Orthogonal multimodality integration and clustering in single-cell data.

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation.

Single cell RNA sequencing (scRNA-seq) technologies provide researchers with an unprecedented opportunity to exploit cell heterogeneity. For example, the sequenced cells belong to various cell lineages, which may have different cell fates in stem and progenitor cells. Those cells may differentiate into various mature cell types in a cell differentiation process. To trace the behavior of cell differentiation, researchers reconstruct cell lineages and predict cell fates by ordering cells chronologically into a trajectory with a pseudo-time. However, in scRNA-seq experiments, there are no cell-to-cell correspondences along with the time to reconstruct the cell lineages, which creates a significant challenge for cell lineage tracing and cell fate prediction. Therefore, methods that can accurately reconstruct the dynamic cell lineages and predict cell fates are highly desirable. In this article, we develop an innovative machine-learning framework called Cell Smoothing Transformation (CellST) to elucidate the dynamic cell fate paths and construct gene networks in cell differentiation processes. Unlike the existing methods that construct one single bulk cell trajectory, CellST builds cell trajectories and tracks behaviors for each individual cell. Additionally, CellST can predict cell fates even for less frequent cell types. Based on the individual cell fate trajectories, CellST can further construct dynamic gene networks to model gene-gene relationships along the cell differentiation process and discover critical genes that potentially regulate cells into various mature cell types.

Synergistic mechanism and degradation kinetics for atrazine elimination by integrated N-ZnO/g-C3N4/solar light/oxidant.

In this study, an N-ZnO/g-C3N4 (g-N-Z) Z-scheme photocatalyst was constructed using hydrothermal and high-temperature calcination. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and other tests were employed to characterise the catalytic material. The results showed that after N-ZnO modification, the separation efficiency of the photoinduced charge pairs and the utilisation of sunlight in the composites were improved. The kinetics experiments indicated that the degradation of atrazine (ATZ) in the g-N-Z/PDS/solar system was significantly better than that in the PDS/solar system. Under the action of the g-N-Z/PDS/solar system, the degradation rate of ATZ reached 83.88%, whereas in the PDS/solar system, it was only 31.76%. In addition, it was found that increasing the PDS concentration, g-N-Z dosage, and solution acidity effectively accelerated the removal of ATZ. The presence of HCO3-/CO32-, Cl-, and natural organic matter (NOM) inhibited the oxidation efficiency of the g-N-Z/PDS/solar system. Moreover, the presence of multiple reactive oxygen species (ROS) was confirmed using radical scavenging experiments to determine the contribution of each active component. Twelve oxidation intermediates of ATZ were obtained via liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the mechanism of enhanced ATZ degradation in the g-N-Z/PDS/solar system was proposed. Actual water and cyclic photocatalytic experiments further suggest that g-N-Z has good application value in water treatment.

Smoothing splines approximation using Hilbert curve basis selection.

Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O(n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O(nq 2). These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly-distributed on a hyper-cube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O[n 2d/{(pr+1)(d +2)}] , where p ∈[1, 2] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors.

Degradation of iopamidol by UV365/NaClO: Roles of reactive species, degradation mechanism, and toxicology.

The degradation of iopamidol (IPM) was investigated using a UV365/NaClO system. The reactive species (HO·, ClO·, ozone, Cl·, and Cl2-·) in the system were identified, and the changing trends of the percentage contributions of these reactive species to IPM removal under various conditions were systematically evaluated. The results showed that ClO· and HO· played the most significant roles in the apparent pseudo-first-order rate constants of IPM degradation (kobs, min-1) in the control experiment, and their percentage contributions to kobs were 41.31% and 34.45%, respectively. In addition, Cl· and Cl2-· together contributed 22% to the kobs. Furthermore, the contribution of ozone to the IPM removal could be neglected. The concentrations of these species increased significantly when the concentration of NaClO was increased from 50 µM to 200 µM, while the percentage contribution of ClO· to kobs was greatly increased. The concentrations and percentage contributions of HO· and ClO· decreased significantly as the solution pH increased from 5 to 9, with Cl2-· playing a greater role in the degradation of IPM under alkaline conditions. While Cl- or HCO3-/CO32- significantly promoted the generation of Cl2-· or CO3-·, neither had an obvious effect on kobs, suggesting that Cl2-· and CO3-· should have a certain reactivity with IPM. Compared with that of Cl2-·, the percentage contribution of ClO· and Cl· to kobs was more likely to be inhibited by NOM. In addition, the organic and inorganic oxidation products of IPM were detected. The oxidation mechanisms of IPM degradation in the UV365/NaClO system, such as the H-extraction reaction, deiodination, substitution reaction, amide hydrolysis, and amine oxidation, were proposed according to the obtained 15 organic products. No effect on acute toxicity towards Vibrio fischeri and Photobacterium phosphoreum was detected during the oxidation of IPM by the UV365/NaClO system. Furthermore, the engineering feasibility of the oxidation system was demonstrated, by the effective degradation of IPM in actual water. However, HOI rapidly accumulated during the removal of IPM in the UV365/NaClO system, which poses certain environmental risks and will needs to be investigated.

Identification of Di/Tripeptide(s) With Osteoblasts Proliferation Stimulation Abilities of Yak Bone Collagen by in silico Screening and Molecular Docking.

Endothelial protein C receptor (EPCR), cannabinoid receptor 2 (CBR2), and estrogen receptor α (ERα) play vital roles in osteoblasts proliferation. Also, collagen peptides have osteoblasts proliferation stimulation abilities, and di/tri-peptides could be absorbed by the intestine more easily. This study obtained three di/tripeptides with potential osteoblasts proliferation stimulation abilities of yak bone collagen, namely, MGF, CF, and MF, by in silico screening. Results suggested that these three peptides exhibited good absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. They also had strong affinities with EPCR, CBR2, and ERα, and the total -CDOCKER energy (-CE) values were 150.9469, 113.1835, and 115.3714 kcal/mol, respectively. However, further Cell Counting Kit-8 (CCK-8) assays indicated that only MGF could significantly (P < 0.05) stimulate osteoblasts proliferation at 0.3 mg/ml. At the same time, the proliferating index (PI) of the osteoblasts treated with MGF increased significantly (P < 0.05), and the alkaline phosphatase (ALP) activity decreased highly significantly (P < 0.01). In summary, MGF exhibited the potential to be an effective treatment for osteoporosis.

Drug screening with zebrafish visual behavior identifies carvedilol as a potential treatment for an autosomal dominant form of retinitis pigmentosa.

Retinitis Pigmentosa (RP) is a mostly incurable inherited retinal degeneration affecting approximately 1 in 4000 individuals globally. The goal of this work was to identify drugs that can help patients suffering from the disease. To accomplish this, we screened drugs on a zebrafish autosomal dominant RP model. This model expresses a truncated human rhodopsin transgene (Q344X) causing significant rod degeneration by 7 days post-fertilization (dpf). Consequently, the larvae displayed a deficit in visual motor response (VMR) under scotopic condition. The diminished VMR was leveraged to screen an ENZO SCREEN-WELL REDOX library since oxidative stress is postulated to play a role in RP progression. Our screening identified a beta-blocker, carvedilol, that ameliorated the deficient VMR of the RP larvae and increased their rod number. Carvedilol may directly on rods as it affected the adrenergic pathway in the photoreceptor-like human Y79 cell line. Since carvedilol is an FDA-approved drug, our findings suggest that carvedilol can potentially be repurposed to treat autosomal dominant RP patients.

Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors.

BACKGROUND: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. METHODS: We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. RESULTS: By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70-87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. CONCLUSION: Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.