Search

To assess the incidence of early blight/brown spot (EB/BS) in Flanders (Belgium), potato fields in 22 locations were monitored and scored over the duration of the growing seasons 2014 and 2015. The average disease incidence was shown to be higher in 2014 than in 2015. Soil type, rainfall and temperature were also analysed in relation to disease incidence. In 2014, potato plants grown in sandy soils had more EB/BS disease than those grown in clay or loamy soils. However, the low disease incidence in 2015 meant that differences in disease levels between soil types could not be discerned for that growing season. A windowpane analysis demonstrated that rainfall and humidity accounted for the differences in disease incidence between the two growing seasons. During the course of the survey, the species composition in leaves with symptoms was assessed using real‐time PCR. Remarkably, small‐spored Alternaria species, such as A. alternata and A. arborescens, rather than the more virulent A. solani were the predominant species on potato leaves throughout the growing season. As the disease progressed, the proportion of A. solani increased. In view of these results, the virulence of a collected set of Alternaria isolates was assessed by an in vitro assay. Despite A. solani being more virulent than A. alternata or A. arborescens, the most abundant species isolated from potato leaves with symptoms was A. arborescens. [ABSTRACT FROM AUTHOR]

Development of materials by mixing different base components is a widespread methodology to create materials with improved properties compared to those of its base components. However, efficient determination of the properties of mixture-based materials during design remains challenging without prior knowledge of the underlying physical phenomena. In this work a new data-based methodology is proposed involving the use of probabilistic, knowledge-guided artificial neural networks to jointly model the properties of the base components, the proportions in which they are mixed, and the processing conditions used during manufacture to predict properties of final products. The method proposed does not involve any assumptions in terms of ideal mixing rules of the base components, and allows for estimation of aleatoric uncertainty in the prediction. Additionally, an extension is presented that incorporates expert knowledge into the model by the implementation of monotonicity constraints between certain inputs and outputs. The methodology is illustrated with a case study involving the formulation of drug products using direct compression. The model is used to predict pharmaceutical tablets' quality attributes (mass variation, tensile strength, disintegration time, friability and ejection force), showing that the method is able to predict properties of the final product overcoming gaps currently present in previous modelling approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innovative techniques. While many cosmetic defects can occur, some are considered more critical than others as they can be harmful to the patient or affect the drug's efficacy. With the rise of artificial intelligence and computer vision technology, faster and more reproducible quality control is possible, allowing real-time monitoring on a continuous manufacturing line. In this study, several continuously freeze-dried samples were prepared using formulations and process settings that lead deliberately to specific defects faced in freeze-drying as well as defect-free samples. Two approaches (i.e. patch-based approach and multi-label classification) capable of handling high-resolution images based on Convolutional Neural Networks were developed and compared to select the optimal one. Additional visualisation techniques were used to enhance model understanding further. The best approach achieved perfect precision and recall on critical defects, with a prediction time of less than 50 ms to make a decision on the acceptance or rejection of vials generated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The lifetime production of dairy cows is a complex trait influenced not only by genetics but also by the environment in which a cow lives and the management practices of the farmer. Moreover, these influential factors show complex interactions with each other, making it difficult to reliably predict the lifetime production of individual animals at birth. However, because well-managed dairy farms often have a surplus of youngstock, reliable lifetime production predictions would offer the opportunity to make more substantiated decisions when selecting calves or heifers to sell. Therefore, using data from Dutch herds, we constructed a dataset capturing information on genetics, environment, and management practices to develop multiple machine learning models capable of predicting the lifetime production of dairy cattle soon after birth. We found that a coupling of trends observed at the country level with farm-specific models largely outperforms off-the-shelf approaches. At birth, our best model could explain