Physiological and molecular studies have identified multiple ERα-rich nuclei in the hypothalamus of the central nervous system (CNS) as sites of actions that mediate effects of estrogens. Much of our understanding of ERα regulation has been obtained using transgenic models such as ERα global or nuclei-specific knockout mice. A fundamental question concerning how ERα is regulated in wild-type animals, including humans, in response to alterations in steroid hormone levels, due to experimental manipulation  or physiological stages , lacks consistent answers. This review discusses how different sex hormones affect ERα expression in the hypothalamus. This information will contribute to the knowledge of estrogen action in the CNS, further our understanding of discrepancies in correlation of altered sex hormone levels with metabolic disturbances when comparing both sexes, and improve health issues in postmenopausal women.Sex differences exist in the complex regulation of energy homeostasis that utilizes central and peripheral systems. It is widely accepted that sex steroids, especially estrogens, are important physiological and pathological components in this sex-specific regulation. Estrogens exert their biological functions via estrogen receptors (ERs). ER Sex differences exist in the epidemic of obesity-related metabolic diseases. According to the recent National Center for Health Statistics , althougIt is well-established that obesity is frequently associated with irregular menstrual cycles, decreased fertility, and altered patterns of various reproductive and metabolic hormones in humans  as well The hypothalamus of the CNS is the integration center that controls homeostatic regulation of energy balance including energy intake and energy expenditure . The majα and ERβ, membrane-localized nuclear ERs, and membrane-bound G protein-coupled ER  [Estrogenic activity is mediated through genomic along with nongenomic mechanisms. Like other sex steroids, estrogens exert their biological functions through binding to ERs, including classic nuclear ERs GPR30) .α is estradiol, ERα also binds to other natural forms of estrogens such as estrone and estriol with lower affinity than estradiol [α, either promoting or disturbing ERα genomic activity [The classic nuclear receptors for sex steroid hormones, including estrogens, progestogens, and androgens, are activated by their respective steroid hormones and undergo conformational changes, dimerize with other hormone receptors, and recruit coactivator/corepressor molecules. The dimers then bind to their respective hormone response elements in the promoter of target genes and function as nuclear transcription factors, to alter transcription and expression of specific target hormone responsive genes and to mediate biological actions of their respective hormones , 42. Sucstradiol . Additioactivity .α and ERβ localized inside nuclei, membrane-initiated nongenomic estrogenic signaling via membrane-bound ERs [ vice versa. Razandi et al. showed that no membrane ER could be detected in ERα and ERβ double knockout mice [α following knockdown or overexpression of membrane ER in breast cancer cells and reported a positive correlation in expressions between nuclear and membrane ERs. Specifically, lowered expression of nuclear ERα was detected when membrane ER expression was decreased and enhanced nuclear ERα expression was detected when membrane ER was increased [α [α have been detected using surface biotinylation and immunocytochemistry [α is distributed on membrane [α are both presented in cell membrane. One possibility is that collaboration between membrane-localized ERα and GPER exists, and GPER could induce the expression of the membrane-localized ERα [α is present at the plasma membrane in many cell types within the CNS, including the hypothalamus.The importance of membrane steroid receptors is increasingly apparent. Besides nuclear-initiated genomic signaling via ERound ERs , 47 is aound ERs , 49. Theout mice , suggestncreased . Thus, teased [α . Similarhemistry , 53. Submembrane , 55. Theized ERα . These s2+-dependent protein kinase C (PKC) pathway [Nongenomic estrogenic signaling occurs more rapidly than the classical genomic estrogenic signaling, typically over a short course of seconds to minutes . This no pathway –61, acti pathway –65, and  pathway –68  is introduced into the nuclear ERα knockout mouse model. Consequently, these ERα knockout mice express a mutant ERα with intact nonclassical signaling capacity but disrupted classical estrogen signaling capacity; thus ERα only signals via nongenomic pathway in this mouse model [α knockout mice are normalized in these mice with nonclassical signaling but lack of genomic estrogen signaling [α ligands at least act via membrane-localized ERα to regulate energy homeostasis.In order to distinguish involvement of classical versus nonclassical ERse model . Interesignaling , confirmα is considered to be the receptor primarily responsible for energy homeostatic modulation in the CNS and in the periphery [α-containing neuronal and nonneuronal cells are mostly located and much more abundant than other types of ERs in the ARC and the ventrolateral part of the VMH [α expression in these nuclei by steroid hormones could provide direct mechanisms underlying how estrogens affect the activity of these brain nuclei to regulate energy balance, body fat distribution, and metabolism [The hypothalamus is structurally and functionally sexually dimorphic  and is ceriphery , 49. ERα the VMH –81, the tabolism –39, 82.α, either whole-body knockout [α gene expression in the steroidogenic factor-1 (SF-1) neurons in the VMH or in the proopiomelanocortin (POMC) neurons in the ARC has a greater impact in females than males to increase food intake and body fat and to decrease energy expenditure and metabolism [α in the hypothalamus but also support that ERα in the neurons in the VMH and ARC, at least SF-1 and POMC expressing neurons, plays important roles in regulating energy balance, especially in females.Deletion of ERknockout , 83 or hknockout , 84, inctabolism , 84. Theα at a much higher concentration than ERβ [There are more nonneuronal cells than neuronal cells within the CNS. Nonneuronal cells encompass diverse cell types, with microglia and astrocytes being the most abundant cell types. Regulation of energy homeostasis involving neuronal cells has been focused in majority studies previously, while microglia and astrocytes have been less studied. Two key points are pertinent to the topic reviewed here. First, microglia and astrocytes locate in the hypothalamic regions that respond to steroid hormones including estrogens –87. Micrthan ERβ . In factthan ERβ , 105. Ththan ERβ , 106.α is expressed in microglia and astrocytes in the hypothalamus has been an ongoing debate. An early study observed ERα immunoreactivity in neuronal cells only but not in any nonneuronal glial cells including microglia and astrocytes [α presence was demonstrated in primary microglial cell culture established from the forebrains of neonatal rats [α was identified as the receptor modulating microglial activity [α protein and gene expression in primary mouse and human microglia [β expression was abundant in microglia [ in vitro studies have reported that both ERα and ERβ are expressed intracellularly and on plasma membrane of astrocytes from cultured CNS cells [β expression in astrocytes was uncertain [α has been found in primary hypothalamic astrocytes cultured from male and female pups [α knockout and ERβ knockout mice, Mong and Blutstein have demonstrated that ERα but not ERβ mediates the action of estradiol on astrocytes in the ARC [α is the receptor mediating the actions of estradiol in astrocyte morphology. To reconcile the literature, most studies on ER expression in microglia and astrocytes have been conducted using in vitro cultures and have demonstrated dominant ERα expression in astrocytes and dominant ERβ expression in microglia. Future in vivo demonstration of ERs in hypothalamic microglia and astrocytes in neonatal and adult brains and assessment of their physiological function in metabolic regulation are needed to further our knowledge of estrogenic metabolic regulation in the CNS.Whether ERtrocytes . Regardital rats  and ERα activity . A few ricroglia –112, wheNS cells , 113–116ncertain . Recentlale pups , 118. Us the ARC , suggestα distribution in the hypothalamus is highly conversed across a number of species, including rat [α is abundantly distributed in several key brain regions implicated in regulating energy homeostasis in both males and females, including the ventral lateral portion of the VMH and the ARC related to the control of food intake, the PVH related to energy expenditure and HPA axis regulation, the MPOA that influences thermogenesis, and the anteroventral periventricular nucleus (AVPV) regulating sexual behavior and HPG axis [α than females in these key hypothalamic nuclei involved in metabolic regulation. The gene expression, protein levels, and distribution pattern of ERα in the hypothalamus can be modified by alterations in circulating steroid hormone levels. Hormone levels can be experimentally manipulated using methods of surgical removal of gonads and hormone replacement, or fluctuate due to natural variation across estrous cycles and during certain physiological stages such as puberty, menopause and pregnancy. The degrees to which the reported differences, either between species or within one species, are indeed due to differences in sex or hormone status remains vague from the current literature.ERding rat , 119, moding rat , hamsterding rat , guinea ding rat , ferret ding rat , opossumding rat , musk shding rat , bird [1ding rat , 126, coding rat , nonhumading rat  and adulding rat , 130, anding rat –133. ERαHPG axis , 134  is more complex than it was previously presumed, especially in humans [α [α gene expression. For example, both human ERα promoters A and C are used in human breast adenocarcinoma cells MCF7 but only promoter A is used in human breast carcinoma cells ZR-75-1 [α. Furthermore, Esr1 promoter region has been shown to undergo epigenetic modification, that is, DNA methylation, under normal and pathological conditions [The exact mechanism regulating ERpression . Recent n humans . Specifiumans [α , 138. Th ZR-75-1 . Considenditions . It is pnditions .α in the hypothalamus of the CNS are regulated by various sex hormones in both males and females from human and animal studies and discuss discrepancy in current literature. This information will contribute to understanding of estrogenic action in the CNS and improve health issues in postmenopausal women.In this review, we gather evidence with regard to how expression and distribution of ERα regulation. In men, androgen levels rise during puberty and decrease gradually over the lifespan [Sex steroid hormones profoundly affect metabolic regulation of many species. Although the distribution pattern of ER-containing cells in the hypothalamus is similar across species, the regulation of ER expression could be relatively dynamic depending on circulating sex hormone levels and reproductive status, indicating the plasticity in ERlifespan . In contlifespan , 144. Adlifespan , 144.