Differential Brain Activation for Four Emotions in VR-2D and VR-3D Modes.

Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal-high pleasure (HAHV), high arousal-low pleasure (HALV), low arousal-low pleasure (LALV), and low arousal-high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation.

Translocation of vaginal and cervical low-abundance non-Lactobacillus bacteria notably associate with endometriosis: A pilot study.

The etiology remains to be understood for endometriosis (EMS) which affected health negatively for 10% of reproductive-age women globally. Emerging studies found the associations of EMS with genital microbiota dysbiosis. However, the role of vaginal and cervical microbiota is not fully understood for Chinese women. This study recruited forty Chinese women (21 healthy women and 19 EMS patients) to analyze vaginal and cervical microbiota using 16S rRNA amplicon sequencing method. For both sites, there were no significant differences for distribution of microbial samples between control and EMS group, which was concordant with dominated Lactobacillus in both groups. In contrast, we observed accumulation of several low-abundance genera in vaginal and cervical microbiota of EMS patients, such as Fannyhessea, Prevotella, Streptococcus, Bifidobacterium, Veillonella, Megasphaera and Sneathia. Random forest analysis found that translocation of these genera had the significant importance in differentiating EMS patients from controls. In addition, cervix/vagina ratio of these genera also associated with EMS severity. And these genera had notable associations with ascending infection-related functional pathways, including flagellar assembly, bacterial motility proteins, bacterial toxins and epithelial cell signaling in Helicobacter pylori infection. These findings suggest that translocation of specific genera between vaginal and cervical sites play a role in EMS.

Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders.

BACKGROUND AND OBJECTIVES: Neuromyelitis optica spectrum disorders (NMOSD) is widely recognized as a CNS demyelinating disease associated with AQP4-IgG (T cell-dependent antibody), and its trigger is still unclear. In addition, although the treatment of NMOSD currently can rely on traditional immunosuppressive and modulating agents, effective methods to predict the efficacy of these therapeutics are lacking. METHODS: In this study, high-throughput T-cell receptor (TCR) sequencing was performed on peripheral blood from 151 pretreatment patients with AQP4-IgG+ NMOSD and 151 healthy individuals. We compared the TCR repertoire of those with NMOSD with that of healthy individuals and identified TCR clones that were significantly enriched in NMOSD. In addition, we treated 28 patients with AQP4-IgG+ NMOSD with immunosuppressants and followed up for 6 months to compare changes in NMOSD-specific TCRs (NMOSD-TCRs) before and after treatment. Moreover, we analyzed transcriptome and single-cell B-cell receptor (BCR) data from public databases and performed T-cell activation experiments using antigenic epitopes of cytomegalovirus (CMV) to further explore the triggers of AQP4-IgG+ NMOSD. RESULTS: Compared with healthy controls, patients with AQP4-IgG+ NMOSD had significantly reduced diversity and shorter CDR3 length of TCRß repertoire. Furthermore, we identified 597 NMOSD-TCRs with a high sequence similarity that have the potential to be used in the diagnosis and prognosis of NMOSD. The characterization of NMOSD-TCRs and pathology-associated clonotype annotation indicated that the occurrence of AQP4-IgG+ NMOSD may be associated with CMV infection, which was further corroborated by transcriptome and single-cell BCR analysis results from public databases and T-cell activation experiments. DISCUSSION: Our findings suggest that the occurrence of AQP4-IgG+ NMOSD may be associated with CMV infection. In conclusion, our study provides new clues to uncover the causative factors of AQP4-IgG+ NMOSD and provides a theoretical foundation for treating and monitoring the disease.

On triangle inequalities of correlation-based distances for gene expression profiles.

