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Interview with… Natàlia Vilor Tejedor

Natàlia VilorNatàlia Vilor-Tejedor holds a BSc in Mathematics and Applied Statistics from the Universitat Autònoma de Barcelona. Additionally, she holds a MSc in Omics Data Analysis from the Universitat de Vic where she also worked at the Bioinformatics and Medical Statistics Research group developing new methods for analyzing the individual evidence of SNPs in biological pathways and was involved in some GWAS data analyses. Currently, she is a PhD student in the Biomedicine programme at the Universitat Pompeu Fabra. At the same time, she is working at the Barcelona Biomedical Research Park (in the Centre for Research in Environmental Epidemiology) where she is working on new statistical methods for integrating different types of Omics, Neuroimaging and Clinical Data from the European BREATHE project. She received one of the two Biostatnet prizes for the best presentations at the JEDE III conference for her talk on “Efficient and Powerful Testing for Gene Set Analysis in Genome-Wide Association Studies”.

Contact info: nvilor(at)creal(dot)cat  linkedin

  1. Why did you choose Biostatistics?

Because I enjoy my work and I think that my research can help to improve different aspects of biomedicine and, in general, the population’s life quality.

  1. Could you give us some insight into your current field of research?

Now I’m just starting my PhD thesis focused on the development of new mathematical methods to better understand the commonalities between Genetics and Neuroimaging, and how both affect Environmental phenotypes.

This is a relatively “new” area that has not been examined in depth, so I’m sure statistics can play an important role.

  1. Coming from a Mathematics/Statistics background, was it difficult to start in the area of Genomics?

The most difficult part is to find a good mentor who is an expert in both areas and who is willing to help you. After that, you need to become familiar and understand different biological concepts that probably you haven’t come across before. Then, you have to extrapolate these concepts to a mathematical point of view, and finally (what is the most important and hardest part), you have to be able convey the information to both, geneticists and mathematicians.

All of these steps require an extra effort that is often difficult to overcome.

  1. Which do you think are the main qualities of a good mentor?

I think the most important quality is knowing how to impart the indispensable knowledge whilst being clear and well organised, but also it is important to instill confidence and provide development opportunities.

  1. What do you think of the situation of young biostatisticians in Spain?

Although we are at a very delicate social-economic moment, I think we have a talented and very well prepared generation of young biostatisticians supported by important national institutions such as the Spanish Biometric Society and the Societat Catalana d’Estadística (in my case) and national networks such as BioStatNet and online resources like this blog that are helping us in our training.

  1. From your experience, would you recommend the area of Genomics as a professional option for statisticians?

I think this is a very interesting, motivating and emerging field of research where a lot of statisticians and mathematicians are required. From my personal experience, the vast majority of mathematicians/statisticians are not comfortable with biology, but I think that promoting interdisciplinarity is essential to ensure biomedical research.

  1. Finally, is there any topic you would like to see covered in the blog?

Of course, even more genetics!

Selected publication and projects:

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Interview with…Manuel G. Bedia

Manuel_GB

Manuel G. Bedia is an assistant professor in the Department of Computer Science at the University of Zaragoza. He is one of the founders of the Spanish Network of Cognitive Science (retecog.net). This network has been established to promote and coordinate research in Cognitive Systems with goals overlapping those of the European Network EUCognition but with more emphasis on the relationships between scientific and educational policies, and the Spanish university system. He holds a BSc in Physics, a MSc.in Technological Innovation management and a Ph.D. in Computer Science and Artificial Intelligence (Best PhD Thesis Award, 2004), all from the University of Salamanca (Spain). He has worked as a Technological Consultant in Innovation and knowledge management (Foundation COTEC, Madrid, Spain) and as a research fellow in the field of artificial cognitive systems in the Department of Computer Science at the University of Salamanca, the Planning and Learning Group at the University Carlos III of Madrid (Visiting Professor, 2005-07) and the Multidisciplinary Institute at the University Complutense of Madrid (2004-07). He has also been a visiting postdoctoral researcher at the Institute of Perception, Action and Behavior (University of Edinburgh, 2005) and the Centre for Computational Neuroscience and Robotics at the University of Sussex, 2008.

