Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

Density related properties of bark insulation boards bonded with tannin hexamine resin

Building owners are increasingly interested in a healthy and sustainable living environment, which is a trend favoring ecological building materials with outstanding structural physical parameters. Insulation boards from particles of larch bark (Larix decidua Mill.) bonded with a formaldehyde-free tannin resin were pressed and evaluated for their mechanical and physical properties. It could be shown that light (target density 250 kg/m³) boards can be pressed, and their thermal conductivity is low (0.065–0.09 W/(m*K)). With regard to mechanical characteristics, the influence of panel density was studied, and it was found that a certain compaction (ρ ≥ 400 kg/m³) is necessary to meet the requirements of the relevant standard. Interestingly, the resin amount did not influence the mechanical board properties as strongly as expected, and panel density is the most important variable in this respect. The study proved that tree bark cannot only be used for substantially upgraded insulation panels but can also be bonded with a formaldehyde free tannin resin.     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

Understanding of copper precipitation under electron or ion irradiations in FeCu0.1 wt% ferritic alloy by combination of experiments and modelling

This work is dedicated to the understanding of the basic processes involved in the formation of copper enriched clusters in low alloyed FeCu binary system (FeCu0.1 wt%) under irradiation at temperature close to 300 °C. Such an alloy was irradiated with electrons or with ions (Fe⁺ or He⁺) in order to deconvolute the effect of displacement cascades and the associated generation of point defect clusters (ion irradiations), and the super-saturation of mono-vacancies and self-interstitial atoms (electron irradiation). The microstructure of this alloy was characterised by tomographic atom probe. Experimental results were compared with results obtained with cluster dynamic model giving an estimation of the evolution of point defects (free or agglomerated) under irradiation on the one hand and describing homogeneous enhanced precipitation of copper on the other hand. The comparison between the results obtained on the different irradiation conditions and the model suggests that the point defect clusters (dislocation loops and/or nano-voids) created in displacement cascades play a major role in copper clustering in low copper alloy irradiated at 573 K.     

Combination of freeze drying and molecular inclusion techniques improves the bioaccessibility of microencapsulated anthocyanins from black rice (Oryza sativa L.) and lavender (Lavandula angustifolia L.) essential oils in a model food system

Black rice and lavender are promising sources of bioactives, in terms of anthocyanins and essential oils. Their bioaccessibility were improved by microencapsulation, followed by mixing in order to benefit both from colour and flavour, along with radical scavenging and biological properties. The mixed powder showed a satisfactory anthocyanins of 2.55 mg g⁻¹ DW, leading to a radical scavenging activity of 65.14 mmol g⁻¹ DW. The powder displayed an inhibitory effect against α-glucosidase (~49%) and α-amylase (39%), respectively, with a controlled release in intestinal environment. To further examine the functional properties, the powder was added to a food model system. During storage, a release in anthocyanins and flavonoids were found, leading to an increase in radical scavenging activity. The sensorial analysis showed that supplemented biscuits were appreciated for colour and lavender aroma. The obtained results were promising in tailoring the health benefits of secondary metabolites, underutilised in human’s nutrition due to their low stability and bioavailability.     

Image denoising by a nonlinear control technique

We propose a technique for image denoising by solving a nonconvex optimal control problem with the state and controller connected on a manifold described by a nonlinear elliptic equation. The existence of a control able to denoise an initial blurred image is proved and the optimality conditions are determined by a passing to the limit technique in an appropriate approximating problem, involving the Legendre–Fenchel relations. An algorithm constructed on the basis of the theoretical results is developed and numerical simulations are provided.     

Influence of cluster mobility on Cu precipitation in α-Fe: A cluster dynamics modeling

A cluster dynamics model has been parametrized to quantitatively reproduce results obtained by atomistic kinetic Monte Carlo (AKMC) modeling on the precipitation of Cu in α-Fe under thermal aging. The cluster mobility, highlighted by AKMC, is shown to have a significant effect on the precipitation kinetics and can reconcile the experimentally observed fast kinetics with the relatively low diffusivity of Cu monomers.     

Automatic detection and segmentation of lymph nodes from CT data

Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.