Decoding imagined speech with delay differential analysis.

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.

Effective identification and differential analysis of anticancer peptides.

Anticancer peptides (ACPs) have recently emerged as promising cancer therapeutics due to their selectivity and lower toxicity. However, the number of experimentally validated ACPs is limited, and identifying ACPs from large-scale sequence data is time-consuming and expensive. Therefore, it is critical to develop and improve upon existing computational models for identifying ACPs. In this study, a computational method named ACP_DA was proposed based on peptide residue composition and physiochemical properties information. To curtail overfitting and reduce computational costs, a sequential forward selection method was utilized to construct the optimal feature groups. Subsequently, the feature vectors were fed into light gradient boosting machine classifier for model construction. It was observed by an independent set test that ACP_DA achieved the highest Matthew's correlation coefficient of 0.63 and accuracy of 0.8129, displaying at least a 2% enhancement compared to state-of-the-art methods. The satisfactory results demonstrate the effectiveness of ACP_DA as a powerful tool for identifying ACPs, with the potential to significantly contribute to the development and optimization of promising therapies. The data and resource codes are available at https://github.com/Zlclab/ACP_DA.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Reference-free inferring of transcriptomic events in cancer cells on single-cell data.

BACKGROUND: Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference. RESULTS: Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment. CONCLUSIONS: Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

The masking phenomenon of microplastics additives on oxidative stress responses in freshwater food chains.

The variability and intrinsic mechanisms of oxidative stress induced by microplastics at different trophic levels in freshwater food chains are not well understood. To comprehensively assess the oxidative stress induced by polystyrene microplastics (PS-MPs) in freshwater food chains, the present study first quantified the oxidative stress induced by PS-MPs in organisms at different trophic levels using factorial experimental design and molecular dynamics methods. Then focuses on analyzing the variability of these responses across different trophic levels using mathematical statistical analysis. Notably, higher trophic level organisms exhibit diminished responses under PS-MPs exposure. Furthermore, the coexistence of multiple additives was found to mask these responses, with antioxidant plastic additives significantly influencing oxidative stress responses. Mechanism analysis using computational chemistry simulation determines that protein structure and amino acid characteristics are key factors driving PS-MPs induced oxidative stress variation in freshwater organisms at different nutrient levels. Increased hydrophobic additives induce protein helicalization and amino acid residue aggregation. This study systematically reveals the variability of biological oxidative stress response under different nutrient levels, emphasizing the pivotal role of chemical additives. Overall, this study offers crucial insights into PS-MPs' impact on oxidative stress responses in freshwater ecosystems, informing future environmental risk assessment.

Differential analysis of immunoglobulin gene expression pattern in chickens of distinct breeds and developmental periods.

Immunoglobulin is an essential component of the body's defense against pathogens, aiding in the recognition and clearance of foreign antigens. Research concerning immunoglobulin gene and its diversity of expression across different breeds within the same species is relatively scarce. In this study, we employed RACE (Rapid Amplification of cDNA Ends) technology, prepared DNA libraries, performed high-throughput sequencing, and conducted related bioinformatics analysis to analyze the differences in immunoglobulin gene diversity and expression at different periods in Hy-line brown hens, Lueyang black-bone chickens, and Beijing-You chickens. The study found that the composition of chicken immunoglobulin genes is relatively simple, with both the light chain and heavy chain having a functional V gene. Additionally, the mechanisms of immunoglobulin diversity generation tended to be consistent among different breeds and periods of chickens, primarily relying on abundant junctional diversity, somatic hypermutation (SHM), and gene conversion (GCV) to compensate for the limitations of low-level V(D)J recombination. As the age increased, the junctional diversity of IgH and IgL tended to diversify and showed similar expression patterns among different breeds. In the three chicken breeds, the predominant types of mutations observed in IGHV and IGLV SHM were A to G and G to A transitions. Specifically, IGLV exhibited a preference for A to G mutations, whereas IGHV displayed a bias toward G to A mutations. The regions at the junctions between framework regions (FR) and complementarity-determining regions (CDR) and within the CDR regions themselves are typically prone to mutations. The locations of GCV events in IGLV and IGHV do not show significant differences, and replacement segments are concentrated in the central regions of FR1, CDR, and FR2. Importantly, gene conversion events are not random occurrences. Additionally, our investigation revealed that CDRH3 in chickens of diverse breeds and periods the potential for diversification through the incorporation of cysteine. This study demonstrates that the diversity of immunoglobulin expression tends to converge among Hy-line brown hens, Lueyang black-bone chickens, and Beijing-You chickens, indicating that the immunoglobulin gene expression mechanisms in different breeds of chickens do not exhibit significant differences due to selective breeding.

