The consequences of urbanisation for Earth's biogeochemical cycles are largely unexplored. Copper (Cu) in urban soils is being accumulated mainly due to anthropogenic activities under rapid urbanisation. The increasing Cu concentrations may contribute to altering soil nitrogen (N) cycling in urban ecosystems through modulating denitrification processes. This research aims to identify how Cu impacts urban soil denitrification functions and denitrifier abundance. An urban park soil with a background total Cu concentration of 7.9μgg–1 was incubated anaerobically with different Cu amendments (10, 20, 40, 80 and 160μg Cu g–1 soil), similar to prevalent Cu contents in urban soils. We evaluated the soil denitrification functions using the acetylene (C2H2) inhibition method and assessed the denitrifier abundance by quantitative polymerase chain reaction (qPCR) analyses of denitrifying marker genes (nirK, nirS and nosZ). At the function level, we observed that both the potential soil denitrification activity and the N2O emission rate due to denitrification were significantly (P<0.05) inhibited by Cu; even the lowest Cu addition (10μg Cu g–1 soil) drastically affected the denitrification function. Moreover, Cu significantly (P<0.05) decreased the abundance of nirK and nirS genes at the additions of 160μg Cu g–1 soil and 40μg Cu g–1 soil, respectively, whereas it had no clear impact on nosZ gene copies. Further correlation analyses revealed that the potential denitrification activity was positively correlated to the copy numbers of nirK and nirS genes, but it was not correlated to nosZ gene abundance. These findings indicate that Cu additions inhibited soil denitrification function and decreased denitrifier abundance in the investigated urban park soil. Our results suggest that Cu accumulation in urban soils, resulting from urbanisation, may generally influence denitrification in urban ecosystems.

Near infrared spectroscopy (NIRS) has the potential to estimate contents of fatty acids (FA) in milk frequently at-farm or during daily milking routine. In this study, a total of 738 raw milk spectra collected from 33 Holstein cows over a period of 30 weeks were recorded. Reference data on FA composition in milk and in milk fat were analysed in laboratory. Calibration models were calculated for single FA and groups of FA in milk and in milk fat. Validation resulted in sufficient Ratio of Prediction to Deviation (RPD) values for some single FA and in higher RPD values for groups of FA when concentrations of FA in milk were predicted. Since the concentrations of most FA in milk are highly correlated with milk fat content, the prediction of FA contents in milk fat is more meaningful when independent predictions are intended. The accuracy of predicting single FA concentrations in milk fat is rather poor for most FA but still comparable to alternative analysing methods such as MIR analysis. The estimation of different groups of FA in milk fat resulted in an improved accuracy based on higher RPD values, which was sufficient to mirror the development in the different lactation phases. The course of cow individual long chain fatty acid (LCFA) concentration in the early lactation stage can be an indicator for body fat mobilisation. The accurate estimation of the extent and duration of body fat mobilisation in cow individuals was rather difficult with NIR predicted LCFA concentrations and would require a higher measuring frequency than applied in this study.

