Can genetics play a role in education and well-being? – USC News

July 4, 2017 student

When Daniel Benjamin was just beginning his PhD program in economics in 2001, he attended a conference with his graduate school advisers. They took in a presentation on neuroeconomics, a nascent field dealing with how the human brain goes about making decisions.

Afterward, as they took a stroll outside, they couldnt stop talking about what they had learned, how novel and intriguing it was. What would be next, they wondered. What would come after neuroeconomics?

The human genome project had just been completed, and we decided that even more fundamental than the brain would be genes, and that someday this was going to matter a lot for social science, said Benjamin, associate professor (research) of economics at the USC Dornsife College of Letters, Arts and Sciences Center for Economic and Social Research (CESR). Indeed, his excitement that day was the foundation of a visionary academic path.

Fast forward to today. Genoeconomics is now an emerging area of social science that incorporates genetic data into the work that economists do. Its based on the idea that a persons particular combination of genes is related to economic behavior and life outcomes such as educational attainment, fertility, obesity and subjective well-being.

Theres this rich new source of data that has only become available recently, said Benjamin, also co-director of the Social Science Genetic Association Consortium, which brings about cooperation among medical researchers, geneticists and social scientists.

Collecting genetic data and creating the large data sets used by economists and other social scientists have become increasingly affordable, and new analytical methods are getting more and more powerful as these data sets continue to grow. The big challenge, he said, is figuring out how scientists can leverage this new data to address a host of important policy questions.

Were ultimately interested in understanding how genes and environments interact to produce the kinds of outcomes people have in their lives, and then what kinds of policies can help people do better. That is really what economics is about and were trying to use genetics to do even better economics.

Only a handful of economists are working with genetics, but this brand of research is perfectly at home at CESR. The center, founded three years ago, was conceived as a place where visionary social science could thrive and where research could be done differently than in the past.

Being in a place where thats the shared vision is pretty rare, said econometrician Arie Kapteyn, professor (research) of economics and CESR director. Theres no restriction on which way you want to go or what you want to do. It doesnt mean that there are no restrictions on resources, but its the opportunity to think about your vision of whats really exciting in social science research. Then being able to actually implement it is absolutely fantastic.

The mission of CESR is discovering how people around the world live, think, interact, age and make important decisions. The centers researchers are dedicated to innovation and combining their analysis to deepen the understanding of human behavior in a variety of economic and social contexts.

What we try to do is mold a disciplinary science in a very broad sense, Kapteyn said. Because todays problems in society, theyre really all multidisciplinary.

Case in point: Benjamins work combining genetics and economics.

The flagship research effort for Benjamins CESR research group deals with genes and education. In a 2016 study, the team identified variants in 74 genes that are associated with educational attainment. In other words, people who carry more of these variants, on average, complete more years of formal schooling.

Benjamin hopes to use this data in a holistic way to create a predictive tool.

Were also creating methods for combining the information in a persons entire genome into a single variable that can be used to partially predict how much education a persons going to get.

Daniel Benjamin

Rather than just identifying specific genes, he said, were also creating methods for combining the information in a persons entire genome into a single variable that can be used to partially predict how much education a persons going to get.

The young field of genoeconomics is still somewhat controversial, and Benjamin is careful to point out that individual genes dont determine behavior or outcome.

The effect of any individual gene on behavior is extremely small, Benjamin explained, but the effects of all the genes combined on almost any behavior were interested in is much more substantial. Its the combined information of many genes that has predictive power, and that can be most useful for social scientists.

While the cohort of researchers actively using the available genome-wide data in this way is still somewhat limited, Benjamin says it is growing quickly.

I think across the social sciences, researchers are seeing the potential for the data, and people are starting to use it in their work and getting excited about it, but right now its still a small band of us trying to lay the foundations.

Were putting together huge data sets of hundreds of thousands of people approaching a million people in our ongoing work on educational attainment because you need those really big sample sizes to accurately detect the genetic influences.

As CESR works to improve social welfare by informing and influencing decision-making in the public and private sectors, big data such as Benjamins is a growing part of that process, according to Kapteyn.

What big data reflects is the fact that nowadays there are so many other ways in which we can learn about behavior, he said. As a result, I think well see many more breakthroughs and gain a much better understanding of whats going on in the world and in social science than in the past.

I think were really at the beginning of something pretty spectacular. What we are doing is really only scratching the surface theres so much more that can be done.

More stories about: Big Data, Economics, Research

Report comes as the university nears the opening of USC Village, the largest economic development project in the history of South Los Angeles.

The USC Dornsife Economics Department launches the USC Economics Review to spotlight students research.

The program at USC Dornsife offers tailored training in preparation for Fall Career Fair.

Conference covers methods of prompting change in human behavior for the public good.

Continued here:
Can genetics play a role in education and well-being? - USC News

Genetics

BRIEF-Interleukin Genetics to explore strategic alternatives, reduce workforce – Reuters

July 4, 2017 student

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Immunology

Immunology, one cell at a time : Nature News & Comment – Nature.com

July 4, 2017 student

Amir Giladi & Ido Amit

Single-cell genomics can identify unique immune cells (red) involved in Alzheimer's disease.

For more than a century, scientists have tried to characterize the different functions of the 10 trillion to 50 trillion cells of the human body from neurons, which can reach 1 metre in length, to red blood cells, which are around 8 micrometres wide. Such efforts have helped to identify important cell types and pathways that are involved in human physiology and pathology.