1.     Your area of research is Cognitive Sciences. Could you give us a brief introduction about the focus of your work? 

Cognitive science is a space for interdisciplinary research where we aim to understand how the mind works. It joins together neuroscientists, psychologists, philosophers, engineers and of course statisticians too!

During the past five decades, analogies between the human mind/brain and computer software/hardware have led the work of researchers trying to understand how we think, reason and solve problems.

However, over the last few years, new conceptions have arisen doubting this conceptualisation. The biggest influence behind this change in perspective has come from engineers rather than scientists; in particular a group of engineers using the disciplinary tools of engineering to generate new scientific hypotheses instead of applying knowledge generated from other areas.

In a reversal of the usual role of engineers using models for the development of artifacts, the process develops tools to think about mind phenomena.

2. Could you give us an example of this?

Imagine we purposefully build a very simple artifact or software program that is capable of performing a certain task in a novel way. This proves the existence of explanatory alternatives to phenomena that were supposed to work in a certain way. In the words of other authors, the models serve as “mental gymnastics”. They are entities equivalent to classical mental experiments: They are artifacts that help our thinking. These tools are the foundations of modelling exercises: dynamic systems, probability theory, etc.

3. Is probability an important tool in your work?

It is indeed very important and relevant at many levels of the research in this area.

At a fundamental level, the mathematical languages that the early Artificial Intelligence (AI) researchers developed were not sufficiently flexible (they were based on the use of logic and rule systems) to capture an important characteristic of our intelligence: its flexibility to interactively reorganise itself. This led to a growing interest in tools that would embrace this uncertainty.

Recently a very interesting approach has been developed in the area where fundamental principles are based on probability: Artificial General Intelligence (AGI). The original goal of the AI field was the construction of “thinking machines” – that is, computer systems with human-like general intelligence. Due to the difficulty of this task, for the last few decades, the majority of AI researchers have focused on what has been called “narrow AI” – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have reapplied themselves to the original goals of the field recognising the necessity and emergent feasibility of treating intelligence holistically. AGI research differs from the ordinary AI research by stressing the versatility and entirety of intelligence. Essentially, its main objective was to develop a theory of Artificial Intelligence based on Algorithmic Probability (further explanations can be found here).

At a more concrete level, there are several examples. For instance, it is well known that the reasoning model of the clinical environment is fundamentally Bayesian. The clinicians analyse and reflect on previous conditions and status of patients, before reaching a diagnosis of their current condition. This fits very well with the whole idea of Bayesian probability. Following the same line of reasoning, probability appears as a fundamental tool to model artificial minds thinking as humans.

In general, this Bayesian framework is the most used in our field.

4. How can this be applied in your area of research?

The Bayesian framework for probabilistic inference provides a general approach to understanding how problems of induction can be solved in principle, and perhaps how they might be solved in the human mind. Bayesian models have addressed animal learning , human inductive learning and generalisation, visual perception, motor control, semantic memory , language processing and acquisition , social cognition, etc.

However, I believe that the most important use comes from the area of neuroscience.

5. So what is the neuroscientific viewpoint in the field of the understanding of our mental functions, the Cognitive Sciences?

Neuroscience intends to understand the brain from the neural correlates that are activated when an individual performs an action. The advances in this area over the years are impressive but this conceptual point of view is not without problems. For instance, as Alva Noë states in his famous book Out of Our Heads, the laboratory conditions under which the measurements are taken substantially affect the observed task…This is a sort of second order cybernetics effect as defined by Margaret Mead decades ago. The history of neuroscience also includes some errors in the statistical analysis and inference phases…

6. Could you explain this further?

In the early 90s, David Poeppel, when researching the neurophysiological foundations of speech perception, found out that none of the six best studies of the topic matched his methodological apparatus (read more here).

Apparently, these issues were solved when functional magnetic resonance imaging (fMRI) emerged. As this technique was affordable it allowed more groups to work on the topic and indirectly forced the analytical methods to become more standardised across the different labs.

However, these images brought in a new problem. In an article in Duped magazine Margaret Talbot described how the single inclusion of fMRI images in papers had arguably increased the probability of these being accepted.

7.  You have also mentioned that big mistakes have been identified in the statistical analysis of data in the area. What is the most common error in your opinion?

In 2011 an eye-opening paper was published on this topic (find it here). The authors focused their research on the misreported significance of differences of significance.

