Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

High-Throughput Transcriptomic Analysis of Circadian Rhythm of Chlorophyll Metabolism under Different Photoperiods in Tea Plants.

Tea plants are a perennial crop with significant economic value. Chlorophyll, a key factor in tea leaf color and photosynthetic efficiency, is affected by the photoperiod and usually exhibits diurnal and seasonal variations. In this study, high-throughput transcriptomic analysis was used to study the chlorophyll metabolism, under different photoperiods, of tea plants. We conducted a time-series sampling under a skeleton photoperiod (6L6D) and continuous light conditions (24 L), measuring the chlorophyll and carotenoid content at a photoperiod interval of 3 h (24 h). Transcriptome sequencing was performed at six time points across two light cycles, followed by bioinformatics analysis to identify and annotate the differentially expressed genes (DEGs) involved in chlorophyll metabolism. The results revealed distinct expression patterns of key genes in the chlorophyll biosynthetic pathway. The expression levels of CHLE (magnesium-protoporphyrin IX monomethyl ester cyclase gene), CHLP (geranylgeranyl reductase gene), CLH (chlorophyllase gene), and POR (cytochrome P450 oxidoreductase gene), encoding enzymes in chlorophyll synthesis, were increased under continuous light conditions (24 L). At 6L6D, the expression levels of CHLP1.1, POR1.1, and POR1.2 showed an oscillating trend. The expression levels of CHLP1.2 and CLH1.1 showed the same trend, they both decreased under light treatment and increased under dark treatment. Our findings provide potential insights into the molecular basis of how photoperiods regulate chlorophyll metabolism in tea plants.

Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association.

High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic traffic settings. First, the improved semantic segmentation network, building on Fast-SCNN, uses the Res2net module instead of the Bottleneck in the global feature extraction to further explore the multi-scale granular features. It achieves the balance between segmentation accuracy and inference speed, leading to consistent performance gains on the coordinated localization task of this paper. Second, a novel scene descriptor combining geometric, semantic, and distributional information is proposed. These descriptors are made up of significant features and their surroundings, which may be unique to a traffic scene, and are used to improve data association quality. Finally, a probabilistic data association is created to find the best estimate using a maximum measurement expectation model. This approach assigns semantic labels to landmarks observed in the environment and is used to correct false negatives in data association. We have evaluated our system with ORB-SLAM2 and DynaSLAM, the most advanced algorithms, to demonstrate its advantages. On the KITTI dataset, the results reveal that our approach outperforms other methods in dynamic traffic situations, especially in highly dynamic scenes, with sub-meter average accuracy.

The Origin, Function, Distribution, Quantification, and Research Advances of Extracellular DNA.

In nature, DNA is ubiquitous, existing not only inside but also outside of the cells of organisms. Intracellular DNA (iDNA) plays an essential role in different stages of biological growth, and it is defined as the carrier of genetic information. In addition, extracellular DNA (eDNA) is not enclosed in living cells, accounting for a large proportion of total DNA in the environment. Both the lysis-dependent and lysis-independent pathways are involved in eDNA release, and the released DNA has diverse environmental functions. This review provides an insight into the origin as well as the multiple ecological functions of eDNA. Furthermore, the main research advancements of eDNA in the various ecological environments and the various model microorganisms are summarized. Furthermore, the major methods for eDNA extraction and quantification are evaluated.

Subcellular engineering of lipase dependent pathways directed towards lipid related organelles for highly effectively compartmentalized biosynthesis of triacylglycerol derived products in Yarrowia lipolytica.

As an alternative to in vitro lipase dependent biotransformation and to traditional assembly of pathways in cytoplasm, the present study focused on targeting lipase dependent pathways to a subcellular compartment lipid body (LB), in combination with compartmentalization of associated pathways in other lipid relevant organelles including endoplasmic reticulum (ER) and peroxisome for efficient in vivo biosynthesis of fatty acid methyl esters (FAMEs) and hydrocarbons, in the context of improving Yarrowia lipolytica lipid pool. Through knock in and knock out of key genes involved in triacylglycerols (TAGs) biosynthesis and degradation, the TAGs content was increased to 51.5%, from 7.2% in parent strain. Targeting lipase dependent pathway to LB gave a 10-fold higher FAMEs titer (1028.0 mg/L) compared to cytosolic pathway (102.8 mg/L). Furthermore, simultaneously targeting lipase dependent pathway to LB, ER and peroxisome gave rise to the highest FAMEs titer (1644.8 mg/L). The subcellular compartment engineering strategy was extended to other lipase dependent pathways for fatty alkene and alkane biosynthesis, which resulted in a 14-fold titer enhancement compared to traditional cytosolic pathways. We developed yeast subcellular cell factories by directing lipase dependent pathways towards the TAGs storage organelle LB for efficient biosynthesis of TAG derived chemicals for the first time. The successful exploration of targeting metabolic pathways towards LB centered organelles is expected to promote subcellular compartment engineering for other lipid derived product biosynthesis.

High-Level Production of a Thermostable Mutant of Yarrowia lipolytica Lipase 2 in Pichia pastoris.

