Project description:Freezing air temperatures kill most leaves, yet the leaves of some species can survive these events. Tracking the temporal and spatial dynamics of freezing remains an impediment to characterizing frost tolerance. Here we deploye time-lapse imaging and image subtraction analysis, coupled with fine wire thermocouples, to discern the in situ spatial dynamics of freezing and thawing. Our method of analysis of pixel brightness reveals that ice formation in leaves exposed to natural frosts initiates in mesophyll before spreading to veins, and that while ex situ xylem sap freezes near 0°C, in situ xylem sap has a freezing point of -2°C in our model freezing-resistant species of Lonicera. Photosynthetic rates in leaves that have been exposed to a rapid freeze or thaw do not recover, but leaves exposed to a slow, natural freezing and thawing to -10°C do recover. Using this method, we are able to quantify the spatial formation and timing of freezing events in leaves, and suggest that in situ and ex situ freezing points for xylem sap can differ by more than 4°C depending on the rate of temperature decline.
Project description:Spatial grain of studies of communities is often based on arbitrary convention. Few studies have examined how spatial scaling of grain size affects estimates of compositional change over time, despite its broad implications.Fish assemblage structure was compared between 1974 and 2014 at 33 sampling locations in the Muddy Boggy River drainage, USA. The two main objectives for this comparison were to quantify change in assemblage structure and to test for a relationship between compositional change and spatial scale. Spatial scale was manipulated by pooling assemblage data into a continuous range of groups, which increased in size from K = 33 pairs (i.e., local scale) to K = 1 pair (i.e., global scale), via clustering algorithm based on pair-wise fluvial distance.Local assemblages (stream reaches) varied in the degree of assemblage change over time (range = 0.10-0.99 dissimilarity; mean = 0.66). The global assemblage (drainage), however, remained relatively similar. A discontinuity in the relationship between compositional change and spatial scale occurred at K = 15 (mean dissimilarity = 0.56; p = .062), and this grouping is roughly the size of the headwater/tributary drainages (i.e., stream order ≤ 3) in the study system.Spatial scale can impact estimates of biodiversity change over time. These results suggest assemblages are more dynamic at individual stream reaches than at the scale of the entire drainage. The decline in assemblage change at the spatial scale of K = 15 deserves further attention given the marginal significance, despite a small sample size (n = 15). This pattern could suggest regional and meta-community processes become more important in shaping assemblage dynamics at the scale of headwater drainages, whereas the factors responsible for driving individual stream reach dynamics (e.g., stochasticity) become less important. Defining assemblages at a larger scale will result in different estimates of species persistence. Biodiversity monitoring efforts must take the effect of spatial scaling into consideration.
Project description:Multicellular organisms rely on intercellular communication to regulate important cellular processes critical to life. To further our understanding of those processes there is a need to scrutinize dynamical signaling events and their functions in both cells and organisms. Here, we report a method and provide MATLAB code that analyzes time-lapse microscopy recordings to identify and characterize network structures within large cell populations, such as interconnected neurons. The approach is demonstrated using intracellular calcium (Ca(2+)) recordings in neural progenitors and cardiac myocytes, but could be applied to a wide variety of biosensors employed in diverse cell types and organisms. In this method, network structures are analyzed by applying cross-correlation signal processing and graph theory to single-cell recordings. The goal of the analysis is to determine if the single cell activity constitutes a network of interconnected cells and to decipher the properties of this network. The method can be applied in many fields of biology in which biosensors are used to monitor signaling events in living cells. Analyzing intercellular communication in cell ensembles can reveal essential network structures that provide important biological insights.
Project description:Water-column bacterial communities are assembled by different mechanisms at different stream network positions, with headwater communities being controlled by mass effects (advection of bacteria from terrestrial soils) while downstream communities are mainly driven by environmental sorting. Conversely, benthic biofilms are colonized largely by the same set of taxa across the entire network. However, direct comparisons of biofilm and bacterioplankton communities along whole stream networks are rare. We used 16S rRNA gene amplicon sequencing to explore the spatiotemporal variability of benthic biofilm (2 weeks old vs. mature biofilm) and water-column communities at different network positions of a subarctic stream from early summer to late autumn. Amplicon sequence variant (ASV) richness of mature biofilm was about 2.5 times higher than that of early biofilm, yet the pattern of seasonality was the same, with the highest richness in midsummer. Biofilm bacterial richness was unrelated to network position whereas bacterioplankton diversity was negatively related to water residence time and distance from the source. This pattern of decreasing diversity along the network was strongest around midsummer and diminished greatly as water level increased towards autumn. Biofilm communities were phylogenetically clustered at all network positions while bacterioplankton assemblages were phylogenetically clustered only at the most downstream site. Both early and mature biofilm communities already differed significantly between upstream (1st order) and midstream (2nd order) sections. Network position was also related to variation in bacterioplankton communities, with upstream sites harbouring substantially more unique taxa (44% of all upstream taxa) than midstream (20%) or downstream (8%) sites. Some of the taxa that were dominant in downstream sections were already present in the upmost headwaters, and even in riparian soils, where they were very rare (relative abundance <0.01%). These patterns in species diversity and taxonomic and phylogenetic community composition of the riverine bacterial metacommunity were particularly strong for water-column communities, whereas both early and mature biofilm exhibited weaker spatial patterns. Our study demonstrated the benefits of studying bacterioplankton and biofilm communities simultaneously to allow testing of ecological hypotheses about biodiversity patterns in freshwater bacteria.
