Month: March 2025

Furthermore, colonization of pathogens during drought may further disrupt the carbon balance of plants as it influences defense and repair, creating a feedback loop that can drive plants toward a mortality tipping point . Thus, while dehydration tolerance may be important during typical seasonal drought conditions, it may be a much riskier strategy and lead to greater mortality during global-change type drought, especially in the presence of pathogens. These frameworks are consistent with our findings and provide further evidence that A. glauca experiencing acute levels of drought stress are highly predisposed to Bot. infection particularly at lower elevations that experience heightened levels of water stress.The results of this study provide strong evidence that A. glauca in the study region are vulnerable to Bot. disease and dieback, and possibly eventual mortality, related to acute drought. This is consistent with Venturas et al. , who found that acute drought in 2014 led to reduced abundance in A. glauca and other obligate seeder chaparral species and even type-conversion in the Santa Monica Mountains of southern California, USA. A review by Jacobsen and Pratt found similar consistencies among shallow-rooted, low round pots obligated seeding shrubs. Clearly, there is strong support that A. glauca populations are at risk for future dieback, and thus should be the focus of more intense studies aimed at understanding the possible mechanisms driving such events.

Manzanita are important members of the chaparral ecosystem and large-scale dieback and mortality of this species could reduce resource availability for wildlife , as well as increase the risk of more intense, fires in an ecosystem already associated with increasingly frequent fire activity. Additionally, our study provides valuable insight into areas of greatest risk for dieback and mortality, which are predominantly in lower elevations. These are important factors to consider when predicting vulnerabilities and potential impacts of future extreme drought events . Mediterranean shrublands like those in southern California already considered high risk for global-change type drought, , and research suggests a general trend of upwards-shifting ranges in southern California chaparral species driven by changes in climate .Therefore, populations of A. glauca occurring at the lower edge of their natural range are at high risk for dieback and mortality, and should be the focus of management efforts. Lastly, while studies on the various physiological mechanisms for plant survival during drought are critical for predicting differential responses to stress, there is an increased emphasis on the importance of understanding the diverse role of pathogens in order to accurately model species vulnerabilities to climate change . Studies that incorporate the impact of pathogens help inform new integrative approaches to protecting plants against drought and biotic infection, rather than treating these influences separately.

Examples include Jactel et al., , whose meta-analysis showed the significant effects of water stress on symptom severity in plants infected with latent pathogens like Bots, and experiments like Drake-Schultheis et al. , who found interactive effects between drought stress and infection from N. australe in driving symptoms of stress and increasing mortality rates in A. glauca. The results of our study align with these frameworks, and provide additional evidence that as climate change models are predicting more intense and frequent drought events, our need to understand the role of latent pathogens in at-risk natural systems is becoming more critical.Reports of large-scale, drought-associated mortality events in forest and woodland systems have been on the rise in recent decades . These reports have spanned across biomes, including in classically drought-tolerant species across Europe , Australia , Africa , and the United States . As a result, interest has been growing in understanding how species that are typically capable of withstanding periodic drought stress may become susceptible to drought and experience significant dieback and even large-scale mortality when exposed to acute or prolonged chronic drought . These droughts of unusual extremes are referred to as “globalchange-type drought” and are becoming more common as the climate warms . While the exact physiological mechanisms leading to dieback and mortality during such events are variable across species and conditions, drought is generally hypothesized to promote physiological decline either via loss of hydraulic functioning or carbon starvation or a combination of both . In the case of hydraulic failure, plants with insufficient soil water experience xylem cavitation , which can ultimately lead to cellular death. Alternatively, plants that avoid drought by closing their stomata to reduce water loss subsequently suffer insufficient carbon supply to meet other metabolic demands.

