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Recent research demonstrates that white matter of the brain contains not only long T2 components, but a minority of ultrashort T2* components. Adiabatic inversion recovery prepared dual echo ultrashort echo time (IR-dUTE) sequences can be used to selectively image the ultrashort T2* components in white matter of the brain using a clinical whole body scanner. The T2*s of the ultrashort T2* components can be quantified using mono-exponential decay fitting of the IR-dUTE signal at a series of different TEs. However, accurate T1 measurement of the ultrashort T2* components is technically challenging. Efficient suppression of the signal from the majority of long T2 components is essential for robust T1 measurement. In this paper we describe a novel approach to this problem based on the use of IR-dUTE data acquisitions with different TR and TI combinations to selectively detect the signal recovery of the ultrashort T2* components. Exponential recovery curve fitting provides efficient T1 estimation, with minimized contamination from the majority of long T2 components. A rubber phantom and a piece of bovine cortical bone were used for validation of this approach. Six healthy volunteers were studied. An averaged T2* of 0.32 ± 0.09 ms, and a short mean T1 of 226 ± 46 ms were demonstrated for the healthy volunteers at 3T.

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.

Obesity is an important and intractable public health problem. In addition to the well-known risk factors of behavior, diet, and genetics, gut microbial communities were recently identified as another possible source of risk and a potential therapeutic target. However, human and animal-model studies have yielded conflicting results about the precise nature of associations between microbiome composition and obesity. In this paper, we use publicly available data from the Human Microbiome Project (HMP) and MetaHIT, both surveys of healthy adults that include obese individuals, plus two smaller studies that specifically examined lean versus obese adults. We find that inter-study variability in the taxonomic composition of stool microbiomes far exceeds differences between lean and obese individuals within studies. Our analyses further reveal a high degree of variability in stool microbiome composition and diversity across individuals. While we confirm the previously published small, but statistically significant, differences in phylum-level taxonomic composition between lean and obese individuals in several cohorts, we find no association between BMI and taxonomic composition of stool microbiomes in the larger HMP and MetaHIT datasets. We explore a range of different statistical techniques and show that this result is robust to the choice of methodology. Differences between studies are likely due to a combination of technical and clinical factors. We conclude that there is no simple taxonomic signature of obesity in the microbiota of the human gut.

Using a prospective, longitudinal study design, this paper addresses the impact of genetic counseling and testing for deafness on deaf adults and the Deaf community. This study specifically evaluated the effect of genetic counseling and Connexin-26 and Connexin-30 genetic test results on participants' deaf identity and understanding of their genetic test results. Connexin-26 and Connexin-30 genetic testing was offered to participants in the context of linguistically and culturally appropriate genetic counseling. Questionnaire data collected from 209 deaf adults at four time points (baseline, immediately following pre-test genetic counseling, 1-month following genetic test result disclosure, and 6-months after result disclosure) were analyzed. Four deaf identity orientations (hearing, marginal, immersion, bicultural) were evaluated using subscales of the Deaf Identity Development Scale-Revised. We found evidence that participants understood their specific genetic test results following genetic counseling, but found no evidence of change in deaf identity based on genetic counseling or their genetic test results. This study demonstrated that culturally and linguistically appropriate genetic counseling can improve deaf clients' understanding of genetic test results, and the formation of deaf identity was not directly related to genetic counseling or Connexin-26 and Connexin-30 genetic test results.

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

It is known that gamma activity is generated by local networks. In this paper we introduced a new approach for estimation of functional connectivity between neuronal networks by measuring temporal relations between peaks of gamma event amplitudes. We have shown in freely moving rats that gamma events recorded between electrodes 1.5 mm apart in the majority of cases, are generated by different neuronal modules interfering with each other. The map of functional connectivity between brain areas during the resting state, created based on gamma event temporal relationships is in agreement with anatomical connections and with maps described by fMRI methods during the resting state. The transition from the resting state to exploratory activity is accompanied by decreased functional connectivity between most brain areas. Our data suggest that functional connectivity between interhemispheric areas depends on GABAergic transmission, while intrahemispheric functional connectivity is kainate receptor dependent. This approach presents opportunities for merging electrographic and fMRI data on brain functional connectivity in normal and pathological conditions.

