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This study was to assess the impact of permanent or temporary restricted feeding on laying hen production traits, physiology, and egg quality. Two hundred and forty individually housed ISA Brown hens were monitored across 2 phases, assigned to 3 treatments: ad libitum feeding (ALF), temporary restricted feeding (TRF) and permanent restricted feeding (PRF), n = 80 hens per treatment. In Phase 1 (P1), 22 to 40 weeks, the TRF and PRF hens were offered 115 g of feed daily. In Phase 2 (P2), 41 to 46 weeks, the TRF hens were transitioned to ALF status while the ALF and PRF hens remained as in P1. From 35 to 40 weeks, eggs were collected once weekly from 15 hens per treatment and assessed for differences in albumen, yolk, and shell variables. At 45 weeks, 10 hens each from the ALF and PRF groups were euthanized and differences in organ characteristics were assessed. In P1, feed intake, feed to egg conversion ratio and body weight (BW) change were lower (P

Aim: The aim of this study was to explore the value of the FRANCE-2 score in associating with clinical outcome in the medium and short-term after TAVI and to compare its relative merits with other risk score models. Methods: 187 consecutive patients undergoing TAVI in a single UK centre were retrospectively studied. The FRANCE-2, logistic EuroSCORE, EuroSCORE II, German AV and STS/ACC TVT risk scores were calculated retrospectively and c-statistics associating with mortality were applied. Survival outcomes were compared between different risk groups according to the FRANCE-2 scores. Results: Of the 187 patients, 57.2% were male and their mean age was 80.9 ± 6.9 years. The c-index of FRANCE-2 score for predicting 30-day mortality was 0.793 (p = 0.009), for 1-year mortality 0.679 (p = 0.016) and for 2-year mortality was 0.613 (p = 0.088). The mean survival time for patients with a high FRANCE-2 score (18.6 months) was significantly less than for patients with low and moderate scores (p = 0.0004). The logistic EuroSCORE and EuroSCORE II were poorly associated with 30-day and 1-year mortality. STS/ACC TVT score was best predictive of 1-year mortality and German AV score was moderately predictive of 30-day mortality. Conclusions: The FRANCE-2 risk score is associated with differential short- and medium-term survival in patients undergoing TAVI. The presence of a high FRANCE-2 score (>5) is associated with poor survival. The FRANCE-2 scoring system could be considered as a useful additional tool by the Heart multidisciplinary team (MDT) in identifying patients who are likely to have limited survival benefit although this requires further prospective evaluation.

From 1976 Australia has experienced seven highly pathogenic avian influenza (HPAI) outbreaks in poultry farms and there have been a total of 16 confirmed low pathogenic avian influenza (LPAI) cases in poultry in Australia at the time of writing. This paper describes all past LPAI and HPAI detections in Australian poultry and reviews avian influenza risk in the Australian commercial chicken industry. The factors that influence this risk are also discussed; notably the nomadic nature of Australian waterfowl, the increasing demand of free range poultry egg and meat production in Australia, and biosecurity practices implemented across farms including farm separations.

Feed efficiency (FE) is an important measure of productivity in the layer industry; however, little is known about how FE differs between individual hens during the egg-laying cycle and the implications for egg quality parameters. Individual 25-week-old ISA Brown hens were observed for 42 days, ranked into three FE groups (n = 48 per High (HFE), Medium (MFE) and Low (LFE) FE groups and then monitored later in the laying cycle from 35–40 weeks. The groups exhibited different feed to egg conversion ratios (p < 0.001) from 35–40 weeks. Average daily feed intake and body weight were highest (p < 0.001) in the LFE group compared to the MFE and HFE groups, while albumen height, Haugh unit and amino acid concentrations of the albumen were significantly higher in the HFE groups compared to the LFE cohort (p < 0.001). This study concludes that FE status established in early lay is a stable variable until at least 40 weeks of age, and overweight, mid-laying hens that had poor FE produced inferior egg albumen quality measurements and composition. The distinct traits of the highly efficient hens and the poor feed efficient hens may provide important information to improving productivity in egg production.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Uniformity in hen and egg traits is an important consideration in commercial layer flocks. There is little information on how individual hen feed consumption and body weight affect egg quality measurements. This study investigated the variation in performance traits of individual hens and associations with egg quality characteristics. Four hundred and fifty-five ISA Brown caged hens in early lay were monitored for 42 days (25 to 30 weeks of age) to collect hen feed consumption and egg production measurements. Forty-four hens from the flock were randomly selected and eggs were collected from the same hen once weekly for albumen, yolk, and shell assessment. The means ± standard deviation of average daily feed intake (ADFI), albumen height, initial body weight (IBW), and final body weight (FBW) were 124 g ± 15, 10.3 mm ± 1.5, 1802 g ± 129, and 2000 g ± 175, respectively. Albumen height was not associated with ADFI (r = 0.18, p = 0.21), IBW (r = −0.04, p = 0.79), or FBW (r = −0.06, p = 0.69). This study showed variation in feed intake, body weight, and albumen quality of individual early-lay hens. Feed intake and body weight did not influence albumen quality.

