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Category: Methods

Posts related to research methods and instrumentation.

Photosynthesis under Light Stress—Methods to Detect Acclimation and Damage to the Photosynthetic Apparatus

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The UV4Plants Training School, Ostrava (14. – 17. 10. 2025): The UV4Plants Training School was organised to provide early-career researchers and students with hands-on experience in methods commonly used to investigate plant responses to ultraviolet (UV) radiation. Instead of working on unrelated samples, participants performed an experiment under near-real research conditions. The central question addressed whether barley leaf angle, vertical or horizontal, modulates the spatial distribution of UV-protective pigments and how this relates to the severity of acute UV-B-induced damage, as evaluated by photosynthetic performance related parameters (Fv/Fm, thylakoid-membrane integrity, and CO₂-fixation rate). The experiment comprised two phases. First, prior to any UV-B exposure, the acclimatory state of leaves grown at different angles, specifically the distribution of UV-absorbing pigments and the associated leaf optical properties,were determined. Second, the acute effects of UV-B were examined in primary leaves developed at these contrasting angles, enabling a direct comparison of pigment distribution and physiological damage. Participants received practical training in: (i) basic HPLC profiling of UV-absorbing pigments; (ii) characterisation of leaf optical properties, including non-destructive in vivo estimation of UV-screening compounds, spectrofluorimetric analysis of chlorophyll excitation spectra in the UV range, and spectroradiometric measurements of leaf reflectance; (iii) functional analysis of Photosystem II…

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Losing Earth: The Decade We Almost Stopped Climate Change (NYT magazine)

An interactive article with photographs, videos and text. This is a long article in two parts. It is extremely interesting with respect to how politics and science intermingle and how science can be ignored or not, and what factors were and are at play. The NYT requires a subscription for regular reading, but everybody has a free quota of four articles. In my opinion, every one of us, doing research on anything with broader implications cannot afford not to read this. On top of it is written almost like a thriller, so an easy read, and includes some interesting ideas of why the Montreal Protocol was easy for politicians and public to accept. https://nyti.ms/2mWMDT8 Our theme disables links on excerpts, please open the post and then follow the link.  

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Follow-up to the statement on p-values by ASA

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The American Statistical Association Says [Mostly] No to p-values Norman Matloff has published a new post after receiving criticism and comments about stating “The ASA says No to p-values” in his post I wrote about some days ago. He defends his interpretation in this new post. However, I think, the interpretation of  the statement in a context different from the “Big data” field to which he is used to does not need to always be “Says No to p-values” but instead in many cases could be “Use p-values to assess the strength of the evidence and nothing else”.  However, “tests” with binary outcomes on probabilities that are essentially continuous, will always be based on an arbitrary threshold and discard a great deal of information. Consequently to me using as suggested by Norman Matloff  “assess” in place of “test” makes a lot of sense. The new post is at https://matloff.wordpress.com/2016/03/09/further-comments-on-the-asa-manifesto/ “Itʹs Not The P-valuesʹ Fault” Joint post by Yoav Benjamini and Tal Galili. The post highlights points raised by Yoav in his official response to the ASA statement… Source: It’s Not The P-values’ Fault – Reflections On The Recent ASA Statement (+relevant R Resources) | R-statistics Blog P-values: the Continuing Saga I highly recommend the blog post by Yoav Benjamini and…

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The American Statistical Association Says No to p-values

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This post is copied from my Blog for Students and is to some extent off-topic but relevant to anybody doing research. Norman Matloff (2016) writes in his post: Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading… Source: After 150 Years, the ASA Says No to p-values | Mad (Data) Scientist Yesterday, the statement by the American Statistics Association was published on-line in the journal “The American Statistician”. Many statisticians have been aware of the problems of significance tests for a long time, but general practice, teaching and journal instructions and editors’  requirements had not changed. Let’s hope the statement will start real changes in everyday practice. John W. Tukey (1991) has earlier written quite boldly about the problem: Statisticians classically asked the wrong question—and were willing to answer with a lie, one that was often a downright lie. They asked “Are the effects of A and B different?” and they were willing to answer “no.” All we know about the world teaches us that the effects of A and B are always different—in some decimal place—for any A and B. Thus…

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