Evidence for significant losses of species richness or biodiversity, even within protected natural areas, is mounting. Managers are increasingly being asked to monitor biodiversity, yet estimating biodiversity is often prohibitively expensive. As a cost-effective option, we estimated the spatial and temporal distribution of species richness for four taxonomic groups (birds, mammals, herpetofauna (reptiles and amphibians), and plants) within Sequoia and Kings Canyon National Parks using only existing biological studies undertaken within the Parks and the Parks' long-term wildlife observation database. We used a rarefaction approach to model species richness for the four taxonomic groups and analyzed those groups by habitat type, elevation zone, and time period. We then mapped the spatial distributions of species richness values for the four taxonomic groups, as well as total species richness, for the Parks. We also estimated changes in species richness for birds, mammals, and herpetofauna since 1980. The modeled patterns of species richness either peaked at mid elevations (mammals, plants, and total species richness) or declined consistently with increasing elevation (herpetofauna and birds). Plants reached maximum species richness values at much higher elevations than did vertebrate taxa, and non-flying mammals reached maximum species richness values at higher elevations than did birds. Alpine plant communities, including sagebrush, had higher species richness values than did subalpine plant communities located below them in elevation. These results are supported by other papers published in the scientific literature. Perhaps reflecting climate change: birds and herpetofauna displayed declines in species richness since 1980 at low and middle elevations and mammals displayed declines in species richness since 1980 at all elevations.

Riboswitches play roles in transcriptional or translational regulation through specific ligand binding of their aptamer domains. Although a number of ligand-bound aptamer complex structures have been solved, it is important to know ligand-free conformations of the aptamers in order to understand the mechanism of specific binding by ligands. In this paper, preQ1 riboswitch aptamer domain from Bacillus subtilis is studied by overall 1.5 μs all-atom molecular dynamics simulations We found that the ligand-free aptamer has a stable state with a folded P1-L3 and open binding pocket. The latter forms a cytosine-rich pool in which the nucleotide C19 oscillates between close and open positions, making it a potential conformation for preQ1 entrance. The dynamic picture further suggests that the specific recognition of preQ1 by the aptamer domain is not only facilitated by the key nucleotide C19 but also aided and enhanced by other cytosines around the binding pocket. These results should help to understand the details of preQ1 binding.

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

The worldwide spread of a novel influenza A (H1N1) virus in 2009 showed that influenza remains a significant health threat, even for individuals in the prime of life. This paper focuses on the unusually high young adult mortality observed during the Spanish flu pandemic of 1918. Using historical records from Canada and the U.S., we report a peak of mortality at the exact age of 28 during the pandemic and argue that this increased mortality resulted from an early life exposure to influenza during the previous Russian flu pandemic of 1889-90. We posit that in specific instances, development of immunological memory to an influenza virus strain in early life may lead to a dysregulated immune response to antigenically novel strains encountered in later life, thereby increasing the risk of death. Exposure during critical periods of development could also create holes in the T cell repertoire and impair fetal maturation in general, thereby increasing mortality from infectious diseases later in life. Knowledge of the age-pattern of susceptibility to mortality from influenza could improve crisis management during future influenza pandemics.

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method.

Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members' decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers. The model presented here is inevitably a simplified representation of a human population, but it provides a basis for future modeling work toward evolutionary explanations of human behavior that consider the influence of both genetic and cultural transmission of behavior.

Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being "leaked" or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.

Activation of the Ah receptor (AhR) by halogenated aromatic hydrocarbons (HAHs), such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, dioxin), can produce a wide variety of toxic and biological effects. While recent studies have shown that the AhR can bind and be activated by structurally diverse chemicals, how widespread of these AhR agonists are in environmental, biological and synthetic materials remains to be determined. Using AhR-based assays, we demonstrate the presence of potent AhR agonists in a variety of common commercial and consumer items. Solvent extracts of paper, rubber and plastic products contain chemicals that can bind to and stimulate AhR DNA binding and/or AhR-dependent gene expression in hepatic cytosol, cultured cell lines, human epidermis and zebrafish embryos. In contrast to TCDD and other persistent dioxin-like HAHs, activation of AhR-dependent gene expression by these extracts was transient, suggesting that the agonists are metabolically labile. Solvent extracts of rubber products produce AhR-dependent developmental toxicity in zebrafish in vivo, and inhibition of expression of the metabolic enzyme CYP1A, significantly increased their toxic potency. Although the identity of the responsible AhR-active chemicals and their toxicological impact remain to be determined, our data demonstrate that AhR active chemicals are widely distributed in everyday products.

