Advanced automatic detection of fetal body movements from multichannel magnetocardiographic signals

OBJECTIVE

Both heart rate (HR) monitoring and detection and description of fetal movements provide essential information of the integrity of in utero development and fetal wellbeing. Our previously described method to identify movements from multichannel magnetocardiographic (MCG) recordings lacks of reliability in some cases. This work is aimed at the improvement of fetal movement detection by means of an advanced signal processing and validation strategy.

APPROACH

The previously proposed methodology of fetal body movement detection from MCG recordings using single space angle (SSA), min-max amplitude (MMA) and a measure of the overall signal strength across (RSS) was extended by moving correlation coefficient (MCC). The methodology was developed with respect to the discrimination between active and quiet sleep, validated by testing its coupling with HR accelerations in a total of 137 recordings lasting 30 min from 98 fetuses aged 34-38 weeks of gestation (WGA) of normal pregnancy.

MAIN RESULTS

The developed algorithm improves the reliable automatic detection of fetal body movements independent of the fetal sleep states and their changes in the individual MCG recordings. In the fetuses aged 34-38 WGA 94% of 15  ×  15 HR accelerations were coupled with detected movements. The visual inspection of the movement graphs of 30 fetuses aged 20-32 WGA supports the transferability of the movement detector to this age. In four subjects MCG-based movement detection and maternal report on percepted fetal movements were consistent.

SIGNIFICANCE

The presented methodology allows the parallel automatic acquisition of precise fetal heart rate variability (HRV) indices based on subsequent beat intervals and of fetal body movements from MCG recordings during late 2nd and 3rd trimester. Potential advantages of parallel monitoring of fetal HRV and movements using MCG compared to established ultrasound technology should be investigated in subsequent studies with respect to the identification of fetuses at risk.

Modifying the transition temperature, 120 K ≤ Tc ≤ 1150 K, of amorphous Fe90-xCoxSc10 with simultaneous alteration of fluctuation of exchange integral up to zero

Amorphous (a-) Fe_90−xCo_xSc₁₀ alloys have been produced by rapid quenching from the melt. The Curie temperature, T_C, was determined using both mean field theory and Landau’s theory of second-order phase transitions in zero and non-zero external fields. The dependence of T_C on the atomic spacing can be explained by the empirical Bethe-Slater curve. The value of T_C of a- Fe₅Co₈₅Sc₁₀, determined by the above theoretical approaches is 1150 K, which is the highest T_C ever measured for amorphous alloys. The flattening of the measured normalized magnetization, M(T)/M(0), as a function of the reduced temperature, T/T_C, is explained within the framework of the Handrich- Kobe model. According to this model the fluctuation of the exchange integral is the main reason for the flattening of M(T)/M(0). In the case of a-Fe₉₀Sc₁₀ without Co, however, the fluctuation of the exchange integral is dominant only at zero external field, B_ex = 0. At B_ex = 9 T, however, the fluctuation of the exchange integral has no conspicuous effect on the reduction of the magnetization. It is shown that at B_ex = 9 T the frozen magnetic clusters control the behaviour of the reduced magnetization as function of T/T_C. In contrast to other ferromagnetic alloys, where the flattening of M(T)/M(0) is characteristic for an amorphous structure, the a- Fe₅Co₈₅Sc₁₀ does not exhibit any trace of the fluctuation of the exchange integral.

The mechanically induced structural disorder in barium hexaferrite, BaFe12O19, and its impact on magnetism

The response of the structure of the M-type barium hexaferrite (BaFe₁₂O₁₉) to mechanical action through high-energy milling and its impact on the magnetic behaviour of the ferrite are investigated. Due to the ability of the ⁵⁷Fe Mössbauer spectroscopic technique to probe the environment of the Fe nuclei, a valuable insight on a local atomic scale into the mechanically induced changes in the hexagonal structure of the material is obtained. It is revealed that the milling of BaFe₁₂O₁₉ results in the deformation of its constituent polyhedra (FeO₆ octahedra, FeO₄ tetrahedra and FeO₅ triangular bi-pyramids) as well as in the mechanically triggered transition of the Fe³⁺ cations from the regular 12k octahedral sites into the interstitial positions provided by the magnetoplumbite structure. The response of the hexaferrite to the mechanical treatment is found to be accompanied by the formation of a non-uniform nanostructure consisting of an ordered crystallite surrounded/separated by a structurally disordered surface shell/interface region. The distorted polyhedra and the non-equilibrium cation distribution are found to be confined to the amorphous near-surface layers of the ferrite nanoparticles with the thickness extending up to about 2 nm. The information on the mechanically induced short-range structural disorder in BaFe₁₂O₁₉ is complemented by an investigation of its magnetic behaviour on a macroscopic scale. It is demonstrated that the milled ferrite nanoparticles exhibit a pure superparamagnetism at room temperature. As a consequence of the far-from-equilibrium structural disorder in the surface shell of the nanoparticles, the mechanically treated BaFe₁₂O₁₉ exhibits a reduced magnetization and an enhanced coercivity.

