Temporal Variance Research Articles

The influence of coyote on Canada lynx populations assessed at two different spatial scales

Previous studies have suggested that the attenuation of Canada lynx (Lynx canadensis) cyclic dynamics with decreasing latitude may be the consequence of a reduced specialization on the lynx’s primary prey, snowshoe hares (Lepus americanus). However, intraguild competitive interactions remain largely unexplored in situations where the temporal dynamics of food resources is pronounced, and lynx populations in the south of their distribution may be negatively affected by interspecific competition with other carnivores. In this paper, we used spectral analysis of fur harvest data collected at the state (US) and province (Canada) level to explore the spatial gradient of cyclic dynamics in lynx. Although some patterns were consistent with the ‘diet specialization’ hypothesis, we found that temporal variance of cycling propensity peaked at mid-latitudes, where transient, non-cyclic periods, coexisted with regular 10-year cycles. In these mid-latitude zones, non-cyclic periods did not coincide with loss of snowshoe hare cycling as demonstrated by historical records, and were not more frequent in recent decades as could be expected under a ‘climatic forcing’ scenario. Instead, we show that non-cyclic periods tended to coincide with periods of high coyote (Canis latrans) abundance and periods when coyotes apparently tracked snowshoe hare abundance as suggested by significant 10-year cycles lagging one or two years behind hare peaks. We used landscape-scale (trapline) fur harvest returns from five provinces in Canada to further probe the importance of interspecific competition in Canada lynx population dynamics. Accounting for coyote distribution and abundance did not bring additional explanatory and predictive power to models based solely on environmental and autecological predictors, suggesting that competition with coyote is not a force driving population abundance and cyclicity among lynx. We discuss the possible factors behind the apparent lack of consistency across spatial scales and recommend that further studies examine species interactions at a smaller (local) scale.

Identifying cyber risk hotspots: A framework for measuring temporal variance in computer network risk

Modern computer networks generate significant volume of behavioural system logs on a daily basis. Such networks comprise many computers with Internet connectivity, and many users who access the Web and utilise Cloud services make use of numerous devices connected to the network on an ad-hoc basis. Measuring the risk of cyber attacks and identifying the most recent modus-operandi of cyber criminals on large computer networks can be difficult due to the wide range of services and applications running within the network, the multiple vulnerabilities associated with each application, the severity associated with each vulnerability, and the ever-changing attack vector of cyber criminals. In this paper we propose a framework to represent these features, enabling real-time network enumeration and traffic analysis to be carried out, in order to produce quantified measures of risk at specific points in time. We validate the approach using data from a University network, with a data collection consisting of 462,787 instances representing threats measured over a 144 hour period. Our analysis can be generalised to a variety of other contexts.

The smell of success: the amount of prey consumed by predators determines the strength and range of cascading non-consumptive effects.

We examined whether chemically mediated risk perception by prey and the effects of changes in prey behavior on basal resources vary as a function of the amount of prey biomass consumed by the predator. We studied these issues using a tritrophic system composed of blue crabs, Callinectes sapidus (top predator), mud crabs Panopeus herbstii (intermediate prey), and oysters Crassostrea virginica (basal resource). Working in a well characterized field environment where experiments preserve natural patterns of water flow, we found that biomass consumed by a predator determines the range, intensity and nature of prey aversive responses. Predators that consume large amounts of prey flesh more strongly diminish consumption of basal resources by prey and exert effects over a larger range (in space and time) compared to predators that have eaten less. Less well-fed predators produce weaker effects, with the consequence that behaviorally mediated cascades preferentially occur in refuge habitats. Well-fed predators affected prey behavior and increased basal resources up to distances of 1–1.5 m, whereas predators fed restricted diet evoked changes in prey only when they were extremely close, typically 50 cm or less. Thus, consumptive and non-consumptive effects may be coupled; predators that have a greater degree of predatory success will affect prey traits more strongly and non-consumptive and consumptive effects may fluctuate in tandem, with some lag. Moreover, differences among predators in their degree of prey capture will create spatial and temporal variance in risk cue availability in the absence of underlying environmental effects.