The worldwide spread of a novel influenza A (H1N1) virus in 2009 showed that influenza remains a significant health threat, even for individuals in the prime of life. This paper focuses on the unusually high young adult mortality observed during the Spanish flu pandemic of 1918. Using historical records from Canada and the U.S., we report a peak of mortality at the exact age of 28 during the pandemic and argue that this increased mortality resulted from an early life exposure to influenza during the previous Russian flu pandemic of 1889-90. We posit that in specific instances, development of immunological memory to an influenza virus strain in early life may lead to a dysregulated immune response to antigenically novel strains encountered in later life, thereby increasing the risk of death. Exposure during critical periods of development could also create holes in the T cell repertoire and impair fetal maturation in general, thereby increasing mortality from infectious diseases later in life. Knowledge of the age-pattern of susceptibility to mortality from influenza could improve crisis management during future influenza pandemics.

Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being "leaked" or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

BackgroundIn Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability.MethodsThis intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention.ResultsForty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents.ConclusionsInsights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family planning preferences and create more sustainable contraceptive service provision with greater impact.

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method.

Spatially discordant alternans (SDA) of action potential duration (APD) is a phenomenon where different regions of cardiac tissue exhibit an alternating sequence of APD that are out-of-phase. SDA is arrhythmogenic since it can induce spatial heterogeneity of refractoriness, which can cause wavebreak and reentry. However, the underlying mechanisms for the formation of SDA are not completely understood. In this paper, we present a novel mechanism for the formation of SDA in the case where the cellular instability leading to alternans is caused by intracellular calcium (Ca) cycling, and where Ca transient and APD alternans are electromechanically concordant. In particular, we show that SDA is formed when rapidly paced cardiac tissue develops alternans over many beats due to Ca accumulation in the sarcoplasmic reticulum (SR). The mechanism presented here relies on the observation that Ca cycling fluctuations dictate Ca alternans phase since the amplitude of Ca alternans is small during the early stages of pacing. Thus, different regions of a cardiac myocyte will typically develop Ca alternans which are opposite in phase at the early stages of pacing. These subcellular patterns then gradually coarsen due to interactions with membrane voltage to form steady state SDA of voltage and Ca on the tissue scale. This mechanism for SDA is distinct from well-known mechanisms that rely on conduction velocity restitution, and a Turing-like mechanism known to apply only in the case where APD and Ca alternans are electromechanically discordant. Furthermore, we argue that this mechanism is robust, and is likely to underlie a wide range of experimentally observed patterns of SDA.

Activation of the Ah receptor (AhR) by halogenated aromatic hydrocarbons (HAHs), such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, dioxin), can produce a wide variety of toxic and biological effects. While recent studies have shown that the AhR can bind and be activated by structurally diverse chemicals, how widespread of these AhR agonists are in environmental, biological and synthetic materials remains to be determined. Using AhR-based assays, we demonstrate the presence of potent AhR agonists in a variety of common commercial and consumer items. Solvent extracts of paper, rubber and plastic products contain chemicals that can bind to and stimulate AhR DNA binding and/or AhR-dependent gene expression in hepatic cytosol, cultured cell lines, human epidermis and zebrafish embryos. In contrast to TCDD and other persistent dioxin-like HAHs, activation of AhR-dependent gene expression by these extracts was transient, suggesting that the agonists are metabolically labile. Solvent extracts of rubber products produce AhR-dependent developmental toxicity in zebrafish in vivo, and inhibition of expression of the metabolic enzyme CYP1A, significantly increased their toxic potency. Although the identity of the responsible AhR-active chemicals and their toxicological impact remain to be determined, our data demonstrate that AhR active chemicals are widely distributed in everyday products.

Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members' decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers. The model presented here is inevitably a simplified representation of a human population, but it provides a basis for future modeling work toward evolutionary explanations of human behavior that consider the influence of both genetic and cultural transmission of behavior.

BackgroundIn Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability.MethodsThis intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention.ResultsForty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents.ConclusionsInsights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family planning preferences and create more sustainable contraceptive service provision with greater impact.