But it has become apparent that the research tools of the past few decades fail to capture the full complexity of cell diversity and function. (These tools include fluorescent tags fused to antibodies that bind to specific proteins on the surfaces of cells, known as cell-surface markers, and sequencing in bulk of the RNA or DNA of thousands of seemingly identical cells.) This failure is partly because many cells with completely different functions have similar shapes or produce the same markers.

Single-cell genomics is transforming the ability of biologists to characterize cells. The new techniques that have emerged aim to capture individual cells and determine the sequences of the molecules of RNA and DNA that they contain. The shift in approach is akin to the change in how cells and molecules could be viewed during the 1980s, following advances in microscopy and the tagging and sorting of cells.

In the past five years, several groups of biologists, including our own laboratory, have gone from measuring the expression of a few genes in a handful of cells to surveying thousands of genes in hundreds of thousands of cells from intact tissues. New cell types1, 2, cellular states and pathways are being uncovered regularly as a result.

Our lab was one of the first to study the immune system using single-cell genomics. The tools are particularly suited to this task because the heterogeneity and plasticity of cells are integral to how the immune system works the nature of each agent that could attack the body being impossible to know ahead of time.

Exploiting single-cell genomics fully will require scientists and clinicians to make experimental and analytical adjustments. In particular, we must be ready to jettison assumptions about cell types and cellular states, and to rebuild representations of cellular networks.

The cells of the immune system, which patrol the blood and dwell in tissues, have many functions. They protect the body from pathogens and cancer, and orchestrate metabolism and the formation of organs. They are involved in almost every activity that regulates the bodys internal environment, from the development and remodelling of tissues to the clearance of dying cells and debris. So their dysfunction can cause many problems. For instance, deregulated immune cells can attack healthy cells and cause autoimmune conditions such as lupus, type 1 diabetes or multiple sclerosis.

A first step towards harnessing the immune system for therapeutic use is to characterize the types of cell that occupy a specific area (such as the surroundings of a tumour). Another is to map the unique processes and pathways that the cells are involved in, the genes they express and the cells interactions and responses to environmental cues. Over the past 40 years, meticulous approaches based on genetic labelling have enabled researchers to identify dozens of types and functions of immune cells. For example, the use of antibodies fused to fluorescent tags that bind to and flag specific cell-surface markers established the basic taxonomy of immune cells including several types of T cells, B cells, monocytes and granulocytes. Such studies also kick-started the search for treatments known as immunotherapies, which harness the body's natural defences to fight disease.

Increasingly, however, these techniques hint that the world of immune cells is more complex than current categories allow. Immune cells seem to change their functions depending on their surroundings. For instance, macrophages (as identified by their cell-surface markers) might have one function in the gut yet a completely different one in the brain3. Also, molecular markers cannot fully describe the functional diversity of cells in different immune contexts. For example, a group of immune cells that suppresses the immune response around tumours (myeloid-derived suppressor cells) has been shown to express markers from both monocytes and granulocytes4.

In short, conventional methods based on populations of cells are proving too blunt a tool with which to tease apart complex immune assemblies5.

In the past five years, technologies for capturing single cells have improved dramatically. Some approaches rely on placing cells inside miniature vessels, one at a time; others capture individual cells inside droplets of oil. Meanwhile, bioinformaticians have built algorithms for representing multidimensional data, identifying distinct cellular states and modelling the transitions between such states6.

Thanks to these developments, researchers can now capture hundreds of thousands (or even millions) of cells and accurately measure the DNA, RNA or protein content of each (see Scale up). Gene-editing tools such as the CRISPRCas9 system can be used to introduce a specific mutation into the genome of one cell, and then a different alteration into the next7. Thus, the function of dozens of genes can be inferred from just one experiment by reading the resulting RNA barcode in parallel with the single-cell genetic information.

With such measurements, researchers can potentially record the functional states of many cells at once8. They can also probe the ancestry of individual cells9 or identify mutations in a particular cells DNA, as well as track communication between cells. In other words, single-cell genomics allows researchers to build an accurate representation of the entire make-up of a tissue10, such as a specific organ or a tumour, or of a multicellular process such as the immune systems response to an infection. Importantly, it enables them to do this without making prior assumptions based on, for instance, the participating cell types and their characteristics.

About 20 labs worldwide have fully embraced single-cell genomics, and even more are trying out the approach. In the past few years, numerous papers have been published that describe new types of immune cell and previously unknown pathways involved in various conditions.

For instance, 15 subtypes of innate lymphoid cell, which are similar to T cells but do not express the T-cell receptor, have been identified in the gut11. Different progenitors of immune-cell lineages have been uncovered in the bone marrow12. Specific types of immune cell have been associated with particular stages of tumour growth13, 14. And various types of microglial cell have been identified in the brain during development.

Last month, our lab reported the discovery of a new type of immune cell in the brain, disease-associated microglia (DAM)15. Our experiments indicate that DAM break down dead cells and protein aggregates called plaques in the brains of mice engineered to express mutated proteins associated with Alzheimers disease.

More than a decade of population-based assays, including cell sorting using specific cell-surface markers and bulk RNA sequencing, had failed to flag these cells. Only by individually sequencing the RNA of each of the immune cells in a sample of brain were we able to find a rare subpopulation of microglia that may open up fresh approaches to treating Alzheimers disease.

It is early days for single-cell genomics. But already, a number of important lessons can be learnt from the experiences of our lab and those of others.

