@article{PaulKollmannsberger2020,
  author    = {Paul, Torsten Johann and Kollmannsberger, Philip},
  title     = {Biological network growth in complex environments: A computational framework},
  series = {PLoS Computational Biology},
  volume    = {16},
  journal   = {PLoS Computational Biology},
  number    = {11},
  doi       = {10.1371/journal.pcbi.1008003},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231373},
  year      = {2020},
  abstract  = {Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function. Author summary We present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.},
  language  = {en}
}
@article{SahlolKollmannsbergerEwees2020,
  author    = {Sahlol, Ahmed T. and Kollmannsberger, Philip and Ewees, Ahmed A.},
  title     = {Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features},
  series = {Scientific Reports},
  volume    = {10},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-020-59215-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229398},
  year      = {2020},
  abstract  = {White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.},
  language  = {en}
}
@article{TrinklKaluzaWallaceetal.2020,
  author    = {Trinkl, Moritz and Kaluza, Benjamin F. and Wallace, Helen and Heard, Tim A. and Keller, Alexander and Leonhardt, Sara D.},
  title     = {Floral Species Richness Correlates with Changes in the Nutritional Quality of Larval Diets in a Stingless Bee},
  series = {Insects},
  volume    = {11},
  journal   = {Insects},
  number    = {2},
  issn      = {2075-4450},
  doi       = {10.3390/insects11020125},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200605},
  pages     = {125},
  year      = {2020},
  abstract  = {Bees need food of appropriate nutritional quality to maintain their metabolic functions. They largely obtain all required nutrients from floral resources, i.e., pollen and nectar. However, the diversity, composition and nutritional quality of floral resources varies with the surrounding environment and can be strongly altered in human-impacted habitats. We investigated whether differences in plant species richness as found in the surrounding environment correlated with variation in the floral diversity and nutritional quality of larval provisions (i.e., mixtures of pollen, nectar and salivary secretions) composed by the mass-provisioning stingless bee Tetragonula carbonaria (Apidae: Meliponini). We found that the floral diversity of larval provisions increased with increasing plant species richness. The sucrose and fat (total fatty acid) content and the proportion and concentration of the omega-6 fatty acid linoleic acid decreased, whereas the proportion of the omega-3 fatty acid linolenic acid increased with increasing plant species richness. Protein (total amino acid) content and amino acid composition did not change. The protein to fat (P:F) ratio, known to affect bee foraging, increased on average by more than 40\% from plantations to forests and gardens, while the omega-6:3 ratio, known to negatively affect cognitive performance, decreased with increasing plant species richness. Our results suggest that plant species richness may support T. carbonaria colonies by providing not only a continuous resource supply (as shown in a previous study), but also floral resources of high nutritional quality.},
  language  = {en}
}
@phdthesis{Leidinger2020,
  author    = {Leidinger, Ludwig Klaus Theodor},
  title     = {How genomic and ecological traits shape island biodiversity - insights from individual-based models},
