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The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R
2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R
2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
Aim
Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small‐bodied species, will increase with increasing temperature. However, the specific responses of single species to rising temperatures, such as spatial range shifts, also alter communities, calling for intensive monitoring of real‐world communities over time.
Location
German Alps and pre‐alpine forests in south‐east Germany.
Methods
We empirically examined the temporal and spatial change in wild bee communities and its drivers along two largely well‐protected elevational gradients (alpine grassland vs. pre‐alpine forest), each sampled twice within the last decade.
Results
We detected clear abundance‐based upward shifts in bee communities, particularly in cold‐adapted bumble bee species, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase in small‐bodied species in low‐ and mid‐elevations along the grassland gradient. Community responses in the pre‐alpine forest gradient were only partly consistent with community responses in alpine grasslands.
Main Conclusion
In well‐protected temperate mountain regions, small‐bodied bees may initially profit from warming temperatures, by getting more abundant and diverse. Less severe warming, and differences in habitat openness along the forested gradient, however, might moderate species responses. Our study further highlights the utility of standardized abundance data for revealing rapid changes in bee communities over only one decade.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Cancer is one of the leading causes of death worldwide. The underlying tumorigenesis is driven by the accumulation of alterations in the genome, eventually disabling tumor suppressors and activating proto-oncogenes.
The MYC family of proto-oncogenes shows a strong deregulation in the majority of tumor entities. However, the exact mechanisms that contribute to MYC-driven oncogenesis remain largely unknown. Over the past decades, the influence of the MYC protein on transcription became increasingly apparent and was thoroughly investigated. Additionally, in recent years several publications provided evidence for so far unreported functions of MYC that are independent of a mere regulation of target genes. These findings suggest an additional role of MYC in the maintenance of genomic stability and this role is strengthened by key findings presented in this thesis.
In the first part, I present data revealing a pathway that allows MYC to couple transcription elongation and DNA double-strand break repair, preventing genomic instability of MYC-driven tumor cells. This pathway is driven by a rapid transfer of the PAF1 complex from MYC onto RNAPII, a process that is mediated by HUWE1. The transfer controls MYC-dependent transcription elongation and, simultaneously, the remodeling of chromatin structure by ubiquitylation of histone H2B. These regions of open chromatin favor not only elongation but also DNA double-strand break repair.
In the second part, I analyze the ability of MYC proteins to form multimeric structures in response to perturbation of transcription and replication. The process of multimerization is also referred to as phase transition. The observed multimeric structures are located proximal to stalled replication forks and recruit factors of the DNA-damage response and transcription termination machinery. Further, I identified the HUWE1-dependent ubiquitylation of MYC as an essential step in this phase transition. Cells lacking the ability to form multimers display genomic instability and ultimately undergo apoptosis in response to replication stress.
Both mechanisms present MYC as a stress resilience factor under conditions that are characterized by a high level of transcriptional and replicational stress. This increased resilience ensures oncogenic proliferation.
Therefore, targeting MYC’s ability to limit genomic instability by uncoupling transcription elongation and DNA repair or disrupting its ability to multimerize presents a therapeutic window in MYC-dependent tumors.
Since the advent of high-throughput sequencing technologies in the mid-2010s, RNA se-
quencing (RNA-seq) has been established as the method of choice for studying gene
expression. In comparison to microarray-based methods, which have mainly been used to
study gene expression before the rise of RNA-seq, RNA-seq is able to profile the entire
transcriptome of an organism without the need to predefine genes of interest. Today,
a wide variety of RNA-seq methods and protocols exist, including dual RNA sequenc-
ing (dual RNA-seq) and multi RNA sequencing (multi RNA-seq). Dual RNA-seq and
multi RNA-seq simultaneously investigate the transcriptomes of two or more species, re-
spectively. Therefore, the total RNA of all interacting species is sequenced together and
only separated in silico. Compared to conventional RNA-seq, which can only investi-
gate one species at a time, dual RNA-seq and multi RNA-seq analyses can connect the
transcriptome changes of the species being investigated and thus give a clearer picture of
the interspecies interactions. Dual RNA-seq and multi RNA-seq have been applied to a
variety of host-pathogen, mutualistic and commensal interaction systems.
We applied dual RNA-seq to a host-pathogen system of human mast cells and Staphylo-
coccus aureus (S. aureus). S. aureus, a commensal gram-positive bacterium, can become
an opportunistic pathogen and infect skin lesions of atopic dermatitis (AD) patients.
Among the first immune cells S. aureus encounters are mast cells, which have previously
been shown to be able to kill the bacteria by discharging antimicrobial products and re-
leasing extracellular traps made of protein and deoxyribonucleic acid (DNA). However,
S. aureus is known to evade the host’s immune response by internalizing within mast
cells. Our dual RNA-seq analysis of different infection settings revealed that mast cells
and S. aureus need physical contact to influence each other’s gene expression. We could
show that S. aureus cells internalizing within mast cells undergo profound transcriptome
changes to adjust their metabolism to survive in the intracellular niche. On the host side,
we found out that infected mast cells elicit a type-I interferon (IFN-I) response in an
autocrine manner and in a paracrine manner to non-infected bystander-cells. Our study
provides the first evidence that mast cells are capable to produce IFN-I upon infection
with a bacterial pathogen.
DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen in several cancers. Any mutations or expression anomalies of genes encoding regulators of DNA methylation may lead to abnormal expression of critical molecules. A comprehensive genomic study encompassing all the genes related to DNA methylation regulation in relation to breast cancer is lacking. We used genomic and transcriptomic datasets from the Cancer Genome Atlas (TGCA) Pan-Cancer Atlas, Genotype-Tissue Expression (GTEx) and microarray platforms and conducted in silico analysis of all the genes related to DNA methylation with respect to writing, reading and erasing this epigenetic mark. Analysis of mutations was conducted using cBioportal, while Xena and KMPlot were utilized for expression changes and patient survival, respectively. Our study identified multiple mutations in the genes encoding regulators of DNA methylation. The expression profiling of these showed significant differences between normal and disease tissues. Moreover, deregulated expression of some of the genes, namely DNMT3B, MBD1, MBD6, BAZ2B, ZBTB38, KLF4, TET2 and TDG, was correlated with patient prognosis. The current study, to our best knowledge, is the first to provide a comprehensive molecular and genetic profile of DNA methylation machinery genes in breast cancer and identifies DNA methylation machinery as an important determinant of the disease progression. The findings of this study will advance our understanding of the etiology of the disease and may serve to identify alternative targets for novel therapeutic strategies in cancer.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Mammalian embryonic development is subject to complex biological relationships that need to be understood. However, before the whole structure of development can be put together, the individual building blocks must first be understood in more detail. One of these building blocks is the second cell fate decision and describes the differentiation of cells of the inner cell mass of the embryo into epiblast and primitive endoderm cells. These cells then spatially segregate and form the subsequent bases for the embryo and yolk sac, respectively. In organoids of the inner cell mass, these two types of progenitor cells are also observed to form, and to some extent to spatially separate. This work has been devoted to these phenomena over the past three years. Plenty of studies already provide some insights into the basic mechanics of this cell differentiation, such that the first signs of epiblast and primitive endoderm differentiation, are the expression levels of transcription factors NANOG and GATA6. Here, cells with low expression of GATA6 and high expression of NANOG adopt the epiblast fate. If the expressions are reversed, a primitive endoderm cell is formed. Regarding the spatial segregation of the two cell types, it is not yet clear what mechanism leads to this. A common hypothesis suggests the differential adhesion of cell as the cause for the spatial rearrangement of cells. In this thesis however, the possibility of a global cell-cell communication is investigated. The approach chosen to study these phenomena follows the motto "mathematics is biology's next microscope". Mathematical modeling is used to transform the central gene regulatory network at the heart of this work into a system of equations that allows us to describe the temporal evolution of NANOG and GATA6 under the influence of an external signal. Special attention is paid to the derivation of new models using methods of statistical mechanics, as well as the comparison with existing models. After a detailed stability analysis the advantages of the derived model become clear by the fact that an exact relationship of the model parameters and the formation of heterogeneous mixtures of two cell types was found. Thus, the model can be easily controlled and the proportions of the resulting cell types can be estimated in advance. This mathematical model is also combined with a mechanism for global cell-cell communication, as well as a model for the growth of an organoid. It is shown that the global cell-cell communication is able to unify the formation of checkerboard patterns as well as engulfing patterns based on differently propagating signals. In addition, the influence of cell division and thus organoid growth on pattern formation is studied in detail. It is shown that this is able to contribute to the formation of clusters and, as a consequence, to breathe some randomness into otherwise perfectly sorted patterns.