Spatially discordant alternans (SDA) of action potential duration (APD) is a phenomenon where different regions of cardiac tissue exhibit an alternating sequence of APD that are out-of-phase. SDA is arrhythmogenic since it can induce spatial heterogeneity of refractoriness, which can cause wavebreak and reentry. However, the underlying mechanisms for the formation of SDA are not completely understood. In this paper, we present a novel mechanism for the formation of SDA in the case where the cellular instability leading to alternans is caused by intracellular calcium (Ca) cycling, and where Ca transient and APD alternans are electromechanically concordant. In particular, we show that SDA is formed when rapidly paced cardiac tissue develops alternans over many beats due to Ca accumulation in the sarcoplasmic reticulum (SR). The mechanism presented here relies on the observation that Ca cycling fluctuations dictate Ca alternans phase since the amplitude of Ca alternans is small during the early stages of pacing. Thus, different regions of a cardiac myocyte will typically develop Ca alternans which are opposite in phase at the early stages of pacing. These subcellular patterns then gradually coarsen due to interactions with membrane voltage to form steady state SDA of voltage and Ca on the tissue scale. This mechanism for SDA is distinct from well-known mechanisms that rely on conduction velocity restitution, and a Turing-like mechanism known to apply only in the case where APD and Ca alternans are electromechanically discordant. Furthermore, we argue that this mechanism is robust, and is likely to underlie a wide range of experimentally observed patterns of SDA.

Deoxyribonucleic acid (DNA) of the human immunodeficiency virus (HIV) provides the most sensitive measurement of residual infection in patients on effective combination antiretroviral therapy (cART). Droplet digital PCR (ddPCR) has recently been shown to provide highly accurate quantification of DNA copy number, but its application to quantification of HIV DNA, or other equally rare targets, has not been reported. This paper demonstrates and analyzes the application of ddPCR to measure the frequency of total HIV DNA (pol copies per million cells), and episomal 2-LTR (long terminal repeat) circles in cells isolated from infected patients. Analysis of over 300 clinical samples, including over 150 clinical samples assayed in triplicate by ddPCR and by real-time PCR (qPCR), demonstrates a significant increase in precision, with an average 5-fold decrease in the coefficient of variation of pol copy numbers and a >20-fold accuracy improvement for 2-LTR circles. Additional benefits of the ddPCR assay over qPCR include absolute quantification without reliance on an external standard and relative insensitivity to mismatches in primer and probe sequences. These features make digital PCR an attractive alternative for measurement of HIV DNA in clinical specimens. The improved sensitivity and precision of measurement of these rare events should facilitate measurements to characterize the latent HIV reservoir and interventions to eradicate it.

Deoxyribonucleic acid (DNA) of the human immunodeficiency virus (HIV) provides the most sensitive measurement of residual infection in patients on effective combination antiretroviral therapy (cART). Droplet digital PCR (ddPCR) has recently been shown to provide highly accurate quantification of DNA copy number, but its application to quantification of HIV DNA, or other equally rare targets, has not been reported. This paper demonstrates and analyzes the application of ddPCR to measure the frequency of total HIV DNA (pol copies per million cells), and episomal 2-LTR (long terminal repeat) circles in cells isolated from infected patients. Analysis of over 300 clinical samples, including over 150 clinical samples assayed in triplicate by ddPCR and by real-time PCR (qPCR), demonstrates a significant increase in precision, with an average 5-fold decrease in the coefficient of variation of pol copy numbers and a >20-fold accuracy improvement for 2-LTR circles. Additional benefits of the ddPCR assay over qPCR include absolute quantification without reliance on an external standard and relative insensitivity to mismatches in primer and probe sequences. These features make digital PCR an attractive alternative for measurement of HIV DNA in clinical specimens. The improved sensitivity and precision of measurement of these rare events should facilitate measurements to characterize the latent HIV reservoir and interventions to eradicate it.

The idea of closed-loop interaction in in vitro and in vivo electrophysiology has been successfully implemented in the dynamic clamp concept strongly impacting the research of membrane and synaptic properties of neurons. In this paper we show that this concept can be easily generalized to build other kinds of closed-loop protocols beyond (or in addition to) electrical stimulation and recording in neurophysiology and behavioral studies for neuroethology. In particular, we illustrate three different examples of goal-driven real-time closed-loop interactions with drug microinjectors, mechanical devices and video event driven stimulation. Modern activity-dependent stimulation protocols can be used to reveal dynamics (otherwise hidden under traditional stimulation techniques), achieve control of natural and pathological states, induce learning, bridge between disparate levels of analysis and for a further automation of experiments. We argue that closed-loop interaction calls for novel real time analysis, prediction and control tools and a new perspective for designing stimulus-response experiments, which can have a large impact in neuroscience research.

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.

Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.

This paper describes the design of a new instrumental technique, Gas Chromatography Recomposition-Olfactometry (GC-R), that adapts the reconstitution technique used in flavor chemistry studies by extracting volatiles from a sample by headspace solid-phase microextraction (SPME), separating the extract on a capillary GC column, and recombining individual compounds selectively as they elute off of the column into a mixture for sensory analysis (Figure 1). Using the chromatogram of a mixture as a map, the GC-R instrument allows the operator to "cut apart" and recombine the components of the mixture at will, selecting compounds, peaks, or sections based on retention time to include or exclude in a reconstitution for sensory analysis. Selective recombination is accomplished with the installation of a Deans Switch directly in-line with the column, which directs compounds either to waste or to a cryotrap at the operator's discretion. This enables the creation of, for example, aroma reconstitutions incorporating all of the volatiles in a sample, including instrumentally undetectable compounds as well those present at concentrations below sensory thresholds, thus correcting for the "reconstitution discrepancy" sometimes noted in flavor chemistry studies. Using only flowering lavender (Lavandula angustifola 'Hidcote Blue') as a source for volatiles, we used the instrument to build mixtures of subsets of lavender volatiles in-instrument and characterized their aroma qualities with a sensory panel. We showed evidence of additive, masking, and synergistic effects in these mixtures and of "lavender' aroma character as an emergent property of specific mixtures. This was accomplished without the need for chemical standards, reductive aroma models, or calculation of Odor Activity Values, and is broadly applicable to any aroma or flavor.

The protein family known as G-protein coupled receptors (GPCRs) comprises an important class of membrane-associated proteins, which remains a difficult family of proteins to characterize because their function requires a native-like lipid membrane environment. This paper focuses on applying a single step method leading to the formation of nanolipoprotein particles (NLPs) capable of solubilizing functional GPCRs for biophysical characterization. NLPs were used to demonstrate increased solubility for multiple GPCRs such as the Neurokinin 1 Receptor (NK1R), the Adrenergic Receptor â2 (ADRB2) and the Dopamine Receptor D1 (DRD1). All three GPCRs showed affinity for their specific ligands using a simple dot blot assay. The NK1R was characterized in greater detail to demonstrate correct folding of the ligand pocket with nanomolar specificity. Electron paramagnetic resonance (EPR) spectroscopy validated the correct folding of the NK1R binding pocket for Substance P (SP). Fluorescence correlation spectroscopy (FCS) was used to identify SP-bound NK1R-containing NLPs and measure their dissociation rate in an aqueous environment. The dissociation constant was found to be 83 nM and was consistent with dot blot assays. This study represents a unique combinational approach involving the single step de novo production of a functional GPCR combined with biophysical techniques to demonstrate receptor association with the NLPs and binding affinity to specific ligands. Such a combined approach provides a novel path forward to screen and characterize GPCRs for drug discovery as well as structural studies outside of the complex cellular environment.

Faeces provide relevant biological information which includes, with the application of genetic techniques, the sex and identity of individuals that defecated, thus providing potentially useful data on the behaviour and ecology of individuals, as well as the dynamics and structure of populations. This paper presents estimates of the sex ratio of different felid species (jaguar, Panthera onca; puma, Puma concolor; and ocelot/margay, Leopardus pardalis/Leopardus wiedi) as observed in field collected faeces, and proposes several hypotheses that could explain the strikingly high proportion of faeces from male jaguars. The proportion of male and female faeces was estimated using a non-invasive faecal sampling method in 14 study areas in Mexico and Brazil. Faecal samples were genetically analysed to identify the species, the sex and the individual (the latter only for samples identified as belonging to jaguars). Considering the three species, 72.6% of faeces (n = 493) were from males; however, there were significant differences among them, with the proportion from males being higher for jaguars than for pumas and ocelots/margays. A male-bias was consistently observed in all study areas for jaguar faeces, but not for the other species. For jaguars the trend was the same when considering the number of individuals identified (n = 68), with an average of 4.2±0.56 faeces per male and 2.0±0.36 per female. The observed faecal marking patterns might be related to the behaviour of female jaguars directed toward protecting litters from males, and in both male and female pumas, to prevent interspecific aggressions from male jaguars. The hypothesis that there are effectively more males than females in jaguar populations cannot be discarded, which could be due to the fact that females are territorial and males are not, or a tendency for males to disperse into suboptimal areas for the species.

