196,252 research outputs found
La Biblioteca di Joaquim de Araújo (1858 – 1917), a cura di Manuel G. Simões e Alessandro Scarsella, schede di Angela de Oliveira, revisione di Vanessa Castagna
No full textVolume in 2e bozze corrette
Milk microbiome: evaluation study on the differences among cows with a different health status classified by leukocyte pattern
No full textSubmitted 2020-06-30 | Accepted 2020-07-25 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.67-73Milk is considered not only a source of nutrient for the offspring but also a font of immunoregulatory compounds capable to predispose the naïve intestinal immune system of the new-born to react to the external environment. In the present study we evaluated the composition of milk microbiome from cows classified according to milk total and differential somatic cell counts. A total of 34, 13 and 13 milk samples of healthy, at risk and subclinical or chronic cows, respectively, were collected during the same milking from a local dairy herd. Through Next Generation Sequencing (NGS) of bacterial 16S rRNA gene, the differences of taxa in terms of relative abundances (RA) and alpha and beta biodiversity were analysed. The RA of several genera were statistically significant in the three groups, such as Arcanobacterium (p=0.001), Rhodococccus (p<0.05) and Rubrobacter (p<0.05), while at species level the presence of Propionibacterium granulosum, Pseudomonas alcaligenes and Prosthecobacter debontii were found. Shannon and Evenness indices computed at the genus level were not significant, while beta biodiversity showed a clear clusterization between groups. The results highlighted that milk microbiome is associated to a different cellular response at udder level, although more specific studies are needed to assess the source of bacteria species identified in milk microbial population of healthy animals.Keywords: milk microbiome; bovine; mastitis; differential cell countReferencesBOLYEN. E.; RIDEOUT. J.R.; DILLON. M.R.; BOKULICH. N.A.; ABNET. C.C.; AL-GHALITH. G.A.; ALEXANDER. H.; ALM. E.J.; ARUMUGAM, M.; ASNICAR. F.; et al. (2019). Reproducible. interactive. scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852-857.BOWEN, J. M.; MCCABE, M.S.; LISTER, S.J.; CORMICAN, P. AND DEWHURST, R.J. (2018). Evaluation of Microbial Communities Associated With the Liquid and Solid Phases of the Rumen of Cattle Offered a Diet of Perennial Ryegrass or White Clover. Frontiers in microbiology, 9, 2389.DERAKHSHANI, H.; FEHR, K.B.; SEPEHRI, S.; FRANCOZ, D.; DE BUCK, J.; BARKEMA, H.W.; PLAIZIER, J.C. AND KHAFIPOUR, E. (2018). Invited review: microbiota of the bovine udder: contributing factors and potential implications for udder health and mastitis susceptibility. J Dairy Sci 101, 10605-10625.GANDA, E.K.; GAETA, N.; SIPKA, A.; POMEROY, B.; OIKONOMOU, G.; SCHUKKEN, Y.H. AND BICALHO, R.C. (2017). Normal milk microbiome is reestablished following experimental infection with Escherichia coli independent of intramammary antibiotic treatment with a third-generation cephalosporin in bovines. Microbiome 5, 74.IVANOVA, N.; SIKORSKI, J.; SIMS, D.; BRETTIN, T.; DETTER, J.C.; HAN, C.; LAPIDUS, A.; COPELAND, A.; GLAVINA DEL RIO, T.; NOLAN, M.; CHEN, F.; LUCAS, S.; TICE, H.; CHENG, J.F.; BRUCE, D.; GOODWIN, L.; PITLUCK, S.; PATI, A.; MAVROMATIS, K.; CHEN, A.; PALANIAPPAN, K.; D'HAESELEER, P.; CHAIN, P.; BRISTOW, J.; EISEN, J.A.; MARKOWITZ, V.; HUGENHOLTZ, P.; GÖKER, M.; PUKALL, R.; KLENK, H.P. AND KYRPIDES, N.C. (2009). Complete genome sequence of Sanguibacter keddieii type strain (ST-74). Stand Genomic Sci 24, 1, 110-118.KEHRLI. M.E. AND HARP, J.A. (2001). Immunity in the mammary gland. Vet Clin North Am Food Anim Pract 17, 495-516.KEIKHA M. (2017). Williamsia spp. are emerging opportunistic bacteria. New Microbes New Infect 21, 88-89.KLINDWORTH. A.; PRUESSE. E.; SCHWEER. T.; PEPLIES. J.; QUAST. C.; HORN. M. AND GLOCKNER. F.O. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41, e1.KUEHN, J.S.; GORDEN, P.J.; MUNRO, D.; RONG, R.; DONG, Q.; PLUMMER, P.J.; WANG, C. AND PHILLIPS, G.J. (2013). Bacterial community profiling of milk samples as a means to understand culture-negative bovine clinical mastitis. PLoS ONE 13, 8, e61959.LIMA, S.F.; BICALHO, M.L.DS. AND BICALHO, R.C. (2018). Evaluation of milk sample fractions for characterization of milk microbiota from healthy and clinical mastitis cows. PLoS ONE 13, e0193671.LOZUPONE, C. AND KNIGHT, R. (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71, 8228‐8235.MACPHERSON, A.J. AND UHR, T. (2004). Induction of protective IgA by intestinal dendritic cells carrying commensal bacteria. Science 303, 1662-1665.METZGER, S.A.; HERNANDEZ, L.L.; SUEN, G. AND RUEGG, P.L. (2018). Understanding the milk microbiota. Vet Clin North Am Food Anim Pract 34, 427-438.OIKONOMOU, G.; BICALHO, M.L.; MEIRA, E.; ROSSI, R.E.; FODITSCH, C.; MACHADO, V.S.; TEIXEIRA, A.G.; SANTISTEBAN, C.; SCHUKKEN, Y.H. AND BICALHO, R.C. (2014). Microbiota of cow's milk; distinguishing healthy, sub-clinically and clinically diseased quarters. PloS one, 9(1), e85904. https://doi.org/10.1371/journal.pone.0085904 (2014) Microbiota of Cow’s Milk: Distinguishing Healthy, Sub-Clinically and Clinically Diseased Quarters. PLoS ONE 9, e85904.OIKONOMOU, G.; MACHADO, V.S.; SANTISTEBAN, C.; SCHUKKEN, Y.H. AND BICALHO, R.C. (2012). Microbial Diversity of Bovine Mastitic Milk as Described by Pyrosequencing of Metagenomic 16s DNA. PLoS ONE 7, e47671.RODRÍGUEZ, J.M. (2014). The origin of human milk bacteria: Is there a bacterial entero-mammary pathway during late pregnancy and lactation? Adv Nutr 5, 779-784.ROUX, M.E.; MCWILLIAMS, M.; PHILLIPS-QUAGLIATA, J.M.; WEISZ-CARRINGTON, P. AND LAMM, M.E. (1977). Origin of IgA-secreting plasma cells in the mammary gland. Journal of Experimental Medicine 146, 1311-1322.STOCCO, G.; SUMMER, A.; CIPOLAT-GOTET, C.; ZANINI, L.; VAIRANI, D.; DADOUSIS, D. AND A. ZECCONI (2020). Differential cell count as a novel indicator of milk quality in dairy cows. Animals 10, 1-14.TAPONEN, S.; MCGUINNESS, D.; HIITIÖ. H.; SIMOJOKI, H.; ZADOKS, R. AND PYÖRÄLÄ, S. (2019). Bovine milk microbiome: a more complex issue than expected. Vet Res 50, 44.TOLLE, A. (1980). The microflora of the udder. Factors influencing the bacteriological quality of raw milk. International Dairy Federation Bulletin Document 120, 4.VANGROENWEGHE, F.; DOSOGNE, H.; MEHRZAD, J. AND BURVENICH, C. (2001). Effect of milk sampling techniques on milk composition, bacterial contamination, viability and functions of resident cells in milk. Vet Res 32, 565-579.VERDIER-METZ, I.; GAGNE, G.; BORNES, S.; MONSALLIER, F.; VEISSEIRE, P.; DELBÈS-PAUS, C. AND MONTEL, M.C. (2012). Cow teat skin, a potential source of diverse microbial populations for cheese production. Appl Environ Microbiol 78,326-333.WEON, H.Y.; LEE, C.M.; HONG, S.B.; KIM, B.Y.; YOO, S.H.; KWON, S.W. AND GO, S.J. (2008). Kaistia soli sp. nov., isolated from a wetland in Korea. Int J Syst Evol Microbiol 58, 1522-1524.YOUNG, W.; HINE, B.C.; WALLACE, O.A.; CALLAGHAN, M. AND BIBILONI, R. (2015). Transfer of intestinal bacterial components to mammary secretions in the cow. PeerJ 3, e888.ZECCONI, A. AND PICCININI R. (2002). Intramammary infections: epidemiology and diagnosis. XXII World Buiatric Congress - Recent developments and perspectives in bovine medicine. HannoverZECCONI, A.; DELL’ORCO, F.; VAIRANI, D.; RIZZI, N.; CIPOLLA, M. AND ZANINI, L. (2020 a). Differential cell count as a marker for changes of milk composition in cows very low somatic cell counts. Animals 10, 1-14.ZECCONI, A.; ZANINI, L.; CIPOLLA, M.; STEFANON, B. (2020 b). Factors Affecting the Patterns of Total Amount and Proportions of Leukocytes in Bovine Milk. Animals 10, 992.ZECCONI, A.; HAMANN, J.; BRONZO, V.; MORONI, P.; GIOVANNINI, G. AND PICCININI, R. (2000). Relationship between teat tissue immune defences and intramammary infections. Adv Exp Med and Biol 480, 287-293.ZECCONI, A.; VAIRANI, D.; CIPOLLA, M.; RIZZI, N. AND ZANINI, L. (2019 a). Assessment of subclinical mastitis diagnostic accuracy by differential cell count in individual cow milk, Italian Journal of Animal Science 18, 460-465.ZECCONI, A.; SESANA, G.; VAIRANI, D.; CIPOLLA, M.; RIZZI, N. AND ZANINI, L. (2019 b). Somatic cell count as a decision tool for selective dry cow therapy in Italy. Italian Journal of Animal Science 18, 435-440.
