Press Release from the John Innes Centre (link)

New evidence has emerged about the agriculturally important process of vernalization in a development that could help farmers deal with financially damaging weather fluctuations.

Vernalization is the process by which plants require prolonged exposure to cold temperature before they transition from the vegetative state to flower. For decades it’s been a key focus of research into plant development and crop productivity.

But how vernalization might work under variable temperatures in the field has been unclear, as have some of the underlying molecular controls of the process.

The research carried out by John Innes Centre scientists in collaboration with colleagues in Hungary and France shows that vernalization is influenced by warm conditions as well as cold, and a much wider temperature range than previously thought.

Led by Dr Laura Dixon, the study began as an exploration into how variance in ambient temperatures might influence flowering regulation in winter wheat. But it unexpectedly uncovered an “extreme vernalization response”.

“We have shown that vernalization responds to warmer conditions than those classically associated with vernalizing. Before this study we thought vernalization only happened up to a maximum of about 12°C, but the true temperature is much higher. This information is immediately useful to breeders,” says Dr Dixon.

The researchers used a panel of 98 wheat cultivars and landraces and exposed them to temperatures ranging from 13 to 25 °C in controlled environments.

Normally, once the vernalization process completes, plant growth is accelerated under warm temperatures. But the team identified one cultivar, named Charger, which did not follow this standard response.

Gene expression analysis revealed that the wheat floral activator gene (VRN-A1) was responsible for this trait. Further experiments showed that expression of genes that delay flowering is reactivated in response to high temperatures (of up to 24 °C), demonstrating that vernalization is not only a consequence of how long the plant experiences continuous cold.

This study published in the journal Development highlights complex workings of a genetic network of floral activators and repressors that coordinate a plant’s response to a range of temperature inputs. It also finds that the Charger cultivar is an extreme version of a response to warmer temperatures that may be prevalent in winter wheat cultivars.

The team is now looking to provide diagnostic genetic markers which will allow breeders to track the distinct allele responsible for this warm-temperature vernalization trait. They also hope to use their new knowledge of warm weather interruption to reduce the length of vernalization in the breeding cycle, so that new wheat lines can be generated more quickly.

Dr Dixon explains: “This study highlights that to understand the vernalization response in agriculture we must dissect the process in the field and under variable conditions. The knowledge can be used to develop new wheat cultivars that are more robust to changing temperatures.”

The full study VERNALIZATION1 controls development response of winter wheat under high ambient temperatures, appears in the journal Development. 

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The Park laboratory has an immediate opening for postdoctoral fellow positions in the Molecular and Integrative Physiological Sciences program at the Harvard T.H. Chan School of Public Health in Boston. The focus of our research is to examine the role of airway epithelial cells in health and disease. We recently discovered striking phenotypical changes of bronchial epithelial cell layer: unjamming and jamming transitions (Nature Materials, Park et al, 2015). These jamming and unjamming behavior could be critical for repair and regeneration of the airway. Also, we found that bronchial epithelial cells are a source of extracellular vesicles, which might be implicated in asthma pathogenesis (Am J Respir Cell Mol Biol, Mitchel JA, 2015). Using primary bronchial epithelial cells and mouse tracheal epithelial cells in air-liquid interface culture and mouse models of allergic asthma, we use biochemical and biophysical tools to determine the link between biophysics and biology in airway epithelial cells and their cooperative role in airway disease.

For more information, please visit: http://www.hsph.harvard.edu/park-lab/

Applicants must have a recent M.D. or Ph.D. in Biology, Biochemistry, Biomedical Engineering, or a related field. Previous experience with lung biology and mouse models of asthma or pulmonary fibrosis is required. Up to 3 years of support is available. Support from an NIH T32 training grant is available in the MIPS program.

Please send a CV, a brief statement of goals, and three references (including names, email addresses, and phone numbers) to hsphparklab@gmail.com.

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The Posfai Lab (www.posfailab.org) at Princeton University is looking to recruit a highly motivated postdoctoral fellow. Our group studies the molecular and cellular mechanisms of cell fate choice and morphogenesis during early embryonic development, using the preimplantation mouse embryo as a model system. Our main approach is combining genetic engineering and quantitative, high-resolution live imaging using light sheet microscopy. The lab’s assets include our recently developed highly efficient genome editing method in mouse embryos and our own light sheet microscope dedicated to preimplantation embryo imaging.

The postdoctoral candidate should have a strong interest in developmental biology and mouse genetics. Experience in light sheet microscopy and/or computational image analysis is preferred, but not necessary. Motivation and excellence is valued more than previous field of study.

