Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning.

In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method. Availability: The proposed method can be freely accessible at https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP. Data will be made available on request.

Interactive gene identification for cancer subtyping based on multi-omics clustering.

Recent advances in multi-omics databases offer the opportunity to explore complex systems of cancers across hierarchical biological levels. Some methods have been proposed to identify the genes that play a vital role in disease development by integrating multi-omics. However, the existing methods identify the related genes separately, neglecting the gene interactions that are related to the multigenic disease. In this study, we develop a learning framework to identify the interactive genes based on multi-omics data including gene expression. Firstly, we integrate different omics based on their similarities and apply spectral clustering for cancer subtype identification. Then, a gene co-expression network is construct for each cancer subtype. Finally, we detect the interactive genes in the co-expression network by learning the dense subgraphs based on the L1 prosperities of eigenvectors in the modularity matrix. We apply the proposed learning framework on a multi-omics cancer dataset to identify the interactive genes for each cancer subtype. The detected genes are examined by DAVID and KEGG tools for systematic gene ontology enrichment analysis. The analysis results show that the detected genes have relationships to cancer development and the genes in different cancer subtypes are related to different biological processes and pathways, which are expected to yield important references for understanding tumor heterogeneity and improving patient survival.

An M cell-targeting recombinant L. lactis vaccine against four H. pylori adhesins.

The acidic environment and enzyme degradation lead to oral vaccines often having little immune effect. Therefore, it is an attractive strategy to study an effective and safe oral vaccine delivery system that can promote gastrointestinal mucosal immune responses and inhibit antigen degradation. Moreover, the antigens uptake by microfold cells (M cells) is the determining step in initiating efficient immune responses. Therefore, M cell-targeting is one promising approach for enhancing oral vaccine potency. In the present study, an M cell-targeting L. lactis surface display system (plSAM) was built to favor the multivalent epitope vaccine antigen (FAdE) to achieve effective gastrointestinal mucosal immunity against Helicobacter pylori. Therefore, a recombinant Lactococcus lactic acid vaccine (LL-plSAM-FAdE) was successfully prepared, and its immunological properties and protective efficacy were analyzed. The results showed that LL-plSAM-FAdE can secretively express the recombinant proteins SAM-FAdE and display the SAM-FAdE on the bacterial cell surface. More importantly, LL-plSAM-FAdE effectively promoted the phagocytosis and transport of vaccine antigen by M cells in the gastrointestinal tract of mice, and simulated high levels of cellular and humoral immune responses against four key H. pylori adhesins (Urease, CagL, HpaA, and Lpp20) in the gastrointestinal tract, thus enabling effective prevention of H. pylori infection and to some extent eliminating H. pylori already present in the gastrointestinal tract. KEY POINTS: • M-cell-targeting L. lactis surface display system LL- plSAM was designed • This system displays H. pylori vaccine-promoted phagocytosis and transport of M cell • A promising vaccine candidate for controlling H. pylori infection was verified.

Biodiversity-safeguarding threshold for urea-fertilizer application on paddy fields: Protozoa-based toxicity tests.

Urea is a widely applied fertilizer to enhance crop yields. Ecological risks associated with the excessive application of urea fertilizer threaten the paddy fields' sustainable agriculture and biodiversity preservation. There are no practical thresholds based on proven data on microbial communities. Protozoa are nitrogen-sensitive organisms. For the first time, this study conducted acute and chronic urea toxicity tests on eight species of organisms. The results indicate that Blepharisma sp. is the most sensitive species to urea exposure and is a suitable indicator for determining the safe threshold of urea. This study estimated the predicted no-effect concentration using species sensitivity distribution curves. Subsequently, it established the threshold for urea application in rice fields based on the fields' area and the surface water's height. The short-term safety threshold for urea in the studied paddy field with black soil is 87.7 mg/L, equivalent to 43.85 kg of urea per hectare for a single nitrogen fertilizer application. The long-term safety threshold is 5.02 mg/L, representing the concentration for re-applicating urea. The biodiversity-safeguarding application threshold provides the basis for developing a urea fertilizer reduction protocol to safeguard the paddy fields' biodiversity.

Identification and Molecular Characterization of a Pellino Protein in Kuruma Prawn (Marsupenaeus Japonicus) in Response to White Spot Syndrome Virus and Vibrio Parahaemolyticus Infection.

Kuruma prawn, Marsupenaeus japonicus, has the third largest annual yield among shrimp species with vital economic significance in China. White spot syndrome virus (WSSV) is a great threat to the global shrimp farming industry and results in high mortality. Pellino, a highly conserved E3 ubiquitin ligase, has been found to be an important modulator of the Toll-like receptor (TLR) signaling pathways that participate in the innate immune response and ubiquitination. In the present study, the Pellino gene from Marsupenaeus japonicus was identified. A qRT-PCR assay showed the presence of MjPellino in all the tested tissues and revealed that the transcript level of this gene was significantly upregulated in both the gills and hemocytes after challenge with WSSV and Vibrio parahaemolyticus. The function of MjPellino was further verified at the protein level. The results of the three-dimensional modeling and protein-protein docking analyses and a GST pull-down assay revealed that the MjPellino protein was able to bind to the WSSV envelope protein VP26. In addition, the knockdown of MjPellino in vivo significantly decreased the expression of MjAMPs. These results suggest that MjPellino might play an important role in the immune response of kuruma prawn.

Overexpression of CDCA7 predicts poor prognosis and induces EZH2-mediated progression of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with high proliferative and metastatic phenotypes. CDCA7, a new member of the cell division cycle associated family of genes, is involved in embryonic development and dysregulated in various types of human cancer. However, the biological role and molecular mechanism of CDCA7 in TNBC have not been defined. Herein, we found that CDCA7 was preferentially and markedly expressed in TNBC cell lines and tissues. High expression of CDCA7 was associated with metastatic relapse status and predicted poorer disease-free survival in patients with TNBC. We observed that CDCA7 silencing in TNBC cell lines effectively impaired cell proliferation, invasion and migration in vitro. Importantly, depletion of CDCA7 strongly reduced the tumorigenicity and distant colonization capacities of TNBC cells in vivo. Furthermore, CDCA7 increased the expression of EZH2, a marker of aggressive breast cancer that is involved in tumor progression, by enhancing the transcriptional activity of its promoter. This increase in EZH2 expression was essential for the CDCA7-mediated effects on TNBC progression. Finally, our immunohistochemical analysis revealed that the CDCA7/EZH2 axis was clinical relevant. These findings suggest CDCA7 plays a crucial role in TNBC progression by transcriptionally upregulating EZH2 and might be a potential prognostic factor and therapeutic target in TNBC.

A strategy for photothermal conversion of polymeric nanoparticles by polyaniline for smart control of targeted drug delivery.

The near-infrared (NIR)-mediated novel strategy to control the drug release from nanocarriers has developed rapidly in recent decades. Polyaniline as a non-cytotoxic and electroactive material for studying cellular proliferation has attracted great attention in recent years. In the present work, polyaniline-mediated polymeric nanoparticles were developed to target the delivery of cisplatin and release it in a controllable way. The prepared polyaniline nanoparticles displayed a size of 90 ± 1.0 nm, a favorable morphology in water, and could be targeted to tumors through the high affinity between trastuzumab and the overexpressed Her2 in tumor cells. In addition, the developed nanoparticles demonstrated exciting photothermal conversion efficiency induced by NIR light and achieved significant cell inhibition efficiency (93.97%) in vitro when exposed to an 808 nm NIR laser with the power of 1.54 W for 5 min. Therefore, the developed external control release delivery system with excellent specificity and high cytotoxicity exhibited great potential in cell research and our research demonstrated that the polyaniline also has potential in the application of photothermal conversion in biomedicine.

