scholarly journals Cell size, photosynthesis and the package effect: an artificial selection approach

2018 ◽  
Vol 219 (1) ◽  
pp. 449-461 ◽  
Author(s):  
Martino E. Malerba ◽  
Maria M. Palacios ◽  
Yussi M. Palacios Delgado ◽  
John Beardall ◽  
Dustin J. Marshall
2018 ◽  
Vol 285 (1884) ◽  
pp. 20181347 ◽  
Author(s):  
Martino E. Malerba ◽  
Maria M. Palacios ◽  
Dustin J. Marshall

Size determines the rate at which organisms acquire and use resources but it is unclear what size should be favoured under unpredictable resource regimes. Some theories claim smaller organisms can grow faster following a resource pulse, whereas others argue larger species can accumulate more resources and maintain growth for longer periods between resource pulses. Testing these theories has relied on interspecific comparisons, which tend to confound body size with other life-history traits. As a more direct approach, we used 280 generations of artificial selection to evolve a 10-fold difference in mean body size between small- and large-selected phytoplankton lineages of Dunaliella tertiolecta , while controlling for biotic and abiotic variables. We then quantified how body size affected the ability of this species to grow at nutrient-replete conditions and following periods of nitrogen or phosphorous deprivation. Overall, smaller cells showed slower growth, lower storage capacity and poorer recovery from phosphorous depletion, as predicted by the ‘fasting endurance hypothesis'. However, recovery from nitrogen limitation was independent of size—a finding unanticipated by current theories. Phytoplankton species are responsible for much of the global carbon fixation and projected trends of cell size decline could reduce primary productivity by lowering the ability of a cell to store resources.


2020 ◽  
Vol 64 (2) ◽  
pp. 383-396
Author(s):  
Lara K. Krüger ◽  
Phong T. Tran

Abstract The mitotic spindle robustly scales with cell size in a plethora of different organisms. During development and throughout evolution, the spindle adjusts to cell size in metazoans and yeast in order to ensure faithful chromosome separation. Spindle adjustment to cell size occurs by the scaling of spindle length, spindle shape and the velocity of spindle assembly and elongation. Different mechanisms, depending on spindle structure and organism, account for these scaling relationships. The limited availability of critical spindle components, protein gradients, sequestration of spindle components, or post-translational modification and differential expression levels have been implicated in the regulation of spindle length and the spindle assembly/elongation velocity in a cell size-dependent manner. In this review, we will discuss the phenomenon and mechanisms of spindle length, spindle shape and spindle elongation velocity scaling with cell size.


2019 ◽  
Vol 83 (3) ◽  
pp. 295-308
Author(s):  
MG Weinbauer ◽  
S Suominen ◽  
J Jezbera ◽  
ME Kerros ◽  
S Marro ◽  
...  

2020 ◽  
Vol 17 (5) ◽  
pp. 243-265 ◽  
Author(s):  
Jiandong Xie ◽  
Sa Xiao ◽  
Ying-Chang Liang ◽  
Li Wang ◽  
Jun Fang

Author(s):  
Zsuzsanna Márton ◽  
Bianka Csitári ◽  
Tamas Felfoldi ◽  
Anna J Szekely ◽  
Attila Szabo

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.


2018 ◽  
Vol 60 (6) ◽  
pp. 583-590 ◽  
Author(s):  
Jinglin Xu ◽  
Jianqing Liu ◽  
Wenbin Gu ◽  
Zhenxiong Wang ◽  
Xin Liu ◽  
...  

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