Polydopamine and Polydopamine-Silane Hybrid Surface Treatments in Structural Adhesive Applications.

Numerous studies have focused on the remarkable adhesive properties of polydopamine, which can bind to substrates with a wide range of surface energies, even under aqueous conditions. This behavior suggests that polydopamine may be an attractive option as a surface treatment in structural bonding applications, where good bond durability is required. Here, we assessed polydopamine as a surface treatment for bonding aluminum plates with an epoxy resin. A model epoxy adhesive consisting of diglycidyl ether of bisphenol A (DGEBA) and Jeffamine D230 polyetheramine was employed, and lap shear measurements (ASTM D1002 10) were made (i) under dry conditions to examine initial bond strength and (ii) after exposure to hot/wet (63 °C in water for 14 days) conditions to assess bond durability. Surprisingly, our results showed that polydopamine alone as a surface treatment provided no benefit beyond that obtained by exposing the substrates to an alkaline solution of tris buffer used for the deposition of polydopamine. This implies that polydopamine has a potential Achilles' heel, namely, the formation of a weak boundary layer that was identified using X-ray photoelectron spectroscopy (XPS) of the fractured surfaces. In fact, for longer deposition times (2.5 and 18 h), the tris buffer-treated surface outperformed the polydopamine surface treatments, suggesting that tris buffer plays a unique role in improving adhesive performance even in the absence of polydopamine. We further showed that the use of polydopamine-3-aminopropyltriethoxysilane (APTES) hybrid surface treatments provided significant improvements in bond durability at extended deposition times relative to both polydopamine and an untreated control.

Evolution of bluetongue virus serotype 1 in northern Australia over 30 years.

UNLABELLED: Bluetongue virus serotype 1 (BTV 1) was first isolated in Australia from cattle blood collected in 1979 at Beatrice Hill Farm (BHF), Northern Territory (NT). From long-term surveillance programs (1977 to 2011), 2,487 isolations of 10 BTV serotypes were made. The most frequently isolated serotype was BTV 1 (41%, 1,019) followed by BTV 16 (17.5%, 436) and BTV 20 (14%, 348). In 3 years, no BTVs were isolated, and in 12 years, no BTV 1 was isolated. Seventeen BTV 1 isolates were sequenced and analyzed in comparison with 10 Australian prototype serotypes. BTV 1 showed an episodic pattern of evolutionary change characterized by four distinct periods. Each period consisted primarily of slow genetic drift which was punctuated from time to time by genetic shifts generated by segment reassortment and the introduction of new genome segments. Evidence was found for coevolution of BTV genome segments. Evolutionary dynamics and selection pressure estimates showed strong temporal and clock-like molecular evolutionary dynamics of six Australian BTV genome segments. Bayesian coalescent estimates of mean substitution rates clustered in the range of 3.5 × 10(-4) to 5.3 × 10(-4) substitutions per site per year. All BTV genome segments evolved under strong purifying (negative) selection, with only three sites identified as under pervasive diversifying (positive) selection. The obligate replication in alternate hosts (insect vector and vertebrate hosts) imposed strong evolutionary constraints. The dominant mechanism generating genetic diversity of BTV 1 at BHF was through the introduction of new viruses and reassortment of genome segments with existing viruses. IMPORTANCE: Bluetongue virus (BTV) is the causative agent of bluetongue disease in ruminants. It is a disease of concern globally and is transmitted by biting midges (Culicoides species). Analysis of the evolutionary and selection pressures on BTV 1 at a single surveillance site in northern Australia showed strong temporal and clock-like dynamics. Obligate replication in alternate hosts of insect and vertebrate imposed strong evolutionary constraints, with all BTV genome segments evolving under strong purifying (negative) selection. Generation of genetic diversity of BTV 1 in northern Australia is through genome segment reassortment and the introduction of new serotypes.

A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects.

PURPOSE: This paper aims to compare the absolute performance of three noncontact sleep measurement devices for measuring sleep parameters in normal subjects against polysomnography and to assess their relative performance. METHODS: The devices investigated were two noncontact radio-frequency biomotion sensors (SleepMinder (SM) and SleepDesign (HSL-101)) and an actigraphy-based system (Actiwatch). Overnight polysomnography measurements were carried out in 20 normal subjects, with simultaneous assessment of sleep parameters using the three devices. The parameters measured included total sleep time (TST), sleep efficiency (SE), sleep-onset latency (SOL), and wake-after-sleep onset (WASO). The per-epoch agreement level for sleep/wake distinction was evaluated. RESULTS: The TSTs reported by the three devices were 426 ± 34, 434 ± 22, and 441 ± 16 min, for the SM, HSL-101, and Actiwatch, respectively, against polysomnogram (PSG)-reported TST of 391 ± 49 min. The SOLs were 10 ± 10, 5 ± 6, and 3 ± 2 min for the SM, HSL-101 and Actiwatch, respectively against PSG SOL of 19 ± 13 min. The WASO times were 46 ± 33, 43 ± 22, and 38 ± 17 min, as against PSG-reported 69 ± 46 min. All three devices had a statistically significant bias to overestimate sleep time and underestimate WASO and SOL compared with PSG. The performance of the three devices was basically equivalent, with only minor interdevice differences. The overall per-epoch agreement levels were 86 % for the SM, 86 % for the HSL-101, and 85 % for the Actiwatch. CONCLUSIONS: Noncontact biomotion approaches to sleep measurement provided reasonable estimates of TST, but with a bias to over-estimation of sleep. The radio-frequency biomotion sensors provided similar accuracies for sleep/wake determination in normal subjects as the actigraph used in this study and slightly improved estimates of TST, SOL, and WASO.

Audio signal analysis in combination with noncontact bio-motion data to successfully monitor snoring.

This paper proposes a novel algorithm for automatic detection of snoring in sleep by combining non-contact bio-motion data with audio data. The audio data is captured using low end Android Smartphones in a non-clinical environment to mimic a possible user-friendly commercial product for sleep audio monitoring. However snore detection becomes a more challenging problem as the recorded signal has lower quality compared to those recorded in clinical environment. To have an accurate classification of snore/non-snore, we first compare a range of commonly used features extracted from the audio signal to find the best subject-independent features. Thereafter, bio-motion data is used to further improve the classification accuracy by identifying episodes which contain high amounts of body movements. High body movement indicates that the subject is turning, coughing or leaving the bed; during these instances snoring does not occur. The proposed algorithm is evaluated using the data recorded over 25 sessions from 7 healthy subjects who are suspected to be regular snorers. Our experimental results showed that the best subject-independent features for snore/non-snore classification are the energy of frequency band 3150-3650 Hz, zero crossing rate and 1st predictor coefficient of linear predictive coding. The proposed features yielded an average classification accuracy of 84.35%. The introduction of bio-motion data significantly improved the results by an average of 5.87% (p<;0.01). This work is the first study that successfully used bio-motion data to improve the accuracy of snore/non-snore classification.