193 research outputs found
Dispersal of Plants by Waterbirds
The widespread distribution of fresh-water
plants and of the lower animals, whether retaining
the same identical form or in some degree modified, I believe mainly depends on the wide
dispersal of their seeds and eggs by animals, more especially by fresh-water
birds, which have
large powers of flight, and naturally travel from one to another and often distant piece of
water. — Charles
Darwin (1859)Peer reviewe
Seed dispersal by dabbling ducks: an overlooked dispersal pathway for a broad spectrum of plant species
1. Dabbling ducks (Anatinae) are omnivorous birds that are widespread, numerous, highly mobile
and often migratory, and therefore have great potential for (long distance) dispersal of other organisms,
including plants. However, their ability to act as plant dispersal vectors has received little
attention compared to frugivores and is often assumed to be relevant only for wetland species.
2. To evaluate the potential for plant dispersal by dabbling ducks, we collated and analysed existing
data. We identified all plant species whose seeds have been recorded in the diets of the seven dabbling
duck (Anas) species in the Western Palaearctic, as reported from gut content analyses. We then
analysed the habitats and traits of these plant species to identify general patterns, and related these
to data on gut passage survival and duck movements.
3. A large number of plant species (> 445 species of 189 genera and 57 families) have been recorded
in the diet of dabbling ducks. These plant species represent a very wide range of habitats, including
almost the full range of site fertility, moisture and light conditions, excluding only very dry and deeply
shaded habitats. The ducks prefer seeds of intermediate sizes (1–10 mm3), which have good chances to
survive gut passage, but also ingest smaller and larger seeds. Ingested seeds represent a wide range of
dispersal syndromes, including fleshy fruits. Many species (62%) were not previously considered animal-
dispersed in plant data bases, and 66% were not identified as bird-dispersed. Rarefaction analyses
suggest that our analysis still greatly underestimates the total number of plant species ingested.
4. Synthesis. Dabbling ducks do not exclusively ingest seeds of wetland plants, which make up only
40% of the ingested species. Rather, they feed opportunistically on a wide cross-section of plant
species available across the landscapes they inhabit. Given the millions of ducks, the hundreds to
thousands of seeds ingested per individual on a daily basis, and known gut passage survival rates,
this results in vast numbers of seeds dispersed by ducks per day. Internal seed dispersal by dabbling
ducks appears to be a major dispersal pathway for a far broader spectrum of plant species than previously
consideredPeer reviewe
Criticism of the Use of Coomassie Brilliant Blue G-250 for the Quantitative Determination of Proteins
Peer Reviewe
Online automatic tuning and control for fed-batch cultivation
Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis
Seed dispersal by waterbirds: a mechanistic understanding by simulating avian digestion
Waterbirds disperse plant species via ingestion and egestion of seeds (endozoochory). However, our understanding about the regulating effects of seed traits, underlying mechanisms and possible (co)evolutionary processes is limited by our traditional reliance on data from feeding experiments with living waterbirds. Here, we overcome these limitations by developing and applying a new bioassay that realistically simulates digestive processes for Anseriformes waterbirds. We test three hypotheses: 1) seed survival and germination are most affected by mechanical digestion in the waterbird gizzard; 2) seed size, hardness, imbibition and shape regulate seed survival; and 3) plants growing in aquatic habitats benefit most from endozoochory by waterbirds. Experiments with 28 200 seeds of 48 plant species demonstrated species-specific seed survival that was entirely determined by digestion in the avian gizzard. Intestinal digestion did not affect seed survival but affected seed establishment (germinability and germination time) for 21% of the species. Large, hard seeds survived the simulations the best, in contrast to generally higher seed survival for smaller seeds during in vivo experiments. This mechanistically explains that small seeds escape digestive processes rather than being inherently more resistant (the ‘escape mechanism'), while large seeds are retained until fully digested or regurgitated (the ‘resistance and regurgitation mechanism'). Plants growing in wetter habitats had similar seed survival, but digestive processes stimulated their germinability and accelerated their germination more than for terrestrial plants. This indicates a relative advantage of endozoochory for plant species growing in wet habitats, possibly reflecting a co-evolutionary response related to dormancy breaking by gut passage. Simulating seed gut passage using a bioassay allowed establishing mechanisms and identifying relevant seed traits involved in seed dispersal by waterbirds. This information enhances our understanding of how animal species shape plant species distributions, which is extremely relevant now that current anthropogenic pressures already severely impact plant dispersal capacities
Modelos propulsivos: novas teorias velhas polémicas
A propulsão no meio aquático é baseada na interacção entre o movimento do nadador e
o meio envolvente. Neste âmbito, o objectivo principal dos movimentos propulsivos é o
de transmitir momento linear ao meio. Esta transferência de momento entre o corpo do
nadador e o meio aquático é mediada pela 3ª Lei de Newton (Lei da acção reacção).
