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BackgroundTrials in Africa indicate that medical adult male circumcision (MAMC) reduces the risk of HIV by 60%. MAMC may avert 2 to 8 million HIV infections over 20 years in sub-Saharan Africa and cost less than treating those who would have been infected. This paper estimates the financial and human resources required to roll out MAMC and the net savings due to reduced infections.MethodsWe developed a model which included costing, demography and HIV epidemiology. We used it to investigate 14 countries in sub-Saharan Africa where the prevalence of male circumcision was lower than 80% and HIV prevalence among adults was higher than 5%, in addition to Uganda and the Nyanza province in Kenya. We assumed that the roll-out would take 5 years and lead to an MC prevalence among adult males of 85%. We also assumed that surgery would be done as it was in the trials. We calculated public program cost, number of full-time circumcisers and net costs or savings when adjusting for averted HIV treatments. Costs were in USD, discounted to 2007. 95% percentile intervals (95% PI) were estimated by Monte Carlo simulations.ResultsIn the first 5 years the number of circumcisers needed was 2 282 (95% PI: 2 018 to 2 959), or 0.24 (95% PI: 0.21 to 0.31) per 10,000 adults. In years 6-10, the number of circumcisers needed fell to 513 (95% PI: 452 to 664). The estimated 5-year cost of rolling out MAMC in the public sector was $919 million (95% PI: 726 to 1 245). The cumulative net cost over the first 10 years was $672 million (95% PI: 437 to 1,021) and over 20 years there were net savings of $2.3 billion (95% PI: 1.4 to 3.4).ConclusionA rapid roll-out of MAMC in sub-Saharan Africa requires substantial funding and a high number of circumcisers for the first five years. These investments are justified by MAMC's substantial health benefits and the savings accrued by averting future HIV infections. Lower ongoing costs and continued care savings suggest long-term sustainability.

For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method--one that is similarly convenient, flexible, and reproducible--exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 microm, cell densities of 10(5)-10(8) cells/cm(3), and total volumes between 1x10(-7) and 8x10(-4) cm(3). By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (10(8)-10(9) cells/cm(3)). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering.

Neuropeptides are an important class of molecules involved in diverse aspects of metazoan development and homeostasis. Insects are ideal model systems to investigate neuropeptide functions, and the major focus of insect neuropeptide research in the last decade has been on the identification of their receptors. Despite these vigorous efforts, receptors for some key neuropeptides in insect development such as prothoracicotropic hormone, eclosion hormone and allatotropin (AT), remain undefined. In this paper, we report the comprehensive cloning of neuropeptide G protein-coupled receptors from the silkworm, Bombyx mori, and systematic analyses of their expression. Based on the expression patterns of orphan receptors, we identified the long-sought receptor for AT, which is thought to stimulate juvenile hormone biosynthesis in the corpora allata (CA). Surprisingly, however, the AT receptor was not highly expressed in the CA, but instead was predominantly transcribed in the corpora cardiaca (CC), an organ adjacent to the CA. Indeed, by using a reverse-physiological approach, we purified and characterized novel allatoregulatory peptides produced in AT receptor-expressing CC cells, which may indirectly mediate AT activity on the CA. All of the above findings confirm the effectiveness of a systematic analysis of the receptor transcriptome, not only in characterizing orphan receptors, but also in identifying novel players and hidden mechanisms in important biological processes. This work illustrates how using a combinatorial approach employing bioinformatic, molecular, biochemical and physiological methods can help solve recalcitrant problems in neuropeptide research.

For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method--one that is similarly convenient, flexible, and reproducible--exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 microm, cell densities of 10(5)-10(8) cells/cm(3), and total volumes between 1x10(-7) and 8x10(-4) cm(3). By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (10(8)-10(9) cells/cm(3)). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering.