Temporal Genetic Variance and Propagule-Driven Genetic Structure Characterize Naturalized Rainbow Trout (Oncorhynchus mykiss) from a Patagonian Lake Impacted by Trout Farming.

Knowledge about the genetic underpinnings of invasions—a theme addressed by invasion genetics as a discipline—is still scarce amid well documented ecological impacts of non-native species on ecosystems of Patagonia in South America. One of the most invasive species in Patagonia’s freshwater systems and elsewhere is rainbow trout (Oncorhynchus mykiss). This species was introduced to Chile during the early twentieth century for stocking and promoting recreational fishing; during the late twentieth century was reintroduced for farming purposes and is now naturalized. We used population- and individual-based inference from single nucleotide polymorphisms (SNPs) to illuminate three objectives related to the establishment and naturalization of Rainbow Trout in Lake Llanquihue. This lake has been intensively used for trout farming during the last three decades. Our results emanate from samples collected from five inlet streams over two seasons, winter and spring. First, we found that significant intra- population (temporal) genetic variance was greater than inter-population (spatial) genetic variance, downplaying the importance of spatial divergence during the process of naturalization. Allele frequency differences between cohorts, consistent with variation in fish length between spring and winter collections, might explain temporal genetic differences. Second, individual-based Bayesian clustering suggested that genetic structure within Lake Llanquihue was largely driven by putative farm propagules found at one single stream during spring, but not in winter. This suggests that farm broodstock might migrate upstream to breed during spring at that particular stream. It is unclear whether interbreeding has occurred between “pure” naturalized and farm trout in this and other streams. Third, estimates of the annual number of breeders (N b) were below 73 in half of the collections, suggestive of genetically small and recently founded populations that might experience substantial genetic drift. Our results reinforce the notion that naturalized trout originated recently from a small yet genetically diverse source and that farm propagules might have played a significant role in the invasion of Rainbow Trout within a single lake with intensive trout farming. Our results also argue for proficient mitigation measures that include management of escapes and strategies to minimize unintentional releases from farm facilities.

Temporal Variance Analysis for Action Recognition.

Slow feature analysis (SFA) extracts slowly varying signals from input data and has been used to model complex cells in the primary visual cortex (V1). It transmits information to both ventral and dorsal pathways to process appearance and motion information, respectively. However, SFA only uses slowly varying features for local feature extraction, because they represent appearance information more effectively than motion information. To better utilize temporal information, we propose temporal variance analysis (TVA) as a generalization of SFA. TVA learns a linear transformation matrix that projects multidimensional temporal data to temporal components with temporal variance. Inspired by the function of V1, we learn receptive fields by TVA and apply convolution and pooling to extract local features. Embedded in the improved dense trajectory framework, TVA for action recognition is proposed to: 1) extract appearance and motion features from gray using slow and fast filters, respectively; 2) extract additional motion features using slow filters from horizontal and vertical optical flows; and 3) separately encode extracted local features with different temporal variances and concatenate all the encoded features as final features. We evaluate the proposed TVA features on several challenging data sets and show that both slow and fast features are useful in the low-level feature extraction. Experimental results show that the proposed TVA features outperform the conventional histogram-based features, and excellent results can be achieved by combining all TVA features.

Biotic and Abiotic Factors Interact to Regulate Northern Peatland Carbon Cycling

Understanding the spatio-temporal variability of controls on peatland carbon (C) cycling is essential to project the effects of future environmental change. While there is understanding of individual drivers of C cycling, the effect of multiple drivers, including interactions, remains poorly understood. Using a spatially and temporally explicit sampling framework, we examined the effects of biotic and abiotic controls on key indicators of peatland functioning: ecosystem respiration (Reco), photosynthesis (Pcal), net ecosystem exchange (NEE), methane (CH4) fluxes, and pore water dissolved organic carbon concentration ([DOC]). Measurements were made over 12 months in a blanket peatland hosting a wind farm in Scotland, UK. Overall, we found that (i) season and plant functional type (PFT) explained most variation in Reco and Pcal, (ii) PFT and spatial location within the wind farm, which integrates several peat properties, were dominant predictors of CH4 fluxes, and (iii) season and location within the wind farm correlated with pore water [DOC]. Examination of predictors indicated that interactions, between and within biotic and abiotic factors, explained a significant amount of variation in greenhouse gas fluxes and [DOC]. These findings indicate that combinations of biotic and abiotic factors could mediate or exacerbate the effects of future environmental change on peatland C cycling. Given this, studies of C cycling need to capture the spatial and temporal variance of biotic and abiotic factors and their interactions to project the likely impacts of environmental change.

