Scots pine (pinus sylvestris L.) growth suppression and adverse effects on human health due to air pollution in the upper Silesian Industrial District (USID), southern Poland

Air pollution emissions were not continually monitored in the Upper Silesian Industrial District (USID), southern Poland, and data is only available for the last 20 years. Long-lasting and severe tree ring reductions in pines growing 5–20 km north of the USID area recorded particularly high levels of air pollution emissions in the period 1950–1990. Especially high amounts of reductions and many missing rings were found in the period 1964–1981. At the same time, pines growing 60 km west of the USID do not record deep ring reductions; this proves that the phenomenon is of a regional nature. Increases in infant mortality and lung, bronchial, and tracheal cancer morbidity rates among males were also recorded in the USID during periods of high air pollution. Infant mortality rates increased several years after the tree ring reductions. Therefore, it may be possible to use tree ring reductions as an early indicator of the occurrence of adverse effects on human health

Allelic losses on chromosome 3p are accumulated in relation to morphological changes of lung adenocarcinoma

We performed allelotyping analysis at nine regions on chromosome 3p using 56 microdissected samples from 23 primary lung adenocarcinomas to examine the process of progression within individual lung adenocarcinoma with various grades of differentiation. Identical allelic patterns among various grades of differentiation were found in eight cases. Accumulation of allelic losses from high to lower differentiated portions was found in seven cases and accumulation of allelic losses from low to higher differentiated portions was found in five cases. Various allelic patterns among various grades of differentiation were found in three cases. These results suggested that allelic losses on 3p play an important role in morphological changes of lung adenocarcinomas. We also investigated the relationship between allelic losses on 3p and histological subtypes of lung adenocarcinoma. The frequencies of allelic losses at 3p14.2 and telomeric region of 3p21.3 were higher in papillary type tumour (nine out of 14, 64% and 11 out of 15, 73%) than in bronchioloalveolar carcinoma-type tumour (one out of 8, 13%; P=0.031 and four out of 12, 33%; P = 0.057). These results indicated that allelic losses at 3p14.2 and telomeric region of 3p21.3 are related to pattern of the proliferation of lung adenocarcinoma

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

High resolution chromosome 3p, 8p, 9q and 22q allelotyping analysis in the pathogenesis of gallbladder carcinoma

Our recent genome-wide allelotyping analysis of gallbladder carcinoma identified 3p, 8p, 9q and 22q as chromosomal regions with frequent loss of heterozygosity. The present study was undertaken to more precisely identify the presence and location of regions of frequent allele loss involving those chromosomes in gallbladder carcinoma. Microdissected tissue from 24 gallbladder carcinoma were analysed for PCR-based loss of heterozygosity using 81 microsatellite markers spanning chromosome 3p (n=26), 8p (n=14), 9q (n=29) and 22q (n=12) regions. We also studied the role of those allele losses in gallbladder carcinoma pathogenesis by examining 45 microdissected normal and dysplastic gallbladder epithelia accompanying gallbladder carcinoma, using 17 microsatellite markers. Overall frequencies of loss of heterozygosity at 3p (100%), 8p (100%), 9q (88%), and 22q (92%) sites were very high in gallbladder carcinoma, and we identified 13 distinct regions undergoing frequent loss of heterozygosity in tumours. Allele losses were frequently detected in normal and dysplastic gallbladder epithelia. There was a progressive increase of the overall loss of heterozygosity frequency with increasing severity of histopathological changes. Allele losses were not random and followed a sequence. This study refines several distinct chromosome 3p, 8p, 9q and 22q regions undergoing frequent allele loss in gallbladder carcinoma that will aid in the positional identification of tumour suppressor genes involved in gallbladder carcinoma pathogenesis

CNN-FM: Personalized Content-Aware Image Tag Recommendation

Social media services allow users to share and annotate their resources freely with keywords or tags that have valuable information to support organizing or searching uploaded images or videos. Tag recommendation is used to encourage users to annotate their resources. Recommending tags of images to users not only depends on user preference but also strongly relies on the contents of images. In this paper, we propose a method for image tag recommendation using both image visual features and user past tagging behaviours by combining convolutional neural networks (CNN), which are widely used and have achieved high performance in image classification and recognition, and factorization machines (FM), since factorization models are the state-of-the-art approach for tag recommendation. Empirically, we demonstrate that learnable features extracted by CNNs can improve up to 7 percent the performance of FMs in image tag recommendation

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis