Precision technologies and data have had relatively modest impacts in grass-based livestock ruminant production systems compared with other agricultural sectors such as arable. Precision technologies ...promise increased efficiency, reduced environmental impact, improved animal health, welfare and product quality. The benefits of precision technologies have, however, been relatively slow to be realised on pasture based farms. Though there is significant overlap with indoor systems, implementing technology in grass-based dairying brings unique opportunities and challenges. The large areas animals roam and graze in pasture based systems and the associated connectivity challenges may, in part at least, explain the comparatively lower adoption of such technologies in pasture based systems. With the exception of sensor and Bluetooth-enabled plate metres, there are thus few technologies designed specifically to increase pasture utilisation. Terrestrial and satellite-based spectral analysis of pasture biomass and quality is still in the development phase. One of the key drivers of efficiency in pasture based systems has thus only been marginally impacted by precision technologies. In contrast, technological development in the area of fertility and heat detection has been significant and offers significant potential value to dairy farmers, including those in pasture based systems. A past review of sensors in health management for dairy farms concluded that although the collection of accurate data was generally achieved, the processing, integration and presentation of the resulting information and decision-support applications were inadequate. These technologies’ value to farming systems is thus unclear. As a result, it is not certain that farm management is being sufficiently improved to justify widespread adoption of precision technologies currently. We argue for a user need-driven development of technologies and for a focus on how outputs arising from precision technologies and associated decision support applications are delivered to users to maximise their value. Further cost/benefit analysis is required to determine the efficacy of investing in specific precision technologies, potentially taking account of several yet to ascertained farm specific variables.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Recently, prebiotic supplementation of infant formula has become common practice; however the impact on the intestinal microbiota has not been completely elucidated. In this study, neonatal piglets ...were randomized to: formula (FORM, n = 8), formula supplemented with 2 g/L each galactooligosaccharides (GOS) and polydextrose (PDX, F+GP, n = 9) or a sow-reared (SOW, n = 12) reference group for 19 days. The ileal (IL) and ascending colon (AC) microbiota were characterized using culture-dependent and -independent methods. 16S amplicon sequencing identified no differences at the genera level in the IL. Interestingly, six genera in the AC were significantly different between FORM and F+GP (P<0.05): Lactobacillus, Ruminococcus, Parabacteroides, Oscillospira, Hydrogenoanaerobacterium and Catabacter. In particular, the relative abundance of AC Lactobacillus was higher (P = 0.04) in F+GP as compared to FORM. Culture-dependent analysis of the IL and AC lactobacilli communities of FORM and F+GP revealed a Lactobacillus spp. composition similar to 16S amplicon sequencing. Additional analysis demonstrated individual Lactobacillus isolates were unable to ferment PDX. Conversely, a majority of lactobacilli isolates could ferment GOS, regardless of piglet diet. In addition, the ability of lactobacilli isolates to ferment the longer chain GOS fragments (DP 3 or greater), which are expected to be present in the distal intestine, was not different between FORM and F+GP. In conclusion, prebiotic supplementation of formula impacted the AC microbiota; however, direct utilization of GOS or PDX does not lead to an increase in Lactobacillus spp.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The objective of this study was to identify and understand grassland management practices employed on dairy farms in the Republic of Ireland, including grazing‐season length, concentrate‐feed input, ...uptake of new grassland‐management technologies and frequency and methods of sward renewal. The sample population for the survey was chosen from a proportionate representation of all milk suppliers taken from three of the largest dairy processors in the Republic of Ireland. The sample was subsequently broken down into three stocking rate (SR) and three size categories of milk quota (Qcat) to investigate their effects on the survey variables. Both SR and Qcat had significant effects on the proportion of participants adopting grass‐based technologies and on the amount of supplementary feed offered. Grazing‐season length increased from 228 d in Qcat1 to 249 d in Qcat 3 but was unaffected by SR (241 d; s.d. 3·05). The proportion of the grazing area reseeded annually was significantly affected by SR, increasing from 0·044 to 0·095 of the grassland area as SR increased from SR1 to SR3, with no effect of Qcat (0·068). The results show that on‐farm grass utilization is low, with significant potential for expansion and increased efficiency through increased SRs, greater adoption of grassland‐management technologies and higher levels of sward renewal.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
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•Differently substituted zinc phthalocyanines were conjugated to graphene quantum dots.•The conjugates exhibited an increase in the triplet quantum yields.•Upon introduction of ...cationic charges, the photodynamic therapy activity increased.
