Towards Context-free Information Importance Estimation

The amount of information contained in heterogeneous text documents such as news articles, blogs, social media posts, scientific articles, discussion forums, and microblogging platforms is already huge and is going to increase further. It is not possible for humans to cope with this flood of information, so that important information can neither be found nor be utilized. This situation is unfortunate since information is the key driver in many areas of society in the present Information Age. Hence, developing automatic means that can assist people to handle the information overload is crucial. Developing methods for automatic estimation of information importance is an essential step towards this goal. The guiding hypothesis of this work is that prior methods for automatic information importance estimation are inherently limited because they are based on merely correlated signals that are, however, not causally linked with information importance. To resolve this issue, we lay in this work the foundations for a fundamentally new approach for importance estimation. The key idea of context-free information importance estimation is to equip machine learning models with world knowledge so that they can estimate information importance based on causal reasons. In the first part of this work, we lay the theoretical foundations for context-free information importance estimation. First, we discuss how the abstract concept of information importance can be formally defined. So far, a formal definition of this concept is missing in the research community. We close this gap by discussing two information importance definitions, which equate the importance of information with its impact on the behavior and the impact on the course of life of the information recipients, respectively. Second, we discuss how information importance estimation abilities can be assessed. Usually, this is done by performing automatic summarization of text documents. However, we find that this approach is not ideal. Instead, we propose to consider ranking, regression, and preference prediction tasks as alternatives in future work. Third, we deduce context-free information importance estimation as a logical consequence of the previously introduced importance definitions. We find that reliable importance estimation, in particular for heterogeneous text documents, is only possible with context-free methods. In the second part, we develop the first machine learning models based on the idea of context-free information importance estimation. To this end, we first tackle the lack of suited datasets that are required to train and test machine learning models. In particular, large and heterogeneous datasets to investigate automatic summarization of multiple source documents are missing, because their construction is complicated and costly. To solve this problem, we present a simple and cost-efficient corpus construction approach and demonstrate its applicability by creating new multi-document summarization datasets. Second, we develop a new machine learning approach for context-free information importance estimation, implement a concrete realization, and demonstrate its advantages over contextual importance estimators. Third, we develop a new method to evaluate automatic summarization methods. Previous works are based on expensive reference summaries and unreliable semantic comparisons of text documents. On the contrary, our approach uses cheap pairwise preference annotations and only much simpler sentence-level similarity estimation. This work lays the foundations for context-free information importance estimation. We hope that future research will explore if this fundamentally new type of information importance estimation can eventually lead to human-level information importance estimation abilities

Learning Analogy-Preserving Sentence Embeddings for Answer Selection

Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similarity-based technique.Comment: To appear in CoNLL1

Proteins from Modern and Ancient Wheat Cultivars: Impact on Immune Cells of Healthy Individuals and Patients with NCGS

In non-celiac gluten sensitivity (NCGS), the elimination of wheat results in a clear symptom improvement, but gluten has still not been proven as (the sole) trigger. Due to the increase in the prevalence of gluten-related diseases, the breeding of high-performance wheat cultivars is discussed as a trigger. To analyze the immune stimulation and signal pathways, the immune cells of healthy subjects and patients with NCGS were stimulated with gliadins from wheat, and the expression and secretion of interleukin 1ß (IL1ß) and interleukin 6 (IL6) were studied. To determine the impact of wheat breeding, the monocyte cell line THP1 and human immune cells were stimulated with gliadin, glutenin, and albumin/globulin fractions of ancient and modern cereals, and expression of inflammatory molecules was checked. Immune cells of patients with NCGS showed an increased expression of IL1ß and IL6 after stimulation with gliadins compared to immune cells of healthy controls. Gliadins caused a strong activation of P-STAT3 in immune cells of healthy controls, and inhibitors of JAK and NFκB pathways considerably reduced this response. In addition to gliadins, we further showed that glutenins and albumin/globulins from all wheat cultivars from the last century, and especially from einkorn and spelt, also markedly induced the expression of inflammatory genes in THP1 and human immune cells. There was no correlation between enhanced immune stimulation and ancient or modern cultivars. This does not support the hypothesis that modern wheat breeding is responsible for the increase in gluten-related diseases. An altered immune situation is suggested in patients with NCGS

Whole-Body Electromyostimulation Combined With Individualized Nutritional Support Improves Body Composition in Patients With Hematological Malignancies – A Pilot Study

