Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection

The words people choose to use hold a lot of power, whether that be in spreading truth or deception. As listeners and readers, we do our best to understand how words are being used. There are many current methods in computer science literature attempting to embed words into numerical information for statistical analyses. Some of these embedding methods, such as Bag of Words, treat words as independent, while others, such as Word2Vec, attempt to gain information about the context of words. It is of interest to compare how well these various methods of translating text into numerical data work specifically with detecting fake news. The term “fake news” can be quite divisive, but we define it as news that is hyper-partisan, filled with untruths, and written to cause anger and outrage, as defined in Potthast & Kiesel (2018). We hypothesize a person’s word choice relates to the factualness of an article. In Chapter 5, we utilize this embedded information in several binary classification methods. We find that words are only marginally valuable in detecting fake news regardless of the embedding or classification method used. However, within natural language processing tasks, there are many preprocessing steps taken to get the text ready for analysis, which is explored in Chapter 6. The embedding methods are confounded with the preprocessing methods used. Preprocessing of text includes, but is not limited to, filtering out words that do not appear a minimum number of times, filtering out stop words, removing numbers, and translating all letters to lower case. We find filtering out stop words and removing words not appearing a minimum number of times have the most significant effect in combination with embedding and classification methods. Finally, in Chapter 7, we extend the classification to six categories ranging from true to pants-on-fire false and found these preprocessing methods are not as influential as they were with the binary outcome. Other predictors outside of the words and word embeddings themselves are necessary for improvement in the detection of fake news. Advisor: Kent Eskridg

Cilengitide induces cellular detachment and apoptosis in endothelial and glioma cells mediated by inhibition of FAK/src/AKT pathway

<p>Abstract</p> <p>Background</p> <p>The antiangiogenic agent cilengitide disrupts integrin binding to the extracellular matrix leading to apoptosis of activated endothelial cells. Integrins are also widely expressed in malignant glioma and integrin inhibitors may directly target tumor cells in this disease. Aim of the current study was to investigate effects of cilengitide on endothelial and glioma cells on molecular and cellular levels.</p> <p>Results</p> <p>Cilengitide caused dose-dependent detachment of endothelial cells from cell culture dishes. Proliferation of endothelial cells was significantly inhibited while the proportion of apoptotic cells was increased. Incubation of integrin-expressing glioma cells with cilengitide caused rounding and detachment after 24 hours as observed with endothelial cells. Cilengitide inhibited proliferation and induced apoptosis in glioma cells with methylated MGMT promotor when given alone or in combination with temozolomide. In endothelial as well as glioma cells cilengitide inhibited phosphorylation of FAK, Src and Akt. Assembly of cytoskeleton and tight junctions was heavily disturbed in both cell types.</p> <p>Conclusion</p> <p>Cilengitide inhibits integrin-dependent signaling, causes disassembly of cytoskeleton, cellular detachment and induction of apoptosis in endothelial and glioma cells thereby explaining the profound activity of integrin inhibitors in gliomas. The combination of cilengitide with temozolomide exerted additive effects in glioma cells as observed clinically.</p

Inspired by Sea Urchins: Warburg Effect Mediated Selectivity of Novel Synthetic Non-Glycoside 1,4-Naphthoquinone-6S-Glucose Conjugates in Prostate Cancer

The phenomenon of high sugar consumption by tumor cells is known as Warburg effect. It results from a high glycolysis rate, used by tumors as preferred metabolic pathway even in aerobic conditions. Targeting the Warburg effect to specifically deliver sugar conjugated cytotoxic compounds into tumor cells is a promising approach to create new selective drugs. We designed, synthesized, and analyzed a library of novel 6-S-(1,4-naphthoquinone-2-yl)-d-glucose chimera molecules (SABs)&mdash;novel sugar conjugates of 1,4-naphthoquinone analogs of the sea urchin pigments spinochromes, which have previously shown anticancer properties. A sulfur linker (thioether bond) was used to prevent potential hydrolysis by human glycoside-unspecific enzymes. The synthesized compounds exhibited a Warburg effect mediated selectivity to human prostate cancer cells (including highly drug-resistant cell lines). Mitochondria were identified as a primary cellular target of SABs. The mechanism of action included mitochondria membrane permeabilization, followed by ROS upregulation and release of cytotoxic mitochondrial proteins (AIF and cytochrome C) to the cytoplasm, which led to the consequent caspase-9 and -3 activation, PARP cleavage, and apoptosis-like cell death. These results enable us to further clinically develop these compounds for effective Warburg effect targeting

Real-world therapy with pembrolizumab: outcomes and surrogate endpoints for predicting survival in advanced melanoma patients in Germany

