Particle characterization in commercial buildings: A cross-sectional study in 40 offices in Singapore.

There is a knowledge gap in understanding how existing office buildings are protecting occupants from exposure to particles from both indoor and outdoor sources. We report a cross-sectional study involving weekly measurements of size-resolved indoor and outdoor particle concentrations in forty commercial building offices in Singapore. The outdoor and indoor particles size distributions were single mode with daytime peak number concentrations at 36.5 nm and 48.7 nm. Outdoor concentrations were significantly greater than indoors for all particle diameters. Indoor particle concentrations were generally low due to: 1) relatively high indoor particle removal (IPR) rates; 2) low indoor source strengths; and 3) low indoor particle of outdoor proportion (IPOP). We found that the ventilation system type had a substantial effect on indoor particle levels, IPR and IPOP. Through linear mixed model analyses, we identified dependencies of IPR rates with the use of MERV13 filters in supply air and filter maintenance frequency, IPOP with the use of MERV13 filters in the fresh air and supply air ducts and low particle source strength with regular daily cleaning presumably due to dust reservoir removal. Lastly, the contribution of outdoor sources was mainly seen for ultrafine and fine particles but less pronounced for coarse particles. This study provided detailed understanding of particle exposure in building offices and their influencing factors, facilitating future research on health impact of particle exposures.

The neurovascular retinal involvement in a large population of patients recovered from COVID-19: an OCT and OCT angiography study.

BACKGROUND: This study aimed to assess the neuronal and microvascular retinal and choroidal involvement in COVID-19 recovered patients using optical coherence tomography (OCT) and OCT angiography (OCTA). METHODS: This observational cross-sectional study recruited patients recovered from COVID-19 and a group of healthy controls for comparisons. OCT (peripapillary scan and macular map) and OCTA (macular map) were performed to obtain: the central subfield thickness (CST), the macular volume (MV), the peripapillary retinal nerve fibre layer (pRNFL) thickness, the vessel area density (VAD), vessel length fraction (VLF), vessel diameter index (VDI) and fractal dimension (FD) of the superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP), and the vessel density (VD), stromal density (SD) and vascular/stromal (V/S) ratio of the choriocapillaris (CC) and choroid (Ch). Data regarding disease severity, administered therapy and prior comorbidities were collected. RESULTS: We recruited 676 eyes from 338 patients and 98 eyes from 49 healthy controls. VAD of all the three retinal plexuses, VLF and VDI of ICP and DCP and VD of CC were significantly reduced in patients versus controls. No differences were found in CST, MV and pRNFL. A multivariate analysis showed that oxygen therapy, previous cardio/cerebrovascular events and hypertension negatively influenced vascular parameters. CONCLUSION: A microvascular retinal and choriocapillaris damage may be identified secondary to SARS-CoV-2 infection, even after recovery. OCTA may represent a reproducible and non-invasive tool to assess microangiopathy in these patients, with particular regard to those with previous cardio/cerebrovascular events, hypertension and those who received oxygen therapy.

Method to evaluate the microbial degradation activity in silage, cow rumen with in vitro test, and in manure and slurry.

Microorganisms are present everywhere and can influence a variety of processes. In agriculture and husbandry, the level of microbial activity can be crucial information, yet the methods for determining microbial activity are usually very long, complex, and costly. In this work, a novel and easy-to-use method, already in use for determining soil microbial activity, named Fertimetro was tested as a fast and cheap solution for measuring microbial activity in silages, in vitro rumen fluids, and manure and slurry. The method was adjusted for the specific conditions of the new testing environments. The results indicate that this method is adequate for measuring cellulolytic microbial activity in vitro rumen fluids, with a coefficient of repeatability (RT%) 92.2 at 24 h and 87.5 at 48 h, and also for cellulolytic microbial activity measures in manure RT% 39.0. While, due to the specific conditions in silages and slurry, this method is less adequate for measuring cellulolytic microbial activity in these environments. This work demonstrates that Fertimetro method can be used in different environments as an easy and cheaper alternative for measuring microbial activity, especially if the interest is only in quantifying the microbial activity and not in knowing the microbial species.1.Fertimetro is an easy-to-use and not costly method to evaluate microbial activity in different environments.2.This method is very adequate for measuring cellulolytic microbial activity in vitro rumen fluids and manure.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows.