In either scenario, the stress that drought places on a plant is likely to cause measurable decreases in physiological functions that may be irreversible . An additional factor that can play a significant role in drought-related dieback and mortality is the presence or introduction of biotic agents. Indeed, introduced plant pathogens have been well documented to cause canopy dieback and dramatically alter community structure in a variety of forested systems . Some well-known examples in the United States include Dutch elm disease , chestnut blight , white pine blister rust , and sudden oak death . Significant pathogen events have also impacted the landscape in wild land shrub communities including sclerophyll shrub woodlands in Australia and salt desert scrub in the western United States . However, large-scale dieback of shrubs has been less documented than their arboreal counterparts, despite evidence of disease from fungal species being abundant in many scrubland systems including southern California chaparral , northern California foothill shrublands , and South African fynbos . Such studies, along with expectations of increasing threats from pathogens due to climate change and accelerating trade/movement of biological materials globally , have led scientists and land managers alike to anticipate introduced pathogens as important contributors to future changes in wild land communities.While both global-change-type drought and pathogens are likely important contributors to plant dieback and mortality, current research suggests that these two factors are not mutually exclusive . Rather, canopy dieback and mortality may result from the combined influences of environmental stress and biotic agents, and theoretical frameworks describing these influences have been put forth . These frameworks incorporate biotic agents into the drought-hydraulics complex described above, whereby pathogens and insects may amplify or be amplified by drought-associated hydraulic failure or carbon starvation . Amplification can occur when biotic agents damage host tissue—by defoliation or blocking transportive vessels, for example—to the extent that the effects of drought are greatly exacerbated . Alternatively, physiological responses to extreme environmental stress can have negative effects on plant defense systems, rendering them susceptible to mortality through biotic infection . In both scenarios, the effects of biotic agents and drought stress are strongly linked, and these interactions have been well documented in drought-tolerant systems such as South African fynbos , red pine forests , eucalyptus forests , and California chaparral . Latent or secondary pathogens are particularly likely to be involved with dieback and mortality events in these systems, as they are known to increase damage in hosts experiencing drought stress . Therefore, while drought events alone are expected to play an important role in reshaping ecosystems as the climate changes, in some cases, synergies between environmental stress and biotic influences might lead to shifts in plant community structure and composition, and thus ecosystems as a whole. In the Santa Ynez Mountains in Santa Barbara County, California, United States, big berry manzanita began exhibiting dramatic canopy dieback during the 2011–2018 drought . Shrubs in the genus Arctostaphylos are common in Mediterranean shrub communities extending from southwest Oregon to northern Baja California . They may occur in monospecific stands or in alliances with other important community members like chamise and Ceanothus spp. . Within these alliances, Arctostaphylos spp. frequently occupy >50% average cover , which along with their nutritious and prolific fruits, and fire-induced regeneration strategies, make them one of the most important members of the chaparral community . In the southern California chaparral ecosystem where hot, dry summers with high vapor pressure deficit are the norm , seasonal drought tolerance has long been considered a common strategy among dominant plant species, including A. glauca. However, the severity of recent canopy dieback observed suggests that this species is reaching a threshold in its drought-resistance capability. Concurrent with observations of canopy dieback, visible symptoms of fungal infection were observed including wood cankers and leaf discoloration , plastic pots 30 liters both of which progress during prolonged drought stress, suggesting that multiple driving forces contribute to manzanita dieback. Molecular sequencing identified the dominant fungal pathogen found on symptomatic A. glauca in this area to be Neofusicoccum australe, a member of the well-known pathogenic Botryosphaeriaceae family . Members of this family are most commonly associated with disease in plant species experiencing severe environmental stress , including Arctostaphylos spp. . They are also known to play a variety of functionally diverse roles, from asymptomatic endophytes to obligate pathogens . Yet, while N. australe has been described around the world , relatively few studies have been conducted on its specific interactions with host species, as it was only fairly recently described .

Historically, Bot. pathogens have most frequently been studied in agricultural host species , and little is known regarding their ecological role in wildland ecosystems , especially with regards to chaparral shrubland systems . The present study was aimed at identifying the possible role of N. australe in A. glauca dieback in Santa Barbara County, particularly in combination with extreme drought. Because this pathogen has only recently been reported on wild shrub species in California and is thought to be an introducedspecies native to Western Australia , this outbreak represents a new and undescribed threat to these wildland plant assemblages. This study addresses the following questions: How does A. glauca respond physiologically to drought and fungal infection, separately and together? Are these responses correlated with visual signs of stress, specifically leaf health? Can drought and fungal presence interact to increase or accelerate plant mortality compared to drought or fungi alone in A. glauca? To address these questions, a greenhouse experiment was conducted in November 2016 through February 2017 manipulating both drought and fungal infection and observing trends in plant stress symptoms, physiological function, and mortality. We predicted that both drought stress and fungal infection would lead to declines in physiological function compared to the control and that these declines would be strongly correlated with increases in stress severity. Furthermore, we expected that those individuals experiencing both drought stress and fungal infection would die sooner than those in all other treatment groups. This experimental study elucidated the potential of the interaction between drought stress and introduced pathogens to significantly impact chaparral shrub health and important implications for the future of these shrubs faced with increasingly frequent global change-type droughts.A completely randomized full-factorial design was used to organize the individuals into four treatment groups: droughted and inoculated with N. australe , droughted and not inoculated , watered and inoculated with N. australe and a control; watered and not inoculated . Data were collected for ~90 days to track declines in health and mortality rates among the different treatments. Drought-treated plants received 1 L of water on the day of inoculation and another 0.5 L on day 38. Those with no drought treatment received 0.5 to 1.0 L of water by hand once per week depending on soil moisture, which was monitored regularly using a TDR machine from Soil Moisture Co. . Soil moisture for non-drought plants was maintained between 15–25% moisture for the entire experiment. Cultures for inoculations were made from re-isolations of field samples that were collected in January 2016 and positively identified to be N. australe . Inoculations took place on 3 November 2016 , using methods adapted from Michailides and Swieckiand Bernhardt . Mycelial plugs were made from 8-d-old cultures growing on half strength potato dextrose agar amended with streptomycin to prevent bacterial contamination. Plants were first sprayed with 70% isopropyl alcohol to sterilize the surfaces and surrounding areas. Mycelial plugs were taken from the advancing margin of N. australe cultures and placed on strips of Parafilm using sterile petroleum jelly for adhesion. Plugs were then placed to superficial wounds made on the main stem . The Parafilm strips were then gently wrapped 2–3 times around the stem to keep the plugs in place and prevent contamination. Those plants not receiving fungal inoculation received a control inoculation with uncultured potato dextrose agar using the same techniques.

