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Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora
Note that a Preprint of this article also exists, first published October 26, 2016.
Carla C.M. Chen1,2, David G. Bourne1,3, Christopher C. Drovandi2,4, Kerrie Mengersen2,4, Bette L. Willis3,5, M. Julian Caley2,4, Yui Sato1
1Australian Institute of Marine Science, Townsville, QLD, Australia
2ARC Centre of Excellence for Mathematical & Statistical Frontiers, Queensland University of Technology, Brisbane, QLD, Australia
3College of Science and Engineering, James Cook University, Townsville, QLD, Australia
4School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
5ARC Centre of Excellence for Coral Reef Studies, College of Science and Engineering, James Cook University, Townsville, QLD, Australia
10.7717/peerj.3438
Ronaldo Francini-Filho
Climate Change Biology, Marine Biology, Natural Resource Management, Statistics
Black Band Disease, Environmental covariates, Coral Disease, Cyanobacterial patches, Transitional probability, Multi-state Markov model, Seasonal variation
© 2017 Chen et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
Chen CCM, Bourne DG, Drovandi CC, Mengersen K, Willis BL, Caley MJ, Sato Y. 2017. Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora. PeerJ 5:e3438 https://doi.org/10.7717/peerj.3438
Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef declines globally. However, little is known about how seasonal scale variations in environmental factors influence disease dynamics at the level of individual coral colonies. In this study, we applied a multi-state Markov model (MSM) to investigate the dynamics of black band disease (BBD) developing from apparently healthy corals and/or a precursor-stage, termed 'cyanobacterial patches' (CP), in relation to seasonal variation in light and seawater temperature at two reef sites around Pelorus Island in the central sector of the Great Barrier Reef. The model predicted that the proportion of colonies transitioning from BBD to Healthy states within three months was approximately 57%, but 5.6% of BBD cases resulted in whole colony mortality. According to our modelling, healthy coral colonies were more susceptible to BBD during summer months when light levels were at their maxima and seawater temperatures were either rising or at their maxima. In contrast, CP mostly occurred during spring, when both light and seawater temperatures were rising. This suggests that environmental drivers for healthy coral colonies transitioning into a CP state are different from those driving transitions into BBD. Our model predicts that (1) the transition from healthy to CP state is best explained by increasing light, (2) the transition between Healthy to BBD occurs more frequently from early to late summer, (3) 20% of CP infected corals developed BBD, although light and temperature appeared to have limited impact on this state transition, and (4) the number of transitions from Healthy to BBD differed significantly between the two study sites, potentially reflecting differences in localised wave action regimes.
Coral disease has contributed to localised declines in coral cover and changes in benthic communities (Weil, Smith & Gil-Agudelo, 2006; Harvell et al., 2007). For example, Acropora palmata and A. cervicornis populations declined in most Caribbean and Gulf of Mexico habitats in part due to diseases, while Orbicella spp. populations have suffered region wide declines due to yellow band disease (Gil-Agudelo et al., 2004; Bruckner & Bruckner, 2006a; Bruckner & Bruckner, 2006b). In the US Virgin Islands, coral disease following a mass beaching event in 2005 resulted in more than a 50% decline in coral cover, and in some areas of the wider Caribbean, repeated outbreaks of white band disease resulted in benthic communities shifting from coral to macroalgae dominated communities (Antonius, 1981; Harvell et al., 2007). The impacts of coral disease on reefs in other regions are not as extensively documented, although outbreaks have been observed across the Indo-Pacific (Raymundo et al., 2005; Weil et al., 2012) and in some areas of Great Barrier Reef (GBR) (Willis, Page & Dinsdale, 2004; Page & Willis, 2006; Sato, Bourne & Willis, 2009; Haapkylä et al., 2010).
Black band disease (BBD) presents as a virulent lesion that infects corals at reef locations worldwide, including the Caribbean, Red Sea and Indo-Pacific (reviewed in Sato et al. (2016)). On the GBR, BBD is also one of the most widespread coral diseases (Page & Willis, 2006). It appears as a darkly pigmented microbial mat occurring as a band at the interface between apparently healthy coral tissue and freshly exposed skeleton. The BBD microbial mat consists of a polymicrobial consortium, composed of a dominant cyanobacterium, sulfate-reducing and sulfide-oxidizing bacteria, and other heterotopic microorganisms, which migrates across colonies killing the underlying coral tissues (Richardson, 2004; Miller & Richardson, 2011; Sato et al., 2016). Linear progression rates of the band of up to 2 cm per day have been reported in the Caribbean (Kuta & Richardson, 1997), although typically it progresses more slowly (average: 0.3 cm/day; Sutherland, Porter & Torres, 2004). The prevalence of BBD on coral reefs is generally low, with only 1–10% of colonies typically infected at any one time (Green & Bruckner, 2000). Outbreaks can occur however, such as observed in the Florida Keys National Marine Sanctuary in 1992, where more than 50% of colonies within a population of Orbicella annularis (formerly known as Montastraea annularis; Budd et al. (2012)) were infected with the disease (Kuta & Richardson, 1996). At one study site on the GBR, BBD infections on approximately 10% of colonies in an assemblage resulted in an average loss of 40% of coral tissue surface area, with colonies having a history of BBD infection being particularly susceptible to re-infection (Sato, Bourne & Willis, 2009). Therefore, even though BBD is potentially part of the natural ecology of coral assemblages (Page & Willis, 2006), an outbreak of BBD is capable of reshaping a coral community (Bruckner & Bruckner, 1997).
Environmental conditions, particularly seawater temperature and light irradiance, combined with demographic factors, such as host diversity and density, have all been linked to the prevalence of a number of different coral diseases (Harvell et al., 2007; Harvell et al., 2009). For BBD specifically, changes in seawater temperature are thought to be a major environmental driver (Antonius, 1981; Edmunds, 1991; Kuta & Richardson, 2002; Rodriguez & Croquer, 2008; Sato, Bourne & Willis, 2009). High seawater temperatures can influence the dynamics of coral diseases through increased pathogen abundance and/or virulence, and/or increased host susceptibility as a result of reduced immune capacity (Burge et al., 2014). However, reports that BBD occurs mostly on corals in shallow habitats and is often absent from highly turbid waters suggest that spatial variation in the occurrence of this disease may be governed by the response of the microbial community associated with the lesion, particularly the dominant cyanobacterium, to different light intensities (Kuta & Richardson, 2002; Page & Willis, 2006; Cróquer & Weil, 2008).
During a two and a half year field monitoring study at a site in the central region of Australia's GBR, cyanobacterium-dominated, green-brown lesions termed cyanobacterial patches (CP) were identified as an early stage in the development of BBD lesions (Sato, Willis & Bourne, 2010). The microbial community of CP was dominated by a cyanobacterium closely related to Blennothrix and Trichodesmium spp., whereas the BBD microbial community was predominately composed of an Oscillatoria sp.-related cyanobacterium (Sato, Willis & Bourne, 2010), currently classified as Roseofilum reptotaenium (Casamatta et al., 2012; Buerger et al., 2016). In approximately 19% of colonies that presented with CP (n = 262 colonies), the lesion on these colonies developed into visually characteristic BBD, although this percentage is likely to be an underestimate because of difficulties accessing the sites during the monitoring period. Although the exact mechanism by which CP transitions to BBD is still unknown, a pathogenesis model proposed by Sato et al. (2016) suggests that light and temperature are key drivers of this transition. A physiological experiment using cyanobacterial cultures suggests that as light levels decrease from seasonal maxima and seawater temperatures approach seasonal maxima, conditions became favourable for the BBD-dominant cyanobacterium to outcompete the CP-associated cyanobacterium, facilitating transitions within the microbial community (Glas et al., 2010; Sato et al., 2016).
