THURSTONIAN MODELS AND VARIANCE I: EXPERIMENTAL CONFIRMATION OF COGNITIVE STRATEGIES FOR DIFFERENCE TESTS AND EFFECTS OF PERCEPTUAL VARIANCE

For the triangle, duo‐trio, same‐different and 2‐AFC methods, using a model system, mean d′ values for the same subjects, discriminating between the same taste stimuli, were not significantly different. This confirmed the postulated cognitive strategies used for these methods in their respective Thurstonian/signal detection models. Introduction of perceptual variance as a result of the effects of sequences of tasting within a test, forgetting stimulus perceptions and τ‐criterion variation resulted in the 2‐AFC eliciting a significantly higher d′ than the other three methods. Yet, after a warm‐up procedure, which not only significantly increased values of d′ for all methods but also aligned subjects' τ‐criteria, the same‐different test had a d′ comparable to that of the 2‐AFC, while both d′ values were significantly higher than those of the triangle and duo‐trio. This suggested that effects of memory were more important those of sequence of tasting.     

REPLICATED SIMILARITY TESTING USING PAIRED COMPARISON METHOD

The present article discusses the replicated similarity testing using a paired comparison method. The “two one‐sided tests” (TOST) combined with a beta‐binomial model are proposed for this testing. Simulation studies provide evidence that the proposed testing is valid and reliable.     

COMPARISON OF METHODS FOR ANALYZING REPLICATED PREFERENCE TESTS

Preference testing is commonly used in consumer sensory evaluation. Traditionally, it is done without replication, effectively leading to a single 0/1 (binary) measurement on each panelist. However, to understand the nature of the preference, replicated preference tests are a better approach, resulting in binomial counts of preferences on each panelist. Variability among panelists then leads to overdispersion of the counts when the binomial model is used and to an inflated Type I error rate for statistical tests of preference. Overdispersion can be adjusted by Pearson correction or by other models such as correlated binomial or beta‐binomial. Several methods are suggested or reviewed in this study for analyzing replicated preference tests and their Type I error rates and power are compared. Simulation studies show that all methods have reasonable Type I error rates and similar power. Among them, the binomial model with Pearson adjustment is probably the safest way to analyze replicated preference tests, while a normal model in which the binomial distribution is not assumed is the easiest.     

The variation of tree beta diversity across a global network of forest plots

Aims With the aim of understanding why some of the world's forests exhibit higher tree beta diversity values than others, we asked: (1) what is the contribution of environmentally related variation versus pure spatial and local stochastic variation to tree beta diversity assessed at the forest plot scale; (2) at what resolution are these beta‐diversity components more apparent; and (3) what determines the variation in tree beta diversity observed across regions/continents? Location World‐wide. Methods We compiled an unprecedented data set of 10 large‐scale stem‐mapping forest plots differing in latitude, tree species richness and topographic variability. We assessed the tree beta diversity found within each forest plot separately. The non‐directional variation in tree species composition among cells of the plot was our measure of beta diversity. We compared the beta diversity of each plot with the value expected under a null model. We also apportioned the beta diversity into four components: pure topographic, spatially structured topographic, pure spatial and unexplained. We used linear mixed models to interpret the variation of beta diversity values across the plots. Results Total tree beta diversity within a forest plot decreased with increasing cell size, and increased with tree species richness and the amount of topographic variability of the plot. The topography‐related component of beta diversity was correlated with the amount of topographic variability but was unrelated to its species richness. The unexplained variation was correlated with the beta diversity expected under the null model and with species richness. Main conclusions Because different components of beta diversity have different determinants, comparisons of tree beta diversity across regions should quantify not only overall variation in species composition but also its components. Global‐scale patterns in tree beta diversity are largely coupled with changes in gamma richness due to the relationship between the latter and the variation generated by local stochastic assembly processes.     