Individual items, or in the case of the current study, individual questions from the RBS-R, were independently assessed for conceptual fit on the factor they most strongly loaded on to determine if it is an appropriate factor fit considering the other items that loaded strongly on the respective factor. The overall goal of EFA was to identify factors, based on a given dataset, and maximize the amount of variance explained by the model . Once a model has been theoretically and/or statistically established and hypotheses have been made, a confirmatory factor analysis can inform the likelihood of the hypothesized results.A Confirmatory Factor Analysis was conducted once the relationships among variables were established through statistical analyses and a theoretical model was evaluated . While the EFA allows for all items to load on any factor, the CFA restricts the factors on which items load. Each item was permitted to load on only one factor. Model fit was determined using recommended indices of model fit including Chi-Squared test, RMSEA, RMR, CFI and TLI. Additionally, the CFA model produced a weighted root mean square residual that is an empirically supported measure of model fit comparable to the other fit indices and is suggested to be highly useful for data that isn’t normally distributed . A WRMR value above 1.0 is considered good model fit. Factor loadings from the CFA were reported as the standardized model estimate loadings and associated standard errors.Cluster analysis provides a unique approach to examining which results in the identification of patterns that organize variables into taxonomies, grouping cases with similar patterns together . For the current study, blueberry grow pot the K-means cluster analysis was run to systematically and conceptually group participants with similar RRB patterns together.

The newly established factors from the CFA of the RBS-R were used to examine the various patterns of RRB presentation for this population. The goal of a k-means clustering is to partition individuals into clusters where every participant belongs to a cluster with others presenting with similar patterns . The optimal number of clusters must strike a balance between successfully compressing the data as a single cluster would, while maintaining maximum accuracy where every participant is assigned to its own cluster. The optimal number of clusters for the data was determined using both theoretical and empirical considerations. Previous research exploring RRBs have defined between two and six distinct types of RRBs; yet, there hasn’t been a clustering of those RRBs into distinct profiles to serve as a comparison or as an empirical rationale to test the fit of a specific number of clusters. Therefore, comparisons of three, four, five and six cluster solutions were conducted. One approach that was used to determine model fit for each cluster was to examine the number of iterations it took to satisfy the convergence criterion . There is no guarantee that data will cluster and iterate to convergence quickly, if at all. Therefore this is a reasonable justification for this approach in determining the fit between the number of clusters and the data being analyzed. Statisticians have concluded that it is acceptable to institute a maximum criterion of between 15 and 20 iterations for the data to reach convergence criterion where the clusters optimally fit the data. Cluster statistics were explored after running three, four, five and six cluster solutions; results are described below.The final research aim was to determine the ability of several behavioral and developmental characteristics to predict cluster membership. Correlation analyses among all predictors were conducted prior to running the MLR to determine presence of collinearity.

A multinomial logistic regression was run with individual cluster assignment as the outcome variable and participants’ standardized scores of ASD severity, nonverbal IQ, hyperactivity, anxiety, and coping skills as predictors. Age differences across clusters was independently examined by running a one-way ANOVA prior to running the MLR to determine if age significantly differed among clusters. The MLR provides a unique approach to determine the odds ratio of an individual being in one cluster relative to the odds of them being in the comparison cluster based on several characteristics . Therefore, it is important to choose a comparison cluster that will provide the most robust information in the analysis of these comparison solutions. Prior to exploring the individual cluster phenotypes to decide on a comparison cluster, the options were carefully considered and a conceptual decision was made. The comparison group should be the one that differs the most from the others, or the group that could be considered the “optimal outcome” group that possesses characteristics that researchers would want to test and discover what makes that group of participants different . Therefore, the cluster with the lowest levels across all RRBs was used as the baseline comparison cluster. Goodness of fit of the MLR model was assessed using the log-likelihood , which sums the probabilities of predicted outcomes and actual outcomes, analogous to the residual sum of squares in typical multiple regression. That is, the LL variable indicates how much unexplained data remains after the model is fit; where large values of the LL statistic tends to describe a poor fit for the model . Results of the multinomial logistic regression produced significance statistical values, which indicated the extent to which individual characteristics were able to significantly predict membership to one cluster over another. The individual parameter estimates for each comparison between the optimal profile group vs. the other profiles were individually examined to determine the significant and non significant results across predictor variables and interactions. The significance values were used to determine which of the characteristics were significant in predicting profile membership, with the odds ratio statistic indicating the odds of a participant being in a cluster when compared to the odds of them being a member of the optimal outcome profile group. Overall model fit statistics as well as individual parameter estimates of the multinomial logistic regression were examined.The recent changes to the DSM have created a more comprehensive list of RRB subtypes than were previously included and set a more stringent benchmark to meet criteria in the RRB domain. Such changes reflect the progression of research supporting the importance and independence of RRBs as an integral component of diagnosis, rather than a by-product of the “core” social communication impairments . From its earliest conception, ASD has been characterized by the presence of frequently and highly repetitious behaviors, with a marked desire for environmental sameness and consistency . Yet, this complex behavioral domain is historically under-represented in research efforts and falls secondary to social communication deficits in ASD research. Reviews of past studies on RRB presentation have highlighted issues including a lack of methodological consistency, with varying approaches to defining, organizing, and measuring RRBs. These discrepancies have led to splintered advancements in understanding the etiology, early behavioral manifestations and longitudinal developmental implications of RRBs . The primary aims of this study were to characterize RRB phenotypes of individuals with ASD and to determine the influence of developmental and behavioral characteristics on RRB profiles. This study revealed that there were five distinct RRB subtypes captured by the RBS-R, with five distinct phenotypic profiles generated from those subtypes. Hyperactivity, hydroponic bucket anxiety and coping skills significantly predicted participants’ RRB phenotype, while IQ and symptom severity had little effect.