α-dihydrotestosterone, estradiol, and progesterone are the most potent respective androgens, estrogens, and progestogens that carry out physiological functions.All circulating androgens in men and most of the estrogens and progestogens in premenopausal women are produced in the gonads, testes in males and ovaries in females. On the other hand, circulating estrogens and progestogens in men, androgens in premenopausal women, and all sex steroids in postmenopausal women are synthesized in tissues other than gonads, including the brain, adipose tissues, and adrenal glands. The sex steroids secreted by nongonad tissues may also act locally as intracrine or paracrine factors, distinct from endocrine hormones. Testosterone and 5α expression in the hypothalamus due to dynamic endocrine status and changes in circulating steroid levels could be region-specific and different among species, depending on dissimilar patterns of reproductive cycles (see below). It is noteworthy, however, that ERα expression could be regulated by organizational effects during developing stages rather than activational effects influenced by steroid hormones. This is demonstrated by a study using castrated male and female rats, both of which lack majority of endogenous sex hormones [α mRNA levels, less than half, than OVX females in the VMH and the ARC [α expression level in the rat hypothalamus since there is no impact of circulating sex hormones in either sex. Thus sex difference in ERα expression may not be merely due to sex hormones. Consequently, data on the sex steroid hormone regulation of sex steroid receptor expression must be interpreted with care.Regulation of ERhormones . Lauber  the ARC . Such fiα expression would affect the sensitivity to steroid hormones and consequently affect their endocrine physiology and behavior, which contribute to the adaptation of neuroendocrine regulation of energy homeostasis in obesogenic environment. In this section, we focus on discussing steroidal regulation of hypothalamic ERα expression in the context of metabolic regulation of energy homeostasis.Differential ERα expression in the hypothalamus changes with estrous cycle as a function of circulating levels of estradiol and progesterone [α expression can be down- or upregulated by circulating levels of estrogens in a brain region-dependent manner across species [α mRNA expression in the VMH, decreases ERα mRNA expression in the lateral septum, and has no effect on ERα mRNA expression in the dorsal hypothalamus in lizards [α immunoreactivity is reduced in the MPOA, is increased in the medial VMH, and is unchanged in the lateral VMH and the ARC by estradiol treatment [α in the hypothalamus by estradiol, either at expression level or protein level, is regulated in a brain nuclei-specific manner.EResterone , 145–149 species , 150–152reatment . Therefoα, similar to what many other hormones do. In agreement with this, several groups have shown that removal of majority of endogenous estradiol by OVX increases the number of ERα mRNA-positive neurons in the VMH of the hypothalamus of female rats [α mRNA in the hypothalamus. Specifically, ERα mRNA levels decline in the VMH and ARC 18–24 hours after estradiol benzoate treatment [α positive neurons reduce in the VMH and anterior hypothalamic area following a 2-week treatment of estradiol benzoate at pharmacologic dosages [α mRNA and protein levels are inversely correlated with circulating estradiol concentration to maintain responsiveness to hormonal activation in ER-containing cells, which would ensure a balanced, homeostatic system to prevent excessive estrogenic action [It is generally assumed that estradiol would downregulate its own receptor ERale rats , 147. Acale rats , 153 or ale rats  treatmenreatment , and the dosages . Thus, ic action –147  surge, ovulation, and formation of the corpus luteum during diestrus and proestrus . The eleProgesterone is an important modulator of estrogenic action in the CNS and it affects LH release and ovulation in a biphasic manner . The rolα expression is supported by the fact that both progesterone receptor (PR) and ERα are coexpressed in certain nuclei in the CNS [ in vivo, ERα binding is significantly reduced in the anterior pituitary, the MPOA, and the medial basal hypothalamus where the VMH and the ARC are located. Such progesterone's effect on ERα expression is not evenly displayed among the brain areas with coexpression of PR and ERα. Additionally, when progesterone and estradiol are treated together to primary disassociated VMH cells in vitro, physiological concentrations of progesterone counteract estradiol-induced elevation of ERα expression, whereas progesterone treatment alone upregulates ERα expression [α expression by progesterone. PR agonist can increase expression of reporter gene in the cells artificially expressing PR or ERs and transfected with vectors containing ERE but not PRE in their promoters [α expression increases after OVX in many species, which could be at least partially due to the removal of progesterone by OVX and thus release inhibition of progesterone on ERα expression [α expression, while when cotreated with estrogens, progesterone downregulates ERα expression.The theory of progesterone regulation on ER the CNS . Blauste the CNS  have reppression  , a type of nuclear receptor that shares similar genomic signaling mechanism as ER and PR. Briefly, upon ligand binding, AR dimerizes and binds with specific DNA motifs androgen response element (ARE) in its target genes . In maleα mRNA in the MPOA [α expression in the CNS is inhibited by androgens. Findings by Wu and Gore support this notion and demonstrate that, in the MPOA, ERα cell density is substantially higher in vehicle-treated compared with testosterone-treated castrated rats of both young and old ages [α cell density but to a lesser extent [α expression in females. Testosterone treatment attenuates ERα mRNA expression in the VMH and ARC [α expression in the hypothalamus is greater in females than in males [Castration and thus reduced androgen levels have been reported to increase ERthe MPOA , 184 andthe MPOA  of male the MPOA , suggestold ages . Additior extent . Further and ARC  and in t and ARC  in both in males , increasα expression in the ovaries of female rats [α expression in the hippocampus of male mice [α expression in the hypothalamus changes in females with high androgen levels.Similarly both men and women could suffer from various forms of metabolic dysfunction and abnormalities due to androgen imbalance, including hypoandrogenism in aging men , 188 andale rats  and decrale mice . It is nα expression: through its aromatization to estradiol or through binding to ARs and inducing genomic changes in AR and ERα coexpressing neurons. The former possibility has been tested by DonCarlos et al., as aromatizable androgen testosterone but not nonaromatizable androgen dihydrotestosterone downregulates the levels of ER mRNA in the POA [There are two possible mechanisms through which androgen treatment may regulate ER the POA , confirmSex differences exist in the prevalence of obesity and related metabolic diseases . Sex steα [α is abundantly located in the MPOA, the VMH, and the ARC of the hypothalamus [α expression in the CNS. In this review, three understudied areas are identified that await further investigation.Recent basic, translational, and clinic studies have greatly advanced our understanding of the mechanism underlying estrogenic effects. Estrogens utilize both genomic and nongenomic signaling mechanisms for estrogenic effects in the central and peripheral systems, primarily via ERα . ERα is thalamus , 81, 104α expression in the hypothalamus (α expression in the hypothalamus. Such effects would disappear or even become opposite if the paradigm and/or dosage are changed. Thus, dosages and combination of hormones are critical components for safe and effective hormone replacement therapies. Second, the importance of membrane steroid receptors is evident. The relationship between genomic and nongenomic estrogenic actions via respective nuclear and membrane-bound estrogen receptors, especially in in vivo physiology and biological functions, is uncertain, due to the fact that limited research has been done. The time courses of genomic and nongenomic estrogenic effects are dramatically different [α being dominant in astrocytes and ERβ being dominant in microglia. Future in vivo studies are needed to assess their physiological function in regulating reproduction and metabolism.First, inconsistency exists in the literature regarding the regulation of estrogen receptors by manipulation of sex hormones. The discrepancy exists in the literature due to different model systems, dosages of hormones, and experimental paradigms being used for testing. It is necessary to gain a more thorough understanding of estrogenic action in order to improve hormone therapeutics in humans. The consensus is that, within the physiological ranges, estrogens or progestogens alone increase ERthalamus , whereasifferent . Underst


We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China’s population-weighted average exposure to PM2.5 was 52 μg/m3. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7–2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO Air pollution is a problem for much of the developing world and is believed to kill more people worldwide than AIDS, malaria, breast cancer, or tuberculosis –4. Airbo2.5), particulate matter < 10 microns (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Provincial governments perform air quality monitoring at 600 additional locations that are not yet integrated into the national system. Previous studies of regional scale air pollution have generally relied on satellite data . This is equivalent to 4 thousand deaths / day or 17% of all deaths in China. Additional details appear in The conversion of pollution concentrations to mortality is complicated. We adopt the framework  develope2.5, some large population centers  emit less than half the PM2.5 of others. Among northeastern cities, Beijing has relatively low emissions except for NO2. Low SO2 fluxes may indicate cities that benefit from lower coal usage or better smokestack pollution controls. Compared to natural gas, coal produces 150 to 400 times more PM for the same energy delivered [Though most of China is subject to potentially harmful levels of PMelivered ,35. Chinelivered , potentiThe methods of this study should be applicable to air quality monitoring in other regions of the world. However, these techniques require an extensive air-quality monitoring network with frequent updates (e.g. hourly), and such networks presently exist in only a few places. We hope that other countries will follow China’s lead and provide both extensive and transparent real-time air quality monitoring.S1 TextAdditional discussion of methods, data handling, and validation.(DOCX)Click here for additional data file.S1 TableProvides pollution concentration and flux data broken down by geographic regions within China. Also provides the details of the associated mortality calculation.