Ruminal in situ incubations are widely used to assess the nutritional value of feedstuffs for ruminants. In in situ methods, feed samples are ruminally incubated in indigestible bags over a predefined timespan and the disappearance of nutrients from the bags is recorded. To describe the degradation of specific nutrients, information on the concentration of feed samples and undegraded feed after in situ incubation (‘bag residues’) is needed. For cereal and pea grains, CP and starch (ST) analyses are of interest. The numerous analyses of residues following ruminal incubation contribute greatly to the substantial investments in labour and money, and faster methods would be beneficial. Therefore, calibrations were developed to estimate CP and ST concentrations in grains and bag residues following in situ incubations by using their near-infrared spectra recorded from 680 to 2500 nm. The samples comprised rye, triticale, barley, wheat, and maize grains (20 genotypes each), and 15 durum wheat and 13 pea grains. In addition, residues after ruminal incubation were included (at least from four samples per species for various incubation times). To establish CP and ST calibrations, 620 and 610 samples (grains and bag residues after incubation, respectively) were chemically analysed for their CP and ST concentration. Calibrations using wavelengths from 1250 to 2450 nm and the first derivative of the spectra produced the best results (R2Validation=0.99 for CP and ST; standard error of prediction=0.47 and 2.10% DM for CP and ST, respectively). Hence, CP and ST concentration in cereal grains and peas and their bag residues could be predicted with high precision by NIRS for use in in situ studies. No differences were found between the effective ruminal degradation calculated from NIRS estimations and those calculated from chemical analyses (P>0.70). Calibrations were also calculated to predict ruminal degradation kinetics of cereal grains from the spectra of ground grains. Estimation of the effective ruminal degradation of CP and ST from the near-infrared spectra of cereal grains showed promising results (R2>0.90), but the database needs to be extended to obtain more stable calibrations for routine use.

Field studies were conducted to characterize the predominant nonstructural carbohydrates in roots and crowns of purple loosestrife and to determine the seasonal fluctuation of root and crown carbohydrates in three Minnesota wetland habitats. Starch was the primary nonstructural carbohydrate present, and concentrations were consistently higher in roots than in crowns. Starch content decreased following shoot emergence and declined until flower bud formation, at which time levels reached seasonal lows. Following bud initiation, levels of starch increased during flowering, and this trend continued through to plant senescence in late September or October. Sucrose was the predominant soluble sugar in roots and crowns of purple loosestrife, but fructose and glucose were detected. Levels of sucrose in roots and crowns followed the same seasonal trends as starch.

Seed composition, including the protein, lipid and sucrose contents of 334 accessions of wild soybean (Glycine soja) collected in Japan, was evaluated using near-infrared reflectance spectroscopy (NIRS) technology. The distribution of protein, lipid and sucrose contents and correlations among these three classes of seed components were determined. Protein, lipid and sucrose levels ranged in accessions from 48.6 to 57.0, 9.0 to 14.3 and 1.24 to 3.53%, respectively. Average levels of protein, lipid and sucrose in the accessions were 54, 11 and 2.5%, respectively. High negative correlations were observed between the protein and lipid contents, and the protein and sucrose contents. Mean levels of the three constituents were compared among collection sites classified by climatic conditions. The total protein content of accessions from regions with a high annual mean temperature was high. The protein content of accessions from the II-1 region was higher than those from the III-3 region, and the sucrose content from the II-1 region was lower than those from regions III-2 and IV-3. The lipid content of plants from the II-1 region was lower than those from other regions, and the accessions in region II had a higher protein content and lower sucrose and lipid contents than the other regions. These results provide diverse and wide-ranged protein, lipid and sucrose contents information of Japanese wild soybean resources according to climatic region; thus, providing a foundation for the future development and selection of new soybean varieties with desired traits in global environmental changes.

The objective of this study was to compare the prediction efficiency of IgG concentration in bovine colostrum by NIRS, using liquid and dried (Dry-Extract Spectroscopy for Infrared Reflectance, DESIR) samples by transflectance and reflectance modes, respectively. Colostrum samples (157), obtained from 2 commercial Holstein dairy farms, were collected within the first hour after calving and kept at −20 °C until analysis. After thawing and homogenisation, a subsample of 500 mg of liquid colostrum was placed in an aluminium mirror transflectance cell (0·1 mm path length), in duplicate, to collect the spectrum. A glass fiber filter disc was infused with another subsample of 500 mg of colostrum, in duplicate, and dried in a forced-air oven at 60 °C for 20 min. The samples were placed in cells for dry samples to collect the spectra. The spectra in the VIS-NIR region (400–2500 nm) were obtained with a NIRSystems 6500 monochromator. Mathematical treatments, scatter correction treatments and number of cross-validation groups were tested to obtain prediction equations for both techniques. Reference analysis for IgG content was performed by radial immunodiffusion. The DESIR technique showed a higher variation in the spectral regions associated with water absorption bands, compared with liquid samples. The best equation for transflectance method (liquid samples) obtained a higher coefficient of determination for calibration (0·95 vs. 0·94, respectively) and cross validation (0·94 vs. 0·91, respectively), and a lower error of cross validation (9·03 vs. 11·5, respectively) than the best equation for reflectance method (DESIR samples). In final, both methods showed excellent capacity for quantitative analysis, with residual predictive deviations above 3. It is concluded that, regarding accuracy of prediction and time for obtaining results of IgG from bovine colostrum, NIRS analysis of liquid samples (transflectance) is recommended over dried samples (DESIR technique by reflectance).

