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Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers.
Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers.
The manuscript describes the phytochemical investigation of the roots, leaves and stem bark of Millettia lasiantha resulting in the isolation of twelve compounds including two new isomeric isoflavones lascoumestan and las-coumaronochromone. The structures of the new compounds were determined using different spectroscopic techniques.
A matter of concern
(2021)
Neurons are post-mitotic cells in the brain and their integrity is of central importance to avoid neurodegeneration. Yet, the inability of self-replenishment of post-mitotic cells results in the need to withstand challenges from numerous stressors during life. Neurons are exposed to oxidative stress due to high oxygen consumption during metabolic activity in the brain. Accordingly, DNA damage can occur and accumulate, resulting in genome instability. In this context, imbalances in brain trace element homeostasis are a matter of concern, especially regarding iron, copper, manganese, zinc, and selenium. Although trace elements are essential for brain physiology, excess and deficient conditions are considered to impair neuronal maintenance. Besides increasing oxidative stress, DNA damage response and repair of oxidative DNA damage are affected by trace elements. Hence, a balanced trace element homeostasis is of particular importance to safeguard neuronal genome integrity and prevent neuronal loss. This review summarises the current state of knowledge on the impact of deficient, as well as excessive iron, copper, manganese, zinc, and selenium levels on neuronal genome stability
Monoclonal antibodies are used worldwide as highly potent and efficient detection reagents for research and diagnostic applications. Nevertheless, the specific targeting of complex antigens such as whole microorganisms remains a challenge. To provide a comprehensive workflow, we combined bioinformatic analyses with novel immunization and selection tools to design monoclonal antibodies for the detection of whole microorganisms. In our initial study, we used the human pathogenic strain E. coli O157:H7 as a model target and identified 53 potential protein candidates by using reverse vaccinology methodology. Five different peptide epitopes were selected for immunization using epitope-engineered viral proteins. The identification of antibody-producing hybridomas was performed by using a novel screening technology based on transgenic fusion cell lines. Using an artificial cell surface receptor expressed by all hybridomas, the desired antigen-specific cells can be sorted fast and efficiently out of the fusion cell pool. Selected antibody candidates were characterized and showed strong binding to the target strain E. coli O157:H7 with minor or no cross-reactivity to other relevant microorganisms such as Legionella pneumophila and Bacillus ssp. This approach could be useful as a highly efficient workflow for the generation of antibodies against microorganisms.
Monoclonal antibodies are used worldwide as highly potent and efficient detection reagents for research and diagnostic applications. Nevertheless, the specific targeting of complex antigens such as whole microorganisms remains a challenge. To provide a comprehensive workflow, we combined bioinformatic analyses with novel immunization and selection tools to design monoclonal antibodies for the detection of whole microorganisms. In our initial study, we used the human pathogenic strain E. coli O157:H7 as a model target and identified 53 potential protein candidates by using reverse vaccinology methodology. Five different peptide epitopes were selected for immunization using epitope-engineered viral proteins. The identification of antibody-producing hybridomas was performed by using a novel screening technology based on transgenic fusion cell lines. Using an artificial cell surface receptor expressed by all hybridomas, the desired antigen-specific cells can be sorted fast and efficiently out of the fusion cell pool. Selected antibody candidates were characterized and showed strong binding to the target strain E. coli O157:H7 with minor or no cross-reactivity to other relevant microorganisms such as Legionella pneumophila and Bacillus ssp. This approach could be useful as a highly efficient workflow for the generation of antibodies against microorganisms.
The spread of shrubs in Namibian savannas raises questions about the resilience of these ecosystems to global change. This makes it necessary to understand the past dynamics of the vegetation, since there is no consensus on whether shrub encroachment is a new phenomenon, nor on its main drivers. However, a lack of long-term vegetation datasets for the region and the scarcity of suitable palaeoecological archives, makes reconstructing past vegetation and land cover of the savannas a challenge.
To help meet this challenge, this study addresses three main research questions: 1) is pollen analysis a suitable tool to reflect the vegetation change associated with shrub encroachment in savanna environments? 2) Does the current encroached landscape correspond to an alternative stable state of savanna vegetation? 3) To what extent do pollen-based quantitative vegetation reconstructions reflect changes in past land cover?
The research focuses on north-central Namibia, where despite being the region most affected by shrub invasion, particularly since the 21st century, little is known about the dynamics of this phenomenon.