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

BACKGROUND

Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities.

RESULTS

We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner.

CONCLUSION

We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions.

Semantic text mining support for lignocellulose research

Background

Biofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties.

Results

Semantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources.

Conclusions

Working closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information.

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

Background

Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.

Results

All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.

The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.

Conclusions

The SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

MOTIVATION

Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation.

RESULTS

We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%.

AVAILABILITY

The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger.

CONTACT

witte@semanticsoftware.info.

Algorithms and semantic infrastructure for mutation impact extraction and grounding

Background

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.

Results

We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.

Conclusion

We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Towards a systematic evaluation of protein mutation extraction systems

The development of text analysis systems targeting the extraction of information about mutations from research publications is an emergent topic in biomedical research. Current systems differ in both scope and approach, thus preventing a meaningful comparison of their performance and therefore possible synergies. To overcome this evaluation bottleneck, we developed a comprehensive framework for the systematic analysis of mutation extraction systems, precisely defining tasks and corresponding evaluation metrics, that will allow a comparison of existing and future applications.

Performance of laser bonded glass/polyimide microjoints in cerebrospinal fluid

In this paper, laser bonded microjoints between glass and polyimide is considered to examine their potential applicability in encapsulating neural implants. To facilitate bonding between polyimide and glass, a thin titanium film with a thickness of 2 microm was deposited on borosilicate glass plates by a physical vapor deposition (PVD) process. Titanium coated glass was then joined with polyimide by using a cw fiber laser emitting at a wavelength of 1.1 microm (1.0 W) to prepare several tensile samples. Some of the samples were exposed to artificial cerebrospinal fluid (aCSF) at 37 degrees C for two weeks to assess long-term integrity of the joints. Both the as-received and aCSF soaked samples were subjected to uniaxial tensile loads for bond strengths measurements. The bond strengths for the as-received and aCSF soaked samples were measured to be 7.31 and 5.33 N/mm, respectively. Although the long-term exposure of the microjoints to aCSF has resulted in 26% reduction of bond strength, the samples still retain considerably high strength as compared with the titanium-polyimide samples. The failed glass/polyimide samples were also analyzed using optical microscopy, and failure mechanisms are discussed. In addition, a two dimensional finite element analysis (FEA) was conducted to understand the stress distribution within the substrate materials while the samples are in tension. The FEA results match reasonably well with the experimental load-displacement curves for as-received samples. Detailed discussion on various stress contours is presented in the paper, and the failure mechanisms observed from the experiment are shown in good agreement with the FEA predicted ones.

A comparison between glass/polyimide and titanium/polyimide microjoint performances in cerebrospinal fluid

Assessment of neural biocompatibility requires that materials be tested with exposure in neural fluids. Laser bonded microjoint samples made from Ti coated glass substrate and polyimide film (GPI) and titanium foil and polyimide film (TIPI) were evaluated for mechanical performance before and after exposure in artificial cerebrospinal fluid (CSF) for two, four, and 12 weeks at 37 degrees C. These samples represent a critical feature, i.e., the microjoint-a major weakness in the bioencapsulation system. Both material systems showed initial degradation up to 4 weeks which then stabilized afterwards and retained similar strength until 12 weeks. The TIPI system appears to exhibit better overall performance with less degradation compared to its as-received strength. The CSF exposed TIPI samples predominantly failed at the interface, while GPI samples had mixed glass and polyimide substrate and interface failure. The amount of glass failure decreases and interface failure increases with increase in CSF exposure time. The failure mechanism of the as-received (not exposed to CSF) GPI samples under tension was predominantly flexure type failure of the glass substrate.

Laser bonded microjoints between titanium and polyimide for applications in medical implants

Bioencapsulation of medical implant devices, and neural implant devices in particular, requires development of reliable hermetic joints between packaging materials that are often dissimilar. Titanium-polyimide is one of the biocompatible material systems, which are of interest to our research groups at Wayne State University and Fraunhofer USA. We have found processing conditions for successful joining of titanium with polyimide using near-infrared diode lasers or fiber lasers along transmission bonding lines with widths ranging from 200 to 300 microm. Laser powers of 2.2 and 3.8 W were used to create these joints. Laser-joined samples were tested in a microtester under tensile loading to determine joint strengths. In addition, finite element analysis (FEA) was conducted to understand the stress distribution within the bond area under tensile loading. The FEA model provides a full-field stress distribution in and around the joint that cause eventual failure. Results from the investigation provide an initial approach to characterize laser-fabricated microjoints between dissimilar materials that can be potentially used in optimization of bio-encapsulation design.