  doi       = {10.25972/OPUS-20730},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207300},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Life on oceanic islands provides a playground and comparably easy\-/studied basis for the understanding of biodiversity in general. Island biota feature many fascinating patterns: endemic species, species radiations and species with peculiar trait syndromes. However, classic and current island biogeography theory does not yet consider all the factors necessary to explain many of these patterns. In response to this, there is currently a shift in island biogeography research to systematically consider species traits and thus gain a more functional perspective. Despite this recent development, a set of species characteristics remains largely ignored in island biogeography, namely genomic traits. Evidence suggests that genomic factors could explain many of the speciation and adaptation patterns found in nature and thus may be highly informative to explain the fascinating and iconic phenomena known for oceanic islands, including species radiations and susceptibility to biotic invasions. Unfortunately, the current lack of comprehensive meaningful data makes studying these factors challenging. Even with paleontological data and space-for-time rationales, data is bound to be incomplete due to the very environmental processes taking place on oceanic islands, such as land slides and volcanism, and lacks causal information due to the focus on correlative approaches. As promising alternative, integrative mechanistic models can explicitly consider essential underlying eco\-/evolutionary mechanisms. In fact, these models have shown to be applicable to a variety of different systems and study questions. In this thesis, I therefore examined present mechanistic island models to identify how they might be used to address some of the current open questions in island biodiversity research. Since none of the models simultaneously considered speciation and adaptation at a genomic level, I developed a new genome- and niche-explicit, individual-based model. I used this model to address three different phenomena of island biodiversity: environmental variation, insular species radiations and species invasions. Using only a single model I could show that small-bodied species with flexible genomes are successful under environmental variation, that a complex combination of dispersal abilities, reproductive strategies and genomic traits affect the occurrence of species radiations and that invasions are primarily driven by the intensity of introductions and the trait characteristics of invasive species. This highlights how the consideration of functional traits can promote the understanding of some of the understudied phenomena in island biodiversity. The results presented in this thesis exemplify the generality of integrative models which are built on first principles. Thus, by applying such models to various complex study questions, they are able to unveil multiple biodiversity dynamics and patterns. The combination of several models such as the one I developed to an eco\-/evolutionary model ensemble could further help to identify fundamental eco\-/evolutionary principles. I conclude the thesis with an outlook on how to use and extend my developed model to investigate geomorphological dynamics in archipelagos and to allow dynamic genomes, which would further increase the model's generality.},
  subject      = {Inselbiogeografie},
  language  = {en}
}
@article{ImhoffRahnKuenzeletal.2020,
  author    = {Imhoff, Johannes F. and Rahn, Tanja and K{\"u}nzel, Sven and Keller, Alexander and Neulinger, Sven C.},
  title     = {Osmotic adaptation and compatible solute biosynthesis of phototrophic bacteria as revealed from genome analyses},
  series = {Microorganisms},
  volume    = {9},
  journal   = {Microorganisms},
  number    = {1},
  issn      = {2076-2607},
  doi       = {10.3390/microorganisms9010046},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220161},
  year      = {2020},
