Random local algorithms

Consider the problem when we want to construct some structure on a bounded degree graph, e.g. an almost maximum matching, and we want to decide about each edge depending only on its constant radius neighbourhood. We show that the information about the local statistics of the graph does not help here. Namely, if there exists a random local algorithm which can use any local statistics about the graph, and produces an almost optimal structure, then the same can be achieved by a random local algorithm using no statistics.Comment: 9 page

Analyzing language sequence in the sequence of language acquisition: Some comments on Major and Ioup.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139747/1/SSLA-Rebuttal-REVISED.pd

From introspections, brain scans, and memory tests to the role of social context:advancing research on interaction and learning

The goal of this epilogue is to use the methodological contributions of the studies presented in this special issue as a starting point for suggestions about methodology in conducting future interaction research. As is the case in most developing fields, interaction research develops methods internally as it continually borrows and extends techniques used in other disciplines and revitalizes older techniques by adding new or different angles unique to interaction. Interaction researchers have also begun to forge relationships in new areas (e.g., by working with psychologists and developing working memory [WM] tests). This sort of cooperation is an important step in the drive to uncover more information about the relationship between interaction and learning. As several contributors to this special issue have noted, the most recent advances in methodology have been driven by questions about how interaction works (as opposed to whether it works). In turn, some of the methodological innovations discussed here will also ultimately allow new questions to be asked. Indeed, the relationship between questions (i.e., suggestions about what needs to be investigated next) and methods (i.e., plans for how to carry out such investigations) is particularly close in interaction research, which is a relatively new but vibrant and quickly developing area. Consequently, this epilogue considers both methods and questions conjointly, beginning with a discussion of methodological issues in the most recent theorizing about the interaction hypothesis

Deconstructing comprehensibility: identifying the linguistic influences on listeners' L2 comprehensibility ratings

Comprehensibility, a major concept in second language (L2) pronunciation research that denotes listeners’ perceptions of how easily they understand L2 speech, is central to interlocutors’ communicative success in real-world contexts. Although comprehensibility has been modeled in several L2 oral proficiency scales—for example, the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS)—shortcomings of existing scales (e.g., vague descriptors) reflect limited empirical evidence as to which linguistic aspects influence listeners’ judgments of L2 comprehensibility at different ability levels. To address this gap, a mixed-methods approach was used in the present study to gain a deeper understanding of the linguistic aspects underlying listeners’ L2 comprehensibility ratings. First, speech samples of 40 native French learners of English were analyzed using 19 quantitative speech measures, including segmental, suprasegmental, fluency, lexical, grammatical, and discourse-level variables. These measures were then correlated with 60 native English listeners’ scalar judgments of the speakers’ comprehensibility. Next, three English as a second language (ESL) teachers provided introspective reports on the linguistic aspects of speech that they attended to when judging L2 comprehensibility. Following data triangulation, five speech measures were identified that clearly distinguished between L2 learners at different comprehensibility levels. Lexical richness and fluency measures differentiated between low-level learners; grammatical and discourse-level measures differentiated between high-level learners; and word stress errors discriminated between learners of all levels