The duality of temporal encoding - the intrinsic and extrinsic representation of time.

While time is well acknowledged for having a fundamental part in our perception, questions on how it is represented are still matters of great debate. One of the main issues in question is whether time is represented intrinsically at the neural level, or is it represented within dedicated brain regions. We used an fMRI block design to test if we can impose covert encoding of temporal features of faces and natural scenes stimuli within category selective neural populations by exposing subjects to four types of temporal variance, ranging from 0% up to 50% variance. We found a gradual increase in neural activation associated with the gradual increase in temporal variance within category selective areas. A second level analysis showed the same pattern of activations within known brain regions associated with time representation, such as the Cerebellum, the Caudate, and the Thalamus. We concluded that temporal features are integral to perception and are simultaneously represented within category selective regions and globally within dedicated regions. Our second conclusion, drown from our covert procedure, is that time encoding, at its basic level, is an automated process that does not require attention allocated toward the temporal features nor does it require dedicated resources.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

Long-term agricultural drainage stimulates CH4 emissions from ditches through increased substrate availability in a boreal peatland

Peatlands have been widely developed for pasture usage through drainage and with the introduction of forage grasses; however, the effects of drainage and plant community change on methane (CH4) emission have not yet been well addressed. In this study, CH4 emissions, environmental factors, and dissolved organic carbon (DOC) in the soil pore water were investigated across a disturbance gradient (i.e., the natural peatlands, the drained peatlands, and an abandoned peatland pasture). Our results show that in addition to temperature and water table, the substrate availability (DOC as a proxy) is also an important regulator of CH4 emission. For example, DOC, peat temperature at 5cm depth (T5), and water table depth (WTD) jointly explained 53% of the temporal variance in the log-transformed CH4 flux for the hollows. Also, DOC was observed to be significantly correlated with the log-transformed CH4 flux for each land form. After the data were split into wet and dry seasons based on the water table, we observed that the T5 was the main driver of the CH4 flux during the wet season (R2=0.13), while WTD replaced it during the dry season (R2=0.23) for the general peatland system (i.e., all plots taken together). For the hummocks, both T5 and WTD together explained 31% of the variance in the log-transformed CH4 flux during the wet season, and peat temperature at 20cm depth explained 16% of the variance in the log-transformed CH4 flux during the dry season, while neither T5 nor WTD was correlated with the log-transformed CH4 flux across the whole season. These results imply that the relationships between environmental factors and CH4 flux depend on the water table and land forms. Our results indicate that water table drawdown is the main driver of the CH4 emission decrease at the non-inundated sites, and it explained 72% of the variance in seasonal mean CH4 emission among sites. The drainage ditches of the drained peatlands and the abandoned pasture, in spite of covering only ∼5% of the land area, contributed 15% and 32% of the CH4 emission of the land use type, respectively, and therefore represent a “hot spot” of CH4 emissions. High CH4 emission rates from the ditches, especially of the abandoned pasture, can be attributed to the higher production and export of DOC as a result of the rigorous vegetation growth. Spatially weighted CH4 emissions from May to October were1.87±0.68g CH4m−2 for the natural peatlands, 1.08±0.35g CH4m−2 for the drained peatlands, and 1.40±0.28g CH4m−2 for the abandoned pasture. Our results suggest that it is critical to measure the CH4 emission from the drainage ditches, where fresher substrate with a higher quality is transferred from the vegetated portion of the drained peatlands, in order to accurately evaluate the effects of agricultural development on the CH4 emissions in boreal peatlands.