up to 47% of the variance in lifetime production, a considerable improvement compared with linear regression on the breeding values supplemented with the average lifetime production at farm level, which could explain only 21.7% of the variance in lifetime production. Moreover, we demonstrated that surplus youngstock selection according to our model could more than double the surplus animal selection effect compared with the benchmark methodology, offering opportunities to significantly increase the average (future) potential lifetime production of the retained heifers. Assuming a static 20% surplus liveborn heifer scenario and random surplus animal selection as the default, our best model for surplus animal selection resulted in a 9.4% greater lifetime production in the retained animals compared with the current Dutch average lifetime production., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The pharmaceutical industry is progressing towards more continuous manufacturing techniques. To dry biopharmaceuticals, continuous freeze drying has several advantages on manufacturing and process analytical control compared to batch freeze-drying, including better visual inspection potential. Visual inspection of every freeze-dried product is a key quality assessment after the lyophilization process to ensure that freeze-dried products are free from foreign particles and defects. This quality assessment is labor-intensive for operators who need to assess thousands of samples for an extensive amount of time leading to certain drawbacks. Applying Artificial Intelligence, specifically computer vision, on high-resolution images from every freeze-dried product can quantitatively and qualitatively outperform human visual inspection. For this study, continuously freeze-dried samples were prepared based on a real-world pharmaceutical product using manually induced particles of different sizes and subsequently imaged using a tailor-made setup to develop an image dataset (with particle sizes from 50μm to 1 mm) used to train multiple object detection models. You Only Look Once version 7 (YOLOv7) outperforms human inspection by a large margin, obtaining particle detection precision of up to 88.9% while controlling the recall at 81.2%, thus detecting most of the object present in the images, with an inference time of less than 1 s per vial., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In most rabbit farms, breeding does kindle and nurse their kits in single-litter cages throughout their entire reproduction cycle. However, the protective behavior can lead to aggressive displays and injuries when the does are housed in groups. This study aimed to evaluate cage enrichment for reducing the agonistic behavior in part-time group-housed does. A total of eighty does with their 22-day-old kits were allocated to 20 multi-litter cages, with each cage housing four does and their litters for 10 days. Each multi-litter group was subjected to one of four treatments: alfalfa blocks as distraction material (A), wooden panels underneath the platforms (P), both alfalfa and wooden panels (AP), or no extra enrichment (controls, C). This experiment was replicated for three consecutive reproduction cycles. The skin injuries of the does and the kits were scored with a tagged visual analog scale before grouping and at one, three, six, eight, and 10 days after grouping. Computer vision techniques were used to continuously monitor rabbit activity and agonistic behavior (aggression and fleeing/chasing) during the first 24 h after grouping, specifically during light hours. During the first day in the group, 67.2% of the does and 13.4% of the kits acquired new injuries. This increased to 82.0 and 33.2%, respectively after 10 days in the group relative to the onset of grouping. The injury scores of the does increased toward the sixth day after grouping compared to the first ( p  < 0.001) and were highest on the tenth day for the kits ( p  < 0.001). On all the observation days, the number of injured does was higher in C compared to A ( p  = 0.04) and AP treatment ( p  = 0.005). There were no other treatment effects observed on the doe or kit skin injuries. Rabbit activity was highest after grouping but decreased after the first and second days ( p  < 0.001). The agonistic interactions between the does involved more fleeing/chasing behavior (62.0%) rather than aggression (38.0%). Although hierarchy fights are likely when unacquainted does are group-housed, the many animals that sustained injuries and the high injury scores confirm that part-time group housing for does is challenging and possibly inevitable. This study has shown that alfalfa, with or without wooden panels, can slightly reduce the number of injured does., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Van Damme, Ipek, Verwaeren, Delezie and Tuyttens.)

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO 3 -N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO 3 -N/L)., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization., (© 2024 Wiley Periodicals LLC.)

