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Aim: CVCs are often used to deliver chemotherapy to patients with cancer. The aim of our study was to compare safety of the CVCs (PICC and PORT) in patients with non-hematological malignancies. Method(s): Eligible patients from three Australian hospitals were prospectively randomised (1:1) and allocated to either a Groshong PICC or a single chamber PORT device. Data was collected every 3 wks from CVC insertion up to treatment completion, removal of device or disease progression, whichever occurred earlier. Result(s): 70 patients (36 males and 34 females) were randomised at 3 centres between Dec 2004- Dec 2009 to PICC (36/70) or PORT (34/70). One patient from each arm withdrew consent prior to CVC insertion. Mean age was comparable in both arms (62 yrs in PICC v 61 yrs in PORT). Prolonged chemotherapy infusion was the commonest indication for CVC insertion (70% in PICC and 74% in PORT). 60% of patients had metastatic disease and colorectal cancer was the leading diagnosis overall (46 out of 70 patients). There were no procedure related complications. Median time to insertion (time from randomisation to CVC insertion) was 7 and 12 days for PICC and PORT respectively. Median dwell time was slightly longer for PORT (160 days) than PICC (112 days). Amongst evaluable patients (68/70), there were more complications observed in patients with PICC lines than with PORTs (47% v 12%; p = 0.001). A higher number of PICC lines were removed prematurely before treatment completion than PORTs (26% v 3%). Four patients needed PICC removal and anticoagulation due to catheter related thrombosis whereas none in PORT arm developed thrombosis. Catheter related infection requiring systemic antibiotics occurred in 1 patient in each group. Conclusion(s): PORT catheters are associated with significantly lower complication rate as compared to PICC in patients with non-haematological cancers receiving chemotherapy.

This work forms part of a larger investigation into fatigue crack detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fatigue crack propagation (FCP) signals and high levels of background noise. An artificial AE fracture source was developed and additionally five sources were used to generate differing artificial AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Furthermore, artificial FCP signals were recorded in the same component under airworthiness test load conditions. PCA was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial FCP signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

The present study evaluated possible modulation of the buck effect by nutritional and metabolic cues during the transition to the breeding season in adult goats with divergent bodyweight (BW) and body condition (BCS) at 27N. In mid-February, goats (Boer Spanish, n ≤ 32) were assigned to receive one of the following two experimental diets to fulfill different allowances of nutritional requirements (1) 100% (n ≤ 16; BW ≤ 52.3 ± 1.5 kg, BCS ≤ 1.6 ± 0.1 units; T-100) or (2) 150% (n ≤ 16; BW ≤ 60.9 ± 2.4 kg, BCS ≤ 1.6 ± 0.1 units; T-150) from February to August. Blood samples were collected to analyse thyroxine (T4), triiodothyronine (T3), non-esterified fatty acids (NEFA), triglycerides (Tg) and progesterone (P4). Final BW and BCS favoured (P < 0.001) the T-150 group (74.9 2.8 v. 56.3 1.4 kg, and 4.4 0.2 v. 1.9 0.1 units, respectively). However, mean values for NEFA, Tg, T3 and T4 did not differ (P > 0.05) between the experimental groups. Thereafter, in early August, half of the does in each diet treatment were randomly selected for determining the response to the 'male effect' (WM), forming the following two treatment groups (1) T-100-WM (n ≤ 8), or (2) T-150-WM (n ≤ 8); the remaining does formed two groups without male exposure (WOM), as follows (3) T-100-WOM (n ≤ 8) and (4) T-150-WOM (n ≤ 8). To evaluate ovarian activity, blood samples were collected from all does on Days 24 during the 14-day period after the male exposure. On Day 12, all does exposed to males (16/16), irrespective of the nutritional treatment, depicted ovulatory activity, whereas only 3/16 (18.75%) T-WOM does did, indicating a significant (P < 0.001) difference between these treatment groups. The increased nutritional level of the T-150 group during the anoestrous season did not result in an early onset of ovulatory activity. Does demonstrated similar metabolic hormones and concentrations of blood metabolites between the two nutritional treatments (100 v. 150% of the nutritional requirement

