Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively-fed dairy cows.

Directly measuring individual cow energy balance is not trivial. Other traits, like body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used world-wide to estimate cow body reserves and the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively-fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. Body condition score change records were merged with milk MIR spectra recorded on the same week. The data set comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec (Canada). Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from 1) MIR spectra only, 2) DIM only, or 3) MIR spectra and DIM together. ΔBCS data in both the first 120 DIM and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40*10-3 BCS units in the 120-d data set and of 3.63*10-3 BCS units in the 305-d data set. 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81*10-3 BCS units and 1.51*10-3 BCS units, respectively, using the 120-d and 305-d data set). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the data set and of the prediction model used, the combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation was reduced up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data was more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.

Peer reviewedNumerous statistical machine learning methods suitable for application to highly correlated features, as exists for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN) and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated (a60, alpha s1 CN, alpha s2 CN, kappa CN, alpha lactalbumin, and beta lactoglobulin B), while NN and RR were the best algorithms for 3 traits each (RCT, k20, and heat stability, and a30, beta CN, and beta lactoglobulin A, respectively), PLSR was best for pH and LASSO was best for CN micelle size. When traits were divided into two classes, SVM had the greatest accuracy for the majority of the traits investigated. While the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error when compared to PLSR from between 0.18% (kappa CN) to 3.67% (heat stability). The use of modern statistical ML methods for trait prediction from MIRS may improve the prediction accuracy for some traits

Associations of sedentary behaviour, physical activity, blood pressure and anthropometric measures with cardiorespiratory fitness in children with cerebral palsy

Background - Children with cerebral palsy (CP) have poor cardiorespiratory fitness in comparison to their peers with typical development, which may be due to low levels of physical activity. Poor cardiorespiratory fitness may contribute to increased cardiometabolic risk. Purpose - The aim of this study was to determine the association between sedentary behaviour, physical activity and cardiorespiratory fitness in children with CP. An objective was to determine the association between cardiorespiratory fitness, anthropometric measures and blood pressure in children with CP. Methods- This study included 55 ambulatory children with CP [mean (SD) age 11.3 (0.2) yr, range 6-17 yr; Gross Motor Function Classification System (GMFCS) levels I and II]. Anthropometric measures (BMI, waist circumference and waist-height ratio) and blood pressure were taken. Cardiorespiratory fitness was measured using a 10 m shuttle run test. Children were classified as low, middle and high fitness according to level achieved on the test using reference curves. Physical activity was measured by accelerometry over 7 days. In addition to total activity, time in sedentary behaviour and light, moderate, vigorous, and sustained moderate-to-vigorous activity (≥10 min bouts) were calculated. Results - Multiple regression analyses revealed that vigorous activity (β = 0.339, p<0.01), sustained moderate-to-vigorous activity (β = 0.250, p<0.05) and total activity (β = 0.238, p<0.05) were associated with level achieved on the shuttle run test after adjustment for age, sex and GMFCS level. Children with high fitness spent more time in vigorous activity than children with middle fitness (p<0.05). Shuttle run test level was negatively associated with BMI (r2 = -0.451, p<0.01), waist circumference (r2 = -0.560, p<0.001), waist-height ratio (r2 = -0.560, p<0.001) and systolic blood pressure (r2 = -0.306, p<0.05) after adjustment for age, sex and GMFCS level. Conclusions - Participation in physical activity, particularly at a vigorous intensity, is associated with high cardiorespiratory fitness in children with CP. Low cardiorespiratory fitness is associated with increased cardiometabolic risk

Precision measurement of the η→π+π−π0\eta\to\pi^+\pi^-\pi^0 Dalitz plot distribution with the KLOE detector

Using $1.6$ fb$^{-1}$ of $e^+ e^-\to\phi\to\eta\gamma$ data collected with the KLOE detector at DA$\Phi$NE, the Dalitz plot distribution for the $\eta \to \pi^+ \pi^- \pi^0$ decay is studied with the world's largest sample of $\sim 4.7 \cdot 10^6$ events. The Dalitz plot density is parametrized as a polynomial expansion up to cubic terms in the normalized dimensionless variables $X$ and $Y$. The experiment is sensitive to all charge conjugation conserving terms of the expansion, including a $gX^2Y$ term. The statistical uncertainty of all parameters is improved by a factor two with respect to earlier measurements.Comment: 11 pages, 9 figures, supplement: an ascii tabl

Connectivity and dispersal patterns of protected biogenic reefs : implications for the conservation of Modiolus modiolus (L.) in the Irish Sea

Biogenic reefs created by Modiolus modiolus (Linnaeus, 1758) (horse mussel reefs) are marine habitats which support high levels of species biodiversity and provide valuable ecosystem services. Currently, M. modiolus reefs are listed as a threatened and/or declining species and habitat in all OSPAR regions and thus are highlighted as a conservation priority under the EU Marine Strategy Framework Directive (MSFD). Determining patterns of larval dispersal and genetic connectivity of remaining horse mussel populations can inform management efforts and is a critical component of effective marine spatial planning (MSP). Larval dispersal patterns and genetic structure were determined for several M. modiolus bed populations in the Irish Sea including those in Wales (North Pen Llŷn), Isle of Man (Point of Ayre) and Northern Ireland (Ards Peninsula and Strangford Lough). Simulations of larval dispersal suggested extant connectivity between populations within the Irish Sea. Results from the genetic analysis carried out using newly developed microsatellite DNA markers were consistent with those of the biophysical model. Results indicated moderately significant differentiation between the Northern Ireland populations and those in the Isle of Man and Wales. Simulations of larval dispersal over a 30 day pelagic larval duration (PLD) suggest that connectivity over a spatial scale of 150km is possible between some source and sink populations. However, it appears unlikely that larvae from Northern Ireland will connect directly with sites on the Llŷn or Isle of Man. It also appears unlikely that larvae from the Llŷn connect directly to any of the other sites. Taken together the data establishes a baseline for underpinning management and conservation of these important and threatened marine habitats in the southern part of the known range