Several differently substituted Zn(II) phthalocyanines (ZnPcs) were prepared and conjugated to GQDs. The photophysical properties were determined for both the Pcs and their respective conjugates including the fluorescence/triplet quantum yields and lifetimes as well as the singlet oxygen generating abilities. Upon conjugation to GQDs, the fluorescence of the Pcs decreased (insignificant decrease in some cases), with an increase in the triplet quantum yields. However, the singlet quantum yields of the Pcs in the conjugates did not show an increase with the increase in the triplet quantum yields, this is suspected to be due to the screening effect. The cytotoxicity of the complexes in vitro decreased upon conjugation, as a result of the reduced actual number of Pcs units provided in the conjugate for therapy. Upon introduction of cationic charges, the photodynamic therapy activity of the complexes increased.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
In Barrett's esophagus (BE), the normal squamous lining of the esophagus is replaced by specialized columnar epithelium. Endoscopic surveillance with autofluorescence imaging (AFI) and ...molecular biomarkers have been studied separately to detect early neoplasia (EN) in BE. The combination of advanced‐imaging modalities and biomarkers has not been investigated; AFI may help detecting biomarkers as a risk‐stratification tool. We retrospectively evaluated a cohort of patients undergoing endoscopy for EN in BE with AFI and correlated five biomarkers (HPP1, RUNX3, p16, cyclin A, and p53) in tissue samples with AFI and dysplasia status. Fifty‐eight samples from a previous prospective study were selected: 15 true‐positive (TP: AFI‐positive, EN), 21 false‐positive (FP: AFI‐positive, no EN), 12 true‐negative (TN1; AFI‐negative, no EN in sample), 10 true‐negative (TN2: AFI‐negative, no EN in esophagus). Methylation‐specific RT‐PCR was performed for HPP1, RUNX3, p16, and immunohistochemistry for cyclin A, p53. P < 0.05 was considered statistically significant. Bonferroni correction was used for multiple comparisons. P16, cyclin A, p53 correlated with dysplasia (P < 0.01, P = 0.003, P < 0.001, respectively). Increased p16 methylation was observed between TP versus TN2 (P = 0.003) and TN1 versus TN2 (P = 0.04) subgroups, suggesting a field defect. Only p53 correlated with AFI‐status (P = 0.003). After exclusion of EN samples, significance was lost. Although correlation with dysplasia status was confirmed for p16, cyclin A and p53, underlining the importance of these biomarkers as an early event in neoplastic progression, none of the investigated biomarkers correlated with AFI status. A larger prospective study is needed to assess the combination of AFI and a larger panel of biomarkers to improve risk stratification in BE.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Wildfires are ecosystem‐level drivers of structure and function in many vegetated biomes. While numerous studies have emphasized the benefits of fire to ecosystems, large wildfires have also been ...associated with the loss of ecosystem services and shifts in vegetation abundance. The size and number of wildfires are increasing across a number of regions, and yet the outcomes of large wildfire on vegetation at large‐scales are still largely unknown. We introduce an exhaustive analysis of wildfire‐scale vegetation response to large wildfires across North America's grassland biome. We use 18 years of a newly released vegetation data set combined with 1,390 geospatial wildfire perimeters and drought data to detect large‐scale vegetation response among multiple vegetation functional groups. We found no evidence of persistent declines in vegetation driven by wildfire at the biome level. All vegetation functional groups exhibited relatively rapid recovery to pre wildfire ranges of variation across the Great Plains ecoregions, with the exception being a persistent decrease in the abundance of trees in the Northwestern Great Plains. Drought intensity magnified immediate vegetation response to wildfire. Persistent declines in vegetation cover were observed at the scale of single pixels (30 m), suggesting that these responses were localized and represent extreme cases within larger wildfires. Our findings echo over a century of research demonstrating a biome resilient to wildfire.
Key Points
All vegetation functional groups exhibited relatively rapid recovery at the biome level
At the ecoregion level, vegetation recovered to prewildfire levels with the exception of one ecoregion for a single functional group
Wildfire‐driven vegetation degradation appears localized and represents extreme cases within larger wildfires
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The preterm infant gut microbiota is influenced by environmental, endogenous, maternal, and genetic factors. Although siblings share similar gut microbial composition, it is not known how genetic ...relatedness affects alpha diversity and specific taxa abundances in preterm infants. We analyzed the 16S rRNA gene content of stool samples, ≤ and >3 weeks postnatal age, and clinical data from preterm multiplets and singletons at two Neonatal Intensive Care Units (NICUs), Tampa General Hospital (TGH; FL, USA) and Carle Hospital (IL, USA). Weeks on bovine milk-based fortifier (BMF) and weight gain velocity were significant predictors of alpha diversity. Alpha diversity between siblings were significantly correlated, particularly at ≤3 weeks postnatal age and in the TGH NICU, after controlling for clinical factors. Siblings shared higher gut microbial composition similarity compared to unrelated individuals. After residualizing against clinical covariates, 30 common operational taxonomic units were correlated between siblings across time points. These belonged to the bacterial classes Actinobacteria, Bacilli, Bacteroidia, Clostridia, Erysipelotrichia, and Negativicutes. Besides the influence of BMF and weight variables on the gut microbial diversity, our study identified gut microbial similarities between siblings that suggest genetic or shared maternal and environmental effects on the preterm infant gut microbiota.