Folate-decorated and NIR-activated nanoparticles based on platinum(IV) prodrugs for targeted therapy of ovarian cancer.

Platinum-based drugs are used to treat a variety of cancers but have many side effects such as nephrotoxicity and neurotoxicity. A folate-decorated nanoparticles system with a good drug payload can selectively deliver drugs into folate receptor (FR)-overexpressing cancer cells to prevent the shortcomings of platinum-based chemotherapy. Here, folate-decorated and near-infrared (NIR) laser-activated nanoparticles (abbreviated as PtIV-FINPs) were prepared via ultrasonic self-assembling of platinum(IV) prodrug c,c,t-Pt(NH3)2Cl2(OOCCH2CH2COOH)2, folic acid (FA)-functionalized lipid DSPE-PEG-FA and NIR fluorescent dye indocyanine green (ICG). The obtained PtIV-FINPs had almost spherical shape with a mean diameter about 100 nm. In vitro cellular uptake, cytotoxicity assays revealed that upon NIR irradiation, PtIV-FINPs further enhanced cellular uptake and generated higher cytotoxicity against human ovarian carcinoma SKOV3 cells than non-targeted or non-NIR activated nanoparticles. Thus, the multifunctional nanoparticles have potential to be developed as an attractive drug delivery system for effective chemotherapy against FR-overexpressing cells.

Crosstalk within and beyond the Polycomb repressive system.

The development of multicellular organisms depends on spatiotemporally controlled differentiation of numerous cell types and their maintenance. To generate such diversity based on the invariant genetic information stored in DNA, epigenetic mechanisms, which are heritable changes in gene function that do not involve alterations to the underlying DNA sequence, are required to establish and maintain unique gene expression programs. Polycomb repressive complexes represent a paradigm of epigenetic regulation of developmentally regulated genes, and the roles of these complexes as well as the epigenetic marks they deposit, namely H3K27me3 and H2AK119ub, have been extensively studied. However, an emerging theme from recent studies is that not only the autonomous functions of the Polycomb repressive system, but also crosstalks of Polycomb with other epigenetic modifications, are important for gene regulation. In this review, we summarize how these crosstalk mechanisms have improved our understanding of Polycomb biology and how such knowledge could help with the design of cancer treatments that target the dysregulated epigenome.

Effect of internal fractures on mechanical properties and failure of sandstone under multi-physical fields.

Under deep mining conditions, rocks are subjected to complex multi-physical fields and can contain numerous pores and fractures. To explore the influence and correlation of these factors on the physical and mechanical properties of fractured rock samples, this study conducted triaxial compression tests on sandstone specimens under various physical conditions using a rock full stress multi-field coupling triaxial tester. Additionally, a random fracture model for multi-field coupling numerical simulation was established. This allowed the study to obtain the mechanical parameters, failure mode, and internal fracture development of rocks under multi-physical field conditions. By analyzing the complete stress-strain curve, mechanical characteristic points, and permeability, a combination of laboratory tests and numerical simulations was used to examine how temperature, seepage, and stress fields affect the development of pores and fractures in rocks. It was found that the temperature field, under conventional geothermal conditions, generates tensile force through thermal expansion and the presence of fluid, thereby promoting fracture development within the rocks. This mechanism is similar to that of seepage. The confining pressure caused by deep geo stress uniformly inhibits the expansion of pores and fissures within the rocks.

Local Second Order MoÌ·ller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: A Distributed Memory Implementation.