The candidate will benefit from an interdisciplinary and collaborative environment at Princeton and the vibrant and supportive atmosphere of a junior lab. Researchers at the rank of Postdoctoral Research Associate are ordinarily appointed for one year at a time. Appointments are reviewed annually to consider reappointment and salary level. The position is benefits-eligible.

To apply, email your CV and cover letter explaining your interests and motivation to Eszter Posfai (eposfai@princeton.edu) and arrange for three reference letters to be sent on your behalf.

For more information, visit www.posfailab.org.

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The Bressan Laboratory (www.bressanlab.com) at the University of North Carolina Chapel Hill is inviting applications for a postdoctoral fellow interested in developmental Cell Biology and Physiology research. The focus of the position will be to explore the genetic and molecular events that control cellular diversity during cardiovascular development. Specifically, candidates will conduct direct in vivo over expression, live imaging, cell sorting, primary culture, and next generation sequencing to explore how alterations in transcriptional activity and cellular mechanics influence physiological fate in the embryonic heart. The applicant is expected to manage an independent research project and to train students and other fellows in the laboratory.

For more information or to apply see (https://unc.peopleadmin.com/postings/155101), or contact the lab directly at www.bressanlab.com.

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A good statistic is the one that you can understand. Mean values are understandable and everybody knows how to calculate them. Most people also realize how the mean value can be skewed by an outlier. So we know what the mean represents and we are aware of its limitations. In sharp contrast, the Null Hypothesis Significance Test (NHST) and the resulting p-values are poorly understood. Although there is nothing wrong with the p-value itself, its calculation and interpretation are far from intuitive (Greenland et al., 2016).

The backward logic of the p-value contributes to its poor understanding (footnote 1). It doesn’t help that the calculation of p-values is usually done with a piece of software, without knowing and understanding the underlying calculation. Notwithstanding these issues, the calculation and reporting of p-values is a standard practice. I think it is very ironic that p-values are widely used and at the same time poorly understood. Since it is unlikely that p-values will disappear any time soon, a user-friendly, understandable calculation of p-values would be very helpful. Below, I will explain a method for calculating a p-value that I find much easier to understand. This method is known as the (approximate) permutation or randomization test. There are also some excellent explanations of this method by others (Hooton, 1991; Nuzzo, 2017).

Before we start: conditions and assumptions

I will deal with the randomization test and treat the case where data is sampled from two independent conditions. The requirements for this test are not different from any other statistical significance test. The data of the samples needs to be random, independent and representative of the population that it was sampled from. A convenient aspect of the randomization test is that it makes no assumption with respect of the data distribution. So, it does not require the data to adhere to a normal distribution (in contrast to the Student’s t-test or Welch’s t-test). 

Example data

I will explain the randomization test with experimental data obtained in our lab (and previously used to explain the calculation of the effect size). There is a “Control” condition (n=74) and a “TIAM” condition (n=110) for which the cell area is determined. These two conditions will be compared in the randomization test.

The null hypothesis

The null hypothesis states that there is no difference between the mean of the reference (“Control”) and mean of the treated (“TIAM”) condition. For the randomization method, we rephrase the null hypothesis: All the observed data is sampled from the same population. As a consequence, there is no difference between the mean value of the two conditions. Note that this rephrased null hypothesis is essentially the same as the classical null hypothesis.

The randomization method

The procedure is broken down in 5 steps. Each step is shown in figure 1 and explained below.

Figure 1: Step-by-step explanation of the randomization method. Each of the steps is explained  in the main text.

Step 1: We calculate the difference between the mean of the two conditions: 1808-1487=321 µm². This is the ‘observed difference’ between the means.

Step 2: We combine the data of both conditions to generate a single aggregated sample. We put all data together because the null hypothesis states that all data was sampled from this distribution. 

Step 3: We draw two random samples from the aggregated sample (the samples have the same size as the original control and treated samples, i.e. n=74 and n=110 respectively). This is repeated many times, typically 1000x.

Step 4: For every single new sample, we calculate the difference between the means. This is a ‘theoretical difference’, since it is a difference that could arise when the samples were obtained from the same population. We end up with a collection of 1000 theoretical differences. The distribution of the theoretical differences is also known as the null distribution. In summary, we have constructed a distribution of theoretical differences that could have been observed when all data came from one and the same population.

Step 5: We compare the observed difference between the means from step 1 with the null distribution that we generated in step 4. This comparison is the same as asking: “How likely is our observed difference (or more extreme differences), given that the data were sampled from the same population?”. We see in figure 1 that the observed difference is quite different from the theoretical differences. In fact, only 1 out of 1000 theoretical differences exceeds the observed difference. Therefore, the probability of our observed difference between the means (or more extreme differences) given that the data are sampled from the same population is 1/000. This probability, or p-value, of 0.001 tells us that we have a rather extreme observation and it provides strong ground for rejecting the null hypothesis.

Beyond the mean

The mean may not be the best measure of centrality due to its sensitivity to outliers. The example data used here seems asymmetric and it that case the median is a better measure of centrality. Consequently, we may be more interested in differences between median values. Luckily, the randomization test is readily adapted to examine a difference in medians (Hooton, 1991). We can repeat the entire procedure for the difference between medians (figure 2). First, the difference in the experimental data is 196 µm². Next, we resample the combined data and calculate the difference in medians for each of the sample. The resulting null distribution is compared with the experimentally observed difference in medians. It shows that the probability of the observation (or more extreme values) is 17 out of 1000. The p-value for the difference in medians is 0.017.

Figure 2: Example of a randomization test for comparing medians, explaining the one-tailed and two-tailed comparison

The devil is in the (de)tails

So far, we have only considered one side of the null distribution. This is known as a one-tailed test. However, we are often interested in finding any difference and we do not consider the directionality. In other words, we are looking for a difference that can be either positive or negative. This is known as a two-tailed test. For a two-tailed test we consider both tails for the difference of medians. We find that in the other tail of the null distribution there are 7 (theoretical) values more extreme than our observed value of 196 µm². Therefore the p-value for the two-tailed test is p=(17+7)/1000=0.024 (figure 2, right panel). Based on this p-value we can reject the null hypothesis and conclude that the two samples do not originate from the same population.

Final words

Like any other statistical test, the permutation and randomization methods are not without limitations or shortcomings (Wilcox and Rousselet, 2018). And regardless of the way a p-value is calculated, it is recommended to supplement p-values with other statistical parameters (Wasserstein and Lazar, 2016). A good alternative for p-values is the calculation of the effect size, because it quantifies the magnitude of the difference. To me, the rather straightforward calculation and more intuitive determination of a p-value by the randomization method is a big advantage over classical null hypothesis tests. Possibly the biggest benefit is that this method makes it clear that we determine the probability of the data in relation to the null hypothesis (instead of determining the probability that the null hypothesis is true as is often mistakingly thought, see also Footnote 1). The randomization method is also well suited for teaching and examining the properties of p-values. To conclude, I hope that the user-friendly p-value will aid in understanding null hypothesis testing and also help in recognizing the limitations of p-values.

DIY

The data and R-script to perform the randomization method is avaliable at Zenodo: http://doi.org/10.5281/zenodo.2553850

A web-based application that calculates p-values by randomization is currently under development and available: http://huygens.science.uva.nl/PlotsOfDifferences/

The source code for the web tool is on GitHub: https://github.com/JoachimGoedhart/PlotsOfDifferences

Footnotes

Footnote 1: The p-value is the probability of the data (or more extreme values) given that the null hypothesis is true. However, its definition is often mistakingly(!) flipped and taken as a probability that the null hypothesis is true (Greenland et al., 2016).

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Every fortnight, between the British Library and the St Pancras station, inside a wooden room in the ground floor of the Francis Crick Institute, four scientists discuss the present breakthroughs in science. Unlike other discussions happening in this room, this one will reach many other Spanish-speaking people around the world. We are talking about “En Fase Experimental”, the science podcast of the Society of Spanish Researchers in the United Kingdom (SRUK),  where the team of hosts Teresa Rayon (The Francis Crick Institute), Berta Verd (Univ. of Cambridge), Cristina Rodríguez (Univ. of Cambridge), and Ruben Perez-Carrasco (UCL), together with the sound technician Carlos Bricio, have the mission to discuss not only current advances in science but also the role and challenges that STEM research faces in the current world.

In each program we interview a researcher actively contributing to the advance of science that provides their personal perspective. This season the program featured researches such as Sergi Castellano (UCL) expert in the extraction and computational analysis of ancient DNA or Javier Carmona (Springer Nature) discussing his new role as a editor in Nature Medicine. These interviews, motivate discussions on current challenges of science ranging from topics such as the impact and repercussion of winning a Nobel prize, to the relevance of open access science in developing countries.

In addition, inspired by the work of Jess Wade (Imperial College), in each program we dedicate a section to narrate the rousing career of female STEM researchers such as the neurobiologist Rita Levi-Montalcini or the computational meteorologist Joanne Simpson.

