Subclinical psychotic experiences in healthy young adults : associations with stress and genetic predisposition

Stress has been identified as a common trigger for psychosis. Dopamine pathways are suggested to be affected by chronic and severe stress and to play an important role in psychosis. This pilot study investigates the potential relationship of stress and psychosis in subclinical psychotic experiences. It was hypothesized that single-nucleotide polymorphisms (SNPs) previously found to be associated with psychiatric disorders would be associated with both stress and subclinical psychotic experiences. University students (N=182) were genotyped for 17 SNPs across 11 genes. Higher stress reporting was associated with rs4680 COMT, rs13211507 HLA region, and rs13107325 SLC39A8. Reports of higher subclinical psychotic experiences were associated with DRD2 SNPs rs17601612 and rs658986 and an AKT1 SNP rs2494732. Replication studies are recommended to further pursue this line of research for identification of markers of psychosis for early diagnosis and intervention.

A little learning is a dangerous thing: Factors influencing the increased identification of special educational needs from the perspective of education policymakers and school practitioners

This article considers the increased identification of special educational needs in Australia’s largest education system from the perspectives of senior public servants, regional directors, principals, school counsellors, classroom teachers, support class teachers, learning support teachers and teaching assistants (n = 30). While their perceptions of an increase generally align with the story told by official statistics, participants’ narratives reveal that school-based identification of special educational needs is neither art nor science. This research finds that rather than an objective indication of the number and nature of children with SEN, official statistics may be more appropriately viewed as a product of funding eligibility and the assumptions of the adults who teach, refer and assess children who experience difficulties in school and with learning.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

On-line estimation of Allan variance parameters

A new online method is presented for estimation of the angular random walk and rate random walk coefficients of IMU (inertial measurement unit) gyros and accelerometers. The online method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph. Allan variance graphs have large off-line computational effort and data storage requirements. The technique proposed here requires no data storage and computational effort of O(100) calculations per data sample.

MIMO and SISO identification of radiation force terms for models of marine structures in waves

This paper presents a discussion on the use of MIMO and SISO techniques for identification of the radiation force terms in models for surface vessels. We compare and discuss two techniques recently proposed in literature for this application: time domain identification and frequency domain identification. We compare the methods in terms of estimates model order, accuracy of the fit, use of the available information, and ease of use and implementation.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

2nd ESMO Consensus Conference on Lung Cancer : non-small-cell lung cancer first-line/second and further lines in advanced disease

To complement the existing treatment guidelines for all tumour types, ESMO organises consensus conferences to focus on specific issues in each type of tumour. The 2nd ESMO Consensus Conference on Lung Cancer was held on 11-12 May 2013 in Lugano. A total of 35 experts met to address several questions on non-small-cell lung cancer (NSCLC) in each of four areas: pathology and molecular biomarkers, first-line/second and further lines in advanced disease, early stage disease and locally-advanced disease. For each question, recommendations were made including reference to the grade of recommendation and level of evidence. This consensus paper focuses on 1st line / 2nd and further lines of treatment in advanced disease. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved.

Nanostructured silver-gold bimetallic SERS substrates for selective identification of bacteria in human blood

Surface-enhanced Raman spectroscopy (SERS) is a potentially important tool in the rapid and accurate detection of pathogenic bacteria in biological fluids. However, for diagnostic application of this technique, it is necessary to develop a highly sensitive, stable, biocompatible and reproducible SERS-active substrate. In this work, we have developed a silver–gold bimetallic SERS surface by a simple potentiostatic electrodeposition of a thin gold layer on an electrochemically roughened nanoscopic silver substrate. The resultant substrate was very stable under atmospheric conditions and exhibited the strong Raman enhancement with the high reproducibility of the recorded SERS spectra of bacteria (E. coli, S. enterica, S. epidermidis, and B. megaterium). The coating of the antibiotic over the SERS substrate selectively captured bacteria from blood samples and also increased the Raman signal in contrast to the bare surface. Finally, we have utilized the antibiotic-coated hybrid surface to selectively identify different pathogenic bacteria, namely E. coli, S. enterica and S. epidermidis from blood samples.

Superstars as drivers of organizational identification : empirical findings from professional soccer

This paper examines the effect of superstars on external stakeholders’ organizational identification through the lens of sport. Drawing on social identity theory and the concept of organizational identification, as well as on role model theories and superstar economics, several hypotheses are developed regarding the influence of soccer stars on their fans’ degree of team identification. Using a proprietary data set that combines archival data on professional German soccer players and clubs with survey data on more than 1,400 soccer fans, this study finds evidence for a positive effect of superstar characteristics and role model perception. Moreover, it is found that players who qualify for the definition of a superstar are more important to fans of established teams than to fans of unsuccessful teams. The player's club tenure, however, seems to have no influence on fans’ team identification. It is further argued that the effect of soccer stars on their fans is comparable to that of executives on external stakeholders, and hence, the results are applied to the business domain. The results of this study contribute to existing research by extending the list of personnel-related determinants of organizational identification.

Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering

This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.

Causal factors of hot air ballooning incidents : identification, frequency, and potential impact

Background: Hot air ballooning incidents are relatively rare, however, when they do occur they are likely to result in a fatality or serious injury. Human error is commonly attributed as the cause of hot air ballooning incidents; however, error in itself is not an explanation for safety failures. This research aims to identify, and establish the relative importance of factors contributing towards hot air ballooning incidents. Methods: Twenty-two Australian Ballooning Federation (ABF) incident reports were thematically coded using a bottom up approach to identify causal factors. Subsequently, 69 balloonists (mean 19.51 years’ experience) participated in a survey to identify additional causal factors and rate (out of seven) the perceived frequency and potential impact to ballooning operations of each of the previously identified causal factors. Perceived associated risk was calculated by multiplying mean perceived frequency and impact ratings. Results: Incident report coding identified 54 causal factors within nine higher level areas: Attributes, Crew resource management, Equipment, Errors, Instructors, Organisational, Physical Environment, Regulatory body and Violations. Overall, ‘weather’, ‘inexperience’ and ‘poor/inappropriate decisions’ were rated as having greatest perceived associated risk. Discussion: Although errors were nominated as a prominent cause of hot air ballooning incidents, physical environment and personal attributes are also particularly important for safe hot air ballooning operations. In identifying a range of causal factors the areas of weakness surrounding ballooning operations have been defined; it is hoped that targeted safety and training strategies can now be put into place removing these contributing factors and reducing the chance of pilot error.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Identification of allanite (Ce, Ca, Y)2(Al, Fe3+)3(SiO4)3OH found in marble from Chillagoe, Queensland using Raman spectroscopy

Samples of marble from Chillagoe, North Queensland have been analyzed using scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) and Raman spectroscopy. Chemical analyses provide evidence for the presence of minerals other than limestone and calcite in the marble, including silicate minerals. Some of these analyses correspond to silicate minerals. The Raman spectra of these crystals were obtained and the Raman spectrum corresponds to that of allanite from the Arizona State University data base (RRUFF) data base. The combination of SEM with EDS and Raman spectroscopy enables the characterization of the mineral allanite in the Chillagoe marble.

A continental-scale tool for acoustic identification of European bats

Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97·6%. The median species-level classification accuracy is 83·7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental- scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.