Chiral Anomaly and Eta-Eta' Mixing

We determine the $\eta-\eta'$ mixing angle via a procedure relatively independent of theoretical assumptions by simultaneously fitting $\eta- eta'$ reactions involving the anomaly--$\eta,\eta'\to\gamma\gamma, \pi^+\pi^-\gamma$. We extract reasonably precise renormalized values of the octet and singlet pseudoscalar decay constants $F_8,F_0$ as well as the mixing angle $\theta$.Comment: 12 page standard Latex file, three figures, added comment

Characterization of copy number variants in a large multibreed population of beef and dairy cattle using high-density single nucleotide polymorphism genotype data

Copy number variants (CNVs) are a form of genomic variation that changes the structure of the genome through deletion or duplication of stretches of DNA. The objective of the present study was to characterize CNVs in a large multibreed population of beef and dairy bulls. The CNVs were called on the autosomes of 5,551 cattle from 22 different beef and dairy breeds, using 2 freely available software suites, QuantiSNP and PennCNV. All CNVs were classified into either deletions or duplications. The median concordance between PennCNV and QuantiSNP, per animal, was 18.5% for deletions and 0% for duplications. The low concordance rate between PennCNV and QuantiSNP indicated that neither algorithm, by itself, could identify all CNVs in the population. In total, PennCNV and QuantiSNP collectively identified 747,129 deletions and 432,523 duplications; 80.2% of all duplications and 69.1% of all deletions were present only once in the population. Only 0.154% of all CNVs identified were present in more than 50 animals in the population. The distribution of the percentage of the autosomes that were composed of deletions, per animal, was positively skewed, as was the distribution for the percentage of the autosomes that were composed of duplications, per animal. The first quartile, median, and third quartile of the distribution of the percentage of the autosomes that were composed of deletions were 0.019%, 0.037%, and 0.201%, respectively. The first quartile, median, and third quartile of the distribution of the percentage of the autosomes that were composed of duplications were 0.013%, 0.028%, and 0.076%, respectively. The distributions of the number of deletions and duplications per animal were both positively skewed. The interquartile range for the number of deletions per animal in the population was between 16 and 117, whereas for duplications it was between 8 and 23. Per animal, there tended to be twice as many deletions as duplications. The distribution of the length of deletions was positively skewed, as was the distribution of the length of duplications. The interquartile range for the length of deletions in the population was between 25 and 101 kb, and for duplications the interquartile range was between 46 and 235 kb. Per animal, duplications tended to be twice as long as deletions. This study provides a description of the characteristics and distribution of CNVs in a large multibreed population of beef and dairy cattle

Development and external validation of a head and neck cancer risk prediction model

\ua9 2024 The Author(s). Head &amp; Neck published by Wiley Periodicals LLC. Background: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. Methods: The IARC-ARCAGE European case–control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74–0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61–0.64). Conclusion: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction

Ophthalmic features of HIV associated cryptococcal meningitis in Malawian Adults: an observational study [version 1; peer review: awaiting peer review]

BACKGROUND: Cryptococcal meningitis (CM) is the commonest neurological complication in patients with advanced HIV. Visual disturbance is a frequent presenting symptom. Papilloedema is commonly reported but other ophthalmic findings are not well described. METHODS: We performed an observational study comparing severely immunocompromised HIV-infected patients with and without CM to determine the nature and prevalence of retinal pathology attributable to CM. 70 adult patients were enrolled in Blantyre Malawi, 35 with CM and 35 HIV-infected patients without CM. RESULTS: 79% (19/24) of CM patients examined on day one had evidence of retinal abnormalities compared to 17% (6/35) of HIV-infected controls (p <0.001). In the CM group, retinal whitening was the commonest abnormality (50%), followed by optic disc swelling (29%), haemorrhage (25%) and vascular abnormalities (7%). Retinal whitening was the only abnormality observed in the comparator group (17%). In CM, there was no significant difference between those with and without retinal abnormalities in fungal burden (13,550 cfu/ml vs. 9,150 cfu/ml; p = 0.65), CD4 count (28 cells/µl vs. 76 cells/µl; p = 0.79) or CSF opening pressure (21cm H20 vs. 27cm H20; p = 0.5). There was no association between presence/absence of retinal abnormalities and death (40% 10-week mortality vs. 26%; p = 0.6). CONCLUSIONS: Whether the presence of CM retinopathy could be used as a marker of disease severity warrants further investigation. The observed ophthalmic findings provide a descriptive framework for CM retinopathy to be utilised in future CM studies. TRIAL REGISTRATION: ISRCTN (ISRCTN45035509) 19/06/2012