Potential methane and nitrous oxide production and respiration rates from penguin and seal colony tundra soils during freezing–thawing cycles under different water contents in coastal Antarctica

In coastal Antarctica, frequent freezing–thawing cycles (FTCs) and changes to the hydrological conditions may affect methane (CH4) and nitrous oxide (N2O) production and respiration rates in tundra soils, which are difficult to observe in situ. Tundra soils including ornithogenic tundra soil (OAS), seal colony soil (SCS) and emperor penguin colony soil (EPS) were collected. In laboratory, we investigated the effects of FTCs and water addition on potential N2O and CH4 production and respiration rates in the soils. The CH4 fluxes from OAS and SCS were much less than that from EPS. Meanwhile, the N2O fluxes from OAS and EPS were much less than that from SCS. The N2O production rates from all soils were extremely low during freezing, but rapidly increased following thawing. In all cases, FTC also induced considerably enhanced soil respiration, indicating that soil respiration response was sensitive to the FTCs. The highest cumulative rates of CH4, N2O and CO2 were 59.5 mg CH4-C·kg−1 in EPS, 6268.8 μg N2O-N·kg−1 in SCS and 3522.1 mg CO2-C·kg−1 in OAS. Soil water addition had no significant effects on CH4 production and respiration rates, but it could reduce N2O production in OAS and EPS, and it stimulated N2O production in SCS. Overall, CH4 and N2O production rates showed a trade-off relationship during the three FTCs. Our results indicated that FTCs greatly stimulated soil N2O and CO2 production, and water increase has an important effect on soil N2O production in coastal Antarctic tundra

Germline-Encoded TCR-MHC Contacts Promote TCR V Gene Bias in Umbilical Cord Blood T Cell Repertoire.

T cells recognize antigens as peptides bound to major histocompatibility complex (MHC) proteins through T cell receptors (TCRs) on their surface. To recognize a wide range of pathogens, each individual possesses a substantial number of TCRs with an extremely high degree of variability. It remains controversial whether germline-encoded TCR repertoire is shaped by MHC polymorphism and, if so, what is the preference between MHC genetic variants and TCR V gene compatibility. To investigate the "net" genetic association between MHC variations and TRBV genes, we applied quantitative trait locus (QTL) mapping to test the associations between MHC polymorphism and TCR β chain V (TRBV) genes usage using umbilical cord blood (UCB) samples of 201 Chinese newborns. We found TRBV gene and MHC loci that are predisposed to interact with one another differ from previous conclusions. The majority of MHC amino acid residues associated with the TRBV gene usage show spatial proximities in known structures of TCR-pMHC complexes. These results show for the first time that MHC variants bias TRBV gene usage in UCB of Chinese ancestry and indicate that germline-encoded contacts influence TCR-MHC interactions in intact T cell repertoires

Ligand-induced unfolding mechanism of an RNA G-quadruplex

The cationic porphyrin, TMPyP4, is a well-established DNA G-quadruplex (G4) binding ligand that can stabilize different topologies via multiple binding modes. However, TMPyP4 has completely opposite destabilizing and unwinding effect on RNA G4 structures. The structural mechanisms that mediate RNA G4 unfolding remains unknown. Here, we report on the TMPyP4-induced RNA G4 unfolding mechanism studied by well-tempered metadynamics (WT-MetaD) with supporting biophysical experiments. The simulations predict a two-state mechanism of TMPyP4 interaction via a groove-bound and a top-face bound conformation. The dynamics of TMPyP4 stacking on the top tetrad disrupts Hoogsteen H-bonds between guanine bases resulting in the consecutive TMPyP4 intercalation from top-to-bottom G-tetrads. The results reveal a striking correlation between computational and experimental approaches and validate WT-MetaD simulations as a powerful tool for studying RNA G4-ligand interactions

Fatty Acid Release and Gastrointestinal Oxidation Status: Different Methods of Processing Flaxseed

Flaxseed has been recognized as a superfood worldwide due to its abundance of diverse functional phytochemicals and nutrients. Various studies have shown that flaxseed consumption is beneficial to human health, though methods of processing flaxseed may significantly affect the absorption and metabolism of its bioactive components. Hence, flaxseed was subjected to various processing methods including microwaving treatment, microwave-coupled dry milling, microwave-coupled wet milling, and high-pressure homogenization. In vitro digestion experiments were conducted to assess the impact of these processing techniques on the potential gastrointestinal fate of flaxseed oil. Even though more lipids were released by the flaxseed at the beginning of digestion after it was microwaved and dry-milled, the full digestion of flaxseed oil was still restricted in the intestine. In contrast, oil droplets were more evenly distributed in wet-milled flaxseed milk, and there was a greater release of fatty acids during simulated digestion (7.33 ± 0.21 μmol/mL). Interestingly, wet-milled flaxseed milk showed higher oxidative stability compared with flaxseed powder during digestion despite the larger specific surface area of its oil droplets. This study might provide insight into the choice of flaxseed processing technology for better nutrient delivery efficiency

Clustering Based on Continuous Hopfield Network

Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms

RNA m6A modification: Mapping methods, roles, and mechanisms in acute myeloid leukemia

N6-Methyladenosine (m6A) is the most abundant modification in eukaryotic mRNA, and plays important biological functions via regulating RNA fate determination. Recent studies have shown that m6A modification plays a key role in hematologic malignancies, including acute myeloid leukemia. The current growth of epitranscriptomic research mainly benefits from technological progress in detecting RNA m6A modification in a transcriptome-wide manner. In this review, we first briefly summarize the latest advances in RNA m6A biology by focusing on writers, readers, and erasers of m6A modification, and describe the development of high-throughput methods for RNA m6A mapping. We further discuss the important roles of m6A modifiers in acute myeloid leukemia, and highlight the identification of potential inhibitors for AML treatment by targeting of m6A modifiers. Overall, this review provides a comprehensive summary of RNA m6A biology in acute myeloid leukemia

Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.

Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features