A forma mais fácil e económica de gerar propulsão seria pela utilização de pontos de
apoio rígidos, nos quais a mão se pudesse fixar, permitindo o deslocamento do corpo do
nadador para a frente, tal como sucede no MAD System (Measuring Active Drag System;
Hollander et al., 1986)
Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms
This paper deals with the estimation of unknown
signals in bioreactors using sliding observers. Particular
attention is drawn to estimate the specific growth rate of
microorganisms from measurement of biomass concentration.
In a recent article, notions of high-order sliding modes have
been used to derive a growth rate observer for batch processes.
In this paper we generalize and refine these preliminary results.
We develop a new observer with a different error structure to
cope with other types of processes. Furthermore, we show that
these observers are equivalent, under coordinate transformations
and time scaling, to the classical super-twisting differentiator
algorithm, thus inheriting all its distinctive features.
The new observers’ family achieves convergence to timevarying
unknown signals in finite time, and presents the best
attainable estimation error order in the presence of noise. In
addition, the observers are robust to modeling and parameter
uncertainties since they are based on minimal assumptions
on bioprocess dynamics. In addition, they have interesting
applications in fault detection and monitoring. The observers
performance in batch, fed-batch and continuous bioreactors is
assessed by experimental data obtained from the fermentation
of Saccharomyces Cerevisiae on glucose.This work was supported by the National University of La Plata (Project 2012-2015), the Agency for the Promotion of Science and Technology ANPCyT (PICT2007-00535) and the National Research Council CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain; and by the project FEDER of the European Union.De Battista, H.; Picó Marco, JA.; Garelli, F.; Navarro Herrero, JL. (2012). Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess and Biosystems Engineering. 35(9):1-11. https://doi.org/10.1007/s00449-012-0752-yS111359Aborhey S, Williamson D (1978) State amd parameter estimation of microbial growth process. Automatica 14:493–498Bastin G, Dochain D (1986) On-line estimation of microbial specific growth rates. Automatica 22:705–709Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier, AmsterdamBejarano F, Fridman L (2009) Unbounded unknown inputs estimation based on high-order sliding mode differentiator. In: Proceedings of the 48th IEEE conference on decision and control, pp 8393–8398Corless M, Tu J (1998) State and input estimation for a class of uncertain systems. Automatica 34(6):757–764Dabros M, Schler M, Marison I (2010) Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass. Bioprocess Biosyst Eng 33:1109–1118Davila A, Moreno J, Fridman L (2010) Variable gains super-twisting algorithm: a lyapunov based design. In: American control conference (ACC), 2010, pp 968–973Dávila J, Fridman L, Levant A (2005) Second-order sliding-mode observer for mechanical systems. IEEE Transact Automatic Control 50(11):1785–1789De Battista H, Picó J, Garelli F, Vignoni A (2011) Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. J Process Control 21:1049–1055Dochain D (2001) Bioprocess control. Wiley, HobokenDochain D (2003) State and parameter estimation in chemical and biochemical processes: a tutorial. J Process Control 13(8):801–818Edwards C, Spurgeon S, Patton R (2000) Sliding mode observers for fault detection and isolation. Automatica 36(2):541–553Evangelista C, Puleston P, Valenciaga F, Fridman L (2012) Lyapunov designed super-twisting sliding mode control for wind energy conversion optimization. Indus Electron IEEE Transact. doi: 10.1109/TIE.2012.2188256Farza M, Busawon K, Hammouri H (1998) Simple nonlinear observers for on-line estimation of kinetic rates in bioreactors. Automatica 34(3):301–318Fridman L, Davila J, Levant A (2008) High-order sliding modes observation. In: International workshop on variable structure systems, pp 203–208Fridman L, Levant A (2002) Sliding mode control in engineering, higher-order sliding