In order to alleviate the computational burden associated with superlinear compute scalings with molecular size in electron correlation methods, researchers have developed local correlation methods that wisely treat relatively small contributions as zeros but still yield accurate energy approximation. Such local correlation techniques can also be combined with parallel computing resources to obtain further efficiency and scalability. This work focuses on the distributed memory parallel implementation of a local correlation method for second order MoÌ·ller-Plesset (MP2) theory. This method also only has a single threshold to control the dropping of terms and accuracy of different computing kernels in the algorithm. The process partitioning strategy and distributed parallel implementation with the message passing interface (MPI) are discussed. In particular, the algorithm relies on a fixed sparsity pattern matrix multiplication and a corresponding distributed conjugate gradient solver, which exhibits almost linear scaling in both strong and weak scaling analyses. Numerical experiments on a range of molecules, including linear chains and molecules with 2 and 3-dimensional characters, are reported. For example, with only 32 MPI ranks, this MP2 implementation can calculate the correlation energy of vancomycin in def2-TZVP basis within 0.003% accuracy (10-6.5 threshold) in half an hour, where the same problem is unfeasible to solve with sequential or pure shared memory implementations.

ß-synuclein regulates the phase transitions and amyloid conversion of α-synuclein.

Parkinson's disease (PD) and Dementia with Lewy Bodies (DLB) are neurodegenerative disorders characterized by the accumulation of α-synuclein aggregates. α-synuclein forms droplets via liquid-liquid phase separation (LLPS), followed by liquid-solid phase separation (LSPS) to form amyloids, how this process is physiologically-regulated remains unclear. ß-synuclein colocalizes with α-synuclein in presynaptic terminals. Here, we report that ß-synuclein partitions into α-synuclein condensates promotes the LLPS, and slows down LSPS of α-synuclein, while disease-associated ß-synuclein mutations lose these capacities. Exogenous ß-synuclein improves the movement defects and prolongs the lifespan of an α-synuclein-expressing NL5901 Caenorhabditis elegans strain, while disease-associated ß-synuclein mutants aggravate the symptoms. Decapeptides targeted at the α-/ß-synuclein interaction sites are rationally designed, which suppress the LSPS of α-synuclein, rescue the movement defects, and prolong the lifespan of C. elegans NL5901. Together, we unveil a Yin-Yang balance between α- and ß-synuclein underlying the normal and disease states of PD and DLB with therapeutical potentials.

Multi-omics clustering for cancer subtyping based on latent subspace learning.

The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.

Sex-specific regulation of the cortical transcriptome in response to sleep deprivation.

Multiple studies have documented sex differences in sleep behaviour, however, the molecular determinants of such differences remain unknown. Furthermore, most studies addressing molecular mechanisms have been performed only in males, leaving the current state of knowledge biased towards the male sex. To address this, we studied the differences in the transcriptome of the cerebral cortex of male and female C57Bl/6 J mice after 6 h of sleep deprivation. We found that several genes, including the neurotrophin growth factor Bdnf, immediate early genes Fosb and Fosl2, and the adenylate cyclase Adcy7 are differentially upregulated in males compared to females. We identified the androgen-receptor activating transcription factor EZH2 as the upstream regulatory element specifying sex differences in the sleep deprivation transcriptome. We propose that the pathways downstream of these transcripts, which impact on cellular re-organisation, synaptic signalling, and learning may underpin the differential response to sleep deprivation in the two sexes.

Local Second-Order Møller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: Theory, Implementation, and Assessment.

It has long been clear that electron correlation methods exhibit unphysical compute scalings with molecular size, which has motivated the development of local correlation methods to discard effectively zero contributions in a controlled way to yield an approximate correlation energy. The ideal local correlation method should have a single numerical threshold that controls the dropping of terms with the ability to have that threshold set small enough so that the correlation energy is reproduced to enough significant figures such that the result is chemically identical. This work reports such a method for the second-order Møller-Plesset (MP2) theory. The theory, implementation, and testing of this local MP2 theory are reported. Thresholds ranging from 10-5 to 10-8 and basis sets ranging from split valence plus polarization through to quadruple-ζ are assessed for local MP2 calculations on a range of molecules, including linear chains and molecules with two- and three-dimensional character. The implementation is shared memory parallel via OpenMP and yields roughly 50% parallel efficiency with 16 cores for a large job. Considerable efforts were made to minimize memory demands, which increased as thresholds were tightened. A variety of relative energy calculations are presented as a function of threshold to provide some guidance to users on how to obtain adequate precision at a low compute cost. It is particularly clear that derivative properties require tighter thresholds in order to achieve an adequate precision.