  abstract  = {Osmotic adaptation and accumulation of compatible solutes is a key process for life at high osmotic pressure and elevated salt concentrations. Most important solutes that can protect cell structures and metabolic processes at high salt concentrations are glycine betaine and ectoine. The genome analysis of more than 130 phototrophic bacteria shows that biosynthesis of glycine betaine is common among marine and halophilic phototrophic Proteobacteria and their chemotrophic relatives, as well as in representatives of Pirellulaceae and Actinobacteria, but are also found in halophilic Cyanobacteria and Chloroherpeton thalassium. This ability correlates well with the successful toleration of extreme salt concentrations. Freshwater bacteria in general lack the possibilities to synthesize and often also to take up these compounds. The biosynthesis of ectoine is found in the phylogenetic lines of phototrophic Alpha- and Gammaproteobacteria, most prominent in the Halorhodospira species and a number of Rhodobacteraceae. It is also common among Streptomycetes and Bacilli. The phylogeny of glycine-sarcosine methyltransferase (GMT) and diaminobutyrate-pyruvate aminotransferase (EctB) sequences correlate well with otherwise established phylogenetic groups. Most significantly, GMT sequences of cyanobacteria form two major phylogenetic branches and the branch of Halorhodospira species is distinct from all other Ectothiorhodospiraceae. A variety of transport systems for osmolytes are present in the studied bacteria.},
  language  = {en}
}
@article{VoulgariKokotaSteffanDewenterKeller2020,
  author    = {Voulgari-Kokota, Anna and Steffan-Dewenter, Ingolf and Keller, Alexander},
  title     = {Susceptibility of Red Mason Bee Larvae to Bacterial Threats Due to Microbiome Exchange with Imported Pollen Provisions},
  series = {Insects},
  volume    = {11},
  journal   = {Insects},
  number    = {6},
  issn      = {2075-4450},
  doi       = {10.3390/insects11060373},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207948},
  year      = {2020},
  abstract  = {Solitary bees are subject to a variety of pressures that cause severe population declines. Currently, habitat loss, temperature shifts, agrochemical exposure, and new parasites are identified as major threats. However, knowledge about detrimental bacteria is scarce, although they may disturb natural microbiomes, disturb nest environments, or harm the larvae directly. To address this gap, we investigated 12 Osmia bicornis nests with deceased larvae and 31 nests with healthy larvae from the same localities in a 16S ribosomal RNA (rRNA) gene metabarcoding study. We sampled larvae, pollen provisions, and nest material and then contrasted bacterial community composition and diversity in healthy and deceased nests. Microbiomes of pollen provisions and larvae showed similarities for healthy larvae, whilst this was not the case for deceased individuals. We identified three bacterial taxa assigned to Paenibacillus sp. (closely related to P. pabuli/amylolyticus/xylanexedens), Sporosarcina sp., and Bacillus sp. as indicative for bacterial communities of deceased larvae, as well as Lactobacillus for corresponding pollen provisions. Furthermore, we performed a provisioning experiment, where we fed larvae with untreated and sterilized pollens, as well as sterilized pollens inoculated with a Bacillus sp. isolate from a deceased larva. Untreated larval microbiomes were consistent with that of the pollen provided. Sterilized pollen alone did not lead to acute mortality, while no microbiome was recoverable from the larvae. In the inoculation treatment, we observed that larval microbiomes were dominated by the seeded bacterium, which resulted in enhanced mortality. These results support that larval microbiomes are strongly determined by the pollen provisions. Further, they underline the need for further investigation of the impact of detrimental bacterial acquired via pollens and potential buffering by a diverse pollen provision microbiome in solitary bees.},
  language  = {en}
}
@phdthesis{Jaslan2020,
  author    = {Jaslan, Dawid Artur},
  title     = {TPC1 mutants provide insight into SV channel function},