So if you want to participate in our discussions, or just want a excuse to learn Spanish, this is the podcast for you. You can find us in Spotify ,Youtube, iVoox, and in social media #EnFaseExperimental @ComunidadCeru.

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PhD student and postdoc positions available in the laboratory headed by Prof. Jussi Taipale and Dr. Norman Zielke at the Finnish Center of Excellence in Tumor Genetics Research (CoETG) at the University of Helsinki.

https://www.helsinki.fi/fi/avoimet-tyopaikat/post-doctoral-position-in-bioinformatics-or-functional-genomics

https://www.helsinki.fi/fi/avoimet-tyopaikat/phd-student-position-in-bioinformatics-or-functional-genomics

Our laboratory employs diverse systems-biology approaches to understand how tissue-specific transcription factors collaborate with oncogenic signals to drive cell proliferation. Current lines of work include computational and experimental identification of TF binding sites, and combination of the generated datasets for identification of regulatory elements that control cell growth during normal development or in cancer. The identified regulatory elements are validated using CRISPR/Cas9-mediated genome editing in cultured cells and model organisms.

Selected publications:

• Palin et al., Contribution of allelic imbalance to colorectal cancer. Nat. Commun. 9, 3664 (2018).
• Zhu et al., The interaction landscape between transcription factors and the nucleosome. Nature (2018).
• Wei et al., A protein activity assay to measure global transcription factor activity reveals determinants of chromatin accessibility. Nat. Biotechnol. (2018).
• Yin et al., Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science. 356 (2017).
• Dave et al., Mice deficient of Myc super-enhancer region reveal differential control mechanism between normal and pathological growth. Elife. 6 (2017),
• Sur et al., Mice lacking a Myc enhancer that includes human SNP rs6983267 are resistant to intestinal tumors. Science. 338, 1360–1363 (2012).
• Zielke et al., Control of Drosophila endocycles by E2F and CRL4(CDT2). Nature. 480, 123–127 (2011).

Requirements PhD student position

A successful candidate has, or is close to obtaining a MSc or in either molecular biology, genetics, biotechnology or computer science or mathematics and most importantly, strong enthusiasm to solve complex biological problems, such as those presented in molecular oncology.

Requirements Postdoc position

The applicant should have a strong background in molecular biology, genetics, biotechnology and/or computer science or mathematics and be interested in working in an interdisciplinary environment that combines experimental and in silico approaches. The candidate should have a recent PhD degree (or be close to completion of their degree) in a relevant subject area and a substantial track record of productivity.

Desired skills in Bioinformatics

The candidate should have solid programming skills (e.g. R, C, Python), knowledge of mathematical network modelling approaches, and ability to work independently in a multi-disciplinary team (i.e. ability to work and communicate with biologists is essential).

Desired skills in Functional Genomics

Experience in at least one of the listed areas is required: CRISPR/CAS9-mediated genome editing, mammalian cell culture with detailed knowledge on lentiviral transfection, Drosophila genetics, ChIP-Seq and related methods, preparation of libraries for next-generation sequencing approaches, data analysis.

Applications containing CV, publication list including impact factors and two letters of reference should be submitted through the University webpage (see links above). The deadline for applications is 31th March 2019. For further information contact: norman.zielke@helsinki.fi

The designated starting date would preferably be between June and October 2019, but can be negotiated under special circumstances. The initial contract of employment will be 2 years and includes a trial period of six months will. Salary will be based on the Universities salary scheme for teaching and research personnel composed of both task specific and personal performance components that will be evaluated during the first year, with typical starting levels of 2/5 (approx. 2400 EUR) for a PhD student, or 5/5 (approx. 3500 EUR) for a post-doc.

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International PhD student opportunity – Australian Regenerative Medicine Institute. Monash University

Studying the clonal dynamics of cartilage stem cells during normal and perturbed bone growth

• Looking for an international PhD applicant to be enrolled in Monash University doctoral program under the supervision of Dr Alberto Rosello-Diez (http://www.rosellodiezlab.com and https://www.armi.org.au/research-leadership/rosello-diez-group).
• Long bones grow by forming a cartilage template that provides a scaffold to be replaced by mineralised bone. The production and replacement of cartilage has to be perfectly balanced in order to sustain growth, but the identity and regulatory logic of the cells involved is not clear. Certain perturbations of this process are promptly compensated, providing a means to study the process (see https://journals.plos.org/plosbiology/article/comments?id=10.1371/journal.pbio.2005086).
• We will use sophisticated mouse models to perform multi-colour lineage tracing of cartilage cells and surrounding tissues in embryonic long bones, in order to: 1) Determine the clonal dynamics of cartilage cells surrounding tissues during normal and perturbed bone growth; 2) Ablate or arrest those cells and study the effect on clonal distribution.
• We will study 4 possible scenarios, arising from the combinations of 2 modes of regulation:

a) Asymmetric division is controlled at the population level (leading to random loss of some clones) vs. at the individual cell level (clones perdure over time).