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

Impact of innovative rearing strategies for the Italian heavy pigs: Technological traits and chemical composition of dry-cured hams.

To explore the influence of 4 feeding strategies on dry-cured ham quality, 336 barrows and gilts (3 batches, 112 pigs/batch) of 90 kg body weight (BW), were divided into 4 groups and housed in 8 pens with automated feeders. In the control group (C), the pigs were fed restrictively medium-protein feeds and slaughtered at 170 kg BW (SW) and 265 d of slaughter age (SA). With the older age (OA) treatment, the pigs were restrictively fed low protein feeds and slaughtered at 170 kg SW and 278 d SA. The other two groups were fed ad libitum high protein feeds, the younger age (YA) group was slaughtered at 170 kg SW and 237 d SA, the greater weight (GW) at 265 d of SA and 194 kg SW. The hams were dry-cured and seasoned for 607 d, weighed before and after seasoning and deboning. Sixty hams were sampled and sliced. The lean and the fat tissues were separated and analyzed for proximate composition and fatty acid profile. The model of analysis considered sex and treatment as fixed factors. With respect to C: i) OA lowered the ham weight, the lean protein content, increased marbling and decreased the PUFA proportion in intramuscular and subcutaneous fat; ii) YA hams had thicker fat cover with lower PUFA in intramuscular and subcutaneous fat; iii) GW increased the deboned ham weight, fat cover depth and marbling, reduced PUFA in intramuscular and subcutaneous fat, without alteration of the lean moisture content. Sex had a negligible impact.

Associations between the detailed milk mineral profile, milk composition, and metabolic status in Holstein cows.

The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, ß-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.

The Disorganization of Retinal Inner Layers Is Correlated to Müller Cells Impairment in Diabetic Macular Edema: An Imaging and Omics Study.

The disorganization of retinal inner layers (DRIL) is an optical coherence tomography (OCT) biomarker strictly associated with visual outcomes in patients with diabetic macular edema (DME) whose pathophysiology is still unclear. The aim of this study was to characterize in vivo, using retinal imaging and liquid biopsy, DRIL in eyes with DME. This was an observational cross-sectional study. Patients affected by center-involved DME were enrolled. All patients underwent spectral domain optical coherence tomography (SD-OCT) and proteomic analysis of aqueous humor (AH). The presence of DRIL at OCT was analyzed by two masked retinal experts. Fifty-seven biochemical biomarkers were analyzed from AH samples. Nineteen eyes of nineteen DME patients were enrolled. DRIL was present in 10 patients (52.63%). No statistically significant difference was found between DME eyes with and without DRIL, considering the AH concentration of all the analyzed biomarkers except for glial fibrillary acidic protein (GFAP), a biomarker of Müller cells dysfunction (p = 0.02). In conclusion, DRIL, in DME eyes, seems to strictly depend on a major dysfunction of Müller cells, explaining its role not only as imaging biomarker, but also as visual function Müller cells-related parameter.

Body and milk production traits as indicators of energy requirements and efficiency of purebred Holstein and 3-breed rotational crossbred cows from Viking Red, Montbéliarde, and Holstein sires.