Learning‐dependent regulation of beta2 and gamma2 subunits of GABAAR mRNA in sensory cortex of mice

GABAA receptors are heteromeric, ligand‐gated ion channels, built up with 19 different subunits. In the somatosensory cortex the most prevalent subunits are: alpha1, beta2 and gamma2. We showed that classical conditioning, in which stimulation of a row of vibrissae (CS) was paired with a tail shock (UCS), results in changes in cortical inhibitory system. Among others, GABAAalpha1 mRNA is up‐regulated in layer IV of cortical representation of trained row of vibrissae. We examined the mRNA expression level of beta2 and gamma2 subunits of GABAAR. For in situ hybridization, 35S‐labeled oligonucleotides were used as antisense probes. The effects were examined 1 h, 24 h and 5 days after the training lasting 3 days. There are no changes observed in beta2 subunits mRNA level. Expression of mRNA of gamma2 subunits increased 5 days after the training, but in contrast to other elements of gabaergic system we investigated, the change was observed in layers II/III. This indicates that learning‐dependent regulation of GABAA receptor phenotype is specific for given neuronal subtype.     

Evaluating Dependence Among Mule Deer Siblings in Fetal and Neonatal Survival Analyses

Abstract: The assumption of independent sample units is potentially violated in survival analyses where siblings comprise a high proportion of the sample. Violation of the independence assumption causes sample data to be overdispersed relative to a binomial model, which leads to underestimates of sampling variances. A variance inflation factor, c, is therefore required to obtain appropriate estimates of variances. We evaluated overdispersion in fetal and neonatal mule deer (Odocoileus hemionus) datasets where more than half of the sample units were comprised of siblings. We developed a likelihood function for estimating fetal survival when the fates of some fetuses are unknown, and we used several variations of the binomial model to estimate neonatal survival. We compared theoretical variance estimates obtained from these analyses with empirical variance estimates obtained from data-bootstrap analyses to estimate the overdispersion parameter, c. Our estimates of c for fetal survival ranged from 0.678 to 1.118, which indicate little to no evidence of overdispersion. For neonatal survival, 3 different models indicated that ĉ ranged from 1.1 to 1.4 and averaged 1.24–1.26, providing evidence of limited overdispersion (i.e., limited sibling dependence). Our results indicate that fates of sibling mule deer fetuses and neonates may often be independent even though they have the same dam. Predation tends to act independently on sibling neonates because of dam-neonate behavioral adaptations. The effect of maternal characteristics on sibling fate dependence is less straightforward and may vary by circumstance. We recommend that future neonatal survival studies incorporate additional sampling intensity to accommodate modest overdispersion (i.e., ĉ = 1.25), which would facilitate a corresponding ĉ adjustment in a model selection analysis using quasi-likelihood without a reduction in power. Our computational approach could be used to evaluate sample unit dependence in other studies where fates of individually marked siblings are monitored.     

A genome‐wide association study reveals a quantitative trait locus for age at first calving in delta/notch‐like EGF repeat containing on chromosome 2 in Japanese Black cattle

Age at first calving (AFC) is an important trait for achieving earlier reproductive performance in cattle. To identify quantitative trait loci for AFC in Japanese Black cattle, we conducted a genome‐wide association study using 866 animals with extreme AFC values selected from a larger group of 52 009 animals. We identified single nucleotide polymorphisms (SNPs) on bovine chromosome 2 that were associated with AFC. These SNPs were located within 112.8‐kbp intronic region of delta/notch‐like EGF repeat containing (DNER) and proved to be in a state of high linkage disequilibrium. The association was replicated in an independent sample of 2963 animals. In the replicated population, the frequency of the reduced AFC allele (Q) was 0.463, and the allele accounts for 8% of the total genetic variance. The effect of allele substitution on AFC was a decrease of 11.54 days. The results suggest that the Q allele could serve as a useful marker in Japanese Black cattle to select animals with superior AFC performance.     