[This corrects the article DOI: 10.1371/journal.pone.0193730.].

This study quantified and compared the probability of avian influenza (AI) spread within and between Australian commercial chicken farms via specified spread pathways using scenario tree mathematical modeling. Input values for the models were sourced from scientific literature, expert opinion, and a farm survey conducted during 2015 and 2016 on Australian commercial chicken farms located in New South Wales (NSW) and Queensland. Outputs from the models indicate that the probability of no establishment of infection in a shed is the most likely end-point after exposure and infection of low-pathogenic avian influenza (LPAI) in one chicken for all farm types (non-free range meat chicken, free range meat chicken, cage layer, barn layer, and free range layer farms). If LPAI infection is established in a shed, LPAI is more likely to spread to other sheds and beyond the index farm due to a relatively low probability of detection and reporting during LPAI infection compared to high-pathogenic avian influenza (HPAI) infection. Among farm types, the median probability for HPAI spread between sheds and between farms is higher for layer farms (0.0019, 0.0016, and 0.0031 for cage, barn, and free range layer, respectively) than meat chicken farms (0.00025 and 0.00043 for barn and free range meat chicken, respectively) due to a higher probability of mutation in layer birds, which relates to their longer production cycle. The pathway of LPAI spread between sheds with the highest average median probability was spread via equipment (0.015; 5–95%, 0.0058–0.036) and for HPAI spread between farms, the pathway with the highest average median probability was spread via egg trays (3.70 × 10−5; 5–95%, 1.47 × 10−6–0.00034). As the spread model did not explicitly consider volume and frequency of the spread pathways, these results provide a comparison of spread probabilities per pathway. These findings highlight the importance of performing biosecurity practices to limit spread of the AI virus. The models can be updated as new information on the mechanisms of the AI virus and on the volume and frequency of movements shed-to-shed and of movements between commercial chicken farms becomes available.

This study investigated the pathways of exposure to low pathogenic avian influenza (LPAI) virus among Australian commercial chicken farms and estimated the likelihood of this exposure occurring using scenario trees and a stochastic modeling approach following the World Organization for Animal Health methodology for risk assessment. Input values for the models were sourced from scientific literature and an on-farm survey conducted during 2015 and 2016 among Australian commercial chicken farms located in New South Wales and Queensland. Outputs from the models revealed that the probability of a first LPAI virus exposure to a chicken in an Australian commercial chicken farms from one wild bird at any point in time is extremely low. A comparative assessment revealed that across the five farm types (non-free-range meat chicken, free-range meat chicken, cage layer, barn layer, and free range layer farms), free-range layer farms had the highest probability of exposure (7.5 × 10−4; 5% and 95%, 5.7 × 10−4—0.001). The results indicate that the presence of a large number of wild birds on farm is required for exposure to occur across all farm types. The median probability of direct exposure was highest in free-range farm types (5.6 × 10−4 and 1.6 × 10−4 for free-range layer and free-range meat chicken farms, respectively) and indirect exposure was highest in non-free-range farm types (2.7 × 10−4, 2.0 × 10−4, and 1.9 × 10−4 for non-free-range meat chicken, cage layer, and barn layer farms, respectively). The probability of exposure was found to be lowest in summer for all farm types. Sensitivity analysis revealed that the proportion of waterfowl among wild birds on the farm, the presence of waterfowl in the range and feed storage areas, and the prevalence of LPAI in wild birds are the most influential parameters for the probability of Australian commercial chicken farms being exposed to avian influenza (AI) virus. These results highlight the importance of ensuring good biosecurity on farms to minimize the risk of exposure to AI virus and the importance of continuous surveillance of LPAI prevalence including subtypes in wild bird populations.

The objective of this study was to elicit experts' opinions and gather estimates on the perceived probability of introduction and spread of avian influenza (AI) virus in the Australian broiler and layer industry. Using a modified Delphi method and a 4-step elicitation process, 11 experts were asked to give initial individual estimates for the various pathways and practices in the presented scenarios using a questionnaire. Following this, a workshop was conducted to present group averages of estimates and discussion was facilitated to obtain final individual estimates. For each question, estimates for all experts were combined using a discrete distribution, with weights allocated representing the level of expertise. Indirect contact with wild birds either via a contaminated water source or fomites was considered the most likely pathway of introduction of low pathogenic avian influenza (LPAI) on poultry farms. Presence of a water body near the poultry farm was considered a potential pathway for introduction only when the operation type was free range and the water body was within 500m distance from the shed. The probability that LPAI will mutate to highly pathogenic avian influenza (HPAI) was considered to be higher in layer farms. Shared personnel, equipment and aerosol dispersion were the most likely pathways of shed to shed spread of the virus. For LPAI and HPAI spread from farm to farm, shared pick-up trucks for broiler and shared egg trays and egg pallets for layer farms were considered the most likely pathways. Findings from this study provide an insight on most influential practices on the introduction and spread of AI virus among commercial poultry farms in Australia, as elicited from opinions of experts. These findings will be used to support parameterization of a modelling study assessing the risk of AI introduction and spread among commercial poultry farms in Australia.

