopic lesions elevate HLA class II complex expression, which stimulates CD4+ T cells and consequently contributes to chronic inflammation. Altogether, our study provides a comprehensive atlas of ovarian endometriosis and highlights potential therapeutic targets for modulating apoptosis and inflammation.

The successful accomplishment of the first telomere-to-telomere human genome assembly, T2T-CHM13, marked a milestone in achieving completeness of the human reference genome. The upcoming era of genome study will focus on fully phased diploid genome assembly, with an emphasis on genetic differences between individual haplotypes. Most existing sequencing approaches only achieved localized haplotype phasing and relied on additional pedigree information for further whole-chromosome scale phasing. The short-read-based Strand-seq method is able to directly phase single nucleotide polymorphisms (SNPs) at whole-chromosome scale but falls short when it comes to phasing structural variations (SVs). To shed light on this issue, we developed a Nanopore sequencing platform-based Strand-seq approach, which we named NanoStrand-seq. This method allowed for de novo SNP calling with high precision (99.52%) and acheived a superior phasing accuracy (0.02% Hamming error rate) at whole-chromosome scale, a level of performance comparable to Strand-seq for haplotype phasing of the GM12878 genome. Importantly, we demonstrated that NanoStrand-seq can efficiently resolve the MHC locus, a highly polymorphic genomic region. Moreover, NanoStrand-seq enabled independent direct calling and phasing of deletions and insertions at whole-chromosome level; when applied to long genomic regions of SNP homozygosity, it outperformed the strategy that combined Strand-seq with bulk long-read sequencing. Finally, we showed that, like Strand-seq, NanoStrand-seq was also applicable to primary cultured cells. Together, here we provided a novel methodology that enabled interrogation of a full spectrum of haplotype-resolved SNPs and SVs at whole-chromosome scale, with broad applications for species with diploid or even potentially polypoid genomes.

The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within an individual cell. To address this, herein, we developed scNanoHi-C, which applies Nanopore long-read sequencing to explore genome-wide proximal high-order chromatin contacts within individual cells. We show that scNanoHi-C can reliably and effectively profile 3D chromatin structures and distinguish structure subtypes among individual cells. This method could also be used to detect genomic variations, including copy-number variations and structural variations, as well as to scaffold the de novo assembly of single-cell genomes. Notably, our results suggest that extensive high-order chromatin structures exist in active chromatin regions across the genome, and multiway interactions between enhancers and their target promoters were systematically identified within individual cells. Altogether, scNanoHi-C offers new opportunities to investigate high-order 3D genome structures at the single-cell level.

N6-methyladenosine (m6A) RNA modification plays important regulatory roles in plant development and adapting to the environment, which requires methyltransferases to achieve the methylation process. However, there has been no research regarding m6A RNA methyltransferases in cotton. Here, a systematic analysis of the m6A methyltransferase (METTL) gene family was performed on twelve cotton species, resulting in six METTLs identified in five allotetraploid cottons, respectively, and three to four METTLs in the seven diploid species. Phylogenetic analysis of protein-coding sequences revealed that METTL genes from cottons, Arabidopsis thaliana, and Homo sapiens could be classified into three clades (METTL3, METTL14, and METTL-like clades). Cis-element analysis predicated the possible functions of METTL genes in G. hirsutum. RNA-seq data revealed that GhMETTL14 (GH_A07G0817/GH_D07G0819) and GhMETTL3 (GH_A12G2586/GH_D12G2605) had high expressions in root, stem, leaf, torus, petal, stamen, pistil, and calycle tissues. GhMETTL14 also had the highest expression in 20 and 25 dpa fiber cells, implying a potential role at the cell wall thickening stage. Suppressing GhMETTL3 and GhMETTL14 by VIGS caused growth arrest and even death in G. hirsutum, along with decreased m6A abundance from the leaf tissues of VIGS plants. Overexpression of GhMETTL3 and GhMETTL14 produced distinct differentially expressed genes (DEGs) in A. thaliana, indicating their possible divergent functions after gene duplication. Overall, GhMETTLs play indispensable but divergent roles during the growth of cotton plants, which provides the basis for the systematic investigation of m6A in subsequent studies to improve the agronomic traits in cotton.