Biohybrid sensor systems for the detection of metal ions in water

Die Wasserverschmutzung durch Seltenen Erden (REEs) und Schwermetallen verursacht viele Probleme für die Umwelt und die menschliche Gesundheit. Daher ist der Nachweis solcher Elemente von hoher Priorität. Derzeit verwendete Methoden haben einige Nachteile, wie hohe Messkosten, beschränke Selektivität, komplexe Handhabung oder der Bedarf von hochqualifiziertem Personal für die Probenanalyse. Die Kombination von biologischen Komponenten und Nanomaterialien zur Sensorentwicklung bietet eine Möglichkeit diese Nachteile ausgleichen. Mikroorganismen haben evolutionäre Strategien entwickelt, um sich vor toxischen Schwermetallen zu schützen, z.B durch Binden der Metallionen an ihrer Zelloberfläche mit speziellen Oberflächenproteinen (S-Layer). Diese bestehen aus einer Monolage identischer (Glyco-) Proteine, die sich selbst assemblieren und eine hochgeordnete kristalline Struktur unterschiedlicher Symmetrie bilden können. Studien haben die Bindung von Metallionen (einschließlich REEs) durch S-Layer-Proteine gezeigt. In dieser Dissertation wurden drei Nanomaterialien (Goldnanopartikel (AuNPs), planare Goldoberflächen und Nanodiamanten (NDs)) mit acht verschiedene S-Layer-Proteinen beschichtet. Ziel war die Entwicklung von Biohybrid-Sensor-Systemen für die Detektion von bis zu 12 Metallionen in Wasser. Ein kolorimetrisches Sensorsystem mit biofunktionalisierten AuNPs zur Detektion von REEs und Schwermetallen, einschließlich der aktuell vermehrt auftretenden Schadstoffe Lanthan und Gadolinium, wurde etabliert. Die Nachweisgrenzen lagen im Bereich vergleichbarer AuNPs-Systeme zum Nachweis von Schwermetallen, während die Slayer-AuNP-Biohybride ein breiteres Spektrum von Metallionen detektieren konnten. Das Screening aller acht S-Layer-AuNP-Biohybride mit 12 Metallsalzlösungen ergab charakteristische Wechselwirkungsmuster für jede der Kombinationen und ermöglichte den spezifischen Nachweis einer einzelnen Metallionenspezies in unbekannten Lösungen. Eine Kosten- und Ressourcenoptimierung ist über die Lagerung bis zu drei Monate und Wiederverwendbarkeit gegeben. Auf planaren Goldoberflächen ermöglichten die SPR-Spektroskopie die Messung der Adsorption von S-Layer-Proteinen, sowie die anschließende Detektion von CuSO4, NiCl2 und YCl3. Die Detektionslimits lagen dabei unter den kolorimetrischen Biohybridsystemen. Die SPR-Chips wurden erfolgreich regeneriert und für mehrere Funktionalisierungen mit S-Layer-Proteinen wiederverwendet. Das S-Layer-Protein SslA von S. ureae ATCC 13881 wurde erstmals an NDs adsorbiert. Die NDs/SslA-Biohybride wurden zur Detektion von CuCl2 und NiCl 2 verwendet, indem die Agglomeration und das Fluoreszenzquenching gemessen wurden. Es hat sich gezeigt, dass die vorgestellten Systeme viele der Nachteile ausgleichen, die mit derzeit verwendeten Systemen verbunden sind. Sie detektieren eine Vielzahl von Metallionen und minimieren so den Bedarf für mehrere Methoden. Die Nachweisgrenzen waren vergleichbar mit aktuellen kolorimetrischen und chemischen Kit-Systemen. Die S-layer-AuNPs und NDs/S-layer-Biohybride waren schnell und einfach zu handhaben, wodurch der Bedarf an hochqualifiziertem Messpersonal minimiert werden kann. Darüber hinaus führt die Verwendung von kostengünstigen Materialien wie NDs und die Wiederverwendbarkeit der Biohybride zu einem ressourceneffizienten und kostengünstigen Nachweissystem. Diese Dissertation hat das enorme Potenzial von S-Layer-Proteinen für den Nachweis von REEs und Schwermetallen in Wasser unter Verwendung verschiedener Nachweissysteme wie kolorimetrischer AuNPs-Assays, SPR-Spektroskopie und NDs gezeigt.The pollution of aqueous systems with rare earth elements (REEs) and heavy metals causes serious problems for environmental and human health. Therefore, the detection of such elements is of uttermost importance. Currently used methods have some disadvantages, such as high measurement costs, limited selectivity, complex sample handling, or the need for highly qualified personnel for sample analysis. The combination of biological components and nanomaterials for sensor development offers a way to offset these disadvantages. Microorganisms have developed strategies to protect themselves from heavy metal toxicity, e.g. by binding the metal ions on their cell surface with special Surface layer (S-layer) proteins. They consist of a monolayer of identical (glyco-) proteins, which can self-assemble and form a highly ordered crystalline structure of varying symmetry. Studies on the heavy metal binding of S-layer proteins have demonstrated their affinity for metal ions, including REE. The combination of nanomaterials with S-layer proteins enables the development of new sensors for these elements. Within this dissertation several nanomaterials in combination with S-layer proteins were investigated to obtain sensors for REEs and heavy metals. Eight different S-layer proteins were used to functionalize AuNPs, flat gold surfaces and nanodiamonds (NDs) for the detection of up to 12 metal ions in water. Colorimetric sensor systems with biofunctionalized AuNPs for the detection of REE and heavy metals, including the newly emerging pollutants lanthanum and gadolinium, were established. The detection limits of reference measurements and spiked tapwater samples were in the range of comparable AuNPs systems for the detection of heavy metals, while offering a broader range of metal ions to detect. The screening of all eight S-layer-AuNP biohybrids with 12 metal ions revealed specific interaction patterns for each of the combinations. The optimization cost and resource is achieved by storage up to three months and reusability of the S-layer-AuNP biohybrids. Surface plasmon resonance (SPR) spectroscopy enabled the measurement of S-layer proteins binding to flat gold surfaces, resulting in a stable protein layer used for the subsequent detection of CuSO4, NiCl2 and YCl3. The SPR chips were succesfully regenerated and reused for multiple functionalizations with S-layer proteins. The S-layer protein SslA from S. ureae ATCC 13881 was successfully adsorbed to the pristine NDs by physical conjugation. The NDs/SslA conjugates were used for the detection of CuCl2 and NiCl2, by measuring the agglomeration of the NDs and fluorescence quenching. The presented systems compensate many of the disadvantages associated with currently used techniques. They detect a broad variety of metal ions, minimizing the need for multiple methods. The detection limits were comparable to current colorimetric and chemical kit systems. The S-layer-AuNPs and NDs/S-layer biohybrids were quick and easy to handle, minimizing the need for highly qualitified personnel. In addition, the use of cost-effective materials such as NDs and the reusability of the biohybrids results in resource-efficient and cost-effective sensor systems. This project has shown the tremendous potential of S-layer proteins for the detection of REE and metal ions in water, by utilizing different detection systems like colorimetric AuNPs assays, SPR spectroscopy and NDs