(XLSX)Click here for additional data file.S2 TableSummary of the empirically determined correlation vs. distance functions for the five pollutants discussed in this study and the effective residence time in the atmosphere of their pollutant plumes in the absence of rain or snow. The correlation functions generally consist of a short range component (influenced largely by source distributions) and a long-range component (influenced by weather patterns). The effective pollutant plume lifetime shows the value empirically estimated for this study. Ranges in brackets indicate alternative lifetimes making different assumptions as discussed in the Supplemental Methods .(DOCX)Click here for additional data file.S1 Movie2.5 concentrations, as inferred by this study, across Eastern China during the four month study interval. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts based on 24-hour exposure. Green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups”, red is “Unhealthy”, purple is “Very Unhealthy”, and dark red is “Hazardous”. Concentrations higher than the upper limit of the maximum EPA classification are shown in gray.Movie showing the evolution of PM(MP4)Click here for additional data file.S1 Fig(Left) Average wind speed pattern across China and surrounding areas during the period of this study according to Global Forecast System data at 80 m altitude. Arrow lengths are proportional to wind speed and the legend indicates the size of arrow consistent with a 10 km/hr average wind speed. (Right) Percentage of hours for each grid cell where rain was experienced during the study period based on observations from the Tropical Rainfall Measurement Mission satellite data. Maps were prepared with MATLAB with political boundaries from the Database of Global Administrative Areas (GADM version 2).(TIF)Click here for additional data file.S2 FigSummary of empirically estimated correlation vs. distance functions for the five pollutants studied. Same values as (EPS)Click here for additional data file.S3 Fig2.5 data from stations in this study. Brighter colors indicate more frequently occurring combinations of R-value and distance, and are normalized for the abundance of comparisons at each distance. The red curve indicates the correlation vs. distance model fit to this data and described in Heat map of correlation vs. distance generated by all possible pairwise comparisons of PM(EPS)Click here for additional data file.S4 Fig2.5 source fluxes for different assumed values of the effective plume lifetime. The upper right panel corresponds to the data reported in the main paper, and has a lifetime chosen such that 5% of the field area is allowed to be negative Click here for additional data file.S5 Fig2.5 concentrations (top) and fluxes (bottom) during the first half of the sampling period (left column) and the second half of the sampling period (right). PM2.5 concentrations are presented using the same health impact associated color scheme as Maps comparing PM(TIF)Click here for additional data file.S6 Fig2.5 concentrations (top) and fluxes (bottom) were repeated in full for both groups. These independent subsamples result in similar concentration and flux patterns, implying the reconstructions are likely to be stable with respect to data selection.The collection of available stations was randomly assigned to two groups, and the analyses for PM(TIF)Click here for additional data file.S7 Fig2.5 concentrations across China. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts. Based on 24-hour exposure green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups” and red is “Unhealthy”.Larger version of panel from (EPS)Click here for additional data file.S8 Fig2.5 source fluxes across China.Larger version of panel from (EPS)Click here for additional data file.S9 Fig10 concentrations across China. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts. Based on 24-hour exposure green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups” and red is “Unhealthy”.Larger version of panel from (EPS)Click here for additional data file.S10 Fig10 source fluxes across China.Larger version of panel from (EPS)Click here for additional data file.S11 Fig2 concentrations across China. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts. Based on 24-hour exposure green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups” and red is “Unhealthy”. The limited coverage of this map is a consequence of the short correlation length for SO2 Click here for additional data file.S12 Fig2 source fluxes across China.Larger version of panel from (EPS)Click here for additional data file.S13 Fig2 concentrations across China. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts. Based on 24-hour exposure green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups” and red is “Unhealthy”.Map of NO(EPS)Click here for additional data file.S14 Fig2 source fluxes across China.Larger version of panel from (EPS)Click here for additional data file.S15 Fig3 concentrations across China. Concentrations are shown using color gradients and contour lines; the colors  represent US EPA qualitative health impacts. Based on 24-hour exposure green is “Good”, yellow is “Moderate”, orange is “Unhealthy for Sensitive Groups” and red is “Unhealthy”.Larger version of panel from (EPS)Click here for additional data file.S16 Fig3 source fluxes across China.Map of O(EPS)Click here for additional data file.


The 45-fold and 2500-fold changes were due to patches of elevated and patches of reduced viral and bacterial abundance. The bacterial and viral hotspots were found over single and multiple sample points and the two groups often coincided whilst the coldspots only occurred over single sample points and the bacterial and viral abundances showed no correlation. The total mean abundances of viruses strongly correlated with bacteria  for all three microplates (n = 1350). Spatial autocorrelation analysis via Moran’s I and Geary’s C revealed non-random distributions in bacterial subpopulations and random distributions in viral subpopulations. The variable distributions of viral and bacterial abundance over centimetre-scale distances suggest that competition and the likelihood of viral infection are higher in the small volumes important for individual cell encounters than bulk measurements indicate. We conclude that large scale measurements are not an accurate measurement of the conditions under which microbial dynamics exist. The high variability we report indicates that few microbes experience the ‘average’ concentrations that are frequently measured.The marine sediment-water interface is an important location for microbially controlled nutrient and gas exchange processes. While microbial distributions on the sediment side of the interface are well established in many locations, the distributions of microbes on the water side of the interface are less well known. Here, we measured that distribution for marine virio- and bacterioplankton with a new two-dimensional technique. Our results revealed higher heterogeneity in sediment-water interface biomass distributions than previously reported with a greater than 45– and 2500-fold change cm Marine ecosystems are dependent on the microbial loop to carry out critical marine biogeochemical processes such as carbon and nitrogen fixation et al.−1There is growing awareness of deficiencies in this mean field approach for productivity, biomass and process estimates Hotspots are areas of high microbial abundance which are believed to result from interactions between microbes and organic matter The presence of these hotspots and coldspots are the units of microscale patchiness. The lower size limit of patches is controlled by the Batchelor scale, which is the smallest scale that nutrient gradients can occur before being dispersed by diffusion et al.et al.Although hotspots are a ubiquitous feature of microbial distributions they are often vaguely defined in studies, primarily being classified as patches of high microbial abundance that ‘exceed’ or are ‘elevated above’ background variation across one or two sampling points −1, with the highest concentrations found 1.5 cm from the sediment-water interface et al.−1 in total virus and total bacteria. However, due to one-dimensional sampling and lack of replication, determining whether these changes in abundance were single or multiple point maxima or surface gradients was unknown.As well as hotspots and coldspots, surface gradients are a common pattern seen in microbial distributions, and are seen when bacterial and viral abundance is highest at an interface and then dissipates into the water column as the distance from the interface increases. These surface gradients arise from the sinking of organic matter and its incorporation into the benthos followed by degradation and transformation by microbes leading to high nutrient concentrations and hence high bacterial and viral concentrations directly above the sediment-water interface et al.et al.et al.The null hypothesis tested in this study was that patchiness has no effect on mean biomass estimates and therefore the mean field approach is an accurate estimate of biomass. To test this hypothesis, two-dimensional sampling with extensive replication was used which builds upon previous work by Seuront th of July 2011 at 11∶20 am; the temperature of the water was 12.3°C with a pH of 9.4, a dissolved O2 level of 14.3 ppm and a conductivity value of 40.7 ppt.Samples were collected from Saint Kilda mangroves and Port Noarlunga in South Australia. These environments were chosen to build on previous work using these sites th of July 2011 at 10∶00 am; the temperature of the water was 11.5°C with a pH of 8.5, a dissolved O2 level of 14.3 ppm and a conductivity value of 41.5 ppt.The St Kilda site was characterised by hypersaline lagoons less than 1 m deep in dense mangrove forests. Previous studies have shown St Kilda mangroves host a microbial community that is characterised by highly productive microbial mats and sulphur-oxidising bacteria Environmental conditions data was collected using a Hydrolab Datasonde 4a sensor. Permission to access the sampling site from the St Kilda mangrove trail site was provided by the National Parks and Wildlife Rangers of the City of Salisbury Council. Specific permission to access the Noarlunga sampling site was not required. The field studies did not involve endangered or protected species.2. This allowed the collection of 8 vertical profiles per microplate, which each consisted of 12 sampling points  using a pipette and stored at 4°C in the dark for 15 minutes. Samples were then snap frozen in liquid nitrogen and stored in a −80°C laboratory freezer 5 beads ml−1 in each sample. Measured flow cytometry parameters were normalised to the fluorescence and concentration of these beads Many previous studies have used flow cytometry for the enumeration of bacteria and viruses due to its rapidity and accuracy Flow cytometry was conducted on a FACSCanto II flow cytometer (BD) equipped with a blue , red  and violet  laser. Forward-angle light scatter (FSC), right-angle light scatter (SSC) and green fluorescence (SYBR I) were acquired for each sample and a phosphate-buffered saline (PBS) solution was used as a sheath fluid. The flow cytometer settings were normalised to fluorescence and bead concentration The resulting cytograms, density plots and histograms acquired from flow cytometry were exported as listmode files and analysed using Win Midi 2.9 (Joseph Trotter) to enumerate the bacterial and viral populations present For this study, rank abundance graphs were used to discriminate between hotspots, coldspots and background values. The background was determined according to Weibe −1.Surfer 10  was used to create two dimensional contour plots of the spatial distribution of viral and bacterial populations. A minimum contour interval value of at least 1000 was chosen when constructing plots due to this being larger than the maximum FACSCanto II flow cytometer error observed. This error refers to the background noise seen within triplicate blank control samples during flow cytometric analysis. This value is conservative as the maximum machine error was less than 36 events µlCorrelations were determined via Pearson’s coefficient with the α of 0.05 being reduced by sequential Bonferroni I spatial autocorrelation statistics test within CrimeStat 3.3  was used to determine the level of randomness within each population. The equation is as follows:i and Xj represent the variable values at a specific locations, i and j . Geostatistical analysis requires each lag distance to have ≥30 pairs of sample points to enable statistical reliability; therefore sample intervals with less than this were not included in the correlograms Geary’s C statistics All terms are the same as Moran’s I. For Geary’s C, the values range from 0 to approximately 2, however there is no definitive upper limit Flow cytometric analysis revealed three distinct bacterial subpopulations and two distinct viral subpopulations at both sites which were comparable to those identified previously in marine systems 6 cells ml−1 , whereas it was 1.3×106 cells ml−1 for the VLP 2 subpopulation   compared to 6.1×105 cells ml−1  for VLP 2  . Hence, or VLP 2 . Hence, 5 cells ml−1  compared to total mean abundances of 3.1×105 cells ml−1  for HDNA 1 and 2.7×105 cells ml−1  for HDNA 2  for LDNA, 2.1×105 cells ml−1  for HDNA 1 and 5.9×105 cells ml−1  for HDNA 2. Hence, the total mean abundance of HDNA 1 was 2-fold less than the total mean abundances of LDNA and HDNA 2  and showed almost identical patterns between the total mean bacterial and total mean viral population , as wellTo determine the presence of hotspots and coldspots in the viral and bacterial distributions, rank abundance graphs were created . In someTwo-dimensional contour plots of both sampling sites showed heterogeneous distributions with patches or hotspots of high abundance that occurred over single or multiple sample points as well as gaps or coldspots of low abundance that occurred only over single sample points .2 particles ml−1 below a background of 1.07×106 particles ml−1 . For these bacterial subpopulations z scores under the assumption of randomisation were much larger than the cut-off point of +1.96, with z scores ranging from 2.5 to 3.8. All the viral subpopulations at Noarlunga and St Kilda had non-significant Moran’s I values.I values  which was more similar to the Geary’s C values of the randomly distributed subpopulations that showed local association of 0.85–0.89. According to Sokal −1 in bacteria and viruses.Taking into account the study by Sokal The microscale spatial heterogeneity of bacterial and viral populations in this study would be underestimated, by more than 3 orders of magnitude in some cases, had the mean field approach been employed −1 found compared to the previously reported 1.5-fold change in abundance cm−1 in bacteria −1 in viruses −1. This large fold-change found cm−1 would have been missed if lower resolution sampling was used. This high level of microscale heterogeneity highlights the importance of analysing microbial spatial distribution on the level of what individual cells experience rather than using a ‘mean field’ approach. In addition, this high level of microscale heterogeneity also illustrates the necessity and applicability of the novel two-dimensional high resolution sampler used in this study which was capable of collecting eight vertical profiles. The use of this sampler allowed the discrimination of single and multiple point hotspots and/or surface gradients, which was not possible in past studies as one-dimensional samplers were used.Spatial heterogeneity in microbial abundance has been found within the 11.3 cm directly above the sediment-water interface at Noarlunga estuary and St Kilda mangroves. This heterogeneity was present in the form of single and multiple point hotspots at Noarlunga and hotspots and surface gradients at St Kilda which resulted in high levels of viral and bacterial abundance being found near the sediment-water interface. These patterns in microbial distributions have been seen in previous studies but to a lesser extent. At St Kilda, gradients in bacterial and viral abundance were much higher than previously reported with a 45- and 10.5-fold change cmI and Geary’s C reveal that spatial autocorrelation amongst bacterial subpopulations at St Kilda show a positive to negative trend 5.9 cm from the sediment-water interface, indicating a mechanism, whether this be sampling alias or particulate resuspension, is present leading to this clustering and dispersal of bacterial populations.Bacterial subpopulations were found to be randomly distributed at Noarlunga and non-randomly distributed at St Kilda with reasons for this finding relating to the low energy-nature of St Kilda allowing the formation of sediment-water interface nutrient and microbial surface gradients. Viral subpopulations only showed random distributions at both sites. Moran’s Despite hotspots being ubiquitous features of microbial distributions, past studies lacked a universal definition. They qualitatively classified patches as regions of high microbial abundance that ‘exceed’ or are ‘elevated above’ background values Figure S1Collection of Vertical Profiles. The use of an 8×12 96-well microplate allowed the two-dimensional array of 8 vertical profiles  per microplate which consisted of 12 sampling wells per vertical profile.(TIF)Click here for additional data file.Figure S2Comparisons of bacterial and viral subpopulations and viral subpopulations at Noarlunga via vertical depth profiles. Bacterial and viral subpopulations of all three microplates (n = 270). A Total mean LDNA, HDNA 1 and HDNA 2; B Total mean VLP 1 and VLP 2. Error bars represent the 95% confidence intervals obtained from each subpopulation of all three replicates (n = 12).(TIF)Click here for additional data file.Figure S3Mean vertical profile of VLP 1 and LDNA, microplate one at Noarlunga (n = 90). Error bars represent the 95% confidence intervals obtained from one replicate (n = 12).(TIF)Click here for additional data file.Figure S4Adjacent orthogonal gradient distributions of bacteria and viruses as rank abundance. St Kilda: A VLP 1, B HDNA 2. Noarlunga:C VLP 2, D HDNA 2. Each distribution is ordered as a rank abundance. In each case the first rank was used for the 2 dimensional plots. A logarithmic trend-line was the best fit for each distribution, with the equations and r2 for A y = −4×106 ln(x)+2×107 (R2 = 0.97), B y = −7×104 ln(x)+4×106 (R2 = 0.99), C y = −2×106 ln(x)+8×106 (R2 = 0.98), D y = −8×104 ln(x)+4×105 (R2 = 0.92). The p values are <<0.05 in all cases.(TIF)Click here for additional data file.Table S1Mean viral abundances per microplate at Noarlunga and St Kilda.(DOCX)Click here for additional data file.Table S2Mean bacterial abundances per microplate at Noarlunga and St Kilda.(DOCX)Click here for additional data file.Table S3Comparison of the Moran’s I values and Geary’s C values obtained for each bacterial subpopulation at Noarlunga and St Kilda.(DOCX)Click here for additional data file.Table S4Comparison of the Moran’s I values and Geary’s C values obtained for each viral subpopulation at Noarlunga and St Kilda.(DOCX)Click here for additional data file.


PPARα, PPARα, TGFβ1, Collagen 1A1 and PDGFα genes. Furthermore, we assessed the impact of age, gender and anatomical location within the liver on patterns of DNA methylation in the same panel of genes.Chronic liver injury can lead to the development of liver fibrosis and cirrhosis but only in a minority of patients. Currently, it is not clear which factors determine progression to fibrosis. We investigated whether DNA\methylation profile as determined by pyrosequencing can distinguish patients with mild from those with advanced/severe fibrosis in non-alcoholic liver disease (NAFLD) and alcoholic liver disease (ALD). To this end, paraffin-embedded liver biopsies were collected from patients with biopsy-proven NAFLD or ALD, as well as paraffin-embedded normal liver resections, genomic DNA isolated, bisulfite converted and pyrosequencing assays used to quantify DNA methylation at specific CpGs within DNA methylation at specific CpGs within genes known to affect fibrogenesis distinguishes between patients with mild from those with severe fibrosis in both NAFLD and ALD, although same CpGs are not equally represented in both etiologies. In normal liver, age, gender or anatomical location had no significant impact on DNA methylation patterns in the liver.DNA methylation status at specific CpGs may be useful as part of a wider set of patient data for predicting progression to liver fibrosis. Chronic liver injury of any aetiology can lead to development of liver fibrosis and ultimately cirrhosis . ProgresEpigenetic processes play a prominent role in a number of complex diseases and may mediate the effects of environmental factors including diet and alcohol ,6. This DNA methylation occurs at the cytosine base within a cytosine-guanine dinucleotide (often referred to as CpG) where DNA methyltransferase catalyses transfer of a methyl group to the fifth carbon atom within the cytosine ring to form 5-methylcytosine . In manyTGFβ1, Collagen 1A1 and PDGFα may be more highly methylated in individuals that remain fibrosis free, whereas anti-fibrogenic genes such as PPARα and PPARδ may have higher DNA methylation levels in individuals experiencing fast progress towards severe fibrosis. Ascertaining DNA methylation in patients is possible only via sampling of liver tissue, either by percutaneous liver biopsy, by direct sampling of the organ during liver resection or by sampling of freshly explanted, cirrhotic liver.Although DNA methylation analysis has not been used to predict liver injury outcome, there have been several studies indicating that changes in locus-specific DNA methylation can affect insulin resistance and severity of non-alcoholic fatty liver disease (NAFLD), as well as predict the response to a low-calorie diet -16. GiveLiver biopsy remains the gold standard for assessment of aetiology and fibrosis staging, although there are drawbacks to this method including its inherent invasiveness and significant sampling variability . In thisWe obtained liver tissue from paraffin-embedded percutaneous needle biopsies carried out in eight NAFLD patients with minimal fibrosis and nine NAFLD patients with advanced fibrosis Tables  and 2. TTGFβ1, Collagen1A1 and PDGFδ) are less methylated in severe NAFLD whereas anti-fibrogenic genes (such as PPARα and PPARδ) might bear higher DNA