SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Long-term efficacy of a recombinant hepatitis E vaccine in adults: 10-year results from a randomised, double-blind, placebo-controlled, phase 3 trial.

BACKGROUND: Hepatitis E virus (HEV) is a frequently overlooked causative agent of acute hepatitis. Evaluating the long-term durability of hepatitis E vaccine efficacy holds crucial importance. METHODS: This study was an extension to a randomised, double-blind, placebo-controlled, phase-3 clinical trial of the hepatitis E vaccine conducted in Dontai County, Jiangsu, China. Participants were recruited from 11 townships in Dongtai County. In the initial trial, a total of 112 604 healthy adults aged 16-65 years were enrolled, stratified according to age and sex, and randomly assigned in a 1:1 ratio to receive three doses of hepatitis E vaccine or placebo intramuscularly at month 0, month 1, and month 6. A sensitive hepatitis E surveillance system including 205 clinical sentinels, covering the entire study region, was established and maintained for 10 years after vaccination. The primary outcome was the per-protocol efficacy of hepatitis E virus vaccine to prevent confirmed hepatitis E occurring at least 30 days after administration of the third dose. Throughout the study, the participants, site investigators, and laboratory staff remained blinded to the treatment assignments. This study is registered with ClinicalTrials.gov (NCT01014845). FINDINGS: During the 10-year study period from Aug 22, 2007, to Oct 31, 2017, 90 people with hepatitis E were identified; 13 in the vaccine group (0·2 per 10 000 person-years) and 77 in the placebo group (1·4 per 10 000 person-years), corresponding to a vaccine efficacy of 83·1% (95% CI 69·4-91·4) in the modified intention-to-treat analysis and 86·6% (73·0 to 94·1) in the per-protocol analysis. In the subsets of participants assessed for immunogenicity persistence, of those who were seronegative at baseline and received three doses of hepatitis E vaccine, 254 (87·3%) of 291 vaccinees in Qindong at the 8·5-year mark and 1270 (73·0%) of 1740 vaccinees in Anfeng at the 7·5-year mark maintained detectable concentrations of antibodies. INTERPRETATION: Immunisation with this hepatitis E vaccine offers durable protection against hepatitis E for up to 10 years, with vaccine-induced antibodies against HEV persisting for at least 8·5 years. FUNDING: National Natural Science Foundation of China, Fujian Provincial Natural Science Foundation, Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, and the Fundamental Research Funds for the Central Universities.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Re-expression of epigenetically silenced PTPRR by histone acetylation sensitizes RAS-mutant lung adenocarcinoma to SHP2 inhibition.

Silenced protein tyrosine phosphatase receptor type R (PTPRR) participates in mitogen-activated protein kinase (MAPK) signaling cascades during the genesis and development of tumors. Rat sarcoma virus (Ras) genes are frequently mutated in lung adenocarcinoma, thereby resulting in hyperactivation of downstream MAPK signaling. However, the molecular mechanism manipulating the regulation and function of PTPRR in RAS-mutant lung adenocarcinoma is not known. Patient records collected from the Cancer Genome Atlas and Gene Expression Omnibus showed that silenced PTPRR was positively correlated with the prognosis. Exogenous expression of PTPRR suppressed the proliferation and migration of lung cancer cells. PTPRR expression and Src homology 2 containing protein tyrosine phosphatase 2 (SHP2) inhibition acted synergistically to control ERK1/2 phosphorylation in RAS-driven lung cancer cells. Chromatin immunoprecipitation assay revealed that HDAC inhibition induced enriched histone acetylation in the promoter region of PTPRR and recovered PTPRR transcription. The combination of the HDAC inhibitor SAHA and SHP2 inhibitor SHP099 suppressed the progression of lung cancer markedly in vitro and in vivo. Therefore, we revealed the epigenetic silencing mechanism of PTPRR and demonstrated that combination therapy targeting HDAC and SHP2 might represent a novel strategy to treat RAS-mutant lung cancer.

Color-Tunable Lead Halide Perovskite Single-Mode Chiral Microlasers with Exceptionally High glum.