First, it is clear that many of the current categories of immune cells, such as T cells or monocytes, encompass heterogeneous populations. To probe cellular complexity, researchers must therefore cast their nets wide, and try to collect all immune cells within a tissue or region of interest. This is a very different approach from that used with methods based on cell-surface markers, which aim to obtain as pure a sample as possible.

Second, success will depend, in part, on the extent to which researchers preserve the states of cells and the original composition of a tissue. Cell stress or death should be minimized to ensure that tissue preparation does not favour specific cell types. (Some are more sensitive to heat stress, for example, than others.)

Single-cell genomics will soon be commonplace in basic and applied immunology research.

Third, bioinformaticians will need to develop scalable and robust algorithms to cope with greater numbers of cells, conflicting or overlapping programs of gene expression and fleeting developmental stages.

Fourth, after researchers have characterized all of the immune cells in a sample, they will need to find molecular markers that can be used to either enrich or deplete certain cell types in further samples. Tissues comprise trillions of cells with myriad molecular characteristics and functions, and the types or states of these cells may vary in abundance by many orders of magnitude. For instance, in the brains of healthy mice, our newly identified population of DAM makes up less than 0.01% of cells15. Thus, repeated unbiased sampling to characterize rare populations will keep on accumulating cells that are not those of interest.

Other experimental, computational and statistical approaches can help to overcome this problem. Importantly, once a rare population of cells is identified using single-cell genomics, they can be purified and experiments conducted only on them. In our recent study, for instance, we used cell-surface markers to isolate DAM and then assessed their role in Alzheimers disease using various techniques, including fluorescent labelling.

A fifth lesson regards a considerable drawback of current single-cell technologies. They capture snapshots of dynamic systems, in which cells are devoid of important context spatial, temporal, clonal and epigenetic. Without knowing where a profiled cell came from, who its neighbours were or what it developed from, it is hard to model complex processes such as tissue formation or a tumours interaction with nearby immune cells.

One way around this problem might be to combine several layers of information from the same cell. Genetic fluorescent labelling, for instance, can be used to track changes in the state of a cell over time or to find exactly where it is in a tissue.

Ultimately, textbook definitions and long-held beliefs about cellular identities, such as the distinction between cell type and cell state, will almost certainly need to be rethought. Some classifications of subgroups based on extra markers may prove helpful in the short term, but can quickly become unwieldy. For example, instead of being able to refer to T-helper (TH) cells, researchers must now refer to one of about a dozen subcategories, including TH1 cells and TH2 cells16. And such an approach may continue to overlook the true functional complexity of the immune ecosystem.

A more workable solution may be for researchers to replace rigid classifications with assemblies of gene-expression programs (see Genetic microscope). These elaborate gene maps could represent all cell types and states, including those from different physiological and pathological contexts. Such maps would allow biologists to define cells not just by one fate, lineage or function, but by the combination of all of these. It would also allow these functional entities to be compared across organisms.

Claire Welsh/Nature

Single-cell genomics will soon be commonplace in basic and applied immunology research. This is thanks to efforts to make the tools affordable, standardized and accessible to academia, the biotech industry and the clinic. We predict that, within a decade, blood samples or biopsies will be routinely sent for single-cell genomic analysis, and the entire immune composition of patients analysed and compared with all known healthy and diseased states.

Also likely to undergo rapid transformation is our understanding of tumours and tumour stem cells, processes such as neuronal development, metabolic disorders and neural function.

Almost every scientific breakthrough has originated from a new measurement or observation that enabled scientists to come up with new hypotheses and merge them into unifying theories. Robert Hookes observation of cells as units of multicellular organisms, James Watson and Francis Cricks discovery of the 3D structure of DNA and Edwin Hubbles detection of galaxies beyond the Milky Way could not have been achieved without new ways of seeing.

The molecular microscope of single-cell genomics is already adding to our knowledge of cell types and gene pathways. But for single-cell genomics to tell us something truly new about the blueprint of humans, we will have to address how individual cells communicate to achieve shared goals.

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Immunology, one cell at a time : Nature News & Comment - Nature.com

Cell Biology

Single-cell sequencing made simple – Nature.com

July 4, 2017 medical

illustration by the project twins

Single-cell biology is a hot topic these days. And at the cutting edge of the field is single-cell RNA sequencing (scRNA-seq).

Conventional bulk methods of RNA sequencing (RNA-seq) process hundreds of thousands of cells at a time and average out the differences. But no two cells are exactly alike, and scRNA-seq can reveal the subtle changes that make each one unique. It can even reveal entirely new cell types.

For instance, after using scRNA-seq to probe some 2,400 immune-system cells, Aviv Regev of the Broad Institute in Cambridge, Massachusetts, and her colleagues came across some dendritic cells that had potent T-cell-stimulating activity (A.-C. Villani et al. Science 356, eaah4573; 2017). Regev says that a vaccine to stimulate these cells could potentially boost the immune system and protect against cancer.

But such discoveries are hard-won. Its much more difficult to manipulate individual cells than large populations, and because each cell yields only a tiny amount of RNA, theres no room for error. Another problem is analysing the enormous amounts of data that result not least because the tools used can be unintuitive.

Typically, RNA-seq data is analysed by laboriously typing commands into a Unix operating system. Data files are passed from one software package to the next, with each tool tackling one step in the process: genome alignment, quality control, variant calling and so on.

The process is complicated. But for bulk RNA-seq, at least, a consensus has emerged as to which algorithms work best for each step and how they should be run. As a result, pipelines now exist that are, if not exactly plug-and-play, at least tractable for non-experts. To analyse differences in gene expression, says Aaron Lun, a computational biologist at Cancer Research UK in Cambridge, bulk RNA-seq is pretty much a solved problem.