Acute Ischemia/Hypoxia in Rat Hippocampal Neurons Activates Nuclear Ubiquitin and Alters Chromatin and DNA
No full textA simple control method for the determination of methylesters in biodiesel fuels
No full textOne of the main problems in the study or industrial application of transesterification processes for vegetable oils is how to measure the methyl ester content. In this work, a quick analytical method was developed for assessing the methyl ester content of purified “fuel grade” transesterification products by applying a simple correlation with viscosity. The correlation was tested on a wide range of samples with various methyl ester contents; the results were in agreement with the values measured by gas-chromatographic analysis. In a defined range of weight fractions the correlation allows for the determination of the methyl ester content of purified transesterification products by a single viscosity measurement. This method is especially suitable for process control purposes as it determines the methyl ester content quickly and simply
New Resistor-Less Electronically Controllable ±C Simulator Employing VCII, DVCC, and a Grounded Capacitor
No full textIn this paper, a new realization of electronically controllable positive and negative floating capacitor multiplier (±C) is presented. The peculiarity of the presented topology is that, for the first time, it implements a floating equivalent capacitor between its two input terminals, rather than a grounded one. To achieve the best performance, we simultaneously use the advantages provided by the current conveyor and its dual circuit, the voltage conveyor. The proposed topology is resistor free and employs one dual-output second-generation voltage conveyor (VCII±) and one electronically tunable differential voltage current conveyor (E-DVCC) as active building blocks (ABBs) and a single grounded capacitor. The value of the simulated capacitor is controlled by means of a control voltage VC which is used to control the current gain between X and Z terminals of E-DVCC. The circuit is free from any matching condition. A complete non-ideal analysis by considering parasitic impedances as well as non-ideal current and voltage gains of the used ABBs is presented. The proposed circuit is designed at the transistor level in 0.18 µm and ±0.9 V supply voltage. Simulation results using the SPICE program show a multiplication factor ranging from ±10 to ±25.4 with a maximum error of 0.56%. As an example, the application of the achieved floating capacitor as a standard high pass filter is also included
Effect of different starch sources in a raw meat-based diet on fecal microbiome in dogs housed in a shelter
No full textA dietary intervention study was assessed to determine if different sources of starch in homemade diets could significantly modify fecal microbiome of dogs. Twenty-seven adult dogs were enrolled and fed a diet based on a mixture of rice and pasta with fresh raw meat (CD). After 90 d, 8 dogs continued to receive CD diet, 10 dogs received a diet made of a raw meat and a complementary food with rice as the main source of starch (B1), and 9 dogs were fed a diet with the same raw meat and a complementary food with potato as the main source of starch (B2). Samples of feces were collected from each dog in the mornings at the beginning of the study and after 15 d and analyzed for pH, ammonia N (N–NH3) and total N, short chain fatty acids (SCFA) and lactic acid. Relative abundance of fecal microbiota was assessed by sequencing and annotating the V3–V4 regions of the 16S rRNA. Total starch intake was similar between diets but differed in the in vitro rate digestion and in the resistant starch, which was higher in B2 than in B1 and CD diets. Dogs fed B2 diet showed lower (P < 0.05) N–NH3 and pH but higher (P < 0.05) molar proportion of lactic acid. Linear discriminant analysis of the genera relative abundances indicated a significant (P < 0.01) increase of SMB53 genus at the end of the study in B1 diet and of Megamonas genus in B1 and B2 diets in comparison to CD diet. These results suggest that changes of starch source in a raw meat-based diet have limited effects on fecal microbiome in healthy dogs, but underline a high variability of microbiota among dogs
Heterogeneous catalysts for hydrothermal liquefaction of lignocellulosic biomass. A review
No full textThe biomass conversion into more valuable fuels represents one of the most viable routes for the exploitation of this material. Hydrothermal liquefaction is currently considered one of the most efficient processes to convert wet biomass into a bio-crude, which however requires expensive upgrading treatments to be used as biofuel. The use of catalysts able to directly improve bio-crude yield and quality during the reaction is of fundamental importance to increase the overall process efficiency. Homogeneous alkaline catalysts are the most studied, but they are not recoverable at the end of the process and so cannot be reused. The use of heterogeneous catalysts allows to overcome this issue making the recovery and reuse possible, maintaining anyway high activity and selectivity in the bio-crude production. The aim of this review is to critically summarize the effect of heterogenous catalyst addition on the hydrothermal liquefaction of lignocellulosic biomass, looking specifically at the improvement in bio-crude yield and quality. On the basis of literature data about the effect of heterogeneous catalyst addition on bio-crude yield and quality in the hydrothermal liquefaction of lignocellulosic biomass, a common catalytic action was identified allowing to group the several catalysts into four classes (alkaline metal oxides, transition metals, lanthanides oxides and zeolites). The hydrodeoxygenation activity of the catalysts, their effect on bio-crude yield and quality and the operating conditions used are highlighted. The highest bio-crude yields are reported using transition metals and lanthanide oxides which are able to guarantee, at the same time, a high-quality bio-crude
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