Estuarine plastisphere as an overlooked source of N2O production

“Plastisphere”, microbial communities colonizing plastic debris, has sparked global concern for marine ecosystems. Microbiome inhabiting this novel human-made niche has been increasingly characterized; however, whether the plastisphere holds crucial roles in biogeochemical cycling remains largely unknown. Here we evaluate the potential of plastisphere in biotic and abiotic denitrification and nitrous oxide (N2O) production in estuaries. Biofilm formation provides anoxic conditions favoring denitrifiers. Comparing with surrounding bulk water, plastisphere exhibits a higher denitrifying activity and N2O production, suggesting an overlooked N2O source. Regardless of plastisphere and bulk water, bacterial and fungal denitrifications are the main regulators for N2O production instead of chemodenitrification. However, the contributions of bacteria and fungi in the plastisphere are different from those in bulk water, indicating a distinct N2O production pattern in the plastisphere. These findings pinpoint plastisphere as a N2O source, and provide insights into roles of the new biotope in biogeochemical cycling in the Anthropocene

Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection

In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection

Seasonal Variation of Biochemical Composition and Non-Volatile Taste Active Compounds in Pearl Oyster <em>Pinctada fucata martensii</em> from Two Selective Strains

Recently, a new pearl oyster Pinctada fucata martensii strain has begun to be cultured as seafood. In the present study, the seasonal variation (February and June) in biochemical composition and flavor compounds in two P. f. martensii strains (strain for pearl production was abbreviated to PP, and seafood was abbreviated to PE) were detected to compare the nutritional and flavor differences between them, and to provide a reference for the seasonal preference of consumers for eating P. f. martensii. The ratio of soft tissues in PE-Feb was significantly higher than that in PP-Feb (p p P. f. martensii strains in the same season, while the contents of these nutrients were significantly higher in February compared to June (p P. f. martensii strain in February were significantly higher than those in June (p p p P. f. martensii strain harvest in February is rich in protein, glycogen, PUFA (DHA and EPA), taurine, succinic acid, and betaine, which could provide healthy nutrition and a good flavor for humans

EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations

Simulation experiments are essential to evaluate epistasis detection methods, which is the main way to prove their effectiveness and move toward practical applications. However, due to the lack of effective simulators, especially for simulating models without marginal effects (eNME models), epistasis detection methods can hardly verify their effectiveness through simulation experiments. In this study, we propose a resampling simulation method (EpiReSIM) for generating the eNME model. First, EpiReSIM provides two strategies for solving eNME models. One is to calculate eNME models using prevalence constraints, and another is by joint constraints of prevalence and heritability. We transform the computation of the model into the problem of solving the under-determined system of equations. Introducing the complete orthogonal decomposition method and Newton&rsquo;s method, EpiReSIM calculates the solution of the underdetermined system of equations to obtain the eNME model, especially the solution of the high-order model, which is the highlight of EpiReSIM. Second, based on the computed eNME model, EpiReSIM generates simulation data by a resampling method. Experimental results show that EpiReSIM has advantages in preserving the biological properties of minor allele frequencies and calculating high-order models, and it is a convenient and effective alternative method for current simulation software

MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions

Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions

Identifying and Predicting the Responses of Multi-Altitude Vegetation to Climate Change in the Alpine Zone

Global climate change has affected vegetation cover in alpine areas. In this paper, we analyzed the correlation between Leaf Area Index (LAI) and climate factors of the Yarlung Tsangpo River basin, and identified their contributions using the quantitative analysis method. The results show that the vegetation cover in the study area generally exhibited an increasing trend. Grassland in the middle- and high-altitude areas was the main contributing area. Temperature is the dominant climatic factor affecting the change, the effect of which increases with the rise in elevation. The influences of precipitation and radiation had obvious seasonality and regionality. The vegetation illustrated a lag response to climate drivers. With the change in the elevation band, the response time to precipitation was significantly less than that to air temperature in the low-elevation area, while the opposite trend was observed in the high-elevation area. In the future, the LAI of the watershed will show different characteristics at different time points, with the increases in the LAI in autumn and winter becoming the main factors for the future increase in the LAI. This provides a crucial basis upon which to explore hydrological and ecological processes as important components of the Third Pole region

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches

Summary: Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making

Mechanistic Investigation on Potassium Amide-Catalyzed Benzylic C–H Bond Addition of Alkylpyridines to Styrenes

The catalytic C–H bond addition of alkylpyridines to olefins represents one of the most efficient approaches to alkyl-substituted pyridine derivatives with 100% atom-economy efficiency. Various catalysts including late transition metal, early transition metal, and s-block metal complexes displayed rich and versatile activity. Potassium amide could selectively achieve the benzylic alkylation of alkylpyridines, displaying distinct activity and regioselectivity. Here, the mechanistic investigation via both kinetic experiments and DFT calculations revealed the dimer structure of potassium amide and confirmed alkylation via a kinetic deprotonative functionalization process

Mechanistic Investigation on Potassium Amide-Catalyzed Benzylic C–H Bond Addition of Alkylpyridines to Styrenes

The catalytic C–H bond addition of alkylpyridines to olefins represents one of the most efficient approaches to alkyl-substituted pyridine derivatives with 100% atom-economy efficiency. Various catalysts including late transition metal, early transition metal, and s-block metal complexes displayed rich and versatile activity. Potassium amide could selectively achieve the benzylic alkylation of alkylpyridines, displaying distinct activity and regioselectivity. Here, the mechanistic investigation via both kinetic experiments and DFT calculations revealed the dimer structure of potassium amide and confirmed alkylation via a kinetic deprotonative functionalization process

Chromosome-level analysis of the Crassostrea hongkongensis genome reveals extensive duplication of immune-related genes in bivalves

Crassostrea hongkongensis is a popular and important native oyster species that is cultured mainly along the coast of the South China Sea. However, the absence of a reference genome has restricted genetic studies and the development of molecular breeding schemes for this species. Here, we combined PacBio and 10 x Genomics technologies to create a C. hongkongensis genome assembly, which has a size of 610 Mb, and is close to that estimated by flow cytometry (similar to 650 Mb). Contig and scaffold N50 are 2.57 and 4.99 Mb, respectively, and BUSCO analysis indicates that 95.8% of metazoan conserved genes are completely represented. Using a high-density linkage map of its closest related species, C. gigas, a total of 521 Mb (85.4%) was anchored to 10 haploid chromosomes. Comparative genomic analyses with other molluscs reveal that several immune- or stress response-related genes extensively expanded in bivalves by tandem duplication, including C1q, Toll-like receptors and Hsp70, which are associated with their adaptation to filter-feeding and sessile lifestyles in shallow sea and/or deep-sea ecosystems. Through transcriptome sequencing, potential genes and pathways related to sex determination and gonad development were identified. The genome and transcriptome of C. hongkongensis provide valuable resources for future molecular studies, genetic improvement and genome-assisted breeding of oysters