BackgroundTrials in Africa indicate that medical adult male circumcision (MAMC) reduces the risk of HIV by 60%. MAMC may avert 2 to 8 million HIV infections over 20 years in sub-Saharan Africa and cost less than treating those who would have been infected. This paper estimates the financial and human resources required to roll out MAMC and the net savings due to reduced infections.MethodsWe developed a model which included costing, demography and HIV epidemiology. We used it to investigate 14 countries in sub-Saharan Africa where the prevalence of male circumcision was lower than 80% and HIV prevalence among adults was higher than 5%, in addition to Uganda and the Nyanza province in Kenya. We assumed that the roll-out would take 5 years and lead to an MC prevalence among adult males of 85%. We also assumed that surgery would be done as it was in the trials. We calculated public program cost, number of full-time circumcisers and net costs or savings when adjusting for averted HIV treatments. Costs were in USD, discounted to 2007. 95% percentile intervals (95% PI) were estimated by Monte Carlo simulations.ResultsIn the first 5 years the number of circumcisers needed was 2 282 (95% PI: 2 018 to 2 959), or 0.24 (95% PI: 0.21 to 0.31) per 10,000 adults. In years 6-10, the number of circumcisers needed fell to 513 (95% PI: 452 to 664). The estimated 5-year cost of rolling out MAMC in the public sector was $919 million (95% PI: 726 to 1 245). The cumulative net cost over the first 10 years was $672 million (95% PI: 437 to 1,021) and over 20 years there were net savings of $2.3 billion (95% PI: 1.4 to 3.4).ConclusionA rapid roll-out of MAMC in sub-Saharan Africa requires substantial funding and a high number of circumcisers for the first five years. These investments are justified by MAMC's substantial health benefits and the savings accrued by averting future HIV infections. Lower ongoing costs and continued care savings suggest long-term sustainability.

Neuropeptides are an important class of molecules involved in diverse aspects of metazoan development and homeostasis. Insects are ideal model systems to investigate neuropeptide functions, and the major focus of insect neuropeptide research in the last decade has been on the identification of their receptors. Despite these vigorous efforts, receptors for some key neuropeptides in insect development such as prothoracicotropic hormone, eclosion hormone and allatotropin (AT), remain undefined. In this paper, we report the comprehensive cloning of neuropeptide G protein-coupled receptors from the silkworm, Bombyx mori, and systematic analyses of their expression. Based on the expression patterns of orphan receptors, we identified the long-sought receptor for AT, which is thought to stimulate juvenile hormone biosynthesis in the corpora allata (CA). Surprisingly, however, the AT receptor was not highly expressed in the CA, but instead was predominantly transcribed in the corpora cardiaca (CC), an organ adjacent to the CA. Indeed, by using a reverse-physiological approach, we purified and characterized novel allatoregulatory peptides produced in AT receptor-expressing CC cells, which may indirectly mediate AT activity on the CA. All of the above findings confirm the effectiveness of a systematic analysis of the receptor transcriptome, not only in characterizing orphan receptors, but also in identifying novel players and hidden mechanisms in important biological processes. This work illustrates how using a combinatorial approach employing bioinformatic, molecular, biochemical and physiological methods can help solve recalcitrant problems in neuropeptide research.

Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people.

The aortic heart valve undergoes geometric and mechanical changes over time. The cusps of a normal, healthy valve thicken and become less extensible over time. In the disease calcific aortic stenosis (CAS), calcified nodules progressively stiffen the cusps. The local mechanical changes in the cusps, due to either normal aging or pathological processes, affect overall function of the valve. In this paper, we propose a computational model for the aging aortic valve that connects local changes to overall valve function. We extend a previous model for the healthy valve to describe aging. To model normal/uncomplicated aging, leaflet thickness and extensibility are varied versus age according to experimental data. To model calcification, initial sites are defined and a simple growth law is assumed. The nodules then grow over time, so that the area of calcification increases from one model to the next model representing greater age. Overall valve function is recorded for each individual model to yield a single simulation of valve function over time. This simulation is the first theoretical tool to describe the temporal behavior of aortic valve calcification. The ability to better understand and predict disease progression will aid in design and timing of patient treatments for CAS.