methylation in the same cohort. To determine if NAFLD-fibrosis severity influences DNA methylation of specific fibrosis-related genes, we isolated genomic DNA from percutaneous needle biopsies in all patients and quantified methylation of specific CpGs within gene regulatory regions of PPARα, PPARδ, TGFβ1, Collagen1A1 and PDGFα as shown in Figure PPARα promoter had significantly higher DNA methylation in the severe NAFLD group (10.9% DNA methylation) when compared to the mild NAFLD patients  had a mean age of 63.3 ± 8.4 years and a 10/7 split of male to female, whereas the ten ALD patients had a younger mean age of 46.3 ± 10.08 years with a 6/4 male to female split , PPARδ (CpG2), TGFβ1 (CpG2) and PDGFα (CpG3) genes that showed differential methylation in either the NAFLD and/or ALD cohort  and PDGFα (CpG3)  and PPARδ (CpG2) show considerably less methylation in the same group  and PPARδ (CpG3) had less methylated DNA in normal livers, which is in line with the results obtained from mild NAFLD. This poses the interesting possibility that the methylation status of a panel of genes may predict predisposition towards development of liver fibrosis irrespective of the liver injury, a very important factor in disease progression that is currently impossible to predict. If a sufficient number of genes harbour differential DNA methylation at particular CpGs, it may be possible to generate an algorithm that can predict which patients are likely to progress on to a severe phenotype versus those that will remain fibrosis free despite the presence of liver injury. As an example, the combination of higher methylation at TGFβ1 (CpG2) and PDGFα (CpG3) with lower level at PPARα (CpG3) and PPARδ (CpG2) may be sufficient to stratify a patient into a rapid progressor cohort in need of continued monitoring or specific therapy. However, the numbers of patients included in our current study are not sufficiently large to carry out a high-powered calculation. If it proved possible to stratify patients into progressors and non-progressors on the basis of DNA methylation fingerprint at defined loci, such knowledge could be useful in defining an appropriate clinical trial cohort, for testing of anti-fibrotic drugs, for example. Such a cohort would be more informative because the drug would be tested only in those patients likely to progress onto fibrotic disease with time.Approximately half of all human gene promoters have dense clustering of CpG dinucleotides, known as CpG islands. As a general rule, hypermethylation of CpG islands is associated with gene repression, whereas hypomethylation is permissive to transcription . As suchIn this study, we have chosen to compare ALD with normal human liver and mild NAFLD with severe NAFLD. To ensure that the chosen cohorts were comparable, ALD livers were always analysed with ‘normal’ livers at the same time and on the same pyrosequencing machine, whereas mild NAFLD samples were analysed with severe NAFLD on a separate machine. The two models, however, were never analysed together at the same time. As such, it is possible that variable factors may exist between the two cohorts that would alter absolute measured values without amending the ratio between the compared patient groups. Hence, the data have been reported separately for ALD and NAFLD cohorts Figures  and 2.A limitation of our study is that the data have been obtained from cross-sectional analyses. It is possible that DNA methylation status changes with disease progression and that the differences we observe are merely a reflection of the liver fibrosis grade rather than a useful predictive or prognostic measure. This question can only be answered using a cohort of patients with progressive disease in whom serial, longitudinal biopsies are collected. In absence of such longitudinal data, we still have to rely on cumulative knowledge from multiple studies, which suggest that DNA methylation does play a role in liver fibrosis as well as being an underpinning cause of other associated comorbidities and complications.It is also worth noting that NAFLD patients will have on-going liver injury at the time of biopsy . In contrast, ALD patients’ injury will turn on and off depending on the extent of their drinking and so an additional factor adding variability will be the pattern of alcohol intake, despite the fact that most of the ALD patients used in this study were cirrhotic. Incidentally, these issues will also apply to the patients with ‘normal’ liver tissue; that is, it is not clear whether any of them were occasional to moderate drinkers? Thus, if DNA methylation is influenced by grade of inflammation, then NAFLD patients will have more stable patterns of disease than ALD patients. This would also offer a possible explanation for the inability to truly replicate DNA methylation disease patterns in ALD.PPARα (CpG3), PPARδ (CpG2), TGFβ1 (CpG2) and PDGFα (CpG3), all of which we had shown to be differentially methylated according to disease state . All samples were collected and used, subject to patient’s written consent.Seventeen patients with biopsy-proven NAFLD and ten patients with histologically proven cirrhotic ALD  from the Freeman Hospital, Newcastle upon Tyne, UK, were included in the study. NAFLD diagnosis was made with abnormal serum transaminases, fatty liver on ultrasound and absence of excess alcohol intake , viral hepatitis (hepatitis B and C and HIV), hereditary hemochromatosis, Wilson’s disease, autoimmune hepatitis, α1 antitrypsin deficiency and drug-induced liver injury. ALD diagnosis was made after the exclusion of other diagnoses as above but including a history of alcohol excess defined as >60 g/day for males and >40 g/day for females. Nineteen non-fibrotic liver tissue samples were obtained from patients who underwent hepatic resections for colorectal cancer liver metastasis. Clinical and laboratory data were collected at the time of biopsy, resection or transplant including basic anthropometrics so that BMI could be calculated. Patients were identified as having type 2 diabetes (T2DM) if they were receiving dietary, oral hypoglycaemic drug or insulin treatment for diabetes or had fasting blood glucose >7.0 mmol/L or glucose >11.1 mmol/L following an oral glucose tolerance test. Percutaneous liver biopsies were performed using a Menghini needle or an 18G BioPince liver biopsy system . Liver specimens were assessed by two experienced hepatopathologists. Histological scoring was performed according to the NIH NAFLD Clinical Research Network criteria for NAFLD biopsies . Mild diDNA was extracted from formalin-fixed paraffin-embedded (FFPE) liver biopsy specimens in the ALD and NAFLD cohort. Two specimens in the ALD cohort were extracted from FFPE cirrhotic explant livers and the rest were needle biopsies. Non-fibrotic liver samples were selected >5 cm away from tumour margin in resected liver tissue. Three curls with the thickness of 10 μm were cut from each paraffin block. FFPE tissues were dewaxed by treating with Clearene  and serially dehydrated in 100% then 70% ethanol. DNA was extracted using QIAamp DNA Micro Kit . Tissues were lysed at 56°C overnight, treated at 70°C for 30 min to remove crosslinks that were formed by formalin, and the lysate was processed and transferred to spin columns as per manufacturer’s instructions. Genomic DNA was quantified using NanoDrop .Sodium bisulfite conversion was performed using EZ DNA Methylation Gold TM Kit . A 2 μg of genomic DNA was bisulphite modified by incubating at 98°C for 10 min and 64°C for 2 h and 30 min. Product was transferred into columns; desulphonated and washed according to manufacturer’s protocol and eluted in 10 μl of elution buffer. A 2 μl of bisulphite modified genomic DNA (400 ng) was amplified in a PCR mix containing 2 μl of forward and reverse primer, 12.5 μl of HotStarTaq Master Mix Kit  and 10.5 μl of water. Amplification of DNA was performed in a thermocycler according to the following PCR conditions: one cycle at 95°C for 6 min, followed by 40 cycles of 95°C for 30 s, annealing temperature of 55°C to 59°C (depending on primer pair) for 30 s and 72°C for 30 s, followed by one cycle at 72°C for 30 s.Methylation of specific cytosines within CpG dinucleotides was quantified by pyrosequencing using a Pyromark Q96 MD (Qiagen) instrument. PCR and sequencing primers were obtained from predesigned assays; HsCOL1A101_PM PyroMark CpG assay , HsTGFB101_PM PyroMark CpG assay , HsPPARA01PM PyroMark CpG assay , HsAC147651.101 PM PyroMark CpG assay  and HsPPARD01PM PyroMark CpG assay . A 10 μl of biotin-labelled PCR product was used in each well and combined by streptavidin-coated sepharose beads, washed in 70% ethanol, denatured in 0.01% sodium azide and washed in a wash buffer . Sequencing primers were annealed to DNA product at 80°C. Samples were run in duplicate. Assay efficiency was validated by unmethylated and methylated DNA . CpG methylation data was analysed by Pyro Q-CpG software 1.0.6. Levels of DNA methylation have been measured independently in the compared cohorts. DNA methylation was measured in ALD livers alongside ‘normal’ livers within the same run using the same pyrosequencing machine to ensure that absolute values of DNA methylation measured were always the same for the specified cohort. DNA methylation in the mild and severe NAFLD samples were also analysed in the same run using a different pyrosequencing machine. The two models were never analysed together at the same time.U test or chi-square test where *P <0.05, **P <0.01 or ***P <0.001 were considered significant.Data are expressed as means ± SEM. GraphPad Instat was used to perform Mann-Whitney