Rapid assessment of the nutritional quality of diets ingested by grazing animals is pivotal for successful cow–calf management in east Mediterranean rangelands, which receive unpredictable rainfall and are subject to hot-spells. Clipped vegetation samples are seldom representative of diets consumed, as cows locate and graze selectively. In contrast, faeces are easily sampled and their near-IR spectra contain information about nutrients and their utilization. However, a pre-requisite for successful faecal near-infrared reflectance spectroscopy (FNIRS) is that the calibration database encompass the spectral variability of samples to be analyzed. Using confined beef cows in Northern and Southern Israel, we calibrated prediction equations based on individual pairs of known dietary attributes and the NIR spectra of associated faeces (n=125). Diets were composed of fresh-cut green fodder of monocots (wheat and barley), dicots (safflower and garden pea) and natural pasture collected at various phenological states over 2 consecutive years, and, optionally, supplements of barley grain and dried poultry litter. A total of 48 additional pairs of faeces and diets sourced from cows fed six complete mixed rations covering a wide range of energy and CP concentrations. Precision (linearity of calibration, R2cal, and of cross-validation, R2cv) and accuracy (standard error of cross-validation, SEcv) were criteria for calibration quality. The calibrations for dietary ash, CP, NDF and in vitro dry matter digestibility yielded R2cal values >0.87, R2cv of 0.81 to 0.89 and SEcv values of 16, 13, 39 and 31 g/kg dry matter, respectively. Equations for nutrient intake were of low quality, with the exception of CP. Evaluation of FNIRS predictions was carried out with grazing animals supplemented or not with poultry litter, and implementation of the method in one herd over 2 years is presented. The potential usefulness of equations was also established by calculating the Mahalanobis (H) distance to the spectral centroid of a calibration population of 796 faecal samples collected throughout 2 years in four herds. Seasonal trends in pasture quality and responses to management practices were identified adequately and H<3.0 for 98% of faecal samples collected. We conclude that the development of FNIRS equations with confined animals is not only unexpensive and ethically acceptable, but their predictions are also sufficiently accurate to monitor dietary composition (but not intake) of beef cattle in east Mediterranean rangelands.

Kinetics of nutrient degradation in the rumen is an important component of feed evaluation systems for ruminants. The in situ technique is commonly used to obtain such dynamic parameters, but it requires cannulated animals and incubations last several days limiting its application in practice. On the other hand, feed industry relies strongly on NIRS to predict chemical composition of feeds and it has been used to predict nutrient degradability parameters. However, most of these studies were feedstuff specific, predicting degradability parameters of a particular feedstuff or category of feedstuffs, mainly forages or compound feeds and not grains and byproducts. Our objective was to evaluate the potential of NIRS to predict degradability parameters and effective degradation utilizing a wide range of feedstuffs commonly used in ruminant nutrition. A database of 809 feedstuffs was created. Feedstuffs were grouped as forages (FF; n=256), non-forages (NF; n=539) and of animal origin (n=14). In situ degradability data for dry matter (DM; n=665), CP (n=682) and NDF (n=100) were collected. Degradability was described in terms of washable fraction (a), slowly degradable fraction (b) and its rate of degradation (c). All samples were scanned from 1100 to 2500 nm using an NIRSystems 5000 scanning in reflectance mode. Calibrations were developed for all samples (ALL), FF and NF. Equations were validated with an external validation set of 20% of total samples. NIRS equations to predict the effective degradability and fractions a and b of DM, CP and NDF could be evaluated from being adequate for screening (r2>0.77; ratio of performance to deviation (RPD)=2.0 to 2.9) to suitable for quantitative purposes (r2>0.84; RPD=3.1 to 4.7), and some predictions were improved by group separation reducing the standard error of prediction. Similarly, the rate of degradation of CP (CPc) and DM (DMc) was predicted for screening purposes (RPD⩾2 and 2.5 for CPc and DMc, respectively). However, the rate of degradation of NDF was not predicted accurately (NDFc: r2<0.75; RDP<2).