Knowledge on the real-world characteristics and outcomes of pembrolizumab-treated advanced melanoma patients in Germany and on the value of different real-world endpoints as surrogates for overall survival (OS) is limited. A sample of 664 pembrolizumab-treated patients with advanced melanoma from the German registry ADOReg was used. We examined OS, real-world progression-free survival (rwPFS), real-world time to next treatment (rwTtNT), and real-world time on treatment (rwToT). Spearman’s rank and iterative multiple imputation (IMI)-based correlation coefficients were computed between the OS and the rwPFS, rwTtNT, and rwToT and reported for the first line of therapy and the overall sample. The median OS was 30.5 (95%CI 25.0–35.4) months, the rwPFS was 3.9 months (95%CI 3.5–4.9), the rwTtNT was 10.7 months (95%CI 9.0–12.9), and the rwToT was 6.2 months (95%CI 5.1–6.8). The rwTtNT showed the highest correlation with the OS based on the IMI (rIMI = 0.83), Spearman rank correlations (rs = 0.74), followed by the rwToT (rIMI = 0.74 and rs = 0.65) and rwPFS (rIMI = 0.69 and rs = 0.56). The estimates for the outcomes and correlations were similar for the overall sample and those in first-line therapy. The median OS was higher compared to recent real-world studies, supporting the effectiveness of pembrolizumab in regular clinical practice. The rwTtNT may be a valuable OS surrogate, considering the highest correlation was observed with the OS among the investigated real-world endpoints

Real-World Therapy with Pembrolizumab: Outcomes and Surrogate Endpoints for Predicting Survival in Advanced Melanoma Patients in Germany

Knowledge on the real-world characteristics and outcomes of pembrolizumab-treated advanced melanoma patients in Germany and on the value of different real-world endpoints as surrogates for overall survival (OS) is limited. A sample of 664 pembrolizumab-treated patients with advanced melanoma from the German registry ADOReg was used. We examined OS, real-world progression-free survival (rwPFS), real-world time to next treatment (rwTtNT), and real-world time on treatment (rwToT). Spearman’s rank and iterative multiple imputation (IMI)-based correlation coefficients were computed between the OS and the rwPFS, rwTtNT, and rwToT and reported for the first line of therapy and the overall sample. The median OS was 30.5 (95%CI 25.0–35.4) months, the rwPFS was 3.9 months (95%CI 3.5–4.9), the rwTtNT was 10.7 months (95%CI 9.0–12.9), and the rwToT was 6.2 months (95%CI 5.1–6.8). The rwTtNT showed the highest correlation with the OS based on the IMI (rIMI = 0.83), Spearman rank correlations (rs = 0.74), followed by the rwToT (rIMI = 0.74 and rs = 0.65) and rwPFS (rIMI = 0.69 and rs = 0.56). The estimates for the outcomes and correlations were similar for the overall sample and those in first-line therapy. The median OS was higher compared to recent real-world studies, supporting the effectiveness of pembrolizumab in regular clinical practice. The rwTtNT may be a valuable OS surrogate, considering the highest correlation was observed with the OS among the investigated real-world endpoints

Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection

The words people choose to use hold a lot of power, whether that be in spreading truth or deception. As listeners and readers, we do our best to understand how words are being used. There are many current methods in computer science literature attempting to embed words into numerical information for statistical analyses. Some of these embedding methods, such as Bag of Words, treat words as independent, while others, such as Word2Vec, attempt to gain information about the context of words. It is of interest to compare how well these various methods of translating text into numerical data work specifically with detecting fake news. The term “fake news” can be quite divisive, but we define it as news that is hyper-partisan, filled with untruths, and written to cause anger and outrage, as defined in Potthast & Kiesel (2018). We hypothesize a person’s word choice relates to the factualness of an article. In Chapter 5, we utilize this embedded information in several binary classification methods. We find that words are only marginally valuable in detecting fake news regardless of the embedding or classification method used. However, within natural language processing tasks, there are many preprocessing steps taken to get the text ready for analysis, which is explored in Chapter 6. The embedding methods are confounded with the preprocessing methods used. Preprocessing of text includes, but is not limited to, filtering out words that do not appear a minimum number of times, filtering out stop words, removing numbers, and translating all letters to lower case. We find filtering out stop words and removing words not appearing a minimum number of times have the most significant effect in combination with embedding and classification methods. Finally, in Chapter 7, we extend the classification to six categories ranging from true to pants-on-fire false and found these preprocessing methods are not as influential as they were with the binary outcome. Other predictors outside of the words and word embeddings themselves are necessary for improvement in the detection of fake news

Ethnische Vielfalt und Mehrsprachigkeit an Schulen. Beispiele aus verschiedenen nationalen Kontexten

Dirim I, Hauschild K, Lütje-Klose B, Löser J, Sievers I, eds. Ethnische Vielfalt und Mehrsprachigkeit an Schulen. Beispiele aus verschiedenen nationalen Kontexten. Bildung in der Weltgesellschaft. Vol 1. Frankfurt am Main: Brandes &amp; Apsel; 2008