BackgroundThe integration of multiple complementary approaches is a powerful way to understand the processes of diversification and speciation. The parasitoid wasp Aphidius transcaspicus Telenga (Hymenoptera: Braconidae) is a parasitoid of Hyalopterus aphids across a wide geographic range. This species shows a remarkable degree of genetic structure among western, central, and eastern Mediterranean population clusters. In this paper we attempt to better characterize this genetic structure.Methodology/principal findingsWe use a Bayesian coalescent analysis of gene flow under the Isolation with Migration model using mitochondrial and microsatellite markers together with climate-based ecological niche models to better understand the genetic structure of A. transcaspicus in the Mediterranean. The coalescent analysis revealed low levels of migration among western and eastern Mediterranean populations (Nm

BackgroundThe integration of multiple complementary approaches is a powerful way to understand the processes of diversification and speciation. The parasitoid wasp Aphidius transcaspicus Telenga (Hymenoptera: Braconidae) is a parasitoid of Hyalopterus aphids across a wide geographic range. This species shows a remarkable degree of genetic structure among western, central, and eastern Mediterranean population clusters. In this paper we attempt to better characterize this genetic structure.Methodology/principal findingsWe use a Bayesian coalescent analysis of gene flow under the Isolation with Migration model using mitochondrial and microsatellite markers together with climate-based ecological niche models to better understand the genetic structure of A. transcaspicus in the Mediterranean. The coalescent analysis revealed low levels of migration among western and eastern Mediterranean populations (Nm

The aortic heart valve undergoes geometric and mechanical changes over time. The cusps of a normal, healthy valve thicken and become less extensible over time. In the disease calcific aortic stenosis (CAS), calcified nodules progressively stiffen the cusps. The local mechanical changes in the cusps, due to either normal aging or pathological processes, affect overall function of the valve. In this paper, we propose a computational model for the aging aortic valve that connects local changes to overall valve function. We extend a previous model for the healthy valve to describe aging. To model normal/uncomplicated aging, leaflet thickness and extensibility are varied versus age according to experimental data. To model calcification, initial sites are defined and a simple growth law is assumed. The nodules then grow over time, so that the area of calcification increases from one model to the next model representing greater age. Overall valve function is recorded for each individual model to yield a single simulation of valve function over time. This simulation is the first theoretical tool to describe the temporal behavior of aortic valve calcification. The ability to better understand and predict disease progression will aid in design and timing of patient treatments for CAS.

Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people.

Understanding the biomechanical properties and the effect of biomechanical force on epithelial cells is key to understanding how epithelial cells form uniquely shaped structures in two or three-dimensional space. Nevertheless, with the limitations and challenges posed by biological experiments at this scale, it becomes advantageous to use mathematical and 'in silico' (computational) models as an alternate solution. This paper introduces a single-cell-based model representing the cross section of a typical tissue. Each cell in this model is an individual unit containing several sub-cellular elements, such as the elastic plasma membrane, enclosed viscoelastic elements that play the role of cytoskeleton, and the viscoelastic elements of the cell nucleus. The cell membrane is divided into segments where each segment (or point) incorporates the cell's interaction and communication with other cells and its environment. The model is capable of simulating how cells cooperate and contribute to the overall structure and function of a particular tissue; it mimics many aspects of cellular behavior such as cell growth, division, apoptosis and polarization. The model allows for investigation of the biomechanical properties of cells, cell-cell interactions, effect of environment on cellular clusters, and how individual cells work together and contribute to the structure and function of a particular tissue. To evaluate the current approach in modeling different topologies of growing tissues in distinct biochemical conditions of the surrounding media, we model several key cellular phenomena, namely monolayer cell culture, effects of adhesion intensity, growth of epithelial cell through interaction with extra-cellular matrix (ECM), effects of a gap in the ECM, tensegrity and tissue morphogenesis and formation of hollow epithelial acini. The proposed computational model enables one to isolate the effects of biomechanical properties of individual cells and the communication between cells and their microenvironment while simultaneously allowing for the formation of clusters or sheets of cells that act together as one complex tissue.

