<jats:p>Ascaris lumbricoides is a major soil-transmitted helminth that is highly infective to humans. The ova of A. lumbricoides are able to survive wastewater treatment, thus making it an indicator organism for effective water treatment and sanitation. Hence, Ascaris ova must be removed from wastewater matrices for the safe use of recycled water. Current microscopic techniques for identification and enumeration of Ascaris ova are laborious and cumbersome. Polymerase chain reaction (PCR)-based techniques are sensitive and specific, however, major constraints lie in having to transport samples to a centralised laboratory, the requirement for sophisticated instrumentation and skilled personnel. To address this issue, a rapid, highly specific, sensitive, and affordable method for the detection of helminth ova was developed utilising recombinase polymerase amplification (RPA) coupled with lateral flow (LF) strips. In this study, Ascaris suum ova were used to demonstrate the potential use of the RPA-LF assay. The method was faster (&lt; 30 min) with optimal temperature at 37 °C and greater sensitivity than PCR-based approaches with detection as low as 2 femtograms of DNA. Furthermore, ova from two different helminth genera were able to be detected as a multiplex assay using a single lateral flow strip, which could significantly reduce the time and the cost of helminth identification. The RPA-LF system represents an accurate, rapid, and cost-effective technology that could replace the existing detection methods, which are technically challenged and not ideal for on-site detection in wastewater treatment plants.</jats:p>

<jats:p>Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.</jats:p>

<jats:p> The cyanobacterium Microcystis aeruginosa has been linked to toxic blooms worldwide. In addition to producing hepatotoxic microcystins, many strains are capable of synthesising a variety of biologically active compounds, including protease and phosphatase inhibitors, which may affect aquatic ecosystems and pose a risk to their use. This study explored the distribution, composition and conservation of known secondary metabolite (SM) biosynthesis gene clusters in the genomes of 27 M. aeruginosa strains isolated from six different Köppen–Geiger climates. Our analysis identified gene clusters with significant homology to nine SM biosynthesis gene clusters spanning four different compound classes: non-ribosomal peptides, hybrid polyketide–non-ribosomal peptides, cyanobactins and microviridins. The aeruginosin, microviridin, cyanopeptolin and microcystin biosynthesis gene clusters were the most frequently observed, but hybrid polyketide–non-ribosomal peptide biosynthesis clusters were the most common class overall. Although some biogeographic relationships were observed, taxonomic markers and geography were not reliable indicators of SM biosynthesis cluster distribution, possibly due to previous genetic deletions or horizontal gene transfer events. The only cyanotoxin biosynthesis gene cluster identified in our screening study was the microcystin synthetase (mcy) gene cluster, suggesting that the production of non-microcystin cyanotoxins by this taxon, such as anatoxin-a or paralytic shellfish poison analogues, is either absent or rare. </jats:p>

Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h2 ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h2 estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.

[This corrects the article DOI: 10.3389/fvets.2019.00351.].

Despite the fact that the sulphur hexafluoride (SF6) tracer technique was developed over 25 years ago to measure methane production from grazing and non-housed animals, no studies have specifically investigated whether ambient wind speed, temperature, relative humidity and rainfall influence the accuracy of the method. The aim of this research was to investigate how these weather factors influence the measurement of enteric methane production by the SF6 technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Methane production from individual cows was measured daily over the last 5 days of each 32-day period. An automated weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily wind speed, average daily temperature, average daily relative humidity and total daily rainfall measurements to dry matter intake, average daily methane production and methane yield of each cohort of cows. It was concluded that the modified SF6 technique can be used outdoors during a range of wind speeds, ambient temperatures, relative humidities and rainfall conditions without causing a significant effect on the measurement of methane production or methane yield of dairy cows.

SARS-CoV-2 causes a spectrum of COVID-19 disease, the immunological basis of which remains ill defined. We analyzed 85 SARS-CoV-2-infected individuals at acute and/or convalescent time points, up to 102 days after symptom onset, quantifying 184 immunological parameters. Acute COVID-19 presented with high levels of IL-6, IL-18, and IL-10 and broad activation marked by the upregulation of CD38 on innate and adaptive lymphocytes and myeloid cells. Importantly, activated CXCR3+cTFH1 cells in acute COVID-19 significantly correlate with and predict antibody levels and their avidity at convalescence as well as acute neutralization activity. Strikingly, intensive care unit (ICU) patients with severe COVID-19 display higher levels of soluble IL-6, IL-6R, and IL-18, and hyperactivation of innate, adaptive, and myeloid compartments than patients with moderate disease. Our analyses provide a comprehensive map of longitudinal immunological responses in COVID-19 patients and integrate key cellular pathways of complex immune networks underpinning severe COVID-19, providing important insights into potential biomarkers and immunotherapies.

Norovirus is estimated to cause 677 million annual cases of gastroenteritis worldwide, resulting in 210,000 deaths. As viral gastroenteritis is generally self-limiting, clinical samples for epidemiological studies only partially represent circulating noroviruses in the population and is biased towards severe symptomatic cases. As infected individuals from both symptomatic and asymptomatic cases shed viruses into the sewerage system at a high concentration, waste water samples are useful for the molecular epidemiological analysis of norovirus genotypes at a population level. Using Illumina MiSeq and Sanger sequencing, we surveyed circulating norovirus within Australia and New Zealand, from July 2014 to December 2016. Importantly, norovirus genomic diversity during 2016 was compared between clinical and waste water samples to identify potential pandemic variants, novel recombinant viruses and the timing of their emergence. Although the GII.4 Sydney 2012 variant was prominent in 2014 and 2015, its prevalence significantly decreased in both clinical and waste water samples over 2016. This was concomitant with the emergence of multiple norovirus strains, including twoGII.4 Sydney 2012 recombinant viruses, GII.P4 New Orleans 2009/GII.4 Sydney 2012 and GII.P16/GII.4 Sydney 2012, along with three other emerging strains GII.17, GII.P12/GII.3 and GII.P16/GII.2. This is unusual, as a single GII.4 pandemic variant is generally responsible for 65-80% of all human norovirus infections at any one time and predominates until it is replaced by a new pandemic variant. In sumary, this study demonstrates the combined use of clinical and wastewater samples provides a more complete picture of norovirus circulating within the population.

Soil-transmitted helminths (STHs) pose a significant public health problem, infecting approximately 2 billion people globally. Despite relatively low prevalence in developed countries, the removal of STHs from wastewater remains crucial to allow the safe use of biosolids or recycled water for agriculture. Wastewater helminth egg count data can contribute to an assessment of the need for, or success of, a parasite management program. Although the World Health Organisation (WHO) has recommended a standard method for counting helminth eggs in raw sewage based on the method of Bailenger (Ayres et al., 1996), the method generally results in low percentage egg recoveries. Given the importance of determining the presence of STHs, it is essential to develop novel techniques that optimise the recovery rate of eggs from raw sewage. In the present study: •The method described by Bowman et al. (2003) was optimized for the concentration and enumeration of helminth eggs in raw sewage from municipal sewage treatment plants.•The method is simple and reproducible and recovers a greater percentage of helminth eggs compared to the WHO method.