BACKGROUND: Distance functions are fundamental for evaluating the differences between gene expression profiles. Such a function would output a low value if the profiles are strongly correlated-either negatively or positively-and vice versa. One popular distance function is the absolute correlation distance, [Formula: see text], where [Formula: see text] is similarity measure, such as Pearson or Spearman correlation. However, the absolute correlation distance fails to fulfill the triangle inequality, which would have guaranteed better performance at vector quantization, allowed fast data localization, as well as accelerated data clustering. RESULTS: In this work, we propose [Formula: see text] as an alternative. We prove that [Formula: see text] satisfies the triangle inequality when [Formula: see text] represents Pearson correlation, Spearman correlation, or Cosine similarity. We show [Formula: see text] to be better than [Formula: see text], another variant of [Formula: see text] that satisfies the triangle inequality, both analytically as well as experimentally. We empirically compared [Formula: see text] with [Formula: see text] in gene clustering and sample clustering experiment by real-world biological data. The two distances performed similarly in both gene clustering and sample clustering in hierarchical clustering and PAM (partitioning around medoids) clustering. However, [Formula: see text] demonstrated more robust clustering. According to the bootstrap experiment, [Formula: see text] generated more robust sample pair partition more frequently (P-value [Formula: see text]). The statistics on the time a class "dissolved" also support the advantage of [Formula: see text] in robustness. CONCLUSION: [Formula: see text], as a variant of absolute correlation distance, satisfies the triangle inequality and is capable for more robust clustering.

TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization.

Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.

Manganese-coordinated mRNA vaccines with enhanced mRNA expression and immunogenicity induce robust immune responses against SARS-CoV-2 variants.

It is urgent to develop more effective mRNA vaccines against the emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants owing to the immune escape. Here, we constructed a novel mRNA delivery system [IC8/Mn lipid nanoparticles (IC8/Mn LNPs)]with high immunogenicity, via introducing a stimulator of interferon genes (STING) agonist [manganese (Mn)] based on a newly synthesized ionizable lipid (IC8). It was found that Mn can not only promote maturation of antigen-presenting cells via activating STING pathway but also improve mRNA expression by facilitating lysosomal escape for the first time. Subsequently, IC8/Mn LNPs loaded with mRNA encoding the Spike protein of SARS-CoV-2 Delta or Omicron variant (IC8/Mn@D or IC8/Mn@O) were prepared. Both mRNA vaccines induced substantial specific immunoglobulin G responses against Delta or Omicron. IC8/Mn@D displayed strong pseudovirus neutralization ability, T helper 1-biased immune responses, and good safety. It can be concluded that IC8/Mn LNPs have great potential for developing Mn-coordinated mRNA vaccines with robust immunogenicity and good safety.

Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis.

Tumor-infiltrating lymphocytes (TILs), especially CD8+ TILs, represent a favorable prognostic factor in high-grade serous ovarian cancer (HGSOC) and other tumor lineages. Here, we analyze the spatial heterogeneity of different TIL subtypes in HGSOC. We integrated RNA sequencing, whole-genome sequencing, bulk T cell receptor (TCR) sequencing, as well as single-cell RNA/TCR sequencing to investigate the characteristics and differential composition of TILs across different HGSOC sites. Two immune "cold" patterns in ovarian cancer are identified: (1) ovarian lesions with low infiltration of mainly dysfunctional T cells and immunosuppressive Treg cells and (2) omental lesions infiltrated with non-tumor-specific bystander cells. Exhausted CD8 T cells that are preferentially enriched in ovarian tumors exhibit evidence for expansion and cytotoxic activity. Inherent tumor immune microenvironment characteristics appear to be the main contributor to the spatial differences in TIL status. The landscape of spatial heterogeneity of TILs may inform potential strategies for therapeutic manipulation in HGSOC.

eSMC: a statistical model to infer admixture events from individual genomics data.

BACKGROUND: Inferring historical population admixture events yield essential insights in understanding a species demographic history. Methods are available to infer admixture events in demographic history with extant genetic data from multiple sources. Due to the deficiency in ancient population genetic data, there lacks a method for admixture inference from a single source. Pairwise Sequentially Markovian Coalescent (PSMC) estimates the historical effective population size from lineage genomes of a single individual, based on the distribution of the most recent common ancestor between the diploid's alleles. However, PSMC does not infer the admixture event. RESULTS: Here, we proposed eSMC, an extended PSMC model for admixture inference from a single source. We evaluated our model's performance on both in silico data and real data. We simulated population admixture events at an admixture time range from 5 kya to 100 kya (5 years/generation) with population admix ratio at 1:1, 2:1, 3:1, and 4:1, respectively. The root means the square error is [Formula: see text] kya for all experiments. Then we implemented our method to infer the historical admixture events in human, donkey and goat populations. The estimated admixture time for both Han and Tibetan individuals range from 60 kya to 80 kya (25 years/generation), while the estimated admixture time for the domesticated donkeys and the goats ranged from 40 kya to 60 kya (8 years/generation) and 40 kya to 100 kya (6 years/generation), respectively. The estimated admixture times were concordance to the time that domestication occurred in human history. CONCLUSION: Our eSMC effectively infers the time of the most recent admixture event in history from a single individual's genomics data. The source code of eSMC is hosted at https://github.com/zachary-zzc/eSMC .