  doi       = {10.25972/OPUS-15731},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157312},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {In the framework of the presented doctoral thesis, the plant ubiquitous, non-selective vacuolar cation channel TPC1/SV was electrophysiologically studied in Arabidopsis thaliana mesophyll vacuoles to further enlighten its physiological role in plant stress responses. For this, the hyperactive channel version fou2 (D454N), gaining a non-functional vacuolar calcium sensor, strong retarded growth phenotype and upregulated JA signalling pathway, and eight fou2 reverting WT-like ouf mutants were used. Except of ouf4, all other seven ouf mutants carried a 2nd mutation in the TPC1 gene. Therefore, the TPC1 electrical features of all ouf mutants were electrophysiologically characterized with the patch clamp method and compared with fou2 and WT. Due to a missense mutation, ouf1 and ouf7 mutants harboured a truncated TPC1 channel protein, resulting in an impaired protein integrity and in turn loss of TPC1 channel activity. Accordingly, ouf1 and ouf7 mimicked the tpc1-2 null mutant with a WT- rather fou2-like phenotype. The ouf2 (G583D D454N) mutant exhibited inactive TPC1 channels, probably because the G583D mutation located in luminal part of the S11 helix caused (i) a shift of the activation threshold to much more positive voltages (i.e. to more than +110 mV) (ii) or channel blockage. As a result of the TPC1 channel inactivity, the ouf2 mutant also imitates the WT-like phenotype of the tpc1-2 null mutant. In the ouf6 mutant (A669V D454N) the 2nd reverting mutation selectively influenced fou2-like SV channel features. Both, the fast activation kinetics and reduced luminal calcium sensitivity were similar in ouf6 and fou2. However, deviations in both, the relative and absolute open channel probability, resulted in strongly reduced (80 \%) current density at 0 mM and channel inactivity in the voltage range between -30 mV to +40 mV compared to fou2 and WT. Furthermore, the TPC1 channels in ouf6 exhibited a higher susceptibility to inhibitory luminal Ca2+ than fou2. As a result of these different effects, the TPC1 channel activity almost vanished at high luminal Ca2+ loads, what is very likely the reason that ouf6 lost the fou2-like phenotype. The ouf4 mutation did not change the fou2 TPC1-channel features like fast channel activation, single channel conductance and voltage-dependent gating behaviour. Nevertheless, the TPC1 current density was 80\% less in ouf4 than in fou2. Since the TPC1 gene was not the target of the 2nd mutation, it can be assumed that it is modulated via external, yet unknown factor. In the ouf8 mutant the TPC1 channels additionally possess M629I mutation within the selectivity filter II resulting in a 50\% decrease in the TPC1 unitary conductance. However, the slightly increased relative open channel probability of the TPC1 channels in ouf8 compared to fou2 appeared to be sufficient to compensate the reduced transport capacity of individual TPC1 channels. As a result, a similar macroscopic outward current density of ouf8 and fou2 was detected in the absence of vacuolar Ca2+. Furthermore, ouf8 mutation did not drastically change the typical fou2 TPC1 channel features such as fast activation, vacuolar calcium insensitivity and voltage dependency. However, a reversible block of the cytosol-directed potassium efflux at increased vacuolar calcium concentration in ouf8 mutant was found. Further inspection of transiently expressed TPC1 channel variants (M629I, M629T) on the single channel level suggest that Met629 of AtTPC1 in the channel pore region is crucial for the unitary channel conductance. Taken together, current membrane recordings from ouf mutants revealed one common feature: All of them lacked or showed a strongly impaired ability for TPC1-mediated potassium release from the vacuole into the cytosol. Additionally, considering the detected dependence of the vacuolar membrane voltage on TPC1 activity, it thus seems that the TPC1-triggered vacuolar membrane depolarization caused by vacuolar K+ release plays a key role in generation of the fou2-like phenotype. Accordingly, one can conclude that TPC1-dependent vacuolar membrane depolarization and initiation of jasmonate production are likely linked. This statement is supported also by the complete restoration of WT-like plant phenotype and JA signalling in the ouf mutants. Finally, as