Torso-based vibrotactile feedback has been shown to improve postural performance during quiet and perturbed stance in healthy young and older adults and individuals with balance impairments. These systems typically include tactors distributed around the torso that are activated when body motion exceeds a predefined threshold. Users are instructed to “move away from the vibration”. However, recent studies have shown that in the absence of instructions, vibrotactile stimulation induces small (~1°) non-volitional responses in the direction of its application location. It was hypothesized that an attractive cuing strategy  could improve postural performance by leveraging this natural tendency.Eight healthy older adults participated in two non-consecutive days of computerized dynamic posturography testing while wearing a vibrotactile feedback system comprised of an inertial measurement unit and four tactors that were activated in pairs when body motion exceeded 1° anteriorly or posteriorly. A crossover design was used. On each day participants performed 24 repetitions of Sensory Organization Test condition 5 (SOT5), three repetitions each of SOT 1–6, three repetitions of the Motor Control Test, and five repetitions of the Adaptation Test. Performance metrics included A/P RMS, Time-in-zone and 95 % CI Ellipse. Performance improved with both cuing strategies but participants performed better when using repulsive cues. However, the rate of improvement was greater for attractive versus repulsive cuing.The results suggest that when the cutaneous signal is interpreted as an alarm, cognition overrides sensory information. Furthermore, although repulsive cues resulted in better performance, attractive cues may be as good, if not better, than repulsive cues following extended training. Vibrotactile feedback has been shown to improve postural performance during quiet and perturbed stance in healthy young adults –4, olderEight healthy older adults  were recruited to participate in two non-consecutive days of computerized dynamic posturography testing . This study was reviewed and approved by the University of Michigan Internal Review Board and all subjects gave written informed consent in accordance with the Declaration of Helsinki. Subjects wore a polyester t-shirt and thin athletic socks. Four tactors  were mounted in a belt with Velcro and placed over the shirt on the torso over the right and left internal oblique (front) and right and left erector spinae (back) locations Fig.  at the lPrior to testing, subjects were given a minimum of 5–10 min, but not more than 15 min, of active training on how to interpret and respond to the vibrotactile feedback during balance testing. Participants were told to “initiate a postural correction (toward or away) when you feel the vibration”. Participants performed the same set of tasks on each testing day. The experimental protocol included three distinct parts: 1) 24 repetitions of Sensory Organization Test (SOT) condition 5 ; 2) three repetitions each of SOT 1 – 6; 3) three repetitions the Motor Control Test  and five repetitions of the Adaptation Test . Data from parts 1 and 3 are included in the present study.th percentile confidence interval ellipse fit to the sway data (95 % CI Ellipse). For the ADT and MCT tests additional performance metrics included maximum A/P tilts and the percentage of trials in which subjects returned to the no feedback zone after the perturbation within the collected time.  Cuing strategy preferences were collected using a questionnaire.Performance metrics included root mean square (RMS) sway in the A/P direction (A/P RMS), percentage time during each trial within a 1° no feedback zone (Time-in-Zone), and a 95nd order Butterworth filter with a cutoff of 10 Hz [Data were processed using Matlab . IMU raw data were low-pass filtered using a 2of 10 Hz . The natAt the end of the second testing day user preferences were collected with a six-question survey. Questions included two Likert-style questions  and four fill in the blank questions .p < 0.001); A/P RMS values were also significantly smaller (~1°) on Day 2 versus Day 1 (p < 0.001) with minimal carry over effects between the two days (p = 0.9). The 95 % CI Ellipse values were significantly smaller (~1 deg2) for repulsive versus attractive cues (p = 0.004) and significantly smaller (~1 deg2) on Day 2 versus Day 1 (p = 0.002) with minimal carry over effects (p = 0.2). Subjects spent slightly more time (~1 %) in the no feedback zone (Time-in-Zone) when using repulsive cues (p < 0.001) and slightly more time (~1 %) in the no feedback zone on Day 2 compared to Day 1 (p = 0.003) with minimal carry over effects (p = 0.6). The slope of the regression analysis fit to the logarithmically transformed A/P RMS data (not shown) was steeper  for attractive versus repulsive cues, however it was not significant (p = 0.2). The slope of the fit to the logarithmically transformed Time-in-Zone data (not shown) was steeper  for attractive versus repulsive cues (p = 0.002). The slopes of the fits to the logarithmically transformed A/P RMS and 95 % CI Ellipse data significantly decreased as a function of trial (p = 0.001) regardless of day or cuing strategy (no interaction effects).According to all metrics, postural performance for SOT 5 was better with repulsive cuing compared to attractive cuing Fig. . Sway inp = 0.001). Also, the percentage of trials in which subjects returned to the no feedback zone after the perturbation was significantly larger for repulsive cuing versus attractive cuing .Peak A/P tilts for the MCT and ADT perturbations Fig.  were sigFrom the survey questionnaire, five subjects preferred repulsive cuing and two preferred attractive cuing (one had no preference) and all eight subjects said that they learned to use repulsive cuing the quickest.Performance improved with both cuing strategies for all conditions ; however, participants performed better when using repulsive cues regardless of whether they completed repulsive cuing trials on the first or second day of testing. The regression analysis demonstrated that participants’ rates of improvement were greater for attractive versus repulsive cuing for the A/P RMS sway and Time-in-Zone metrics.The results do not fully support our initial hypothesis based on the benefit of congruence between sensory and cognitive information. It appears that when a sensory signal is attributed an “alarm” significance, the corresponding messages are interpreted according to the cognitive assignation and not as the natural feedback. This suggests that avoidance behavior, derived from life experience and/or default sensorimotor pathways as in the withdrawal reflex, prevail over apparently conflicting sensory information. It may be hypothesized that the cognitive system resolves a possible conflict by choosing a low cost (known) solution that may not be optimal in terms of response time. The lower initial performance with attractive cuing, particularly when it is presented on Day 1 (Group 2) may be indicative of a cognitive “incongruence” engendered by the less natural/common requirement of attraction toward a perturbation. Although in the end both strategies led to a similar level of performance, the findings would tend to support the use of a repulsive cuing strategy when time to learn to use a torso-based vibrotactile feedback system is limited. However, based on the steeper slopes of the fits to the logarithmically transformed SOT 5 data (not shown in the figures), performance using attractive cues following a training period may be as good, if not better, than performance using repulsive cues. Future work should involve extended within and across session training protocols.


Here we report high in-plane dielectric permittivity (ε’ ∼2500) and low dielectric loss (tan δ < 0.01) obtained on Bi0.95Y0.05FeO3 films epitaxially grown on SrTiO3 (001) by pulsed laser deposition. High resolution transmission electron microscopy and geometric phase analysis evidenced nanostripe domains with alternating compressive/tensile strain and slight lattice rotations. Nanoscale mixed phase/domain ensembles are commonly found in different complex materials with giant dielectric/electromechanical (ferroelectric/ relaxors) or magnetoresistance (manganites) response. Our work brings insight into the joined role of chemical pressure and epitaxial strain on the appearance of nanoscale stripe structure which creates conditions for easy reorientation and high dielectric response, and could be of more general relevance for the field of materials science where engineered materials with huge response to external stimuli are a highly priced target.BiFeO Multiferroics (MF) are materials which have simultaneously more ferroic properties, such as ferroelectric, antiferrodistortive or magnetic order3 (BFO) is certainly the most investigated due to both polar and magnetic orders coexisting at room temperature, which are of interest for potential applications in nonvolatile memories, spintronics, piezoelectric devices etcTc ≈ 1100 K, BFO transforms from the paraelectric orthorhombic (O) Pnma phase into the rhombohedral (R) R3c ferroelectric (FE) phase. The rhombohedral R3c ground state is characterized by antiphase octahedral tilts about the [111]c directions and ionic displacements from the centrosymmetric positions along the same axis, with pseudocubic lattice parameters a = 3.965 Å and α = 89.4oTN ∼ 640 K BFO has also a G-type antiferromagnetic (AFM) ordering of Fe3+ ionsAmong MF, BiFeOTcAmong the drawbacks of BFO, the small dielectric constant, high dielectric loss and high leakage currents, which are detrimental for its applications in electromechanical devices, must be pointed out. The intrinsic dielectric constant at radio frequencies was measured to be about 30O Pnma) at room temperature while, at lower pressures, several low symmetry (monoclinic) phases are stabilized at room temperatureM) structure for compressive strain93 (STO) (001) substrateset al.There are two main ways to control the phase stability and transition temperatures in BFO: one is through strain engineering and the other is through chemical substitutions. Both aim to induce, by different means  a mixed state, characterized by coexisting phases, in order to improve its dielectric and electromechanical response. The two degrees of freedom, polar displacements and oxygen octahedral tilts which coexist in a wide temperature range, can react differently to chemical substitutions or external parameter variations and thus lead to a multitude of phases. Thus BFO is very sensitive to the application of an external pressure: above 10 GPa the pressure brings the high temperature phase  substituted BFO thin films1517R phase to an O phase, with enhancement in the dielectric constant and piezoelectric coefficient. For example in Bi1−xSmxFeO3 films grown on STO (001) substrates the MPB around room temperature was found for compositions with x ≅ 0.14= Gd3+, Dy3+) the MPB occurred at smaller x values (0.12 for Gd and 0.07 for Dy). In all Bi1−xRExFeO3 compositions it was found that the transformation from the R phase to the O phase at MPB is associated with a nanoscale phase mixture composed of the parent BFO R lattice and lamellar ferroelectric nanodomains with alternating polarizations161−xRExFeO3  compositions at room temperatureThe size of RE ions controls also the stability of the structural phase, as shown by the temperature dependence of the lattice parameters3+) with smaller ionic radius has been chosen. We show that Y-BFO films with thickness about 100 nm display very high in-plane dielectric constant. We relate this huge enhancement to the destabilizing influence of the chemical pressure induced by the small size ion substitution and the peculiar nanostripe formation due to partial strain relaxation. Our results can be of more general relevance for other materials where properties improvement is pursued through the combined role of chemical pressure (doping) and epitaxial strain.We take further this investigation and address the problem of concomitant influence of RE-doping and epitaxial strain on BFO properties. For doping, a RE ion (Y1−xYxFeO3 (Y-BFO) epitaxial thin films with thickness of about 100 nm on STO (001) single crystal substrates. For comparison, pure BiFeO3 (BFO) thin films have been grown also, on the same type of substrates. As further described in the Experimental section, a relatively low growth rate (∼0.1 Å/s) and high deposition temperature (∼700 oC) have been selected for deposition. The deposition conditions have a strong influence on the final lattice parameters and tetragonality of the BFO films. Indeed it has been shown that tetragonal-like BFO films can be grown even on low-mismatch substrates such as STO by controlling the growth rate and deposition temperature202122We have employed pulsed laser