Immunoglobulins play a key role in the organism's defense against pathogens, and their diverse expression allows the body to generate a wide array of antibodies. This diversity serves as a critical safeguard for the immune system against various pathogens. Natural geographical variances and artificial breeding and selection can potentially lead to different immune responses in distinct populations of the same species when confronted with the same pathogen. In this study, we investigated the diversity of immunoglobulin gene expression in the natural state of different chicken breeds (Hy-line brown hens, Lueyang black-bone chickens, and Beijing-You chickens) and at different periods from the perspective of immunoglobulin gene expression mechanism. We analyzed the diversity of immunoglobulin based on the results of high-throughput sequencing by extracting Fabricius bursa RNA, RACE (Rapid Amplification of cDNA Ends) technique, and constructing DNA libraries. Our study reveals that the junctional diversity, somatic hypermutation, CDR3 diversity, and gene conversion expression of immunoglobulins in Hy-line brown hens, Lueyang black-bone chickens, and Beijing-You chickens converge during the same time period. This indicates that the immunoglobulin gene expression mechanisms in different chicken breeds do not exhibit significant variations as a result of selective breeding.

A systematic comparison of salt removal efficiency in washing treatment by using fly ashes from 13 MSWI plants in China.

Municipal solid waste incineration (MSWI) fly ash contains large amounts of Ca, Si, and other elements, giving it the potential to be used as a raw material for cement production. However, fly ash often contains a high content of salts, which greatly limits its blending ratio during cement production. These salts are commonly removed via water washing, but this process is affected by the nature and characteristics of fly ash. To clarify the influence of the ash characteristics on salt removal, a total of 60 fly ash samples from 13 incineration plants were collected, characterized, and washed. The ash characterization and cluster analysis showed that the incinerator type and flue gas purification technology/process significantly influenced the ash characteristics. Washing removed a high percentage of salts from fly ash, but the removal efficiencies varied significantly from each other, with the chlorine removal efficiency ranging from 73.76% to 96.48%, while the sulfate removal efficiency ranged from 6.92% to 51.47%. Significance analysis further revealed that the salt removal efficiency varied not only between the ash samples from different incinerators, but also between samples collected at different times from the same incinerator. The high variance of the 60 ash samples during salt removal was primarily ascribed to their different mineralogical and chemical characteristics. Mineralogical analysis of the raw and washed ash samples showed that the mineralogical forms and proportion of these salts in each ash sample greatly influenced their removal. The presence of less-soluble and insoluble chloride salts (e.g., CaClOH, Ca2Al(OH)6(H2O)2Cl etc.) in fly ash significantly affected the chlorine removal efficiency. This study also found that Fe, Mn, and Al in fly ash were negatively correlated with the dechlorination efficiency of fly ash. In summary, the different physical and chemical properties of fly ash caused great discrepancies in salt removal. Consequently, it is suggested to consider the potential impact of the ash source and ash generation time on salt removal to ensure a reliable treatment efficiency for engineering applications.

Effects of exogenous N-acyl homoserine lactones (AHLs) on methanogenic activities and microbial community differences during anaerobic digestion.

