Adjusting for protein quality by food source may affect nutrient density metrics

Methods to assess nutrient density of foods, commonly known as nutrient profiling (NP), typically include protein as a component. In this study, the goal was to apply a correction for protein quality by food source to selected NP algorithms. Analyses of 378 component foods of the Fred Hutch food frequency questionnaire showed that animal-source foods (ie, meat, eggs, and dairy) along with some soy products and nuts were the only foods that provided > 20% of the daily value (DV) of protein per 100 g or per 100 kcal. Most beans, pulses, legumes, grains, and vegetables provided <10% DV of protein per 100 g or per 100 kcal. Adjusting for protein quality using a simplified Protein Digestibility Corrected Amino Acid Score (PDCAAS) had consequences for point-based NP models (namely, Nutri-Score) and for continuous nutrient density scores (namely, Nutrient Rich Foods). Quantitative methods that use protein content to capture nutrient density may require a protein-quality adjustment, especially when adapted for use in low- and middle-income countries where protein quality is an issue of public health concern.

Review Study on the Accumulation and Release of Trace Metal Elements on Aluminum Containing Sediments in Drinking Water Distribution System

Accumulation and releasing of trace metal elements on aluminum containing sediments of inner drinking water pipe is discussed, as studied from five variations effecting: raw water quality, chemical reagents, solution pH and drinking water flow condition . In order to decrease the release of trace metal elements, and to ensure the pipe operation and human safety, water quality adjustment is suggested to avoid aluminum containing sediments formation in drinking distribution system. The maximum amounts of accumulation of common trace metal elements are given. Future trends of development in this field are also proposed.

Comparing environmental microbiota of different growth stages of fresh water turtle Chinemys reevesii in pond and greenhouse cultivation

The microbial community structure is an important indicator for evaluating the water quality of the aquaculture environment. In this study, V4 regions of 16S rRNA gene of pond (PC) and greenhouse cultured (GC) C. reevesii were sequenced. Results showed that a total of 1,993,090 high quality counts and 105,159 observed OUTs were obtained; and the Chao1 richness estimator of PC was significantly higher than that of GC groups. Beta-diversity showed that the microbiota of two groups were isolated from each other. In addition, the correlation analysis of environmental factors showed that NO2-N, PH, PO4-P, and stocking density played significant roles in the bacterial community composition. The dominant phyla in PC groups were cyanobacteria, proteobacteria, actinobacteria, bacteroidetes, verrucomicrobia, planctomycetes; and in GC groups were proteobacteria, bacteroidetes, firmicutes, cyanobacteria, chloroflexi, actinobacteria. The functional prediction showed that the top5 Picrust prediction gene functions were protein processing in endoplasmic reticulum, retinol metabolism, proteasome, glycan binding proteins, and stilbenoid, diarylheptanoid and gingerol biosynthesis. Meanwhile, the numbers and types of KEGG pathway annotations showed a significant difference between the two cultivation environments. The prediction of bacterial phenotype implied that the GC environment is more likely to deteriorate, and turtles are more susceptible to pathogens than those of PC environment. This is the first report to explore and understand the difference of microbiota characteristics between different cultivation environments in different growth stages of C. reevesii, which will provide basic data for water quality adjustment, disease prevention, and healthy breeding of turtle.