Field-based vegetation data were compared with modern pollen data to assess their correspondence in terms of composition and diversity along precipitation and grazing intensity gradients. In addition, two sediment cores from Lake Otjikoto were analysed to reveal changes in vegetation composition that have occurred in the region over the past 170 years and their possible drivers. For this, a multiproxy approach (fossil pollen, sedimentary ancient DNA (sedaDNA), biomarkers, compound specific carbon (δ13C) and deuterium (δD) isotopes, bulk carbon isotopes (δ13Corg), grain size, geochemical properties) was applied at high taxonomic and temporal resolution. REVEALS modelling of the fossil pollen record from Lake Otjikoto was run to quantitatively reconstruct past vegetation cover. For this, we first made pollen productivity estimates (PPE) of the most relevant savanna taxa in the region using the extended R-value model and two pollen dispersal options (Gaussian plume model and Lagrangian stochastic model). The REVEALS-based vegetation reconstruction was then validated using remote sensing-based regional vegetation data.
The results show that modern pollen reflects the composition of the vegetation well, but diversity less well. Interestingly, precipitation and grazing explain a significant amount of the compositional change in the pollen and vegetation spectra. The multiproxy record shows that a state change from open Combretum woodland to encroached Terminalia shrubland can occur over a century, and that the transition between states spans around 80 years and is characterized by a unique vegetation composition. This transition is supported by gradual environmental changes induced by management (i.e. broad-scale logging for the mining industry, selective grazing and reduced fire activity associated with intensified farming) and related land-use change. Derived environmental changes (i.e. reduced soil moisture, reduced grass cover, changes in species composition and competitiveness, reduced fire intensity) may have affected the resilience of Combretum open woodlands, making them more susceptible to change to an encroached state by stochastic events such as consecutive years of precipitation and drought, and by high concentrations of pCO2. We assume that the resulting encroached state was further stabilized by feedback mechanisms that favour the establishment and competitiveness of woody vegetation.
The REVEALS-based quantitative estimates of plant taxa indicate the predominance of a semi-open landscape throughout the 20th century and a reduction in grass cover below 50% since the 21st century associated with the spread of encroacher woody taxa. Cover estimates show a close match with regional vegetation data, providing support for the vegetation dynamics inferred from multiproxy analyses. Reasonable PPEs were made for all woody taxa, but not for Poaceae.
In conclusion, pollen analysis is a suitable tool to reconstruct past vegetation dynamics in savannas. However, because pollen cannot identify grasses beyond family level, a multiproxy approach, particularly the use of sedaDNA, is required. I was able to separate stable encroached states from mere woodland phases, and could identify drivers and speculate about related feedbacks. In addition, the REVEALS-based quantitative vegetation reconstruction clearly reflects the magnitude of the changes in the vegetation cover that occurred during the last 130 years, despite the limitations of some PPEs.
This research provides new insights into pollen-vegetation relationships in savannas and highlights the importance of multiproxy approaches when reconstructing past vegetation dynamics in semi-arid environments. It also provides the first time series with sufficient taxonomic resolution to show changes in vegetation composition during shrub encroachment, as well as the first quantitative reconstruction of past land cover in the region. These results help to identify the different stages in savanna dynamics and can be used to calibrate predictive models of vegetation change, which are highly relevant to land management.
Objective
The Caribbean is an important global biodiversity hotspot. Adaptive radiations there lead to many speciation events within a limited period and hence are particularly prominent biodiversity generators. A prime example are freshwater fish of the genus Limia, endemic to the Greater Antilles. Within Hispaniola, nine species have been described from a single isolated site, Lake Miragoâne, pointing towards extraordinary sympatric speciation. This study examines the evolutionary history of the Limia species in Lake Miragoâne, relative to their congeners throughout the Caribbean.
Results
For 12 Limia species, we obtained almost complete sequences of the mitochondrial cytochrome b gene, a well-established marker for lower-level taxonomic relationships. We included sequences of six further Limia species from GenBank (total N = 18 species). Our phylogenies are in concordance with other published phylogenies of Limia. There is strong support that the species found in Lake Miragoâne in Haiti are monophyletic, confirming a recent local radiation. Within Lake Miragoâne, speciation is likely extremely recent, leading to incomplete lineage sorting in the mtDNA. Future studies using multiple unlinked genetic markers are needed to disentangle the relationships within the Lake Miragoâne clade.