Statistical methods for studying disease transitions are well established for many host-pathogen interactions, and multi-state Markov models (MSMs) are particularly suitable for describing processes whereby an individual progresses through different states in a disease continuum and for exploring the roles of covariates in the process. For example, MSMs have been widely used in studies of human diseases, such as HIV/AIDS (Gentleman et al., 1994; Aalen et al., 1997; Mathieu et al., 2005), breast cancer (Meier-Hirmer & Schumacher, 2013) and dementia (Joly et al., 2002), however the use of such models to describe coral disease transitions has yet to be explored. Here, we apply an MSM to describe the development of BBD in 355 coral colonies monitored on the inshore central GBR to examine how changes in seasonal environmental conditions, in particular temperature and light, influence transitions between Healthy, CP and BBD states. Specifically, we (1) model the effects of seasonal changes in temperature and light on progression of BBD lesions, (2) test conclusions of the pathogenesis model proposed by Sato et al. (2016), and (3) provide a case study for applying such model-based approaches to understand drivers of coral disease outbreaks.
The dynamics of the coral diseases CP and BBD were monitored in two Montipora spp.- dominated coral assemblages between September 2006 and January 2009, at sites in the central GBR located at North-East (18°32.5′S, 146°30.0′E) and South–East (18°33.6′S, 146°30.1′E) Pelorus Island (Map of the location is in Fig. S3), as detailed in Sato, Bourne & Willis (2009) and Sato, Willis & Bourne (2010). The average sea water temperature and photosynthetically active radiation recorded at the Pelorus Island site in the summer were 28.6°C and 497.0 µmol m−2s−1 and reduced to 22.6°C and 333.04 µmol m−2s−1in the winter.
Data from this intensive field monitoring program were used to develop modelling approaches for assessing drivers of disease transitions within coral populations. Both sites have limited exposure to terrestrial run-off but are exposed to strong wave energy year-round caused by south-easterly trade winds. The site at NE Pelorus is relatively more protected from waves than the SE Pelorus site. At each site, three replicate 10 m × 10 m permanent quadrants were haphazardly placed 5–10 m apart between 2.5 and 3.0 m depth. In this study, only coral colonies had signs of BBD during the study period were followed. A total of 355 coral colonies were individually tagged and photographed (239 colonies from SE Pelorus; 116 colonies from NE Pelorus), and the state of each coral colony was recorded in repeated surveys between September 2006 and January 2009 (see Sato, Bourne & Willis, 2009; Sato, Willis & Bourne, 2010 for full details). Due to logistical limitations in accessing study sites caused by poor weather conditions, surveys were done at irregular intervals (i.e., at one to three month intervals). The data was collected under the Great Barrier Marine Park Authority permits (No. G09/31013.1 and G09/30237.1.2).
Average daily seawater temperature and light irradiance levels were obtained from a weather station operated by the Australian Institute of Marine Science located at nearby Orpheus Island, approximately 8 km from the study sites. Seawater temperature was measured at 6 m depth and light at the surface as photosynthetically active radiation (PAR, µmol photons m−2s−1). As seawater temperature is a partial function of solar energy absorbed by the ocean, seasonal patterns of light and seawater temperature are highly correlated (Fig. S1). However, seasonal patterns in seawater temperature lag behind seasonal light patterns, thus light levels reach seasonal maxima/minima before seawater temperature. To incorporate the individual effects of both light and seawater temperature and account for the lag between the two variables, a new metric of environmental condition was developed by identifying four phases in annual light and seawater temperature cycles: rising (↑), maximum (Max), declining (↓), and minimum (Min). To determine the seawater temperature phase at time t, a non-linear sinusoidal model was first fitted to each of the datasets. The water temperature phase at time t was then determined by the value of the slope of the non-linear function at point t, which is the first derivative of the function. Even though a slope of zero is the theoretical turning point of functions (i.e., slope = 0, either at the maximum or the minimum; slope > 0, rising phase; slope < 0, decreasing phase), a wider range of values was used here to reflect that water temperature often remains relatively steady for a period before declining or increasing. Exploratory analysis suggested that a threshold slope value of ≈0.7 best described the data. Therefore, when the slope was greater than 0.7, temperature was deemed to be rising, and decreasing when the slope was less than −0.7. Slopes between −0.7 and 0.7 were categorised as being either maxima or minima, depending on the observed value (Fig. 1).
Figure 1: Seasonal variation in seawater temperature at 6 m from January 2006 to January 2009, showing four seasonal phases.
Black line: daily mean temperature; blue lines: time period encompassing temperature minima; green lines: period when temperature is decreasing; orange lines: period when light is rising; and red lines: period when temperature at maxima.
DOI: 10.7717/peerj.3438/fig-1
A similar approach was used to derive light phases from daily average data. However, due to large annual variation in light cycles (Fig. 2), different cosine functions were fitted to each of the annual light cycles between July 2005 and July 2009. An annual light cycle was defined as 365 days starting from the lowest light period in July, and light data from July 2005 to July 2009 was used. Different threshold values were chosen for each annual cycle, based on the closest fit to natural patterns in an exploratory analysis (i.e., 0.9 for 2006, 0.8 for 2007 and 2008, and 0.7 for 2009; Fig. 2).
Figure 2: Seasonal variation in light from July 2006 to July 2009 and corresponding phases.
Blue lines correspond to the light at trough, green lines are when light is at dropping phase, orange lines are when light is at rising phase and red lines are the light at crest.
The converted categorical variables of light and seawater temperature were combined to form a single environmental metric using eight possible combinations ("Max↑": light at maxima and water temperature rising; "MaxMax": both light and water temperature at maxima; "↓Max": light dropping and water temperature at maxima; "↓↓": both light and water temperature dropping; "Min↓": light at minima and water temperature dropping; "MinMin": both light and water temperature at minima; "↑Min": light rising and water temperature at minima; and "↑↑": both light and water temperature rising). However, due to the logistics of assessing study sites in poor weather conditions, only one observation for each of "↑Min", "Min↓" and "↓Max" phases was available. Therefore, samples from these phases were combined with the nearest class (by date), hence we used five possible phases of microclimatic conditions, "MaxMax", "Max↑", "↓↓","↑↑" and "MinMin".
Application of a multi-state model to explain CP-BBD disease transitions
A multi-state Markov model (MSM) was used to model transitions between disease states and refine environmental factors contributing to these transitions. This model is particularly useful when observations are made at irregular time intervals, the exact transition time is unknown, subjects are recruited progressively, and survival times are right censored (e.g., death of some subjects is not reached by the end of study). In a MSM, the probability of transition, i.e., moving from state r to state s, is governed by transitional intensity (qrs), which is the instantaneous risk of moving between two states (i.e., r to s), and the time interval between observations (t). When the effects of covariates are of interest, covariates are often regressed on the transitional intensity using the proportional hazard model, which assumes covariate effects are multiplicative, i.e., q r s x = q rs 0 exp β r s x where q r s 0 is the baseline intensity and βrs is the effect size of covariate x.
Figure 3: Modelling development of BBD lesions on the coral Montipora spp.
