Recently, we reported that some dairy cows could produce high amounts of milk with high amounts of protein (defined as milk protein yield MPY) when a population was raised under the same nutritional ...and management condition, a potential new trait that can be used to increase high-quality milk production. It is unknown to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to MPY. Here, analysis of rumen metagenomics and metabolomics, together with serum metabolomics was performed to identify potential regulatory mechanisms of MPY at both the rumen microbiome and host levels.
Metagenomics analysis revealed that several Prevotella species were significantly more abundant in the rumen of high-MPY cows, contributing to improved functions related to branched-chain amino acid biosynthesis. In addition, the rumen microbiome of high-MPY cows had lower relative abundances of organisms with methanogen and methanogenesis functions, suggesting that these cows may produce less methane. Metabolomics analysis revealed that the relative concentrations of rumen microbial metabolites (mainly amino acids, carboxylic acids, and fatty acids) and the absolute concentrations of volatile fatty acids were higher in the high-MPY cows. By associating the rumen microbiome with the rumen metabolome, we found that specific microbial taxa (mainly Prevotella species) were positively correlated with ruminal microbial metabolites, including the amino acids and carbohydrates involved in glutathione, phenylalanine, starch, sucrose, and galactose metabolism. To detect the interactions between the rumen microbiome and host metabolism, we associated the rumen microbiome with the host serum metabolome and found that Prevotella species may affect the host's metabolism of amino acids (including glycine, serine, threonine, alanine, aspartate, glutamate, cysteine, and methionine). Further analysis using the linear mixed effect model estimated contributions to the variation in MPY based on different omics and revealed that the rumen microbial composition, functions, and metabolites, and the serum metabolites contributed 17.81, 21.56, 29.76, and 26.78%, respectively, to the host MPY.
These findings provide a fundamental understanding of how the microbiome-dependent and host-dependent mechanisms contribute to varied individualized performance in the milk production quality of dairy cows under the same management condition. This fundamental information is vital for the development of potential manipulation strategies to improve milk quality and production through precision feeding. Video Abstract.
Lactation is extremely important for dairy cows; however, the understanding of the underlying metabolic mechanisms is very limited. This study was conducted to investigate the inherent metabolic ...patterns during lactation using the overall biofluid metabolomics and the metabolic differences from non-lactation periods, as determined using partial tissue-metabolomics. We analyzed the metabolomic profiles of four biofluids (rumen fluid, serum, milk and urine) and their relationships in six mid-lactation Holstein cows and compared their mammary gland (MG) metabolomic profiles with those of six non-lactating cows by using gas chromatography-time of flight/mass spectrometry.
In total, 33 metabolites were shared among the four biofluids, and 274 metabolites were identified in the MG tissues. The sub-clusters of the hierarchical clustering analysis revealed that the rumen fluid and serum metabolomics profiles were grouped together and highly correlated but were separate from those for milk. Urine had the most different profile compared to the other three biofluids. Creatine was identified as the most different metabolite among the four biofluids (VIP = 1.537). Five metabolic pathways, including gluconeogenesis, pyruvate metabolism, the tricarboxylic acid cycle (TCA cycle), glycerolipid metabolism, and aspartate metabolism, showed the most functional enrichment among the four biofluids (false discovery rate < 0.05, fold enrichment >2). Clear discriminations were observed in the MG metabolomics profiles between the lactating and non-lactating cows, with 54 metabolites having a significantly higher abundance (P < 0.05, VIP > 1) in the lactation group. Lactobionic acid, citric acid, orotic acid and oxamide were extracted by the S-plot as potential biomarkers of the metabolic difference between lactation and non-lactation. The TCA cycle, glyoxylate and dicarboxylate metabolism, glutamate metabolism and glycine metabolism were determined to be pathways that were significantly impacted (P < 0.01, impact value >0.1) in the lactation group. Among them, the TCA cycle was the most up-regulated pathway (P < 0.0001), with 7 of the 10 related metabolites increased in the MG tissues of the lactating cows.
The overall biofluid and MG tissue metabolic mechanisms in the lactating cows were interpreted in this study. Our findings are the first to provide an integrated insight and a better understanding of the metabolic mechanism of lactation, which is beneficial for developing regulated strategies to improve the metabolic status of lactating dairy cows.