Chiral microlasers hold great promise for optoelectronics from integrated photonic devices to high-density quantum information processing. Despite significant progress in lead-halide perovskite emitters, chiral lasing with high dissymmetry factors (glum) has not yet been realized. Here, we demonstrate chiral single-mode microlasers with exceptional stability and tunable emission across the visible range by combining CsPbClxBr3-x perovskite microrods (MRs) with a cholesteric liquid crystal (CLC) layer. The MRs lase via a whispering gallery mode (WGM) microcavity and confer chirality through the encapsulated CLC layer, thus exhibiting circularly polarized lasing with dissymmetry factors reaching 1.62. Importantly, we demonstrate wavelength-tunable high dissymmetry chiral lasers in a broad spectral range by tuning the halide composition and using CLC layers with the desired photonic bandgap (PBG). This facile approach to generate chiral lasing not only is applicable to semiconductor nano- and microcrystals but also paves the way for potential integration into nanoscale photonic devices.

Harnessing Smectic Ordering for Electric-Field-Driven Guided-Growth of Surface Topography in a Liquid Crystal Polymer.

The guided-growth strategy has been widely explored and proved its efficacy in fabricating surface micro/nanostructures in a variety of systems. However, soft materials like polymers are much less investigated partly due to the lack of strong internal driving mechanisms. Herein, the possibility of utilizing liquid crystal (LC) ordering of smectic liquid crystal polymers (LCPs) to induce guided growth of surface topography during the formation of electrohydrodynamic (EHD) patterns is demonstrated. In a two-stage growth, regular stripes are first found to selectively emerge from the homogeneously aligned region of an initially flat LCP film, and then extend neatly along the normal direction of the boundary line between homogeneous and homeotropic alignments. The stripes can maintain their directions for quite a distance before deviating. Coupled with the advanced tools for controlling LC alignment, intricate surface topographies can be produced in LCP films starting from relatively simple designs. The regularity of grown pattern is determined by the LC ordering of the polymer material, and influenced by conditions of EHD growth. The proposed approach provides new opportunities to employ LCPs in optical and electrical applications.

A Self-Assembled 3D/0D Quasi-Core-Shell Structure as Internal Encapsulation Layer for Stable and Efficient FAPbI3 Perovskite Solar Cells and Modules.

FAPbI3 perovskites have garnered considerable interest owing to their outstanding thermal stability, along with near-theoretical bandgap and efficiency. However, their inherent phase instability presents a substantial challenge to the long-term stability of devices. Herein, this issue through a dual-strategy of self-assembly 3D/0D quasi-core-shell structure is tackled as an internal encapsulation layer, and in situ introduction of excess PbI2 for surface and grain boundary defects passivating, therefore preventing moisture intrusion into FAPbI3 perovskite films. By utilizing this method alone, not only enhances the stability of the FAPbI3 film but also effectively passivates defects and minimizes non-radiative recombination, ultimately yielding a champion device efficiency of 23.23%. Furthermore, the devices own better moisture resistance, exhibiting a T80 lifetime exceeding 3500 h at 40% relative humidity (RH). Meanwhile, a 19.51% PCE of mini-module (5 × 5 cm2) is demonstrated. This research offers valuable insights and directions for the advancement of stable and highly efficient FAPbI3 perovskite solar cells.

A long-term experiment reveals no trade-off between seed persistence and seedling emergence.

Information on seed persistence and seedling emergence from the soil seed bank is critical for understanding species coexistence and predicting community dynamics. However, quantifying seed persistence in the soil is challenging; thus, its association with other life-history traits is poorly known on a broad scale. Using germination phenology for 349 species in a 42-yr experiment, we quantified the persistence-emergence correlations and their associations with intrinsic regeneration traits using Bayesian phylogenetic multilevel models. We showed no trade-off between seed persistence and seedling emergence. Physically dormant seeds were more persistent but exhibited lower emergence than nondormant seeds. Monocarpic species had both higher persistence and emergence than polycarpic species. Seed mass posed a marginal proxy for persistence, while emergence almost doubled from the smallest to the largest seeds. This study challenges the traditional assumption and is the first demonstration of noncorrelation between persistence and emergence, probably owing to the complexity of regenerative strategies. Species with short persistence and low emergence would be the most vulnerable for in situ conservation. Our analyses of this unique, long-term dataset provide a strong incentive for further experimental studies and a rich data resource for future syntheses.

Association between adult food insecurity and mortality among adults aged 20-79 years with diabetes: A population-based retrospective cohort study.