Toxicological sensitivity of protozoa to pesticides and nanomaterials: A prospect review.

Protozoa are sensitive indicators of pollutant toxicity. This review presents and discusses the toxicological studies of protozoa and the toxicological conventional test species (Daphnia magna) by pesticides and nanomaterials, particularly comparing the sensitivity of through relative tolerance analysis, Z-score, and species sensitivity index. The sensitivity of different species of protozoa varies greatly. The protozoa Paramecium sp. and Tetrahymena sp. are not sensitive species; conversely, Urostyla sp. is sensitive to dimethoate and nanomaterials Ag-NPs, respectively ZnO-NPs, and CuO-NPs, fits the use as an indicator species on these substances. The prospects to explore scientific toxicity exposure protocols, expand the protozoan species examined, and screen the sensitive species under the protocols are discussed. This prospect review advances the knowledge for including the sensitive protozoa as an indicator species in comprehensive toxicological analysis for pesticides and nanomaterials.

SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder.

OBJECTIVE: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. METHODS: In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. RESULTS: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. CONCLUSION: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. SIGNIFICANCE: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation.

Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two of these aspects. In this paper, we present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach. Specifically, we compute a multiscale affinity field for each pixel, capturing its semantic relationships with neighboring pixels in the predicted mask image. This field represents the local geometry of vessel segments of different sizes, allowing us to learn spatial- and scale-aware adaptive weights to strengthen vessel features. We evaluate our AFN on four different types of vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV), digital subtraction angiography cerebrovascular vessel dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental results demonstrate that our AFN outperforms the state-of-the-art methods in terms of both higher accuracy and topological metrics, while also being more robust to various contrast changes.

Electrical Synchrony Optimization for Left Bundle Branch Area Pacing in Patients With Bradycardia and Heart Failure.

Left bundle branch area pacing (LBBAP) has emerged as a promising physiological pacing modality. This study was designed to investigate the acute impact of the atrioventricular delay (AVD) on cardiac electrical characteristics and identify an optimal range of AVDs for LBBAP to achieve electrical atrioventricular and interventricular synchrony. Patients indicated for ventricular or biventricular pacing were studied during routine follow-ups at least 3 months after LBBAP implantation. Patients were excluded if they had a complete AV block or persistent atrial fibrillation. AVD was programed from 40 to 240 ms or until intrinsic conduction occurred. Optimal AVD was determined by the electrocardiography criteria, including QRS duration, reduced R-wave in lead V1, reduced notching or slurring in lateral leads, and more desirable precordial QRS transition. A total of 38 patients (age 68.7 ± 10.3 years; 16 male (42%); 18 dual-chamber pacemakers and 20 cardiac resynchronization therapy devices; average follow-up period 15.1 ± 10.2 months) were included. The fusion of LBBAP and intrinsic right ventricular conduction occurred in 21 patients with corresponding optimal AVD determined. A great proportion (∼85%) of the optimal AVDs ranged from 50% to 80% of the observed atrium-to-left bundle branch-sensing (A-LBBS) intervals. The linear correlation between the optimal AVD and corresponding A-LBBS interval (optimal AVD = 0.84 × [A-LBSs interval] - 36 ms) produced R = 0.86 and p <0.0001. In conclusion, AVD selection during LBBAP greatly impacted the ventricular electrical characteristics and the optimal AVD was linearly correlated with the corresponding A-LBBS interval.

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation.

Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://github.com/TY-Shi/LIOT.