As a promising biocatalyst, Yarrowia lipolytica lipase 2 (YlLip2) is limited in its industrial applications due to its low thermostability. In this study, a thermostable YlLip2 mutant was overexpressed in Pichia pastoris and its half-life time was over 30 min at 80 °C. To obtain a higher protein secretion level, the gene dosage of the mutated lip2 gene was optimized and the lipase activity was improved by about 89%. Then, the YlLip2 activity of the obtained strain further increased from 482 to 1465 U/mL via optimizing the shaking flask culture conditions. Subsequently, Hac1p and Vitreoscilla hemoglobin (VHb) were coexpressed with the YlLip2 mutant to reduce the endoplasmic reticulum stress and enhance the oxygen uptake efficiency in the recombinant strains, respectively. Furthermore, high-density fermentations were performed in a 3 L bioreactor and the production of the YlLip2 mutant reached 9080 U/mL. The results demonstrated that the expression level of the thermostable YlLip2 mutant was predominantly enhanced via the combination of these strategies in P. pastoris, which forms a consolidated basis for its large-scale production and future industrial applications.

The impact of Bayesian optimization on feature selection.

Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.

Diastereoselective Synthesis of 4-Hydroxy-2-quinolinones via Formal [2 + 4] Cycloaddition Reactions Using α-Diazo Pyrazoleamides as C2 Synthons.

A rhodium-catalyzed highly stereoselective formal [2 + 4]-cycloaddition reaction of α-diazo pyrazoleamides and 2-aminophenyl ketones that produces 4-hydroxy-2-quinolinones in good yields with excellent diastereoselectivities has been developed. A pyrazolium ylide species that is generated from α-diazo pyrazoleamides is used as a C2 synthon for this cycloaddition. This protocol offers an efficient approach to a variety of 4-hydroxy-2-quinolinones featuring sequential quaternary centers.

Effects of combined nitrogen and phosphorus application on soil phosphorus fractions in alfalfa (Medicago sativa L.) production in China.

Nitrogen (N) and phosphorus (P) fertilizers change the morphological structure and effectiveness of P in the soil, which in turn affects crop growth, yield, and quality. However, the effects and mechanism of combined N and P application on the content of P fractions and the transformation of effective forms in alfalfa (Medicago sativa L.) production is unclear. This experiment was conducted with four levels of N: 0 (N0), 60 (N1), 120 (N2) and 180 kg·ha-1 (N3); and two levels of P (P2O5): 0 (P0) and 100 kg·ha-1 (P1). The results indicated that, under the same N level, P application significantly increased soil total N, and total P, available P, and content of various forms of inorganic P when compared to no P application, while decreasing the content of various forms of organic P and pH value. In general, under P0 conditions, soil total N content tended to increase with increasing N application, while total P, available P content, pH, inorganic P content in all forms, and organic P content in all forms showed a decreasing trend. When compared to no N application, insoluble P (Fe-P, O-P, Ca10-P) of the N application treatments was reduced 2.80 - 22.72, 2.96 - 20.42, and 5.54 - 20.11%, respectively. Under P1 conditions, soil total N and O-P tended to increase with increasing N application, while, pH, Ca2-P, Al-P, Fe-P, Ca10-P, and organic P content of each form tended to decrease. Total P, available P, and labile organic P (LOP) of N application reduced 0.34 - 8.58, 4.76 - 19.38, and 6.27 - 14.93%, respectively, when compared to no application. Nitrogen fertilization reduced the soil Ca2-P ratio, while P fertilization reduced soil Fe-P, moderately resistant organic P (MROP), and highly resistant P (HROP) ratios, and combined N and P elevated the Ca8-P to LOP ratio. The results of redundancy analysis showed that soil total N content, available P content, and pH were the key factors affecting the conversion of P fractions in the soil. Nitrogen and P reduced the proportion of soil insoluble P, promoted the activation of soil organic P, resulting in accumulation of slow-acting P in the soil, thereby improving the efficiency of soil P in alfalfa production.

Multifunctional bacterial cellulose­derived carbon hybrid aerogel for ultrabroad microwave absorption and thermal insulation.

Carbon aerogel has gained intense attention as one of the most promising microwave absorption materials. It can overcome severe electromagnetic pollution, thanks to its 3D macroscopic structure and superb conductive loss capacity. However, there is still a big challenge to endow multifunctionality to carbon aerogel while maintaining its good electromagnetic wave absorption (EWA) so as to adapt wide practical application. Herein, a novel carbon-based aerogel consisting of Cu and TiO2 nanoparticles dispersed on carbon nanofiber framework was derived from carbonized bacterial cellulose (CBC) decorated with its mother bacteria via freeze-drying, in situ growth and carbonization strategies. The synthesized carbon-based CBC/Cu/TiO2 aerogel achieved an excellent EWA performance with a broad effective absorption bandwidth (EAB) of 8.32 GHz. It is attributed to the synergistic loss mechanism from multiple scattering, conductive network loss, interfacial polarization loss and dipolar polarization relaxation. Meanwhile, the obtained aerogel also shows an excellent thermal insulation with a 3-mm-thick sample generating a temperature gradient of over 42 °C at 85 °C and a maximum radar cross-section (RCS) reduction of 23.88 dB m2 owing to the cellular structure and synergistic effects of multi-components. Therefore, this study proposes a feasible design approach for creating lightweight, effective, and multifunctional CBC-based EWA materials, which offer a new platform to develop ultrabroad electromagnetic wave absorber under the guidance of RCS simulation.