a control element of the vacuolar membrane voltage TPC1 is probably upstream located in JA signalling pathway and therefore a perfect junction for linking multiple physiological stimuli and response to them. Im Rahmen der vorgelegten Doktorarbeit wurde der in Pflanzen ubiquit{\"a}r exprimierte, nicht-selektive vakuol{\"a}re Kationenkanal TPC1/SV elektrophysiologisch in Arabidopsis thaliana Mesophyllvakuolen untersucht, um seine physiologische Rolle in der pflanzlichen Stressantwort weiter aufzukl{\"a}ren. Hierf{\"u}r wurde die hyperaktive Kanalvariante fou2 (D454N), die einen nicht-funktionalen vakuol{\"a}ren Calciumsensor, ein stark verz{\"o}gertes Pflanzenwachstum und einen hochregulierten Jasmons{\"a}ure-Signalweg aufweist, sowie acht ouf Mutanten mit fou2-umkehrenden Ph{\"a}notyp benutzt. Mit Ausnahme von ouf4 enthalten alle anderen ouf Mutanten eine weitere Mutation im TPC1-Gen. Daher wurden die elektrischen Eigenschaften von TPC1 in allen ouf Mutanten elektrophysiologisch mittels der Patch clamp Technik charakterisiert und mit fou2 und dem Wildtyp verglichen. Aufgrund einer Missense-Mutation beinhalten die Mutanten ouf1 und ouf7 ein verk{\"u}rztes TPC1 Protein, woraus eine gest{\"o}rte Proteinintegrit{\"a}t resultiert und daraus wiederum ein Fehlen der TCP1-Kanalaktivit{\"a}t. Dementsprechend {\"a}hneln ouf1 und ouf7 der tpc1-2 Nullmutante mit einem WT- oder eher fou2-artigen Ph{\"a}notyp. Wahrscheinlich weist die ouf2 (G583D D454N) Mutante einen inaktiven TPC1-Kanal auf, weil die G583D Mutation, die in einem luminalen Teil der S11 Helix sitzt, eine Verschiebung der Aktivierungsschwelle hin zu einer h{\"o}heren Spannung (z. B. mehr als +110 mV) oder einen Kanalblock verursacht. Als Folge der TPC1 Kanal Inaktivit{\"a}t, ahmt die ouf2 Mutante auch den WT-{\"a}hnlichen Ph{\"a}notyp der tpc1-2 Nullmutante nach. In der ouf6 Mutante (A669V D454N) beeinflusst die zweite Mutation selektiv die fou2-{\"a}hnlichen SV-Kanaleigenschaften. Sowohl die schnelle Aktivierungskinetik als auch die verringerte luminale Calciumsensitivit{\"a}t waren denen von ouf6 und fou2 {\"a}hnlich. Die Abweichungen in der relativen sowie der absoluten Offenwahrscheinlichkeit resultierten jedoch in einer stark reduzierten (80 \%) Stromdichte bei 0 mM luminalem Calcium verglichen mit fou2 und dem WT, sowie einer Kanalinaktivit{\"a}t bei Spannungen zwischen -30 mV und +40 mV. Dar{\"u}ber hinaus zeigten die TPC1 Kan{\"a}le in ouf6 eine h{\"o}here Anf{\"a}lligkeit f{\"u}r inhibitorisches, luminales Calcium als die in fou2. Das Ergebnis der beiden unterschiedlichen Effekte ist, dass die TPC1 Kanalaktivit{\"a}t bei einer hohen luminalen Calciumkonzentration fast verschwindet, woraus zu schließen ist, dass ouf6 den fou2-{\"a}hnlichen Ph{\"a}notyp verlor. Die ouf4 Mutation ver{\"a}nderte nicht die fou2 TPC1 Kanaleigenschaften, wie die schnelle Kanalaktivierung, die Einzelkanalleitf{\"a}higkeit und das spannungsabh{\"a}ngige Verhalten. Nichtsdestotrotz war die TCP1 Stromdichte in ouf4 um 80 \% geringer als in fou2. Da das TPC1 Gen nicht das Ziel der zweiten Mutation war, kann angenommen werden, dass es durch {\"a}ußere, bisher noch unbekannte Faktoren, reguliert wird. In der ouf8 Mutante haben die TPC1 Kan{\"a}le zus{\"a}tzlich eine M629I Mutation innerhalb des zweiten Selektivit{\"a}tsfilters, welche in einem 50 \% R{\"u}ckgang der TCP1 Einzelkanalleitf{\"a}higkeit resultiert. Jedoch scheint die leicht erh{\"o}hte Offenwahrscheinlichkeit der TCP1 Kan{\"a}le in ouf8, verglichen mit fou2, ausreichend zu sein, um die reduzierte Transportkapazit{\"a}t der individuellen TPC1 Kan{\"a}le zu kompensieren. Schlussfolgernd wurde eine {\"a}hnliche makroskopische ausw{\"a}rts gerichtete Stromdichte des ouf8 und des fou2 in Abwesenheit vakuol{\"a}ren Calciums entdeckt. Des Weiteren {\"a}nderte eine ouf8 Mutation die fou2 TPC1 Kanaleigenschaften wie eine schnelle Aktivierung, vakuol{\"a}re Calciuminsensitivit{\"a}t und die Spannungsabh{\"a}ngigkeit nicht drastisch. Jedoch wurde ein reversibler Block des Zytosol-gerichteten Kalium Ausstroms bei