The same cannot be said for scRNA-seq: researchers are still working out what they can do with the data sets and which algorithms are the most useful.

But a range of online resources and tools are beginning to ease the process of scRNA-seq data analysis. One page at GitHub, called Awesome Single Cell (go.nature.com/2rmb1hp), catalogues more than 70 tools and resources, covering every step of the analysis process. The field has spawned a cottage industry of computational-biology tools, says Cole Trapnell, a biologist at the University of Washington in Seattle.

Lana Garmire, a bioinformatician at the University of Hawaii in Honolulu, laid out the basic steps of scRNA-seq data analysis (and some 48 tools to perform them) in a review published last year (O. B. Poirion et al. Front. Genet. 7, 163; 2016). Although each experiment is unique, she says, most analysis pipelines follow the same steps to clean up and filter the sequencing data, work out which transcripts are expressed and correct for differences in amplification efficiency. Researchers then run one or more secondary analyses to detect subpopulations and other functions.

In many cases, says Christina Kendziorski, a biostatistician at the University of WisconsinMadison, the tools used in bulk RNA-seq can be applied to scRNA-seq. But fundamental differences in the data mean that this is not always possible. For one thing, single-cell data are noisier, says Lun. With so little RNA to work with, small changes in amplification and capture efficiencies can produce large differences from cell to cell and day to day that have nothing to do with biology. Researchers must therefore be vigilant for batch effects, in which seemingly identical cells prepared on different days differ for purely technical reasons, and for dropouts genes that are expressed in the cell but not picked up in the sequence data.

Another challenge is the scale, says Joshua Ho, a bioinformatician at the Victor Chang Cardiac Research Institute in Sydney, Australia. A typical bulk RNA-seq experiment involves a handful of samples, but scRNA-seq studies can involve thousands. Tools that can handle a dozen samples often slow to a crawl when confronted with ten or a hundred times as many. (Hos Falco software taps on-demand cloud-computing resources to address that problem.)

Even the seemingly simple question of what constitutes a good cell preparation is complicated in the world of scRNA-seq. Luns workflow assumes that most of the cells have approximately equivalent RNA abundances. But that assumption isnt necessarily true, he says. For instance, he says, naive T cells, which have never been activated by an antigen and are relatively quiescent, tend to have less messenger RNA than other immune cells and could end up being removed during analysis because a program thinks there is insufficient RNA for processing.

Perhaps most significantly, researchers performing scRNA-seq tend to ask different questions from those analysing bulk RNA. Bulk analyses typically investigate how gene expression differs between two or more treatment conditions. But researchers working with single cells are often aiming to identify new cell types or states or reconstruct developmental cellular pathways. Because the aims are different, that necessarily requires a different set of tools to analyse the data, says Lun.

One common type of single-cell analysis, for instance, is dimensionality reduction. This process simplifies data sets to facilitate the identification of similar cells. According to Martin Hemberg, a computational biologist at the Wellcome Trust Sanger Institute in Cambridge, UK, scRNA-seq data represent each cell as a list of 20,000 gene-expression values. Dimensionality-reduction algorithms such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) effectively project those shapes into two or three dimensions, making clusters of similar cells apparent. Another popular application is pseudo-time analysis. Trapnell developed the first such tool, called Monocle, in 2014. The software uses machine learning to infer from an scRNA-seq experiment the sequence of gene-expression changes that accompany cellular differentiation, much like inferring the path of a foot race by photographing the runners from the air, Trapnell says.

Other tools address subpopulation detection (for instance, Pagoda, from Peter Kharchenko at Harvard Medical School in Boston, Massachusetts) and spatial positioning, which uses data on the distribution of gene expression in tissues to determine where in a tissue each transcriptome arose. Rahul Satija of the New York Genome Center in New York City, who developed one such tool, Seurat, as a postdoc with Regev, says that the software uses these data to position cells as points in 3D space. Thats why we named the package Seurat, he explains, because the dots reminded us of points on a pointillist painting.

Although targeted to specific tasks, these tools often address multiple functions. Seurat, for instance, powered the cell-subpopulation analysis Regevs team performed to identify new classes of immune cells.

Most scRNA-seq tools exist as Unix programs or packages in the programming language R. But relatively few biologists are comfortable working in those environments, says Gene Yeo, a bioinformatician at the University of California, San Diego. Even if they are, they may lack the time required to download and configure everything to make such tools work.

Some ready-to-use pipelines have been developed. And there are end-to-end graphical tools too, including the commercial GenSeq package from FlowJo, as well as a pair of open-source web tools: Granatum from Garmires group, and ASAP (the Automated Single-cell Analysis Pipeline) from the lab of Bart Deplancke, a bioengineer at the Swiss Federal Institute of Technology in Lausanne.

ASAP and Granatum use a web browser to provide relatively simple, interactive workflows that allow researchers to explore their data graphically. Users upload their data and the software walks them through the steps one by one. For ASAP, that means taking data through preprocessing, visualization, clustering and differential gene-expression analysis; Granatum allows pseudo-time analyses and the integration of protein-interaction data as well.

According to both Garmire and Deplancke, ASAP and Granatum were designed to allow researchers and computational biologists to work together. Researchers used to think of [bioinformaticians] as magical creatures who just get the data and magically generate the result, says Xun Zhu, a PhD student at the University of Hawaii at Manoa, and lead developer on Granatum. Now they can participate a little bit in terms of tuning the parameters. And thats a good thing.