The findings in this study provide a unique perspective when conceptualizing ASD symptomology and the influence of non-ASD specific traits on this core domain.The five-factor model result from the factor analyses of the RBS-R exhibits substantial consistency with previous studies examining the factor structure of the RBS-R . Comparisons between factor results of the RBS-R can be seen in Figure 3. Most notably, the current study excluded 3 items from the original compulsive scale as the item factor loadings were above .4 on more than two newly calculated factors. These results indicated that there wasn’t a single factor that accounted for the variability of each item, forcing those items to be excluded. Similarly, five items on the original ritualistic scale were excluded which included items regarding eating, sleeping, travel, play and self-care as they were highly loaded on multiple factors. These findings indicate they may not be sufficiently differentiating types of RRBs measured by each question, which leads investigators to wonder if the questions are adequately differentiating between RRB subtypes. Bishop, et al. investigated RRB data from both the ADI-R and the RBS-R and found that the ADI-R items resulted in a two-factor model, whereas the RBS-R resulted in a five-factor model as the best fit. When examined in conjunction with findings from the Lam & Aman study as well as the current results, it is evident that using a measure with a wider range of questions such as the RBS-R provides more in-depth and informative results when examining the specific types and severities of RRBs. Despite the utility of a measure dedicated to specific RRB types and severity, factor results from previous studies fail to be substantiated with each study, leading to the conclusion that a final set of RRB subtypes have yet to be established unequivocally across studies. Further, each analytic result has not been entirely consistent with the six conceptually derivedsub-scales that Bodfish, et al. originally established. Discrepancies between the subtypes and the original sub-scales, as well as between the previously proposed models can be seen below in Figure 3. As previously discussed, RRBs comprise a complex and heterogeneous set of behaviors that vary greatly depending on the population being measured; therefore, it is not a complete surprise that each factor analytic study has resulted in slightly altered structures. However, given the vast age range include in the current study and largest number of participants to date for an RBS-R factor analysis, the resulting factor structure warrants consideration as an organizational RRB factor structure to be analyzed for confirmatory analyses in future studies using the RBS-R. As seen in Figure 3, researchers who organized and defined more than two categories of RRBs had one striking consistency, the inclusion of an independent category of self-injurious behavior . Further, self- injury is arguably the most recognizable and disruptive RRB consistently found to be related to greater impairment with significantly lower IQ and higher severity of ASD symptoms . In fact, the most recent study examining RRB subtypes concluded that SI behaviors create significant difficulty in dichotomizing RRBs, as the SI items fail to load with the repetitive sensory motor category or with the insistence on sameness supporting the existence of additional subcategories . Further, SI is the only RRB subtype to consistently load identically as an entire sub-scale in every factor analytic study of the RBS-R, which was also true in the current study, indicating its distinctiveness .The RRB subtype found most consistently across studies has been self-injurious behaviors. As seen in Figure 3, researchers who organized and defined more than two categories of RRBs had one striking consistency, the inclusion of an independent category of self-injurious behavior . Further, self- injury is arguably the most recognizable and disruptive RRB consistently found to be related to greater impairment with significantly lower IQ and higher severity of ASD symptoms . In fact, the most recent study examining RRB subtypes concluded that SI behaviors create significant difficulty in dichotomizing RRBs, as the SI items fail to load with the repetitive sensory motor category or with the insistence on sameness supporting the existence of additional subcategories . Further, SI is the only RRB subtype to consistently load identically as an entiresub-scale in every factor analytic study of the RBS-R, which was also true in the current study, indicating its distinctiveness .Cluster analysis provides a novel approach to statistically explore phenotypic profiles and the co-occurrence of RRB types and severity across individuals with ASD. This is the first study of its kind to statistically generate clustered phenotypes, each consisting of multiple RRB subtypes. RRBs don’t occur in isolation; the pattern of behavior is fluid with minimal evidence to explain the variations seen across and within individuals. By studying RRBs in a way that allows for multiple RRBs to co-occur at varying levels, researchers may gain a more accurate and informative picture of how these behaviors manifest across individuals with ASD. However, when researchers rely solely on parent report measures, there is a limited scope of distinct behaviors from which combination or cluster phenotypes can be derived.