This study aimed to compare rotational 3-breed crossbred cows of Viking Red, Montbéliarde, and Holstein breeds with purebred Holstein cows for a range of body measurements, as well as different metrics of the cows' productivity and production efficiency. The study involved 791 cows (440 crossbreds and 351 purebreds), that were managed across 2 herds. Within each herd, crossbreds and purebreds were reared and milked together, fed the same diets, and managed as one group. The heart girth, height at withers, and body length were measured, and body condition score (BCS) was determined on all the cows on a single test day. The body weight (BW) of 225 cows were used to develop an equation to predict BW from body size traits, parity, and days in milk, which was then used to estimate the BW of all the cows. Equations from the literature were used to estimate body protein and lipid contents using the predicted BW and BCS. Evidence suggests that maintenance energy requirements may be closely related to body protein mass, and Holstein and crossbred cows may be different in body composition. Therefore, we computed the requirements of net energy for maintenance (NEM) on the basis either of the metabolic weight (NEM-MW: 0.418 MJ/kg of metabolic BW) or of the estimated body protein mass according to a coefficient (NEM-PM: 0.631 MJ/kg body protein mass) computed on the subset comprising the purebred Holstein. On the same day when body measurements were collected, individual test-day milk yield and fat and protein contents were retrieved once from the official Italian milk recording system, and milk was sampled to determine fresh cheese yield. Measures of NEM were used to scale the production traits. Statistical analyses of all variables included the fixed effects of herd, days in milk, parity, and genetic group (purebred Holstein and crossbred), and the herd × genetic group interaction. External validation of the equation predicting BW yielded a correlation coefficient of 0.94 and an average bias of -4.95 ± 36.81 kg. The crossbreds had similar predicted BW and NEM-MW compared with the Holsteins. However, NEM-PM of crossbreds was 3.8% lower than that of the Holsteins, due to their 11% greater BCS and different estimated body composition. The crossbred cows yielded 4.8% less milk and 3.4% less milk energy than the purebred Holsteins. However, the differences between genetic groups were no longer significant when the production traits were scaled on NEM-PM, suggesting that the crossbreds and purebreds have the same productive ability and efficiency per unit of body protein mass. In conclusion, measures of productivity and efficiency that combine the cows' production capability with traits related to body composition and the energy cost of production seem to be more effective criteria for comparing crossbred and purebred Holstein cows than just milk, fat, and protein yields.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle.

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by ß-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Milk metagenomics and cheese-making properties as affected by indoor farming and summer highland grazing.

The study of the complex relationships between milk metagenomics and milk composition and cheese-making efficiency as affected by indoor farming and summer highland grazing was the aim of the present work. The experimental design considered monthly sampling (over 5 mo) of the milk produced by 12 Brown Swiss cows divided into 2 groups: the first remained on a lowland indoor farm from June to October, and the second was moved to highland pastures in July and then returned to the lowland farm in September. The resulting 60 milk samples (2 kg each) were used to analyze milk composition, milk coagulation, curd firming, and syneresis processes, and to make individual model cheeses to measure cheese yields and nutrient recoveries in the cheese. After DNA extraction and Illumina Miseq sequencing, milk microbiota amplicons were also processed by means of an open-source pipeline called Quantitative Insights Into Microbial Ecology (Qiime2, version 2018.2; https://qiime2.org). Out of a total of 44 taxa analyzed, 13 bacterial taxa were considered important for the dairy industry (lactic acid bacteria, LAB, 5 taxa; and spoilage bacteria, 4) and for human (other probiotics, 2) and animal health (pathogenic bacteria, 2). The results revealed the transhumant group of cows transferred to summer highland pastures showed an increase in almost all the LAB taxa, bifidobacteria, and propionibacteria, and a reduction in spoilage taxa. All the metagenomic changes disappeared when the transhumant cows were moved back to the permanent indoor farm. The relationships between 17 microbial traits and 30 compositional and technological milk traits were investigated through analysis of correlation and latent explanatory factor analysis. Eight latent factors were identified, explaining 75.3% of the total variance, 2 of which were mainly based on microbial traits: pro-dairy bacteria (14% of total variance, improving during summer pasturing) and pathogenic bacteria (6.0% of total variance). Some bacterial traits contributed to other compositional-technological latent factors (gelation, udder health, and caseins).

Personal comfort models based on a 6-month experiment using environmental parameters and data from wearables.

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using a smartwatch for 180 days. We collected more than 1080 field-based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of the time to want no change in their thermal environment despite completing 75% of these surveys at temperatures higher than 26.6°C. All but one personal comfort model had a median prediction accuracy of 0.78 (F1-score). Skin, indoor, near body temperatures, and heart rate were the most valuable variables for accurate prediction. We found that ≈250-300 data points per participant were needed for accurate prediction. We, however, identified strategies to significantly reduce this number. Our study provides quantitative evidence on how to improve the accuracy of personal comfort models, prove the benefits of using wearable devices to predict thermal preference, and validate results from previous studies.

Diurnal trends of indoor and outdoor fluorescent biological aerosol particles in a tropical urban area.