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.

Congenital heart defects are the most common cause of death in infants and young children in the developed world. As the mortality in this population has declined to less than 5%, more attention is being focused now on reducing post-procedural morbidities that may seriously impact the patient and their families. Because of multiple reasons, paediatric cardiac surgery and anaesthesia is a perfect model for studying human errors and their impact on patient safety. Congenital cardiac disease is a common lesion causing much morbidity, pain, and loss of life. Over 44,000 surgical procedures are performed yearly to repair congenital cardiac problems in the United States alone. The reduction or elimination of iatrogenic adverse outcomes, given the current mortality rates of 4.2%–4.5%, might lead to as many as 500 children achieving better outcomes or shorter hospitalizations.

Efforts to quantify the frequency of complications related to anaesthesia in patients undergoing congenital cardiac surgery have been difficult to date because of the low occurrence of this surgery compared to other surgeries on children and the relatively rare incidence of complications related to anaesthesia in this population. Anaesthesiologists play a crucial role in the reduction, recognition, and timely treatment of medical errors that impact this morbidity. Paediatric cardiac surgery encompasses many complex procedures that are highly dependent upon a sophisticated organizational structure, effective communication, coordinated efforts of multiple individuals working as a team, and high levels of cognitive and technical performance. Human factor error analysis in this patient population has shown how frequently both minor and major errors occur. The goal of this paper is to outline the frequency and sources of these errors and to suggest treatment strategies which may minimize their occurrence.

Infrared (IR) spectroscopy is based on the principle that the chemical bonds in organic molecules absorb or emit infrared light when their vibrational state changes. In the near IR part of the spectrum, large changes in vibrational state are observed (overtones), while in the mid IR region, primary vibrations are produced. The latter yields sharper, more clearly defined peaks that are better suited for quantitative purposes. Raman spectroscopy, in which the decay of the vibration is observed after strong excitation of the sample, is a variant on mid IR spectroscopy. A major challenge in applying IR spectroscopy to animal production is sample presentation. Transmission is the most powerful method well suited to liquids and gases but is inappropriate for undiluted solids. Although reflection offers an alternative for solids, it is less than ideal for quantitative purposes as the path length is not known. For pastes and opaque liquids, attenuated total reflectance offers good possibilities for the future as it acts like a transmission device but sample application is simple. A novel method is photo-acoustics in which the heating of a sample (as it absorbs the IR light) is measured using a microphone. IR spectroscopy is typically fast and easy to use. In feedmills it allows the quality (e.g. proximate and nutritionally relevant parameters such as metabolisable energy) of feed ingredients and complete feeds to be monitored. In meat processing IR spectroscopy offers the opportunity to assess meat and fat quality, and perhaps even palatability (texture and flavour). New developments in IR spectroscopy will expand its applications further. These include hand-held instruments that may find use in determining digestive disorders among birds in the field, fibreoptics that will allow instantaneous measurements to be made in almost any part of a plant, tunable lasers (with their much stronger signals) that will make IR spectroscopy much more powerful, and imaging IR spectroscopy which may be used to determine the homogeneity of meat (e.g. colour). IR spectroscopy, with its speed, ease of use and versatility, could be about to become one of the most powerful analytical techniques available to the animal production industries. It promises to allow for improved quality control in virtually every aspect of production, from feed manufacture to final product evaluation.