Square boxes represent coral states and arrows denote the direction of disease development. Except for the death states, transitions between transient states are bi-directional. (A) is the initial disease model, and (B) is the final model implemented in the analysis.
We used a time-homogeneous Markov model to explain the development of BBD lesions. This model assumes the transition intensity is constant as a function of time, t, and independent of the history of the process, but only dependent on the state that the coral currently occupies. The time unit here is a month and a detailed description of the model is available in Supplemental Information 1. In our study, BBD disease development is specified to have four discrete states, including three transient states (Healthy, CP and BBD) and one absorbing state (Dead) (Fig. 3A). The healthy state was defined as a colony lacking any visual signs of CP or BBD lesions when examined. This state included colonies that showed no disease signs, although they may have had a lesion previously that has since disappeared. Death was defined here as mortality of an entire coral colony. To investigate the effects of light and water temperature conditions on the transition between healthy to diseased states, recovery, and between two diseased states, covariates were applied to transitions between H → CP, CP → H, CP → BBD, H → BBD and BBD → H. In addition to light and seawater temperature phase, study sites (NE and SE Pelorus) and disease density were also included as covariates. Disease density was defined as the number of infected coral colonies per 100 m2 quadrat at the time observed. Disease density was then categorized as: low (≤10 colonies), median (11–20 colonies) or high (≥21 colonies). These thresholds were selected based on the density of observed diseased colonies over the sampling period. The maximum disease density during the study period was 33 coral colonies per 100 m2, and there were three distinct clusters in the disease density (Fig. S2). The ranges of clusters were 0–10, 10–20, and 20 +.
The msm package (Jackson, 2007) in R was used for model fitting. Parameter estimation was done using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Likelihood ratio tests were used to prevent overfitting, and the assumption of time–homogenous transition intensity was examined using the method suggested by Kalbfleisch & Lawless (1985), which involves fitting a time-dependent model, i.e q s r t = q s r e λ t and testing if λ = 0. All statistical analyses were carried out in R (R Core Team, 2016). The data and code used in this manuscript is available via https://github.com/cewels/Modelling-environmental-drivers-of-BBD-outbreak.
Disease states of 239 and 116 colonies of Montipora sp. from SE and NE Pelorus reefs, respectively, were repeatedly recorded between September 2006 and January 2009 (17 observations per colony at SE Pelorus; 13 observations per colony at NE Pelorus). The median duration between two observations was 1.67 months (range: 0.33–3.76 months). During the study period, the mean coral densities for SE and NE Pelorus were 227 and 289 colonies per 100 m2, and the mean BBD densities for both sites were 3.14 and 3.94 colonies per 100 m2, respectively. Although the size of each individual colony was not recorded, the surface area of most colonies was over 100 cm2 at both study sites. The majority of corals within each assemblage remained in the Healthy state between observations, and 214 transitions from Healthy to the CP state and 166 direct transitions from Healthy to the BBD state were also observed (Table 1). Eleven colonies that had no visible signs of disease died during the study and the cause of their mortality could not be assigned. For corals with CP, 160 transitioned back to the Healthy state, 87 remained in the CP state and 43 progressed to BBD by the next survey. On only two occasions did corals in the CP state die without a BBD lesion being observed (Table 1), hence transitions from CP to death were omitted from the subsequent MSM analyses (Table 2). For corals displaying visible signs of BBD, 150 returned to a Healthy state, 116 remained in the BBD state, and 11 colonies died. The transition from BBD to CP was observed 5 times; however, these represented new CP lesions elsewhere on the host coral after the original BBD lesions had disappeared, indicating that these BBD lesions did not transition back to the CP stage. Therefore, the transition from the BBD to CP state was also excluded from the MSM analysis. The final disease model is shown in Fig. 3B. The difference between the log likelihood of the time-dependent and time-independent models was small (−2 log-likelihood are 4354.405 and 4376.467). Therefore, the assumption of a time-independent MSM appears to be justified.
Total number of state transitions occurring between 5,030 pairs of consecutive observations from September 2006 to January 2009.
The number in the bracket is the proportion of the transitions in all observations.
From Healthy 4,065 (0.81) 214 (0.043) 166 (0.03) 11 (0.002)
CP 160 (0.03) 87 (0.017) 43 (0.01) 2 (0.0004)
BBD 150 (0.03) 5 (0.001) 116 (0.02) 11 (0.002)
DOI: 10.7717/peerj.3438/table-1
The effect of light-water temperature phase on the transition between two states.
The light-water temperature is a categorical variable, thus the magnitude of phase effect is estimated using odds ratio. For example, the transition from healthy to CP was 2.85 higher during the ↑↑ phase comparing to ↓↓ phase. The two columns on the right are the estimated 95% confidence interval of the estimated odds ratio. MinMin and MaxMax symbolize phases when both light and water temperature are at minima and maxima, respectivly; ↑↑ and ↓↓ represent when both light and water temperature are rising and dropping, respectively; and Max↑ represents when the light is at maxima while seawater temperatures are rising.
Light-water temperature phase
Odds Ratio
Lower 95% CI
Upper 95% CI
(A) Healthy → CP MinMin∕↓↓ 2.04 0.95 4.36
↑↑∕↓↓ 2.85 1.35 6.02
Max↑∕↓↓ 3.56 1.66 7.62
MaxMax∕↓↓ 1.29 0.36 4.67
(B) Healthy → BBD MinMin∕↑↑ 0.07 0.001 3.28
Max↑/↑↑ 5.21 2.71 10.01
MaxMax∕↑↑ 3.50 1.46 8.39
↓↓∕↑↑ 1.54 0.58 4.06
(C) CP → Healthy MinMin∕↑↑ 1.37 0.68 2.80
Max↑∕↑↑ 3.36 1.92 5.89
MaxMax∕↑↑ 7.43 2.98 18.54
(D) BBD → Healthy MinMin∕↑↑ 1.54 0.77 3.08
Max↑∕↑↑ 8.27 4.37 15.67
↓↓∕↑↑ 0.01 0.000 31.96
The final model fit was significantly better than the model without covariates (LR = 392.546, DF = 17, p < 0.001), but the likelihood ratio test demonstrated that not all covariates influenced the transitions between all states. Site and light-temperature phases were important for transitions from H → BBD, however only light-temperature phases were important for the transition intensities of H → CP, CP → H, and BBD → H. Disease density did not significantly affect the transition between H → CP or H → BBD.
Transitions between healthy and disease states (CP or BBD) were affected by the light-temperature phases. During the period when temperature was rising and light was either rising or at its annual maximum (i.e., ↑↑ or Max↑), the transition intensity from H → CP was significantly higher than the period when both light and temperature were in decline (↓↓; Table 2A); transition intensities from H → CP were low and did not differ significantly among ↓↓, MinMin and MaxMax phases. This suggests that healthy coral colonies were more likely to be affected by CP during the spring. In contrast, transitions from H → BBD occurred more frequently later in the summer season. During the Max↑ and MaxMax phases, healthy coral colonies were 5.21 and 3.5 times, respectively, more likely to be affected by BBD than during the ↑↑ phase (Table 2B). However, there was little difference in the transition intensities between the MinMin, ↑↑ and ↓↓ phases.
The MSM results also showed strong spatial variation between the NE and SE Pelorus sites. The estimated instantaneous transitions from H → BBD (qH→BBD) at NE Pelorus were 2.41 times (95% CI [1.67–3.50]) higher than at SE Pelorus. This suggests that healthy corals at NE Pelorus more frequently contracted BBD than corals at the SE Pelorus site.
After accounting for the effects of covariates, the estimated baseline monthly transition intensity, q H → cp 0 , from healthy into CP (H → CP) was slightly higher than transitioning from Healthy to BBD ( q H → BBD 0 ) ( q H → cp 0 , mean monthly transition intensity from H to CP was 0.05, 95% CI [0.040–0.065]; q H → BBD 0 , mean monthly transition intensity from H to BBD was 0.019, 95% CI [0.007–0.046], Table 3), however this difference was not significant. This suggested that without the influence of site and light-temperature phase, at any given time interval, the probability for H → CP is similar to H → BBD.
