Demographic and Clinical characteristic of 57 patients with aspiration-related deaths.

<p>Demographic and Clinical characteristic of 57 patients with aspiration-related deaths.</p

Undiagnosed Aspiration Cases (n = 19): Comparison of Postmortem and Clinician Diagnoses.

<p>ALS = amyotrophic lateral sclerosis.</p

Precipitating factors in 57 aspiration related deaths.

<p>*Compromised airway defense consisted of vocal cord immobility, oropharyngeal deformities caused by surgery or radiation therapy, and endotracheal intubation.</p

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

Unsupervised Full-color Cellular Image Reconstruction through Disordered Optical Fiber

Recent years have witnessed the tremendous development of fusing fiber-optic imaging with supervised deep learning to enable high-quality imaging of hard-to-reach areas. Nevertheless, the supervised deep learning method imposes strict constraints on fiber-optic imaging systems, where the input objects and the fiber outputs have to be collected in pairs. To unleash the full potential of fiber-optic imaging, unsupervised image reconstruction is in demand. Unfortunately, neither optical fiber bundles nor multimode fibers can achieve a point-to-point transmission of the object with a high sampling density, as is a prerequisite for unsupervised image reconstruction. The recently proposed disordered fibers offer a new solution based on the transverse Anderson localization. Here, we demonstrate unsupervised full-color imaging with a cellular resolution through a meter-long disordered fiber in both transmission and reflection modes. The unsupervised image reconstruction consists of two stages. In the first stage, we perform a pixel-wise standardization on the fiber outputs using the statistics of the objects. In the second stage, we recover the fine details of the reconstructions through a generative adversarial network. Unsupervised image reconstruction does not need paired images, enabling a much more flexible calibration under various conditions. Our new solution achieves full-color high-fidelity cell imaging within a working distance of at least 4 mm by only collecting the fiber outputs after an initial calibration. High imaging robustness is also demonstrated when the disordered fiber is bent with a central angle of 60{\deg}. Moreover, the cross-domain generality on unseen objects is shown to be enhanced with a diversified object set

Temporal-dependent diversity (Pi) of four main genotypes.

Temporal-dependent diversity (Pi) of four main genotypes.</p

Mutation accumulation and phylogenetic analysis of Spike variants in SARS-CoV-2.

A and B, Genome accumulation curve of S_614G and S_614D as counted by the sample collection date; C and D, Correlation analysis between genomes of S_614G and S_614D and confirmed cases; E and F, Mutation (Pi) accumulation curves of genotype S_614G (E) and S_614D (F); G, Maximum Likelihood tree of gene sequences of Spike as inferred by using the Tamura-Nei model [44]; H, Evolutionary tree of peptide sequences of Spike as inferred by using the Neighbor-Joining method [46]. The bootstrap supports by 1000 replicates are shown next to the branches. All positions with less than 95% site coverage were eliminated. Evolutionary analyses were conducted in MEGA11 [36].</p

Molecular phylogenetic trees inferred by using the genome sequences.

A, ME tree, B, MP tree. All ambiguous positions were removed for each sequence pair in ME tree, or positions with less than 95% site coverage were eliminated in MP tree. The percentage in 1000 replicates of trees in which the associated taxa clustered together is shown next to the branches. (TIF)</p

Mutation accumulation and phylogenetic analysis of NS8 variants in SARS-CoV-2.

A and B, Genome accumulation curve of NS8_84L and NS8_84S as counted by the sample collection date; C and D, Correlation analysis between genomes of genotypes and confirmed cases; E and F, Mutation (Pi) accumulation dynamics of genotype NS8_84L (E) and NS8_84S (F); G, Maximum Likelihood tree of peptide sequences of NS8 as inferred by using the JTT matrix-based model [43]. The bootstrap supports by 1000 replicates are shown next to the branches. H, Maximum Likelihood tree of gene sequences of NS8 as inferred by using the Tamura-Nei model [44]. The bootstrap supports are shown above the branches. All positions with less than 95% site coverage were eliminated. Evolutionary analyses were conducted in MEGA11 [36].</p

Table_3_Cultivars identification of oat (Avena sativa L.) seed via multispectral imaging analysis.docx

Cultivar identification plays an important role in ensuring the quality of oat production and the interests of producers. However, the traditional methods for discrimination of oat cultivars are generally destructive, time-consuming and complex. In this study, the feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis. The principal component analysis (PCA), linear discrimination analysis (LDA) and support vector machines (SVM) were applied to classify seeds of 16 oat cultivars according to their morphological features, spectral traits or a combination thereof. The results demonstrate that clear differences among cultivars of oat seeds could be easily visualized using the multispectral imaging technique and an excellent discrimination could be achieved by combining data of the morphological and spectral features. The average classification accuracy of the testing sets was 89.69% for LDA, and 92.71% for SVM model. Therefore, the potential of a new method for rapid and nondestructive identification of oat cultivars was provided by multispectral imaging combined with multivariate analysis.</p