Two-Year Monitoring of Water Samples from Dam of Iskar and the Black Sea, Bulgaria, by Molecular Analysis: Focus on Mycobacterium spp

The coast of the Bulgarian Black Sea is a popular summer holiday destination. The Dam of Iskar is the largest artificial dam in Bulgaria, with a capacity of 675 million m3. It is the main source of tap water for the capital Sofia and for irrigating the surrounding valley. There is a close relationship between the quality of aquatic ecosystems and human health as many infections are waterborne. Rapid molecular methods for the analysis of highly pathogenic bacteria have been developed for monitoring quality. Mycobacterial species can be isolated from waste, surface, recreational, ground and tap waters and human pathogenicity of nontuberculose mycobacteria (NTM) is well recognized. The objective of our study was to perform molecular analysis for key-pathogens, with a focus on mycobacteria, in water samples collected from the Black Sea and the Dam of Iskar. In a two year period, 38 water samples were collected-24 from the Dam of Iskar and 14 from the Black Sea coastal zone. Fifty liter water samples were concentrated by ultrafiltration. Molecular analysis for 15 pathogens, including all species of genus Mycobacterium was performed. Our results showed presence of Vibrio spp. in the Black Sea. Rotavirus A was also identified in four samples from the Dam of Iskar. Toxigenic Escherichia coli was present in both locations, based on markers for stx1 and stx2 genes. No detectable amounts of Cryptosporidium were detected in either location using immunomagnetic separation and fluorescence microscopy. Furthermore, mass spectrometry analyses did not detect key cyanobacterial toxins. On the basis of the results obtained we can conclude that for the period 2012-2014 no Mycobacterium species were present in the water samples. During the study period no cases of waterborne infections were reported.

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Quantum-state transfer in imperfect artificial spin networks

High-fidelity quantum computation and quantum state transfer are possible in short spin chains. We exploit a system based on a dispersive qubit-boson interaction to mimic XY coupling. In this model, the usually assumed nearest-neighbor coupling is no longer valid: all the qubits are mutually coupled. We analyze the performances of our model for quantum state transfer showing how preengineered coupling rates allow for nearly optimal state transfer. We address a setup of superconducting qubits coupled to a microstrip cavity in which our analysis may be applied.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.