Assessing the impact of glazing and window shade systems on view clarity.

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.

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.

Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R2 = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties.

Indoor environmental quality in WELL-certified and LEED-certified buildings.

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.

Detailed mineral profile of milk, whey, and cheese from cows, buffaloes, goats, ewes and dromedary camels, and efficiency of recovery of minerals in their cheese.

Milk and dairy products are important in the human diet not only for the macro nutrients, such as proteins and fats, that they provide, but also for the supply of essential micronutrients, such as minerals. Minerals are present in milk in soluble form in the aqueous phase and in colloidal form associated with the macronutrients of the milk. These 2 forms affect the nutritional functions of the minerals and their contribution to the technological properties of milk during cheese-making. The aim of the present work was to study and compare the detailed mineral profiles of dairy foods (milk, whey, and cheese) obtained from cows, buffaloes, goats, ewes and dromedary camels, and to analyze the recovery in the curd of the individual minerals according to a model cheese-making procedure applied to the milk of these 5 dairy species. The detailed mineral profile of the milk samples was obtained by inductively coupled plasma - optical emission spectroscopy (ICP - OES). We divided the 21 minerals identified in the 3 different matrices into essential macro- and micro-minerals, and environmental micro-minerals, and calculated the recovery of the individual minerals in the cheeses. The complete mineral profiles and the recoveries in the cheeses were then analyzed using a linear mixed model with Species and Food, and their interaction included as fixed effects, and Sample within Species as a random effect. The mineral profiles of each food matrix were then analyzed separately with a general linear model in which only the fixed effect of Species was included. The results showed that the species could be divided into 2 groups: those producing a more diluted milk characterized by a higher content of soluble minerals (in particular K), and those with a more concentrated milk with a higher colloidal mineral content in the skim of the milk (such as Ca and P). The recoveries of the minerals in the curd were in line with the initial content in the milk, and also highlighted the fact that the influence of the brine was not limited to the Na content but to its whole mineral makeup. These results provide valuable information for the evaluation of the nutritional and technological properties of milk, and for the uses made of the byproducts of cheese making from the milk of different species.

Characterization of dry-cured ham microbiota at 12 months of seasoning obtained from different rearing strategies using 16S rRNA profiling.

In this study, we investigated the microbiota of 72 Italian ham samples collected after 12 months of seasoning. The hams were elaborated from pigs fed different rearing methods, including the traditional restricted medium protein diet chosen as control (C group); restrictive low protein diet (LP group); two ad libitum high-protein diet groups (HP9M group: slaughter at 9 months of age; HP170 group: slaughter at 170 kg). A multi-amplicon 16S metabarcoding approach was used, and a total of 2845 Amplicon Sequence Variants were obtained from the 72 ham samples. Main phyla included: Firmicutes (90.8%), Actinobacteria (6.2%), Proteobacteria (2.7%), and Bacteroidota (0.12%). The most common genera were Staphylococcus, Tetragenococcus, and Brevibacterium. Shannon index for α-diversity was found statistically significant, notably for the HP9M group, indicating higher diversity compared to C. PERMANOVA test on ß-diversity showed significant differences in rearing methods between HP170 and C, HP170 and LP, and HP9M vs. C. All three rearing methods revealed associations with characteristic communities: the HP9M group had the highest number of associations, many of which were due to spoilage bacteria, whereas the LP group had the highest number of seasoning-favourable genera.

Effects of slaughter weight and backfat depth on trimming, curing, and deboning losses and quality traits of Italian dry-cured ham.

This study aimed at assessing the effects of two infra-vitam traits, specifically the slaughter weight (SW) and the ultrasound backfat depth (BCKF) on several post-mortem and quality traits of typical Prosciutto Veneto protected designation of origin (PDO) dry-cured ham. The trial was conducted on a population of 423 pigs fed using different strategies to generate a high variation in SW (175 ± 15.5 kg) and BCKF (23.16 ± 4.14 mm). All the left thighs were weighed at slaughter and the ham factory during the different processing phases. The fat cover depth of green trimmed hams was measured. Data were analyzed with a linear model including SW classified in tertiles, BCKF as a covariate, SW × BCKF interaction, sex, batch, and pen nested within batch. Our results highlighted that, for each 10 kg increase in SW, trimmed and seasoned ham weights increased by 0.76 and 0.54 kg, respectively. The increase in SW significantly reduced relative curing and deboning losses but did not affect ham fat cover depth and trimming losses. A rise in BCKF increased the ham fat cover depth and trimming losses and decreased the curing and deboning losses. Increases in SW and BCKF improved quality traits of the seasoned ham including fat cover depth, visible marbling, inner lean firmness, and fat color. These findings confirm the feasibility of increasing SW and BCKF, which will result in a reduction in the relative losses associated with the dry-curing process while improving the quality of the seasoned ham.

Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.

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.

Passive and low-energy strategies to improve sleep thermal comfort and energy resilience during heat waves and cold snaps.

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.

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.