5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark that regulates gene expression. Charting the landscape of 5hmC in human tissues is fundamental to understanding its regulatory functions. Here, we systematically profiled the whole-genome 5hmC landscape at single-base resolution for 19 types of human tissues. We found that 5hmC preferentially decorates gene bodies and outperforms gene body 5mC in reflecting gene expression. Approximately one-third of 5hmC peaks are tissue-specific differentially-hydroxymethylated regions (tsDhMRs), which are deposited in regions that potentially regulate the expression of nearby tissue-specific functional genes. In addition, tsDhMRs are enriched with tissue-specific transcription factors and may rewire tissue-specific gene expression networks. Moreover, tsDhMRs are associated with single-nucleotide polymorphisms identified by genome-wide association studies and are linked to tissue-specific phenotypes and diseases. Collectively, our results show the tissue-specific 5hmC landscape of the human genome and demonstrate that 5hmC serves as a fundamental regulatory element affecting tissue-specific gene expression programs and functions.

The newly identified Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has resulted in a global health emergency because of its rapid spread and high mortality.The molecular mechanism of interaction between host and viral genomic RNA is yet unclear.We demonstrate herein that SARS-CoV-2 genomic RNA,as well as the negative-sense RNA,is dynamically N6-methyladenosine(m6A)-modified in human and monkey cells.Combined RIP-seq and miCLIP analyses identified a total of 8 m6A sites at single-base resolution in the genome.Especially,epidemic strains with mutations at these identified m6A sites have emerged worldwide,and formed a unique cluster in the US as indicated by phylogenetic analysis.Further functional experiments showed that m6A methylation negatively regulates SARS-CoV-2 infection.SARS-CoV-2 infection also triggered a global increase in host m6A methylome,exhibiting altered localization and motifs of m6A methylation in mRNAs.Altogether,our results identify m6A as a dynamic epitranscriptomic mark mediating the virus-host interaction.

Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery. Traditional machine learning methods have achieved certain beneficial effects in medical image segmentation, but they have problems such as low classification accuracy and poor robustness. Deep learning theory has good generalizability and feature extraction ability, which provides a new idea for solving medical image segmentation problems. However, deep learning has problems in terms of its application to medical image segmentation: one is that the deep learning network structure cannot be constructed according to medical image characteristics; the other is that the generalizability y of the deep learning model is weak. To address these issues, this paper first adapts a neural network to medical image features by adding cross-layer connections to a traditional convolutional neural network. In addition, an optimized convolutional neural network model is established. The optimized convolutional neural network model can segment medical images using the features of two scales simultaneously. At the same time, to solve the generalizability problem of the deep learning model, an adaptive distribution function is designed according to the position of the hidden layer, and then the activation probability of each layer of neurons is set. This enhances the generalizability of the dropout model, and an adaptive dropout model is proposed. This model better addresses the problem of the weak generalizability of deep learning models. Based on the above ideas, this paper proposes a medical image segmentation algorithm based on an optimized convolutional neural network with adaptive dropout depth calculation. An ultrasonic tomographic image and lumbar CT medical image were separately segmented by the method of this paper. The experimental results show that not only are the segmentation effects of the proposed method improved compared with those of the traditional machine learning and other deep learning methods but also the method has a high adaptive segmentation ability for various medical images. The research work in this paper provides a new perspective for research on medical image segmentation.

N-6-methyladenosine (m(6)A), the most abundant internal mRNA modification, and N-6,2'-O-dimethyladenosine (m(6)Am), found at the first-transcribed nucleotide, are two reversible epitranscriptomic marks. However, the profiles and distribution patterns of m(6)A and m(6)Am across human and mouse tissues are poorly characterized. Here, we report the m(6)A and m(6)Am methylome through profiling of 43 human and 16 mouse tissues and demonstrate strongest tissue specificity for the brain tissues. A small subset of tissue-specific m(6)A peaks can also readily classify tissue types. The overall m(6)A and m(6)Am level is partially correlated with the expression level of their writers and erasers. Additionally, the m(6)A-containing regions are enriched for SNPs. Furthermore, cross-species analysis revealed that species rather than tissue type is the primary determinant of methylation. Collectively, our study provides an in-depth resource for dissecting the landscape and regulation of the m(6)A and m(6)Am epitranscriptomic marks across mammalian tissues.

