Tumor cell-selective apoptosis induction through targeting of KV10.1 via bifunctional TRAIL antibody

<p>Abstract</p> <p>Background</p> <p>The search for strategies to target ion channels for therapeutic applications has become of increasing interest. Especially, the potassium channel K_V10.1 (Ether-á-go-go) is attractive as target since this surface protein is virtually not detected in normal tissue outside the central nervous system, but is expressed in approximately 70% of tumors from different origins.</p> <p>Methods</p> <p>We designed a single-chain antibody against an extracellular region of K_V10.1 (scFv62) and fused it to the human soluble TRAIL. The K_V10.1-specific scFv62 antibody -TRAIL fusion protein was expressed in CHO-K1 cells, purified by chromatography and tested for biological activity.</p> <p>Results</p> <p>Prostate cancer cells, either positive or negative for K_V10.1 were treated with the purified construct. After sensitization with cytotoxic drugs, scFv62-TRAIL induced apoptosis only in K_V10.1-positive cancer cells, but not in non-tumor cells, nor in tumor cells lacking K_V10.1 expression. In co-cultures with K_V10.1-positive cancer cells the fusion protein also induced apoptosis in bystander K_V10.1-negative cancer cells, while normal prostate epithelial cells were not affected when present as bystander.</p> <p>Conclusions</p> <p>K_V10.1 represents a novel therapeutic target for cancer. We could design a strategy that selectively kills tumor cells based on a K_V10.1-specific antibody.</p

Potassium channels as tumour markers

AbstractAn increasing number of ion channels are being found to be causally involved in diseases, giving rise to the new field of “channelopathies”. Cancer is no exception, and several ion channels have been linked to tumour progression. Among them is the potassium channel EAG (Ether-a-go-go). Over 75% of tumours have been tested positive using a monoclonal antibody specific for EAG, while inhibition of this channel decreased the proliferation of EAG expressing cells. The inhibition of EAG is accomplished using RNA interference, functional anti-EAG1 antibodies, or (unspecific) EAG channel blockers. Fluorescently labelled recombinant Fab fragments recognizing EAG allow the distribution of EAG to be visualized in an in vivo mouse tumour model

When Fiction Is Just as Real as Fact: No Differences in Reading Behavior between Stories Believed to be Based on True or Fictional Events

Experiments have shown that compared to fictional texts, readers read factual texts faster and have better memory for described situations. Reading fictional texts on the other hand seems to improve memory for exact wordings and expressions. Most of these studies used a “newspaper” vs. “literature” comparison. In the present study, we investigated the effect of reader's expectation to whether information is true or fictional with a subtler manipulation by labeling short stories as either based on true or fictional events. In addition, we tested whether narrative perspective or individual preference in perspective taking affects reading true or fictional stories differently. In an online experiment, participants (final N = 1,742) read one story which was introduced as based on true events or as fictional (factor fictionality). The story could be narrated in either 1st or 3rd person perspective (factor perspective). We measured immersion in and appreciation of the story, perspective taking, as well as memory for events. We found no evidence that knowing a story is fictional or based on true events influences reading behavior or experiential aspects of reading. We suggest that it is not whether a story is true or fictional, but rather expectations toward certain reading situations (e.g., reading newspaper or literature) which affect behavior by activating appropriate reading goals. Results further confirm that narrative perspective partially influences perspective taking and experiential aspects of reading

Preventive health examinations: protocol for a prospective cross-sectional study of German employees aged 45 to 59 years (Ü45-check)

