New solutions for the color-flavor locked strangelets

Recent publications rule out the negatively charged beta equilibrium strangelets in ordinary phase, and the color-flavor locked (CFL) strangelets are reported to be also positively charged. This letter presents new solutions to the system equations where CFL strangelets are slightly negatively charged. If the ratio of the square-root bag constant to the gap parameter is smaller than 170 MeV, the CFL strangelets are more stable than iron and the normal unpaired strangelets. For the same parameters, however, the positively charged CFL strangelets are more stable.Comment: 5 pages, 4 figures, Revtex4 styl

Effects of different probiotics on the gut microbiome and metabolites in the serum and caecum of weaning piglets

The objective of the study was to determine the effects of antibiotics, yeast culture (YC), and Lactobacillus culture (LC) on the gut microbiome and metabolites in the serum and caecum of weaning piglets. Twenty-four weaning piglets were divided into four treatment groups: control, antibiotic (1% chlortetracycline), 1.8% yeast culture (YC), and 1.6% Lactobacillus culture groups (LC). Each group had six replicated pens with one pig per pen. Feed and water were available ad libitum. Dietary supplementation with antibiotics, YC and LC increased the abundance of phylum, Firmicutes, and decreased the abundance of phylum, Proteobacteria. Beneficial bacteria such as Lactobacillus and Megasphaera in YC and LC groups increased, whereas the proportion of Shigella was decreased. Genera Alloprevotella and Lachnospira were biomarkers in the control and antibiotic groups, respectively. Phylum, Bacteroidetes, and genus, Collinsella, were biomarkers in the YC group, and Mitsuokella, Anaerotruncus, Syntrophococcus and Sharpea were biomarkers in the LC group. Dietary supplementation with different probiotics changed the serum and caecum metabolite profiles too. Antibiotic supplementation increased the levels of D-mannose, D-glucose, and hexadecanoic acid in the serum, and the levels of myo-inositol, D-mannose and benzenepropanoic acid in the caecum. LC increased the concentrations of D-mannose, cis-9-hexadecenoic acid and heptadecanoic acid in caecum compared with the control group. YC and LC supplementation in the weaning diet could improve the abundance of beneficial bacteria by changing the concentrations of some metabolites in the serum and caecum. Therefore, dietary supplementation with YC or LC could be used as additives instead of antibiotics in weaning piglets.Keywords: antibiotic; lactobacillus culture; yeast culture; high-throughput sequencing; gas chromatography mass spectrometr

Comparison of endosperm amyloplast development and degradation in waxy and non-waxy wheat

The waxy wheat shows special starch quality due to high amylopectin content. However, little information is available concerning the development and degradation of amyloplast from waxy wheat endosperm. To address this problem, waxy wheat variety, Yangnuo 1, and a non-waxy wheat variety, Yangmai 13, were chosen to investigate the development and degradation of endosperm amyloplast during wheat caryopsis development and germination stage respectively using histochemical staining and light microscopy. Changes of morphology, the soluble sugar and total starch content were indistinguishable in the process of caryopsis development of two wheat varieties. The developing endosperm of non-waxy was stained blue-black by I2-KI while the endosperm of waxy wheat was stained reddish-brown, but the pericarp of waxy and non-waxy wheat was stained blue-black. In contrast to nonwaxy wheat, endosperm amyloplast of waxy wheat had better development status and higher proportion of small amyloplast. During seed germination many small dissolution pores appeared on the surface of endosperm amyloplast and the pores became bigger and deeper until amyloplast disintegrated. The rate of degradation of waxy wheat endosperm amyloplast was faster than non-waxy wheat. Our results may also be helpful to the use of waxy starch in food and nonfood industry

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Upscaling wetland methane emissions from the FLUXNET‐CH4 eddy covariance network (UpCH4 v1.0): model development, network assessment, and budget comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253)

Material properties and impact resistance of a new lightweight engineered cementitious composite

Engineered Cementitious Composite (ECC) is a unique type of cement mixture that exhibited superior tensile strain-hardening compared to normal fibre reinforced concrete (FRC). ECC contains a mix of cement/fly ash, sand, water, chemical adhesives and a relatively low volume (typically <2%) of short discrete low modulus fibres, such as polyvinyl-Alcohol (PVA) fibre. The interaction between the microfibers and other materials create flat steady state multi-cracking of concrete when under stress. In this paper, an innovative lightweight ECC material is developed by replacing a fraction volume of cement with lightweight hollow glass microsphere additives. It is expected that the material will have a lower density than the normal ECC, better energy absorption and improved compressive strength. Both static testing and impact testing using Split Hopkinson Bar (SHPB) have been conducted