b) Control by extrinsic vs. intrinsic signals

• Part of the project involves modelling of clone behaviour in collaboration with BD Simons (U. of Cambridge) http://www.tcm.phy.cam.ac.uk/~bds10/dir/research_bio.html
• Motivated students are encouraged to contact alberto.rosellodiez@monash.edu. A first-author paper and good English level (IELTS ≥ 6.5) are highly recommended in order to get a Monash scholarship (deadline end of March).

PhD student_Cartilage stem cells_Rosello-Diez

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A Postdoctoral Research Associate position is available in the Spagnoli lab. in the Centre for Stem Cell and Regenerative Medicine at King’s College London. The candidate will work on an exciting EU-funded FET-Open consortium to bioengineer pancreatic tissue (https://www.pan3dp-project.eu/).

The Spagnoli lab (https://www.spagnolilab.org/) studies the mechanisms regulating cell identity and plasticity of metabolic organs, such as the pancreas and the liver. We use a combination of genetic approaches with genomic strategies and imaging in mouse embryos and human stem cells to study how distinct cell types, such as liver and pancreas, arise from common progenitors and acquire specialized shape to form functional organs.

The lab. is coordinating a multi-disciplinary FET European consortium with the ambitious goal of developing an innovative bio-engineering approach for generating pancreatic tissue. Tissues and organs comprise multiple cell types with specific biological functions that must be recapitulated in engineered tissue. We will biomimic developmental processes to fabricate 3D pancreatic tissue units that allow sustained cell viability, expansion and functional differentiation ex vivo.

This position seeks a highly motivated and creative individual with a strong interest in developmental biology. The candidate will be primarily responsible for establishing a 3D-imaging and transcriptome atlas of pancreatic cells and defining cellular sources for engineering pancreatic tissue units. This is a highly collaborative project, in which the successful candidate will work closely to all members of our team and participants of the interdisciplinary FET consortium.

The Centre for Stem Cells & Regenerative Medicine (CSCRM) at King’s College London acts as the nucleus for a vibrant and collaborative stem cell research community, being an ideal environment for a successful postdoc experience.

Qualifications:

Applicants should have a recent Ph.D. degree or M.D./Ph.D. degree. Candidates with experience in stem cell and developmental biology, confocal microscopy, image acquisition and analysis will be preferred.

Requirements:

A CV and a statement of research interests along with names of 2 referees should be sent via online application (see below).

Contact:

francesca.spagnoli@kcl.ac.uk

https://www.spagnolilab.org/

https://www.kcl.ac.uk/lsm/research/divisions/gmm/departments/stemcells/people/dr-francesca-spagnoli.aspx

If you are interested in applying for this role, please apply via our King’s College London link below:

https://my.corehr.com/pls/kingrecruit/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=009906

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We seek an outstanding postdoctoral candidate to join the Yeh Laboratory at Texas A&M University in College Station (http://biomed.tamu.edu/tml).  Our group uses interdisciplinary and quantitative approaches to study the molecular and cellular basis of embryonic development, with specific focus on brain development.  In this context, we are interested in understanding how complex multi-enhancer regulatory landscapes interact with gene promoters through the application of super-resolution, live cell imaging.  A particular strength of the lab is the development of custom, state-of-the-art microscopy systems for applications in the life sciences.

The candidate will be independent, creative, motivated, and able to work collaboratively with a group of researchers with expertise in microscopy, physics, and developmental biology.  A PhD in the biological sciences (or related fields) with at least 3 years of laboratory research experience in zebrafish developmental biology is required.  Experience with quantitative imaging, in addition to experience in zebrafish development, will be considered positively, but is not required.

This is a renewable, two-year position with full benefits, based upon good performance of the candidate and availability of funds.  Salary will be competitive and dependent on the level of experience of the candidate.  Applications submitted before March 8, 2019, will receive full consideration, but the position will remain open until filled.

Applicants should provide,

• cover letter describing research interests and career goals,
• CV,
• letters of recommendation from at least two references.

Submit applications to Alvin Yeh, ayeh@tamu.edu

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