A fully-stochasticized, age-structured population model for population viability analysis of fish: Lower Missouri River endangered pallid sturgeon example

We develop a fully-stochasticized, age-structured population model suitable for population viability analysis (PVA) of fish and demonstrate its use with the endangered pallid sturgeon (Scaphirhynchus albus) of the Lower Missouri River as an example. The model incorporates three levels of variance: parameter variance (uncertainty about the value of a parameter itself) applied at the iteration level, temporal variance (uncertainty caused by random environmental fluctuations over time) applied at the time-step level, and implicit individual variance (uncertainty caused by differences between individuals) applied within the time-step level. We found that population dynamics were most sensitive to survival rates, particularly age-2+ survival, and to fecundity-at-length. The inclusion of variance (unpartitioned or partitioned), stocking, or both generally decreased the influence of individual parameters on population growth rate. The partitioning of variance into parameter and temporal components had a strong influence on the importance of individual parameters, uncertainty of model predictions, and quasiextinction risk (i.e., pallid sturgeon population size falling below 50 age-1+ individuals). Our findings show that appropriately applying variance in PVA is important when evaluating the relative importance of parameters, and reinforce the need for better and more precise estimates of crucial life-history parameters for pallid sturgeon.

Temporal averaging of atmospheric turbulence-induced optical scintillation.

Based on the Rytov approximation we have developed for weak scintillation conditions a general expression for the temporal averaged variance of irradiance. The present analysis provides, for what we believe is the first time, a firm theoretical basis for the often-observed reduction of irradiance fluctuations of an optical beam due to atmospheric turbulence. Accurate elementary analytic approximations are presented here for plane, spherical and beam waves for predicting the averaging times required to obtain an arbitrary value of the ratio of the standard deviation to the mean of an optical beam propagating through an arbitrary path in the atmosphere. In particular, a novel application of differential absorption measurement for the purpose of measuring column-integrated concentrations of various so-called greenhouse gas (GHG) atmospheric components is considered where the results of our analysis indicates that relatively short averaging times, on the order of a few seconds, are required to reduce the irradiance fluctuations to a value precise enough for GHG measurements of value to climate related studies.

Local Facilitation May Cause Tipping Points on a Landscape Level Preceded by Early-Warning Indicators.

Positive biotic interactions play a significant role in shaping ecological communities. We used an individual-based model to demonstrate that plant facilitation on a microscale may cause ecosystem shifts on a landscape scale that can be announced by generic early-warning indicators. Recruitment of woody plants in harsh environments such as drylands often depends on nurse plants that ameliorate stressful conditions and facilitate the establishment of seedlings under their canopy. We found that these facilitative interactions may cause a treeless and a woodland state to be alternative stable states on a landscape scale if nurse plant effects are strong and if the environment is harsh enough to make facilitation necessary for seedling survival. A corollary is that under such conditions environmental change can bring drylands to tipping points for woody plant encroachment or woodland collapse. We show that the proximity of tipping points may be indicated by slowness of recovery of woody vegetation cover from small perturbations as well as by elevated temporal and spatial autocorrelation and variance. These signs are known to be indicators of critical slowing down. This is the first demonstration that the systemic phenomena of tipping points, announced by critical slowing down as a warning signal, may plausibly arise from microscale individual interactions, such as plant facilitation.

Hand bacteria as an identifier: a biometric evaluation

Molecular and soft bio-molecular biometrics are an advancing field that involves the analysis of a person’s unique biological markers at a molecular level to ascertain identity. Bacteria communities found on the skin of the human hand have shown to be highly diverse and to have a low percentage of similarity between individuals. The goal of this research effort is to see if a person’s demographics, primarily ethnicity, share a relationship with the bacteria communities that exist on their hand. A sample collection was carried out in which the left and right inner palms of 250 individuals were swabbed to obtain a total of 500 bacteria samples. Of these, 104 samples from 52 individuals (left and right hands) covering a range of age, gender, and ethnicity of the participants were sequenced using 150 paired-end multiplex reads on an Illumina MiSeq. The reads contained the third hypervariable region DNA of the microbial 16S rRNA gene commonly used for microbial identification. Sequences were analyzed using a combination of commercial and custom bioinformatics tools. Results indicated that women who participated in the sample collection had a 15.7 % higher diversity of bacteria at the genus level than men. Using a support vector machine with a 60 % train and 40 % test approach, ethnicities of individuals who provided samples could be classified with a range of 72–94 % accuracy depending on the method used. Principal coordinate plots generated using the unique fraction (UniFrac) algorithm devised by Lozupone et al. at University of Colorado at Boulder showed that similar clustering appeared with people of Turkish, Asian Indian, and Middle Eastern descent and less clustering with people of Caucasian and African American descent. Although focused on a small subset of the human population with no temporal variance in bacterial diversity explored, these results provide a basis for performing identification based on human bacteria that can be expanded upon using time varying sampling and other regions of the 16S rRNA gene.

