CIMAWA : development and implementation of a text-based association measuring method

Die vorliegende Arbeit stellt die Entwicklung und Anwendung einer textbasierten Assoziations - Berechnungsmethode vor. Das Verfahren trägt den Titel CIMAWA (Concept for the Imitation of the Human Ability of Word Association) und berechnet die Stärke der Beziehungen zwischen Worten. CIMAWA orientiert sich dabei an der menschlichen Wortassoziation und versucht diese möglichst exakt nachzubilden. Basierend auf großen Textsammlungen, werden statistische Auswertungen über gemeinsames Vorkommen von Worten und deren Häufigkeit dazu verwendet, die Stärke der Assoziationen zwischen Begriffen zu berechnen. Die Ergebnisse der CIMAWA-Berechnungen werden in mehreren Fallstudien mit Assoziationstests an menschlichen Probanden verglichen. Zusätzlich wurden aus der Literatur bekannte Assoziations – Berechnungsmethoden implementiert und in ihrer Leistungsfähigkeit bewertet. Die detaillierte Erläuterung der Berechnung und die Herleitung der Parameter werden komplettiert durch die Darstellung der konzeptuellen Unterschiede zwischen den bekannten Berechnungsverfahren und CIMAWA. Die vielseitige Anwendbarkeit und praktische Relevanz der CIMAWA-Assoziationsberechnung wird durch vier Umsetzungen aus verschiedenen Anwendungsgebieten gezeigt. Die erste Anwendung zeigt, wie CIMAWA zur Erkennung von Multi-Themenstrukturen in Textdokumenten eingesetzt wird. Die Metaanalyse von Textdokumenten im Instandhaltungsmanagement wird zum Gegenstand der zweiten Anwendung und die kontextbasierte Bereitstellung von Texten im Produktverbesserungsprozess ist im dritten Beispiel behandelt. Ein assoziatives Suchverfahren für die Wissensbasis von Unternehmen bildet die abschließende CIMAWA-Anwendung.The present work discusses the development and application of a novel method for text-based word association measuring. The method is entitled as CIMAWA which stands for the ‘Concept for the Imitation of the Human Ability of Word Association‘. CIMAWA calculates the strength of the relationship between words. Taking into account the human ability of word association as an archetype, CIMAWA is aimed at simulating the existing but not necessarily discovered associations. It applies statistical analysis to detect co-occurring terms and frequencies based on huge collections of texts, and uses the outcomes for the calculation of the strength of the relation. CIMAWA is verified in several case studies, especially in comparison with free association tests of human test subjects. In addition the literatures of association measuring are reviewed and the most common methods are implemented and compared with CIMAWA’s outcomes. A detailed explanation of the calculation and the parameters are given, as well as a demonstration of the conceptual differences between CIMAWA and other measurement methods. The multilateral areas of application and the practical adaptability are shown in four independent software applications. The first application shows how CIMAWA is utilized to detect multi-topic structures in text documents. The second discusses the meta-analysis of text documents in maintenance management. The third presents a CIMAWA based recommender system for text documents towards improving quality of industrial goods. Finally, the fourth application is developed for associative search engine in companies

Closely related Campylobacter jejuni strains from different sources reveal a generalist rather than a specialist lifestyle

Background: Campylobacter jejuni and Campylobacter coli are human intestinal pathogens of global importance. Zoonotic transmission from livestock animals or animal-derived food is the likely cause for most of these infections. However, little is known about their general and host-specific mechanisms of colonization, or virulence and pathogenicity factors. In certain hosts, Campylobacter species colonize persistently and do not cause disease, while they cause acute intestinal disease in humans. Results: Here, we investigate putative host-specificity using phenotypic characterization and genome-wide analysis of genetically closely related C. jejuni strains from different sources. A collection of 473 fresh Campylobacter isolates from Germany was assembled between 2006 and 2010 and characterized using MLST. A subset of closely related C. jejuni strains of the highly prevalent sequence type ST-21 was selected from different hosts and isolation sources. PCR typing of strain-variable genes provided evidence that some genes differed between these strains. Furthermore, phenotypic variation of these strains was tested using the following criteria: metabolic variation, protein expression patterns, and eukaryotic cell interaction. The results demonstrated remarkable phenotypic diversity within the ST-21 group, which however did not correlate with isolation source. Whole genome sequencing was performed for five ST-21 strains from chicken, human, bovine, and food sources, in order to gain insight into ST-21 genome diversity. The comparisons showed extensive genomic diversity, primarily due to recombination and gain of phage-related genes. By contrast, no genomic features associated with isolation source or host were identified. Conclusions: The genome information and phenotypic data obtained in vitro and in a chicken infection model provided little evidence of fixed adaptation to a specific host. Instead, the dominant C. jejuni ST-21 appeared to be characterized by phenotypic flexibility and high genetic microdiversity, revealing properties of a generalist. High genetic flexibility might allow generalist variants of C. jejuni to reversibly express diverse fitness factors in changing environments

Autoantibodies against chemokines post-SARS-CoV-2 infection correlate with disease course

Infection with severe acute respiratory syndrome coronavirus 2 associates with diverse symptoms, which can persist for months. While antiviral antibodies are protective, those targeting interferons and other immune factors are associated with adverse coronavirus disease 2019 (COVID-19) outcomes. Here we discovered that antibodies against specific chemokines were omnipresent post-COVID-19, were associated with favorable disease outcome and negatively correlated with the development of long COVID at 1 yr post-infection. Chemokine antibodies were also present in HIV-1 infection and autoimmune disorders, but they targeted different chemokines compared with COVID-19. Monoclonal antibodies derived from COVID-19 convalescents that bound to the chemokine N-loop impaired cell migration. Given the role of chemokines in orchestrating immune cell trafficking, naturally arising chemokine antibodies may modulate the inflammatory response and thus bear therapeutic potential

A Global Overview of the Genetic and Functional Diversity in the Helicobacter pylori cag Pathogenicity Island

The Helicobacter pylori cag pathogenicity island (cagPAI) encodes a type IV secretion system. Humans infected with cagPAI–carrying H. pylori are at increased risk for sequelae such as gastric cancer. Housekeeping genes in H. pylori show considerable genetic diversity; but the diversity of virulence factors such as the cagPAI, which transports the bacterial oncogene CagA into host cells, has not been systematically investigated. Here we compared the complete cagPAI sequences for 38 representative isolates from all known H. pylori biogeographic populations. Their gene content and gene order were highly conserved. The phylogeny of most cagPAI genes was similar to that of housekeeping genes, indicating that the cagPAI was probably acquired only once by H. pylori, and its genetic diversity reflects the isolation by distance that has shaped this bacterial species since modern humans migrated out of Africa. Most isolates induced IL-8 release in gastric epithelial cells, indicating that the function of the Cag secretion system has been conserved despite some genetic rearrangements. More than one third of cagPAI genes, in particular those encoding cell-surface exposed proteins, showed signatures of diversifying (Darwinian) selection at more than 5% of codons. Several unknown gene products predicted to be under Darwinian selection are also likely to be secreted proteins (e.g. HP0522, HP0535). One of these, HP0535, is predicted to code for either a new secreted candidate effector protein or a protein which interacts with CagA because it contains two genetic lineages, similar to cagA. Our study provides a resource that can guide future research on the biological roles and host interactions of cagPAI proteins, including several whose function is still unknown

Autoantibodies against chemokines post-SARS-CoV-2 infection correlate with disease course

Infection with severe acute respiratory syndrome coronavirus 2 associates with diverse symptoms, which can persist for months. While antiviral antibodies are protective, those targeting interferons and other immune factors are associated with adverse coronavirus disease 2019 (COVID-19) outcomes. Here we discovered that antibodies against specific chemokines were omnipresent post-COVID-19, were associated with favorable disease outcome and negatively correlated with the development of long COVID at 1 yr post-infection. Chemokine antibodies were also present in HIV-1 infection and autoimmune disorders, but they targeted different chemokines compared with COVID-19. Monoclonal antibodies derived from COVID-19 convalescents that bound to the chemokine N-loop impaired cell migration. Given the role of chemokines in orchestrating immune cell trafficking, naturally arising chemokine antibodies may modulate the inflammatory response and thus bear therapeutic potential

Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders

Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain

US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study

BackgroundBlack women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. ObjectiveThis study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. MethodsTweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. ResultsA variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%. ConclusionsThe results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts

Feature selection with the CLOP package

We used the datasets of the NIPS 2003 challenge on feature selection as part of the practical work of an undergraduate course on feature extraction. The students were provided with a toolkit implemented in Matlab. Part of the course requirements was that they should outperform given baseline methods. The results were beyond expectations: the student matched or exceeded the performance of the best challenge entries and achieved very effective feature selection with simple methods. We make available to the community the results of this experiment and the corresponding teaching material