N6-methyladenosine (m6A), the most abundant internal mRNA modification, and N6,2’-O-dimethyladenosine (m6Am), found at the first-transcribed nucleotide, are two examples of dynamic and reversible epitranscriptomic marks. However, the profiles and distribution patterns of m6A and m6Am across different human and mouse tissues are poorly characterized. Here we report the m6A and m6Am methylome through an extensive profiling of 42 human tissues and 16 mouse tissue samples. Globally, the m6A and m6Am peaks in non-brain tissues demonstrates mild tissue-specificity but are correlated in general, whereas the m6A and m6Am methylomes of brain tissues are clearly resolved from the non-brain tissues. Nevertheless, we identified a small subset of tissue-specific m6A peaks that can readily classify the tissue types. The number of m6A and m6Am peaks are partially correlated with the expression levels of their writers and erasers. In addition, them6A- and m6Am-containing regions are enriched for single nucleotide polymorphisms. Furthermore, cross-species analysis of m6A and m6Am methylomes revealed that species, rather than tissue types, is the primary determinant of methylation. Collectively, our study provides an in-depth resource for dissecting the landscape and regulation of the m6A and m6Am epitranscriptomic marks across mammalian tissues.

Images, as one of the important carriers for information exchange, play an important role in work and daily life. Image encryption technology has received a lot of attention, and varieties of encryption technologies for images have emerged. Early image encryption technologies have shortcomings such as simple algorithm structure, small key space, and poor resistance to plaintext attacks. These algorithms have been unable to meet the needs of information security at this stage. Based on the wavelet algorithm, chaos algorithm, and cyclic encryption algorithm, combined with frequency domain encryption and spatial domain encryption, a digital image encryption algorithm based on adaptive wavelet is proposed in this paper. In terms of frequency, this paper enhances the adaptive ability of the wavelet algorithm by convex optimization-particle swarm optimization (PSO). At the same time, the chaotic algorithm is used to scramble the low-frequency coefficients. In the aspect of airspace, this paper uses the block-encryption adaptive encryption algorithm to rescramble the wavelet-reconstructed image. And the SHA-1 algorithm is introduced into the plaintext image generation key sequence as a cyclic index value for cyclic encryption. The key composition of the encryption algorithm proposed in this paper is complex, and the idea of a one-time pad is used, which makes the improved algorithm increase the key space and improve the ability to resist the choice of plaintext attack. In each cyclic encryption of the airspace, mutual encryption is performed by image subblocks, and the algorithm adaptability is improved. Through the experimental test of statistical characteristics, key space, and key sensitivity, the encryption performance of the algorithm is verified, and it has strong antiattack and interference ability. It is a relatively secure encryption algorithm.

The development of fabricated buildings has become the main trend of the developm ent of modern construction industry in China. As the main tool of building information, BIM (b uilding information modeling) has greatly promoted the development of construction industry. Based on the review of the papers about the fabricated buildings and BIM technology in recent years, this paper analyzes the advantages of fabricated buildings and BIM technology, then exp lores the application of BIM technology in fabricated buildings. It aims to realize the rationaliz ation and scientification of project lifecycle management in fabricated construction project, and finally form a coherent information platform in the fabricated building.