Increasing temperature can accelerate metabolism, including sugar biosynthesis and transport, but the increase in metabolism is not uniform. For example, the increase in anthocyanin concentration during the ripening phase is not affected as much as the increase in sugar concentration. These responses vary with the cultivar, complicating this kind of analysis even further. Direct studies of temperature effects on Cabernet Sauvignon berry composition also are consistent with our data. In one study, the composition of Cabernet Sauvignon berries was altered substantially for vines grown in phytotrons at 20 or 30 °C temperatures. Cooler temperatures promoted anthocyanin development and malate concentrations and higher temperatures promoted TSS and proline concentrations. In a second study, vines were grown at 20 or 30 °C day temperatures with night temperatures 5 °C cooler than the day. In this study, higher temperatures increased berry volume and veraison started earlier by about 3 to 4 weeks. The authors concluded that warmer temperatures hastened berry development. In a third study, Cabernet Sauvignon berry composition was affected in a similar manner by soil temperatures that differed by 13 °C. TSS concentrations are also affected by light and the vine water status. Light is generally not a factor because there is usually a large enough leaf area and sufficient light levels to saturate this source to sink relationship. Sun-exposed Cabernet Sauvignon berries in the vineyard had higher TSS than shaded berries. This sunlight effect was attributed largely to an increase in berry temperature rather than an increase in the fluence rate perse.

A higher grapevine water status results in larger berry size and lower sugar concentrations and water deficit is known to increase sugar concentrations in Cabernet Sauvignon. However, nft hydroponic system temperature is thought to have the largest effect on sugar concentrations. Other transcriptomic data in the present study indicated that BOD berries were more mature at a lower sugar level than RNO berries. These included the transcript abundance profiles of genes involved in autophagy, auxin and ABA signaling, iron homeostasis and seed development. Many of these DEGs had an accelerated rate of change in BOD berries. While these transcripts are in the skins, they may be influenced by signals coming from the seed. In addition, there was a higher transcript abundance for most genes involved with the circadian clock in BOD berries. PHYB can regulate the circadian clock and PHYB activity is very sensitive to night temperatures ; PHYB reversion is accelerated to the inactive form at warmer temperatures. The inactivity of phytochrome promotes the expression of RVE1, which promotes auxin concentrations and seed dormancy. Thus, all things considered, it is likely that temperature and/or the temperature differentials between day and night significantly contributed to the differences in the rate of berry development and sugar accumulation in the two locations.Determining maturity of grapes is a difficult and error prone process. Reliable markers could aid in the decision of when to harvest the grapes. “Optimum” maturity is a judgement call and will ultimately depend on the winemaker’s or grower’s specific goals or preferences. A combination of empirical factors can be utilized including °Brix, total acidity, berry tasting in the mouth for aroma and tannins, seed color, etc. °Brix or total soluble solids by itself may not be the best marker for berry ripening as it appears to be uncoupled from berry maturity by temperature.

Phenylpropanoid metabolism, including anthocyanin metabolism, is also highly sensitive to both abiotic and biotic stresses and may not be a good indicator of full maturity. Thus, color may not be a good indicator either. Specific developmental signals from the seed or embryo, such as those involved with auxin and ABA signaling, may provide more reliable markers for berry ripening in diverse environments, but will not be useful in seedless grapes. Aromatic compounds may also be reliable markers but they will need to be generic, developmental markers that are not influenced by the environment. This study revealed many genes that are not reliable markers because they were expressed differently in different environments. One candidate marker that is noteworthy is ATG18G . Its transcript abundance increased and was relatively linear with increasing °Brix and these trends were offset at the two locations relative to their level of putative fruit maturity . ATG18G is required for the autophagy process and maybe important during the fruit ripening phase. It was found to be a hub gene in a gene subnetwork associated with fruit ripening and chloroplast degradation. Further testing will be required to know if it is essential for fruit ripening and whether its transcript abundance is influenced by abiotic and biotic stresses in grape berry skins.The ultimate function of a fruit is to produce fully mature seeds in order to reproduce another generation of plants. The ripe berry exhibits multiple traits that signal to other organisms when the fruit is ready for consumption and seed dispersal. In this study, we show that there were large differences in transcript abundance in grape skins in two different locations with different environments, confirming our original hypothesis. We also identified a set of DEGs with common profiles in the two locations.