We evaluated diurnal trends of size-resolved indoor and outdoor fluorescent biological airborne particles (FBAPs) and their contributions to particulate matter (PM) within 0.5-20 µm. After a ten-week continuous sampling via two identical wideband integrated bioaerosol sensors, we found that both indoor and outdoor diurnal trends of PM were driven by its bioaerosol component. Outdoors, the median [interquartile range] FBAP mass concentration peaked at 8.2 [5.8-9.9] µg/m3 around sunrise and showed a downtrend from 6:00 to 18:00 during the daytime and an uptrend during the night. The nighttime FBAP level was 1.8 [1.4-2.2] times higher than that during the daytime, and FBAPs accounted for 45 % and 56 % of PM during daytime and nighttime, respectively. Indoors, the rise in concentrations of FBAPs smaller than 1 µm coincided with the starting operation of the heating, ventilation, and air conditioning (HVAC) system at 6:00, and the concentration peaked at 8:00 and dropped to the daily average by noontime. This indicated that the starting operation of the HVAC system dislodged the overnight settled and accumulated fine bioaerosols into the indoor environment. For particles larger than 1 µm, the variation of mass concentration was driven by occupancy. Based on regression modeling, the contributions of indoor PM, non-FBAP, and FBAP sources to indoor mass concentrations were estimated to be 93 %, 67 %, and 97 % during the occupied period.

A Global Building Occupant Behavior Database.

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants' schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

Associations between Milk Fatty Acid Profile and Body Condition Score, Ultrasound Hepatic Measurements and Blood Metabolites in Holstein Cows.

Dairy cows have high incidences of metabolic disturbances, which often lead to disease, having a subsequent significant impact on productivity and reproductive performance. As the milk fatty acid (FA) profile represents a fingerprint of the cow's nutritional and metabolic status, it could be a suitable indicator of metabolic status at the cow level. In this study, we obtained milk FA profile and a set of metabolic indicators (body condition score, ultrasound liver measurements, and 29 hematochemical parameters) from 297 Holstein-Friesian cows. First, we applied a multivariate factor analysis to detect latent structure among the milk FAs. We then explored the associations between these new synthetic variables and the morphometric, ultrasonographic and hematic indicators of immune and metabolic status. Significant associations were exhibited by the odd-chain FAs, which were inversely associated with ß-hydroxybutyrate and ceruloplasmin, and positively associated with glucose, albumin, and γ-glutamyl transferase. Short-chain FAs were inversely related to predicted triacylglycerol liver content. Rumen biohydrogenation intermediates were associated with glucose, cholesterol, and albumin. These results offer new insights into the potential use of milk FAs as indicators of variations in energy and nutritional metabolism in early lactating dairy cows.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Impact of Rearing Strategies on the Metabolizable Energy and SID Lysine Partitioning in Pigs Growing from 90 to 200 kg in Body Weight.

The current nutrient recommendations focus on pigs fed ad libitum up to 140 kg in body weight (BW). It remains unclear whether this applies to pigs weighing above 140 kg in BW under different rearing conditions. This study aimed to estimate protein (Pd) and lipid (Ld) depositions and the metabolizable energy (ME), standardized ileal digestible lysine (SID lysine) requirement and partitioning in 224 C21 Goland pigs (90−200 kg in BW). The control pigs (C) received diets limiting ME up to 170 kg in slaughter weight (SW) at 9 months of age (SA); older (OA) pigs had restricted diets limiting ME and SID lysine up to 170 kg in SW at >9 months SA; younger (YA) pigs were fed nonlimited amounts of ME and SID lysine up to 170 kg in SW at <9 months SA; and greater weight (GW) pigs were fed as the YA group, with 9 months SA at >170 kg in SW. The estimated MEm averaged 1.03 MJ/kg0.60. An 11% increase in MEm was observed in OA pigs compared to the controls. Energy restriction had negligible effects on the estimated MEm. The marginal efficiency of SID lysine utilization for Pd averaged 0.725, corresponding to a SID lysine requirement of 9.8 g/100 g Pd.

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle.

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

Genes Related to Fat Metabolism in Pigs and Intramuscular Fat Content of Pork: A Focus on Nutrigenetics and Nutrigenomics.