Let’s assume one effect is statistically significantly different from controls (i.e. p<0.05), while another is not (p>0.05). On the surface, this sounds reasonable, but it is flawed because it doesn’t say anything about how different the two effects are from one another. To do this, researchers need to separately test for a significant interaction between the two results in question. Nieuwenhuis and his co-workers summed up the solution concisely: ‘…researchers need to report the statistical significance of their difference rather than the difference between their significance levels.’

The authors had the impression that this type of error was widespread in the neuroscience community. To test this idea, they went hunting for ‘difference of significance’ errors in a set of very prestigious neuroscience articles.

The authors analysed 513 papers in cognitive neurosciences in the five journals of highest impact (Science, Nature, Nature Neuroscience, Neuron and The Journal of Neuroscience). Out of the 157 papers that could have made the mistake, 78 use the right approach whereas 79 did not.

After finding this, they suspected that the problem could be more generalised and went to analyse further papers. Out of these newly sampled 120 articles on cellular and molecular neuroscience published in Nature Neuroscience between 2009 and 2010, not a single publication used correct procedures to compare effect sizes. At least 25 papers erroneously compared significance levels either implicitly or explicitly.

8. What was the origin of this mistake?

The authors suggest that it could be due to the fact that people are generally tempted to attribute too much meaning to the difference between significant and not significant. For this reason, the use of confidence intervals may help prevent researchers from making this statistical error. Whatever the reasons behind the mistake, its ubiquity and potential effect suggest that researchers and reviewers should be more aware that the difference between significant and not significant events is not itself necessarily significant.

I see this as a great opportunity and a challenge for the statistical community, i.e., to contribute to the generation of invaluable knowledge in the applied areas that make use of their techniques.

Selected publications:

Bedia, M. & Di Paolo (2012). Unreliable gut feelings can lead to correct decisions: The somatic marker hypothesis innon-linear decision chains. FRONTIERS IN PSYCHOLOGY. 3 – 384, pp. 1 – 19 pp. 2012. ISSN 1664-1078

Aguilera, M., Bedia, M., Santos, B. and Barandiaran, X. (2013). The situated HKB model: How sensorimotor spatialcoupling can alter oscillatory brain dynamics. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE. 2013. ISSN 1662-5188

De Miguel, G and Bedia, M.G. (2012). The Turing Test by Computing Interaction Coupling. HOW THE WORLD COMPUTES: TURING CENTENARY CONFERENCE AND 8TH CONFERENCE ON COMPUTABILITY IN EUROPE, CIE 2012. Cambridge, ISBN 3642308694

Santos, B., Barandiaran, X., Husband, P., Aguilera, M. and Bedia, M. (2012). Sensorimotor coordination and metastability in a situated HKB model. CONNECTION SCIENCE. 24 – 4, pp. 143 – 161. 2012. ISSN 0954-0091

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Interview with… Anabel Forte

foto_anabel

Anabel Forte Deltell, graduated in mathematics and statistics, holds a PhD in Statistics from the Univeristy of Valencia. Now she is a lecturer at the economics department of Universitat Jaume I at Castellón. Her research topics cover many fields of statistics as, for instance, spatial analysis or joint modeling of longitudinal (or panel) data and time-to-event data with her main interest beeing Bayesian model selection and, in particular, Objective Bayesian variable selection. Her dissertation (2011) entitled “Objective Bayes Criteria for Variable Selection” focuses on this last topic. She has published various articles in international journals and participated in many national and international meetings.

Email: forte@uji.es

 1.   Why do you like Biostatistics?

Researching in biostatistics is researching in real life and it makes me feel like I’m helping to improve people´s life conditions.

 2. Could you give us some insight in your current field of research?

Despite my main line of research being Bayesian model selection, I’m actually working in joint modeling of longitudinal data and time-to-event data. Nowadays many practitioners are moving to what it is called personalized medicine with medical decisions, practices, and/or products being tailored to the individual patient. In this scenario longitudinal data seems to be of great importance since it allows for the consideration of several sources of uncertainty including patient-specific variability. Moreover, quantifying the risk of suffering a certain event of a patient given its trajectory seems really sensible and can be done using joint modeling.

 3. Which are, in your opinion, the main advantages of being a researcher?

For me, being a researcher is simply a way of life. I can not imaging myself doing anything else.