The observations made in this study provide lists of such genes and generated a large number of hypotheses to be tested in the future. WGCNA was particularly powerful and enhanced our analyses. The transcript abundance during the late stages of berry ripening was very dynamic and may respond to many of the environmental and developmental factors identified in this study. Functional analysis of the genes and GO enrichment analysis were very useful tools to elucidate these factors. Some of the factors identified were temperature, moisture, light and biotic stress. The results of this study indicated that berries still have a “sense of place” during the late stages of berry ripening. Future studies are required to follow up on these observations. It appears that fruit ripening is very malleable. Manipulation of the canopy may offer a powerful lever to adjust gene expression and berry composition, since these parameters are strongly affected by light and temperature.The ability of a genotype to produce different phenotypes as a function of environmental cues is known as phenotypic plasticity . Phenotypic plasticity is considered one of the main processes by which plants, as sessile organisms, can face and adapt to the spatio-temporal variation of environmental factors . Grapevine berries are characterized by high phenotypic plasticity and a genotype can present variability within berries, among berriesin a cluster, and among vines . Berry phenotypic traits, such as the content of sugars, acids, phenolic, anthocyanins, and flavor compounds, are the result of cultivar and environmental influences , and often strong G × E interactions . Although grapevine plasticity in response to environmental conditions and viticulture practices may provide advantages related to the adaptation of a cultivar to specific growing conditions, it may also cause irregular ripening and large inter-seasonal fluctuations , which are undesirable characteristics for wine making . Due to its complex nature, hydroponic nft system the study of phenotypic plasticity is challenging and the mechanisms by which the genes affecting plastic responses operate are poorly characterized . In fact it is often difficult to assess the performance of different phenotypes in different environments . It has been suggested that genetic and epigenetic regulation of gene expression might be at the basis of phenotypic plasticity through the activation of alternative gene pathways or multiple genes . Epigenetics has been proposed as crucial in shaping plant phenotypic plasticity, putatively explaining the rapid and reversible alterations in gene expression in response to environmental changes. This fine-tuning of gene expression can be achieved through DNA methylation, histone modifications and chromatin remodeling . Small non-coding RNAs are ubiquitous and adjustable repressors of gene expression across a broad group of eukaryotic species and are directly involved in controlling, in a sequence specific manner, multiple epigenetic phenomena such as RNA-directed DNA methylation and chromatin remodeling and might play a role in genotype by environment interactions. In plants, small ncRNAs are typically 20–24 nt long RNA molecules and participate in a wide series of biological processes controlling gene expression via transcriptional and post-transcriptional regulation . Moreover, small RNAs have been recently shown to play an important role in plants environmental plasticity . Fruit maturation, the process that starts with fruit-set and ends with fruit ripening , has been largely investigated in fleshy fruits such as tomato and grapevine. These studies highlighted, among others, the vast transcriptomic reprogramming underlying the berry ripening process , the extensive plasticity of berry maturation in the context of a changing environment , and the epigenetic regulatory network which contributes to adjust gene expression to internal and external stimuli . In particular, small RNAs, and especially microRNAs , are involved, among others, in those biological processes governing fruit ripening . In this work, we assessed the role of small ncRNAs in the plasticity of grapevine berries development, by employing next-generation sequencing.

We focused on two cultivars of Vitis vinifera, Cabernet Sauvignon, and Sangiovese, collecting berries at four different developmental stages in three Italian vineyards, diversely located. First, we described the general landscape of small RNAs originated from hotspots present along the genome, examining their accumulation according to cultivars, environments and developmental stages. Subsequently, we analyzed miRNAs, identifying known and novel miRNA candidates and their distribution profiles in the various samples. Based on the in silico prediction of their targets, we suggest the potential involvement of this class of small RNAs in GxE interactions. The results obtained provide insights into the complex molecular machinery that connects the genotype and the environment.Two V. vinifera varieties Sangiovese , a red Italian grape variety, and Cabernet Sauvignon , an international variety, were grown side by side in three different Italian locations, representing traditional areas of Sangiovese cultivation in Italy with a long-standing wine making tradition. In order to reduce factors of variation, the age of the plants , the clone type , the rootstock , the cultivation techniques and the health status were the same among all the locations. Further details on the environmental conditions of the vineyards are provided in Supplementary Figure 1. Berries from four developmental stages were collected in two biological replicates, during the 2011 growing season, for a total of 48 samples . The four sampled stages corresponded to pea size , representing the first stage of berry development in this experimental plan, bunch closure also known as Lag Phase, 19–20 ◦Brix , which corresponds to 50% of sugar accumulation in berries, and harvest , when the berries are fully ripened and the onset of sugar accumulation is over. About 200 berries per each developmental berry stage were sampled from upper, central and lower part of cluster, both from sun exposed and shaded side and split in two biological replicates. Per each vineyard, the berries were collected from about 20 vines selected in a single uniform row and immediately frozen in liquid nitrogen and stored at −80◦C prior to analysis. The libraries were named using the initials of the vineyard where the berries were collected, followed by the initial of the cultivar and the developmental stage. For example, the sample containing berries of Sangiovese, collected in Montalcino at pea size, was named Mont_SG_ps.RNA extraction was performed as described in Kullan et al. . Briefly, total RNA was extracted from 200 mg of ground berries pericarp tissue using 1 ml of Plant RNA Isolation Reagent following manufacturer’s recommendations. The small RNA fraction was isolated from the total RNA using the mirPremier R microRNA Isolation kit and dissolved in DEPC water. All the steps suggested in the technical bulletin for small RNA isolation of plant tissues were followed except the “Filter Lysate” step, which was omitted. The quality and quantity of small RNAs were evaluated by a NanoDrop 1000 spectrometer , and their integrity assessed by an Agilent 2100 Bioanalyzer using a small RNA chip according to the manufacturer’s instructions. Small RNA libraries were prepared using the TruSeq Small RNA Sample Preparation Kit , following all manufacturers’ instructions. The quality of each library was assessed using an Agilent DNA 1000 chip for the Agilent 2100 Bioanalyzer. Libraries were grouped in pools with six libraries each . The pools of libraries were sequenced on an Illumina Hiseq 2000 at IGA Technology Services . The sequencing data were submitted to GEO–NCBI under the accession number GSE85611.