AIMS: There is limited research on the relationship between food insecurity and mortality among individuals with diabetes. This study aims to investigate the impact of food insecurity on all-cause and cause-specific mortality in adults with diabetes. RESEARCH DESIGN AND METHODS: This study included 5749 adults with diabetes from the National Health and Nutrition Examination Survey cycles 2003-2018 and followed up until 31 December 2019. Food insecurity was measured by the Food Security Survey Module. Cox proportional hazard models were employed to estimate hazard ratios (HRs) and 95% confidence intervals for both all-cause mortality and cause-specific mortality. RESULTS: The weighted prevalence of full food security, marginal food security, low food security, and very low food security was 70.8%, 11.0%, 10.4%, and 7.8%, respectively. Food insecurity demonstrated a significant correlation with diminished diet quality and reduced consumption of healthy foods. Over the course of 42,272.0 person-years of follow-up, we documented 1091 deaths, of which 370 were attributed to cardiovascular disease and 180 to cancer. After adjusting for multiple variables, food insecurity scores were significantly and linearly associated with increased all-cause mortality. Comparing to full food security, participants experiencing very low food security had a multivariate-adjusted HR of 1.48 (1.12, 1.95) for all-cause mortality (ptrend = 0.010). CONCLUSIONS: Food insecurity was associated with increased all-cause mortality and compromised diet quality, especially in individuals experiencing very low food security. Future strategies may necessitate the monitoring of and interventions for food insecurity among individuals with diabetes.

Systematic review of the osteogenic effect of rare earth nanomaterials and the underlying mechanisms.

Rare earth nanomaterials (RE NMs), which are based on rare earth elements, have emerged as remarkable biomaterials for use in bone regeneration. The effects of RE NMs on osteogenesis, such as promoting the osteogenic differentiation of mesenchymal stem cells, have been investigated. However, the contributions of the properties of RE NMs to bone regeneration and their interactions with various cell types during osteogenesis have not been reviewed. Here, we review the crucial roles of the physicochemical and biological properties of RE NMs and focus on their osteogenic mechanisms. RE NMs directly promote the proliferation, adhesion, migration, and osteogenic differentiation of mesenchymal stem cells. They also increase collagen secretion and mineralization to accelerate osteogenesis. Furthermore, RE NMs inhibit osteoclast formation and regulate the immune environment by modulating macrophages and promote angiogenesis by inducing hypoxia in endothelial cells. These effects create a microenvironment that is conducive to bone formation. This review will help researchers overcome current limitations to take full advantage of the osteogenic benefits of RE NMs and will suggest a potential approach for further osteogenesis research.

Proangiogenic effect and underlying mechanism of holmium oxide nanoparticles: a new biomaterial for tissue engineering.

BACKGROUND: Early angiogenesis provides nutrient supply for bone tissue repair, and insufficient angiogenesis will lead tissue engineering failure. Lanthanide metal nanoparticles (LM NPs) are the preferred materials for tissue engineering and can effectively promote angiogenesis. Holmium oxide nanoparticles (HNPs) are LM NPs with the function of bone tissue "tracking" labelling. Preliminary studies have shown that HNPs has potential of promote angiogenesis, but the specific role and mechanism remain unclear. This limits the biological application of HNPs. RESULTS: In this study, we confirmed that HNPs promoted early vessel formation, especially that of H-type vessels in vivo, thereby accelerating bone tissue repair. Moreover, HNPs promoted angiogenesis by increasing cell migration, which was mediated by filopodia extension in vitro. At the molecular level, HNPs interact with the membrane protein EphrinB2 in human umbilical vein endothelial cells (HUVECs), and phosphorylated EphrinB2 can bind and activate VAV2, which is an activator of the filopodia regulatory protein CDC42. When these three molecules were inhibited separately, angiogenesis was reduced. CONCLUSION: Overall, our study confirmed that HNPs increased cell migration to promote angiogenesis for the first time, which is beneficial for bone repair. The EphrinB2/VAV2/CDC42 signalling pathway regulates cell migration, which is an important target of angiogenesis. Thus, HNPs are a new candidate biomaterial for tissue engineering, providing new insights into their biological application.

The role of Neutrophil counts, infections and Smoking in mediating the Effect of Bronchiectasis on Chronic Obstructive Pulmonary Disease: a mendelian randomization study.

