Self-organised droplet flow patterns in microchannels

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.In this work, we have investigated the generation and behaviour of self-organised droplet flow patterns in microchannels. The water droplets, which are generated at a T-junction where the carrier is oil, move into an expanded channel and are self reorganised into various flow patterns: single-profile, double-helix-profile, triple-helix-profile, and more. We find that increasing water/oil flow rate ratio and Capillary number lead to more densely packed droplet flow patterns. The channel geometry also plays an essential role where the 300-μm-deep expansion channel can form multiple layers of droplets while only single layer of droplets can be observed in the 200-μm-deep expansion channel

Decline in Health-Related Quality of Life reported by more than half of those waiting for joint replacement surgery: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>In many healthcare systems, people with severe joint disease wait months to years for joint replacement surgery. There are little empirical data on the health consequences of this delay and it is unclear whether people with substantial morbidity at entry to the waiting list continue to deteriorate further while awaiting surgery. This study investigated changes in Health-Related Quality of Life (HRQoL), health status and psychological distress among people waiting for total hip (THR) and knee replacement (TKR) surgery at a major metropolitan Australian public hospital.</p> <p>Methods</p> <p>134 patients completed questionnaires including the Assessment of Quality of Life (AQoL) instrument, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Kessler Psychological Distress Scale after entering an orthopaedic waiting list (baseline) and before surgery (preadmission). To quantify potential decline in wellbeing, we calculated the proportion of people experiencing clinically important deterioration using published guidelines and compared HRQoL and psychological distress outcomes with population norms.</p> <p>Results</p> <p>Most participants (69%) waited ≥6 months for surgery (median 286 days, IQR 169-375 days). Despite poor physical and psychological wellbeing at baseline, there was an overall deterioration in HRQoL during the waiting period (mean AQoL change -0.04, 95%CI -0.08 to -0.01), with 53% of participants experiencing decline in HRQoL (≥0.04 AQoL units). HRQoL prior to surgery remained substantially lower than Australian population norms (mean sample AQoL 0.37, 95%CI 0.33 to 0.42 vs mean population AQoL 0.83, 95%CI 0.82 to 0.84). Twenty-five per cent of participants showed decline in health status (≥9.6 WOMAC units) over the waiting period and prevalence of high psychological distress remained high at preadmission (RR 3.5, 95%CI 2.8 to 4.5). Most participants considered their pain (84%), fatigue (76%), quality of life (73%) and confidence in managing their health (55%) had worsened while waiting for surgery.</p> <p>Conclusions</p> <p>Despite substantial initial morbidity, over half of the participants awaiting joint replacement experienced deterioration in HRQoL during the waiting period. These data provide much-needed evidence to guide health professionals and policymakers in the design of care pathways and resource allocation for people who require joint replacement surgery.</p

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

Applications, potentialities and limitations of adsorptive stripping analysis on mercury film electrodes

This article reviews the field of adsorptive stripping analysis (AdSA) on mercury film electrodes (MFEs). Mercury thin films deposited on conductive substrates can be used as electrodes in the reductive mode of AdSA since they retain most of the favourable features of the hanging mercury drop electrode while offering the advantages of solid electrodes at the same time. The practical aspects of the application of MFEs in AdSA are discussed and some applications in the field of inorganic and organic trace analysis are presented

Selective determination of Ni(II) and Co(II) by flow injection analysis and adsorptive cathodic stripping voltammetry on a wall jet mercury film electrode

Ni(II) and Co(II) have been determined simultaneously by means of adsorptive cathodic stripping voltammetry (AdCSV) in a computerised flow injection system. The working electrode was a glassy carbon disk that was fitted in a wall-jet flow cell. The electrode was initially electrochemically coated with a mercury film at - 1.0 V by injecting a HE(II) solution in the flow stream. Then, the sample, containing Ni(II) and Co(II), was mixed on-line with a solution containing dimethylgyoxime (DMG) at pH 9 in order to selectively complex the metal ions and was injected in the flow system. After a number of successive injections during which accumulation took place under controlled potentiostatic conditions, the surface-bound complexes were reduced in ammonia buffer at pH 9 by a cathodic scan of the potential of the working electrode in the square wave mode and the current-potential response was recorded. Finally, the electrode surface was regenerated by a potentiostatic polarisation at - 1.4 V in the same buffer. The apparatus could be easily converted for continuous flow accumulation in order to increase the sensitivity; in this mode of operation, instead of performing discrete injections, the sample was continuously pumped through the cell. Various parameters associated with the preconcentration. stripping and regeneration steps were optimised for the determination of Ni(II) and Co(II). The selectivity of the method was demonstrated for the analysis of high purity iron; the accuracy for the determination of Ni(II) and Co(II) was 11 and 3%, respectively while the coefficient of variation was 10 and 8%, respectively. (C) 1998 Elsevier Science B.V. All rights reserved