Double-blind, randomized study of primary hormonal treatment of stage D2 prostate carcinoma: flutamide versus diethylstilbestrol

PURPOSE

Patients with stage D2 prostate carcinoma are often treated initially with hormones to decrease endogenous testosterone. Therapy with diethylstilbestrol (DES), leuprolide, or bilateral orchiectomy has been reported to be equivalent. DES is the cheapest preparation, but has the potential for serious cardiovascular or thromboembolic complications. Flutamide is a novel antiandrogen with fewer side effects.

PATIENTS AND METHODS

The Eastern Cooperative Oncology Group (ECOG) conducted a double-blind, randomized study to compare the efficacy of flutamide (250 mg three times daily) to DES (1 mg three times daily) as the primary hormonal therapy for patients with stage D2 prostate cancer. Patients were stratified by performance status, disease sites, and history of cardiovascular disease at randomization.

RESULTS

Forty-eight patients received DES and 44 flutamide. Patient characteristics were evenly distributed between the two treatments. The overall response rate was similar (DES, 62%; flutamide, 50%). Grade III or worse cardiovascular or thromboembolic toxicity developed in 33.3% of patients on DES and in 17.6% on flutamide (P = .051). Other toxicities were similar between the two treatment arms. However, DES produced significantly longer time to treatment failure (26.4 v 9.7 months, P = .016) and longer survival than flutamide (43.2 v 28.5 months, P = .040).

CONCLUSION

As the primary hormonal therapy for stage D2 prostate cancer, DES caused more serious cardiovascular or thromboembolic complications than flutamide. Despite this, flutamide was not as active an initial agent as DES. However, the effectiveness of flutamide in conjunction with other agents compared with DES remains undetermined, and the optimal initial hormone therapy of stage D2 prostate cancer requires further studies.

A wind tunnel for measuring the gaseous losses of environmental chemicals from the soil/plant system under field-like conditions

Volatilization from treated areas is a major source of pesticide residues in air, fog, and rain. This may lead to long-range transport of pesticide residues to remote areas. Up to now most information on pesticide volatilization has come from laboratory experiments under controlled conditions. A new system has been designed and developed to measure the volatile losses of(14)C-labelled chemicals after application; the method compares with agricultural practice of treating soils or plants grown in lysimeters. Sensitive analytical methods guarantee a distinction between residues of unchanged pesticide, its metabolites or(14)CO2 as a mineralization product released into the air.

Patchwork access. Primary care in EDs on the rise

It's been long known that EDs in the nation's largest cities routinely provide non-emergent primary care. But a new study by the AHA and several allied hospital associations has found that a very high proportion of ED visits in each of the four communities studied could have been handled outside the ED.

Long-term survival of patients treated with combination chemotherapy for metastatic breast cancer

Long-term survival of patients with metastatic breast cancer treated on two prospective stratified randomised trials has been analysed. Patients on study B122 received either cyclophosphamide, methotrexate and 5-fluorouracil (CMF) or cyclophosphamide, doxorubicin and 5-fluorouracil (CAF). On study B141 patients received CAF or mitolactol (dibromodulcitol), doxorubicin and vincristine alternating after every three cycles with three cycles of CMF (DAV/CMF). Long-term follow-up of 172 patients showed no significant survival difference (in multivariate regression models) for treatment with either CMF vs. CAF or CAF vs. DAV/CMF. The difference in median survival times between CMF and CAF showed a trend in favour of CAF. Advances in the management of metastatic breast cancer in postmenopausal women obtained by doxorubicin regimens have had a small but measurable impact on survival, but known patient discriminants were not overridden by the treatment regimens investigated in these studies.

Integrated Academic Information Management Systems (IAIMS). Part II. Planning and implementing integrated information services. IAIMS infrastructure: technological base

The IAIMS concept calls for the logical integration of information derived from databases in the functional areas of administration, clinical care, education, libraries, and research. To accomplish this task a technological base must be established. It is suggested that such an infrastructure will consist of a systems component, the linked databases component, and a management component. An IAIMS should rapidly evolve into an information utility and, within a decade, provide a knowledge management system.