The tools arent perfect for every situation, of course. A pipeline that excels at identifying cell types, for instance, might stumble with pseudo-time analysis. Plus, appropriate methods are very data-set dependent, says Sandrine Dudoit, a biostatistician at the University of California, Berkeley. The methods and tuning parameters may need to be adjusted to account for variables such as sequencing length. But Marioni says its important not to put complete faith in the pipeline. Just because the satellite navigation tells you to drive into the river, you dont drive into the river, he says.

For beginners, caution is warranted. Bioinformatics tools can almost always yield an answer; the question is, does that answer mean anything? Dudoits advice is do some exploratory analysis, and verify that the assumptions underlying your chosen algorithms make sense.

Some analytical tasks still remain challenging, says Satija, including comparing data sets across experimental conditions or organisms and integrating data from different omics. (A planned update to Seurat should address the former issue, he notes.)

But enough tools exist to keep most researchers occupied. Kendziorski suggests that people who are interested just dive in. Each new tool can unveil another facet of biology; just keep your eyes on the science, and be judicious in your choice.

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Single-cell sequencing made simple - Nature.com

Cell Biology

‘Cellular mosh pit’ helps researchers understand tissue formation – Phys.Org

July 4, 2017 student

July 3, 2017 by Grant Hill

Researchers led by the University of Dundee have developed a way of exploring a 'cellular mosh pit' that may shed light on processes such as embryo development, wound healing and cancer growth.

Working with colleagues at the University of Aberdeen, they have developed the Active Vertex Model (AVM), a new computational model that allows scientists to examine in greater depth than ever before how cells move in a variety of biological processes.

Epithelial tissues, such as the skin or lining of the internal organs, act as barriers to the environment. To form an effective barrier, cells in epithelia have to be closely packed together. These epithelial tissues are formed and shaped during embryonic development, while not disrupting the tissue's connectivity.

This is achieved via carefully orchestrated exchanges between neighbours so-called cell intercalations. These intercalations also play key roles during tissue repair and regeneration. The mechanisms behind intercalations a process of fundamental importance for proper tissue function are not fully understood.

The AVM will allow much larger areas of individual cells to be studied, almost 10 times the size previously possible. This will provide scientists with a greater understanding of these active systems and the mechanics of tissues, something has previously been likened to watching fans mosh away at gigs.

"Understanding the emergence of collective behaviour of cells in tissues is what our model is interested in explaining," said lead author Dr Rastko Sknepnek, a lecturer in Physics within Dundee's Division of Computational Biology. "This behaviour has hallmarks of an active system. Active systems can be a school of fish, a developing embryo or even a mosh pit at a rock concert, which is quite a well-known analogy among people working in this area.

"Each person in a mosh pit has their own choice on where to move but is also affected by those around them. If you compare the biology we are interested in with this scenario, each person is like a cell, and we have built a model that can look at the activity and movement of the people in the mosh pit."

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The AVM combines the physics of active systems, which is credited with describing behaviours of systems such as flocks of birds, schools of fish and human crowds, with the Vertex Model commonly used to study mechanical properties of epithelial tissues. The AVM not only allows for very efficient computations but also incorporates the cell intercalation events in a natural way.

The interdisciplinary project combined the biological expertise of Professor Kees Weijer, from the University's School of Life Sciences, with the modelling knowledge of Dr Sknepnek and Dr Silke Henkes, a lecturer in Physics at the Institute for Complex Systems and Mathematical Biology at the University of Aberdeen. Much of the work was carried out by Daniel Barton, a postgraduate student in Dr Sknepnek's lab.

The next stage of the project will see the research team apply the model to Professor Weijer's research on cell and tissue dynamics during embryogenesis, the process by which the embryo forms and develops.

"We will now carry out work with existing biological research that will to improve the model further," said Dr Sknepnek. "We want to work with other researchers to expand the model to other systems, in particular curved surfaces such as those found in the gut."

Owing to its efficiency, the AVM will allow researchers to explore cell motion patterns over previously inaccessible sizes, while retaining the resolution of individual cells. This may help understand how collectives of cells organise and control their behaviour at the scale of the entire tissue, providing new insights into processes such as development of embryos and cancer metastasis.

The AVM is publicly available under a non-restrictive open source licence and can downloaded at https://github.com/sknepneklab/SAMoS.

The research was funded by BBSRC and is published in the latest edition of the journal PLoS Computer Biology.

Explore further: Physical basis of tissue coordination uncovered

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'Cellular mosh pit' helps researchers understand tissue formation - Phys.Org

Cell Biology

How proteins bring together membrane blebs – Phys.Org

July 4, 2017 medical

July 3, 2017

Researchers have gained new insights into the mechanisms with which certain proteins help the immune defence mechanism in the human body. Pathogens such as viruses or bacteria are wrapped in membrane blebs and rendered harmless there. What are known as guanylate-binding proteins are crucial in this. How they contribute to the process that was investigated by researchers from Ruhr-Universitt Bochum, the Paul-Ehrlich-Institut and the University of Cologne, together with other partners from Erlangen and Geneva.

The team led by Prof Dr Christian Herrmann and Dr Sergii Shydlovskyi from the Bochum cluster of excellence Resolv and Dr Gerrit Praefcke, formerly of the University of Cologne, now at the Paul-Ehrlich-Institut in Langen, reports on the study in the journal Proceedings of the National Academy of Sciences.