Understanding the biomechanical properties and the effect of biomechanical force on epithelial cells is key to understanding how epithelial cells form uniquely shaped structures in two or three-dimensional space. Nevertheless, with the limitations and challenges posed by biological experiments at this scale, it becomes advantageous to use mathematical and in silico (computational) models as an alternate solution. This paper introduces a single-cell-based model representing the cross section of a typical tissue. Each cell in this model is an individual unit containing several sub-cellular elements, such as the elastic plasma membrane, enclosed viscoelastic elements that play the role of cytoskeleton, and the viscoelastic elements of the cell nucleus. The cell membrane is divided into segments where each segment (or point) incorporates the cells interaction and communication with other cells and its environment. The model is capable of simulating how cells cooperate and contribute to the overall structure and function of a particular tissue; it mimics many aspects of cellular behavior such as cell growth, division, apoptosis and polarization. The model allows for investigation of the biomechanical properties of cells, cell-cell interactions, effect of environment on cellular clusters, and how individual cells work together and contribute to the structure and function of a particular tissue. To evaluate the current approach in modeling different topologies of growing tissues in distinct biochemical conditions of the surrounding media, we model several key cellular phenomena, namely monolayer cell culture, effects of adhesion intensity, growth of epithelial cell through interaction with extra-cellular matrix (ECM), effects of a gap in the ECM, tensegrity and tissue morphogenesis and formation of hollow epithelial acini. The proposed computational model enables one to isolate the effects of biomechanical properties of individual cells and the communication between cells and their microenvironment while simultaneously allowing for the formation of clusters or sheets of cells that act together as one complex tissue.

Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2].

Craniosynostosis (CS), the premature ossification of cranial sutures, is attributed to increased osteogenic potential of resident osteoblasts, yet the contribution of the surrounding extracellular matrix (ECM) on osteogenic differentiation is unclear. The osteoblast-secreted ECM provides binding sites for cellular adhesion and regulates the transport and signaling of osteoinductive factors secreted by the underlying dura mater. The binding affinity of each osteoinductive factor for the ECM may amplify or mute its relative effect, thus contributing to the rate of suture fusion. The purpose of this paper was to examine the role of ECM composition derived from calvarial osteoblasts on protein binding and its resultant effect on cell phenotype. We hypothesized that potent osteoinductive proteins present during sutural fusion (e.g., bone morphogenetic protein-2 (BMP-2) and transforming growth factor beta-1 (TGF-β1)) would exhibit distinct differences in binding when exposed to ECMs generated by human calvarial osteoblasts from unaffected control individuals (CI) or CS patients. Decellularized ECMs produced by osteoblasts from CI or CS patients were incubated in the presence of BMP-2 or TGF-β1, and the affinity of each protein was analyzed. The contribution of ECM composition to protein binding was interrogated by enzymatically modulating proteoglycan content within the ECM. BMP-2 had a similar binding affinity for each ECM, while TGF-β1 had a greater affinity for ECMs produced by osteoblasts from CI compared to CS patients. Enzymatic treatment of ECMs reduced protein binding. CS osteoblasts cultured on enzymatically-treated ECMs secreted by osteoblasts from CI patients in the presence of BMP-2 exhibited impaired osteogenic differentiation compared to cells on untreated ECMs. These data demonstrate the importance of protein binding to cell-secreted ECMs and confirm that protein-ECM interactions have an important role in directing osteoblastic differentiation of calvarial osteoblasts.

Craniosynostosis (CS), the premature ossification of cranial sutures, is attributed to increased osteogenic potential of resident osteoblasts, yet the contribution of the surrounding extracellular matrix (ECM) on osteogenic differentiation is unclear. The osteoblast-secreted ECM provides binding sites for cellular adhesion and regulates the transport and signaling of osteoinductive factors secreted by the underlying dura mater. The binding affinity of each osteoinductive factor for the ECM may amplify or mute its relative effect, thus contributing to the rate of suture fusion. The purpose of this paper was to examine the role of ECM composition derived from calvarial osteoblasts on protein binding and its resultant effect on cell phenotype. We hypothesized that potent osteoinductive proteins present during sutural fusion (e.g., bone morphogenetic protein-2 (BMP-2) and transforming growth factor beta-1 (TGF-β1)) would exhibit distinct differences in binding when exposed to ECMs generated by human calvarial osteoblasts from unaffected control individuals (CI) or CS patients. Decellularized ECMs produced by osteoblasts from CI or CS patients were incubated in the presence of BMP-2 or TGF-β1, and the affinity of each protein was analyzed. The contribution of ECM composition to protein binding was interrogated by enzymatically modulating proteoglycan content within the ECM. BMP-2 had a similar binding affinity for each ECM, while TGF-β1 had a greater affinity for ECMs produced by osteoblasts from CI compared to CS patients. Enzymatic treatment of ECMs reduced protein binding. CS osteoblasts cultured on enzymatically-treated ECMs secreted by osteoblasts from CI patients in the presence of BMP-2 exhibited impaired osteogenic differentiation compared to cells on untreated ECMs. These data demonstrate the importance of protein binding to cell-secreted ECMs and confirm that protein-ECM interactions have an important role in directing osteoblastic differentiation of calvarial osteoblasts.