Estimation of calf:cow ratios of Pacific walruses for use in population modeling and monitoring

We report on a method of visually classifying Pacific walruses (Odobenus rosmarus) to sex and age class with the goal of estimating calf:cow ratios. Development of this method began in 1958 by Dr. F. H. Fay and was used during six surveys in the Chukchi Sea between 1981 and 1999. We estimate calf:cow ratios using beta‐binomial models to allow for overdispersion and use Monte Carlo simulations to assess the reliability of prior surveys and quantify sample sizes required for future surveys. Calf:cow ratios did not vary by region, date, or by the number of cows in a group. However, higher ratios were observed in the morning and evening than during the day, indicating haul out behavior of cows varies by reproductive status. Adjusted for solar noon, few calves were observed in 1981 (3:100), 1984 (6:100), and 1998 (5:100), while substantially more were observed in 1982 (15:100) and 1999 (13:100). Classifying between 200 and 300 groups with cows (~1,600–2,300 individual cows) will yield calf:cow ratios with ~20%–30% relative precision. Tagging studies that examine hauling‐out behavior of cows with and without calves relative to time‐of‐day are necessary to better understand how to interpret calf:cow ratios.     

Semi‐parametric analysis of overdispersed count and metric data with varying follow‐up times: Asymptotic theory and small sample approximations

Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum‐likelihood estimators. Although this approach can account for heterogeneity it postulates a common overdispersion parameter across groups. Such parametric assumptions are usually difficult to verify, especially in small trials. Therefore, novel procedures that are based on asymptotic results for newly developed rate and variance estimators are proposed in a general framework. Moreover, in case of small samples the procedures are carried out using permutation techniques. Here, the usual assumption of exchangeability under the null hypothesis is not met due to varying follow‐up times and unequal overdispersion parameters. This problem is solved by the use of studentized permutations leading to valid inference methods for situations with (i) varying follow‐up times, (ii) different overdispersion parameters, and (iii) small sample sizes.     

Is optimal foraging a realistic expectation in orb‐web spiders?

Abstract 1. Explanations for web relocation invoking optimal foraging require reliable differentiation between individual sites and overall habitat quality. We characterised natural conditions of resource variability over 20 days in artificial webs of the orb‐web spider Gasteracantha fornicata to examine this requirement. 2. Variability in catch success was high. Day‐to‐day catch success in 90% (18/20) catch sites fitted negative binomial distributions, whereas 10% fitted Poisson distributions. Considered across trap sites (overall habitat), variance in catch success increased proportionally faster than the mean (i.e. Taylor’s Power Law, variance = 0.54mean^1.764). 3. We compared the confidence intervals for the expected cumulative catch in randomly drawn sequential samples from a frequency distribution representing the overall habitat (based on the parameters for Taylor’s power law) and the frequency distribution of expected cumulative catch within each individual catch site [via randomisation based on the mean and negative binomial exponent (k)]. 4. In all cases and across all sample sizes, median values for the power to differentiate habitat and catch sites never exceeded 0.2, suggesting that principles involved in optimal foraging, if operating, must be accompanied by a very high degree of uncertainty. 5. Under conditions of high resource variability, many days must be spent in a single catch site if movement decisions are based on an ability to differentiate current catch site from overall habitat. Empirical evidence suggests this is never met. This may explain why proximal mechanisms that illicit quickly resolved behavioural responses have been more successful in describing web relocation patterns than those associated with optimal foraging.     