Precursor of vesicle fusion

With a combination of cell biology and biochemical experiments, the researchers explored the function of human guanylate-binding protein 1 (hGBP1). In cells, it interacts with the energy storage molecule GTP, from which it can split off one or two phosphate groups, in order to release energy.

In the current study, the researchers discovered that hGBP1 uses energy released during splitting to change its structure: it unveils a lipid anchor. Using this anchor, it can form larger ring-shaped polymers with other hGBP1 proteins. With the aid of artificial vesicles, the team also found that hGBP1 uses the anchor to bind to the vesicle membrane. In this way, it brings together many such membrane blebs, which the researchers assume could be a precursor to vesicle fusion.

Demonstrated in cells

This kind of fusion is crucial for the immune defence mechanism: pathogens are trapped in the human body in vesicles, which merge with certain cell organelles, lysosomes. The latter contain enzymes that degrade pathogens. In the current study, the team also demonstrated that the protein hGBP1 in living cells is actually involved in the signal path, which leads via the lysosomes to the degradation of viruses and bacteria.

Explore further: Research describes missing step in how cells move their cargo

More information: Sergii Shydlovskyi et al. Nucleotide-dependent farnesyl switch orchestrates polymerization and membrane binding of human guanylate-binding protein 1, Proceedings of the National Academy of Sciences (2017). DOI: 10.1073/pnas.1620959114

Every time a hormone is released from a cell, every time a neurotransmitter leaps across a synapse to relay a message from one neuron to another, the cell must undergo exocytosis. This is the process responsible for transporting ...

In order for cells to function properly, cargo needs to be constantly transported from one point to another within the cell, like on a goods station. This cargo is located in or on intracellular membranes, called vesicles. ...

Movement of secretory molecules, such as hormones and digestive enzymes, out of the cell is known as exocytosis. This process is guided by SNARE proteins, which help the fusion of secretory vesicles with the plasma membrane. ...

The protein that helps the sperm and egg fuse together in sexual reproduction can also fuse regular cells together. Recent findings by a team of biomedical researchers from the Technion-Israel Institute of Technology, Argentina, ...

Small "bubbles" frequently form on membranes of cells and are taken up into their interior. The process involves EHD proteins - a focus of research by Prof. Oliver Daumke of the MDC. He and his team have now shed light on ...

The many factors that contribute to how cells communicate and function at the most basic level are still not fully understood, but researchers at Baylor College of Medicine have uncovered a mechanism that helps explain how ...

The mass extinction that obliterated three-fourths of life on Earth, including non-avian dinosaurs, set the stage for the swift rise of frogs, a new study shows.

The town of Escalante in southern Utah is no small potatoes when it comes to scientific discovery; a new archaeological finding within its borders may rewrite the story of tuber domestication.

New research into the way that honeybees see colour could pave the way for more accurate cameras in phones, drones and robots.

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How proteins bring together membrane blebs - Phys.Org

Physiology

Mark Hargreaves Receives 2017 ACSM Citation Award … – https://ryortho.com/ (press release) (subscription)

July 4, 2017 student

Tracey Romero Mon, July 3rd, 2017 Print this article Mark Hargreaves, Ph.D., FACSM

Mark Hargreaves, Ph.D., FACSM, a professor of physiology at the University of Melbourne in Australia received the 2017 American College of Sports Medicine Citation Award at the associations recent annual meeting in Denver, Colorado.

Hargreaves was awarded for his contributions to sports medicine and exercise sciences research. His main research focus has been on better understanding the cellular mechanisms that regulate muscle metabolism during exercise and what effect training and nutritional manipulations may have on those mechanisms. His research has been funded by the Australian Sports Commission, the National Health and Medical Research Council of Australia, the Australian Research Council and the Diabetes Australia Research Trust.

Citation Award winners are selected for their leadership and contributions in the areas of research and scholarship, clinical care, administrative services or educational services, said Walter Thompson, FASCM, president of the American College of Sports Medicine (ACSM) in a press release. We are happy to recognize Dr. Hargreaves tremendous accomplishments. Hargreaves work has been published in more than 120 peer-reviewed journals and 65 book chapters and invited reviews, and has been cited more than 5,600 times. He has also received the American College of Sports Medicines Young Investigator Award and the Australian Physiological Societys McIntyre Prize, both in 1994.

One of the most recent studies he participated in, which was published in the June issue of the Journal of Science & Medicine in Sport, evaluated the physical activity training in Australian medical school. The researchers found that while most schools included some physical activity training, they did not always include national strength recommendations.

Hargreaves has served on the ACSMs board of trustees as a foreign corresponding editor of Medicine & Science in Sports & Exercise, associate editor of Exercise and Sport Sciences Reviews and consulting editor of the Journal of Applied Physiology. He received his masters degree in exercise physiology from Ball State University in 1984 and his Ph.D. in physiology from the University of Melbourne in 1989.

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Physiology

The Five Secrets Of Lifelong Health – FemaleFirst.co.uk

July 4, 2017 student

3 July 2017

The media today can make you think that living a long, healthy (and happy!) life is quite complicated. There are diet wars, exercise wars, and competing tips on how to have a successful date, sex life, marriage or family. The boring (but fabulous) truth, is that it is actually fairly simple. And at the same time, completely particular to YOU. As human animals, our physiology is 10,000 years old, but life in the industrialized, digital modern world is quite new. The key to making choices that extend your health and your life is to get in touch with what your body was made forthe evolutionary health that your physiology thrives in.