Identifying essential factors in cellular interactions and organized movement of cells is important in predicting behavioral phenotypes exhibited by many bacterial cells. We chose to study Myxococcus xanthus, a soil bacterium whose individual cell behavior changes while in groups, leading to spontaneous formation of aggregation center during the early stage of fruiting body development. In this paper, we develop a cell-based computational model that solely relies on experimentally determined parameters to investigate minimal elements required to produce the observed social behaviors in M. xanthus. The model verifies previously known essential parameters and identifies one novel parameter, the active turning, which we define as the ability and tendency of a cell to turn to a certain angle without the presence of any obvious external factors. The simulation is able to produce both gliding pattern and spontaneous aggregation center formation as observed in experiments. The model is tested against several known M. xanthus mutants and our modification of parameter values relevant for the individual mutants produces good phenotypic agreements. This outcome indicates the strong predictive potential of our model for the social behaviors of uncharacterized mutants and their expected phenotypes during development.

Identifying essential factors in cellular interactions and organized movement of cells is important in predicting behavioral phenotypes exhibited by many bacterial cells. We chose to study Myxococcus xanthus, a soil bacterium whose individual cell behavior changes while in groups, leading to spontaneous formation of aggregation center during the early stage of fruiting body development. In this paper, we develop a cell-based computational model that solely relies on experimentally determined parameters to investigate minimal elements required to produce the observed social behaviors in M. xanthus. The model verifies previously known essential parameters and identifies one novel parameter, the active turning, which we define as the ability and tendency of a cell to turn to a certain angle without the presence of any obvious external factors. The simulation is able to produce both gliding pattern and spontaneous aggregation center formation as observed in experiments. The model is tested against several known M. xanthus mutants and our modification of parameter values relevant for the individual mutants produces good phenotypic agreements. This outcome indicates the strong predictive potential of our model for the social behaviors of uncharacterized mutants and their expected phenotypes during development.

UnlabelledAll the opinions in this article are those of the authors and should not be construed to reflect, in any way, those of the Department of Veterans Affairs.BackgroundOur study purpose was to assess the predictive validity of reviewer quality ratings and editorial decisions in a general medicine journal.MethodsSubmissions to the Journal of General Internal Medicine (JGIM) between July 2004 and June 2005 were included. We abstracted JGIM peer review quality ratings, verified the publication status of all articles and calculated an impact factor for published articles (Rw) by dividing the 3-year citation rate by the average for this group of papers; an Rw>1 indicates a greater than average impact.ResultsOf 507 submissions, 128 (25%) were published in JGIM, 331 rejected (128 with review) and 48 were either not resubmitted after revision was requested or were withdrawn by the author. Of 331 rejections, 243 were published elsewhere. Articles published in JGIM had a higher citation rate than those published elsewhere (Rw: 1.6 vs. 1.1, p = 0.002). Reviewer quality ratings of article quality had good internal consistency and reviewer recommendations markedly influenced publication decisions. There was no quality rating cutpoint that accurately distinguished high from low impact articles. There was a stepwise increase in Rw for articles rejected without review, rejected after review or accepted by JGIM (Rw 0.60 vs. 0.87 vs. 1.56, p

UnlabelledAll the opinions in this article are those of the authors and should not be construed to reflect, in any way, those of the Department of Veterans Affairs.BackgroundOur study purpose was to assess the predictive validity of reviewer quality ratings and editorial decisions in a general medicine journal.MethodsSubmissions to the Journal of General Internal Medicine (JGIM) between July 2004 and June 2005 were included. We abstracted JGIM peer review quality ratings, verified the publication status of all articles and calculated an impact factor for published articles (Rw) by dividing the 3-year citation rate by the average for this group of papers; an Rw>1 indicates a greater than average impact.ResultsOf 507 submissions, 128 (25%) were published in JGIM, 331 rejected (128 with review) and 48 were either not resubmitted after revision was requested or were withdrawn by the author. Of 331 rejections, 243 were published elsewhere. Articles published in JGIM had a higher citation rate than those published elsewhere (Rw: 1.6 vs. 1.1, p = 0.002). Reviewer quality ratings of article quality had good internal consistency and reviewer recommendations markedly influenced publication decisions. There was no quality rating cutpoint that accurately distinguished high from low impact articles. There was a stepwise increase in Rw for articles rejected without review, rejected after review or accepted by JGIM (Rw 0.60 vs. 0.87 vs. 1.56, p