Objective
The Caribbean is an important global biodiversity hotspot. Adaptive radiations there lead to many speciation events within a limited period and hence are particularly prominent biodiversity generators. A prime example are freshwater fish of the genus Limia, endemic to the Greater Antilles. Within Hispaniola, nine species have been described from a single isolated site, Lake Miragoâne, pointing towards extraordinary sympatric speciation. This study examines the evolutionary history of the Limia species in Lake Miragoâne, relative to their congeners throughout the Caribbean.
Results
For 12 Limia species, we obtained almost complete sequences of the mitochondrial cytochrome b gene, a well-established marker for lower-level taxonomic relationships. We included sequences of six further Limia species from GenBank (total N = 18 species). Our phylogenies are in concordance with other published phylogenies of Limia. There is strong support that the species found in Lake Miragoâne in Haiti are monophyletic, confirming a recent local radiation. Within Lake Miragoâne, speciation is likely extremely recent, leading to incomplete lineage sorting in the mtDNA. Future studies using multiple unlinked genetic markers are needed to disentangle the relationships within the Lake Miragoâne clade.
AAA+ proteins (ATPases associated with various cellular activities) catalyze the energy-dependent movement or rearrangement of macromolecules. A new study addresses the important question of how to design a selective chemical inhibitor for specific proteins in this diverse superfamily. The powerful chemical genetics approach adds to a growing toolbox of applications that allow dissection of the functions of distinct AAA+ proteins in vivo, facilitating the first steps toward effective drug development.
Iron-sulfur clusters are essential enzyme cofactors. The most common and stable clusters are [2Fe-2S] and [4Fe-4S] that are found in nature. They are involved in crucial biological processes like respiration, gene regulation, protein translation, replication and DNA repair in prokaryotes and eukaryotes. In Escherichia coli, Fe-S clusters are essential for molybdenum cofactor (Moco) biosynthesis, which is a ubiquitous and highly conserved pathway. The first step of Moco biosynthesis is catalyzed by the MoaA protein to produce cyclic pyranopterin monophosphate (cPMP) from 5’GTP. MoaA is a [4Fe-4S] cluster containing radical S-adenosyl-L-methionine (SAM) enzyme. The focus of this study was to investigate Fe-S cluster insertion into MoaA under nitrate and TMAO respiratory conditions using E. coli as a model organism. Nitrate and TMAO respiration usually occur under anaerobic conditions, where oxygen is depleted. Under these conditions, E. coli uses nitrate and TMAO as terminal electron. Previous studies revealed that Fe-S cluster insertion is performed by Fe-S cluster carrier proteins. In E. coli, these proteins are known as A-type carrier proteins (ATC) by phylogenomic and genetic studies. So far, three of them have been characterized in detail in E. coli, namely IscA, SufA, and ErpA. This study shows that ErpA and IscA are involved in Fe-S cluster insertion into MoaA under nitrate and TMAO respiratory conditions. ErpA and IscA can partially replace each other in their role to provide [4Fe-4S] clusters for MoaA. SufA is not able to replace the functions of IscA or ErpA under nitrate respiratory conditions.
Nitrate reductase is a molybdoenzyme that coordinates Moco and Fe-S clusters. Under nitrate respiratory conditions, the expression of nitrate reductase is significantly increased in E. coli. Nitrate reductase is encoded in narGHJI genes, the expression of which is regulated by the transcriptional regulator, fumarate and nitrate reduction (FNR). The activation of FNR under conditions of nitrate respiration requires one [4Fe-4S] cluster. In this part of the study, we analyzed the insertion of Fe-S cluster into FNR for the expression of narGHJI genes in E. coli. The results indicate that ErpA is essential for the FNR-dependent expression of the narGHJI genes, a role that can be replaced partially by IscA and SufA when they are produced sufficiently under the conditions tested. This observation suggests that ErpA is indirectly regulating nitrate reductase expression via inserting Fe-S clusters into FNR.
Most molybdoenzymes are complex multi-subunit and multi-cofactor-containing enzymes that coordinate Fe-S clusters, which are functioning as electron transfer chains for catalysis. In E. coli, periplasmic aldehyde oxidoreductase (PaoAC) is a heterotrimeric molybdoenzyme that
consists of flavin, two [2Fe-2S], one [4Fe-4S] cluster and Moco. In the last part of this study, we investigated the insertion of Fe-S clusters into E. coli periplasmic aldehyde oxidoreductase (PaoAC). The results show that SufA and ErpA are involved in inserting [4Fe-4S] and [2Fe-2S] clusters into PaoABC, respectively under aerobic respiratory conditions.