Recent research of task-irrelevant perceptual learning (TIPL) demonstrates that stimuli that are consistently presented at relevant point in times (e.g. with task-targets or rewards) are learned, even in the absence of attention to these stimuli. However, different research paradigms have observed different results for how salient stimuli are learned; with some studies showing no learning, some studies showing positive learning and others showing negative learning effects. In this paper we focused on how the level of processing of stimuli impacts fast-TIPL. We conducted three different experiments in which the level of processing of the information paired with a target was manipulated. Our results indicated that fast-TIPL occurs when participants have to memorize the information presented with the target, but also when they just have to process this information for a secondary task without an explicit memorization of those stimuli. However, fast-TIPL does not occur when participants have to ignore the target-paired information. This observation is consistent with recent models of TIPL that suggest that attentional signals can either enhance or suppress learning depending on whether those stimuli are distracting or not to the subjects' objectives. Our results also revealed a robust gender effect in fast-TIPL, where male subjects consistently show fast-TIPL, whereas the observation of fast-TIPL is inconsistent in female subjects.

Genetic exchange between isolated populations, or introgression between species, serves as a key source of novel genetic material on which natural selection can act. While detecting historical gene flow from DNA sequence data is of much interest, many existing methods can be limited by requirements for deep population genomic sampling. In this paper, we develop a scalable genealogy-based method to detect candidate signatures of gene flow into a given population when the source of the alleles is unknown. Our method does not require sequenced samples from the source population, provided that the alleles have not reached fixation in the sampled recipient population. The method utilizes recent advances in algorithms for the efficient reconstruction of ancestral recombination graphs, which encode genealogical histories of DNA sequence data at each site, and is capable of detecting the signatures of gene flow whose footprints are of length up to single genes. Further, we employ a theoretical framework based on coalescent theory to test for statistical significance of certain recombination patterns consistent with gene flow from divergent sources. Implementing these methods for application to whole-genome sequences of environmental yeast isolates, we illustrate the power of our approach to highlight loci with unusual recombination histories. By developing innovative theory and methods to analyze signatures of gene flow from population sequence data, our work establishes a foundation for the continued study of introgression and its evolutionary relevance.

Eucalypt species are a group of flowering trees widely used in pulp production for paper manufacture. For several decades, the wood pulp industry has focused research and development efforts on improving yields, growth rates and pulp quality through breeding and the genetic improvement of key tree species. Recently, this focus has shifted from the production of high quality pulps to the investigation of the use of eucalypts as feedstocks for biofuel production. Here the structure and chemical composition of the heartwood and sapwood of Eucalyptus dunnii, E. globulus, E. pillularis, E. urophylla, an E. urophylla-E. grandis cross, Corymbia citriodora ssp. variegata, and Acacia mangium were compared using nuclear magnetic resonance spectroscopy (NMR), X-ray diffraction (XRD) and biochemical composition analysis. Some trends relating to these compositions were also identified by Fourier transform near infrared (FT-NIR) spectroscopy. These results will serve as a foundation for a more comprehensive database of wood properties that will help develop criteria for the selection of tree species for use as biorefinery feedstocks.

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.

This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement.

ObjectiveTo evaluate the reliability and validity of the London Measure of Unplanned Pregnancy (a U.K.-developed measure of pregnancy intention), in English and Spanish translation, in a U.S. population of women.MethodsA psychometric evaluation study of the London Measure of Unplanned Pregnancy (LMUP), a six-item, self-completion paper measure was conducted with 346 women aged 15-45 who presented to San Francisco General Hospital for termination of pregnancy or antenatal care. Analyses of the two language versions were carried out separately. Reliability (internal consistency) was assessed using Cronbach's alpha and item-total correlations. Test-retest reliability (stability) was assessed using weighted Kappa. Construct validity was assessed using principal components analysis and hypothesis testing.ResultsPsychometric testing demonstrated that the LMUP was reliable and valid in both U.S. English (alpha = 0.78, all item-total correlations >0.20, weighted Kappa = 0.72, unidimensionality confirmed, hypotheses met) and Spanish translation (alpha = 0.84, all item-total correlations >0.20, weighted Kappa = 0.77, unidimensionality confirmed, hypotheses met).ConclusionThe LMUP was reliable and valid in U.S. English and Spanish translation and therefore may now be used with U.S. women.

This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement.