modes. Marcel Dekker, Inc., New York, pp 53–101Fridman L, Shtessel Y, Edwards C, Yan X (2008) Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems. Int J Robust Nonlinear Control 18(3–4):399–412Gauthier J, Hammouri H, Othman S (1992) A simple observer for nonlinear systems: applications to bioreactors. IEEE Transact Automatic Control 37(6):875–880Gnoth S, Jenzsch M, Simutis R, Lubbert A (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31(1):21–39Hitzmann B, Broxtermann O, Cha Y, Sobieh O, Stärk E, Scheper T (2000) The control of glucose concentration during yeast fed-batch cultivation using a fast measurement complemented by an extended kalman filter. Bioprocess Eng 23(4):337–341Kiviharju K, Salonen K, Moilanen U, Eerikainen T (2008) Biomass measurement online: the performance of in situ measurements and software sensors. J Indus Microbiol Biotechnol 35(7):657–665Levant A (1998) Robust exact differentiation via sliding mode technique. Automatica 34(3):379–384Levant A (2003) Higher-order sliding modes, differentiation and output-feedback control. Int J Control 76(9/10):924–941Lubenova V, Rocha I, Ferreira E (2003) Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses. Bioprocess Biosyst Eng 25:395–406Moreno J, Alvarez J, Rocha-Cozatl E, Diaz-Salgado J (2010) Super-twisting observer-based output feedback control of a class of continuous exothermic chemical reactors. In: Proceedings of the 9th IFAC international symposium on dynamics and control of process systems, pp 719–724. Leuven, BelgiumMoreno J, Osorio M (2008) A Lyapunov approach to second-order sliding mode controllers and observers. In: Proceedings of the 47th IEEE conference on decision and control. Cancún, México, pp 2856–2861Moreno J, Osorio M (2012) Strict Lyapunov functions for the super-twisting algorithm. IEEE Transact Automatic Control 57:1035–1040Navarro J, Picó J, Bruno J, Picó-Marco E, Vallés S (2001) On-line method and equipment for detecting, determining the evolution and quantifying a microbial biomass and other substances that absorb light along the spectrum during the development of biotechnological processes. Patent ES20010001757, EP20020751179Neeleman Boxtel (2001) Estimation of specific growth rate from cell density measurements. Bioprocess Biosyst Eng 24(3):179–185November E, van Impe J (2002) The tuning of a model-based estimator for the specific growth rate of Candidautilis. Bioprocess Biosyst Eng 25:1–12Park Y, Stein J (1988) Closed-loop, state and input observer for systems with unknown inputs. Int J Control 48(3):1121–1136Perrier M, de Azevedo SF, Ferreira E, Dochain D (2000) Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters. Control Eng Pract 8:377–388Picó J, De Battista H, Garelli F (2009) Smooth sliding-mode observers for specific growth rate and substrate from biomass measurement. J Process Control 19(8):1314–1323. Special section on hybrid systems: modeling, simulation and optimizationSchenk J, Balaszs K, Jungo C, Urfer J, Wegmann C, Zocchi A, Marison I, von Stockar U (2008) Influence of specific growth rate on specific productivity and glycosylation of a recombinant avidin produced by a Pichia pastoris Mut + strain. Biotecnol Bioeng 99(2):368–377Shtessel Y, Taleb M, Plestan F (2012) A novel adaptive-gain supertwisting sliding mode controller: Methodol Appl Automatica (in press)Soons Z, van Straten G, van der Pol L, van Boxtel A (2008) On line automatic tuning and control for fed-batch cultivation. Bioprocess Biosyst Eng 31(5):453–467Utkin V, Poznyak A, Ordaz P (2011) Adaptive super-twist control with minimal chattering effect. In: Proceedings of 50th IEEE conference on decision and control and European control conference. Orlando, pp 7009–7014Veloso A, Rocha I, Ferreira E (2009) Monitoring of fed-batch E. coli fermentations with software sensors. Bioprocess Biosyst Eng 32(3):381–388Venkateswarlu C (2004) Advances in monitoring and state estimation of bioreactors. J Sci Indus Res 63:491–498Zamboni N, Fendt S, Rühl M, Sauer U (2009) 13c-based metabolic flux analysis. Nat Protocols 4:878–892Zorzetto LFM, Wilson JA (1996) Monitoring bioprocesses using hybrid models and an extended kalman filter. Comput Chem Eng 20(Suppl 1):S689–S69
- …