Background: Chronic indwelling central venous catheters (CVC) are being used increasingly to enable delivery of intravenous chemotherapy. Prospective randomised data comparing different types of CVC is lacking. Aim : To compare the safety of PICC vs PORT used to deliver chemotherapy in patients (PTS) with non-haematological malignancies. Method(s): We conducted a prospective multicentre randomised (1:1) clinical trial comparing a Groshong* PICC to a single chamber subcutaneously implanted port device (infusaport*, vitalcath*, powerport*). Data was collected every 3 wks from CVC insertion date. The primary endpoint of the study was the major complication rate. Secondary endpoints included CVC dwell-time, total complication rate, time to insertion of CVC and quality of life. Result(s): 68 pts from 3 centres were randomised between Dec 2004 - Jan 2010. Data has been analysed from 63 pts (32 male and 31 female; 32 PICC and 31 PORT). One pt from each arm withdrew consent prior to CVC insertion. Colon cancer was the most frequent diagnosis (61% PICC and 71% PORT). Most common indication for CVC insertion was prolonged (> 24 hours) chemotherapy infusion (75% PICC and 74% PORT). There were no complications related to the insertion procedures. Median time to insertion of CVC from randomisation was 8 days in PICC (range 1 - 28 days) and 12 days in PORT arm (range 4 - 24 days). Median dwell-time was longer for PORT (160 days) than PICC (115 days). The overall complication rate was higher in the PICC arm (45%vs 10%). Major complications occurred in 8 pts (26%) with PICC and only 1 (3%) with PORT. Of these, 4 pts in the PICC arm developed CVC thrombosis (3 within 12 wks of insertion) and another 4 pts required PICC removal due to blockage or dislodgement. The only major PORT complication was a twisted chamber. No patients in the PORT arm developed thrombosis. None of the patients had catheter related major infection requiring systemic antibiotics. Conclusion(s): PORT catheters a

A standard technique in the field of non-destructive evaluation is to use acoustic emissions to characterise and locate the damage events that generate them. The location problem is typically posed in terms of the times of flight of the waves and results in an optimisation problem, which can at times be ill-posed. A method is proposed here for learning the relationship between time of flight differences and damage location using data generated by artificially stimulated acoustic emission (AE)-a classic problem of regression. A structure designed to represent a complicated aerospace component was interrogated using a laser to thermoelastically generate AE at multiple points across the structure's surface. Piezoelectric transducers were mounted on the surface of the structure, and the resulting waveforms were recorded. A Gaussian process (GP) with RBF kernels was chosen for regression. Since during AE monitoring not all events can be guaranteed to be detected by all sensors, a GP was trained on data for all possible combinations (subsets) of sensors. The inputs to the GPs were the differences in time of flight between sensors in the set, and the targets were the locations of the source of ultrasonic stimulation. Subsequent (test) data points were located by every possible GP, given the active sensors. It is shown that maps learned on a given structure can generalise effectively to nominally identical structures.

This paper details progress in the application of a methodology for acoustic emission (AE) detection and interpretation for the monitoring of fatigue fractures in large-scale industrial environments. The approach makes use of a number of novel signal processing techniques. An online radius-based clustering algorithm (ORACAL) is used to identify clusters of data, both in the spatial domain (locating AE sources) and in the feature domain (identifying candidate fracture processes). The paper proposes a new approach to the identification of AE waveforms produced by crack propagation; rather than seeking to identify the waveform features characteristic of a fracture event, the new method looks for specific patterns of clustering in the feature space. The approach is validated by a full-scale experiment. An artificial acoustic emission source, representative of a fatigue fracture, was injected into a test of a substantial landing gear component. A commercial AE monitoring system was then used to successfully locate and identify the source in a blind test using the new signal processing methodology. The method was successful on two of three experiments performed and the position of the artificial source was determined accurately; further analysis shows that the unsuccessful test appears to have occurred due to incorrect mounting of the artificial source.

Structural health monitoring (SHM) is of paramount importance in the aircraft industry: not only to ensure the safety and reliability of aircraft in flight and to ensure timely maintenance of critical components, but also increasingly to monitor structures under test for airworthiness certification of new designs. This article highlights some of the recent advances in the acoustic emission (AE) technique as applied to SHM, and the new approaches that are crucial for the successful use of AE data for diagnostic purposes. These include modal analysis, enhanced location techniques, and novel signal processing approaches. A case study is presented on a landing gear component undergoing fatigue loading in which a linear location analysis using conventional techniques identified the position of fracture and final rupture of the specimen. A principal component analysis approach was used to separate noise signals from signals arising from fatigue cracks, which identified and located further fatigue crack positions, subsequently confirmed by magnetic particle inspection. Kernel probability density functions are used to aid visualization of the damage location.

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent.