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Pain localised to the metacarpophalangeal (MCP) and metatarsophalangeal (MTP) region represents a frequent cause of lameness in sport horses, and standing magnetic resonance imaging (MRI) of these regions is increasingly being acquired. This multicentre retrospective study describes the ranges of abnormalities identified on standing MRI of the MCP/MTP region and compares patterns of abnormalities between forelimbs, hindlimbs and different sports disciplines. In total, 341 MRI reports were reviewed. Subchondral bone plate irregularities, condylar and proximal phalanx pathologies were frequently identified with subchondral bone defect, sclerosis and increased intensity on STIR images often described. Medial pathology was frequently identified in the forelimbs, and more lateral pathology was reported in the hindlimbs, which could potentially reflect differences in the loading patterns. Significant differences in MRI findings were found between different sports activities, with MCP/MTP bone pathology occurring more frequently in MRI reports from race and endurance horses and MCP/MTP soft tissue injuries being reported more frequently in dressage and show-jumping horses, particularly in the suspensory apparatus, including the distal sesamoidean ligaments. The findings of this study identify patterns of MCP/MTP abnormalities detected using standing MRI, with differences seen between forelimbs and hindlimbs and between different sports disciplines.

Abstract In the context of a global food system, the dynamics associated to international food trade have become key determinants of food security. In this paper, we resort to a diffusion model to simulate how shocks to domestic food production propagate through the international food trade network and study the relationship between trade openness and vulnerability. The results of our simulations suggest that low-income and food insecure countries tend to be the more exposed to external shocks and, at the same time, they are usually not in a position to take full advantage of international food trade when it comes to shield themselves from shocks to domestic production. We also study and discuss how nodes characteristics are associated with the propagation dynamics and with countries’ vulnerability, finding that simple centrality measures can significantly predict the magnitude of the shock experienced by individual countries.

[This corrects the article DOI: 10.1371/journal.pone.0200639.].

Food Security is a long-standing concern worldwide. The expansion of global food markets brings benefits but also risks, such as shock transmission within the global network of trade relations. We focus on this last issue, from an empirical point of view, by analysing the diffusion of trade shocks-defined as relevant drops in exported quantities-during the period 1986-2011, for four major staples (wheat, maize, rice, and soy-beans) both at country level and at global scale. We find that: (i) income per capita of importing countries is relevant in shock propagation; (ii) developing countries tend to absorb most of the negative export variation (i.e., the trade shock), and (iii) global food prices and real (tonnes) flows of commodities are only weakly correlated, meaning that a quantity-based investigation provides additional information with respect to a price-based analysis. This work offers a novel framework, complementary to the price-based literature, for the measurement of the propagation of international food shocks.

This study aimed at evaluating the environmental impact (EI) of herds representative of the Italian heavy pig production system to investigate the EI variation associated with farm management (FM), diet formulation (DF) and animal response (AR) variables. Data originated from 8 breeding farms, which included the sows and preweaning piglets (Site1) and the post-weaned piglets (7.5–30 kg body weight – BW, Site2) and 10 growing–fattening farms, dealing with 30–170 kg BW pigs (Site3). A cradle-to-farm gate Life Cycle Assessment was used, with 1 kg BW gain (BWG) as functional unit. Impact categories were global warming (GWP), acidification (AP) and eutrophication (EP) potentials and land occupation (LO). Whole-production cycle EI (Site123, 0–170 kg BW) was obtained summing up EIs of 0–30 and 30–170 kg BW animals. Impacts were analysed with one- or two-way PERMANOVA to test the effect of FM, DF and AR variables. The Site123-related EI averaged nearly 3.1 kg CO2-eq (GWP), 50 g SO2-eq (AP), 22 g PO4-eq (EP) and 4.9 m2/y (LO) per kg BWG. Site3 contributed nearly 80% of the whole impact. Regarding Site1, GWP resulted mitigated by increasing sow productivity. Site2 EI resulted mitigated by decreasing feed conversion ratio and increasing average daily gain, whereas Site3 EI by increasing feed self-sufficiency and decreasing dietary crude protein and mortality. In perspective, given the relevance of Site3 on the whole-pig system EI, a deeper integration between the farm agronomical management and the growing–finishing pigs’ diets formulation would support the environmental sustainability of heavy pig operations.

Over recent decades, there was a substantial evolution in the productive management of dairy animals worldwide with a consequent boost in individual milk yield. This evolution positioned the milk production as the central metabolic priority around which all other physiological functions are coordinated and partially minimised. This shift underscores the crucial role of effectively managing stressful phases in intensive dairy farming systems, also highlighting the resilience exhibited by the animals. Indeed, monitoring the nutritional and health status becomes paramount, aiming for an early detection of (sub)clinical health impairments. Given the mammary gland’s centrality in high-yielding dairy breeds, it’s unsurprising that the milk matrix provides insights about udder itself but also systemic metabolic function. The emerging field of milk phenomics explores links between milk components and animal health, holding great promise for studying dairy cow resilience. The use of infrared spectroscopy on milk to predict indicators and complex traits at the herd level is a promising approach. In the dairy sector, the available infrared instruments mainly implement the Fourier transform infrared (FTIR) spectroscopy. This method is widely employed in milk recording schemes worldwide for animal monitoring and breeding purposes. In addition, visible and near-infrared (NIR) spectroscopy is increasingly integrated into milking systems for daily on-farm monitoring of milk quality and animals’ physiological status. This review examines the topic of milk phenomics together with potential and challenges of infrared spectroscopy to predict indicators and complex traits related to health and nutritional disorders exploiting the biological bonds that exist between milk and blood in dairy animals.HIGHLIGHTS Milk is a valuable source of information for assessing nutritional and health proxies in dairy animals. Infrared spectroscopy is used within routine milk recording systems for the determination of major components, but other useful applications exist. This review summarises research on milk phenomics carried out by mean of infrared spectroscopy to predict complex traits related to animals’ health and nutritional disorders.

ABSTRACT: Fertility is a crucial aspect of dairy herd efficiency and sustainability. Among factors influencing fertility in dairy cattle, metabolic stress and systemic inflammation of animals are of main relevance, especially in the postpartum stage when ovarian activity begins and cows are inseminated. Our study aimed to infer the associations between milk infrared-predicted blood biomarkers of stress resilience and fertility traits, namely the interval from calving to first service (iCF), days open (DO), and the pregnancy rate at first service (PRF) in a multibreed population of 89,097 dairy cows. The blood metabolites (15 blood biomarkers related to hepatic damage and function, oxidative stress, inflammation, and innate immunity) were predicted using milk Fourier-transform mid-infrared (MIR) spectroscopy. A gradient boosting machine approach with leave-one-batch-out cross-validation (R2 range from 0.45 to 0.82) was implemented to an independent calibration database of 1,367 lactating cows reared in 5 herds. Calibration equations were then applied to a population database of 1,799,186 MIR milk spectral data, that were then merged with fertility data collected by the Breeders Federation of Alto Adige (Bolzano province, Italy) generating a final database of 285,145 records. The 2 databases were merged according to the milk test day (and thus, the MIR spectrum) closest to the date of insemination. The interval fertility traits were fitted as the hazard of either receiving the first service after calving at time t for iCF or becoming pregnant after calving at time t for DO in a Cox proportional-hazards model. Statistical analyses were performed including in the model the number of lactations, year of calving, and herd as fixed effects. The independent effect of the MIR-based predictions of metabolites was also included with each metabolite evaluated separately and discretized into 7 levels based on percentiles. Pregnancy rate at first service, however, was analyzed using logistic regression and the same explanatory variables. The metabolites linked to liver function and damage, such as aspartate aminotransferase, total bilirubin, and alkaline phosphatase, had a relevant influence on iCF and DO in terms of the hazard ratio (HR). Relevant results were also obtained for the biomarkers related to oxidative stress, inflammation, and innate immunity. Specifically, increasing levels of ceruloplasmin, total reactive oxygen metabolites, and advanced oxidation protein products resulted in a relevant decrease in the HR of cows becoming pregnant. The logistic regression analysis did not reveal any significant effect of the aforementioned biomarkers on PRF, indicating that the effects of the stress response mainly concern the resumption of the ovarian cycle after calving. The results for the associations of the predicted biomarkers of the stress response with iCF and DO were consistent with expected physiological patterns. In conclusion, the predicted biomarkers investigated revealed to be promising novel phenotypes for assessing animal health and welfare, in the view of enhancing fertility in dairy cattle also through selective breeding, thus improving the overall efficiency of dairy herds.

Abstract Windows provide access to daylight and outdoor views, influencing building design. Various glazing and window shade materials are used to mitigate glare, overheating and privacy issues, and they affect view clarity. Among them, we evaluated the effect of window films, electrochromic (EC) glass, and fabric shades on view clarity. We conducted an experiment with 50 participants using visual tests adapted from clinical vision tests (visual acuity, contrast sensitivity, color sensitivity) and images displayed on a computer monitor in a controlled laboratory. Window films and EC glass tints outperformed fabric shades in visual acuity, contrast sensitivity and view satisfaction with the exception of the darkest EC tint state and dark grey VLT 3% shade for color sensitivity and view satisfaction. The EC tints pose internal reflection issues and fabric shades are preferred for visual privacy. Window films and EC glass hinder participants’ blue–green color discrimination while fabric shades also decrease red–yellow color discrimination. Visual acuity predicts view satisfaction and contrast sensitivity is the strongest predictor for visual privacy. Generally, higher visible light transmittance and lower solar reflectance (darker color) enhance human visual performance. The proposed workflow provides an experimental procedure, identifies the primary variables and establishes a predictive framework for assessing view clarity of fenestration.

Abstract International building certification systems, such as the WELL and Leadership in Energy and Environmental Design (LEED) standards, play a pivotal role in the design of healthy and sustainable buildings. While LEED adopts a holistic approach to designing healthy and sustainable buildings, the WELL standard has a strong emphasis on human health, comfort, and well-being. Although prior research has revealed inconsistent results for occupant satisfaction in office buildings with WELL certification compared to buildings without WELL certification, or are certified using another certification system (e.g., LEED), most of these comparisons tend to lack methodological rigor. This study used a statistical procedure to match and compare 1634 occupant surveys from LEED-certified buildings to 1634 surveys from WELL-certified buildings. Six important architectural and experiential parameters were matched, masking their influence on the outcome. Overall building and workspace satisfaction was high in both WELL-certified buildings (94% and 87%) and LEED-certified (73% and 71%). We found that there is a 39% higher probability of finding occupants who are more satisfied in WELL-certified buildings compared to LEED-certified buildings, indicating occupant satisfaction is higher in buildings with WELL certification. Although we were unable to pinpoint the reason for higher occupant satisfaction in WELL-certified buildings, the results consistently showed that perceived indoor environmental quality was enhanced across all parameters except for the amount of space.

Abstract Background Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. Results Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (−log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. Conclusion Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

Abstract Sleep is a pillar of human health and wellbeing. In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment in the bedroom. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive—problems that may increase due to climate change. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. We used a thermal manikin to experimentally show that many passive and low-energy strategies are highly effective in supplementing or replacing HVAC systems during sleep. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69–91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.

The effects of ceiling fans on the transmission of infectious aerosols remain poorly understood, leading to conflicting recommendations. We conducted repeated experiments in a well-controlled chamber with a typical mixing ventilation system at three different ventilation rates with and without ceiling fans. We evaluated airborne infection risks for short- and long-range transmission routes based on size-resolved tracer particles measured at various locations. We found that the mixing ventilation without fans only effectively diluted the airborne particle concentration for the long-range route but not for the short-range. By using ceiling fans to enhance air mixing, tracer particles were distributed more homogeneously throughout the room, leading to up to 77 % reduction in short-range particle exposure while a slight increase of less than 14 % in long-range exposure. Based on the dilution-based Wells-Riley model, the changes in particle concentration translated to a maximum 47 % reduction in short-range infection risk and a marginal 4 % increase for long-range transmission. Based on the dilution factors obtained from the experiments, we developed a decision-making tool that uses the ventilation rate, the number of individuals at short- and long-range, and the disease's transmissibility to decide whether the use of ceiling fans is beneficial. Deploying ceiling fans always reduces the concentration of particles in the short range and, assuming a relationship between particles and pathogens, this directly translates to a diminished short-range risk. Based on the modeling of the overall risk, the benefits of fans are highest when the room is ventilated according to code, when masking measures are in place, and when the pathogen is not highly contagious.

Research on triethylene glycol (TEG) use to disinfect airborne microorganisms have been conducted in non-realistic chamber settings. This study assesses how air temperature, humidity, HVAC settings, and filtration impact TEG's effectiveness in deactivating a common SARS-CoV-2 substitute, MS2 bacteriophage, in a simulated non-occupied office-sized chamber. The chamber was served by a dedicated HVAC system operating at 22.0, 23.5 and 25.0 °C, at 40, 55 and 70 % relative humidity, at 0, 3 and 6 air change per hour (ACH) recirculation, at 0.8, 2.5 and 5.0 ACH outdoor ventilation and at no, MERV8 and MERV14 filtration status. Airborne MS2 log10 reductions in the presence of TEG increased linearly over time and we noted a higher MS2 inactivation rate with higher TEG concentration. The estimated TEG concentration needed for a one-log inactivation of the MS2 within an hour was 0.44 mg/m3. The efficacy of TEG declined with the increase in temperature from 22.0 to 25.0 °C, peaked at 55 % RH, increased with higher recirculation rates but decreased with increasing ventilation rates and higher efficiency filters. The results of our study suggest that the optimum environmental and building conditions for TEG performance is at 22.0 or 23.5 °C air temperature, 55 % relative humidity, 0.8 ACH ventilation rate and 6 ACH recirculation rate. By conducting experiments in simulated office conditions, this study closes significant knowledge gaps in TEG performance application.

The aim of this study was to characterize the highly detailed fatty acid (FA) profiles of 258 cheeses of 18 different categories of cheese collected in the mountains and on the plains of the Veneto region (Italy). The results clearly showed that, aside from the distinctive FA profiles of goat cheeses (more short-chain FAs and fewer MUFAs), the three categories of Formaggio di Malga (artisanal cheeses produced on temporary summer farms on Alpine pastures where transhumance is practiced) were very different from the other cheese categories in terms of their much higher CLA and omega-3 contents. Two categories of cheese from permanent farms in the mountains (Morlacco del Grappa and Monte Veronese PDO) were intermediate, and two other categories of cheeses originating in the mountains (Asiago PDO and Montasio PDO), but now produced mainly on the plains, were not distinguishable from the other cheese categories. The very detailed profile (65 individual FA, 11 isomers, and 12 groups of FAs) and the large number of cheese types analyzed (18) may represent a useful reference for future investigations, especially on the causes of variability in FAs and on their relationships with sensory properties and nutrition/health in humans.

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.

ABSTRACT: 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.