BACKGROUND: The causality of the relationship between bronchiectasis and chronic obstructive pulmonary disease (COPD) remains unclear. This study aims to investigate the potential causal relationship between them, with a specific focus on the role of airway inflammation, infections, smoking as the mediators in the development of COPD. METHODS: We conducted a two-sample Mendelian randomization (MR) analysis to assess: (1) the causal impact of bronchiectasis on COPD, sex, smoking status, infections, eosinophil and neutrophil counts, as well as the causal impact of COPD on bronchiectasis; (2) the causal effect of smoking status, infections and neutrophil counts on COPD; and (3) the extent to which the smoking status, infections and neutrophil counts might mediate any influence of bronchiectasis on the development of COPD. RESULTS: COPD was associated with a higher risk of bronchiectasis (OR 1.28 [95% CI 1.05, 1.56]). Bronchiectasis was associated with a higher risk of COPD (OR 1.08 [95% CI 1.04, 1.13]), higher levels of neutrophil (OR 1.01 [95% CI 1.00, 1.01]), higher risk of respiratory infections (OR 1.04 [95% CI 1.02, 1.06]) and lower risk of smoking. The causal associations of higher neutrophil cells, respiratory infections and smoking with higher COPD risk remained after performing sensitivity analyses that considered different models of horizontal pleiotropy, with OR 1.17, 1.69 and 95.13, respectively. The bronchiectasis-COPD effect was 0.99, 0.85 and 122.79 with genetic adjustment for neutrophils, respiratory infections and smoking. CONCLUSION: COPD and bronchiectasis are mutually causal. And increased neutrophil cell count and respiratory infections appears to mediate much of the effect of bronchiectasis on COPD.

Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia.

AIM: Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients. METHODS: Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR. RESULTS: The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data. CONCLUSION: HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.

Monomeric CXCL12-Engineered Adipose-Derived Stem Cells Transplantation for the Treatment of Ischemic Stroke.

Adipose-derived stem cells (ASCs) possess therapeutic potential for ischemic brain injury, and the chemokine CXCL12 has been shown to enhance their functional properties. However, the cumulative effects of ASCs when combined with various structures of CXCL12 on ischemic stroke and its underlying molecular mechanisms remain unclear. In this study, we genetically engineered mouse adipose-derived ASCs with CXCL12 variants and transplanted them to the infarct region in a mice transient middle cerebral artery occlusion (tMCAO) model of stroke. We subsequently compared the post-ischemic stroke efficacy of ASC-mCXCL12 with ASC-dCXCL12, ASC-wtCXCL12, and unmodified ASCs. Neurobehavior recovery was assessed using modified neurological severity scores, the hanging wire test, and the elevated body swing test. Changes at the tissue level were evaluated through cresyl violet and immunofluorescent staining, while molecular level alterations were examined via Western blot and real-time PCR. The results of the modified neurological severity score and cresyl violet staining indicated that both ASC-mCXCL12 and ASC-dCXCL12 treatment enhanced neurobehavioral recovery and mitigated brain atrophy at the third and fifth weeks post-tMCAO. Additionally, we observed that ASC-mCXCL12 and ASC-dCXCL12 promoted angiogenesis and neurogenesis, accompanied by an increased expression of bFGF and VEGF in the peri-infarct area of the brain. Notably, in the third week after tMCAO, the ASC-mCXCL12 exhibited superior outcomes compared to ASC-dCXCL12. However, when treated with the CXCR4 antagonist AMD3100, the beneficial effects of ASC-mCXCL12 were reversed. The AMD3100-treated group demonstrated worsened neurological function, aggravated edema volume, and brain atrophy. This outcome is likely attributed to the interaction of monomeric CXCL12 with CXCR4, which regulates the recruitment of bFGF and VEGF. This study introduces an innovative approach to enhance the therapeutic potential of ASCs in treating ischemic stroke by genetically engineering them with the monomeric structure of CXCL12.

Chemical Derivatization and Paper Spray Ionization Mass Spectrometry for Fast Screening of Retinoic Acid in Cosmetics.

As a prescription drug, retinoic acid is listed as a banned cosmetic additive in the EU and China regulations. Currently, spectrophotometric methods, including thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and HPLC-MS/MS, are commonly used for the determination of retinoic acid. As these conventional methods require complex pretreatment and are time-consuming, chemical derivatization combined with paper spray ionization mass spectrometry was developed for the fast detection of retinoic acid in cosmetics. N,N-dimethylpiperazine iodide (DMPI) was utilized as a derivatization reagent. Carboxylic acid in retinoic acid was derivatized to carry a positive charge and was subjected to mass spectrometry analysis. Results showed that compared with non-derivatized compounds, the detection limit was increased by about 50 times. The linearity in the range of 0.005-1 µg·mL-1 was good. The limit of detection (LOD) was 0.0013 µg·mL-1, and the limit of quantification (LOQ) was 0.0043 µg·mL-1. The recoveries of spiked samples were in the range of 95-105%, and the RSDs were below 5%. Derivatization and paper spray ionization MS render a quick, sensitive, and accurate method for the detection of retinoic acid in a complex matrix.

Relationship between interincisal angles and TMJ morphology/position and trabecular structure: a retrospective study.