Denver frequently ranks among the most expensive housing markets in the United States, and a recent market analysis concluded that nine of the state’s largest cities witnessed median home prices double in less than a decade . While this has provided homeowners with greater equity, it has also made property taxes and housing affordability a pressing issue for Colorado voters and politicians alike. Despite the public’s desire for property tax relief, Colorado voters decisively rejected Proposition HH, which sought to cut property tax rates and curtail the fiscal constraints imposed by TABOR. Democrats in the General Assembly referred the measure to the ballot over considerable Republican opposition in May 2023. Republican lawmakers walked out of the House chamber in protest as lawmakers proceeded to a vote on final passage. This first legislative walkout in more than 20 years resulted in a House vote of 44-2 vote in favor with 19 absent Republicans marked “excused with protest.” Speaking in objection, the House Minority Leader highlighted the ideological and urban-rural divides in the state by claiming that the walkout’s intentions to send a message to Democratic lawmakers “who are mostly metro area Democrats, stacking pots that our state includes much more than the concrete and steel parts of Colorado” . After a successful party line vote in the Senate and an unsuccessful legal challenge that the measure violated the state’s one-subject rule, the measure was cleared for the ballot allowing voters to decide its fate.

Supporters of Proposition HH enjoyed a financial advantage as the primary committee backing the measure raised nearly $3 million, which exceeded the primary opposition committee by about $1 million . Despite a considerable fundraising advantage and strong endorsement from the Democratic Party and many prominent Democrats across the state, including Governor Jared Polis, voters defeated the measure with 59.3% of ballots voting opposed. The Denver Post described the result as a “shellacking” that delivered a “double blow to Gov. Jared Polis and fellow Democrats,” which resulted in Governor Polis calling a special legislative session to address the property tax issue since inaction would result in a property tax increase . Legislation enacted during the special session reduced the residential property tax rate to 6.7% from 6.765% while increasing each home value’s tax exemption from $15,000 to $55,000. The General Assembly also passed an expansion of the Earned Income Tax Credit to provide economic relief for renters, appropriated funds to local governments to help offset taxation losses from the increase in the tax exemption value, and standardized equal TABOR refunds next year across all income tax brackets. To research additional policy reforms, the legislature further created a 19-member property tax task force to study and provide the governor with “recommendations for a permanent and sustainable property tax structure for the state” . Although growing property tax burdens caused economic anxiety for many across the state, other aspects of the state economy provide causes for optimism.By most indicators Colorado’s economy remains robust and trending upward.

A recent economic forecast projected “continued moderate expansion” in 2024, albeit at a slower pace than the year prior. The state’s economic analysis further posits that eases in inflation will allow the state to outperform national economic trends with “comparable employment growth in 2024, higher income growth, and lower inflation” . Data from the first quarter of 2024 place inflation year-over-year in the Denver metro area at less than 3%, which is slightly better than the national figure of 3.2%. The Bureau of Economic Analysis estimates a gross state product growth rate of 2.3% . Median incomes in Denver and Colorado each surpass the national median income of $75,149. Per capita income in both areas likewise exceed the national figure. Economic indicators suggest that Coloradans are better off than their counterparts in other states, especially regarding labor force participation and income levels. In 2023, the rate of personal income growth in Colorado was estimated to be the same as the national percentage of 5.2. Estimates of civilian labor force participation in Colorado and Denver both exceed the United States overall . The state’s unemployment rate of 3.8% in June 2024 remained below the national figure of 4.1% . The state’s economic forecast notes that while employers continue to add jobs each month with some exceptions, the pace of job creation has slowed considerably over the past year. Monthly job creation averaged 4,800 in 2023, which represents a substantial decrease from the 6,900 jobs averaged in 2022 . Concerns about affordable housing and cost of living consistently appear among the most important issues for voters in the state . Census data estimate the share of state residents living in poverty at 9.4%, which is less than the reported 11.5% living in poverty nationwide.