Iron sulfur (Fe-S) clusters are important biological cofactors present in proteins with crucial biological functions, from photosynthesis to DNA repair, gene expression, and bioenergetic processes. For the insertion of Fe-S clusters into proteins, A-type carrier proteins have been identified. So far, three of them have been characterized in detail in Escherichia coli, namely, IscA, SufA, and ErpA, which were shown to partially replace each other in their roles in [4Fe-4S] cluster insertion into specific target proteins. To further expand the knowledge of [4Fe-4S] cluster insertion into proteins, we analyzed the complex Fe-S cluster-dependent network for the synthesis of the molybdenum cofactor (Moco) and the expression of genes encoding nitrate reductase in E. coli. Our studies include the identification of the A-type carrier proteins ErpA and IscA, involved in [4Fe-4S] cluster insertion into the radical Sadenosyl-methionine (SAM) enzyme MoaA. We show that ErpA and IscA can partially replace each other in their role to provide [4Fe-4S] clusters for MoaA. Since most genes expressing molybdoenzymes are regulated by the transcriptional regulator for fumarate and nitrate reduction (FNR) under anaerobic conditions, we also identified the proteins that are crucial to obtain an active FNR under conditions of nitrate respiration. We show that ErpA is essential for the FNR-dependent expression of the narGHJI operon, a role that cannot be compensated by IscA under the growth conditions tested. SufA does not appear to have a role in Fe-S cluster insertion into MoaA or FNR under anaerobic growth employing nitrate respiration, based on the low level of gene expression. <br /> IMPORTANCE Understanding the assembly of iron-sulfur (Fe-S) proteins is relevant to many fields, including nitrogen fixation, photosynthesis, bioenergetics, and gene regulation. Remaining critical gaps in our knowledge include how Fe-S clusters are transferred to their target proteins and how the specificity in this process is achieved, since different forms of Fe-S clusters need to be delivered to structurally highly diverse target proteins. Numerous Fe-S carrier proteins have been identified in prokaryotes like Escherichia coli, including ErpA, IscA, SufA, and NfuA. In addition, the diverse Fe-S cluster delivery proteins and their target proteins underlie a complex regulatory network of expression, to ensure that both proteins are synthesized under particular growth conditions.
Populations adapt to novel environmental conditions by genetic changes or phenotypic plasticity. Plastic responses are generally faster and can buffer fitness losses under variable conditions. Plasticity is typically modeled as random noise and linear reaction norms that assume simple one-to- one genotype–phenotype maps and no limits to the phenotypic response. Most studies on plasticity have focused on its effect on population viability. However, it is not clear, whether the advantage of plasticity depends solely on environmental fluctuations or also on the genetic and demographic properties (life histories) of populations. Here we present an individual-based model and study the relative importance of adaptive and nonadaptive plasticity for populations of sexual species with different life histories experiencing directional stochastic climate change. Environmental fluctuations were simulated using differentially autocorrelated climatic stochasticity or noise color, and scenarios of directiona
climate change. Nonadaptive plasticity was simulated as a random environmental effect on trait development, while adaptive plasticity as a linear, saturating, or sinusoidal reaction norm. The last two imposed limits to the plastic response and emphasized flexible interactions of the genotype with the environment. Interestingly, this assumption led to (a) smaller phenotypic than genotypic variance in the population (many-to- one genotype–phenotype map) and the coexistence of polymorphisms, and (b) the maintenance of higher genetic variation—compared to linear reaction norms and genetic determinism—even when the population was exposed to a constant environment for several generations. Limits to plasticity led to genetic accommodation, when costs were negligible, and to the appearance of cryptic variation when limits were exceeded. We found that adaptive plasticity promoted population persistence under red environmental noise and was particularly important for life histories with low fecundity. Populations produing more offspring could cope with environmental fluctuations solely by genetic changes or random plasticity, unless environmental change was too fast.