BackgroundDrinking water contaminated by chemicals or pathogens is a major public health threat in the developing world. Responses to this threat often require water consumers (households or communities) to improve their own management or treatment of water. One approach hypothesized to increase such positive behaviors is increasing knowledge of the risks of unsafe water through the dissemination of water contamination data. This paper reviews the evidence for this approach in changing behavior and subsequent health outcomes.Methods/principal findingsA systematic review was conducted for studies where results of tests for contaminants in drinking water were disseminated to populations whose water supply posed a known health risk. Studies of any design were included where data were available from a contemporaneous comparison or control group. Using multiple sources >14,000 documents were located. Six studies met inclusion criteria (four of arsenic contamination and two of microbiological contamination). Meta-analysis was not possible in most cases due to heterogeneity of outcomes and study designs. Outcomes included water quality, change of water source, treatment of water, knowledge of contamination, and urinary arsenic. Source switching was most frequently reported: of 5 reporting studies 4 report significantly higher rates of switching (26-72%) among those who received a positive test result and a pooled risk difference was calculate for 2 studies (RD = 0.43 [CI0.4.0-0.46] 6-12 months post intervention) suggesting 43% more of those with unsafe wells switched source compared to those with safe wells. Strength of evidence is low since the comparison is between non-equivalent groups. Two studies concerning fecal contamination reported non-significant increases in point-of-use water treatment.ConclusionDespite the publication of some large cohort studies and some encouraging results the evidence base to support dissemination of contamination data to improve water management is currently equivocal. Rigorous studies on this topic are needed, ideally using common outcome measures.

BackgroundDrinking water contaminated by chemicals or pathogens is a major public health threat in the developing world. Responses to this threat often require water consumers (households or communities) to improve their own management or treatment of water. One approach hypothesized to increase such positive behaviors is increasing knowledge of the risks of unsafe water through the dissemination of water contamination data. This paper reviews the evidence for this approach in changing behavior and subsequent health outcomes.Methods/principal findingsA systematic review was conducted for studies where results of tests for contaminants in drinking water were disseminated to populations whose water supply posed a known health risk. Studies of any design were included where data were available from a contemporaneous comparison or control group. Using multiple sources >14,000 documents were located. Six studies met inclusion criteria (four of arsenic contamination and two of microbiological contamination). Meta-analysis was not possible in most cases due to heterogeneity of outcomes and study designs. Outcomes included water quality, change of water source, treatment of water, knowledge of contamination, and urinary arsenic. Source switching was most frequently reported: of 5 reporting studies 4 report significantly higher rates of switching (26-72%) among those who received a positive test result and a pooled risk difference was calculate for 2 studies (RD = 0.43 [CI0.4.0-0.46] 6-12 months post intervention) suggesting 43% more of those with unsafe wells switched source compared to those with safe wells. Strength of evidence is low since the comparison is between non-equivalent groups. Two studies concerning fecal contamination reported non-significant increases in point-of-use water treatment.ConclusionDespite the publication of some large cohort studies and some encouraging results the evidence base to support dissemination of contamination data to improve water management is currently equivocal. Rigorous studies on this topic are needed, ideally using common outcome measures.