 4. Your whole professional experience has been within the public sector and the University. How do you see the present and future of research in the Spanish public sector?

From my point of view if things do not change greatly, researching in Spain, at least in the public sector, has a really black future. As I see it, the only way researchers can manage to get money is to call attention of private investors. But private money can compromise the objectivity of research and at the same time avoid researching in some fields that are not so “attractive” for industry. Hence it is something that has to be studied carefully… but something has to be done, that´s for sure.

 5. What do you think of the situation of young biostatisticians in Spain?

As I see it, young biostatisticians have to make a great effort to show their enormous value for private companies as, for instance, food processing companies or health care related companies among others… And I think so because, pitifully, nowadays the Spanish public system can not assume all the biostatistical research that is needed.

 6. What would be the 3 main characteristics or skills you would use to describe a good biostatistician?

For me it is really important to have a good mathematical/probabilistic base and to be well organized when working. But the most important of all is enjoying research.

 7. Which do you think are the main qualities of a good mentor?

For me, a good mentor is someone that supervises your work and is always there for you but at the same time he or she should give you some space for you to fail and learn… in other words someone that teaches you what researching really is.

Selected publications:

  • Francisco Pozo-Rodríguez; Jose Luis López-Campos; Carlos J. Álvarez-Martínez; Ady Castro-Acosta; Ramón Agüero; Javier Hueto; Jesús Hernández-Hernández; Manuel Barrón; Victor Abraira; Anabel Forte; Juan Miguel Sanchez Nieto; Encarnación Lopez-Gabaldón; Borja G. Cosío; Alvar Agusti. Clinical Audit of COPD patients requiring hospital admissions in Spain: AUDIPOC Study.Plos One. 7 – 7, (USA): 2012.
  • M. J. Bayarri; J. O. Berger; A. Forte; G. García-Donato. Criteria fo Bayesian model choice with application to variable selection. Annals of Statistics. 40 – 3, pp. 1550 – 1577. (USA): 2012.
  • C. Armero; A. Forte; A. López-Quílez. Geographical variation in pharmacological prescription. Mathematical and Computer Modelling. 50, pp. 921 – 928. (UK): 2009.
  • Allepuz; A. López-Quílez; A. Forte; G. Fernández; J. Casal. Spatial Analysis of Bovine Spongiform Encephalopathy in Galicia, Spain (2000-05). Preventive Veterinary Medicine. 79 – 2, pp. 174 – 185. (Holland): 2007.
  • López-Quílez; C. Armero; A. Forte. Geographical variation of Pharmacological prescription with Bayesian Hierarchical models.Value In Health. 10 – 6, (USA): 2007.
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Interview with…Laetitia Teixeira

Laetitia_TeixeiraLaetitia is a graduate in Applied Mathematics and she also has a Master’s degree in Applied Statistics and Modelling from the University of Porto, Portugal. At present, she is a PhD student on Applied Mathematics and she works in the Research Unit UNIFAI (Institute of Biomedical Sciences Abel Salazar, University of Porto, Portugal) 

Email: laetitiateixeir@gmail.com

1. Why do you like Biostatistics?

Biostatistics allows for the application of statistical theory to various areas of research practice and to work in various areas. Statistics and medicine are two areas of great interest for me and biostatistics allows me to work in both.

2. Could you give us some insight in your current field of research?

My PhD work focuses on survival analysis in the presence of competing risks. All the practical work is based on end-stage renal disease patients with peritoneal dialysis as renal function replacement therapy. We explore several statistical approaches, such as regression models taking competing risks into account, multistate models and joint models for longitudinal and competing risks data. Using these approaches, we can give more and better information about the disease progression, helping clinicians in the evaluation of patients and treatment planning.

Combined with my PhD, I am a fellow researcher at UNIFAI/ICBAS-UP, a Research Unit specialized in ageing and health.

3. Which are, in your opinion, the main advantages of being a researcher?

The opportunity to work in several areas with multidisciplinary teams.

4. What do you think of the situation of young biostatisticians in Portugal?

In Portugal, biostatisticians are mostly present in higher education institutions. Some public and private enterprises have been integrating some young biostatisticians, however in a very limited number. Some colleagues have gone to other European countries, where they have found better opportunities in this area.