N-acyl homoserine lactones (AHLs) function as signaling molecules influencing microbial community dynamics. This study investigates the impact of exogenously applied AHLs on methane production during waste-activated sludge (WAS) anaerobic digestion (AD). Nine AHL types, ranging from 10-4 to 10 µg/g VSS, were applied, comparing microbial community composition under optimal AHL concentrations. Firmicutes, Bacteroidetes, Chloroflexi, and Proteobacteria were identified in anaerobic digesters with C4-HSL, C6-HSL, and C8-HSL. Compared to the control, Halobacterota increased by 19.25%, 20.87%, and 9.33% with C7-HSL, C10-HSL, and C12-HSL. Exogenous C7-HSL enhanced the relative abundance of Methanosarcina, Romboutsia, Sedimentibacter, Proteiniclasticum, Christensenellaceae_R-7_group. C10-HSL increased Methanosarcina abundance. C4-HSL, C6-HSL, C8-HSL, C10-HSL, and C12-HSL showed potential to increase unclassified_Firmicutes. Functional Annotation of Prokaryotic Taxa (FAPROTAX) predicted AHLs' impact on related functional genes, providing insights into their role in AD methanogenesis regulation. This study aimed to enhance the understanding of the influence of different types of exogenous AHLs on AD and provide technical support for regulating the methanogenesis efficiency of AD by exogenous AHLs.

Toxin metalloproteinases exert a dominant influence on pro-inflammatory response and anti-inflammatory regulation in jellyfish sting dermatitis.

Toxin metalloproteinases are the primary components responsible for various toxicities in jellyfish venom, and there is still no effective specific therapy for jellyfish stings. The comprehension of the pathogenic mechanisms underlying toxin metalloproteinases necessitates further refinement. In this study, we conducted a differential analysis of a dermatitis mouse model induced by jellyfish Nemopilema nomurai venom (NnNV) samples with varying levels of metalloproteinase activity. Through skin tissue proteomics and serum metabolomics, the predominant influence of toxin metalloproteinase activity on inflammatory response was revealed, and the signal pathway involved in its regulation was identified. In skin tissues, many membrane proteins were significantly down-regulated, which might cause tissue damage. The expression of pro-inflammatory factors was mainly regulated by PI3K-Akt signaling pathway. In serum, many fatty acid metabolites were significantly down-regulated, which might be the anti-inflammation feedback regulated by NF-κB p65 signaling pathway. These results reveal the dermatitis mechanism of toxin metalloproteinases and provide new therapeutic targets for further studies. SIGNIFICANCE: Omics is an important method to analyze the pathological mechanism and discover the key markers, which can reveal the pathological characteristics of jellyfish stings. Our research first analyzed the impact of toxin metalloproteinases on jellyfish sting dermatitis by skin proteomics and serum metabolomics. The present results suggest that inhibition of toxin metalloproteinases may be an effective treatment strategy, and provide new references for further jellyfish sting studies.

Weighted gene co-expression network analysis for hub genes in colorectal cancer.

BACKGROUND: This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA). METHODS: Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established. RESULTS: After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC. CONCLUSIONS: CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Integrative analysis reveals key mRNA and long non-coding RNA interaction in idiopathic pulmonary arterial hypertension.

Introduction: Idiopathic pulmonary arterial hypertension (IPAH) is a rare and sporadic form of pulmonary arterial hypertension (PAH), characterized by elevated pulmonary arterial resistance leading to right heart failure. However, molecular mechanisms of PAH development are still not completely understood. Material and methods: In this study, we aimed to uncover key mRNAs and long non-coding RNA (lncRNAs), functional modules and pathways. Moreover, to detect the dysregulated pathway or biological function, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. PPI and co-expression networks were constructed to reveal the potential roles of PAH-related mRNAs and lncRNAs. Results: A total of 3,134 genes, including 945 up-regulated and 2,189 down-regulated genes, were identified to be differentially expressed in IPAH by differential expression analysis. We identified T cell differentiation and the T cell receptor signaling pathway as up-regulated in IPAH by using GO and KEGG analysis. Based on the PPI module analysis, we identified that the pro-inflammatory genes, such as OAS1, CXCL10, STAT1 and TLR4, were the hub genes in the PPI modules. To link the lncRNAs to the PPI modules, we calculated the Spearman correlation coefficient for lncRNA-DE-mRNA pairs to identify the modules with high correlation with each lncRNA. Conclusions: Notably, 6 of these lncRNAs were associated with modules characterized by the NOD-like receptor signaling pathway and chemokine signaling pathway, suggesting that these lncRNAs may promote the occurrence of IPAH via participating in the pro-inflammatory pathways. In conclusion, our systematic analysis not only improved our understanding of the molecular mechanism, but also provided potential lncRNA biomarkers for further research.

