No abstract

This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.

Particle transport phenomena in the deep alveolated airways of the lungs (i.e. pulmonary acinus) govern deposition outcomes following inhalation of hazardous or pharmaceutical aerosols. Yet, there is still a dearth of experimental tools for resolving acinar particle dynamics and validating numerical simulations. Here, we present a true-scale experimental model of acinar structures consisting of bifurcating alveolated ducts that capture breathing-like wall motion and ensuing respiratory acinar flows. We study experimentally captured trajectories of inhaled polydispersed smoke particles (0.2 to 1 μm in diameter), demonstrating how intrinsic particle motion, i.e. gravity and diffusion, is crucial in determining dispersion and deposition of aerosols through a streamline crossing mechanism, a phenomenon paramount during flow reversal and locally within alveolar cavities. A simple conceptual framework is constructed for predicting the fate of inhaled particles near an alveolus by identifying capture and escape zones and considering how streamline crossing may shift particles between them. In addition, we examine the effect of particle size on detailed deposition patterns of monodispersed microspheres between 0.1–2 μm. Our experiments underline local modifications in the deposition patterns due to gravity for particles ≥0.5 μm compared to smaller particles, and show good agreement with corresponding numerical simulations.

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

During cell corpse removal, dynamin's self-assembly and GTP hydrolysis activities establish a precise dynamic control of DYN-1's transient association to its target membranes. Dynamin's dynamic membrane association controls the mechanism that underlies the recruitment of downstream effectors, such as small GTPases RAB-5 and RAB-7, to target membranes.