Population‐level consequences for wild fish exposed to sublethal concentrations of chemicals – a critical review

AbstractConcentrated chemical spills have been shown to impact adversely on fish populations and even cause localized population extinctions. Evaluating population‐level impacts of sublethal exposure concentrations is, however, complex and confounded by other environmental pressures. Applying effect measures derived from laboratory‐based chemical exposures to impacts in wild fish populations is constrained by uncertainty on how biochemical response measures (biomarkers) translate into health outcomes, lack of available data for chronic exposures and the many uncertainties in available fish population models. Furthermore, wild fish show phenotypic plasticity and local adaptations can occur that adds geographic and temporal variance on responses. Such population‐level factors are rarely considered in the chemical risk assessment process and can probably be derived only from studies on wild fish. Molecular technologies, including microsatellite and SNP genotyping, and RNASeq for gene expression studies, are advancing our understanding of mechanisms of eco‐toxicological response, tolerance, adaptation and selection in wild populations. We examine critically the application of such approaches with examples including using microsatellites that has identified roach (Rutilus rutilus) populations living in rivers contaminated with sewage effluents that are self‐sustaining, and studies of stickleback (Gasterosteus aculeatus) and killifish (Fundulus heteroclitus) that have identified genomic regions under selection putatively related to pollution tolerance. Integrating data on biological effects between laboratory‐based studies and wild populations, and building understanding on adaptive responses to sublethal exposure are some of the priority research areas for more effective evaluation of population risks and resilience to contaminant exposure.

Decomposing the variance in southern elephant seal weaning mass: partitioning environmental signals and maternal effects

Predator populations are likely to respond to bottom‐up processes, but there remains limited understanding of how wide‐ranging marine predators respond to environmentally driven temporal variation in food availability. Widespread declines of several Southern Ocean predators, including southern elephant seals Mirounga leonina, have been attributed to decreases in food availability following environmental changes. We used linear mixed models to examine temporal process variance in weaning mass (a key fitness component) of southern elephant seals at Marion Island over a 27‐year period (1986–2013). We quantified the contribution of within‐ and between‐year covariates to the total phenotypic variance in weaning mass and determined whether the observed reversal of population decline was associated with a continued increase in weaning mass, suggesting improvement in per capita food availability to adult females. Weaning mass initially increased rapidly with maternal age, but reached an asymptote when females were nine years old. Longitudinal data examining between‐individual maternal differences suggested latent, age‐independent maternal influences on weaning mass. Between‐year differences accounted for only 6% of the total phenotypic variance in weaning mass. We found no evidence for a systematic trend in weaning mass, but model predicted weaning mass was 8.70 kg (95% CI = 2.14–14.73) lower during the 1980s, suggesting that food limitation may have been most severe during these years when the population was declining. Model support for a population size effect was entirely driven by the low weaning mass and comparatively high (but declining) population size from 1986 to 1988; subsequent variation in population size had no detectable influence on weaning mass. Remotely sensed chlorophyll‐a concentration within the seals' foraging distribution explained 45% of the between‐year variation (1998–2013, n = 9) in weaning mass, with higher weaning mass in years of positive chlorophyll‐a anomalies. Environmental variation associated with variability in the Southern Annular Mode poorly predicted temporal variation in weaning mass. Our long‐term data on elephant seal weaning mass provides a perspective on variation in food availability in a pelagic environment which is poorly known. Examining the long‐term regionally specific effects of environmental variability aids our understanding of how these predators interact with their environment.