[This corrects the article DOI: 10.1371/journal.pone.0188505.].

This paper describes the level of adoption of biosecurity practices performed on Australian commercial chicken meat and layer farms and farmer-perceived importance of these practices. On-farm interviews were conducted on 25 free range layer farms, nine cage layer farms, nine barn layer farms, six free range meat chicken farms and 15 barn meat chicken farms in the Sydney basin bioregion and South East Queensland. There was a high level of treatment of drinking water across all farm types; town water was the most common source. In general, meat chicken farms had a higher level of adoption of biosecurity practices than layer farms. Cage layer farms had the shortest median distance between sheds (7.75m) and between sheds and waterbodies (30m). Equipment sharing between sheds was performed on 43% of free range meat chicken farms compared to 92% of free range layer farms. There was little disinfection of this shared equipment across all farm types. Footbaths and visitor recording books were used by the majority of farms for all farm types except cage layer farms (25%). Wild birds in sheds were most commonly reported in free range meat chicken farms (73%). Dogs and cats were kept across all farm types, from 56% of barn layer farms to 89% of cage layer farms, and they had access to the sheds in the majority (67%) of cage layer farms and on the range in some free range layer farms (44%). Most biosecurity practices were rated on average as 'very important' by farmers. A logistic regression analysis revealed that for most biosecurity practices, performing a practice was significantly associated with higher perceived farmer importance of that biosecurity practice. These findings help identify farm types and certain biosecurity practices with low adoption levels. This information can aid decision-making on efforts used to improve adoption levels.

There are few published studies describing the unique management practices, farm design and housing characteristics of commercial meat chicken and layer farms in Australia. In particular, there has been a large expansion of free range poultry production in Australia in recent years, but limited information about this enterprise exists. This study aimed to describe features of Australian commercial chicken farms, with particular interest in free range farms, by conducting on-farm interviews of 25 free range layer farms, nine cage layer farms, nine barn layer farms, six free range meat chicken farms and 15 barn meat chicken farms in the Sydney basin bioregion and South East Queensland. Comparisons between the different enterprises (cage, barn and free range) were explored, including stocking densities, depopulation procedures, environmental control methods and sources of information for farmers. Additional information collected for free range farms include range size, range characteristics and range access. The median number of chickens per shed was greatest in free range meat chicken farms (31,058), followed by barn meat chicken (20,817), free range layer (10,713), barn layer (9,300) and cage layer farms (9,000). Sheds had cooling pads and tunnel ventilation in just over half of both barn and free range meat chicken farms (53%, n = 8) and was least common in free range layer farms (16%, n = 4). Range access in free range meat chicken farms was from sunrise to dark in the majority (93%, n = 14) of free range meat chicken farms. Over half of free range layer farms (56%, n = 14) granted range access at a set time each morning; most commonly between 9:00 to 10.00am (86%, n = 12), and chickens were placed back inside sheds when it was dusk.

Campylobacter spp. are important causes of bacterial gastroenteritis in humans in developed countries. Among Campylobacter spp. Campylobacter jejuni (C. jejuni) and C. coli are the most common causes of human infection. In this study, a multiplex PCR (mPCR) and high resolution melt (HRM) curve analysis were optimized for simultaneous detection and differentiation of C. jejuni and C. coli isolates. A segment of the hippuricase gene (hipO) of C. jejuni and putative aspartokinase (asp) gene of C. coli were amplified from 26 Campylobacter isolates and amplicons were subjected to HRM curve analysis. The mPCR-HRM was able to differentiate between C. jejuni and C. coli species. All DNA amplicons generated by mPCR were sequenced. Analysis of the nucleotide sequences from each isolate revealed that the HRM curves were correlated with the nucleotide sequences of the amplicons. Minor variation in melting point temperatures of C. coli or C. jejuni isolates was also observed and enabled some intraspecies differentiation between C. coli and/or C. jejuni isolates. The potential of PCR-HRM curve analysis for the detection and speciation of Campylobacter in additional human clinical specimens and chicken swab samples was also confirmed. The sensitivity and specificity of the test were found to be 100% and 92%, respectively. The results indicated that mPCR followed by HRM curve analysis provides a rapid (8 hours) technique for differentiation between C. jejuni and C. coli isolates.