MBE: model-based enrichment estimation and prediction for differential sequencing data.

Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.

A distinct circular DNA profile intersects with proteome changes in the genotoxic stress-related hSOD1G93A model of ALS.

BACKGROUND: Numerous genes, including SOD1, mutated in familial and sporadic amyotrophic lateral sclerosis (f/sALS) share a role in DNA damage and repair, emphasizing genome disintegration in ALS. One possible outcome of chromosomal instability and repair processes is extrachromosomal circular DNA (eccDNA) formation. Therefore, eccDNA might accumulate in f/sALS with yet unknown function. METHODS: We combined rolling circle amplification with linear DNA digestion to purify eccDNA from the cervical spinal cord of 9 co-isogenic symptomatic hSOD1G93A mutants and 10 controls, followed by deep short-read sequencing. We mapped the eccDNAs and performed differential analysis based on the split read signal of the eccDNAs, referred as DifCir, between the ALS and control specimens, to find differentially produced per gene circles (DPpGC) in the two groups. Compared were eccDNA abundances, length distributions and genic profiles. We further assessed proteome alterations in ALS by mass spectrometry, and matched the DPpGCs with differentially expressed proteins (DEPs) in ALS. Additionally, we aligned the ALS-specific DPpGCs to ALS risk gene databases. RESULTS: We found a six-fold enrichment in the number of unique eccDNAs in the genotoxic ALS-model relative to controls. We uncovered a distinct genic circulome profile characterized by 225 up-DPpGCs, i.e., genes that produced more eccDNAs from distinct gene sequences in ALS than under control conditions. The inter-sample recurrence rate was at least 89% for the top 6 up-DPpGCs. ALS proteome analyses revealed 42 corresponding DEPs, of which 19 underlying genes were itemized for an ALS risk in GWAS databases. The up-DPpGCs and their DEP tandems mainly impart neuron-specific functions, and gene set enrichment analyses indicated an overrepresentation of the adenylate cyclase modulating G protein pathway. CONCLUSIONS: We prove, for the first time, a significant enrichment of eccDNA in the ALS-affected spinal cord. Our triple circulome, proteome and genome approach provide indication for a potential importance of certain eccDNAs in ALS neurodegeneration and a yet unconsidered role as ALS biomarkers. The related functional pathways might open up new targets for therapeutic intervention.

Characterization and comparison of cardiomyocyte protection activities of non-starch polysaccharides from six ginseng root herbal medicines.

Ginseng is rich of polysaccharides, however, the evidence supporting polysaccharides to distinguish various ginseng species is rarely reported. Focusing on six root ginseng (e.g., Panax ginseng-PG, P. quinquefolius-PQ, P. notoginseng-PN, red ginseng-RG, P. japonicus-PJ, and P. japonicus var. major-PJM), the contained non-starch polysaccharides (NPs) were structurally characterized and compared by both the chemical and biological evaluation. Holistic fingerprinting at three levels (the NPs and the acid hydrolysates involving oligosaccharides and monosaccharides) utilized various chromatography methods, and the treatment of H9c2 cells with the NPs by OGD and H2O2-induced injury models was used to assess the protective effect. NPs from six Panax herbal medicines occupied about 20 % of the total polysaccharides, which were of the highest content in RG and the lowest in PN. NPs from six ginseng exhibited weak differentiations in the molecular weight distribution, while marker oligosaccharides were found to distinguish PN and RG from the others. Glc and GalA were more abundant in the NPs for PG and RG, respectively. NPs from PQ (100/200 µg/mL) showed significant cardiomyocyte protection effect by regulating the mitochondrial functions. This work further testifies the role of polysaccharides in quality control of herbal medicine, with new markers discovered beneficial to distinguish the ginseng.

CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data.

Antibody microarray data provides a powerful and high-throughput tool to monitor global changes in cellular response to perturbation or genetic manipulation. However, while collecting such data has become increasingly accessible, a lack of specific computational tools has made their analysis limited. Here we present CAT PETR, a user friendly web application for the differential analysis of expression and phosphorylation data collected via antibody microarrays. Our application addresses the limitations of other GUI based tools by providing various data input options and visualizations. To illustrate its capabilities on real data, we show that CAT PETR both replicates previous findings, and reveals additional insights, using its advanced visualization and statistical options.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

Molecular Dynamics Simulation of Self-Assembly Processes of Diphenylalanine Peptide Nanotubes and Determination of Their Chirality.

In this work, we further developed a new approach for modeling the processes of the self-assembly of complex molecular nanostructures using molecular dynamics methods; in particular, using a molecular dynamics manipulator. Previously, this approach was considered using the example of the self-assembly of a phenylalanine helical nanotube. Now, a new application of the algorithm has been developed for implementing a similar molecular dynamic self-assembly into helical structures of peptide nanotubes (PNTs) based on other peptide molecules-namely diphenylalanine (FF) molecules of different chirality L-FF and D-FF. In this work, helical nanotubes were assembled from linear sequences of FF molecules with these initially different chiralities. The chirality of the obtained nanotubes was calculated by various methods, including calculation by dipole moments. In addition, a statistical analysis of the results obtained was performed. A comparative analysis of the structures of nanotubes was also performed using the method of visual differential analysis. It was found that FF PNTs obtained by the MD self-assembly method form helical nanotubes of different chirality. The regimes that form nanotubes of right chirality D from initial L-FF dipeptides and nanotubes of left chirality L from D-FF dipeptides are revealed. This corresponds to the law of changing the sign of the chirality of molecular helical structures as the level of their hierarchical organization becomes more complicated.

Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.

Three-dimensional (3D) genome architecture is characterized by multi-scale patterns and plays an essential role in gene regulation. Chromatin conformation capturing experiments have revealed many properties underlying 3D genome architecture, such as the compartmentalization of chromatin based on transcriptional states. However, they are complex, costly and time consuming, and therefore only a limited number of cell types have been examined using these techniques. Increasing effort is being directed towards deriving computational methods that can predict chromatin conformation and associated structures. Here we present DNA-delay differential analysis (DDA), a purely sequence-based method based on chaos theory to predict genome-wide A and B compartments. We show that DNA-DDA models derived from a 20 Mb sequence are sufficient to predict genome wide compartmentalization at the scale of 100 kb in four different cell types. Although this is a proof-of-concept study, our method shows promise in elucidating the mechanisms responsible for genome folding as well as modeling the impact of genetic variation on 3D genome architecture and the processes regulated thereby.

Comparative analysis between high-grade serous ovarian cancer and healthy ovarian tissues using single-cell RNA sequencing.

Introduction: High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates. Methods: In this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer cells using single-cell RNA sequencing (ScRNA-seq). We delved deeper into the differences between healthy ovarian and ovarian cancer cells at different levels, and performed specific analysis on endothelial cells. Results: We obtained scRNA-seq data of 6867 and 17056 cells from healthy ovarian samples and ovarian cancer samples, respectively. The transcriptional profiles of the groups differed at various stages of ovarian cell development. A detailed comparison of the cell cycle, and cell communication of different groups, revealed significant differences between healthy ovarian and ovarian cancer cells. We also found that apoptosis-related genes, URI1, PAK2, PARP1, CLU and TIMP3, were highly expressed, while immune-related genes, UBB, RPL11, CAV1, NUPR1 and Hsp90ab1, were lowly expressed in ovarian cancer cells. The results of the ScRNA-seq were verified using qPCR. Discussion: Our findings revealed differences in function, gene expression and cell interaction patterns between ovarian cancer and healthy ovarian cell populations. These findings provide key insights on further research into the treatment of ovarian cancer.