Various types of cancer involve aberrant cell cycle regulation. Among the pathways responsible for tumor growth, the YAP oncogene, a key downstream effector of the Hippo pathway, is responsible for oncogenic processes including cell proliferation, and metastasis by controlling the expression of cell cycle genes. In turn, the MMB multiprotein complex (which is formed when B-MYB binds to the MuvB core) is a master regulator of mitotic gene expression, which has also been associated with cancer. Previously, our laboratory identified a novel crosstalk between the MMB-complex and YAP. By binding to enhancers of MMB target genes and promoting B-MYB binding to promoters, YAP and MMB co-regulate a set of mitotic and cytokinetic target genes which promote cell proliferation. This doctoral thesis addresses the mechanisms of YAP and MMB mediated transcription, and it characterizes the role of YAP regulated enhancers in transcription of cell cycle genes.
The results reported in this thesis indicate that expression of constitutively active, oncogenic YAP5SA leads to widespread changes in chromatin accessibility in untransformed human MCF10A cells. ATAC-seq identified that newly accessible and active regions include YAP-bound enhancers, while the MMB-bound promoters were found to be already accessible and remain open during YAP induction. By means of CRISPR-interference (CRISPRi) and chromatin immuniprecipitation (ChIP), we identified a role of YAP-bound enhancers in recruitment of CDK7 to MMB-regulated promoters and in RNA Pol II driven transcriptional initiation and elongation of G2/M genes. Moreover, by interfering with the YAP-B-MYB protein interaction, we can show that binding of YAP to B-MYB is also critical for the initiation of transcription at MMB-regulated genes. Unexpectedly, overexpression of YAP5SA also leads to less accessible chromatin regions or chromatin closing. Motif analysis revealed that the newly closed regions contain binding motifs for the p53 family of transcription factors. Interestingly, chromatin closing by YAP is linked to the reduced expression and loss of chromatin-binding of the p53 family member Np63. Furthermore, I demonstrate that downregulation of Np63 following expression of YAP is a key step in driving cellular migration.
Together, the findings of this thesis provide insights into the role of YAP in the chromatin changes that contribute to the oncogenic activities of YAP. The overexpression of YAP5SA not only leads to the opening of chromatin at YAP-bound enhancers which together with the MMB complex stimulate the expression of G2/M genes, but also promotes the closing of chromatin at ∆Np63 -bound regions in order to lead to cell migration.
Coxiella burnetii, a Gram negative obligate intracellular bacterium, is the causative
agent of Q fever. It has a world wide distribution and has been documented to
be capable of causing infections in several domestic animals, livestock species,
and human beings. Outbreaks of Q fever are still being observed in livestock
across animal farms in Europe, and primary transmission to humans still oc-
curs especially in animal handlers. Public health authorities in some countries
like Germany are required by law to report human acute cases denoting the
significance of the challenge posed by C. burnetii to public health.
In this thesis, I have developed a platform alongside methods to address the
challenges of genomic analyses of C. burnetii for typing purposes. Identification
of C. burnetii isolates is an important task in the laboratory as well as in the
clinics and genotyping is a reliable method to identify and characterize known
and novel isolates. Therefore, I designed and implemented several methods
to facilitate the genotyping analyses of C. burnetii genomes in silico via a web
platform. As genotyping is a data intensive process, I also included additional
features such as visualization methods and databases for interpretation and
storage of obtained results. I also developed a method to profile the resistome
of C. burnetii isolates using a machine learning approach. Data about antibiotic
resistance in C. burnetii are scarce majorly due to its lifestyle and the difficulty
of cultivation in laboratory media. Alternative methods that rely on homology
identification of resistance genes are also inefficient in C. burnetii, hence, I
opted for a novel approach that has been shown to be promising in other
bacteria species. The applied method relied on an artificial neural network as
well as amino acid composition of position specific scoring matrix profile for
feature extraction. The resulting model achieved an accuracy of ≈ 0.96 on test
data and the overall performance was significantly higher in comparison to
existing models. Finally, I analyzed two new C. burnetii isolates obtained from
an outbreak in Germany, I compared the genome to the RSA 493 reference
isolate and found extensive deletions across the genome landscape.
This work has provided a new digital infrastructure to analyze and character-
ize C. burnetii genomes that was not in existence before and it has also made a
significant contribution to the existing information about antibiotic resistance
genes in C. burnetii.