Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Terroir Dynamics: Impact of Vineyard and Canopy Treatment with Chitosan on Anthocyanins, Phenolics, and Volatile and Sensory Profiles of Pinot Noir Wines from South Tyrol
    (MDPI (Basel, Switzerland), 2024-04-23) Tchouakeu Betnga PF; Poggesi S; Darnal A; Longo E; Rudari E; Boselli E; Rolle L
    The effects of canopy treatment with chitosan and the effects of the vineyard location on the quality parameters, volatile and non-volatile profiles, and sensory profile of Pinot Noir wines from South Tyrol (Italy) were studied. Multivariate statistical analysis was applied to identify the most relevant compounds associated with the variability in phenolics and anthocyanins (analyzed by UHPLC-MS), volatile components (HS-SPME-GCxGC-ToF/MS), and basic enological parameters. A clear separation of low-altitude wines (350 m.a.s.l.), which had a high concentration of most of the identified volatile compounds, compared to high-altitude wines (800 and 1050-1150 m.a.s.l.) was pointed out. Low altitude minimized the concentration of the most significant anthocyanins in wines from a valley bottom, presumably due to reduced sun exposure. Wines obtained from chitosan-treated canopies, and, more particularly, those subjected to multiple treatments per year showed a higher amount of the main non-volatile phenolics and were sensorially described as having "unpleasant flavors" and "odors", which might suggest that grape metabolism is slightly altered compared to untreated grapevines. Thus, optimization of the treatment with chitosan should be further investigated.
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    Genetic Association of PPARGC1A Gene Single Nucleotide Polymorphism with Milk Production Traits in Italian Mediterranean Buffalo.
    (Hindawi Limited, 2021-03-20) Hosseini SM; Tingzhu Y; Pasandideh M; Liang A; Hua G; Farmanullah; Schreurs NM; Raza SHA; Salzano A; Campanile G; Gasparrini B; Yang L; Kontos CK
    PPARGC1A gene plays an important role in the activation of various important hormone receptors and transcriptional factors involved in the regulation of adaptive thermogenesis, gluconeogenesis, fiber-type switching in skeletal muscle, mitochondrial biogenesis, and adipogenesis, regulating the reproduction and proposed as a candidate gene for milk-related traits in cattle. This study identified polymorphisms in the PPARGC1A gene in Italian Mediterranean buffaloes and their associations to milk production and quality traits (lactation length, peak milk yield, fat and protein yield, and percentage). As a result, a total of seven SNPs (g.-78A>G, g.224651G>C, g.286986G>A, g.304050G>A, g.325647G>A, g.325817T>C, and g.325997G>A) were identified by DNA pooled sequencing. Analysis of productivity traits within the genotyped animals revealed that the g.286986G>A located at intron 4 was associated with milk production traits, but the g.325817T>C had no association with milk production. Polymorphisms in g.-78A>G was associated with peak milk yield and milk yield, while g.304050G>A and g.325997 G>A were associated with both milk yield and protein percentage. Our findings suggest that polymorphisms in the buffalo PPARGC1A gene are associated with milk production traits and can be used as a candidate gene for milk traits and marker-assisted selection in the buffalo breeding program.
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    A Statistical Model for Earthquake And/Or Rainfall Triggered Landslides
    (Frontiers Media S.A., 2021-02-04) Frigerio Porta G; Bebbington M; Xiao X; Jones G; Xu C
    Natural hazards can be initiated by different types of triggering events. For landslides, the triggering events are predominantly earthquakes and rainfall. However, risk analysis commonly focuses on a single mechanism, without considering possible interactions between the primary triggering events. Spatial modeling of landslide susceptibility (suppressing temporal dependence), or tailoring models to specific areas and events are not sufficient to understand the risk produced by interacting causes. More elaborate models with interactions, capable of capturing direct or indirect triggering of secondary hazards, are required. By discretising space, we create a daily-spatio-temporal hazard model to evaluate the relative and combined effects on landslide triggering due to earthquakes and rainfall. A case study on the Italian region of Emilia-Romagna is presented, which suggests these triggering effects are best modeled as additive. This paper demonstrates how point processes can be used to model the triggering influence of multiple factors in a large real dataset collected from various sources.