We are made to move

And this is the other key, that you are unique! Genetically unique and socially and environmentally unique. You need to be bodywise, to listen to YOUR bodys needs and responses when considering competing health advice or making decisions about what to have for dinner or when to go to bed. With body intelligence (your BQ) as your navigational guide and your bodys earth-adapted physiology as your map, it is simple to make choices that help your body (and your life) hum with vibrancy and wellness.

Here are the Five Secrets to Lifelong Evolutionary Health that every major health advocate can agree upon, and that you can decide upon according to your own body intelligence.

Here are the principles of a healthy diet according to the worlds longest lived peoples.

Let your body guide you as to which grains and how many carbohydrates make you feel energetic and happy, or whether meat or dairy products agree with your digestion. Listen to your body and let it guide you to YOUR healthy diet, within these evolutionary guidelines.

The average person in modern societies sleeps 6.5 hours and we need, on average, 8! How much sleep does your body intelligence say you need to wake up rested and refreshed? Adequate sleep reduces pain, anxiety, depression, infections and weight gain. Sleep is your most important anti-aging activity.

We are made to move. How can you be more active in your everyday life and what kind of activities does your body wisdom lead you to? Dancing? Biking? Yoga? Exercise is the best treatment for depression, high blood pressure and the best prevention for heart disease and stroke.

Loneliness will kill you faster than cigarettes. What kinds of love and affection does your body crave? Cuddling with friends (or your dog!)? Hot sex with your lover? Sweet, affectionate family time with kids, siblings, parents or grandparents? How can you get a regular dose of love and affection in your life? Love truly is our greatest healer, halving the risk of heart attack and reducing your risk of cancer, stroke and all chronic disease.

A sense of pupose can extend your life by 50%what are you committed to? What kinds of creativity, service or work make create peace, satisfaction or excitement inside you?

If you use your body intelligence to guide you in these five evolutionary fundamentals of health, you will live long and prosper, and benefit the world as well. Blessings on your bodywise path!

BodyWise: Discovering Your Body's Intelligence for Lifelong Health and Healing by Dr Rachel Carlton Abrams is published by Bluebird and priced 12.99

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The Five Secrets Of Lifelong Health - FemaleFirst.co.uk

Neuroscience

Applying neuroscience to Cannes-winning work: Coca-Cola Pool Boy – AdNews

July 4, 2017 medical

AdNews has partnered with Neuro-Insight to bring an analysis of some of this year's winning Cannes work to understand what it is that made them successful through a neuroscience lens.

Sex sells, but are consumers still buying the narrative? Following Coca-Colas Pool Boy ad winning at Cannes, the analysts and Neuro-Insight wanted to find out. So what can the brain tell us about brand effectiveness? And what is our subconscious response to the ad?

Coca-Cola Pool Boy

Coca-Colas Pool Boy ad by Santo, received a Bronze Lion award in the creative category this year at Cannes. Launched in Australia early may, as part of the Taste the Feeling global campaign, the ad narrates a brother and sister racing to offer a Pool boy a bottle of Coca-Cola to ultimately find that their mother beat them to it. The combination of creative style, diversity, humour and story telling aid in the overall entertainment of the ad, but what does this mean in terms of brand effectiveness? As a part of Neuro-Insights partnership with AdNews for the sixth year running; NIs Cannes on the Brain series, unlocks via neuroscience, the subconscious response to this lovable ad.

How we did it

Neuro-Insight measured brain activity to see how 50 females and 50 males responded to the ad. The specific technology used by Neuro-Insight is founded in work originally developed for academic and neuroscience research, and has been used to analyse the effectiveness of Cannes award winners for over four years. The technology allows us to simultaneously record viewers second-by-second changes in approach (like)/withdraw (dislike), emotional intensity, engagement and memory whilst watching advertisements. The measure Neuro-Insight predominantly focusses on is Long-term Memory Encoding, based on its strong and highly researched link to actual consumer behaviour. This measure reveals, second by second, what the brain is storing (or encoding) into conscious and unconscious long-term memory and is plotted in the form of a time series graph. The higher the lines on the graph, the more strongly that moment in the ad is stored in memory and the more likely it is to influence consumer behaviour.

Time Series

Below is Neuro-Insights video timeseries showing how viewers brains respond to the Pool Boy ad. Immediately, you can see multiple strong peaks in long term memory encoding (NIs key indicator for ad effectiveness). This suggests that information processed at these moments, has been effectively encoded into memory. We also see a fairly even balance in the way viewers process the information of the ad, as indicated by the red and blue trace. The red trace corresponds with memory encoding from the left hemisphere, which is primarily responsible for the encoding of the detail in experiences, such as text, dialogue or micro features. In contrast the right hemisphere, which is represented by the blue line, is responsible with the storing of global elements, such as soundtracks, scenery and broad themes, as well as the emotional underpinnings of a particular experience.

Long term memory encoding for all viewers

Early in the ad we see two powerfully encoded scenes (see highlighted sections below), at this point in the ad viewers are introduced to the admiring sister as she as looks longingly out the window to the pool boy. Cleverly integrated, in both of these scenes, lies the iconic Coca-Cola bottle. Whilst the different creative elements have ensued an effective memory encoding response, the branding has successfully linked itself to the narrative and stored into viewers memory. As well as memory encoding, NI also evaluate viewers engagement ( a measure associated with personal relevance and relatability) and emotional intensity (relating to the strength of emotion being experienced). As indicated by the peak in engagement, it appears viewers respond to the sister daydreaming out of her window with high relatability and relevance this highlights the effective way in which Coca-Cola have engaged viewers in an everyday kind of moment. This response reflects objectives mentioned by Lisa Winn, Coca-Cola South Pacific marketing director, whom has stated As a brand we are constantly looking for ways to keep our work fresh, exciting and engaging to our consumers. We do this by tapping into everyday moments and telling universal stories that connect with our consumers around the world.