BACKGROUND: TMJ morphology/position and trabecular structure are influenced by various factors. The role of the interincisal angle, an indicator of the anterior occlusal relationship, on TMJ remains unclear. This study aims to investigate the morphology, trabecular bone structure, and position of the condyle, as well as the glenoid fossa's morphology in skeletal class II populations with different interincisal angles. MATERIALS AND METHODS: A total of 150 adult patients with normodivergent facial types and skeletal class II malocclusions were selected and divided into three groups based on their interincisal angles: normal, small, and large angle groups. The indexes of TMJ were measured using cone-beam computed tomography (CBCT) data and analyzed using Dolphin Imaging, Mimics, and ImageJ. RESULTS: The small angle group had the smallest anteroposterior diameter (APD), while the large angle group had a greater mediolateral diameter (MLD). The large angle group exhibited significantly the largest maximum axial area, bone surface area, and bone volume (P < 0.05). Small and large angle groups exhibited greater bone trabeculae (Tb. N) and thinner trabecular thickness (Tb. Th). Compared to the normal angle group, the small angle group exhibited a larger horizontal condylar angle and smaller bilateral condylar angles on the axial plane, while the large angle group showed the opposite trend. Small and large angle groups showed a reduced vertical condyle angle on the coronal plane, with the largest reduction observed in the large angle group (P < 0.05). Small and large angle groups had higher heights of the glenoid fossa (GFH). The large angle group exhibited the greatest GFH and width of the glenoid fossa (GFW) (P < 0.05). CONCLUSION: The large angle group had elongated oval and large condyles, and deeper glenoid fossae, while a flattened-oval and smaller condyle, and wider and shallower glenoid fossae were observed in the small angle group. Small and large interincisal angle affects the structure of condylar trabeculae, resulting in thinner Tb. Th and greater Th. N. In the condylar position, small and large angle groups exhibit condylar rotation in the axial and coronal planes. Therefore, the interincisal angle affects the morphology, position, and trabecular structure of the TMJ. This implies that we must pay attention to the impact of the interincisal angle on TMJ, and it is crucial to restore the normal interincisal angle during orthodontic treatment.

Adaptive inverse mapping: a model-free semi-supervised learning approach towards robust imaging through dynamic scattering media.

Imaging through scattering media is a useful and yet demanding task since it involves solving for an inverse mapping from speckle images to object images. It becomes even more challenging when the scattering medium undergoes dynamic changes. Various approaches have been proposed in recent years. However, none of them are able to preserve high image quality without either assuming a finite number of sources for dynamic changes, assuming a thin scattering medium, or requiring access to both ends of the medium. In this paper, we propose an adaptive inverse mapping (AIP) method, which requires no prior knowledge of the dynamic change and only needs output speckle images after initialization. We show that the inverse mapping can be corrected through unsupervised learning if the output speckle images are followed closely. We test the AIP method on two numerical simulations: a dynamic scattering system formulated as an evolving transmission matrix and a telescope with a changing random phase mask at a defocused plane. Then we experimentally apply the AIP method to a multimode-fiber-based imaging system with a changing fiber configuration. Increased robustness in imaging is observed in all three cases. AIP method's high imaging performance demonstrates great potential in imaging through dynamic scattering media.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning.

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

ABLNCPP: Attention Mechanism-Based Bidirectional Long Short-Term Memory for Noncoding RNA Coding Potential Prediction.

With the continuous development of ribosome profiling, sequencing technology, and proteomics, evidence is mounting that noncoding RNA (ncRNA) may be a novel source of peptides or proteins. These peptides and proteins play crucial roles in inhibiting tumor progression and interfering with cancer metabolism and other essential physiological processes. Therefore, identifying ncRNAs with coding potential is vital to ncRNA functional research. However, existing studies perform well in classifying ncRNAs and mRNAs, and no research has been explicitly raised to distinguish whether ncRNA transcripts have coding potential. For this reason, we propose an attention mechanism-based bidirectional LSTM network called ABLNCPP to assess the coding possibility of ncRNA sequences. Considering the sequential information loss in previous methods, we introduce a novel nonoverlapping trinucleotide embedding (NOLTE) method for ncRNAs to obtain embeddings containing sequential features. The extensive evaluations show that ABLNCPP outperforms other state-of-the-art models. In general, ABLNCPP overcomes the bottleneck of ncRNA coding potential prediction and is expected to provide valuable contributions to cancer discovery and treatment in the future. The source code and data sets are freely available at https://github.com/YinggggJ/ABLNCPP.