Eucalypt species are a group of flowering trees widely used in pulp production for paper manufacture. For several decades, the wood pulp industry has focused research and development efforts on improving yields, growth rates and pulp quality through breeding and the genetic improvement of key tree species. Recently, this focus has shifted from the production of high quality pulps to the investigation of the use of eucalypts as feedstocks for biofuel production. Here the structure and chemical composition of the heartwood and sapwood of Eucalyptus dunnii, E. globulus, E. pillularis, E. urophylla, an E. urophylla-E. grandis cross, Corymbia citriodora ssp. variegata, and Acacia mangium were compared using nuclear magnetic resonance spectroscopy (NMR), X-ray diffraction (XRD) and biochemical composition analysis. Some trends relating to these compositions were also identified by Fourier transform near infrared (FT-NIR) spectroscopy. These results will serve as a foundation for a more comprehensive database of wood properties that will help develop criteria for the selection of tree species for use as biorefinery feedstocks.

UnlabelledThis paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects.The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300-700 ms after the target image onset, an alpha band (12 Hz) power boosting 500-1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects.Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane.AvailabilityThe data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html.

Genetic exchange between isolated populations, or introgression between species, serves as a key source of novel genetic material on which natural selection can act. While detecting historical gene flow from DNA sequence data is of much interest, many existing methods can be limited by requirements for deep population genomic sampling. In this paper, we develop a scalable genealogy-based method to detect candidate signatures of gene flow into a given population when the source of the alleles is unknown. Our method does not require sequenced samples from the source population, provided that the alleles have not reached fixation in the sampled recipient population. The method utilizes recent advances in algorithms for the efficient reconstruction of ancestral recombination graphs, which encode genealogical histories of DNA sequence data at each site, and is capable of detecting the signatures of gene flow whose footprints are of length up to single genes. Further, we employ a theoretical framework based on coalescent theory to test for statistical significance of certain recombination patterns consistent with gene flow from divergent sources. Implementing these methods for application to whole-genome sequences of environmental yeast isolates, we illustrate the power of our approach to highlight loci with unusual recombination histories. By developing innovative theory and methods to analyze signatures of gene flow from population sequence data, our work establishes a foundation for the continued study of introgression and its evolutionary relevance.

Faeces provide relevant biological information which includes, with the application of genetic techniques, the sex and identity of individuals that defecated, thus providing potentially useful data on the behaviour and ecology of individuals, as well as the dynamics and structure of populations. This paper presents estimates of the sex ratio of different felid species (jaguar, Panthera onca; puma, Puma concolor; and ocelot/margay, Leopardus pardalis/Leopardus wiedi) as observed in field collected faeces, and proposes several hypotheses that could explain the strikingly high proportion of faeces from male jaguars. The proportion of male and female faeces was estimated using a non-invasive faecal sampling method in 14 study areas in Mexico and Brazil. Faecal samples were genetically analysed to identify the species, the sex and the individual (the latter only for samples identified as belonging to jaguars). Considering the three species, 72.6% of faeces (n = 493) were from males; however, there were significant differences among them, with the proportion from males being higher for jaguars than for pumas and ocelots/margays. A male-bias was consistently observed in all study areas for jaguar faeces, but not for the other species. For jaguars the trend was the same when considering the number of individuals identified (n = 68), with an average of 4.2±0.56 faeces per male and 2.0±0.36 per female. The observed faecal marking patterns might be related to the behaviour of female jaguars directed toward protecting litters from males, and in both male and female pumas, to prevent interspecific aggressions from male jaguars. The hypothesis that there are effectively more males than females in jaguar populations cannot be discarded, which could be due to the fact that females are territorial and males are not, or a tendency for males to disperse into suboptimal areas for the species.