Estimated baseline monthly transitional intensity, q r s 0 (instantaneous probability of transitioning from state r to s in a month) and 95% confidence interval between two included states.
These are monthly transitional intensities without the effect of other covariates. For example, the monthly transition from CP to BBD was significantly lower than the transition from CP to Healthy, as the mean estimates were 0.19 (95% CI [0.132–0.274]) and 0.68 (95% CI [0.51–0.905]), respectively.
Mean estimates
Healthy → CP 0.051 0.04 0.065
Healthy → BBD 0.019 0.007 0.046
Healthy → Death 0.001 0.0003 0.003
CP → Healthy 0.680 0.51 0.905
CP → BBD 0.190 0.132 0.274
BBD → Healthy 0.301 0.086 1.04
BBD → Death 0.036 0.020 0.067
Even though the transition from CP to healthy predominately occurred during the Max↑, MaxMax and ↓↓ phases (Table 2C), the effects of differing light-seawater temperature phases on this transition was less clear. This was because a higher number of HCP transitions occurred in the ↑↑ phase (period immediately before Max↑ phase) and the estimated mean sojourn time for CP (i.e., the time remaining in the CP state) was 1.14 months (95% CI [0.91–1.43]). Therefore, it is unclear if the higher number of CP → H transitions was the result of light-temperature conditions or the development of a host immune response to the disease. Similarly, we were unable to identify the effect of light-seawater temperature phases on the transition from BBD → H, even though a high number of transitions from BBD → H were observed during the Max↑ and MaxMax phases (Table 2D), as the estimated mean sojourn time for BBD was 2.67 months (95% CI [0.98–7.34]). Furthermore, we found no significant difference in the observed sojourn times of BBD among different light-seawater temperature phases (Likelihood ratio test of models with and without the inclusion of light-seawater temperature phases, test statistic = 2.17, DF = 4, p = 0.704), suggesting that reverting to the healthy state is likely due to the development of a host immune response to the disease.
After removing the covariate effect, coral colonies with CP were approximately four times more likely to revert to a healthy state than progress into the BBD state (monthly transition intensities for CP → H and CP → BBD were q cp → H 0 = 0 . 68 vs. q cp → BBD 0 0 . 19 , Table 3. The ratio of two intensities is approximately 4). Once a coral colony exhibited a BBD lesion, the estimated probability of recovery (BBD → H) in three months was approximately 53%. However, once a coral colony presented BBD, mortality was at least 30 times higher than a healthy colony ( q BBD → D 0 = 0 . 036 v.s q H → D 0 = 0 . 001 ; Table 3).
This study demonstrates the use of a multi-state analysis to understand the dynamics of a BBD disease within a Montipora spp. coral assemblage and elucidate how the covariate effects of light and temperature influence lesion state-transitions within individual colonies. Results highlight that the combined effect of seasonal variation in light and seawater temperature is an important driver for transitions of individual healthy Montipora sp. corals into either CP or BBD disease states. The transition into each of the two disease states occurred mostly from spring to summer, when light and seawater temperatures were rising or at their maxima (↑↑, Max↑, MaxMax). The transition between H → CP occurred slightly earlier in the spring/summer season than H → BBD, suggesting that CP may act as a precursor to BBD infections in some cases, although CP was more likely to heal (CP → H) than transition to BBD, as found in a field study (Sato, Willis & Bourne, 2010). Overall, healthy corals were more likely to develop CP lesions than BBD lesions, and the likelihood of CP developing was greater during spring when seawater temperatures and light were increasing or at their maxima (↑↑, Max↑), compared to autumn months when temperature and light were declining (↓↓). The transition from healthy to CP subsided when light and temperature both reached maxima (i.e., MaxMax), suggesting that rising seawater temperatures are favourable for the development of CP lesions but high temperatures above a certain threshold inhibited development of CP lesions. This interpretation is supported by laboratory-based studies, which found that high temperatures at summer maxima negatively affected growth of the dominant cyanobacterium within CP lesions (Glas et al., 2010). Thus, lower growth rates of the dominant cyanobacterium within CP lesions likely explains the lower probability of CP-development when both light and temperature were at maxima. In contrast, evidence that growth of CP-derived cyanobacteria in cultures was positively correlated with light intensity (Glas et al., 2010) explains why the highest intensity of H → CP transitions occurred when light was at its maximum (Max↑).
The transition intensity between healthy and BBD states peaked when light was at its maximum and water temperature was rising or at its maximum (Max↑, MaxMax). The 3–5 times greater probability of developing BBD during the Max↑ andMaxMax phases than when both light and temperature were rising (↑↑) suggests that certain light and potentially temperature thresholds need to be reached before corals are susceptible to BBD. Previous field studies have showed that BBD abundance is positively correlated with temperature and light intensity (Antonius, 1981; Edmunds, 1991; Kuta & Richardson, 2002; Page & Willis, 2006; Voss & Richardson, 2006; Weil & Cróquer, 2008; Sato, Bourne & Willis, 2009; Zvuloni et al., 2009; Muller & Van Woesik, 2011). Culture-based studies of the locally dominant cyanobacterium in BBD lesions show that its growth is enhanced at seasonal temperature maxima, while light has little impact on its growth (Glas et al., 2010), corroborating our field-based results. Aquarium-based experimental studies have also shown that both high light and temperature can cause stress in coral hosts and are linked to an increase in BBD virulence (Boyett, Bourne & Willis, 2007; Sato, Bourne & Willis, 2011). Furthermore, a recent metagenomic and metatranscriptomic-based study on the in-situ development of BBD derived from CP showed that increased cyanobacterial photosynthesis, which introduces fixed carbohydrates into the microbial community, is a key to the development of BBD pathogenesis (Sato et al., 2017). However, our modelling approach did not detect a role for combined light and temperature variation to drive the CP to BBD transition. However, this result is likely due to the small number of CP-infected colonies developing into BBD (43 cases). Hence more observations are required to help elucidate the impact of light and temperature on the transition between the two disease states.
In addition to seasonal variation, our results also suggest strong spatial variation in the likelihood that colonies of Montipora transition from healthy to BBD states. Significantly more transitions from healthy to BBD were recorded at the NE than the SE Pelorus site. Considering that the distance between these two sites is less than 5 km, the difference in BBD susceptibility is likely to reflect localised environmental conditions, particularly differences in local wave action. Reefs at SE Pelorus are typically exposed to high wave action, whereas the NE Pelorus site is comparatively protected by a local headland. Constant surface disturbances and turbidity caused by wave surge would reduce light intensity reaching the reef substratum, thus light levels may regularly be lower at SE than at NE Pelorus, accounting for differences in disease dynamics between the two sites.
The low explanatory power of BBD-infected coral density on the probability of BBD development suggests that environmental factors are more important drivers of disease occurrence than the density of potential pathogen sources. Evidence of spatial clumping of BBD-infected corals in past monitoring studies led to the proposal that BBD spreads from infected corals to new corals in a density-dependant manner (Bruckner, Bruckner & Williams, 1997; Page & Willis, 2006; Voss & Richardson, 2006). In contrast, Edmunds (1991) reported that distributions of BBD-infected corals were not clumped nor dependant on host-coral density, suggesting that BBD is not highly contagious. The present study supports this latter hypothesis and suggests that the clumped distribution of BBD may result from patchy distributions of other local environmental conditions within reefs, such as bottom topology, light availability, and/or sedimentation rates.