deposition (PLD) technique to grow Bi0.95Y0.05FeO3. -a-a- system in Glazer’s notationThe selected composition was Bi0.95Y0.05FeO3 in the context of temperature - average ionic radiusIt is clear that the smaller its radius is the larger is the available space for the tilting of oxygen octahedra in the structure and easier is the appearance of a new structural phase. c has been found to be 4.0042 Å for Y-BFO film, slightly larger in comparison with the BFO film (3.9983 Å). We recall that for Bi0.95Y0.05FeO3 ceramics the pseudocubic lattice parameter is about 3.94 Åc of the Y-BFO films exceeds the bulk pseudocubic lattice parameter, while the in-plane lattice parameter a is about 3.9266 Å, fairly close to the cubic STO substrate lattice parameter (3.905 Å). The tetragonality ratio c/a has been found to be about 1.02, slightly larger in comparison with the value obtained for the BFO film. The difference between the out-of-plane and in-plane lattice constants is attributed to compressive coherent in-plane strain induced by the STO substrate which causes a cell elongation in the out-of-plane direction. In 132627o apart from each other. This indicates the presence of fourfold symmetry with a “cube-on-cube” epitaxial growth on the STO (001) substrate.The degree of in-plane orientation was assessed by the XRD Φ –scans. As shown in The full width at half maximum (FWHM) values of the rocking curve of the (002) Y-BFO diffraction peak is larger in comparison with the (002) diffraction peak of the pure BFO film due to both the monoclinic distortion in the out-of-plane direction and dopant-induced lattice modifications . SimilarL⊥ and the heterogeneous microstrain Bragg reflection ε⊥ are derived from the Williamson-Hall plotsω-2θ scan) directions of (00l) symmetric reflections, by using the following relationThe substitution with Y on the B-site apparently causes a decrease of the in-plane lattice parameter and an increase of c/a ratio with respect to BFO film lattice . In orde2929β cos θ)/λ versus (sin θ)/λ gives the value of the strain from the slope and the vertical coherence length from the ordinate intercept.where λ is the X-ray wavelength, β - the integral breadth  and θ - the Bragg angle. The plot of (ω scan) of the same symmetric (00l) reflections, using a similar Williamson−Hall approach proposed by Mentzger et al.L|| and the tilt angle αtilt could be extracted. All the XRD results are summarized in From the broadening of the rocking curve Comparative HRTEM and stress/strain GPA maps of the Y-BFO and BFO films are shown in There are multiple evidences that, in partially relaxed films, the in-plane strain does not relax to zero but locally oscillates, by different mechanisms, including monoclinic twinning rotationThe additional distortions associated with the presence of nanodomains are related to the elastic bending and rotation of the lattice planes in order to fit the adjacent domains. They result into nanostripes with contrast difference as seen in °.In = 21 finger pairs of length L = 464 μm and width 10 μm. The interspace between fingers is 10 μm, thus the distance D between finger centres is 20 μm. A second set of similar samples but provided with IDE of different dimensions  has been prepared and investigated, in order to rule out the possible influence of IDE characteristics on the measured values.Dielectric spectroscopy measurements have been made on interdigital electrodes (IDE) deposited on top surfaces of the films (10 IDE structures on each sample). A sketch of the IDE structure is shown in the inset in et al.Eq. (2)The in-plane dielectric constant of the Y-BFO thin film capacitors with the interdigital electrode configuration was calculated by using the analytical model derived by Farnell S is the dielectric constant of the substrate, h is the film thickness (h = 100 nm), CK is a constant depending on IDE geometry and Cn is the measured capacitance of the IDE normalized to the finger length (L) and to the number of fingers (2N−1). In the case of IDE patterns with equal finger width and spacing the constant CK = 4.53 pF/m35where εThe Y-BFO dielectric constant and loss values measured on 6 representatives IDE are plotted in The values obtained for the dielectric constant are by far the highest obtained for BFO, bulk or thin film, pure or doped. In order to verify the experimental procedure and the calculation method, measurements have been repeated on IDE electrodes directly deposited on the substrate and the same model has been applied to calculate the dielectric constant. Perfect agreement has been obtained between the measured and the data sheet value of the STO dielectric constant.Dielectric measurements have been made on a second set of Y-BFO films with IDE electrodes with different dimensions and number of fingers and the results are similar which rule out possible effects of geometric configuration on the measurements see . For comWe have analyzed above the effects of strain on the Y-BFO films, which manifest by the appearance of a stripe nanodomain structure with monoclinic symmetry. Stripe-like domain patterns have been identified also in other ferroelectric393+ ions on the microstructure and dielectric response of Y-BFO films, by taking advantage of the reported findings on the universal phase diagram of RE-substituted BFOav) of Bi1−xRExFeO3, as proposed in ref. R3c and paraelectric O Pnma, is the bridging mixed phase formed by nanometer-size antipolar domains in the R3c matrix16av on the dopant concentration x for the different RE ionic radii, including Y3+. The corresponding ionic radii of the trivalent A ions in 12-fold coordination as given in refs Bi (1.36 Å) >RSm (1.28 Å) >RGd (1.27 Å) >RDy (1.24 Å)>RY (1.22 Å). For every Bi1−xRExFeO3 composition the average ionic radius is plotted against x in av is called critical  since it is common for all compositions, marking the transition from the R3c to the Pnma phase. Its significance is related to the difference in the free energy density between these two phases, ΔU (Rav), which is given by the relationship17We believe that this peculiar nanostructure could be at the origin of the observed huge dielectric response. However we need to analyze also the influence of small radius YBFO is the energy density difference for pure BFO and k is a constant. At the critical average ionic radius, Rav = Rav,c, ΔU  = 0.where ΔU= 0.05), represented by the square point on the Y line and by the red square symbol in O Pnma phase is favoured by tilt distortions of the oxygen octahedra which, at their turn, are promoted by small size ions occupying the A-site positions. It has been previously evidenced161718It can be observed that the corresponding compositions are placed within the limits of the MPB region. Moreover, Y-BFO films composition  and misfit strain caused by the epitaxial growth on the STO substrate.We have seen that, in Y-BFO, the conditions for coexisting phases and nanodomain formations are created by Y-doping and epitaxial strain. We can infer therefore that the high dielectric permittivity of the Bi3 films epitaxially grown on SrTiO3(001) by pulsed laser deposition. Our experimental results reveal that the previously evidenced limitations on these values can be surmounted in high quality epitaxial films by promoting the formation of mixed states at nanoscale level. High resolution transmission electron microscopy and geometric phase analysis evidenced nanostripe domains with alternating compressive and tensile strain. Our work brings insight into the combined role of chemical pressure and epitaxial strain on the destabilization of ferroelectric phase and appearance of nanoscale stripe structure.We have obtained very high in-plane dielectric permittivity (ε′∼2500) and low dielectric loss (tanδ<0.01) on Y-doped BiFeO3 (STO) single crystal substrates, from ceramic targets with composition Bi0.95Y0.05FeO3 (Y-BFO) and BiFeO3 (BFO). The thickness of the films was set around 100 nm, in order to preserve the strained condition. The deposition has been made under 13 Pa oxygen partial pressure, by using an ArF excimer laser (193 nm wavelength) with pulse frequency 5 Hz. The laser pulse energy was 20 mJ and the substrate temperature was 700 oC. In these conditions the growth rate was about 0.1 Å/s.The films were grown by pulsed laser deposition on (001) SrTiOX-ray diffraction (XRD) at room temperature was performed with a PANalytical X’Pert MRD diffractometer, by using a line focused parallel monochromatic beam with CuKα1 radiation (0.1540598 nm) provided by a hybrid monochromator 2xGe(220) asymmetric.The HR-TEM investigations have been performed on a JEM ARM 200F microscope.Piezoforce microscopy (PFM) measurements have been carried out on a commercial AFM  in order to evidence the local ferroelectric domain orientation.For dielectric measurements two sets of Au interdigital electrodes with different dimensions have been deposited on top surfaces of the films by lift-off technique. Low-signal dielectric spectroscopy measurements have been carried out by using an HP4194A impedance bridge equipped with a special holder for contacting the IDE electrodes. In order to verify the correctness of the measurement method and of the calculation model, IDE structures have been deposited also directly on the STO substrate, and its dielectric constant has been measured and compared with datasheet value.How to cite this article: Scarisoreanu, N. D. et al. Joining Chemical Pressure and Epitaxial Strain to Yield Y-doped BiFeO3 Thin Films with High Dielectric Response. Sci. Rep.6, 25535; doi: 10.1038/srep25535 (2016).


Mesenchymal Stem Cells (MSCs) are mobilized in response to injury to initiate healing and remodelling. TGFβ1 is known to induce MSC migration and homing in various tissues including bone. However, the molecular mechanisms involved are poorly understood. TGFβ1 signalling has recently been linked to the primary cilium in fibroblasts. This cellular microdomain, enriched in transmembrane receptors, could be a specialized centre for TGFβ signalling. Therefore, the aim of this study is to investigate the role of the primary cilium in TGFβ1-induced MSC migration.TGFβ1 induced MSC migration was investigated using a Boyden chamber approach. The localization of TGFβ1 signalling components within MSCs was investigated through immunocytochemistry. Deletion of the primary cilia was performed with siRNA targeting Ift88.Firstly, the constitutively active TGFβRII is expressed in human MSCs and localizes preferentially to primary cilia in up to 80% of ciliated cells. TGFβRI, recruited upon ligand binding to TGFβRII is also localized to the primary cilia (14% of ciliated cells), and this specific localization increases after TGFβ1 stimulation (33% of ciliated cells). This result suggests a recruitment of the receptor into the ciliary microdomain. Moreover, the downstream signalling phosphorylated Smad2 and Smad4 localized to primary cilia. Furthermore, TGFβ1 induces human and mouse MSCs chemotaxis and interestingly, after deletion of the primary cilia, the migration capacity is decreased.These results reveal that TGFβ1 signalling cascade occurs within the microdomain of the primary cilium and more importantly the primary cilium is involved in TGFβ1-induced MSC migration.