Rather incredibly, the strongest elicited response occurs as the brother and sister meet at the fridge with the Coca-Cola bottles clearly presented in the foreground. The Coca-Cola iconography is yet again, effectively stored in viewers long-term memory encoding. Shortly after this powerful moment, we see a drop in viewers responses, indicative of a phenomenon called Conceptual Closure. Conceptual Closure occurs when the brain perceives an event boundary (i.e a narrative sequence has come to an end), the brain then takes a brief period to process and store the previous experience (i.e the brain takes a break). In this case, it appears that viewers process the notion that both the brother and sister seek-out the Coca-Cola bottle as the solution to gaining the attention of the pool boy. The Conceptual Closure occurrence is not viewed as bad thing, as the dynamic and humorous story telling recaptures viewers high levels of processing.

As viewers follow the entertaining battle to the pool boy, with the brother and sister grasping their Coca-Cola bottles, wee see high levels of memory encoding, engagement and emotional intensity (see below). As the brother and sister ultimately discover their mother has beat them to it and delivered the Coca-Cola bottle, we see a drop in processing again suggesting Conceptual Closure. Viewers are met with the humorous twist and end to the story. The ad concludes with the emerging Coca-Cola branding logo, successfully retriggering viewers memory encoding for the final time. NIs analysis is able to objectively showcase the effectiveness of creativity and branding, and how an entertaining and dynamic narrative has effectively and emotively communicated the Coca-Cola brand.

Have something to say on this? Share your views in the comments section below. Or if you have a news story or tip-off, drop us a line at adnews@yaffa.com.au

Sign up to the AdNews newsletter, like us on Facebook or follow us on Twitter for breaking stories and campaigns throughout the day. Need a job? Visit adnewsjobs.com.au.

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Applying neuroscience to Cannes-winning work: Coca-Cola Pool Boy - AdNews

Embryology

Cancer-surviving women a third less likely to become pregnant … – The Guardian

July 4, 2017 medical

A proton beam irradiating a brain tumour (circled in white) many anti-cancer therapies destroy fertility either chemically or through radiation. Photograph: Alamy Stock Photo

Women who survived cancer in the past 30 years were a third less likely to become pregnant than women in the general population, according to study into the impact of the disease and its treatment on patients.

The research provides the first broad assessment of how cancer, the fertility-harming therapies that patients receive, and the decisions women make on leaving hospital, can affect their plans for a family.

This really allows us to quantify the effects of cancer and its treatment, in the broadest sense, on women and girls having a pregnancy afterwards, said Richard Anderson, professor of clinical reproductive science, who led the work at Edinburgh University.

The scientists analysed medical records for more than 23,000 women in Scotland who survived cancer after being diagnosed between 1981 and 2012. The cancer survivors had only 6,627 pregnancies, far fewer than the 11,000 or so expected for an age-matched group of women in the general population.

A 30-year-old who has chemotherapy will have the reproductive potential of a 40-year-old

The impact of the disease was most striking for women who had not carried a baby before their diagnosis. The records showed that these women were about half as likely to conceive as similar but healthy women, with pregnancy rates of 21% versus 39%.

Many anti-cancer therapies are known to destroy fertility either chemically or through radiation, but many other factors will affect whether or not cancer survivors go on to have children. As well as the treatment damaging their fertility, its also women choosing not to complete their family, said Anderson. Some women may not want to bring another child into the world when they are not sure about their own health.

While the findings highlight the serious impact that cancer can have on female fertility and the choices women make around having children, the records point to a stark improvement in recent years, with some types of cancer now taking far less of a toll. In the 1980s, women who survived cancer were half as likely to conceive as others, but since 2005 pregnancy rates have risen to 75% of that seen in the healthy population.

Speaking from the European Society of Human Reproduction and Embryology in Geneva, Anderson said that doctors had seen clear improvements in pregnancy rates among survivors of some cancers but not others. For example, girls diagnosed with Hodgkin lymphoma today have far less radiotherapy than 30 years ago, causing less damage to their fertility. Similar improvements have not been seen in other cancers such as leukaemia, however.

The work highlights the need for more widespread access to new procedures that aim to preserve the fertility of girls and women who face cancer therapy. One approach is to remove ovarian tissue from the patient and freeze it until the patient has the all-clear and the tissue can be re-implanted. Last year, Anderson announced the first British birth using frozen ovarian tissue, to a 33-year-old woman who had part of an ovary removed 11 years earlier. Anderson said the latest findings should help doctors to counsel women who are diagnosed with cancer and direct services, such as ovarian tissue preservation, to where it is needed most.

Gillian Lockwood at Midland Fertility Services said that chemotherapy could add a decade to a womans reproductive age, an issue that must be taken into account in patient counselling. A 30-year-old who has chemotherapy will have the reproductive potential of a 40-year-old, which is not good, she said. Its important for these young women to know that even though their life expectancy thanks to good, modern oncology treatment is near normal, their reproductive life expectancy may not be as good.

Nick Macklon, professor of obstetrics and gynaecology at Southampton University, said the results were positive for many cancer patients. The knowledge that they can have a good chance of having a baby will be very important to women, and the addition of fertility preservation over the past few years has really changed the scene for them. Not so long ago, having a cancer diagnosis was seen as the end of your chances of having a baby, he said.