The acute effects of eccentrically-biased versus conventional weight training in older adults : a randomised controlled cross-over study

Background Whilst resistance training has been proven to convey considerable benefits to older people; immediately post-exercise there may be elevated transient risks for cardiac events and falls. Objectives and Measurements We assessed the acute effects of eccentrically-biased (EB) and conventional (CONV) resistance exercise on: platelet number, activation and granule exocytsosis; and mean velocity of centre of pressure displacement (Vm). Design, Setting, Participants and Intervention Ten older adults (7 males, 3 females; 69 ± 4 years) participated in this randomised controlled cross-over study in which they performed EB and CONV training sessions that were matched for total work and a control condition. Results Immediately post-exercise there was a statistically significant difference in platelet count between the control condition, in which it fell (pre 224 ± 35 109/L; post 211 ± 30 109/L: P < 0.05) and CONV in which it increased (pre 236 ± 55 109/L; post 242 ± 51 109/L: P > 0.05). There was no change in platelet activation and granule exocytsosis or Vm following EB and CONV. Conclusions Overall, while minor differences between regimens were observed, no major adverse effect on parameters of platelet function or centre of pressure displacement were observed acutely following either regimen. Eccentrically-biased and conventional resistance exercise training regimens do not appear to present an elevated acute risk in the context of changes to platelet function contributing to a cardiac event or postural stability increasing falls risk for apparently healthy older adults.

Resistance to exercise-induced weight loss : compensatory behavioral adaptations

In many interventions that are based on an exercise program intended to induce weight loss, the mean weight loss observed is modest and sometimes far less than what the individual expected. The individual responses are also widely variable, with some individuals losing a substantial amount of weight, others maintaining weight, and a few actually gaining weight. The media have focused on the subpopulation that loses little weight, contributing to a public perception that exercise has limited utility to cause weight loss. The purpose of the symposium was to present recent, novel data that help explain how compensatory behaviors contribute to a wide discrepancy in exercise-induced weight loss. The presentations provide evidence that some individuals adopt compensatory behaviors, that is, increased energy intake and/or reduced activity, that offset the exercise energy expenditure and limit weight loss. The challenge for both scientists and clinicians is to develop effective tools to identify which individuals are susceptible to such behaviors and to develop strategies to minimize their effect.

The adaptive metabolic response to exercise-induced weight loss influences both energy expenditure and energy intake

BACKGROUND/OBJECTIVEs A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS Thirty overweight and obese women (body mass index (BMI)=30.6±3.6 kg/m2) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention (MApost), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI=31.0±3.9 kg/m2). RESULTS Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (−102.9±77.5 kcal per day). MApost was associated with the change in leptin (r=0.47; P=0.04), and the change in resting fat (r=0.52; P=0.01) and carbohydrate oxidation (r=−0.44; P=0.02). Furthermore, MApost was also associated with the change in EI following EX (r=−0.44; P=0.01). CONCLUSIONS Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance.

Evaluation of two-view geometry methods with automatic ground-truth generation

A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.

Mawson the astrobiologist rover : towards automatic recognition of stromatolites

This work aims at developing a planetary rover capable of acting as an assistant astrobiologist: making a preliminary analysis of the collected visual images that will help to make better use of the scientists time by pointing out the most interesting pieces of data. This paper focuses on the problem of detecting and recognising particular types of stromatolites. Inspired by the processes actual astrobiologists go through in the field when identifying stromatolites, the processes we investigate focus on recognising characteristics associated with biogenicity. The extraction of these characteristics is based on the analysis of geometrical structure enhanced by passing the images of stromatolites into an edge-detection filter and its Fourier Transform, revealing typical spatial frequency patterns. The proposed analysis is performed on both simulated images of stromatolite structures and images of real stromatolites taken in the field by astrobiologists.

Reliable automatic camera-laser calibration

Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.

Validity of maternal-reported weight and height of children 2-5 year olds

To the Editor: In affluent-urban areas of India, overweight (6 %) and obesity (8 %) are prevalent in children as young as 2–5 y [1]. A potential risk factor for childhood obesity could be parent’s under-reporting their child’s anthropometry. In Indian culture, a larger body size is typically acceptable, and mothers may consider a chubby baby as healthy [2]. Therefore, it was proposed that Indian mothers may under-report their child’s weight status. The present study examined the validity of maternal reported height and weight of young, urban-affluent Indian children aged 2–5 y. After receiving approval from the QUT Human Research Ethics Committee, Australia 111 mothers with children aged 2–5 y attending private medical clinics (n = 5) in the affluent areas of Mumbai were recruited. Child’s height and weight were measured by the researcher using standard equipment/protocols. Mothers also reported their child’s height and weight.

Accuracy of mothers’ perceptions of their child’s weight status

The study examined the accuracy of maternal perceived child weight. Urban-affluent mothers with 111 children aged 2-5 years were recruited. Nearly a quarter of mothers overestimated their underweight child as healthyweight and all overweight/obese children were perceived as healthyweight. Mothers therefore were unable to recognize their child’s true weight status.

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Making YAWL and SmartPM interoperate : managing highly dynamic processes by exploiting automatic adaptation features

In the last years, the trade-o between exibility and sup- port has become a leading issue in work ow technology. In this paper we show how an imperative modeling approach used to de ne stable and well-understood processes can be complemented by a modeling ap- proach that enables automatic process adaptation and exploits planning techniques to deal with environmental changes and exceptions that may occur during process execution. To this end, we designed and imple- mented a Custom Service that allows the Yawl execution environment to delegate the execution of subprocesses and activities to the SmartPM execution environment, which is able to automatically adapt a process to deal with emerging changes and exceptions. We demonstrate the fea- sibility and validity of the approach by showing the design and execution of an emergency management process de ned for train derailments.

Using ambient ozone for assignment of double bond position in unsaturated lipids

Unsaturated lipids deposited onto a range of materials are observed to react with the low concentrations of ozone present in normal laboratory air. Parent lipids and ozonolysis cleavage products are both detected directly from surfaces by desorption electrospray ionisation mass spectrometry (DESI-MS) with the resulting mass spectra providing clear evidence of the double bond position within these molecules. This serendipitous process has been coupled with thin-layer chromatography (TLC) to provide a simple but powerful approach for the detailed structural elucidation of lipids present in complex biological extracts. Lipid extracts from human lens were deposited onto normal phase TLC plates and then developed to separate components according to lipid class. Exposure of the developed plates to laboratory air for ca. 1 h prior to DESI-MS analysis gave rise to ozonolysis products allowing for the unambiguous identification of double bond positions in even low abundant, unsaturated lipids. In particular, the co-localization of intact unsaturated lactosylceramides (LacCer) with products from their oxidative cleavage provide the first evidence for the presence of three isomeric LacCer (d18:0/24:1) species in the ocular lens lipidome, i.e., variants with double bonds at the n-9, n-7 and n-5 positions.