Metformin was proposed to be a candidate for host-directed therapy for COVID-19. However, its efficacy remains to be validated. In this study, we compared the outcome of metformin users and nonusers ...in hospitalized COVID-19 patients with diabetes. Hospitalized diabetic patients with confirmed COVID-19 in the Tongji Hospital of Wuhan, China, from January 27, 2020 to March 24, 2020, were grouped into metformin and no-metformin groups according to the diabetic medications used. The demographics, characteristics, laboratory parameters, treatments, and clinical outcome in these patients were retrospectively assessed. A total of 283 patients (104 in the metformin and 179 in the no-metformin group) were included in this study. There were no significant differences between the two groups in gender, age, underlying diseases, clinical severity, and oxygen-support category at admission. The fasting blood glucose level of the metformin group was higher than that of the no-metformin group at admission and was under effective control in both groups after admission. Other laboratory parameters at admission and treatments after admission were not different between the two groups. The length of hospital stay did not differ between the two groups (21.0 days for metformin versus 19.5 days for no metformin,
= 0.74). However, in-hospital mortality was significantly lower in the metformin group (3/104 (2.9%) versus 22/179 (12.3%),
= 0.01). Antidiabetic treatment with metformin was associated with decreased mortality compared with diabetics not receiving metformin. This retrospective analysis suggests that metformin may offer benefits in patients with COVID-19 and that further study is indicated.
In this study, polyhedral oligomeric silsesquioxane (POSS) based giant triblock molecules with precisely defined regio‐configuration are modularly prepared through highly efficient coupling ...reactions. The length of the linker connecting neighboring nanoparticles is elaborately designed to regulate the geometric constraints. The triblock molecules adopt a folded packing during phase separation, and the regio‐configuration imparts direct influence on the self‐assembly behaviors. The ortho‐isomers form periodic structures with a larger domain size, larger interfacial curvature, and enhanced phase stability. The regio‐effect is closely related to the length and symmetry of the linker. As the linker extends, the neighboring particles gradually decouple, and the regio‐effect diminishes. The symmetry of the linker shows an even more profound impact. This work quantitatively scrutinized the role of the linker, opening an avenue for engineering the assembled structures with molecular precision.
Regio‐configuration has profound impacts on the self‐assembly behaviors, which can be regulated by the length and symmetry of the linker.
Abstract
Harmonics are quite common in pulsating stars. They are always considered to mimic the behaviors of their independent parent pulsation modes, and are not taken for key information in ...asteroseismology. Here, we report an SX Phoenicis star XX Cygni, whose periodogram is dominated by the fundamental frequency
f
0
= 7.41481 ± 0.00004 c day
−1
and its 19 harmonics. According to the analysis of the archival data from the Transiting Exoplanet Survey Satellite (TESS), we find that both the amplitudes and frequencies of the fundamental mode and the harmonics vary within TESS Sectors 14–17 and 54–57, which might be caused by the contamination of neighboring stars. What is more interesting is that the harmonics show significantly uncorrelated amplitude and frequency variations over time. Some possible origins and interesting issues are proposed to scheme further research of this hidden corner in current asteroseismology.
Background The safety of discontinuing oral anticoagulant (OAC) therapy after atrial fibrillation (AF) ablation remains controversial. A meta-analysis was performed to assess the safety and ...feasibility of discontinuing OAC therapy after successful AF ablation. Methods PubMed and Embase were searched up to October 2020 for prospective cohort studies that reported the risk of thromboembolism (TE) after successful AF ablation in off-OAC and on-OAC groups. The primary outcome was the incidence of TE events. The Mantel-Haenszel method with random-effects modeling was used to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs). Results A total of 11,148 patients (7,160 in the off-OAC group and 3,988 in the on-OAC group) from 10 studies were included to meta-analysis. No significant difference in TE between both groups was observed (OR, 0.73; 95%CI, 0.51-1.05; I.sup.2 = 0.0%). The risk of major bleeding in off-OAC group was significantly lower compared to the on-OAC group (OR, 0.18; 95%CI, 0.07-0.51; I.sup.2 = 51.7%). Conclusions Our study suggests that it may be safe to discontinue OAC therapy in patients after successful AF ablation. Additionally, an increased risk of major bleeding was observed in patients on OAC. However, the results of this meta-analysis should be interpreted with caution because of the heterogeneity among the included study designs. Large-scale and adequately powered randomized controlled trials are warranted to confirm these findings.