The two ears are linked with a neural pathway such that stimulation of one ear has a modulating effect on the contralateral cochlea. This is mediated by cochlear afferent neurons connecting with olivo-cochlear efferents. The monitoring of this pathway is easily achieved by measuring contralateral suppression of otoacoustic emissions, and there is some clinical value in the ability to assess the integrity of this pathway. An important step in an evaluation of clinical utility is to assess any age-related changes. Accordingly, in the present study we measure the dynamics of contralateral DPOAE suppression in a population of normal hearing subjects of different ages.1-f2; f2 = 4.4 kHz; f2/f1 = 1.22) input–output functions were measured. In response to contralateral broadband noise, dynamic aspects of DPOAE suppression were measured, specifically suppression onset latency and time constants.Using a real-time DPOAE recording method we assessed contralateral DPOAE suppression in 95 ears from 51 subjects (age range 2–52 years). DPOAE  we find an increased latency that may reflect deterioration in auditory brainstem function. Regarding clinical utility, it is possible that the changes observed may reflect an aspect of age-related hearing loss that has not been previously considered. Otoacoustic emission (OAE) recording has become an important clinical tool for objective testing of cochlear function, in particular to verify the integrity of the outer haircell system. Distortion product otoacoustic emissions (DPOAEs)  are wideIn our previous studies of contralateral DPOAE suppression or modulation ,16,22,23DPOAE levels and the effects of contralateral acoustic stimulation were determined in 95 ears from 51 randomly chosen subjects across a wide age range (2 – 52 years) with no history of sensorineural or conductive hearing loss. We did not formally assess audiometric thresholds and did not exclude subjects because of hearing status because our aim was to assess DPOAE suppression dynamics in the general population. We divided our subjects into five age groups as follows: 1–10 years, 13 subjects (24 ears); 11–20 years, 14 subjects (26 ears); 21–30 years, 8 subjects (15 ears); 31–40 years, 10 subjects (19 ears), > 41 years, 6 subjects (11 ears). This study was approved by the Research Ethics Board at the Hospital for Sick Children, Toronto, and was carried out with the consent of adult participants or parents of children prior to testing.1-f2 distortion product . The stimulus levels used for DPOAE recording were adjusted for each subject based on individual input–output (I/O) functions. Thus, I/O functions were recorded with f2=4.4 kHz, at levels of L2 from 50 - 70 dB SPL, and the stimulus level chosen was that resulting in a DPOAE signal at 0.5 – 0.7 of maximum level [DPOAEs were recorded in real-time using a custom research device . The device uses narrow bandpass digital filtering and signal modeling to determine DPOAE levels in real-time with a temporal resolution of <2 ms . All recum level . ContralFor each ear we have measured baseline DPOAE levels, and then in response to contralateral stimulation the amplitude of DPOAE suppression, and the dynamic aspects, specifically the onset latency and time constants of suppression. The suppression onset latency was measured as the interval between the onset of contralateral noise stimulus and the initial change point in DPOAE calculated from the intercept of linear regressions plotted through the baseline and first part of the suppression response . Occasio2 = 4.4 kHz) as a function of subject age is plotted in Figure 2 = 50 dB SPL. In graph B, L2 = 60 dB SPL. The data (N = 89 ears) indicates a clear decrement of DPOAE amplitude with age. A linear regression analysis shows a correlation between the DPOAE amplitude and age, and that DPOAE amplitude decreases significantly with age. Thus in plot A (L2 = 50 dB) the correlation coefficient = −0.49 (p < 0.001), and in plot B (L2 = 60 dB) correlation coefficient = −0.64 (p < 0.001). See Table The DPOAE amplitude  are shown in Figure Previous reports have described a reduction in amplitudes of OAEs with aging -8,25. InRecent studies have reported a degradation of contralateral suppression of transient evoked OAE in human with advancing age ,31. OlivWe note that our mean DPOAE suppression amplitude was relatively small (0.13 dB) compared to some other reports. For example Bassim et al.  reportedIn addition to our own studies ,22,23 onIn humans there is still much uncertainty about the actual latency of the olivo-cochlear reflex as revealed by contralateral OAE suppression. Mott et al. reported that contralateral stimulation changes spontaneous OAEs with latencies in the range of 40–200 ms (median = 120 ms); their methodology had a temporal resolution of 40 ms . Lind reIt is useful perhaps to dissect out the timing at points along the pathway involved in contralateral suppression of DPOAE as follows: (i) stimulus transduction delay, from acoustic signal onset to the activation of inner hair cells; (ii) afferent neural transmission delay from the cochlea to  cochlear nucleus, to the superior olivary complex; (iii) inter-neuronal connection to cochlear efferent neurons; (iv) efferent neural delay from superior olive to OHCs via the crossed olivo-cochlear bundle; and (v) synaptic and mechanical activation times at OHC level including time for same to be manifest as DPOAE changes. The delay of step (i) is relatively small (<3 ms) and the timing of (ii) is similar to the latency of ABR wave PIII, approximately 6 ms. We will consider delay (iii) later. Regarding to the neural delay of (iv), Fex reported the efferent neural delay is 1.9-3.9 ms in the cat . Since, Regarding cochlear events, we report here that DPOAE suppression time constants did not show any significant age related changes Figures  and 5. WWe report that DPOAE amplitude and DPOAE suppression by contralateral stimulation are degraded with aging, and some dynamic aspects of suppression are also changed with age. Our study was prompted by questions about the clinical utility of DPOAE suppression testing. We conclude that in neonates and young children, contralateral suppression of DPOAEs can be reliably measured, and can potentially inform us about the integrity of OHCs, of cochlear afferent and efferent function and also auditory brainstem mechanisms. We are particularly enthusiastic about our own real-time DPOAE measurement technique because it easily allows the DPOAE suppression dynamics  to be quantified. We questioned whether such clinical testing was feasible in older subjects and find that the degree and detectability of DPOAE suppression falls off with age. For a clinical test to be widely applicable this may be considered a problem. On the other hand it may be revealing an important parameter of age related hearing loss that we have not been addressing previously.The authors declare that they have no competing interests.RVH and ALJ formulated the experimental questions, supervised the research and contributed to the manuscript writing. UK and SK conducted all of the experimental testing of subjects, analyzed the data and produced a draft paper. All authors have read and approved of the final manuscript.


The protein kinase C (PKC) is a family of serine/threonine kinases that consists of 12 different isoforms. Since PKC isoform expressions are known to be specific for different cell types and postnatal developmental stages, we aimed to determine immunolocalizations and protein expression levels of different PKC isoforms in pre-pubertal, pubertal and adult mouse ovaries.Ovaries were obtained from postnatal day 1 (PND1) and PND7 of pre-pubertal, PND21 of pubertal and PND60 of adult mice. Immunolocalizations of PKCα, PKCδ and PKCε isoforms were determined and immunostainings in different cellular components of all follicular stages were evaluated by H-Score. PKCα, PKCδ and PKCε protein expression levels were determined by Western blot. The bands were quantified via ImageJ software. The data obtained from H-Score and ImageJ evaluations were analyzed by ANOVA statistical test.PKCα immunostainings were more intense in oocytes when compared to granulosa and theca cells at different follicular stages of all groups. The Western blot analysis revealed that PKCα expression was significantly higher in PND60 adult ovaries. Conversely, PKCδ immunostainings were more intense in granulosa cells. According to the Western blot analysis, PKCδ protein expression was also higher in PND60 and significantly lower in PND1 ovaries. PKCε immunostaining was more apparent in oocytes. PKCε protein expression was significantly higher in adult PND60 and pubertal PND21 ovaries when compared to pre-pubertal PND7 and PND1 ovaries. Interestingly, PKCε immunostaining was significantly higher in primordial follicles, though PKCα and PKCδ immunostainings were more apparent in larger follicles. PKCα immunostainings of corpora lutea (CL) were significantly higher when compared to follicles in PND60 ovaries.This study demonstrates that PKCα, PKCδ and PKCε isoforms are differentially expressed in particular cellular components of pre-pubertal, pubertal and adult mouse ovarian follicles. Therefore, we suggest that each PKC isoform has unique functions that are controlled by gonadotropin dependent mechanisms during follicular growth, oocyte maturation, ovulation and luteinization. Main ovarian functions related to female fertility include folliculogenesis, oocyte maturation, ovulation and luteinization processes consecutively controlled by gonadotropin induced signal transduction pathways. The activation of these pathways shows difference between pre-pubertal, pubertal and adult ovaries due to variation of gonadotropin levels.As reviewed by Richards J. et al. , particu2+ and diacylglycerol (DAG) , (b) novel isoforms that are activated by DAG for activation , and (c) atypical isoforms that are activated independently of Ca2+ or DAG  i within a physiological range [2+]i in granulosa cells i in bovine luteal cells and in porcine granulosa cells leading to activation of PKC [2α – induced rise in [Ca2+]i i is decreased in luteal cells and consequently the ability of PGF2α to inhibit LH-stimulated P4 secretion becomes restricted leading to inadequate extension of the luteal life span [PGFmulation . Calciumn of PKC . When PKife span . On the ife span .Though it is documented that PKCε is related to progesterone synthesis/secretion in luteal cells in earlier studies , in the PKC is found to be participated in the induction process of ovulatory Prkg2 gene through stimulation of ERK, but not JNK . Thus, PThough isoform specific PKC antibodies can detect multiple isotypes to some extent, based on our observations in Western blot experiments, we concluded that the antibodies are notably specific for the studied isoforms for Western blot as well as immunohistochemistry.In addition to our findings through immunohistochemistry and Western blotting, the existence of mRNA of specific PKC isoforms in different cells of the ovary can be revealed by application of q-PCR on isolated cells from follicles at specific developmental stages without any concern about the amount of specimen. Besides, proteomics technology can clearly reveal the structure and function of different PKC isoforms on isolated cells of the ovary.We conclude that PKCα, PKCδ and PKCε isoforms show different expression profiles in mouse ovary at pre-pubertal, pubertal and adult stages. Thus, considering the differential expression levels of these isoforms in specific cellular components of the ovary, we suggest that these isoforms have particular roles in ovarian function. The isoform specifity of PKC family must be further studied to reveal undiscovered mechanisms of follicular development, oocyte maturation, ovulation and luteal cell functions. The proof of PKC isoform specifity for particular ovarian functions will allow the use of isoform specific inhibitors or activators to compensate ovarian dysfunction which is one of the major causes of female infertility.