Salmon‐derived nutrients drive diatom beta‐diversity patterns

1. Pacific salmon are a textbook example of migratory animals that transfer nutrients between ecosystems, but little is known about how salmon‐derived nutrients (SDN) affect the biodiversity of recipient freshwater ecosystems. We examined paleolimnological records from six Alaskan lakes to define how changes in SDN from sockeye salmon (Oncorhynchus nerka) influenced sedimentary diatom community structure and beta‐diversity among lakes and through time. 2. Using an isotopic mixing model, we showed that SDN loading could account for >80% of the lake total nitrogen budgets and strongly regulated diatom community composition. Spatial dissimilarity in diatom communities was positively related to differences in SDN among lakes (r² = 0.69, P < 0.01, n = 10). Likewise, temporal dissimilarity in diatom communities was positively related to differences in SDN in a sediment core with substantial variation in salmon spawner dynamics between 1700 and 1950 AD (r² = 0.34, P < 0.01, n = 19). Finally, beta‐diversity metrics quantifying temporal turnover within each lake’s sediment record were also positively related to the variance in SDN loading among lakes (r² = 0.88, P < 0.05, n = 5). Mean SDN was only negatively correlated to temporal diatom beta‐diversity. 3. Spatially subsidised systems often receive temporally variable resource inputs, and thus, it is not surprising that, unlike previous studies, we found that resource variability was the key driver of community composition and beta‐diversity. In habitats that receive strongly fluctuating external nutrient loads, environment heterogeneity may overweigh stochastic community processes. In addition, freshwater diatoms are characterised by great dispersal capabilities and short life cycles and therefore may be a more sensitive indicator for evaluating the role of resource variability than previously used model organisms. These results suggest that productivity–diversity relationship vary with the nature of nutrient loading and the life history of the community studied. 4. Overall, our study highlights that the transport of nutrients by sockeye salmon across ecosystem boundaries is a significant driver of algal community and biodiversity in nursery lakes, mainly through changing the magnitude of nutrient variation. As such, freshwater species diversity in regions like the U.S. Pacific Northwest may become impoverished where there have been long‐term declines in salmon populations and decreases in nutrient variability among lakes.     

Efficient Bayesian Sample Size Calculation for Designing a Clinical Trial with Multi‐Cluster Outcome Data

Health care utilization and outcome studies call for hierarchical approaches. The objectives were to predict major complications following percutaneous coronary interventions by health providers, and to compare Bayesian and non‐Bayesian sample size calculation methods. The hierarchical data structure consisted of: (1) Strata: PGY4, PGY7, and physician assistant as providers with varied experiences; (2) Clusters: k_s providers per stratum; (3) Individuals: n_s patients reviewed by each provider. The main outcome event illustrated was mortality modeled by a Bayesian beta‐binomial model. Pilot information and assumptions were utilized to elicit beta prior distributions. Sample size calculations were based on the approximated average length, fixed at 1%, of 95% posterior intervals of the mean event rate parameter. Necessary sample sizes by both non‐Bayesian and Bayesian methods were compared. We demonstrated that the developed Bayesian methods can be efficient and may require fewer subjects to satisfy the same length criterion.     

The effect of fire interval on post‐fire understorey communities in Yellowstone National Park

Questions: How does the time interval between subsequent stand‐replacing fire events affect post‐fire understorey cover and composition following the recent event? How important is fire interval relative to broad‐ or local‐scale environmental variability in structuring post‐fire understorey communities? Location: Subalpine plateaus of Yellowstone National Park (USA) that burned in 1988. Methods: In 2000, we sampled understorey cover and Pinus contorta density in pairs of 12–yr old stands at 25 locations. In each pair, the previous fire interval was either short (7–100 yr) or long (100–395 yr). We analysed variation in understorey species richness, total cover, and cover of functional groups both between site pairs (using paired t‐tests) and across sites that experienced the short fire intervals (using regression and ordination). We regressed three principal components to assess the relative importance of disturbance and broad or local environmental variability on post‐fire understorey cover and richness. Results: Between paired plots, annuals were less abundant and fire‐intolerant species (mostly slow‐growing shrubs) were more abundant following long intervals between prior fires. However, mean total cover and richness did not vary between paired interval classes. Across a gradient of fire intervals ranging from 7–100 yr, total cover, species richness, and the cover of annuals and nitrogen‐fixing species all declined while the abundance of shrubs and fire‐intolerant species increased. The few exotics showed no response to fire interval. Across all sites, broad‐scale variability related to elevation influenced total cover and richness more than fire interval. Conclusions: Significant variation in fire intervals had only minor effects on post‐fire understorey communities following the 1988 fires in Yellowstone National Park.     

