Towards a smart cancer registry

Aims Pathology notification for a Cancer Registry is regarded as the most valid information for the confirmation of a diagnosis of cancer. In view of the importance of pathology data, an automatic medical text analysis system (Medtex) is being developed to perform electronic Cancer Registry data extraction and coding of important clinical information embedded within pathology reports. Methods The system automatically scans HL7 messages received from a Queensland pathology information system and analyses the reports for terms and concepts relevant to a cancer notification. A multitude of data items for cancer notification such as primary site, histological type, stage, and other synoptic data are classified by the system. The underlying extraction and classification technology is based on SNOMED CT1 2. The Queensland Cancer Registry business rules3 and International Classification of Diseases – Oncology – Version 34 have been incorporated. Results The cancer notification services show that the classification of notifiable reports can be achieved with sensitivities of 98% and specificities of 96%5, while the coding of cancer notification items such as basis of diagnosis, histological type and grade, primary site and laterality can be extracted with an overall accuracy of 80%6. In the case of lung cancer staging, the automated stages produced were accurate enough for the purposes of population level research and indicative staging prior to multi-disciplinary team meetings2 7. Medtex also allows for detailed tumour stream synoptic reporting8. Conclusions Medtex demonstrates how medical free-text processing could enable the automation of some Cancer Registry processes. Over 70% of Cancer Registry coding resources are devoted to information acquisition. The development of a clinical decision support system to unlock information from medical free-text could significantly reduce costs arising from duplicated processes and enable improved decision support, enhancing efficiency and timeliness of cancer information for Cancer Registries.

BDNF and TNF-α polymorphisms in memory

Here, we investigate the genetic basis of human memory in healthy individuals and the potential role of two polymorphisms, previously implicated in memory function. We have explored aspects of retrospective and prospective memory including semantic, short term, working and long-term memory in conjunction with brain derived neurotrophic factor (BDNF) and tumor necrosis factor-alpha (TNF-alpha). The memory scores for healthy individuals in the population were obtained for each memory type and the population was genotyped via restriction fragment length polymorphism for the BDNF rs6265 (Val66Met) SNP and via pyrosequencing for the TNF-alpha rs113325588 SNP. Using univariate ANOVA, a significant association of the BDNF polymorphism with visual and spatial memory retention and a significant association of the TNF-alpha polymorphism was observed with spatial memory retention. In addition, a significant interactive effect between BDNF and TNF-alpha polymorphisms was observed in spatial memory retention. In practice visual memory involves spatial information and the two memory systems work together, however our data demonstrate that individuals with the Val/Val BDNF genotype have poorer visual memory but higher spatial memory retention, indicating a level of interaction between TNF-alpha and BDNF in spatial memory retention. This is the first study to use genetic analysis to determine the interaction between BDNF and TNF-alpha in relation to memory in normal adults and provides important information regarding the effect of genetic determinants and gene interactions on human memory.

Social media analytics: an interdisciplinary approach and its implications for information systems

Social Media Analytics is an emerging interdisciplinary research field that aims on combining, extending, and adapting methods for analysis of social media data. On the one hand it can support IS and other research disciplines to answer their research questions and on the other hand it helps to provide architectural designs as well as solution frameworks for new social media-based applications and information systems. The authors suggest that IS should contribute to this field and help to develop and process an interdisciplinary research agenda.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Introduction to the special issue on next generation network architectures, protocols, theory, systems and applications

This special issue of Networking Science focuses on Next Generation Network (NGN) that enables the deployment of access independent services over converged fixed and mobile networks. NGN is a packet-based network and uses the Internet protocol (IP) to transport the various types of traffic (voice, video, data and signalling). NGN facilitates easy adoption of distributed computing applications by providing high speed connectivity in a converged networked environment. It also makes end user devices and applications highly intelligent and efficient by empowering them with programmability and remote configuration options. However, there are a number of important challenges in provisioning next generation network technologies in a converged communication environment. Some preliminary challenges include those that relate to QoS, switching and routing, management and control, and security which must be addressed on an urgent or emergency basis. The consideration of architectural issues in the design and pro- vision of secure services for NGN deserves special attention and hence is the main theme of this special issue.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