erh{\"o}hten vakuol{\"a}ren Calcium Konzentrationen in ouf8 gefunden. Eine weitere Betrachtung transient exprimierter TPC1 Kanalvarianten (M629I, M629T) auf Einzelkanalebene weist darauf hin, dass das Met629 des AtTPC1 in der Kanalporenregion entscheidend ist f{\"u}r die Einzelkanalleitf{\"a}higkeit. Zusammengefasst zeigt der {\"u}ber die Membran von ouf Mutanten gemessene Strom eine Gemeinsamkeit: Alle zeigten keinen oder einen stark beeintr{\"a}chtigten TPC1-vermittelten Kaliumausstrom aus der Vakuole ins Zytosol. Unter Ber{\"u}cksichtigung der beobachteten Abh{\"a}ngigkeit der vakuol{\"a}ren Membranspannung von der TPC1 Aktivit{\"a}t, scheint es, als ob die durch TPC1 angeregte Depolarisation der Vakuolenmembran, welche durch die vakuol{\"a}re Kaliumfreisetzung bedingt wird, in der Ausbildung des fou2 Ph{\"a}notyps eine Rolle spielt. Daraus l{\"a}sst sich ableiten, dass die TPC1-abh{\"a}ngige Depolarisation der Vakuolenmembran und die Jasmonat Bildung vermutlich verbunden sind. Diese Behauptung wird auch gest{\"u}tzt durch die komplette Wiederherstellung des WT-{\"a}hnlichen Pflanzenph{\"a}notyps und des Jasmons{\"a}ure Signalwegs in den ouf Mutanten. Letztendlich ist TPC1 als kontrollierendes Element der vakuol{\"a}ren Membranspannung wahrscheinlich dem Jasmons{\"a}ure Signalweg vorgeschaltet und deswegen ein perfekter Knotenpunkt, der verschiedene physiologische Stimuli und ihre Antworten verbindet.},
  language  = {en}
}
@article{PookFreudenthalKorteetal.2020,
  author    = {Pook, Torsten and Freudenthal, Jan and Korte, Arthur and Simianer, Henner},
  title     = {Using Local Convolutional Neural Networks for Genomic Prediction},
  series = {Frontiers in Genetics},
  volume    = {11},
  journal   = {Frontiers in Genetics},
  doi       = {10.3389/fgene.2020.561497},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216436},
  year      = {2020},
  abstract  = {The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular, local convolutional neural networks (LCNN) for genomic prediction, as a region-specific filter corresponds much better with our prior genetic knowledge on the genetic architecture of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000; p = 34,595) and real Arabidopsis data (n = 2,039; p = 180,000) for a variety of traits based on their predictive ability. The baseline LCNN, containing one local convolutional layer (kernel size: 10) and two fully connected layers with 64 nodes each, is outperforming commonly proposed ANNs (multi layer perceptrons and convolutional neural networks) for basically all considered traits. For traits with high heritability and large training population as present in the simulated data, LCNN are even outperforming state-of-the-art methods like genomic best linear unbiased prediction (GBLUP), Bayesian models and extended GBLUP, indicated by an increase in predictive ability of up to 24\%. However, for small training populations, these state-of-the-art methods outperform all considered ANNs. Nevertheless, the LCNN still outperforms all other considered ANNs by around 10\%. Minor improvements to the tested baseline network architecture of the LCNN were obtained by increasing the kernel size and of reducing the stride, whereas the number of subsequent fully connected layers and their node sizes had neglectable impact. Although gains in predictive ability were obtained for large scale data sets by using LCNNs, the practical use of ANNs comes with additional problems, such as the need of genotyping all considered individuals, the lack of estimation of heritability and reliability. Furthermore, breeding values are additive by design, whereas ANN-based estimates are not. However, ANNs also comes with new opportunities, as networks can easily be extended to account for additional inputs (omics, weather etc.) and outputs (multi-trait models), and computing time increases linearly with the number of individuals. With advances in high-throughput phenotyping and cheaper genotyping, ANNs can become a valid alternative for genomic prediction.},
  language  = {en}
}