Biotic homogenisation and differentiation as directional change in beta diversity: synthesising driver–response relationships to develop conceptual models across ecosystems

Biotic homogenisation is defined as decreasing dissimilarity among ecological assemblages sampled within a given spatial area over time. Biotic differentiation, in turn, is defined as increasing dissimilarity over time. Overall, changes in the spatial dissimilarities among assemblages (termed ‘beta diversity’) is an increasingly recognised feature of broader biodiversity change in the Anthropocene. Empirical evidence of biotic homogenisation and biotic differentiation remains scattered across different ecosystems. Most meta-analyses quantify the prevalence and direction of change in beta diversity, rather than attempting to identify underlying ecological drivers of such changes. By conceptualising the mechanisms that contribute to decreasing or increasing dissimilarity in the composition of ecological assemblages across space, environmental managers and conservation practitioners can make informed decisions about what interventions may be required to sustain biodiversity and can predict potential biodiversity outcomes of future disturbances. We systematically reviewed and synthesised published empirical evidence for ecological drivers of biotic homogenisation and differentiation across terrestrial, marine, and freshwater realms to derive conceptual models that explain changes in spatial beta diversity. We pursued five key themes in our review: (i) temporal environmental change; (ii) disturbance regime; (iii) connectivity alteration and species redistribution; (iv) habitat change; and (v) biotic and trophic interactions. Our first conceptual model highlights how biotic homogenisation and differentiation can occur as a function of changes in local (alpha) diversity or regional (gamma) diversity, independently of species invasions and losses due to changes in species occurrence among assemblages. Second, the direction and magnitude of change in beta diversity depends on the interaction between spatial variation (patchiness) and temporal variation (synchronicity) of disturbance events. Third, in the context of connectivity and species redistribution, divergent beta diversity outcomes occur as different species have different dispersal characteristics, and the magnitude of beta diversity change associated with species invasions also depends strongly on alpha and gamma diversity prior to species invasion. Fourth, beta diversity is positively linked with spatial environmental variability, such that biotic homogenisation and differentiation occur when environmental heterogeneity decreases or increases, respectively. Fifth, species interactions can influence beta diversity via habitat modification, disease, consumption (trophic dynamics), competition, and by altering ecosystem productivity. Our synthesis highlights the multitude of mechanisms that cause assemblages to be more or less spatially similar in composition (taxonomically, functionally, phylogenetically) through time. We consider that future studies should aim to enhance our collective understanding of ecological systems by clarifying the underlying mechanisms driving homogenisation or differentiation, rather than focusing only on reporting the prevalence and direction of change in beta diversity, per se.     

Climate as a driver of population variability in breeding Gentoo Penguins Pygoscelis papua at the Falkland Islands

Detecting and predicting how populations respond to environmental variability are eminent challenges in conservation research and management. This is particularly true for wildlife populations at high latitudes, many of which demonstrate changes in population dynamics associated with global warming. The Falkland Islands (Southwest Atlantic) hold one of the largest Gentoo Penguin Pygoscelis papua populations in the world, representing c. 34% of the global population. The numbers of breeding Gentoo Penguins at the Falkland Islands have shown a high degree of inter‐annual variability since monitoring commenced in 1990. However, proximate causes of annual variability in breeding numbers have not been explored. Here we examine 21 years of Gentoo Penguin breeding surveys from the Falkland Islands and assess whether inter‐annual variability in the number of breeding pairs were correlated with proxies of environmental variability. There was a positive correlation between the number of breeding pairs and a broad‐scale climatic variation index, the Southern Oscillation Index (SOI). In turn, the SOI was significantly correlated with spring sea surface temperature anomalies, indicating a more immediate atmospherically forced response to El Niño Southern Oscillation variability in the Southwest Atlantic than previously reported. However, we also describe a non‐linear response to environmental variability that may highlight foraging plasticity and/or the complexity of regional ecosystem interactions that operate across a range of different scales.     