5. What would be the 3 main characteristics or skills you would use to describe a good biostatistician?

Interested in research, versatile and good communicator.

6. Which do you think are the main qualities of a good mentor?

Motivator, interested in research and dedicated, good communicator.

7. Finally, is there any topic you would like to see covered in the blog?

A list of working groups organized by research themes. This list would be important for young biostatisticians to find people according to working area and would allow students/researchers to create other networks.

Selected publications:

  • Teixeira, L., Rodrigues, A., Carvalho, M.J., Cabrita, A. & Mendonça, D. (2013). Modeling Competing Risks in Nephrology Research: An Example in Peritoneal Dialysis. BMC Nephrology 2013, 14:110 doi:10.1186/1471-2369-14-110
  • Cotovio, P., Rocha, A., Carvalho, M.J., Teixeira, L., Mendonça, D., Cabrita, A., & Rodrigues, A. (2013). Better Outcomes of Peritoneal Dialysis in Diabetic Patients in Spite of Risk of Loss of Autonomy for Hemodialysis. Accepted – Peritoneal Dialysis International.
  • Rocha, A., Rodrigues, A., Teixeira, L., Carvalho, M. J., Mendonça, D., & Cabrita, A. (2012). Temporal Trends in Peritonitis Rates, Microbiology and Outcomes: The Major Clinical Complication of Peritoneal Dialysis. Blood Purification, 33(4), 284-291.
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Interview with… Guillermo Vinué Visús

Guillermo Vinué Visús completed his degree in Mathematics in 2008, granted by the Universitat de València (Spain). He also holds a Master’s degree in Biostatistics from the same university. After working at the Drug Research Center of the Santa Creu i Sant Pau Hospital in Barcelona (Spain) for a year, he is currently a PhD student in the Department of Statistics and Operations Research at the Universitat de València. His doctoral thesis focuses on Statistics applied to Anthropometry.

1. Why do you like Biostatistics?

I like Biostatistics because it allows me to apply Maths to different real life problems.

 2. Could you give us some insight in your current field of research?

 I am working on  a research project aiming to develop statistical methodologies to deal with anthropometric data, in order to tackle some statistical problems related to Anthropometry and Ergonomics.

3. Which are, in your opinion, the main advantages of being a researcher?

The main advantage is the possibility to learn everyday a bit more. Research is a continuous learning process.

4. Your whole professional experience has been within the public sector and  the University. How do you see the present and future of research in the Spanish public sector?

The current situation of economic difficulties has caused that unfortunately the government budget for scientific research is more and more limited, so I am concerned about both present and future of the public Spanish research.

5. What do you think of the situation of young biostatisticians in Spain?

Neither better nor worse than other young people in the country. Nowadays, I guess the best way to make progress is to move abroad.

6. What would be the 3 main characteristics or skills you would use to describe a good biostatistician?

 Enthusiasm, effort and a little bit of R knowledge.

7. Which do you think are the main qualities of a good mentor?

To be attentive and available when needed.

Selected publications:

  • Ibañez M. V., Vinué G., Alemany S., Simó A., Epifanio I., Domingo J., Ayala G., “Apparel sizing using trimmed PAM and OWA operators”, Expert Systems with Applications 39, 10512-10520, 2012, http://dx.doi.org/10.1016/j.eswa.2012.02.127
  • Epifanio I., Vinué G., Alemany S., “Archetypal analysis: Contributions for estimating boundary cases in multivariate accommodation problem”, Computers & Industrial Engineering 64, 757- 765, 2013, http://dx.doi.org/10.1016/j.cie.2012.12.011

Interview with…Moisés Gómez Mateu

Moisés

Moisés Gómez Mateu is a PhD student at the Universitat Politècnica de Catalunya (UPC) where he works as research assistant. 

Contact Moisés

1. Why do you like Biostatistics?

I have been very curious since I was a child; that’s why I like statistics. Moreover, statistics related to biology and medicine allows helping people to improve their quality of life.

 2. Could you give us some insight in your current field of research?

I focus my thesis on survival analysis, especially on the issue of composite endpoints in clinical trials. The main aim is to analyze what is the best primary endpoint to use, extend the statistical theory, and make practical tools available to researchers by means of a library in R, an on-line platform, etc.

