Effects of drying temperature and relative humidity on spaghetti characteristics

This study aimed at investigating the effect of drying conditions on spaghetti properties, i.e., its color, surface structure, rupture strength, rehydration characteristics, texture, and sauce retention capacity. The effects of temperature and humidity were independently examined under constant drying conditions, which were compared to those applied industrially, where the temperature and relative humidity are changed stepwise with time. The knowledge obtained in this study is considered useful for reasonably determining the drying conditions for producing spaghetti with desired properties.     

First law analysis for viscous dissipation in liquid flows in micro-channels

This paper focuses on temperature rise due to the viscous dissipation in liquids flowing through micro-channels. In the past, equations for the prediction of the temperature rise have been obtained as a function of the friction factor, Reynolds number and Eckert number or a similar form, starting from Navier–Stokes equation and energy equation under the assumption of fully developed laminar flow by researchers. The temperature rises calculated from the equations have been compared with experimental data and the equations have been validated. However, in this paper, a new equation for the prediction of the temperature rise is simply obtained from the first law of thermodynamics without restriction of fully developed laminar flow.     

Type IIa RPTPs and Glycans: Roles in Axon Regeneration and Synaptogenesis

Type IIa receptor tyrosine phosphatases (RPTPs) play pivotal roles in neuronal network formation. It is emerging that the interactions of RPTPs with glycans, i.e., chondroitin sulfate (CS) and heparan sulfate (HS), are critical for their functions. We highlight here the significance of these interactions in axon regeneration and synaptogenesis. For example, PTPσ, a member of type IIa RPTPs, on axon terminals is monomerized and activated by the extracellular CS deposited in neural injuries, dephosphorylates cortactin, disrupts autophagy flux, and consequently inhibits axon regeneration. In contrast, HS induces PTPσ oligomerization, suppresses PTPσ phosphatase activity, and promotes axon regeneration. PTPσ also serves as an organizer of excitatory synapses. PTPσ and neurexin bind one another on presynapses and further bind to postsynaptic leucine-rich repeat transmembrane protein 4 (LRRTM4). Neurexin is now known as a heparan sulfate proteoglycan (HSPG), and its HS is essential for the binding between these three molecules. Another HSPG, glypican 4, binds to presynaptic PTPσ and postsynaptic LRRTM4 in an HS-dependent manner. Type IIa RPTPs are also involved in the formation of excitatory and inhibitory synapses by heterophilic binding to a variety of postsynaptic partners. We also discuss the important issue of possible mechanisms coordinating axon extension and synapse formation.     

Identification of enzyme responsible for erythritol utilization and reaction product in yeast Lipomyces starkeyi

We have identified the enzyme responsible for erythritol utilization and its reaction product in the yeast Lipomyces starkeyi CBS 1807. The enzyme, a polyol dehydrogenase requiring NAD+ as a coenzyme, was induced by erythritol in this yeast. We confirmed that the enzyme product was L-erythrulose by MS, NMR, and polarimeter analyses, meaning that we clarified the first step of erythritol utilization in yeasts for the first time. In the case of the oxidative reaction, D-threitol, (2R,3R)-2,3-butanediol, and erythritol were much better substrates than 21 other polyols tested. These three substrates are tetroses and have an R configuration at C-3, and whose third carbon results in easiest oxidation in this enzyme. The research of the substrate specificity in the reductive reaction demonstrated that L-erythrulose and dihydroxyacetone were better substrates, that D-acetoin was inactive and L-erythrose (aldose) was slightly active.     

Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.     

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.     

A general model-based approach to two-dimensional infrared correlation spectroscopy incorporating the global phase angle

We describe a theoretical framework for a model-based approach to two-dimensional correlation spectroscopy that is generally applicable to any arbitrary model function. The method is based on the correlation between spectral data and a set of model waveforms with a varying correlation index, the global phase angle Theta. When experimental spectral intensity variations are expressed as sinusoidal, exponential, Lorentzian, or quadratic functions, the proposed approach allows us to estimate the quantitative values of the target parameters in those expressions. In addition, this method enables us to assess the sequential order in a series of bands undergoing non-identical intensity changes in a dynamic data set. We present both simulated and experimentally obtained data that illustrate that the deviations from linearity of the absorption band intensity waveforms are clearly detected and can be quantitatively estimated using quadratic functions.     

Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: Trial application to patients in an intensive care unit

We have applied a new methodology for noninvasive continuous blood glucose monitoring, proposed in our previous paper, to patients in ICU (intensive care unit), where strict controls of blood glucose levels are required. The new methodology can build calibration models essentially from numerical simulation, while the conventional methodology requires pre-experiments such as sugar tolerance tests, which are impossible to perform on ICU patients in most cases. The in vivo experiments in this study consisted of two stages, the first stage conducted on healthy subjects as preliminary experiments, and the second stage on ICU patients. The prediction performance of the first stage was obtained as a correlation coefficient (r) of 0.71 and standard error of prediction (SEP) of 28.7 mg/dL. Of the 323 total data, 71.5% were in the A zone, 28.5% were in the B zone, and none were in the C, D, and E zones for the Clarke error-grid analysis. The prediction performance of the second stage was obtained as an r of 0.97 and SEP of 27.2 mg/dL. Of the 304 total data, 80.3% were in the A zone, 19.7% were in the B zone, and none were in the C, D, and E zones. These prediction results suggest that the new methodology has the potential to realize a noninvasive blood glucose monitoring system using near-infrared spectroscopy (NIRS) in ICUs. Although the total performance of the present monitoring system has not yet reached a satisfactory level as a stand-alone system, it can be developed as a complementary system to the conventional one used in ICUs for routine blood glucose management, which checks the blood glucose levels of patients every few hours.