Protein Paucimannosylation Is an Enriched N‐Glycosylation Signature of Human Cancers

While aberrant protein glycosylation is a recognized characteristic of human cancers, advances in glycoanalytics continue to discover new associations between glycoproteins and tumorigenesis. This glycomics‐centric study investigates a possible link between protein paucimannosylation, an under‐studied class of human N‐glycosylation [Man_1‐3GlcNAc₂Fuc_0‐1], and cancer. The paucimannosidic glycans (PMGs) of 34 cancer cell lines and 133 tissue samples spanning 11 cancer types and matching non‐cancerous specimens are profiled from 467 published and unpublished PGC‐LC‐MS/MS N‐glycome datasets collected over a decade. PMGs, particularly Man_2‐3GlcNAc₂Fuc₁, are prominent features of 29 cancer cell lines, but the PMG level varies dramatically across and within the cancer types (1.0–50.2%). Analyses of paired (tumor/non‐tumor) and stage‐stratified tissues demonstrate that PMGs are significantly enriched in tumor tissues from several cancer types including liver cancer (p = 0.0033) and colorectal cancer (p = 0.0017) and is elevated as a result of prostate cancer and chronic lymphocytic leukaemia progression (p < 0.05). Surface expression of paucimannosidic epitopes is demonstrated on human glioblastoma cells using immunofluorescence while biosynthetic involvement of N‐acetyl‐β‐hexosaminidase is indicated by quantitative proteomics. This intriguing association between protein paucimannosylation and human cancers warrants further exploration to detail the biosynthesis, cellular location(s), protein carriers, and functions of paucimannosylation in tumorigenesis and metastasis.     

Landscape partitioning by nocturnal rodent assemblages in the Llanos de Ojuelos,in Mexico’s Central High Plateau

Aim The Llanos de Ojuelos in Mexico’s Central High Plateau supports unique Opuntia scrublands and the southernmost Chihuahuan grasslands. Although human activities have modified strongly its landscape and impacted its biodiversity at an unknown scale, such impacts are poorly known. We aimed at understanding how nocturnal rodent species distributed across the landscape and formed assemblages and on the role and integration of the different habitats at the landscape level. Location The study was carried out at 43 sites in a study area of approximately 3350 km² in the Llanos de Ojuelos. Methods During the Spring of 2008, we surveyed nocturnal rodents, through live‐trapping. We redefined habitat classes based on log‐linear multinomial regressions of rodent captures. Species rarefaction curves and true alpha, beta and gamma diversities were calculated for the different habitat classes. A map of the different habitats was constructed based on Landsat imagery. Results We captured 458 individuals of 20 rodent species. Multinomial regressions caused the merger of 11 a priori defined habitat classes into 7. Leguminous scrub and mixed nopaleras, both secondary habitats, had the highest alpha and gamma diversity values. Closed arboreal nopaleras and grasslands had the highest within‐habitat variability (¹D_β) and the lowest area coverage. Within‐habitat ¹D_β was larger than landscape ¹D_β, because of the great overlap in rodent assemblage composition between the habitats. Main conclusions There are no ‘typical’ rodent assemblages per habitat class, but they are organized loosely and have fuzzy borders. Rodent community organization was highly species‐centred. At the landscape level, secondary habitats have a profound effect on rodent diversity and should be included in management schemes for biological conservation. The most endangered habitats seem to be the closed arboreal nopaleras and grasslands. Any conservation efforts must consider their conservation and an increase in the size of remaining patches.     