Human papillomavirus integration perspective in small cell cervical carcinoma.

Small cell cervical carcinoma (SCCC) is a rare but aggressive malignancy. Here, we report human papillomavirus features and genomic landscape in SCCC via high-throughput HPV captured sequencing, whole-genome sequencing, whole-transcriptome sequencing, and OncoScan microarrays. HPV18 infections and integrations are commonly detected. Besides MYC family genes (37.9%), we identify SOX (8.4%), NR4A (6.3%), ANKRD (7.4%), and CEA (3.2%) family genes as HPV-integrated hotspots. We construct the genomic local haplotype around HPV-integrated sites, and find tandem duplications and amplified HPV long control regions (LCR). We propose three prominent HPV integration patterns: duplicating oncogenes (MYCN, MYC, and NR4A2), forming fusions (FGFR3-TACC3 and ANKRD12-NDUFV2), and activating genes (MYC) via the cis-regulations of viral LCRs. Moreover, focal CNA amplification peaks harbor canonical cancer genes including the HPV-integrated hotspots within MYC family, SOX2, and others. Our findings may provide potential molecular criteria for the accurate diagnosis and efficacious therapies for this lethal disease.

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs.

BACKGROUND: Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. RESULT: Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3-11 (children), 12-18 (teens), 19-25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. CONCLUSIONS: We introduced a novel deep learning approach DENSEN to estimate a subject's age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.

Characterization of somatic structural variations in 528 Chinese individuals with Esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) demonstrates high genome instability. Here, we analyze 528 whole genomes to investigate structural variations' mechanisms and biological functions. SVs show multi-mode distributions in size, indicating distinct mutational processes. We develop a tool and define five types of complex rearrangements with templated insertions. We highlight a type of fold-back inversion, which is associated with poor outcomes. Distinct rearrangement signatures demonstrate variable genomic metrics such as replicating time, spatial proximity, and chromatin accessibility. Specifically, fold-back inversion tends to occur near the centrosome; TD-c2 (Tandem duplication-cluster2) is significantly enriched in chromatin-accessibility and early-replication region compared to other signatures. Analyses of TD-c2 signature reveal 9 TD hotspots, of which we identify a hotspot consisting of a super-enhancer of PTHLH. We confirm the oncogenic effect of the PTHLH gene and its interaction with enhancers through functional experiments. Finally, extrachromosomal circular DNAs (ecDNAs) are present in 14% of ESCCs and have strong selective advantages to driver genes.

The association of cervicovaginal Langerhans cells with clearance of human papillomavirus.

Human papillomavirus (HPV) clearance is important in eliminating cervical cancer which contributes to high morbidity and mortality in women. Nevertheless, it remains largely unknown about key players in clearing pre-existing HPV infections. HPV antigens can be detected by the most important cervical antigen-presenting cells (Langerhans cells, LCs), of which the activities can be affected by cervicovaginal microbiota. In this review, we first introduce persistent HPV infections and then describe HPV-suppressed LCs activities, including but not limited to antigen uptake and presentation. Given specific transcriptional profiling of LCs in cervical epithelium, we also discuss the impact of cervicovaginal microbiota on LCs activation as well as the promise of exploring key microbial players in activating LCs and HPV-specific cellular immunity.

Nucleophilicity Prediction Using Graph Neural Networks.