Understanding the synoptic variability of stratocumulus cloud liquid water path over the Southeastern Pacific

The spatial and temporal variance of stratocumulus cloud liquid water path (CLWP) over Southeastern Pacific has been investigated by combining satellite moderate resolution imaging spectroradiometer cloud products, CLWP from advanced microwave scanning radiometer-EOS observations and NCEP final analysis atmospheric products with empirical orthogonal function (EOF) Analysis. CLWP variance is the most complicated factor among three fundamental cloud quantities (the microphysical cloud droplet concentration, and the macrophysical CLWP and cloud fractional cover). The results show that EOF/PC1 of CLWP represents the variation of domain-averaged CLWP, which is mainly controlled by surface meteorological factors. Sea surface temperature and cold advection drive the synoptic and seasonal scales of variance of CLWP, while surface wind speed plays a fundamental role in stratocumulus cloud formation and daily variance of CLWP. EOF/PC2 of CLWP describes the spatial variance of CLWP. This daily spatial variance of CLWP is controlled by the factors of lower tropospheric stability and cloud top relative humidity, which determine cloud thickness and, consequently, CLWP through thermodynamic and entrainment processes. Further study indicates a twofold interaction of the surface wind speed on stratocumulus CLWP: (1) dynamically through modulation of surface latent heat and sensible heat fluxes and (2) microphysically through enhanced marine aerosol production.

Small quench dynamics as a probe for trapped ultracold atoms

Finite systems of bosons and/or fermions described by the Hubbard model can be realized using ultracold atoms confined in optical lattices. The ground states of these systems often exhibit a coexistence of compressible superfluid and incompressible Mott insulating regimes. We analyze such systems by studying the out-of-equilibrium dynamics following a weak sudden quench of the trapping potential. In particular, we show how the temporal variance of the site occupations reveals the location of spatial boundaries between compressible and incompressible regions. The feasibility of this approach is demonstrated for several models using numerical simulations. We first consider integrable systems, hard-core bosons (spinless fermions) confined by a harmonic potential, where space separated Mott and superfluid phases coexist. Then, we analyze a nonintegrable system, a $J-V-V'$ model with coexisting charge density wave and superfluid phases. We find that the temporal variance of the site occupations is a more effective measure than other standard indicators of phase boundaries such as a local compressibility. Based on these examples, we argue that analyzing temporal fluctuations is a valuable experimental tool for exploring phase boundaries in trapped atom systems.

Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.

To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy-seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho = -0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = -0.52). Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS.

Preliminary field study of soil TKN in a wastewater land application system

Abstract An accurate nitrogen mass balance is necessary when designing a municipal wastewater land application system in order to reduce the potential for nitrogen contamination of groundwater. Limited information on soil total Kjehldahl nitrogen (TKN) is available to estimate the TKN variance in the soil that can be used for calculating the nitrogen mass balance. This study investigated the soil TKN temporal variance at two different depth ranges within three different soil types irrigated with municipal wastewater effluent in a short-term at a long-term running site under field conditions. Three plots were chosen with each plot growing different plants and with different soil types in Lubbock, Texas, USA. Kruskal–Wallis one way analysis of variance on ranks and Mann–Whitney rank sum test were employed to compare the differences of medians of many groups of data and of two groups of data at p

Genome-Wide Gene Expression in relation to Age in Large Laboratory Cohorts of Drosophila melanogaster.

Aging is a complex process characterized by a steady decline in an organism's ability to perform life-sustaining tasks. In the present study, two cages of approximately 12,000 mated Drosophila melanogaster females were used as a source of RNA from individuals sampled frequently as a function of age. A linear model for microarray data method was used for the microarray analysis to adjust for the box effect; it identified 1,581 candidate aging genes. Cluster analyses using a self-organizing map algorithm on the 1,581 significant genes identified gene expression patterns across different ages. Genes involved in immune system function and regulation, chorion assembly and function, and metabolism were all significantly differentially expressed as a function of age. The temporal pattern of data indicated that gene expression related to aging is affected relatively early in life span. In addition, the temporal variance in gene expression in immune function genes was compared to a random set of genes. There was an increase in the variance of gene expression within each cohort, which was not observed in the set of random genes. This observation is compatible with the hypothesis that D. melanogaster immune function genes lose control of gene expression as flies age.