Persons in poverty are slightly greater in Denver; however, both areas report fewer shares of individuals without health insurance . Until recently, Colorado’s population growth consistently ranked among the nation’s largest. The state’s 16.9% population growth from 2000 to 2010 placed it in the top quintile. Colorado’s population growth of 14.8% from 2010 to 2020 was the sixth largest in the nation and exactly double the national average . As a result of its rapidly growing population of 5.8 million, Colorado received an additional seat in the U.S. House of Representatives following the decennial reapportionment process. This raised its House delegation size to eight, which in 2024 included five Democrats and three Republicans. The past four years, however, have witnessed more sluggish population growth. According to the state demographer, population growth of less than 0.5% from 2021 to 2022 was the state’s lowest since 1989 . Also abnormal was the out-migration of residents moving to other states surpassing the number of migrants moving into Colorado in 2022. The state reports in-migration and out-migration by year from 2005 to the present, and 2022 was the first year with a net population loss when comparing the two figures across nearly two decades. Twenty-five counties in the state decreased in population relative to 2022. Among the counties that grew, only Weld and Douglas counties exceeded 2% growth. Affordable housing, cost of living, and taxes are commonly cited as motivating factors for those leaving the state. Most recent census data estimate that about half of the state’s 5.8 million residents live in the Denver metropolitan statistical area. The city and county of Denver is the largest in the state with an estimated population of 716,577 . Though it has slowed, the state’s population growth of nearly 2% since 2020 doubled the national population growth during this period. The number of residents in the city and county of Denver, however, has remained essentially constant since 2020 with a growth rate of just 0.1%. Both Denver and Colorado have proportionally fewer residents under 18 years of age and over 65 years of age relative to national averages. The percent of adult residents aged 19 to 64 in Colorado and Denver both exceed the national share with the difference between Denver and the United States figures nearly reaching 10%. Denver and Colorado continue to have less racial diversity than the nation as a whole, although each has a greater share of residents with Hispanic or Latinx origin. The census reports that 80% of Denver residents are white, nft hydroponic which is less than the 86% of whites residing in Colorado overall. While Denver and Colorado have fewer Black residents than the national average of 13.7%, the proportion of Hispanic or Latinx residents in each location exceeds the national percentage of 19.5% by about 8% and 3%, respectively. The census estimates that 13.9% of Denver residents were born outside of the United States, which is slightly greater than the United States as a whole. Approximately 1 in 10 residents across the state of Colorado were foreign born .

Fifty-seven percent of Denver residents possess a college degree compared to 45.9% of Coloradans and 34.3% of all residents of the United States.The Colorado General Assembly remained in Democratic control following the 2022 elections. The 100 lawmakers elected to the 74th meeting of the Colorado General Assembly were the most diverse group in history, which continued a diversification trend. Notably, Colorado became just the second state in the nation to elect a majority female legislature with several women serving in key leadership positions, including Julie McCluskie as Speaker of the House. First-time legislators comprised about one-third of the total. Regarding racial diversity in the legislature, nearly all legislators currently representing rural districts are white, while those representing urban areas are increasingly Black or Latinx . One hundred percent of Senators and 94% of Representatives elected from rural districts are white. White legislators also commonly represent suburban districts, constituting 78% of suburban House districts and 92% of suburban Senate districts. Much greater racial and ethnic diversity exists among those representing urban districts. One-third of senators elected from urban districts are either Black or Latinx, compared to 42% in the House Representatives. The political divisions between representatives of rural and urban that exist in many other states likewise exist in Colorado . Divergent views on many issue areas commonly overlap with important differences in legislator identity, experience, and perspective . During the 2022 election cycle Democrats picked up an additional five seats in the state House and two seats in the state Senate for majorities of 46-19 and 23-12, respectively. Unified Democratic government has existed in Colorado since Democrats regained majority-party status in the Senate after the 2018 elections. A Democratic super majority in the House and near super majority in the Senate represent the largest partisan advantage in the state legislature in nearly a century. Prospects for Republicans to regain a majority in either chamber appear dismal in the near future barring a seismic disruption to the status quo. Republican candidates for state and federal office have a similarly abysmal record across the past several election cycles. Former President Donald Trump held a double-digit disapproval rating in the state upon leaving office, and Republican candidates of all stripes have struggled to overcome Trump’s unpopularity as the party’s standard-bearer. Trump lost the 2024 presidential election in Colorado by 11%, which is more than twice as large as his margin of defeat to Hillary Clinton in 2016. The erosion of support for Republican candidates for statewide public office has occurred rapidly over the past decade. Before Trump took office, Republican candidates commonly garnered electoral majorities sufficient to win elections. In the 2014 midterm elections, for example, Republicans won four of the five statewide races including an upset defeat of Democratic incumbent Senator Mark Udall. The only Democrat to win the state that year was incumbent Governor John Hickenlooper who narrowly defeated his Republican opponent by just three percentage points. Six years later, Democrats won all six statewide elections and majorities in both legislative chambers, while also holding both U.S. Senate seats and most of the state’s congressional delegation. Neither party had accomplished such a feat since the Democrats did in 1936 . Though it may have seemed implausible at the time, Republicans performed even worse in 2022 as Democrats expanded their majority in the congressional delegation by winning the highly competitive new 8th congressional district and made further gains in each chamber of the state legislature. Completely uncompetitive, Republican candidates for U.S. Senate, governor, secretary of state, attorney general, and treasurer lost by an average of 14 points. The 2022 governor’s race exemplified the Republican Party’s current inability to complete at the state level. A contested GOP primary witnessed entrepreneur and University of Colorado Regent, Heidi Ganahl emerge victorious. Although Colorado is one of five states in the Union that has never elected a woman to serve as U.S. senator or governor, many considered Ganahl a strong candidate given her business credentials and electoral experience as the last Republican candidate to win a statewide race. In her prior campaign for CU Regent at-large, Ganahl received52% of the vote, which gave Republicans a one seat majority on the University of Colorado Board of Regents. A comparison of voter registration data at the time of the November 2014 elections to the present also provides evidence of the state’s ongoing political transformation. Once a reliable red state in presidential politics, Colorado only cast its electoral votes for the Democratic presidential ticket once in the ten elections from 1968 to 2004.