This study aimed to assess the environmental impact (cradle-to-farm gate LCA) of milk production of cereal-based dairy farms in northern Italy and to analyse which traits related to farm management (MAN_F), lactating cows’ ration composition (DIET_F), and animal responses (ANI_F) could be identified as main determinants to address specific mitigation strategies. Data originated from 28 dairy farms. The functional unit was 1 kg of fat- and protein-corrected milk (FPCM). Impact categories were global warming (GWP), acidification (AP) and eutrophication (EP) potentials and land occupation (LO). Impact values were analysed with a linear model to test separately the effects of MAN_F, DIET_F and ANI_F variables. One kg FPCM was associated with an average impact of 1.10 kg CO2-eq (GWP), 17.1 g SO2-eq (AP), 6.0 g PO4-eq (EP), and 1.3 m2/y (LO). Regarding MAN_F, GWP and LO linearly decreased with increasing values of stocking rate and feed self-sufficiency (FSS), whereas minimum EP was found for intermediate FSS values (50% DM). Regarding DIET_F, GWP linearly decreased at increasing levels of dietary inclusion of cereal silages, whereas AP and EP linearly decreased at decreasing dietary crude protein (CP) content values. Among ANI_F, GWP and EP were quadratically associated with milk yield (MY), evidencing the lowest values at intermediate MY (9100 ± 477 kg FPCM/cow per 305d-lactation). The GWP, AP and EP decreased with decreasing age at first calving. These results can contribute at drawing good practices for farmers and consultants to promote more environmentally sustainable dairy production while supporting farm functioning and farmers’ income.HIGHLIGHTS Farm and animal determinants of Life Cycle Assessment of cereal-based dairy farms were studied. Mitigation effects can be achieved by operating on farm organisation, cow’s diet, and animal responses. Good practices for farmers and consultants can be proposed for a better dairy production sustainability.

The milk Fourier-transform infra-red spectrometry (FTIRS) can be used to predict the enteric methane emissions (EME) at population level. In this study, the variability in FTIRS predicted EME traits due to the breed of cow, farm, the production level of individual cows within herds, and their interactions were evaluated. A dataset obtained from milk recordings, which covered four breeds (Holstein, Brown Swiss, Simmental and Alpine Grey), 6,430 herds, 115,819 cows, and 1,759,706 test-day milk/spectra records was used. The herds were stratified into 5 production levels considering their average daily milk energy production; individual cows within herd were similarly stratified considering their individual production levels. The EME traits were predicted directly from milk FTIR spectra or indirectly from six informative milk FA predicted from milk spectra. The statistical model included, separately for each trait and method, breed, herd intensiveness level, cow production level, and their interactions, year, month, parity, and lactation stage. The direct and indirect methods yielded similar results in predicting CH4 yield per kg of DMI and CH4 intensity per kg of fat-protein corrected milk. The indirect method was reliable in predicting daily EME production per cow, the indirect one did not. EME was affected by the breed x herd production intensiveness interaction, and to a lesser degree, by the breed x cow production level interaction. A better understanding of the complex interactions influencing EME in dairy herds was achieved. This would be useful for the genetic improvement, the environmental certification of farms, and for setting prices in milk payment schemes.HIGHLIGHTS Causes of variation of the FTIR predicted EME were studied at population level using data from 6,430 herds and 1,759,706 milk spectra records Reliable daily EME estimates were achieved from 6 six informative fatty acids predicted from milk spectra EME was influenced by the interaction breed × level of herd intensiveness and, to a lesser extent, by the individual production level

As the global trend for healthy eating grows, firms are emphasising dairy functional foods (DFFs). This study looks into the relationship between consumer attitudes and readiness to consume DFFs, taking into account how a healthy lifestyle might both motivate and deter people from using them. Hungary was chosen because consumer behaviour towards functional foods is under-researched in comparison to Western Europe. Data were generated using a survey questionnaire from 313 respondents. The results of Partial Least Squares (PLS) analysis revealed that consumer attitudes had a considerable influence on the desire to consume DFFs. Furthermore, the motivators and barriers had a direct influence on willingness through the consumer attitudes that serve as mediators. These findings show the need for targeting consumer attitudes and lifestyle characteristics in order to increase the market acceptance of DFFs.

ABSTRACT: 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, β-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 decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability.

ABSTRACT: 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.

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.