In recent years, computer vision has contributed significantly to the study of farm animal behavior. In complex environments such as commercial farms, however, the automated detection of social behavior and specific interactions between animals can be improved. The present study addresses the automated detection of agonistic interactions between caged animals in a complex environment, relying solely on computer vision. An automated pipeline including group-level temporal action segmentation, object detection, object tracking and rule-based action classification for the detection of agonistic interactions was developed and extensively validated at a level unique in the field. Comparing with observations made by human observers, our pipeline reaches 77% precision and 85% recall using a 5-min tolerance interval for the detection of agonistic interactions. Results obtained using this pipeline allow to construct time-dependent socio-matrices of a group of animals and derive metrics on the dominance hierarchy in a semi-automated manner. Group-housed breeding rabbits (does) with their litters in commercial farms are the main use-case in this work, but the idea is probably also applicable to other social farm animals., (© 2023. Springer Nature Limited.)

Wood identification is a key step in the enforcement of laws and regulations aimed at combatting illegal timber trade. Robust wood identification tools, capable of distinguishing a large number of timbers, depend on a solid database of reference material. Reference material for wood identification is typically curated in botanical collections dedicated to wood consisting of samples of secondary xylem of lignified plants. Specimens from the Tervuren Wood Collection, one of the large institutional wood collections around the world, are used as a source of tree species data with potential application as timber. Here, we present SmartWoodID, a database of high-resolution optical scans of the end-grain surfaces enriched with expert wood anatomical descriptions of macroscopic features. These can serve as annotated training data to develop interactive identification keys and artificial intelligence for computer vision-based wood identification. The first edition of the database consists of images of 1190 taxa, with a focus on potential timber species from the Democratic Republic of the Congo with at least four different specimens per species included. Database URL https://hdl.handle.net/20.500.12624/SmartWoodID&#95;first&#95;edition., (© The Author(s) 2023. Published by Oxford University Press.)

The use of phosphate solubilizing bacteria (PSB) as inoculants for the rhizosphere is a well-known strategy to mitigate P-deficiency in plants. However, despite the multiple modes of action to render P available for plants, PSB often fail to deliver in the field as their selection is often based on a single P-solubilizing trait assessed in vitro. Anticipating these shortcomings, we screened 250 isolates originating from rhizosphere-based enriched consortia for the main in vitro P-solubilizing traits, and subsequently grouped the isolates through trait-based HCPC (hierarchical clustering on principal components). Representative isolates of each cluster were tested in an in planta experiment to compare their in vitro P-solubilizing traits with their in planta performance under conditions of P-deprivation. Our data convincingly show that bacterial consortia capable to mitigate P-deficiency in planta were enriched in bacterial isolates that had multiple P-solubilizing traits in vitro and that had the capacity to mitigate plant P-stress in planta under P-deprived conditions. Furthermore, although it was assumed that bacteria that looked promising in vitro would also have a positive effect in planta , our data show that this was not always the case. Opposite, lack of performance in vitro did not automatically result in a lack of performance in planta . These results corroborate the strength of the previously described in planta -based enrichment and selection technique for the isolation of highly efficient rhizosphere competent PSB. IMPORTANCE With the growing awareness on the ecological impact of chemical phosphate fertilizers, research concerning the use of phosphate solubilizing bacteria (PSB) as a sustainable alternative for, or addition to these fertilizers is of paramount importance. In previous research, we successfully implemented a plant-based enrichment technique for PSB, which simultaneously selected for the rhizosphere competence and phosphate solubilizing characteristics of bacterial suspensions. Current research follows up on our previous findings, whereas we screened 250 rhizobacteria for their P-solubilizing traits and were able to substantiate the results obtained from the enriched suspensions at a single-isolate level. With this research, we aim for a paradigm shift toward the plant-based selection of PSB, which is a more holistic approach compared to the plate-based methods. We emphasize the strength of the previously described plant-based enrichment and selection technique for the isolation of highly efficient and diverse PSB.