Quantitative description of cell mechanics has challenged biological scientists for the past two decades. Various structural models have been attempted to analyze the structure of the cytoskeleton. One important aspect that has been largely ignored in all these modeling approaches is related to the flexural and buckling behavior of microtubular filaments. The objective of this paper is to explore the influence of this flexural and buckling behavior in cytoskeletal mechanics.In vitro the microtubules are observed to buckle in the first mode, reminiscent of a free, simply-supported beam. In vivo images of microtubules, however, indicate that the buckling mostly occurs in higher modes. This buckling mode switch takes place mostly because of the lateral support of microtubules via their connections to actin and intermediate filaments. These lateral loads are exerted throughout the microtubule length and yield a considerable bending behavior that, unless properly accounted for, would produce erroneous results in the modeling and analysis of the cytoskeletal mechanics.One of the promising attempts towards mechanical modeling of the cytoskeleton is the tensegrity model, which simplifies the complex network of cytoskeletal filaments into a combination merely of tension-bearing actin filaments and compression-bearing microtubules. Interestingly, this discrete model can qualitatively explain many experimental observations in cell mechanics. However, evidence suggests that the simplicity of this model may undermine the accuracy of its predictions, given the model's underlying assumption that "every single member bears solely either tensile or compressive behavior," i.e. neglecting the flexural behavior of the microtubule filaments. We invoke an anisotropic continuum model for microtubules and compare the bending energy stored in a single microtubule with its axial strain energy at the verge of buckling. Our results suggest that the bending energy can exceed the axial energy of microtubules by 40 folds. A modification to tensegrity model is, therefore, proved necessary in order to take into account the flexural response of microtubules. The concept of "bendo-tensegrity" is proposed as a modification to contemporary cytoskeletal tensegrity models.

Quantitative description of cell mechanics has challenged biological scientists for the past two decades. Various structural models have been attempted to analyze the structure of the cytoskeleton. One important aspect that has been largely ignored in all these modeling approaches is related to the flexural and buckling behavior of microtubular filaments. The objective of this paper is to explore the influence of this flexural and buckling behavior in cytoskeletal mechanics.In vitro the microtubules are observed to buckle in the first mode, reminiscent of a free, simply-supported beam. In vivo images of microtubules, however, indicate that the buckling mostly occurs in higher modes. This buckling mode switch takes place mostly because of the lateral support of microtubules via their connections to actin and intermediate filaments. These lateral loads are exerted throughout the microtubule length and yield a considerable bending behavior that, unless properly accounted for, would produce erroneous results in the modeling and analysis of the cytoskeletal mechanics.One of the promising attempts towards mechanical modeling of the cytoskeleton is the tensegrity model, which simplifies the complex network of cytoskeletal filaments into a combination merely of tension-bearing actin filaments and compression-bearing microtubules. Interestingly, this discrete model can qualitatively explain many experimental observations in cell mechanics. However, evidence suggests that the simplicity of this model may undermine the accuracy of its predictions, given the models underlying assumption that every single member bears solely either tensile or compressive behavior, i.e. neglecting the flexural behavior of the microtubule filaments. We invoke an anisotropic continuum model for microtubules and compare the bending energy stored in a single microtubule with its axial strain energy at the verge of buckling. Our results suggest that the bending energy can exceed the axial energy of microtubules by 40 folds. A modification to tensegrity model is, therefore, proved necessary in order to take into account the flexural response of microtubules. The concept of bendo-tensegrity is proposed as a modification to contemporary cytoskeletal tensegrity models.

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.

BackgroundOcular albinism type 1, an X-linked disease characterized by the presence of enlarged melanosomes in the retinal pigment epithelium (RPE) and abnormal crossing of axons at the optic chiasm, is caused by mutations in the OA1 gene. The protein product of this gene is a G-protein-coupled receptor (GPCR) localized in RPE melanosomes. The Oa1-/- mouse model of ocular albinism reproduces the human disease. Oa1 has been shown to immunoprecipitate with the Gαi subunit of heterotrimeric G proteins from human skin melanocytes. However, the Gαi subfamily has three highly homologous members, Gαi1, Gαi2 and Gαi3 and it is possible that one or more of them partners with Oa1. We had previously shown by in-vivo studies that Gαi3-/- and Oa1-/- mice have similar RPE phenotype and decussation patterns. In this paper we analyze the specificity of the Oa1-Gαi interaction.MethodologyBy using the genetic mouse models Gαi1-/-, Gαi2-/-, Gαi3-/- and the double knockout Gαi1-/-, Gαi3-/- that lack functional Gαi1, Gαi2, Gαi3, or both Gαi1 and Gαi3 proteins, respectively, we show that Gαi3 is critical for the maintenance of a normal melanosomal phenotype and that its absence is associated with changes in melanosomal size and density. GST-pull-down and immunoprecipitation assays conclusively demonstrate that Gαi3 is the only Gαi that binds to Oa1. Western blots show that Gαi3 expression is barely detectable in the Oa1-/- RPE, strongly supporting a previously unsuspected role for Gαi3 in melanosomal biogenesis.ConclusionOur results identify the Oa1 transducer Gαi3 as the first downstream component in the Oa1 signaling pathway.