Project description:Cells elongate on a surface with nanogrooved (NG) patterns and align along that pattern. Although various models have been proposed for how this occurs, much remains to be clarified. Studies with fixed cells do not lend themselves to answering some of these open questions. In this study, the dynamic behaviours of living mesenchymal stem cells on an NG substrate with a 200 nm groove depth, an 870 nm ridge width and a 670 nm groove width were observed using time-lapse microscopes. We found that filopodia moved as if they were probing the surroundings of the cell protrusion, and then some cell protrusions invaded the probed areas. Cell protrusions that extended perpendicular to the NG direction tended to retract more rapidly than those parallel to the grooves. From these facts, we think that the retracting phase of cell protrusions play a rule in cell alignment along the NG patterns.
Project description:Contemporary models of psychosis suggest that a continuum of severity of psychotic symptoms exists, with subthreshold psychotic experiences (PEs) potentially reflecting some genetic and environmental risk factors shared with clinical psychosis. Thus, identifying abnormalities in brain activity that manifest across this continuum can shed new light on the pathophysiology of psychosis. Here, we investigated the moment-to-moment engagement of brain networks ("states") in individuals with schizophrenia (SCZ) and PEs and identified features of these states that are associated with psychosis-spectrum symptoms. Transient brain states were defined by clustering "single snapshots" of blood oxygen level-dependent images, based on spatial similarity of the images. We found that individuals with SCZ (n = 35) demonstrated reduced recruitment of three brain states compared to demographically matched healthy controls (n = 35). Of these three illness-related states, one specific state, involving primarily the visual and salience networks, also occurred at a lower rate in individuals with persistent PEs (n = 22), compared to demographically matched healthy youth (n = 22). Moreover, the occurrence rate of this marker brain state was negatively correlated with the severity of PEs (r = -0.26, p = 0.003, n = 130). In contrast, the spatial map of this state appeared to be unaffected in the SCZ or PE groups. Thus, reduced engagement of a brain state involving the visual and salience networks was demonstrated across the psychosis continuum, suggesting that early disruptions of perceptual and affective function may underlie some of the core symptoms of the illness.
Project description:We present a 3D time-lapse imaging method for monitoring mitochondrial dynamics in living HeLa cells based on photothermal optical coherence microscopy and using novel surface functionalization of gold nanoparticles. The biocompatible protein-based biopolymer coating contains multiple functional groups which impart better cellular uptake and mitochondria targeting efficiency. The high stability of the gold nanoparticles allows continuous imaging over an extended time up to 3000 seconds without significant cell damage. By combining temporal autocorrelation analysis with a classical diffusion model, we quantify mitochondrial dynamics and cast these results into 3D maps showing the heterogeneity of diffusion parameters across the whole cell volume.
Project description:Under a warmer future climate, thermal refuges could facilitate the persistence of species relying on cold-water habitat. Often these refuges are small and easily missed or smoothed out by averaging in models. Thermal infrared (TIR) imagery can provide empirical water surface temperatures that capture these features at a high spatial resolution (<1 m) and over tens of kilometers. Our study examined how TIR data could be used along with spatial stream network (SSN) models to characterize thermal regimes spatially in the Middle Fork John Day (MFJD) River mainstem (Oregon, USA). We characterized thermal variation in seven TIR longitudinal temperature profiles along the MFJD mainstem and compared them with SSN model predictions of stream temperature (for the same time periods as the TIR profiles). TIR profiles identified reaches of the MFJD mainstem with consistently cooler temperatures across years that were not consistently captured by the SSN prediction models. SSN predictions along the mainstem identified ~80% of the 1-km reach scale temperature warming or cooling trends observed in the TIR profiles. We assessed whether landscape features (e.g., tributary junctions, valley confinement, geomorphic reach classifications) could explain the fine-scale thermal heterogeneity in the TIR profiles (after accounting for the reach-scale temperature variability predicted by the SSN model) by fitting SSN models using the TIR profile observation points. Only the distance to the nearest upstream tributary was identified as a statistically significant landscape feature for explaining some of the thermal variability in the TIR profile data. When combined, TIR data and SSN models provide a data-rich evaluation of stream temperature captured in TIR imagery and a spatially extensive prediction of the network thermal diversity from the outlet to the headwaters.
Project description:To quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36-months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non-cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.
Project description:Nitric oxide (NO) is a gaseous signaling molecule that plays an important role in neurovascular coupling. NO produced by neurons diffuses into the smooth muscle surrounding cerebral arterioles, driving vasodilation. However, the rate of NO degradation in hemoglobin is orders of magnitude higher than in brain tissue, though how this might impact NO signaling dynamics is not completely understood. We used simulations to investigate how the spatial and temporal patterns of NO generation and degradation impacted dilation of a penetrating arteriole in cortex. We found that the spatial location of NO production and the size of the vessel both played an important role in determining its responsiveness to NO. The much higher rate of NO degradation and scavenging of NO in the blood relative to the tissue drove emergent vascular dynamics. Large vasodilation events could be followed by post-stimulus constrictions driven by the increased degradation of NO by the blood, and vasomotion-like 0.1-0.3 Hz oscillations could also be generated. We found that these dynamics could be enhanced by elevation of free hemoglobin in the plasma, which occurs in diseases such as malaria and sickle cell anemia, or following blood transfusions. Finally, we show that changes in blood flow during hypoxia or hyperoxia could be explained by altered NO degradation in the parenchyma. Our simulations suggest that many common vascular dynamics may be emergent phenomena generated by NO degradation by the blood or parenchyma.