Emoji have been proposed as a way to get additional insights in how consumers perceive food products. Recent works have indicated that emoji are able to provide distinctive emotional associations with food products, regardless of whether one is using the check-all-that-apply (CATA) or the rate-all-that-apply (RATA) scaling approach. Typically, in examining emotional associations one can work with either a general list which can be used with all food products or a product-specific emotion list. To date, a comparison between the performance of a general and product-specific emoji list with adults is lacking. Moreover, it is unclear to which extent emotional data of emoji help to better predict the actual food choice of adult consumers. Using five samples of chocolates, this study compared the use of a general list of 39 emoji with a product-specific list of 20 emoji (based upon input of 32 consumers). In total, 138 consumers assessed the samples using the general list while 136 consumers evaluated the samples with the product-specific emoji list. The RATA approach was used for the evaluation of the samples and the actual food choice was registered as participants received a snack portion of the chosen sample to take home. Results indicated that, considering the frequency of selection, 10 emoji discriminated between the samples for both the general and product-specific lists. Similar results were obtained when considering the rating intensities. Including emoji did not lead to a significant increase in the food choice prediction regardless the type of list used. However, emoji data obtained from the product-specific emoji list was able to predict the food choice as accurate as the liking data when using the RATA intensity scores. This study suggests that both general and product-specific emoji lists are able to generate distinguishing emotional profiles for chocolate samples. While further research is necessary with other food products and measurement methods (e.g. CATA), this study proposes that emoji measurements might be an alternative to liking data in order to be better understand of consumers' food choice., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance., Results: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy., Conclusions: Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance., (© 2022. The Author(s).)

Phosphorus (P) is despite its omnipresence in soils often unavailable for plants. Rhizobacteria able to solubilize P are therefore crucial to avoid P deficiency. Selection for phosphate-solubilizing bacteria (PSB) is frequently done in vitro; however, rhizosphere competence is herein overlooked. Therefore, we developed an in planta enrichment concept enabling simultaneous microbial selection for P-solubilization and rhizosphere competence. We used an ecologically relevant combination of iron- and aluminium phosphate to select for PSB in maize (Zea mays L.). In each consecutive enrichment, plant roots were inoculated with rhizobacterial suspensions from plants that had grown in substrate with insoluble P. To assess the plants' P statuses, non-destructive multispectral imaging was used for quantifying anthocyanins, a proxy for maize's P status. After the third consecutive enrichment, plants supplied with insoluble P and inoculated with rhizobacterial suspensions showed a P status similar to plants supplied with soluble P. A parallel metabarcoding approach uncovered that the improved P status in the third enrichment coincided with a shift in the rhizobiome towards bacteria with plant growth-promoting and P-solubilizing capacities. Finally, further consecutive enrichment led to a functional relapse hallmarked by plants with a low P status and a second shift in the rhizobiome at the level of Azospirillaceae and Rhizobiaceae., (© 2021 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.)

Rhizospheric microorganisms can alter plant physiology and morphology in many different ways including through the emission of volatile organic compounds (VOCs). Here we demonstrate that VOCs from beneficial root endophytic Serendipita  spp. are able to improve the performance of in vitro grown Arabidopsis seedlings, with an up to 9.3-fold increase in plant biomass. Additional changes in VOC-exposed plants comprised petiole elongation, epidermal cell and leaf area expansion, extension of the lateral root system, enhanced maximum quantum efficiency of photosystem II (F v /F m ), and accumulation of high levels of anthocyanin. Notwithstanding that the magnitude of the effects was highly dependent on the test system and cultivation medium, the volatile blends of each of the examined strains, including the references S. indica and S. williamsii , exhibited comparable plant growth-promoting activities. By combining different approaches, we provide strong evidence that not only fungal respiratory CO 2 accumulating in the headspace, but also other volatile compounds contribute to the observed plant responses. Volatile profiling identified methyl benzoate as the most abundant fungal VOC, released especially by Serendipita cultures that elicit plant growth promotion. However, under our experimental conditions, application of methyl benzoate as a sole volatile did not affect plant performance, suggesting that other compounds are involved or that the mixture of VOCs, rather than single molecules, accounts for the strong plant responses. Using Arabidopsis mutant and reporter lines in some of the major plant hormone signal transduction pathways further revealed the involvement of auxin and cytokinin signaling in Serendipita VOC-induced plant growth modulation. Although we are still far from translating the current knowledge into the implementation of Serendipita VOCs as biofertilizers and phytostimulants, volatile production is a novel mechanism by which sebacinoid fungi can trigger and control biological processes in plants, which might offer opportunities to address agricultural and environmental problems in the future., (Copyright © 2020 Venneman, Vandermeersch, Walgraeve, Audenaert, Ameye, Verwaeren, Steppe, Van Langenhove, Haesaert and Vereecke.)