High pressure in situ diffraction studies of metal–hydrogen systems

“Hybrid” hydrogen storage, where hydrogen is stored in both the solid material and as a high pressure gas in the void volume of the tank can improve overall system efficiency by up to 50% compared to either compressed hydrogen or solid materials alone. Thermodynamically, high equilibrium hydrogen pressures in metal–hydrogen systems correspond to low enthalpies of hydrogen absorption–desorption. This decreases the calorimetric effects of the hydride formation–decomposition processes which can assist in achieving high rates of heat exchange during hydrogen loading—removing the bottleneck in achieving low charging times and improving overall hydrogen storage efficiency of large hydrogen stores. Two systems with hydrogenation enthalpies close to −20 kJ/mol H2 were studied to investigate the hydrogenation mechanism and kinetics: CeNi5–D2 and ZrFe2−xAlx (x = 0.02; 0.04; 0.20)–D2. The structure of the intermetallics and their hydrides were studied by in situ neutron powder diffraction at pressures up to 1000 bar and complementary X-ray diffraction. The deuteration of the hexagonal CeNi5 intermetallic resulted in CeNi5D6.3 with a volume expansion of 30.1%. Deuterium absorption filled three different types of interstices, Ce2Ni2 and Ni4 tetrahedra, and Ce2Ni3 half-octahedra and was accompanied by a valence change for Ce. Significant hysteresis was observed between deuterium absorption and desorption which profoundly decreased on a second absorption cycle. For the Al-modified Laves-type C15 ZrFe2−xAlx intermetallics, deuteration showed very fast kinetics of H/D exchange and resulted in a volume increase of the FCC unit cells of 23.5% for ZrFe1.98Al0.02D2.9(1). Deuterium content, hysteresis of H/D uptake and release, unit cell expansion and stability of the hydrides systematically change with the amount of Al content. In the deuteride D atoms exclusively occupy the Zr2(Fe,Al)2 tetrahedra. Observed interatomic distances are Zr–D = 1.98–2.11; (Fe, Al)–D = 1.70–1.75A˚ . Hydrogenation slightly increases the magnetic moment of the Fe atoms in ZrFe1.98Al0.02 and ZrFe1.96Al0.04 from 1.9 �B at room temperature for the alloy to 2.2 �B for its deuteride.

Reasoning paradigms in legal decision support systems

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

Joint motion control and control allocation design for UAS flight control systems

As Unmanned Aircraft Systems (UAS) grow in complexity, and their level of autonomy increases|moving away from the concept of a remotely piloted systems and more towards autonomous systems|there is a need to further improve reliability and tolerance to faults. The traditional way to accommodate actuator faults is by using standard control allocation techniques as part of the flight control system. The allocation problem in the presence of faults often requires adding constraints that quantify the maximum capacity of the actuators. This in turn requires on-line numerical optimisation. In this paper, we propose a framework for joint allocation and constrained control scheme via vector input scaling. The actuator configuration is used to map actuator constraints into the space of the aircraft generalised forces, which are the magnitudes demanded by the light controller. Then by constraining the output of controller, we ensure that the allocation function always receive feasible demands. With the proposed framework, the allocation problem does not require numerical optimisation, and since the controller handles the constraints, there is not need to implement heuristics to inform the controller about actuator saturation.

Indexes of insulin resistance and secretion in obese children and adolescents : a validation study

OBJECTIVE To assess the concurrent validity of fasting indexes of insulin sensitivity and secretion in - obese prepubertal (Tanner stage 1) children and pubertal (Tanner stages 2-5) glucose tolerance test (FSIVGTT) as a criterion measure. RESEARCH DESIGN AND METHODS Eighteen obese children and adolescents (11 girls and 7 boys, mean age 12.2 +/- 2.4 years, mean BMI 35.4 +/- 6.2 kg/m(2), mean BMI-SDS 3.5 +/- 0.5, 7 prepubertal and I I pubertal) participated in the study. All participants underwent an insulin-modified FSIVGTT on two occasions, and 15 repeated this test a third time (mean 12.9 and 12.0 weeks apart). S-i measured by the FSIVGTT was compared with homeostasis model assessment (HOMA) of insulin resistance (HOMA-IR), quantitative insulin-sensitivity check index (QUICKI), fasting glucose-to-insulin ratio (FGIR), and fasting insulin (estimates of insulin sensitivity derived from fasting samples). The acute insulin response (AIR) measured by the FSIVGTT was compared with HOMA of percent beta-cell function (HOMA-beta%), FGIR, and fasting insulin (estimates of insulin secretion derived from fasting samples). RESULTS There was a significant negative correlation between HOMA-IR and S-i (r = -0.89, r = -0.90, and r = -0.81, P < 0.01) and a significant positive correlation between QUICKI and S-i (r = 0.89, r = 0.90, and r = 0.81, P < 0.01) at each time point. There was a significant positive correlation between FGIR and S-i (r = 0.91, r = 0.91, and r = 0.82, P < 0.01) and a significant negative correlation between fasting insulin and S-i (r = -90, r = -0.90, and r = -0.88, P < 0.01). HOMA-beta% was not as strongly correlated with AIR (r = 0.60, r = 0.54, and r = 0.61, P < 0.05). CONCLUSIONS HOMA-IR, QUICKI, FGIR, and fasting insulin correlate strongly with S-i assessed by the FSIVGTT in obese children and adolescents. Correlations between HOMA-β% FGIR and fasting insulin, and AIR were not as strong. Indexes derived from fasting samples are a valid tool for assessing insulin sensitivity in prepubertal and pubertal obese children.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