LiNiPO4 (LNP) ceramics were synthesized using a conventional solid‐state reaction method and different sintering temperatures. Differential scanning calorimetry (DSC), thermogravimetric (TG) ...analysis, and X‐ray diffraction (XRD) measurements indicated that single‐ phase olivine (Pnma, No. 62) was formed above 750°C, and dense LNP ceramic with a theoretical density of more than 95% was obtained at 825°C. Raman and far‐infrared (IR) vibrational modes were assigned and discussed in detail. The intrinsic dielectric properties of the samples were calculated using the four‐parameter semi‐quantum (FPSQ) model based on far‐IR reflectance spectroscopy and were in good agreement with the measured values. A positive relationship existed between the Raman shift of the υ1 mode (attributed to the symmetric vibration of PO43−) and the corrected permittivity, and the opposite correlation was observed between the quality factor (Q × f) and the damping of the υs mode as well as the distortion of the NiO6 octahedra. The optimized microwave dielectric properties of the LNP ceramics sintered at 825°C include an ultralow dielectric constant (5.18) and a good quality factor (24 076 GHz, f = 17.2 GHz).
Error-correcting output coding (ECOC) is one of the most widely used strategies for dealing with multi-class problems by decomposing the original multi-class problem into a series of binary ...sub-problems. In traditional ECOC-based methods, binary classifiers corresponding to those sub-problems are usually trained separately without considering the relationships among these classifiers. However, as these classifiers are established on the same training data, there may be some inherent relationships among them. Exploiting such relationships can potentially improve the generalization performances of individual classifiers, and, thus, boost ECOC learning algorithms. In this paper, we explore to mine and utilize such relationship through a joint classifier learning method, by integrating the training of binary classifiers and the learning of the relationship among them into a unified objective function. We also develop an efficient alternating optimization algorithm to solve the objective function. To evaluate the proposed method, we perform a series of experiments on eleven datasets from the UCI machine learning repository as well as two datasets from real-world image recognition tasks. The experimental results demonstrate the efficacy of the proposed method, compared with state-of-the-art methods for ECOC-based multi-class classification.
Many enveloped viruses utilize endocytic pathways and vesicle trafficking to infect host cells, where the acidification of virus‐containing endosomes triggers the virus‐endosome fusion events. ...Therefore, simultaneous correlation of intracellular location, local pH, and individual virus dynamics is important for gaining insight into viral infection mechanisms. Here, an imaging approach is developed for spatiotemporal quantification of endosomal acidification on the viral journey in host cells using a fluorescence resonance energy transfer based ratiometric pH sensor consisting of a photostable and high‐brightness QD, pH‐sensitive fluorescent dyes, and virus‐binding proteins. Ratiometric analysis of sensor‐based single‐virus tracking data enables to dissect a two‐step endosomal acidification process during the infection of influenza viruses and elucidates the occurrence of the fission and sorting of virus‐containing endosomes to recycling endosomes after initial acidification. This technique should serve as a robust approach for in situ quantification of endosomal acidification on the viral journey.
A spatiotemporal imaging method is developed to quantify endosomal acidification of viruses in host cells using a fluorescence resonance energy transfer based ratiometric pH sensor, and it is demonstrated that influenza viruses undergo a two‐step endosomal acidification process during their infection.
As one of the most critical molecular parameters, molecular weight distribution has a profound impact on the structure and properties of polymers. Quantitative and comprehensive understanding, ...however, has yet to be established, mainly due to the challenge in the precise control and regulation of molecular weight distribution. In this work, we demonstrated a robust and effective approach to artificially engineer the molecular weight distribution through precise recombination of discrete macromolecules. The width, symmetry, and other characteristics of the distribution can be independently manipulated to achieve absolute control, serving as a model platform for highlighting the importance of chain length heterogeneity in structural engineering. Different from their discrete counterparts, each individual component in dispersed samples experiences a varied degree of supercooling at a specific crystallization temperature. Non-uniform crystal nucleation and growth kinetics lead to distinct molecular arrangements. This work could bridge the gap between discrete and dispersed macromolecules, providing fundamental perspectives on the critical role of molecular weight distribution.
Modulating MWD through precise blending of discrete macromolecules provides a model platform for highlighting the importance of chain length heterogeneity.
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