The current researches on risk assessment of geological disasters mainly focus on unexpected disasters such as collapses, landslides and mud-rock flows etc. As the convergence zone of land and sea, coastal zone is the most active and complex area of interactions of lithosphere, hydrosphere, atmosphere, biosphere and anthroposphere. The ecological environment of coastal zone is very fragile, so further systematical research on coastal geological hazard assessment and prevention is in urgent need. The author begins with the definition and research contents and selects three typical coastal geological disasters, namely, the seawater intrusion, coastline change and sea-level rise as the objects of study. The systematic analysis and study on assessment system and methods are conducted, hazard assessment factors are selected, and a completely set of coastal disaster assessment system is established based on the technique of GIS. We took Bao'an District of Shenzhen City as an example and carried out a case study.

Contractors for the construction of the risk in the contract clause clear analysis is an important content of construction project risk management. Based on the 1999 edition of FIDIC contract conditions for construction of the demonstration text as the research object, according to the risk and responsibility on the contractor risk clause matching principle clear classification. To provide the contractor some experience for reference which the contractor could better control of engineering project contract risk.

In order to solve the incompatible problems between each evaluation index when evaluated,the superiority evaluation method was applied in the process of Bid Evaluation of architectural engineering. Using the CRITIC method to determine the weight coefficient of each index , which take into account both the contrast intensity between the indexes and introduces the correlation between indicators to determine the weight coefficient, so that more objective. Calculating the bidders value of superiority degree and maximal is selected as the most suitable bidder, results show that the method is relatively objective evaluation, the calculation is simple and practical.

The project is a complicated systems engineering. The theory of whole life cycle cost considers roundly each phase of the project such as design. life cycle cost theory is applied to fields of maintenance and reinforcement about existing building structure, modeling with a reinforced concrete frame structure deterioration, it made a research about the influence of maintenance, repair and reinforcement measures to the reliability and deterioration speed of existing structure while analyzing its life cycle cost, considering economic optimization problem so as to select economy and reasonable maintenance scheme from multiple plans.

Coal and gas outburst disasters are usually accompanied by some of the characteristics of events, through the analysis of gas accident monitoring data, we can draw the pattern characteristics of a gas accident, which can highlight the situation in the future identification of gas according to features of the database, support vector machines in a small sample, high-dimensional pattern recognition has shown great advantages, the combination of VC dimension theory and structural risk minimization principle, the limited sample modal learning experience, can effectively achieve the effect of classification and pattern recognition, In combination with rough set theory, the original sample data reduction, support vector machine so as to post the data to facilitate processing and pattern identification, and finally validated through case study and practical validity of the model.

As enterprise's informatization is a long and complicated process, it is necessary to make a risk assessment before doing the action of informatization. With the unascertained theory, this paper made a qualitative and quantitative analysis about the risk factors of enterprise informatization process. According to the enterprise informatization risk management of diamond model, it extracted comprehensive measures for information risk evaluation and made the risk evaluation. Through the unascertained theory in the risk assessment the paper made a quantitative evaluation about the index which is difficult to quantify. And it also provides feasible risk assessment methods for the implementation of enterprise's informatization.

This paper starts from the production of green supply chain. it describes the concepts of green supply chain and performance evaluation. According to the idea of green supply chain and considering the characteristics of the Green Supply Chain, this paper established a green supply chain performance evaluation system. This system made clear choice of evaluation index. Finally, by improving the data envelopment analysis (DEA) and MATLAB software-assisted calculation complete the evaluation of green supply chain performance. And combined with examples, this paper explainsed the improved data envelopment analysis (DEA) is more conducive to the whole sort of decision-making unit than the traditional data envelopment analysis (DEA).

By using GIS method, this paper designs and develops an intellectualization risk management system of city gas pipeline network. This system embeds the MapX components provided by the MapInfo into Visual development languages - Visual Basic 6.0 to finish the secondary development based on the MapInfo 7.0 Platform and SQL Server 2000 database management system. The design idea, overall structure and function, and the implementation of the function of intellectualization risk management system of city gas pipeline network are mainly introduced in this paper. This system realizes efficient and seamless systems integration, dynamically displays risk grade and accidents range of influence of each pipeline of gas pipeline network and automatically generates emergency response measures and the path. It represents powerful support for the intellectualization risk management of city gas pipeline network and administration department's assistance decision and emergency response.