Objective: Early identification of health-related risk factors is of great importance for maintaining workability. Screening examinations can help to detect diseases at an early stage and provide more needs-based recommendations. This study aims (1) to assess the individual need for prevention or rehabilitation based on preventive health examinations compared to a questionnaire survey, (2) to assess the results of the preventive health examinations compared to the Risk Index – Disability Pension (RI-DP), (3) to assess the results of the questionnaire survey compared to the RI-DP, (4) to assess the general health status of the sample (target population > 1,000) in German employees aged 45–59, (5) to identify the most common medical conditions. A further study question aims, and (6) to investigate the general health status of the specific occupational groups. Methods: Comprehensive diagnostics including medical examination, anamnesis, anthropometric measurements, bioelectrical impedance analysis (BIA), handgrip strength, resting electrocardiogram (ECG), resting blood pressure, pulse wave velocity (PWV), and laboratory blood analyses added by a questionnaire are conducted. The research questions are analyzed in an exploratory manner. Results and conclusion: We expect that the results will allow us to formulate recommendations regarding screening for prevention and rehabilitation needs on a more evidence-based level. Clinical Trial Registration: DRKS ID: DRKS00030982.Peer Reviewe

Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups

Hartung M, Klinger R, Schmidtke F, Vogel L. Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Stroudsburg PA: Association for Computational Linguistics; 2017: 24-33.Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extrem- ist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right- wing extremism, we frame the task as ranking each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust performance (up to 0.81 F1 score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements

Laser power stabilization for improved ablation depth uniformity

Due to low heat deposition into the surrounding material, ps-lasers are well suited for a precise ablation of micro structures. However, to obtain constant removal rates the laser parameters as well as the environmental conditions need to be kept stable. For our investigations we used a frequency tripled ps-laser operating at 355 nm. Without any additional power stabilization system, long term micro structuring processes of OFE-copper typically resulted in depth variations of about +/- 10 % which are strongly correlated with detected laser power fluctuations. The developed system for laser power stabilization takes advantage of the linear polarized radiation of our laser source. With a fast photoelectric detector the power fluctuations are measured. A specifically designed feedback control system turns a half wave plate mounted in a piezo driven rotation stage to decouple fluctuating power fractions via a polarizing beam splitter. We demonstrate a nearly constant laser power at the work piece. Substantially improved ablation depth uniformity is verified by a profilometric analysis of directly ablated diffractive structures

Morality is in the eye of the beholder: the neurocognitive basis of the “anomalous-is-bad” stereotype

Are people with flawed faces regarded as having flawed moral characters? An “anomalous-is-bad” stereotype is hypothesized to facilitate negative biases against people with facial anomalies (e.g., scars), but whether and how these biases affect behavior and brain functioning remain open questions. We examined responses to anomalous faces in the brain (using a visual oddball paradigm), behavior (in economic games), and attitudes. At the level of the brain, the amygdala demonstrated a specific neural response to anomalous faces—sensitive to disgust and a lack of beauty but independent of responses to salience or arousal. At the level of behavior, people with anomalous faces were subjected to less prosociality from participants highest in socioeconomic status. At the level of attitudes, we replicated previously reported negative character evaluations made about individuals with facial anomalies, and further identified explicit biases directed against them as a group. Across these levels of organization, the specific amygdala response to facial anomalies correlated with stronger just-world beliefs (i.e., people get what they deserve), less dispositional empathic concern, and less prosociality toward people with facial anomalies. Characterizing the “anomalous-is-bad” stereotype at multiple levels of organization can reveal underappreciated psychological burdens shouldered by people who look different

Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach

Hartung M, Klinger R, Schmidtke F, Vogel L. Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach. In: Frascinar F, Ittoo A, Nguyen LM, Métais E, eds. Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems (NLDB 2017). Lecture Notes in Computer Science. Vol 10260. Springer International Publishing; 2017: 320-325.Social media platforms are used by an increasing number of extremist political actors for mobilization, recruiting or radicalization purposes. We propose a machine learning approach to support manual monitoring aiming at identifying right-wing extremist content in German Twitter profiles. We frame the task as profile classification, based on textual cues, traits of emotionality in language use, and linguistic patterns. A quantitative evaluation reveals a limited precision of 25 % with a close-to-perfect recall of 95 %. This leads to a considerable reduction of the workload of human analysts in detecting right-wing extremist users