Near infrared reflectance spectroscopy (NIRS) was used to study the reflectance properties of intact and minced lamb muscles in two presentations to the instrument to predict their chemical composition. A total of 306 muscles were examined from 51 lambs, consisting of the following muscles: longissimus dorsi, supraspinatus, infraspinatus, semimembranosus, semitendinosus and rectus femoris. Modified partial least squares (MPLS) regression models of chemical variables yielded R2 and standard error of cross-validation (SECV) of 0·76 (SECV: 10·4), 0·83 (SECV: 5·5) and 0·73 (SECV: 4·7) for moisture, crude protein and intramuscular fat in the minced samples expressed as g/kg on a fresh-weight basis, respectively. Calibrations for intact samples had lower R2 and higher standard error of cross validation (SECV) compared with the minced samples.

Ninety-four silages were made over 5 years from predominantly perennial ryegrass swards using a range of cutting dates (19 May to 18 September), wilting periods (0 to 48 h) and additives (none, acids, inoculants, sugar, sugar + acids, sugar + inoculants). A wide range of silage composition was achieved (CV for dry matter (DM), crude protein (CP), digestible organic matter (DOMD), lactic acid, total volatile fatty acids (VFA) and sugar were 0–2. Silage dry-matter intake (SDMI) was measured for 88 silages using lambs (mean live weight (M) 28 kg) given silage as their sole diet in four incomplete block design experiments using four lambs per silage and a standard hay given every third period for covariance correction. Thirty-four of the silages were also evaluated using early lactation cows (M, 561 kg and milk yield 27 kg/day) with 7 kg/day of concentrate in eight incomplete block change-over experiments each using 12 cows.

Intakes (SDMI mean, range, s.d. glkg M075) were 56, 25 to 84, 13·7 for lambs and 90, 64 to 119, 13·4 for cows. Scaling lamb SDMI by M1'47 accounted best for the effect of lamb weight on intake (mean, range 5·07, 2·43 to 7·68). Silage predictors were grouped as follows: traditional values (BASAL) - DM, CP, organic matter (OM), DOMD,

neutral-detergent fibre (NDF), acid-detergent fibre (ADF), ammonia nitrogen (NH3N), pH, acid hydrolysed ether extract (AHEE); silage fermentation values obtained by high-performance liquid chromatography (HPLC); or by electrometric titration (ET); and near infra-red reflectance spectra (NIRS) obtained on either 100°C dried (NIRSdry) orfreshsamples (NIRSwetl using a vertical transport mechanism and NIRSwet2 using a rotating cup). The most useful predictors within each group were firstly identified by step-wise multiple linear regression and models were then derived by partial least squares.

Standard errors of cross validation (SECV) obtained by the ‘leave one out’ method were for lamb SDMI (g/M1·47) 0·81, 0·81, 0·75, 0·52, 0·82 and 0·56 for BASAL, BASAL + HPLC, BASAL + ET, NIRSdry, NIRSwetl and NIRSwet2 respectively. Corresponding values for cows (g/M0·75) were 7·3, 7·3, 5·9, 5·1, 6·2, and 2·5. Inclusion of fermentation measurements made by ET, but not by HPLC, improved SDMI prediction over that obtainedfromthe BASAL set. However, NIRSdry and NIRSwet2 were the most accurate methods giving values for s.d. (reference population)ISECV of 2·27 and 2·13 for lambs and 2·65 and 5·28 for dairy cows. Use of these methods in advisory silage evaluation should substantially reduce the errors of predicting the intake potential ofgrass silages.