The aim of this study was to investigate the effects of two feed supplements on rumen bacterial communities of heifers fed a high grain diet. Six Holstein-Friesian heifers received one of the following dietary treatments according to a Latin square design: no supplement (control, C), 60 g/day of fumarate-malate (organic acid, O) and 100 g/day of polyphenol-essential oil (P). Rumen fluid was analyzed to assess the microbial population using Illumina sequencing and quantitative real time PCR. The P treatment had the highest number of observed species (P < 0.10), Chao1 index (P < 0.05), abundance based coverage estimated (ACE) (P < 0.05), and Fisher’s alpha diversity (P < 0.10). The O treatment had intermediate values between C and P treatments with the exception of the Chao1 index. The PCoA with unweighted Unifrac distance showed a separation among dietary treatments (P = 0.09), above all between the C and P (P = 0.05). The O and P treatments showed a significant increase of the family Christenenellaceae and a decline of Prevotella brevis compared to C. Additionally, the P treatment enhanced the abundance of many taxa belonging to Bacteroidetes, Firmicutes and Tenericutes phyla due to a potential antimicrobial activity of flavonoids that increased competition among bacteria. Organic acid and polyphenols significantly modified rumen bacterial populations during high-grain feeding in dairy heifers. In particular the polyphenol treatment increased the richness and diversity of rumen microbiota, which are usually high in conditions of physiological rumen pH and rumen function.

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

The aim of this study was to investigate the effects of long-distance road transport (19 h, from Poland to Italy) during 2 seasons (summer vs. winter) on clinical and hematological variables in calves. The environmental temperature range that could compromise the thermoregulation system (thermal stress) of the calves was tested. For the 7 Holstein calves in each transport, the BW and rectal temperature (RT) were measured, and blood samples were collected at the farm of origin, before loading at the transit center (T2), after unloading at the farm of destination (T3), and 1, 2, 3, and 4 d after arrival. The body temperature (BT) and heart rate (HR) were continuously monitored from T2 to T3. The data were statistically analyzed according to a mixed model that considered the fixed effects of transport (repeated measurements), season of journey, and their interaction. Within the observed temperature-humidity index (THI) range (30 to 80), effective thermoregulation allowed the calves to maintain their BT with small physiologic changes to prevent thermal stress, particularly in the summer. With no seasonal differences, the HR was greater at loading than unloading (120 vs. 115 beats per min; P = 0.012). As for the transport effect, the BW was less (P < 0.001) after unloading, and the RT was greater (P = 0.004). This effect was more marked in summer. The hematological variables indicated a moderate effect of transport on the hydration condition, reactive and muscular systems, and metabolism, although hematocrit (P = 0.004), erythrocytes, cortisol, NEFA, β-hydroxybutyrate, lactate, creatine kinase, lactate dehydrogenase, and aspartate aminotransferase activity (P < 0.001), and total protein (P = 0.007) were greater after unloading. This was confirmed by a moderate decrease in total leukocytes (P = 0.031) and glucose concentration (P = 0.002). The changes in the clinical variables were similar for both seasons even though in the summer, hematocrit (P < 0.001), urea (P = 0.008), and total protein (P = 0.010) increased and glucose concentration (P = 0.038) decreased. In conclusion, the data did not show a pronounced effect attributable to the season of the journey. Long-distance road transport leads to notable changes in clinical and hematological variables at the end of the journey. However, these variables remained within their physiological ranges and returned to basal values within a few days after the journey.

Mastitis is the most frequent and costly disease in dairy herds, as it negatively affects yield and milk quality. The presence of clinical mastitis is quite easy to asses, whereas the diagnosis of the subclinical form can be more difficult and requires laboratory assays. Somatic cell count (SCC) is widely used as a rapid and low-cost indicator of mastitis, even if is not useful in discriminating between the clinical and subclinical form. As amyloid A has been investigated as a marker of mastitis, the aim of this study was to assess the potential value of measuring amyloid A in serum and milk and the correlation with SCC in the diagnosis of subclinical mastitis. The reliability of two different ELISA kits for the measurement of amyloid A in milk was also tested. During a 1-month trial period, 21 cows were assigned to three experimental groups according to their health status: 6 cows with clinical mastitis (CM), 10 cows with subclinical mastitis (SM) and 5 healthy cows (HE). Amyloid A was measured both in serum (SAA) and in quarter milk samples (mAA) with a serum ELISA kit, and in quarter milk samples (MAA) with a milk ELISA kit. SCC, total microbial count (TMC) and bacterial examination of the milk were also carried out. After a log transformation, the data were submitted to ANOVA and linear regression. TMC was significantly higher in cows with clinical mastitis, while no differences were observed between the other two experimental groups. SCC and MAA levels were significantly different among the three groups. mAA concentrations were similar between cows with subclinical and clinical mastitis, and SAA was not affected by mastitis. A significant correlation between SCC and MAA or mAA was detected, while no correlation was recorded between SAA and mAA. A close relationship between MAA and mAA was noticeable even at low concentrations, suggesting MAA as a potential physiological marker of subclinical mastitis.