3. Did you find it difficult to move from the private sector to the University?

No. In fact, I left my job as a consultant in a marketing research company to study the MSc Statistics at the UPC, and I think it was a very good decision.

 4. Which are, in your opinion, the main advantages of being a researcher?

It is very satisfactory. The results you get or the research you conduct have nothing to do with the private sector. One usually investigates issues that are related to thing you like and to help improving science in general, not only to earn money.

 5. What do you think of the situation of young biostatisticians in Spain?

The reality is that several colleagues and friends are working-studying abroad or looking for opportunities …

6. What would be the 3 main characteristics or skills you would use to describe a good biostatistician?

 Curiosity, Analytical skills and Creativity.

 7. Which do you think are the main qualities of a good mentor?

Expertise, Modesty and Open-mindedness.

Selected publications:

  • Gómez G, Gómez-Mateu M, Dafni U. Informed Choice of Composite Endpoints in Cardiovascular Trials.Submitted.
  • Gómez G, Gómez-Mateu M. The Asymptotic Relative Efficiency and the ratio of sample sizes when testing two different null hypotheses. Submitted.

Interview with…Natàlia Adell Calvet

Natàlia AdellNatàlia Adell is a graduate in Statistics from the Universitat Politècnica de Catalunya. She also has a master´s degree in Statistical and Operations Resarch from the same University. She worked in KantarMedia and in the Statistical Service of the Universitat Autònoma de Barcelona. At present, she works in the Statistical Assessment Unit of the Research Technical Services of the University of Girona.

Contact Natalia

+34 680778844

http://www.udg.edu/str/uae

1. Why do you like Biostatistics?

Because I like applied Statistics and if you can contribute to a good cause such as decreasing the number of illnesses, you will have all the right ingredients for good science.

2. Could you give us some insight in the work you develop at the Statistical Assessment Unit of the UdG´s Research Technical Services?

My main perception is that people need statisticians to help with a part of their research, studies… Statistics is a science that other scientists need and the Statistical Assessment Unit tries to provide it.

3. What were the main difficulties you found when setting up the unit?

The main difficulty was getting started. We had to organise the unit, establish all the procedures, and also let the community know about us. The most important thing I had was the support of all the people around me, who helped every time I needed it (and still do).

4. Is it possible to combine consultancy/advice and research?

Well, in our case, we dedicate ourselves just to the consultancy and giving advice because doing research is not the aim of the Statistical Assessment Unit. But it might be possible to combine both, because some doubts arise from research, and some questions need a research approach so they can be related.

5. What do you think of the situation of young biostatisticians in Spain?

I think  biostatisticians usually work alone, without the support of other statisticians and, in my opinion, it would be interesting to share knowledge with other biostatisticians. So I hope that BioStatNet and FreshBiostats will allow that! 🙂

6. What would be the 3 main characteristics or skills you would use to describe a good biostatistician?

Listening, communicating and having a deep knowledge of Statistics. If you have these three characteristics, you can be a good biostatistician.

7. What do you think are the main qualities of a good mentor?

I think the most important skill is to be organised, knowing the steps you need to take to achieve your goal.  Explaining difficult technics in a clear way will also be appreciated.

8. Finally, is there any topic you would like to see covered in the blog?

Sample size could be a theme of interest!

Selected publications:

  • Adell, N., Puig P., Rojas-Olivares, A., Caja, G., Carné, S. and Salama, A.A.K. A bivariate model for retinal image identification. Computers and Electronics in Agriculture. 2012; 87: 108-112. Epub 2012 June.
  • M. A. Rojas-Olivares, G. Caja, S. Carné, A. A. K. Salama, N. Adell, and P.Puig. Determining the optimal age for recording the retinal vascular pattern image of lambs.  Journal of Animal Science. 2012; 90 (3): 1040-6. Epub 2011 Nov 7.
  • Rojas-Olivares M.A., Caja G., Carné S., Salama A.A.K., Adell N., Puig P. Retinal image recognition for verifying the identity of fattening and replacement lambs. Journal of Animal Science. 2011; 89 (8): 2603-13. Epub 2011 Feb 4.
  • Martínez-Vilalta J, López BC, Adell N, Badiella L & Ninyerola M (2008). Twentieth century increase of Scots pine radial growth in NE Spain shows strong climate interactions. Global change biology. 2008; 14, nº 12: 2868-2881.