BackgroundOcular albinism type 1, an X-linked disease characterized by the presence of enlarged melanosomes in the retinal pigment epithelium (RPE) and abnormal crossing of axons at the optic chiasm, is caused by mutations in the OA1 gene. The protein product of this gene is a G-protein-coupled receptor (GPCR) localized in RPE melanosomes. The Oa1-/- mouse model of ocular albinism reproduces the human disease. Oa1 has been shown to immunoprecipitate with the Gαi subunit of heterotrimeric G proteins from human skin melanocytes. However, the Gαi subfamily has three highly homologous members, Gαi1, Gαi2 and Gαi3 and it is possible that one or more of them partners with Oa1. We had previously shown by in-vivo studies that Gαi3-/- and Oa1-/- mice have similar RPE phenotype and decussation patterns. In this paper we analyze the specificity of the Oa1-Gαi interaction.MethodologyBy using the genetic mouse models Gαi1-/-, Gαi2-/-, Gαi3-/- and the double knockout Gαi1-/-, Gαi3-/- that lack functional Gαi1, Gαi2, Gαi3, or both Gαi1 and Gαi3 proteins, respectively, we show that Gαi3 is critical for the maintenance of a normal melanosomal phenotype and that its absence is associated with changes in melanosomal size and density. GST-pull-down and immunoprecipitation assays conclusively demonstrate that Gαi3 is the only Gαi that binds to Oa1. Western blots show that Gαi3 expression is barely detectable in the Oa1-/- RPE, strongly supporting a previously unsuspected role for Gαi3 in melanosomal biogenesis.ConclusionOur results identify the Oa1 transducer Gαi3 as the first downstream component in the Oa1 signaling pathway.

Uracil is removed from DNA by the conserved enzyme uracil DNA N-glycosylase (UNG). Previously, we observed that inhibiting UNG in Xenopus egg extracts blocked assembly of CENP-A, a histone H3 variant. CENP-A is an essential protein in all species, since it is required for chromosome segregation during mitosis. Thus, the implication of UNG in CENP-A assembly implies that UNG would also be essential, but UNG mutants lacking catalytic activity are viable in all species. In this paper, we present evidence that UNG2 colocalizes with CENP-A and H2AX phosphorylation at centromeres in normally cycling cells. Reduction of UNG2 in human cells blocks CENP-A assembly, and results in reduced cell proliferation, associated with increased frequencies of mitotic abnormalities and rapid cell death. Overexpression of UNG2 induces high levels of CENP-A assembly in human cells. Using a multiphoton laser approach, we demonstrate that UNG2 is rapidly recruited to sites of DNA damage. Taken together, our data are consistent with a model in which the N-terminus of UNG2 interacts with the active site of the enzyme and with chromatin.

Uracil is removed from DNA by the conserved enzyme uracil DNA N-glycosylase (UNG). Previously, we observed that inhibiting UNG in Xenopus egg extracts blocked assembly of CENP-A, a histone H3 variant. CENP-A is an essential protein in all species, since it is required for chromosome segregation during mitosis. Thus, the implication of UNG in CENP-A assembly implies that UNG would also be essential, but UNG mutants lacking catalytic activity are viable in all species. In this paper, we present evidence that UNG2 colocalizes with CENP-A and H2AX phosphorylation at centromeres in normally cycling cells. Reduction of UNG2 in human cells blocks CENP-A assembly, and results in reduced cell proliferation, associated with increased frequencies of mitotic abnormalities and rapid cell death. Overexpression of UNG2 induces high levels of CENP-A assembly in human cells. Using a multiphoton laser approach, we demonstrate that UNG2 is rapidly recruited to sites of DNA damage. Taken together, our data are consistent with a model in which the N-terminus of UNG2 interacts with the active site of the enzyme and with chromatin.

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.

Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.