Patients undergoing the complex treatment for hematological malignancies are exposed to a high physiological and psychological distress inducing fatigue and physical inactivity. In line with cancer-related metabolic changes patients are predisposed for skeletal muscle mass loss that leads to a functional decline, affects therapeutic success, and quality of life. Benefits of physical exercise and nutritional interventions on muscle maintenance are observed in solid cancer patients, but marginally investigated in patients with hematological cancer. We here studied the effects of a combined supportive exercise and nutrition intervention using whole-body electromyostimulation (WB-EMS) training and individualized nutritional support in patients actively treated for hematological malignancy. In a controlled pilot trial, 31 patients (67.7% male; 58.0 ± 16.7 years) with various hematological cancers were allocated to a control group (n = 9) receiving nutritional support of usual care regarding a high protein intake (>1.0 g/kg/d) or to a physical exercise group (n = 22) additionally performing WB-EMS training twice weekly for 12 weeks. Bodyweight and body composition assessed by bioelectrical impedance analysis were measured every 4 weeks. Physical function, blood parameters, quality of life and fatigue were assessed at baseline and after 12 weeks. No WB-EMS-related adverse effects occurred. Patients attending the exercise program presented a higher skeletal muscle mass than controls after 12-weeks (1.51 kg [0.41, 2.60]; p = 0.008). In contrast, patients of the control group showed a higher fat mass percentage than patients of the WB-EMS group (-4.46% [-7.15, -1.77]; p = 0.001) that was accompanied by an increase in serum triglycerides in contrast to a decrease in the WB-EMS group (change ± SD, control 36.3 ± 50.6 mg/dl; WB-EMS -31.8 ± 68.7 mg/dl; p = 0.064). No significant group differences for lower limb strength, quality of life, and fatigue were detected. However, compared to controls the WB-EMS group significantly improved in physical functioning indicated by a higher increase in the 6-min-walking distance (p = 0.046). A combined therapeutic intervention of WB-EMS and protein-rich nutritional support seems to be safe and effective in improving skeletal muscle mass and body composition in hematological cancer patients during active oncological treatment.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT02293239

Muscle-derived cytokines reduce growth, viability and migratory activity of pancreatic cancer cells

The evidence that regular physical exercise reduces the risk of developing cancer is well described. However, the interaction between physical exercise and cancer is not fully clarified yet. Several myokines released by skeletal muscle appear to have a direct anti-tumour function. There are few data on myokine secretion after exercise in patients with advanced tumours. Pancreatic cancer (PC) is a very aggressive and usually fatal cancer. To investigate the effects of exercise in PC, the blood of advanced-stage PC patients was analysed after 12 weeks of resistance training using whole-body electromyostimulation. After the 12-week training period, the patient serum inhibited the proliferation and the motility of PC cells and enhanced PC cell apoptosis. The impact of exercise training was also investigated in an exercise-mimicking in vitro model using electric pulse stimulation of human myotubes and revealed similar anti-tumour effects on PC cells, clearly indicating direct cancer-protective properties of activated skeletal muscle. Protein and gene expression analyses in plasma from exercise-trained patients and in myotube cultures after in vitro exercise showed that interleukin 10 (IL10), C-X-C motif ligand 1 (CXCL1) and C-C motif chemokine ligand 4 (CCL4) are myokines released from activated skeletal muscle. In accordance with the effects of serum from exercise-trained patients, the supplementation with recombinant IL10, CXCL1 and CCL4 impaired growth and migration of PC cells. Treatment of PC cells with these myokines upregulated caspase 3/7 expression and the cleavage of poly(ADP-ribose) polymerase, leading to enhanced PC cell death. The identification of myokines with anti-tumour properties in advanced-stage PC patients after exercise opens a new perspective in supportive therapy with sports and exercise for cancer patients

I-scan optical enhancement for the in vivo prediction of diminutive colorectal polyp histology:results from a prospective three-phased multicentre trial

BACKGROUND AND AIMS:Dye-less chromoendoscopy is an emerging technology for colorectal polyp characterization. Herein, we investigated whether the newly introduced I-scan optical enhancement (OE) can accurately predict polyp histology in vivo in real-time. METHODS:In this prospective three-phased study, 84 patients with 230 diminutive colorectal polyps were included. During the first two study phases, five endoscopists assessed whether analysis of polyp colour, surface and vascular pattern under i-scan OE can differentiate in vivo between adenomatous and hyperplastic polyps. Finally, junior and experienced endoscopists (JE, EE, each n = 4) not involved in the prior study phases made a post hoc diagnosis of polyp histology using a static i-scan OE image database. Histopathology was used as a gold-standard in all study phases. RESULTS:The overall accuracy of i-scan OE for histology prediction was 90% with a sensitivity, specificity, positive (PPV) and negative prediction value (NPV) of 91%, 90%, 86% and 94%, respectively. In high confidence predictions, the diagnostic accuracy increased to 93% with sensitivity, specificity, PPV and NPV of 94%, 91%, 89% and 96%. Colonoscopy surveillance intervals were predicted correctly in ≥ 90% of patients. In the post hoc analysis EE predicted polyp histology under i-scan OE with an overall accuracy of 91%. After a single training session, JE achieved a comparable diagnostic performance for predicting polyp histology with i-scan OE. CONCLUSION:The histology of diminutive colorectal polyps can be accurately predicted with i-scan OE in vivo in real-time. Furthermore, polyp differentiation with i-scan OE appears to require only a short learning curve