Integer‐valued functional data analysis for measles forecasting

Measles presents a unique and imminent challenge for epidemiologists and public health officials: the disease is highly contagious, yet vaccination rates are declining precipitously in many localities. Consequently, the risk of a measles outbreak continues to rise. To improve preparedness, we study historical measles data both prevaccine and postvaccine, and design new methodology to forecast measles counts with uncertainty quantification. We propose to model the disease counts as an integer‐valued functional time series: measles counts are a function of time‐of‐year and time‐ordered by year. The counts are modeled using a negative‐binomial distribution conditional on a real‐valued latent process, which accounts for the overdispersion observed in the data. The latent process is decomposed using an unknown basis expansion, which is learned from the data, with dynamic basis coefficients. The resulting framework provides enhanced capability to model complex seasonality, which varies dynamically from year‐to‐year, and offers improved multimonth‐ahead point forecasts and substantially tighter forecast intervals (with correct coverage) compared to existing forecasting models. Importantly, the fully Bayesian approach provides well‐calibrated and precise uncertainty quantification for epi‐relevant features, such as the future value and time of the peak measles count in a given year. An R package is available online.     

Maresin Conjugates in Tissue Regeneration 1 improves alveolar fluid clearance by up‐regulating alveolar ENaC,Na, K‐ATPase in lipopolysaccharide‐induced acute lung injury

Maresin Conjugates in Tissue Regeneration 1 (MCTR1) is a newly identified macrophage‐derived sulfido‐conjugated mediator that stimulates the resolution of inflammation. This study assessed the role of MCTR1 in alveolar fluid clearance (AFC) in a rat model of acute lung injury (ALI) induced by lipopolysaccharide (LPS). Rats were intravenously injected with MCTR1 at a dose of 200 ng/rat, 8 hours after administration of 14 mg/kg LPS. The level of AFC was then determined in live rats. Primary rat ATII (Alveolar Type II) epithelial cells were also treated with MCTR1 (100 nmol/L) in a culture medium containing LPS for 8 hours. MCTR1 treatment improved AFC (18.85 ± 2.07 vs 10.11 ± 1.08, P < .0001) and ameliorated ALI in rats. MCTR1 also significantly promoted AFC by up‐regulating epithelial sodium channel (ENaC) and Na⁺‐K⁺‐adenosine triphosphatase (Na, K‐ATPase) expressions in vivo. MCTR1 also activated Na, K‐ATPase and elevated phosphorylated‐Akt (P‐Akt) by up‐regulating the expression of phosphorylated Nedd4‐2 (P‐Nedd4‐2) in vivo and in vitro. However, BOC‐2 (ALX inhibitor), KH7 (cAMP inhibitor) and LY294002 (PI3K inhibitor) abrogated the improved AFC induced by MCTR1. Based on the findings of this study, MCTR1 may be a novel therapeutic approach to improve reabsorption of pulmonary oedema during ALI/acute respiratory distress syndrome (ARDS).     

Salidroside induces rat mesenchymal stem cells to differentiate into dopaminergic neurons

Parkinson's disease (PD) is a neurodegenerative disorder characterised by the loss of substantia nigra dopaminergic neurons that leads to a reduction in striatal dopamine (DA) levels. Replacing lost cells by transplanting dopaminergic neurons has potential value to repair the damaged brain. Salidroside (SD), a phenylpropanoid glycoside isolated from plant Rhodiola rosea, is neuroprotective. We examined whether salidroside can induce mesenchymal stem cells (MSCs) to differentiate into neuron‐like cells, and convert MSCs into dopamine neurons that can be applied in clinical use. Salidroside induced rMSCs to adopt a neuronal morphology, upregulated the expression of neuronal marker molecules, such as gamma neuronal enolase 2 (Eno2/NSE), microtubule‐associated protein 2 (Map2), and beta 3 class III tubulin (Tubb3/β‐tubulin III). It also increased expression of brain‐derived neurotrophic factor (BDNF), neurotrophin‐3 (NT‐3) and nerve growth factor (NGF) mRNAs, and promoted the secretion of these growth factors. The expression of dopamine neurons markers, such as dopamine‐beta‐hydroxy (DBH), dopa decarboxylase (DDC) and tyrosine hydroxylase (TH), was significantly upregulated after treatment with salidroside for 1–12 days. DA steadily increased after treatment with salidroside for 1–6 days. Thus salidroside can induce rMSCs to differentiate into dopaminergic neurons.