Effect of Ultrasonic Irradiation on the Probability of Primary Nucleation of L-Arginine Hydrochloride

Most of the amino acids that are utilized as medical raw materials and food additives show polymorphism. To improve the functionality of amino acid crystals, an effective method of polymorph control is required in the crystallization process. Here, primary nucleation of L-arginine hydrochloride by ultrasonication was investigated. L-Arginine hydrochloride exhibits polymorphism, and it crystallizes into three distinct crystal forms. A cooling crystallization experiment was performed, and nucleation of each polymorph upon ultrasonication was observed. In addition, the nucleation was analyzed using the nucleation probability theory. The results indicate that ultrasonic irradiation would significantly induce the nucleation of a particular polymorph.     

Chemical Synthesis of a Synthetic Analogue of the Sialic Acid‐Binding Lectin Siglec‐7

As a basis for the development of an artificial carbohydrate‐binding lectin, we chemically synthesized a domain of siglec‐7, a well‐characterized sialic‐acid‐binding lectin. The full polypeptide (127 amino acids) was constructed by sequential native chemical ligation (NCL) of five peptide segments. Because of poor cysteine availability for NCL, cysteine residues were introduced at suitable ligation sites; these cysteine residues were alkylated in order to mimic native glutamine or asparagine residues, or converted to an alanine residue by desulfurization after NCL. After folding the full‐length polypeptide, the sialic‐acid‐binding activity of the synthetic siglec‐7 was clearly demonstrated by STD NMR and ELISA experiments. We succeeded in the synthesis of siglec‐7 by installing three extra cysteine residues with side‐chain modifications and found that these modifications did not affect the binding activity.     

Comparison of isolation methods using commercially available kits for obtaining extracellular vesicles from cow milk

Extracellular vesicles (EV) are important for delivering biologically active substances to facilitate cell-to-cell communication. Milk-derived EV are widely known because of their potential for immune enhancement. However, procedures for isolating milk-derived EV have not been fully established. To obtain pure milk-derived EV and accurately reveal their function, such procedures must be established. The aim of the present study was to compare methods using commercially available kits for isolating milk-derived EV. Initially, we investigated procedures to remove casein, which is the major obstacle in determining milk-derived EV purity. We separated whey using centrifugation only, acetic acid precipitation, and EDTA precipitation. Then, we isolated milk-derived EV by ultracentrifugation, membrane affinity column, size exclusion chromatography (SEC), polymer-based isolation, or phosphatidylserine-affinity isolation. Using EV count per milligram of protein, which is a good indicator of purity, we determined that acetic acid precipitation was the best method for removing casein. Using nanoparticle tracking analysis, protein quantity analysis, and RNA quantity analysis, we comprehensively compared each isolation method for its purity and yield. We found that SEC-based qEV column (Izon Science) could collect purer milk-derived EV at higher quantities. Thus, a combination of acetic acid precipitation and qEV can effectively isolate high amounts of pure extracellular vesicles from bovine milk.     

Robust comprehension of natural language instructions by a domestic service robot

We present a method through which domestic service robots can comprehend natural language instructions. For each action type, a variety of natural language expressions can be used, for example, the instruction, ‘Go to the kitchen’ can also be expressed as ‘Move to the kitchen.’ We are of the view that natural language instructions are intuitive and, therefore, constitute one of the most user-friendly robot instruction methods. In this paper, we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language. The proposed method combines action-type classification, which is based on a support vector machine, and slot extraction, which is based on conditional random fields, both of which are required in order for a robot to execute an action. Further, by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score, the proposed method can avoid degradation of the robot’s comprehension accuracy in noisy environments, where inaccurate speech recognition can be problematic. We conducted experiments using a Japanese instruction data-set collected using a questionnaire-based survey. Experimental results show that the robot’s comprehension accuracy is higher in a noisy environment using our method than when using a baseline method with only a 1-best speech recognition result.     

Degradation behavior of moduli in extruded pure magnesium during low- to giga-scale cyclic tension fatigue

The mechanical properties of extruded pure magnesium during cyclic tension fatigue in the low- to giga-scale regime at room temperature have been investigated using ultrasonic reflection methods with longitudinal and shear waves. The acoustic velocities and calculated Young’s and shear moduli decreased by a large percentage with an increase in the number of cycles in all cycle modes due to growth of grain boundary voids. The eventual degradation of the properties was largest during giga-cycle fatigue, in which the moduli decreased by ～9%. The elastic behavior depended on the drive stress and the number of cycles rather than on fatigue time. Longitudinal and shear wave propagation characteristics and investigations of a grain boundary before and after fatigue using electron backscatter diffraction based on field-emission scanning electron microscopy and focused ion beam transmission electron microscopy revealed that the largest boundary void gap width was less than several nanometers (almost closed). The Poisson’s ratio and bulk modulus were affected notably by the void gap, in which the threshold corresponds to the longitudinal wave amplitude. Other damage phase data were determined using scanning electron microscopy, Vickers hardness, and surface roughness tests under progressive fatigue; these results also indicated slight grain boundary degradation.     

Theoretical study of the temperature dependent hydrogen storage capacity of Pd and Ti nanoparticles

A hybrid simulation was carried out using discrete variational (DV) Xα molecular orbital and molecular dynamics methods to examine the storage of hydrogen in metal nanoparticles. The calculation load was light and therefore could be performed quickly on a generic personal computer. The simulation investigates the electronic states of hydrogen on Pd and Ti nanoparticles. The hydrogen dissociation behavior on the metal surface was reproduced, and the calculated bonding orbitals of Pd and hydrogen are consistent with other reports. The diffusion coefficient of hydrogen inside the metal displays the same temperature dependence as the theoretical results. This simplified calculation, which produces results in agreement with experimental/theoretical values, could lead to improved simulation methods for hydrogen storage materials.