Developing food-based dietary guidelines (FBDGs) for infants and toddlers is a complex task that few countries have attempted.
Our objectives are to describe the process of food pattern modeling ...(FPM) conducted to develop FBDGs for the Dietary Guidelines for Americans, 2020–2025 for infants 6 to <12 mo and toddlers 12 to <24 mo of age, as well as the implications of the results and areas needing further work.
The US 2020 Dietary Guidelines Advisory Committee, with the support of federal staff, conducted FPM analyses using 5 steps: 1) identified energy intake targets; 2) established nutritional goals; 3) identified food groupings and expected amounts, using 3 options for the amount of energy from human milk in each age interval; 4) estimated expected nutrient intakes for each scenario, based on nutrient-dense representative foods; and 5) evaluated expected nutrient intakes against nutritional goals.
For human milk–fed infants (and toddlers), example combinations of complementary foods and beverages were developed that come close to meeting almost all nutrient recommendations if iron-fortified infant cereals are included at 6 to <12 mo of age. These combinations would also be suitable for formula-fed infants. For toddlers not fed human milk, 2 patterns were developed: the Healthy US-Style Pattern and the Healthy Vegetarian Pattern (a lacto-ovo vegetarian pattern). Achieving nutrient recommendations left virtually no remaining energy for added sugars.
It is challenging to meet all nutrient needs during these age intervals. Added sugars should be avoided for infants and toddlers <2 y of age. Further work is needed to 1) establish a reference human milk composition profile, 2) update and strengthen the DRI values for these age groups, and 3) use optimization modeling, in combination with FPM, to identify combinations of foods that meet all nutritional goals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Identification of nutrients of public health concern has been a hallmark of the Dietary Guidelines for Americans (DGA); however, a formal systematic process for identifying them has not been ...published.
We aimed to propose a framework for identifying “nutrients or food components” (NFCs) of public health relevance to inform the DGA.
The proposed framework consists of 1) defining terminology; 2) establishing quantitative thresholds to identify NFCs; and 3) examining national data. The proposed framework utilizes available data from 3 key data sources or “prongs”: 1) dietary intakes; 2) biological endpoints; and 3) clinical health consequences such as prevalence of health conditions, directly or indirectly through validated surrogate markers.
In identifying potential NFCs of public health concern, the 2020 DGA Committee developed a decision-tree framework with suggestions for combining the 3 prongs. The identified NFCs of public health concern for Americans ≥1 y old included fiber, calcium (≥2 y old), vitamin D, and potassium for low intakes and sodium, added sugars, and saturated fats (≥2 y old) for high intakes that were associated with adverse health consequences. Iron was identified among infants ages 6–12 mo fed human milk. For reproductive-aged and pregnant females, iron (all trimesters) and folate (first trimester) were identified for low intake, based on dietary and biomarker data (iron) or the severity of the consequence (folic acid and neural tube defects). Among pregnant women, low iodine was of potential public health concern based on biomarker data. Other NFCs that were underconsumed, overconsumed, and pose special challenges were identified across the life course.
The proposed decision-tree framework was intended to streamline and add transparency to the work of this and future Dietary Guidelines Advisory Committees to identify NFCs that need to be encouraged or discouraged in order to help reduce risk of chronic disease and promote health and energy balance in the population.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Complementary foods and beverages (CFBs) are key components of an infant’s diet in the second 6 months of life. This article summarizes nutrition and feeding practices examined by the 2020 Dietary ...Guidelines Advisory Committees during the CFB life stage. Breastfeeding initiation is high (84%), but exclusive breastfeeding at 6 months (26%) is below the Healthy People 2030 goal (42%). Most infants (51%) are introduced to CFBs sometime before 6 months. The primary mode of feeding (ie, human milk fed HMF; infant formula or mixed formula and human milk fed FMF) at the initiation of CFBs is associated with the timing of introduction and types of CFBs reported. FMF infants (42%) are more likely to be introduced to CFBs before 4 months compared with HMF infants (19%). Different dietary patterns, such as higher prevalence of consumption and mean amounts, were observed, including fruit, grains, dairy, proteins, and solid fats. Compared with HMF infants of the same age, FMF infants consume more total energy (845 vs 631 kcal) and protein (22 vs 12 g) from all sources, and more energy (345 vs 204 kcal) and protein (11 vs 6 g) from CFBs alone. HMF infants have a higher prevalence of risk of inadequate intakes of iron (77% vs 7%), zinc (54% vs <3%), and protein (27% vs <3%). FMF infants are more likely to have an early introduction (<12 months) to fruit juice (45% vs 20%) and cow’s milk (36% vs 24%). Registered dietitian nutritionists and nutritional professionals should consider tailoring their advice to caregivers on dietary and complementary feeding practices, taking into account the primary mode of milk feeding during this life stage to support infants’ nutrient adequacy. National studies that address the limitations of this analysis, including small sample sizes and imputed breast milk volume, could refine findings from this analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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