MSM has commonly been used in medical research to understand the development of human diseases. Our application of this approach to the dynamics of the virulent coral disease BBD, specifically the effect of environmental covariates on the probability of transitioning between healthy and disease states in Montipora spp., provides empirical support for the light-seawater temperature hypothesis established in Sato et al. (2016). Although this study would have benefited from a longer time series of observations made at shorter time-intervals, as well as more comprehensive and localised measurements of environmental covariates at each study site, it does provide a model-based framework for identifying the drivers of disease transitions at fine spatial and temporal resolution. As the frequency of disease outbreaks is predicted to increase with global changes in climate (Maynard et al., 2015), identifying the drivers of finer spatial and temporal heterogeneity of disease outbreaks and spread is becoming important, particularly for understanding the resilience of corals to climate change. Our findings provide novel insights into disease dynamics at the scale of individual coral colonies and identify environmental drivers leading to development of CP and BBD lesions on corals.
Supplementary Method
DOI: 10.7717/peerj.3438/supp-1
Authors thank staff of James Cook University's Orpheus Island Research Station for their logistic support. K Chong-Seng, R Littman, D Loong, A Lutz, T Mannering, B Olson, A Paley, A Ridep-Morris, K Schmidt, F Seneca, P Warner and K Winters are also thanked for their support in collection of specimens.
Additional Information and Declarations
The authors declare there are no competing interests.
Carla C.M. Chen conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
David G. Bourne wrote the paper, reviewed drafts of the paper.
Christopher C. Drovandi and Kerrie Mengersen contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Bette L. Willis conceived and designed the experiments, reviewed drafts of the paper.
M. Julian Caley reviewed drafts of the paper.
Yui Sato conceived and designed the experiments, performed the experiments, wrote the paper, reviewed drafts of the paper.
Field Study Permissions
The following information was supplied relating to field study approvals (i.e., approving body and any reference numbers):
Field experiments were approved by the great barrier Reef Marine Park Authority permit number G09/31013.1 and G09/30237.1.2.
The following information was supplied regarding data availability:
The data and code used in this study and the description of variables are now available via Github: https://github.com/cewels/Modelling-environmental-drivers-of-BBD-outbreak.
This study was funded by an Australian Institute of Marine Science research grant, an Australian Research Council: Centre of Excellence for Mathematical and Statistical Frontiers, an Australian Research Council grant to B Willis administered through the Australian Research Council Centre of Excellence for Coral Reef Studies, and the Disease Working Group in the Coral Reef Targeted Research and Capacity Building for Management Program, Australian Coral Reef Society research fund and Research grant from Mitsubishi Corporation and Earthwatch Australia, and was logistically supported by AIMS@JCU. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Aalen OO, Farewell VT, De Angelis D, Day NE, Nöel Gill O. 1997. A Markov model for HIV disease progression including the effect of HIV diagnosis and treatment: application to AIDS prediction in England and Wales. Statistics in Medicine 16:2191-2210
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Bruckner A, Bruckner R. 2006a. The recent decline of Montastraea annularis (complex) coral populations in western Curaçao: a cause for concern? Revista de Biología Tropical 54:45-58
Bruckner AW, Bruckner RJ. 2006b. Consequences of yellow band disease (YBD) on Montastraea annularis (species complex) populations on remote reefs off Mona Island, Puerto Rico. Diseases of Aquatic Organisms 69:67-73
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Buerger P, Alvarez-Roa C, Weynberg KD, Baekelandt S, Van Oppen MJ. 2016. Genetic, morphological and growth characterisation of a new Roseofilum strain (Oscillatoriales, Cyanobacteria) associated with coral black band disease. PeerJ 4:e2110
Burge CA, Mark Eakin C, Friedman CS, Froelich B, Hershberger PK, Hofmann EE, Petes LE, Prager KC, Weil E, Willis BL, Ford SE, Harvell CD. 2014. Climate change influences on marine infectious diseases: implications for management and society. Annual Review of Marine Science 6:249-277
Casamatta D, Stanic D, Gantar M, Richardson LL. 2012. Characterization of Roseofilum reptotaenium (Oscillatoriales, Cyanobacteria) gen. et sp. nov. isolated from Caribbean black band disease. Phycologia 51:489-499
Cróquer A, Weil E. 2008. Spatial variability in distribution and prevalence of Caribbean scleractinian coral and octocoral diseases-II Genera-level analysis. Diseases of Aquatic Organisms 83:209
Edmunds PJ. 1991. Extent and effect of black band disease on a Caribbean reef. Coral Reefs 10:161-165
Gentleman R, Lawless J, Lindsey J, Yan P. 1994. Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease. Statistics in Medicine 13:805-821
Gil-Agudelo DL, Smith GW, Garzón-Ferreira J, Weil E, Petersen D. 2004. Dark spots disease and yellow band disease, two poorly known coral diseases with high incidence in Caribbean reefs. In: Coral health and disease. Springer. 337-349
Glas MS, Motti CA, Negri AP, Sato Y, Froscio S, Humpage AR, Krock B, Cembella A, Bourne DG. 2010. Cyanotoxins are not implicated in the etiology of coral black band disease outbreaks on Pelorus Island, Great Barrier Reef. FEMS Microbiology Ecology 73:43-54
Green EP, Bruckner AW. 2000. The significance of coral disease epizootiology for coral reef conservation. Biological Conservation 96:347-361
Haapkylä J, Melbourne-Thomas J, Flavell M, Willis B. 2010. Spatiotemporal patterns of coral disease prevalence on Heron Island, Great Barrier Reef, Australia. Coral Reefs 29:1035-1045
Harvell D, Altizer S, Cattadori IM, Harrington L, Weil E. 2009. Climate change and wildlife diseases: when does the host matter the most? Ecology 90:912-920
Harvell D, Jordán-Dahlgren E, Merkel S, Rosenberg E, Raymundo L, Smith G, Weil E, Willis B. 2007. Coral disease, environmental drivers, and the balance between coral and microbial associates. Oceanography 20:172-195
Jackson C. 2007. Multi-state modelling with R: the msm package.
Joly P, Commenges D, Helmer C, Letenneur L. 2002. A penalized likelihood approach for an illness–death model with interval-censored data: application to age-specific incidence of dementia. Biostatistics 3:433-443
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Kuta K, Richardson L. 1997. Black band disease and the fate of diseased coral colonies in the Florida Keys. In: Proc 8th int coral reef symp. 575-578
Kuta K, Richardson L. 2002. Ecological aspects of black band disease of corals: relationships between disease incidence and environmental factors. Coral Reefs 21:393-398
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Muller E, Van Woesik R. 2011. Black-band disease dynamics: prevalence, incidence, and acclimatization to light. Journal of Experimental Marine Biology and Ecology 397:52-57
Page C, Willis B. 2006. Distribution, host range and large-scale spatial variability in black band disease prevalence on the Great Barrier Reef, Australia. Diseases of Aquatic Organisms 69:41-51
R Core Team. 2106. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. software
Raymundo LJ, Rosell KB, Reboton CT, Kaczmarsky L. 2005. Coral diseases on Philippine reefs: genus Porites is a dominant host. Diseases of Aquatic Organisms 64:181-191
Richardson LL. 2004. Black band disease. In: Coral health and disease. Springer. 325-336
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Sato Y, Bourne DG, Willis BL. 2009. Dynamics of seasonal outbreaks of black band disease in an assemblage of Montipora species at Pelorus Island (Great Barrier Reef, Australia) Proceedings of the Royal Society of London B 276:2795-2803
Sato Y, Bourne D, Willis B. 2011. Effects of temperature and light on the progression of black band disease on the reef coral, Montipora hispida. Coral Reefs 30:753-761
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text:
package com.netflix.genie.web.agent.services;