The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important representatives. However, the determination of Mayr's nucleophilicity parameter N often requires time-consuming experiments with reference electrophiles in the solvent. Several machine learning (ML)-based models have been proposed to realize the data-driven prediction of N in recent years. However, in addition to DFT-calculated electronic descriptors, most of them also use a set of artificially predefined structural descriptors as input, which may result in a biased representation of the nucleophile's structural information depending on descriptors' definition preference. Compared with traditional ML algorithms, graph neural networks (GNNs) can naturally take the molecule's structural information into account by applying the message passing technique. We herein proposed a SchNet-based GNN model that only takes the molecular conformation and solvent type as input. The model achieves a comparable performance to the previous benchmark study on 10-fold cross-validation of 894 data points (R2 = 0.91, RMSE = 2.25). To enhance the model's ability to capture the molecule's electronic information, some DFT-calculated parameters are then incorporated into the model via graph global features, and substantial improvement is achieved in the prediction precision (R2 = 0.95, RMSE = 1.63). These results demonstrate that both structural and electronic information are important for the prediction of N, and GNN can integrate these two kinds of information more effectively.

Profiling the peripheral blood T cell receptor repertoires of gastric cancer patients.

Cancer driven by somatic mutations may express neoantigens that can trigger T-cell immune responses. Since T-cell receptor (TCR) repertoires play critical roles in anti-tumor immune responses for oncology, next-generation sequencing (NGS) was used to profile the hypervariable complementarity-determining region 3 (CDR3) of the TCR-beta chain in peripheral blood samples from 68 gastric cancer patients and 49 healthy controls. We found that most hyper-expanded CDR3 are individual-specific, and the gene usage of TRBV3-1 is more frequent in the tumor group regardless of tumor stage than in the healthy control group. We identified 374 hyper-expanded tumor-specific CDR3, which may play a vital role in anti-tumor immune responses. The patients with stage IV gastric cancer have higher EBV-specific CDR3 abundance than the control. In conclusion, analysis of the peripheral blood TCR repertoires may provide the biomarker for gastric cancer prognosis and guide future immunotherapy.

Mining bacterial NGS data vastly expands the complete genomes of temperate phages.

Temperate phages (active prophages induced from bacteria) help control pathogenicity, modulate community structure, and maintain gut homeostasis. Complete phage genome sequences are indispensable for understanding phage biology. Traditional plaque techniques are inapplicable to temperate phages due to their lysogenicity, curbing their identification and characterization. Existing bioinformatics tools for prophage prediction usually fail to detect accurate and complete temperate phage genomes. This study proposes a novel computational temperate phage detection method (TemPhD) mining both the integrated active prophages and their spontaneously induced forms (temperate phages) from next-generation sequencing raw data. Applying the method to the available dataset resulted in 192 326 complete temperate phage genomes with different host species, expanding the existing number of complete temperate phage genomes by more than 100-fold. The wet-lab experiments demonstrated that TemPhD can accurately determine the complete genome sequences of the temperate phages, with exact flanking sites, outperforming other state-of-the-art prophage prediction methods. Our analysis indicates that temperate phages are likely to function in the microbial evolution by (i) cross-infecting different bacterial host species; (ii) transferring antibiotic resistance and virulence genes and (iii) interacting with hosts through restriction-modification and CRISPR/anti-CRISPR systems. This work provides a comprehensively complete temperate phage genome database and relevant information, which can serve as a valuable resource for phage research.

Cervicovaginal microbiota significantly changed for HPV-positive women with high-grade squamous intraepithelial lesion.

Lower female genital tract is colonized by a variety of microbes (cervicovaginal microbiota, CVM) which associate with the risk of genital infection. This study characterized CVM for 149 Chinese women with different status of human papillomavirus (HPV) infection and squamous intraepithelial lesion (SIL): no HPV infection (HPV-), HPV infection without significant SIL (HPV+NoSIL), HPV infection with low-grade SIL (HPV+LSIL) and HPV infection with high-grade SIL (HPV+HSIL). Analysis results showed CVM has dramatically changed in HPV+HSIL group when compared to HPV+LSIL group, but it exhibited no significant differences between HPV- and HPV+NoSIL groups as well as between HPV+NoSIL and HPV+LSIL groups. In consistence, random forest analysis found more notable differences in HPV+HSIL vs HPV+LSIL comparison than in other comparisons. In addition, depletion of Lactobacillus in CVM was more to be frequently identified in SIL-positive women as compared to SIL-negative individuals. Our findings suggested that significant CVM differences occurred when SIL developed to HSIL which was caused by persistent HPV infection.