Despite the high economic importance of cacao beans, few RNA-based studies have been conducted on this plant material and hence no optimal RNA-extraction has been reported. Moreover, extraction of high-quality RNA from recalcitrant cacao bean tissue has shown many difficulties and requires optimization. Furthermore, cacao beans are mostly found at remote and under-resourced locations, which pressures the outsourcing of such analysis and thereby demands RNA-stable preservation and transportation of cacao beans. This study aims to select an appropriate RNA extraction and preservation/transportation method for cacao beans. For this purpose, three sample homogenization and five extraction protocols on cacao beans were compared. In addition, 13 preservation conditions-differing in tissue crushing degree, preservation method, duration, and temperature-were compared and evaluated. A comparative analysis revealed that CTAB-based homogenization and extraction outcompeted all tested commercial protocols in RNA yield and integrity, respectively. Preservation at -80°C affected RNA quality the least, whereas freeze-drying was most suitable for transportation at room temperature for maximum 1 week. The cacao bean RNA obtained from the selected methods were compatible for downstream applications. The results of this study will facilitate on-field sampling and transportation of genetically sensitive cacao material prior to cacao bean transcriptomic studies. In addition, valuable insights on sample homogenization, extraction, preservation, and transportation have been provided, which is of interest to every plant geneticist., (Copyright © 2020 De Wever, Tulkens, Verwaeren, Everaert, Rottiers, Dewettinck, Lefever and Messens.)

Sourdough is a very interesting natural preservation system to prolong mould free shelf-life of bread. Numerous studies have reported that the antifungal activity of sourdough is mainly correlated with the presence of lactic (LA) and acetic acid (AA), but very few information is available on the effect of undissociated acid concentrations in the aqueous phase of bread (C HA ; mmole/L). This study was conducted to provide additional information about the mode of action of the acids in sourdough bread, enabling a better shelf-life prediction. This study was divided into two parts. In part 1, three industrial biological sourdoughs were characterized (dough yield, pH, a w , fermentation quotient, microbiota). During 7 weeks, a shelf-life test with natural flora was conducted with daily checks of visible mould growth (21 °C). In part 2, the effect of the acids present in the antifungal active sourdough breads was validated in chemically acidified wheat breads. Complete growth inhibition was observed in full-baked sourdough bread (30 g/100 g dough) containing Lactobacillus sanfranciscensis and Saccharomyces cerevisiae as dominant sourdough micro-organisms, whereas in control bread the shelf-life was limited to 4.4-9.2 days. These full-baked sourdough breads contained 36 mmole undissociated LA/L and 220 mmole undissociated AA/L. The data were used to make General Linear Regression models for shelf-life prediction and resulted in a fit of R 2  = 0.79 when expressing the shelf-life in function of C HA,LA and C HA,AA . In acidified breads, the role of lactic acid was not significant and only impacted shelf-life indirectly through acidification. No difference between antifungal activity of sourdough breads and chemically acidified bread with comparable C HA,AA concentrations was observed. Shelf-life increased when 150-200 mmole undissociated AA/L aqueous phase in bread was present. To conclude, this study showed the importance of the undissociated acid fraction of acetic acid in relation to bread shelf-life, together with bread pH and moisture content., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Black carbon is often used as an indicator for combustion-related air pollution. In urban environments, on-road black carbon concentrations have a large spatial variability, suggesting that the personal exposure of a cyclist to black carbon can heavily depend on the route that is chosen to reach a destination. In this paper, we describe the development of a cyclist routing procedure that minimizes personal exposure to black carbon. Firstly, a land use regression model for predicting black carbon concentrations in an urban environment is developed using mobile monitoring data, collected by cyclists. The optimal model is selected and validated using a spatially stratified cross-validation scheme. The resulting model is integrated in a dedicated routing procedure that minimizes personal exposure to black carbon during cycling. The best model obtains a coefficient of multiple correlation of R=0.520. Simulations with the black carbon exposure minimizing routing procedure indicate that the inhaled amount of black carbon is reduced by 1.58% on average as compared to the shortest-path route, with extreme cases where a reduction of up to 13.35% is obtained. Moreover, we observed that the average exposure to black carbon and the exposure to local peak concentrations on a route are competing objectives, and propose a parametrized cost function for the routing problem that allows for a gradual transition from routes that minimize average exposure to routes that minimize peak exposure., (Copyright © 2019 Elsevier Inc. All rights reserved.)