This paper describes the design of a new instrumental technique, Gas Chromatography Recomposition-Olfactometry (GC-R), that adapts the reconstitution technique used in flavor chemistry studies by extracting volatiles from a sample by headspace solid-phase microextraction (SPME), separating the extract on a capillary GC column, and recombining individual compounds selectively as they elute off of the column into a mixture for sensory analysis (Figure 1). Using the chromatogram of a mixture as a map, the GC-R instrument allows the operator to "cut apart" and recombine the components of the mixture at will, selecting compounds, peaks, or sections based on retention time to include or exclude in a reconstitution for sensory analysis. Selective recombination is accomplished with the installation of a Deans Switch directly in-line with the column, which directs compounds either to waste or to a cryotrap at the operator's discretion. This enables the creation of, for example, aroma reconstitutions incorporating all of the volatiles in a sample, including instrumentally undetectable compounds as well those present at concentrations below sensory thresholds, thus correcting for the "reconstitution discrepancy" sometimes noted in flavor chemistry studies. Using only flowering lavender (Lavandula angustifola 'Hidcote Blue') as a source for volatiles, we used the instrument to build mixtures of subsets of lavender volatiles in-instrument and characterized their aroma qualities with a sensory panel. We showed evidence of additive, masking, and synergistic effects in these mixtures and of "lavender' aroma character as an emergent property of specific mixtures. This was accomplished without the need for chemical standards, reductive aroma models, or calculation of Odor Activity Values, and is broadly applicable to any aroma or flavor.

Recent research of task-irrelevant perceptual learning (TIPL) demonstrates that stimuli that are consistently presented at relevant point in times (e.g. with task-targets or rewards) are learned, even in the absence of attention to these stimuli. However, different research paradigms have observed different results for how salient stimuli are learned; with some studies showing no learning, some studies showing positive learning and others showing negative learning effects. In this paper we focused on how the level of processing of stimuli impacts fast-TIPL. We conducted three different experiments in which the level of processing of the information paired with a target was manipulated. Our results indicated that fast-TIPL occurs when participants have to memorize the information presented with the target, but also when they just have to process this information for a secondary task without an explicit memorization of those stimuli. However, fast-TIPL does not occur when participants have to ignore the target-paired information. This observation is consistent with recent models of TIPL that suggest that attentional signals can either enhance or suppress learning depending on whether those stimuli are distracting or not to the subjects' objectives. Our results also revealed a robust gender effect in fast-TIPL, where male subjects consistently show fast-TIPL, whereas the observation of fast-TIPL is inconsistent in female subjects.

The protein family known as G-protein coupled receptors (GPCRs) comprises an important class of membrane-associated proteins, which remains a difficult family of proteins to characterize because their function requires a native-like lipid membrane environment. This paper focuses on applying a single step method leading to the formation of nanolipoprotein particles (NLPs) capable of solubilizing functional GPCRs for biophysical characterization. NLPs were used to demonstrate increased solubility for multiple GPCRs such as the Neurokinin 1 Receptor (NK1R), the Adrenergic Receptor â2 (ADRB2) and the Dopamine Receptor D1 (DRD1). All three GPCRs showed affinity for their specific ligands using a simple dot blot assay. The NK1R was characterized in greater detail to demonstrate correct folding of the ligand pocket with nanomolar specificity. Electron paramagnetic resonance (EPR) spectroscopy validated the correct folding of the NK1R binding pocket for Substance P (SP). Fluorescence correlation spectroscopy (FCS) was used to identify SP-bound NK1R-containing NLPs and measure their dissociation rate in an aqueous environment. The dissociation constant was found to be 83 nM and was consistent with dot blot assays. This study represents a unique combinational approach involving the single step de novo production of a functional GPCR combined with biophysical techniques to demonstrate receptor association with the NLPs and binding affinity to specific ligands. Such a combined approach provides a novel path forward to screen and characterize GPCRs for drug discovery as well as structural studies outside of the complex cellular environment.

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.

ObjectiveTo evaluate the reliability and validity of the London Measure of Unplanned Pregnancy (a U.K.-developed measure of pregnancy intention), in English and Spanish translation, in a U.S. population of women.MethodsA psychometric evaluation study of the London Measure of Unplanned Pregnancy (LMUP), a six-item, self-completion paper measure was conducted with 346 women aged 15-45 who presented to San Francisco General Hospital for termination of pregnancy or antenatal care. Analyses of the two language versions were carried out separately. Reliability (internal consistency) was assessed using Cronbach's alpha and item-total correlations. Test-retest reliability (stability) was assessed using weighted Kappa. Construct validity was assessed using principal components analysis and hypothesis testing.ResultsPsychometric testing demonstrated that the LMUP was reliable and valid in both U.S. English (alpha = 0.78, all item-total correlations >0.20, weighted Kappa = 0.72, unidimensionality confirmed, hypotheses met) and Spanish translation (alpha = 0.84, all item-total correlations >0.20, weighted Kappa = 0.77, unidimensionality confirmed, hypotheses met).ConclusionThe LMUP was reliable and valid in U.S. English and Spanish translation and therefore may now be used with U.S. women.