Fat metabolism and intramuscular fat (IMF) are qualitative traits in pigs whose development are influenced by several genes and metabolic pathways. Nutrigenetics and nutrigenomics offer prospects in estimating nutrients required by a pig. Application of these emerging fields in nutritional science provides an opportunity for matching nutrients based on the genetic make-up of the pig for trait improvements. Today, integration of high throughput "omics" technologies into nutritional genomic research has revealed many quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs) for the mutation(s) of key genes directly or indirectly involved in fat metabolism and IMF deposition in pigs. Nutrient-gene interaction and the underlying molecular mechanisms involved in fatty acid synthesis and marbling in pigs is difficult to unravel. While existing knowledge on QTLs and SNPs of genes related to fat metabolism and IMF development is yet to be harmonized, the scientific explanations behind the nature of the existing correlation between the nutrients, the genes and the environment remain unclear, being inconclusive or lacking precision. This paper aimed to: (1) discuss nutrigenetics, nutrigenomics and epigenetic mechanisms controlling fat metabolism and IMF accretion in pigs; (2) highlight the potentials of these concepts in pig nutritional programming and research.

Influence of Slaughter Weight and Sex on Growth Performance, Carcass Characteristics and Ham Traits of Heavy Pigs Fed Ad-Libitum.

Slaughter weight (SW) is critical for dry-cured ham production systems with heavy pigs. A total of 159 C21 Goland pigs (gilts and barrows) at 95 ± 9.0 kg body weight (BW) from three batches were used to investigate the impact of ad libitum feeding on SW, growth performance, feed efficiency, and carcass and green ham characteristics. Diets contained 10 MJ/kg of net energy and 7.4 and 6.0 g/kg of SID-lysine. Slaughter weight classes (SWC) included <165, 165-180, 180-110 and >210 kg BW. In each batch, pigs were sacrificed at 230 or 258 d of age. Left hams were scored for round shape, fat cover thickness, marbling, lean colour, bicolour and veining. Data were analyzed with a model considering SWC, sex and SWC × Sex interactions as fixed factors and the batch as a random factor. The linear, quadratic and cubic effects of SWC were tested, but only linear effects were found. Results showed that pigs with greater SWC had greater average daily gain and feed consumption, with similar feed efficiency and better ham quality traits: greater ham weight, muscularity, and fat coveringin correspondence of semimembranosus muscle. Barrows were heavier and produced hams with slightly better characteristics than gilts.

Composition and aptitude for cheese-making of milk from cows, buffaloes, goats, sheep, dromedary camels, and donkeys.

Bovines produce about 83% of the milk and dairy products consumed by humans worldwide, the rest represented by bubaline, caprine, ovine, camelid, and equine species, which are particularly important in areas of extensive pastoralism. Although milk is increasingly used for cheese production, the cheese-making efficiency of milk from the different species is not well known. This study compares the cheese-making ability of milk sampled from lactating females of the 6 dairy species in terms of milk composition, coagulation properties (using lactodynamography), curd-firming modeling, nutrients recovered in the curd, and cheese yield (through laboratory model-cheese production). Equine (donkey) milk had the lowest fat and protein content and did not coagulate after rennet addition. Buffalo and ewe milk yielded more fresh cheese (25.5 and 22.9%, respectively) than cow, goat, and dromedary milk (15.4, 11.9, and 13.8%, respectively). This was due to the greater fat and protein contents of the former species with respect to the latter, but also to the greater recovery of fat in the curd of bubaline (88.2%) than in the curd of camelid milk (55.0%) and consequent differences in the recoveries of milk total solids and energy in the curd; protein recovery, however, was much more similar across species (from 74.7% in dromedaries to 83.7% in bovine milk). Compared with bovine milk, the milk from the other Artiodactyla species coagulated more rapidly, reached curd firmness more quickly (especially ovine milk), had a more pronounced syneresis (especially caprine milk), had a greater potential asymptotical curd firmness (except dromedary and goat milk), and reached earlier maximum curd firmness (especially caprine and ovine milk). The maximum measured curd firmness was greater for bubaline and ovine milk, intermediate for bovine and caprine milk, and lower for camelid milk. The milk of all ruminant species can be used to make cheese, but, to improve efficiency, cheese-making procedures need to be optimized to take into account the large differences in their coagulation, curd-firming, and syneresis properties.