666 I. Introduction 667 II. Biosynthesis 667 III. Meta-analysis 669 IV. The type of stress influences the total amount of GLVs released 669 V. Herbivores can modulate the wound-induced release of GLVs 669 VI. Fungal infection greatly induces GLV production 672 VII. Monocots and eudicots respond differentially to different types of stress 673 VIII. The type of stress does not influence the proportion of GLVs per chemical class 673 IX. The type of stress does influence the isomeric ratio within each chemical class 674 X. GLVs: from signal perception to signal transduction 676 XI. GLVs influence the C/N metabolism 677 XII. Interaction with plant hormones 678 XIII. General conclusions and unanswered questions 678 Acknowledgements 679 References 679 SUMMARY: Plants respond to stress by releasing biogenic volatile organic compounds (BVOCs). Green leaf volatiles (GLVs), which are abundantly produced across the plant kingdom, comprise an important group within the BVOCs. They can repel or attract herbivores and their natural enemies; and they can induce plant defences or prime plants for enhanced defence against herbivores and pathogens and can have direct toxic effects on bacteria and fungi. Unlike other volatiles, GLVs are released almost instantly upon mechanical damage and (a)biotic stress and could thus function as an immediate and informative signal for many organisms in the plant's environment. We used a meta-analysis approach in which data from the literature on GLV production during biotic stress responses were compiled and interpreted. We identified that different types of attackers and feeding styles add a degree of complexity to the amount of emitted GLVs, compared with wounding alone. This meta-analysis illustrates that there is less variation in the GLV profile than we presumed, that pathogens induce more GLVs than insects and wounding, and that there are clear differences in GLV emission between monocots and dicots. Besides the meta-analysis, this review provides an update on recent insights into the perception and signalling of GLVs in plants., (© 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.)

In the last decade, there has been an increasing focus on the implementation of plant growth-promoting (PGP) organisms as a sustainable option to compensate for poor soil fertility conditions in developing countries. Trap systems were used in an effort to isolate PGP fungi from rhizospheric soil samples collected in the region around Kisangani in the Democratic Republic of Congo. With sudangrass as a host, a highly conducive environment was created for sebacinalean chlamydospore formation inside the plant roots resulting in a collection of 51 axenically cultured isolates of the elusive genus Piriformospora (recently transferred to the genus Serendipita ). Based on morphological data, ISSR fingerprinting profiles and marker gene sequences, we propose that these isolates together with Piriformospora williamsii constitute a species complex designated Piriformospora (= Serendipita ) ' williamsii. ' A selection of isolates strongly promoted plant growth of in vitro inoculated Arabidopsis seedlings, which was evidenced by an increase in shoot fresh weight and a strong stimulation of lateral root formation. This isolate collection provides unprecedented opportunities for fundamental as well as translational research on the Serendipitaceae, a family of fungal endophytes in full expansion.

A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the "decoy" database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs). Nokoi was trained on a very large collection of heterogeneous data using ranks supplied by the Mascot search engine to label correct and incorrect PSMs. We show that Nokoi outperforms Mascot and achieves a performance very close to that of Percolator at substantially higher processing speeds.