import java.util.Map;

/**
 * Service that provides agent runtime configuration (i.e. independent of the job being executed).
 *
 * @author mprimi
 * @since 4.0.0
 */
public interface AgentConfigurationService {

    /**
     * Produce a list of properties to be sent down to the agents running jobs.
     *
     * @return a map of properties and values
     */
    Map<String, String> getAgentProperties();
}

redpajama_set_name: RedPajamaGithub


text:
Q: How can two quadwords be compared in fasm? I need to check that a quadword is not less than zero.
I store them in MMX registers:
 movq mm0, [inputNum]
 movq mm1, [zero]

But i can't find an instruction for comparring two qwords in mmx or sse... Is there a way to do this?

redpajama_set_name: RedPajamaStackExchange


Cute, Charming and freshly remodeled & updated home! Close to parks, shopping, and schools! Hardwood floors throughout the home. 5 minute drive to downtown La Grange and the awesome dining & Shopping Options! BRAND NEW Kitchen cabinets, sink, fixtures & stainless steel appliances. This 2 bedroom, 2 Bath home on a nicely landscaped lot features built-in cabinets in many rooms, TWO fireplaces - One wood burning in the living room & a gas fireplace in the lower level, large 28 x 28 deck, extra-deep 2.5 car garage, beautiful trees and a fenced-in yard. Great location! Blue Ribbon winning Spring Avenue School and fantabulous LTHS school district 204.
Additional Information: This property is located in the La Grange neighborhood, IL. This 2 bedrooms detached single property is priced at $372.2/sqft.


text:
Kelsea Ballerini's New Song 'Club' Is Infectious [Listen]
Kelsea Ballerini is on a hitmaking streak with her latest release, "Club." The 26-year-old released the infectious new track on Friday (Nov. 8).
"I don't wanna go to the club / I don't wanna watch everybody around me try to hook up / And say stuff they don't mean / And get drunk and get cheap / So what's wrong with me? / 'Cause I don't wanna go to the club," the songstress sings in the chorus.
What begins as a softer slow tune slowly builds into a full-out jam about not wanting to deal with all the struggles and negative connotations that the club life brings.
The new track brings the "Love Me Like You Mean It" singer's pop influence to light. The blend of country with hints of pop creates a song that is sure to be stuck in your head for days to come. "Club" showcases Ballerini's sassy side in the melody of the track, while showing off her lyrical ability.
The single artwork features personal polaroids of Ballerini and friends on a table with confetti and shoes laying on the ground—a perfect fit for the song!
The new release follows up her emotional track "Homecoming Queen?" The new music seems to be off of Ballerini's forthcoming third studio album that is reportedly being released in 2020.
Ballerini will be performing at E!'s People's Choice Awards on Sunday (Nov. 10). Fans can tune into the show at 8 PM ET. She hinted on Twitter that she will be giving the debut performance of her new single.
29 Songs From Women in Country That Demand Attention:
Source: Kelsea Ballerini's New Song 'Club' Is Infectious [Listen]
Filed Under: kelsea ballerini
Categories: Country, Music
When to Take Down Christmas Decorations? Kelsea Ballerini's Stance Is Solid
Kelsea Ballerini Bought Her New 'Dream Home' From Kacey Musgraves
Kelsea Ballerini Reaches Divorce Settlement With Morgan Evans
2023 CMT Music Awards Moving to Austin, Texas
Kelsea Ballerini Reveals Girl Power Triple Collaboration for New Song
Luke Bryan Kicking Off His 2023 Tour Close to Home in Central NY
Thanks Homeboy! Special Pre-Sale Tickets Now Available for Eric Church
Brooks & Dunn 'Boot Scootin' Boogie-ing' Their Way to Western NY
All Aboard! Scenic Train Ride Takes You Through The Beautiful Finger Lakes

redpajama_set_name: RedPajamaCommonCrawl


This historic football book makes an ideal and unique football gift for any Blackburn Rovers fan. The book covers Blackburn's history over the last century, featuring famous League and Cup wins, the memorable games, and the stars past and present. The football clubs history is seen through the eyes of newspaper reporters who were present at the games at the time.
A unique and wonderful gift and must have for all Blackburn Rovers fans.


