A Novel Design Science Approach for Integrating Chinese User-Generated Content in Non-Chinese Market Intelligence

Market research has long relied on reactive means of data gathering, such as questionnaires or focus groups. With the wide-spread use of social media, millions of comments about customer opinions and feedback regarding products and brands are available. However, before using this ‘wisdom of the crowd’ as a source for marketing research, several challenges have to be tackled: the sheer volume of posts, their unstructured format, and the dozens of different languages used on the internet. All of them make automated usage of this data challenging. In this paper, we draw on dashboard design principles and follow a design science research approach to develop a framework for search, integration, and analysis of cross-language user-generated content. With ‘MarketMiner’, we implement the framework in the automotive industry by analyzing Chinese auto forums. The results are promising in that MarketMiner can dramatically improve utilization of foreign-language social media content for market intelligence purposes

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Introduction Treating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients. Methods We conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality. Results Thirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model. Conclusions While the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality

Intraspecific mating system evolution and its effect on complex male secondary sexual traits: Does male–male competition increase selection on size or shape?

Sexual selection is generally held responsible for the exceptional diversity in secondary sexual traits in animals. Mating system evolution is therefore expected to profoundly affect the covariation between secondary sexual traits and mating success. Whereas there is such evidence at the interspecific level, data within species remain scarce. We here investigate sexual selection acting on the exaggerated male fore femur and the male wing in the common and widespread dung flies Sepsis punctum and S. neocynipsea (Diptera: Sepsidae). Both species exhibit intraspecific differences in mating systems and variation in sexual size dimorphism (SSD) across continents that correlates with the extent of male–male competition. We predicted that populations subject to increased male–male competition will experience stronger directional selection on the sexually dimorphic male foreleg. Our results suggest that fore femur size, width and shape were indeed positively associated with mating success in populations with male‐biased SSD in both species, which was not evident in conspecific populations with female‐biased SSD. However, this was also the case for wing size and shape, a trait often assumed to be primarily under natural selection. After correcting for selection on overall body size by accounting for allometric scaling, we found little evidence for independent selection on any of these size or shape traits in legs or wings, irrespective of the mating system. Sexual dimorphism and (foreleg) trait exaggeration is therefore unlikely to be driven by direct precopulatory sexual selection, but more so by selection on overall size or possibly selection on allometric scaling

Survival and prognostic factors in conventional G1 chondrosarcoma

Background Chondrosarcoma is the second most frequent malignant bone tumor. Grade I chondrosarcoma (syn.: atypical cartilaginous tumor) is classified as an intermediately and locally aggressive neoplasm and typically is treated less aggressively (i.e., by intralesional curettage). Does the data regarding local recurrence (LR) and metastatic disease justify this? Methods From 1982 to 2014, 37 consecutive patients with G1 chondrosarcoma had been resected or curetted. The margin was defined as R0 (wide resection) or R1 (marginal resection). All patients were followed for evidence of local recurrence or metastatic disease. Overall and recurrence-free survival were calculated, and various potentially prognostic factors were evaluated. Results In 23 patients (62%), the tumor was widely (R0) resected, whereas in 14 patients, (38%) the resection was marginal (R1). Overall survival was 97% after 5 years, 92% after 10 years, and 67% after 20 years. Five-year local recurrence-free survival was 96%. Ten-year local recurrence-free survival was 83%. Local recurrence-free survival showed a significant correlation to margin status but no correlation to location or age. None of the patients with local recurrence died during the follow-up. One patient had metastatic disease at initial presentation, and a further five patients developed metastatic disease during follow-up. Metastatic disease proofed to be a highly significant factor for survival but was not correlated to local recurrence. Conclusions There was no significant correlation between the outcome and the primary tumor location. Marginal resection was a risk factor for LR, but there was no significant difference in the overall survival in patients with or without LR. Metastatic disease (16%) was more common than expected from the literature and a significant predictor for poor overall survival

Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models

To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification

Insights into the structural basis of amyloid resistance provided by cryo-EM structures of AApoAII amyloid fibrils

Amyloid resistance is the inability or the reduced susceptibility of an organism to develop amyloidosis. In this study we have analysed the molecular basis of the resistance to systemic AApoAII amyloidosis, which arises from the formation of amyloid fibrils from apolipoprotein A-II (ApoA-II). The disease affects humans and animals, including SAMR1C mice that express the C allele of ApoA-II protein, whereas other mouse strains are resistant to development of amyloidosis due to the expression of other ApoA-II alleles, such as ApoA-IIF. Using cryo-electron microscopy, molecular dynamics simulations and other methods, we have determined the structures of pathogenic AApoAII amyloid fibrils from SAMR1C mice and analysed the structural effects of ApoA-IIF-specific mutational changes. Our data show that these changes render ApoA-IIF incompatible with the specific fibril morphologies, with which ApoA-II protein can become pathogenic in vivo

Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning

Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. Methods: This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists. Results: All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75-0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit. Conclusions: Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool

Cryo-EM demonstrates the in vitro proliferation of an ex vivo amyloid fibril morphology by seeding

Several studies showed that seeding of solutions of monomeric fibril proteins with ex vivo amyloid fibrils accelerated the kinetics of fibril formation in vitro but did not necessarily replicate the seed structure. In this research we use cryo-electron microscopy and other methods to analyze the ability of serum amyloid A (SAA)1.1-derived amyloid fibrils, purified from systemic AA amyloidosis tissue, to seed solutions of recombinant SAA1.1 protein. We show that 98% of the seeded fibrils remodel the full fibril structure of the main ex vivo fibril morphology, which we used for seeding, while they are notably different from unseeded in vitro fibrils. The seeded fibrils show a similar proteinase K resistance as ex vivo fibrils and are substantially more stable to proteolytic digestion than unseeded in vitro fibrils. Our data support the view that the fibril morphology contributes to determining proteolytic stability and that pathogenic amyloid fibrils arise from proteolytic selection

Summary of Milestones 2030 : Elements and milestones for the development of a stable and sustainable bioenergy strategy

This publication is the English version of the summary of the German report „Meilensteine 2030“ (THRÄN et al. 2015) which is published in the series of the funding programme “Biomass energy use”. The report describes elements and milestones for the development of a stable and sustainable bioenergy strategy