Das digitale Ich. Zwischen Gemeinschaft und Abgrenzung - drei Fallstudien

Dieses E-Book enthält drei sozialwissenschaftliche Fallstudien, die Studierende der Hochschule der Medien Stuttgart im Wintersemester 2015/16 in einem Master-Kurs erstellt haben. Die Projekte behandelten neue Trends in der zunehmend digitalisierten Gesellschaft. Thematisch umfassen die drei Teilstudien das "Vertrauen in der Sharing Economy", die "digitale Diaspora" am Beispiel polnischstämmiger Migranten und die "Ethik der Privatheit"

A pre-registered, multi-lab non-replication of the Action-sentence Compatibility Effect (ACE)

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.Fil: Morey, Richard. Cardiff University; Reino UnidoFil: Kaschak, Michael. Florida State University; Estados UnidosFil: Díez Álamo, Antonio. Universidad de Salamanca; España. Arizona State University; Estados UnidosFil: Glenberg, Arthur. Arizona State University; Estados Unidos. Universidad de Salamanca; EspañaFil: Zwaan, Rolf A.. Erasmus University Rotterdam; Países BajosFil: Lakens, Daniël. Eindhoven University of Technology; Países BajosFil: Ibáñez, Santiago Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Adolfo Ibañez; Chile. Trinity College Dublin; IrlandaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Universidad de Santiago de Chile; ChileFil: Gianelli, Claudia. Universitat Potsdam; Alemania. Scuola Universitaria Superiore; ItaliaFil: Jones, John L.. Florida State University; Estados UnidosFil: Madden, Julie. University of Tennessee; Estados UnidosFil: Alifano Ferrero, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bergen, Benjamin. University of California at San Diego; Estados UnidosFil: Bloxsom, Nicholas G.. Ashland University; Estados UnidosFil: Bub, Daniel N.. University of Victoria; CanadáFil: Cai, Zhenguang G.. The Chinese University; Hong KongFil: Chartier, Christopher R.. Ashland University; Estados UnidosFil: Chatterjee, Anjan. University of Pennsylvania; Estados UnidosFil: Conwell, Erin. North Dakota State University; Estados UnidosFil: Wagner Cook, Susan. University of Iowa; Estados UnidosFil: Davis, Joshua D.. University of California at San Diego; Estados UnidosFil: Evers, Ellen R. K.. University of California at Berkeley; Estados UnidosFil: Girard, Sandrine. University of Carnegie Mellon; Estados UnidosFil: Harter, Derek. Texas A&m University Commerce; Estados UnidosFil: Hartung, Franziska. University of Pennsylvania; Estados UnidosFil: Herrera, Eduar. Universidad ICESI; ColombiaFil: Huettig, Falk. Max Planck Institute for Psycholinguistics; Países BajosFil: Humphries, Stacey. University of Pennsylvania; Estados UnidosFil: Juanchich, Marie. University of Essex; Reino UnidoFil: Kühne, Katharina. Universitat Potsdam; AlemaniaFil: Lu, Shulan. Texas A&m University Commerce; Estados UnidosFil: Lynes, Tom. University of East Anglia; Reino UnidoFil: Masson, Michael E. J.. University of Victoria; CanadáFil: Ostarek, Markus. Max Planck Institute for Psycholinguistics; Países BajosFil: Pessers, Sebastiaan. Katholikie Universiteit Leuven; BélgicaFil: Reglin, Rebecca. Universitat Potsdam; AlemaniaFil: Steegen, Sara. Katholikie Universiteit Leuven; BélgicaFil: Thiessen, Erik D.. University of Carnegie Mellon; Estados UnidosFil: Thomas, Laura E.. North Dakota State University; Estados UnidosFil: Trott, Sean. University of California at San Diego; Estados UnidosFil: Vandekerckhove, Joachim. University of California at Irvine; Estados UnidosFil: Vanpaeme, Wolf. Katholikie Universiteit Leuven; BélgicaFil: Vlachou, Maria. Katholikie Universiteit Leuven; BélgicaFil: Williams, Kristina. Texas A&m University Commerce; Estados UnidosFil: Ziv Crispel, Noam. BehavioralSight; Estados Unido