Populations adapt to novel environmental conditions by genetic changes or phenotypic plasticity. Plastic responses are generally faster and can buffer fitness losses under variable conditions. Plasticity is typically modeled as random noise and linear reaction norms that assume simple one-to- one genotype–phenotype maps and no limits to the phenotypic response. Most studies on plasticity have focused on its effect on population viability. However, it is not clear, whether the advantage of plasticity depends solely on environmental fluctuations or also on the genetic and demographic properties (life histories) of populations. Here we present an individual-based model and study the relative importance of adaptive and nonadaptive plasticity for populations of sexual species with different life histories experiencing directional stochastic climate change. Environmental fluctuations were simulated using differentially autocorrelated climatic stochasticity or noise color, and scenarios of directiona
climate change. Nonadaptive plasticity was simulated as a random environmental effect on trait development, while adaptive plasticity as a linear, saturating, or sinusoidal reaction norm. The last two imposed limits to the plastic response and emphasized flexible interactions of the genotype with the environment. Interestingly, this assumption led to (a) smaller phenotypic than genotypic variance in the population (many-to- one genotype–phenotype map) and the coexistence of polymorphisms, and (b) the maintenance of higher genetic variation—compared to linear reaction norms and genetic determinism—even when the population was exposed to a constant environment for several generations. Limits to plasticity led to genetic accommodation, when costs were negligible, and to the appearance of cryptic variation when limits were exceeded. We found that adaptive plasticity promoted population persistence under red environmental noise and was particularly important for life histories with low fecundity. Populations produing more offspring could cope with environmental fluctuations solely by genetic changes or random plasticity, unless environmental change was too fast.
Background
Nicotine consumption during pregnancy and advanced maternal age are well known independent risk factors for poor pregnancy outcome and therefore serious public health problems.
Objectives
Considering the ongoing trend of delaying childbirth in our society, this study investigates potential additive effects of nicotine consumption during pregnancy and advanced maternal age on foetal growth.
Sample and Methods
In a medical record-based study, we analysed the impact of maternal age and smoking behaviour before and during pregnancy on newborn size among 4142 singleton births that took place in Vienna, Austria between 1990 and 1995.
Results
Birth weight (H=82.176, p<0.001), birth length (H=91.525, p<0.001) and head circumference (H=42.097, p<0.001) differed significantly according to maternal smoking behaviour. For birth weight, the adjusted mean differences between smokers and non-smokers increased from 101.8g for the < 18-year-old mothers to 254.8g for >35 year olds, with the respective values for birth length being 0.6 cm to 0.7cm, for head circumference from 0.3 cm to 0.6 cm.
Conclusion
Increasing maternal age amplified the negative effects of smoking during pregnancy on newborn parameters. Our findings identify older smoking mothers as a high-risk group which should be of special interest for public health systems.
Due to their electrically polarized air-filled internal pores, optimized ferroelectrets exhibit a remarkable piezoelectric response, making them suitable for energy harvesting. Expanded polytetrafluoroethylene (ePTFE) ferroelectret films are laminated with two fluorinated-ethylene-propylene (FEP) copolymer films and internally polarized by corona discharge. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-coated spandex fabric is employed for the electrodes to assemble an all-organic ferroelectret nanogenerator (FENG). The outer electret-plus-electrode double layers form active device layers with deformable electric dipoles that strongly contribute to the overall piezoelectric response in the proposed concept of wearable nanogenerators. Thus, the FENG with spandex electrodes generates a short-circuit current which is twice as high as that with aluminum electrodes. The stacking sequence spandex/FEP/ePTFE/FEP/ePTFE/FEP/spandex with an average pore size of 3 mu m in the ePTFE films yields the best overall performance, which is also demonstrated by the displacement-versus-electric-field loop results. The all-organic FENGs are stable up to 90 degrees C and still perform well 9 months after being polarized. An optimized FENG makes three light emitting diodes (LEDs) blink twice with the energy generated during a single footstep. The new all-organic FENG can thus continuously power wearable electronic devices and is easily integrated, for example, with clothing, other textiles, or shoe insoles.
Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows
(2021)
Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. <br /> Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.
A symmetry-breaking mechanism is investigated that creates bistability between fully and partially synchronized states in oscillator networks. Two populations of oscillators with unimodal frequency distribution and different amplitudes, in the presence of weak global coupling, are shown to simplify to a modular network with asymmetrical coupling. With increasing the coupling strength, a synchronization transition is observed with an isolated fully synchronized state. The results are interpreted theoretically in the thermodynamic limit and confirmed in experiments with chemical oscillators.
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.