The Rover RS2500 Curved Shaft Line Trimmer has a 25cc, 2-stroke engine for greater durability. Featuring Spring Assist™ starting technology to reduce your starting effort, a convenient adjustable deluxe handle and a curved, two piece shaft for greater balance.
After using this a couple of times with the edger attached I am quite happy with it overall. Will have to see how it performs over a longer period of time but I have high hopes for this little beast. Soon I will be looking to get the pruning pole attachment so it will be interesting to see how that goes too.
When looking at this machine I think it's in its own little category as it's a budget trimmer/engine with swappable tools. If you were to get this in any other brand you would be paying at least 2-3 times as more than this unit. If you're a domestic user and going to use it for normal house usage then this would be fine and a really good idea given the attachment selections available for it.


22 replies to "? President Trump AMAZING Speech in Kansas City, Missouri – December 7, 2018"
It is evident to me the Trump haters could not have heard this entire speech. Sad deep state sheep.
Excellent speech President Trump. ???
Donnie says hes a good judge of character. That's why they are going to jail. People cant flip on you dumbass if you havent committed any crimes. No other president ever talked like that. I've heard mob bosses talk that way. I guess trump supporters like criminals.
He is such a moron. Talk about himself and how great he is. If you have to go around telling people how great you are, its obviously not great.
DEMOCRAT FORGATED DIABEL AMERICAN PEOPLE AFTER GWH BUSH 41 PASST A WAY ??????????????????
DREAM IS FOR AMERICAN OR DREAMER ILLIGALY OR CARAVAN OR DACA??????
YOU FORGATEN FORMER PRESIDENT GWH BUSH SERVICE IN MILITARY AND SINGNED FOR DISABEL PEOPLE?????????????????
CARAVAN OR DACA OR ILLIGALY PEOPE HAVE A DREAM OR AMERICAN PEOPLE LONG TIME AGO HARD WORK PAY TAX AND THIS TIME LOST EVRYTHING IN MOTHER NATUR OR HOMELESS PEOPE IS NOT DIFFRENT BETWEEN THEM ALL IS HOMELESS ?????????????
THEY LOST EVRY THING WITH MOTHER NATUR OR DEMOCRAT PARTY CHOICE?????????????????WHO NEED A HELP????????????????????
Trump is The Greatest President in History!
lock him up ? ?
Such ignorant trolls here. They wouldn't know the truth if it slapped them in the face. WWG1WGA 1????????????
When Donald and his pretty wife walked into the funeral services of Bush, what did we see? They looked like a orange haired gangster and a Slovakian prostitute. Come January, their lives will become a nightmare. God, I will love 2019!
The law gave us: Obamacare, WW1, WW2, Iraq war, Gulf War, Vietnam, the SS ponzi scheme, the largest most powerful socialist banking cartel ever created, a $20T debt sword of Damocles, the biggest financial collapse in history (housing crisis), Medicare, Medicaid, $1T/y budget deficit, and the Drug War which created a huge Mexican drug cartel who sent millions of M13 drug mules across our border to terrorize us and vote. Need I go on? We dont want law and order to return, for law and order is the fire in which we burn.
Proud saying i was watching his motorcade drive past in KC today. Hell of a sight seeing that crest a hill coming at you!


text:
Clean Air Task Force
For General Support
General Support/Organization
www.catf.us
114 State Street 6th Floor, Boston, MA, 02109, United States
for the Federal Power Plant Regulation and Policy project
The Clean Air Task Force utilizes research, education, advocacy, and litigation to reduce air pollution. This project seeks to defend the authority of the EPA to address local air pollution and greenhouse gas pollution from power plants. The project will also engage in administrative advocacy to persuade the EPA to address greenhouse gas emissions from existing power plants.
for general operating support
The Clean Air Task Force uses expert forums and policy advocacy and engages directly with private sector partners to promote policies that protect public health and promote innovation. Its mission is to accelerate zero carbon energy and other climate-beneficial technologies. The Task Force is focusing its efforts on promoting carbon capture use and storage in the United States and China. This grant is related to our work in the coal sector.
Latino leaders on the power of community-led conservation
How communities of color lead on climate solutions — and ways to support them
Statement on the signing of the Inflation Reduction Act


#SundayBumday #LazyMondayThroughSaturday and any other hashtag to promote being absolutely lazy and loving it is my choice of self-care this week.
Like the title says, sometimes we can do everything in the world, and be everybody for someone in the world, but at the end of the day, you STILL don't feel like doing anything.
Being lazy has got to be one of the simplest forms of self-care there is; just simply lay around and do NOTHING!
The best way to achieve this method of self-care, just simply give yourself the opportunity to enjoy doing nothing. We work so hard and have so much to do, that sometimes we deserve to not do absolutely anything. There have been days that I would sit around and not do a thing and it truly feels very refreshing and uplifting to be an unapologetic lazy adult for a day. Other times, the "lazy" seeems to creep up on me, and before I have realized it, I have spent an entire day being unproductive, but it oddly feels pretty great at times.
As adults, we don't give ourselves many opportunities to take advantage of a lazy day. We're always finding a reason to be busy, or guilting ourselves into thinking that we don't need to waste time lounging around the house for no reason. I think the exact opposite; as adults that work hard, we deserve the abililty to treat ourselves with some time to kick our feet up, in our best sweatpants and big t-shirt and lay around doing nothing at all.
This week, I encourage you to take one day and spend it doing nothing. Turn your adulting off for a day and see how beneficial it will be to get the undisturbed rest and relaxation you need.


