Multiple-line inference of selection on quantitative traits

Trait differences between species may be attributable to natural selection. However, quantifying the strength of evidence for selection acting on a particular trait is a difficult task. Here we develop a population-genetic test for selection acting on a quantitative trait which is based on multiple-line crosses. We show that using multiple lines increases both the power and the scope of selection inference. First, a test based on three or more lines detects selection with strongly increased statistical significance, and we show explicitly how the sensitivity of the test depends on the number of lines. Second, a multiple-line test allows to distinguish different lineage-specific selection scenarios. Our analytical results are complemented by extensive numerical simulations. We then apply the multiple-line test to QTL data on floral character traits in plant species of the Mimulus genus and on photoperiodic traits in different maize strains, where we find a signatures of lineage-specific selection not seen in a two-line test.Comment: 21 pages, 11 figures; to appear in Genetic

ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion. We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (n = 8689) and randomly selected from PubMed (n = 1500) we iteratively developed a set of derived keyword categories. ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code). To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.73 and specificity of 0.97. Open Data was detected for 11.5% (n = 91) of publications. Open Code was detected for 1.4% (n = 11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science. Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub

Inference of natural selection on quantitative traits

The concept of evolution, which was introduced by Charles Darwin in 1859, and also its mathematical description by the theory of population genetics are well-established. Population genetics describes the development of a population under the influence of mutations, creating new genetic variants, and natural selection, increasing the frequency of favorable phenotypes. Yet, the experimental verification of selective forces acting on species has proven difficult. With new experimental techniques that have been established in the field of quantitative genetics, like the sequencing of DNA or measurements of gene expression levels, it has become possible to find signs of natural selection on the level of the genome. In this thesis, I develop a statistical test based on population genetics theory that can infer lineage-specific differences in selection between multiple lines of a species. The test employs data from quantitative trait experiments and uses a log-likelihood scoring to quantify the evidence for different selective scenarios. I show that the use of multiple lines increases both the power and the scope of selection inference. Extensive numerical simulations demonstrate that the test can distinguish selection from neutral evolution as well as different scenarios of lineage-specific evolution. The principle of maximum entropy is used to derive a modified version of the selection test that accounts for the multiple testing problem arising when many traits are tested for selection at the same time. The developed test is applied to two published plant datasets and a published dataset of gene expression levels in three yeast lines. In all cases, I find signs of selection not seen with a two-line test. For the yeast dataset I find pervasive adaptation linked to stress resistance both on the level of individual genes as well as for larger gene modules consisting of several genes, like protein complexes and pathways. This adaptation signal is also reflected on the protein levels

Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry

Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra-and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters

From Missing Links to New Records: A Series of Novel Polychlorine Anions

Herein we report the synthesis and structural characterization of four novel polychloride compounds. The compounds [CCl(NMe2)2][Cl(Cl2)3] and [NPr4][Cl(Cl2)4] have been obtained from the reaction of the corresponding chloride salts with elemental chlorine at low temperature. They are the missing links in the series of polychloride monoanions [Cl(Cl)n]− (n=1–6). Additionally, the reaction of decamethylferrocene with elemental chlorine was studied yielding [Cp*2Fe]2[Cl20], which contains the largest known polychloride [Cl20]2− to date, and [Cp*2Fe][Cl(Cl2)4(HF)], which is the first example of a polychloride‐HF network stabilized by strong hydrogen and halogen bonding. All compounds have been characterized by single‐crystal X‐ray diffraction, Raman spectroscopy and quantum‐chemical calculations

Non-classical polyinterhalides of chlorine monofluoride: experimental and theoretical characterization of [F(ClF)3]−

We present the synthesis and characterization of the first non-classical Cl(I) polyinterhalide [NMe4][F(ClF)3] as well as the homologous polychloride [NPr3Me][Cl7]. Both salts were obtained from the reaction of the corresponding ammonium chlorides with ClF or Cl2, respectively. Quantum-chemical investigations predict an unexpected planar structure for the [F(ClF)3]− anion

Montagegerechte Gestaltungsrichtlinien mittels Deep Learning

Die Anwendung von Deep Learning in der manuellen Montage birgt großes Potenzial, Montagezeiten zu reduzieren und Montagefehler zu vermeiden. Indem der Montageablauf mithilfe einer Kamera erfasst und die aufgezeichneten Bilder durch einen Objekterkennungsalgorithmus analysiert werden, lassen sich Position, Lage und Art der montierten Bauteile bestimmen. Daraus lassen sich wiederum Informationen über Arbeitsschritte, Montagefehler oder den aktuellen Zustand des Produkts ableiten, sodass die Mitarbeiter bei der Montage durch entsprechende Anweisungen unterstützt werden können. Es stellt sich jedoch die Frage, inwieweit gegenwärtige Produkte für den Einsatz von Deep Learning geeignet sind. Nur wenn die zu montierenden Bauteile sicher erkannt werden, ist der Einsatz in der manuellen Montage sinnvoll. Bestehende Gestaltungsrichtlinien adressieren diesen Aspekt bislang nicht. Im Forschungsprojekt wurde daher untersucht, welche Eigenschaften Produkte aufweisen sollten, um eine optimale Objekterkennung zu ermöglichen. Dazu wurden Hypothesen zu positiven und negativen Bauteileigenschaften hinsichtlich der Erkennungsgenauigkeit formuliert und in praktischen Versuchen überprüft. Dabei konnte gezeigt werden, dass alle untersuchten Objekte durch den eingesetzten Objekterkennungsalgorithmus sehr gut detektiert werden. Aus den vorliegenden Forschungsergebnissen lassen sich daher keine Einschränkungen in der Produktgestaltung ableiten

Triphenylene-Derived Electron Acceptors and Donors on Ag(111):Formation of Intermolecular Charge-Transfer Complexes with Common Unoccupied Molecular States

Over the past years, ultrathin films consisting of electron donating and accepting molecules have attracted increasing attention due to their potential usage in optoelectronic devices. Key parameters for understanding and tuning their performance are intermolecular and molecule–substrate interactions. Here, the formation of a monolayer thick blend of triphenylene‐based organic donor and acceptor molecules from 2,3,6,7,10,11‐hexamethoxytriphenylene (HAT) and 1,4,5,8,9,12‐hexaazatriphenylenehexacarbonitrile (HATCN), respectively, on a silver (111) surface is reported. Scanning tunneling microscopy and spectroscopy, valence and core level photoelectron spectroscopy, as well as low‐energy electron diffraction measurements are used, complemented by density functional theory calculations, to investigate both the electronic and structural properties of the homomolecular as well as the intermixed layers. The donor molecules are weakly interacting with the Ag(111) surface, while the acceptor molecules show a strong interaction with the substrate leading to charge transfer and substantial buckling of the top silver layer and of the adsorbates. Upon mixing acceptor and donor molecules, strong hybridization occurs between the two different molecules leading to the emergence of a common unoccupied molecular orbital located at both the donor and acceptor molecules. The donor acceptor blend studied here is, therefore, a compelling candidate for organic electronics based on self‐assembled charge‐transfer complexes