The collision induced loss of carbon monoxide from deprotonated benzyl benzoate in the gas phase. An anionic 1,2-Wittig type rearrangement

The ion PhCO2--CHPh, upon collision activation, undergoes competitive losses of CO and CO2 of which the former process produces the base peak of the spectrum. Product ion and substituent effect (Hammett) studies indicate that PhCO2--CHPh cyclises to a deprotonated hydroxydiphenyloxirane which ring opens to PhCOCH(O-)Ph. This anion then undergoes an anionic 1,2-Wittig type rearrangement {through [PhCO- (PhCHO)]} to form Ph2CHO- and CO. The mechanism of the 1,2-rearrangement has been probed by an ab initio study [at MP4(SDTQ)/6-31++G(d,p) level] of the model system HCOCH2O- →; MeO- + CO The analogous system RCO2--CHPh (R = alkyl) similarly loses CO, and the migratory aptitudes of the alkyl R groups in this reaction are Bu′ > Me > Et ∼Pri). This trend correlates with the order of anion basicities (i.e. the order of ΔG○acid values of RH), supporting the operation of an anion migration process. The loss of CO2 from PhCO2--CHPh yields Ph2CH- as the anionic product: several mechanistic scenarios are possible, one of which involves an initial ipso nucleophilic substitution.

MMP-9 secretion and MMP-2 activation distinguish invasive and metastatic sublines of a mouse mammary carcinoma system showing epithelial-mesenchymal transition traits

We have investigated the gelatinase profiles and invasiveness of clonal tumour sublines derived from a spontaneously arising mammary tumour in a Balb/cfC3H mouse. The 67NR, 66c14 and 4T1.2 sublines have low, intermediate and high metastatic potential respectively. In Boyden chamber studies, Matrigel invasion was seen to be progressively higher in the more metastatic lines 4T1.2>66c14>67NR, consistent with MMP-2 activation potential, MMP-9 secretion, and migration over either type I or IV collagen, which were low in both 67NR and 66c14 cells compared to 4T1.2 cells. These attributes are consistent with those seen in human breast cancer cell lines which appear to have undergone an epithelial-mesenchymal transition (EMT) as indicated by vimentin expression. We were, however, surprised to find vimentin expression, MT1-MMP expression and stellate Matrigel outgrowth in the non-invasive, non-metastatic 67NR cells, indicating that they had undergone an EMT despite not being invasive. We conclude that the EMT is manifested to differing degrees in these three clonal cell lines, and that the 67NR cells have either undergone a partial EMT or have since lost certain important attributes of the EMT-derived phenotype. This model should prove useful in further characterizing the regulation of MT1-MMP mediated MMP-2 activation and delineating the EMT in breast cancer progression.

Neurological consequences of diabetic ketoacidosis at initial presentation of type 1 diabetes in a prospective cohort study of children

OBJECTIVE To investigate the impact of new-onset diabetic ketoacidosis (DKA) during child- hood on brain morphology and function. RESEARCH DESIGN AND METHODS Patients aged 6–18 years with and without DKA at diagnosis were studied at four time points: <48 h, 5 days, 28 days, and 6 months postdiagnosis. Patients under- went magnetic resonance imaging (MRI) and spectroscopy with cognitive assess- ment at each time point. Relationships between clinical characteristics at presentation and MRI and neurologic outcomes were examined using multiple linear regression, repeated-measures, and ANCOVA analyses. RESULTS Thirty-six DKA and 59 non-DKA patients were recruited between 2004 and 2009. With DKA, cerebral white matter showed the greatest alterations with increased total white matter volume and higher mean diffusivity in the frontal, temporal, and parietal white matter. Total white matter volume decreased over the first 6 months. For gray matter in DKA patients, total volume was lower at baseline and increased over 6 months. Lower levels of N-acetylaspartate were noted at base- line in the frontal gray matter and basal ganglia. Mental state scores were lower at baseline and at 5 days. Of note, although changes in total and regional brain volumes over the first 5 days resolved, they were associated with poorer delayed memory recall and poorer sustained and divided attention at 6 months. Age at time of presentation and pH level were predictors of neuroimaging and functional outcomes. CONCLUSIONS DKA at type 1 diabetes diagnosis results in morphologic and functional brain changes. These changes are associated with adverse neurocognitive outcomes in the medium term.