A Moving Update With Pics!
Sometime within the next day or so, I will hit 50,000 visits here at the blog. Yay! With only 2 days before we pick up the keys this coming Monday, most of the packing is done. I really only have the kitchen which is half done and our walk in wardrobe left to do, which I can mostly finish before we pick up the keys. It didn't seem to take as long as I was expecting, and I've had time to do other things this week as well which I do appreciate – and some of you might too, because I've been commenting when I've had the chance.
Above is how my bookshelves look now. I can't bear to pack my Aromatherapy stuff until the last minute, and I've been burning oils and incense every day this week. Click any of these pics for a slightly bigger image.
This is how my desk currently looks. It's a lot messier than I like and I can't wait to get into the new place with new ways of organising things like pens and stationery – I have plans for how I'm going to do it.
Above you see my daily checklist, which I have been sticking to and finding really helpful especially in the midst of apparent chaos. I've been trying to do the reply to comments daily this week and I'd have to go back and check but I believe I have achieved it the next day after a post is posted. I used to find I'd put that job off and tend to do it twice a week or so. If it doesn't get done the tick doesn't get put there until it does get done, to remind me.
The Art/Dining room was the first room we completely emptied, and we have now filled it with packed boxes and furniture ready to be moved.
Here is a view of the art/dining room from another angle and you can see my favourite cabinet, the tall red one with glass. I think this was the best bargain we ever bought, it was just over $100AUD at an import clearance of furniture from Bali. There's a smaller one which matches it, which we had bought in a different state at least a year before, you can see it in the previous photo under the lightning picture. We were lucky to find these!
This is a photo of the kitchen/pool table room. The pool table is the first thing to be moved, it is costing us an absolute fortune because it has to go upstairs. Please cross your fingers that they can actually manage to get it up the stairs. :) You can also see our outdoor setting, which all needs washing off before we move it, and the lanterns in the gardens are ours as well, they're going to need a good spray with spider spray. At our new place, we are "borrowing" my parents water feature because they can't use it where they are now. ;) It'll look great on our balcony.
Here you can see my packing area. This is where anything glass, ceramic, or generally breakable gets packed.
This is many of my sentimental much loved knick knacks, which take a lot of time and effort to pack. The ceramic castle I made myself, when I used to do ceramics as a hobby – I also did that lovely green candlestick holder, which is going to have pride of place on my dining table – which will be used for *dining* in the new place *shock* *boggle* *awe* rather than art. The dragon you can see the back of with the big copper wings, The Other Half did that himself at Ceramics. We used to do it together once a week. No, it's not girly! It's creative art!
Nothing you see here cost over $10AUD. I love collecting glass and you can probably tell blue glass is my favourite. The tall red and black thing is actually fortune telling sticks which my parents brought back from Japan many years ago. I love them. Some of these items, though inexpensive, are my most treasured things.
A not so empty room – this is The Other Half's office. He is refusing to pack it till the last minute. Can you tell this is somewhat.. annoying to me? I mean sure, leave the computer there, but the little stuff which he's not going to use, surely he can pack?
Thanks to all my regular readers for hanging in there with me during this time of upheaval and chaos, but I am hopeful it's going to be worth it in the end – for me because I'll be walking every day and attempting a healthier lifestyle – and some menu planning, and for you because you'll get a lot more beautiful photos, and more community orientated kinds of tales. I do intend to become involved in the community down there, getting out and meeting new people and being a part of things. I may even begin selling my art at the markets from time to time.
So, it's not much longer now. I will be around a bit during the week but there will be at least one day I will be at the new house cleaning for most of the day.
Heartbreak leads me to Stumble..
← A Different Kind Of Life In The Country.
I always wanted to have a lamp like that when I was a kid and my parents would never let me. It was one of the first things I bought for me when I earned money. I have seen a bunch of these in different colors and I want to collect them, they're not very expensive. They have a cobalt blue one…. my favourite!
You're so organized with your moving! Ideally I'm like that, and was when I moved out of a shared apartment into my own place, but when I moved in with Boyfriend things were a total disaster and I was so unprepared. I'm still not unpacked either (it's way too hot – about 105F/44C). Maybe the next move will be better.
Can't wait to see pics of your new place!
Hope the moves goes well, and soon you'll be having a glass of something to celebrate the new home. I am so enjoying your work… I look forward to checking out your new stuff. Glad you'll be walking your health is the most important..without it you can't accomplish anything. Nor are you able to enjoy the good things in life.
I once moved a sealed box labelled ornamental knick-knacks through three houses since I couldn't be bothered unpacking it and finding places to display them. At the fourth house I donated the still unopened box to the nearest Salvation army store. I still don't remember what was in it and now I don't care. I'm just glad I didn't have to dust them. Of course now there's a new bunch of "things" which require dusting so every year or so I dispose of a few. But then No. 2 husband goes for a walk past the $2 shop and comes home with bits and pieces……..
I see you're not the only one having problems with a pool table.
My other half and I are living with my parents (in my childhood bedroom, mind) and he has just gone and bought an $800 pool table. And we have nowhere to put it. I am not happy!
You are very organised, by the way! Loved the virtual tour!


text:
Kreační a anihilační operátory byly původně zavedeny jako pomůcka pro řešení lineárního harmonického oscilátoru. Později nalezly značné uplatnění ve formalizmu obsazovacích čísel u nerozlišitelných částic, případně teorii pole.

Lineární harmonický oscilátor
U lineárního harmonického oscilátoru zavádíme anihilační operátor v energiové bázi takto:

Matice tohoto operátoru má tedy tvar:

Hermitovským sdružením matice získáme vztah pro operátor kreační:

Speciálně od je zřejmé, že:

Případně, že:

Vidíme tedy, že n-tý energetický stav je až na konstantu dán n-násobným působením kreačního operátoru na vektor , který nazýváme vakuum.

Pro další výpočty je užitečná komutační relace, kterou lze snadno odvodit z definice:

Z definic je taktéž zřejmé, že definujeme-li operátor  jako

,

pak pro diagonální maticové elementy platí:

Tento operátor tedy odpovídá pozorovatelné veličině, jenž udává v kolikáté energetické hladině se systém nalézá. Z důvodu, jenž bude zřejmý později, tento operátor nazýváme operátorem počtu částic.

Reprezentace obsazovacích čísel
Reprezentace obsazovacích čísel je s výhodou používána u systémů skládajícího se z několika identických částic, bosonů nebo fermionů. Výhodou tohoto popisu je skutečnost, že takto zapsané vektory automaticky splňují podmínky související s nerozlišitelností částic.

Lineární algebra

redpajama_set_name: RedPajamaWikipedia


text:
Q: Ubuntu 16.04, Nvidia driver issues Ok first off I tried installing the nvidia-current-updates but upon reboot both Unity and Cinnamon crashed.  Removed them then tried the official 361-42 drivers.  I can login to a desktop but there's no window panes nor any panels.
Im running an Asus ROG G751 with a GTX 965M

A: I am also running an asus ROG G751 with GTX 965M, and I was also having these problems. I fixed it by disabling Secure Boot.

*

*Restart and F2 to enter Bios

*Go to Security tab and select the Secure Boot option menu

*Disable Secure Boot, F10 to save and exit


création branding et développement de la nouvelle identité visuelle du Giant Steppes Festival.
the Giant Steppes Festival is organized by the Giant Steppes of Jazz NGO The Festival's objectives are those of the NGO – to promote jazz to the general audience in Mongolia as well as to provide jazz education and career development opportunities for Mongolian musicians. To accomplish these ends, the event combines performance, workshops and collaborative efforts between Mongolian and International musicians.
To answer all these goals, I proposed a full creation and development of GSOJ visual identity into several entities which could be developed through the time.
In addition to the branding concept work, I proposed venue location and stage design to develop the festival in several Ulaanbaatar locations.
new logotype for master series concert @ Ulaanbaatar Opera.
new logotype for UB Jazz Club mixing traditional mogolian morin kuur string instrument and guitar, including the « UB » as Ulaanbaatar.
new logotype for Giant Steppes Educational Program, inspired by primary Giant Steppes of Jazz logotype.