Disrupting the methionine biosynthetic pathway in Candida guilliermondii: characterization of the MET2 gene as counter‐selectable marker

Candida guilliermondii (teleomorph Meyerozyma guilliermondii) is an ascomycetous species belonging to the fungal CTG clade. This yeast remains actively studied as a result of its moderate clinical importance and most of all for its potential uses in biotechnology. The aim of the present study was to establish a convenient transformation system for C. guilliermondii by developing both a methionine auxotroph recipient strain and a functional MET gene as selection marker. We first disrupted the MET2 and MET15 genes encoding homoserine‐O‐acetyltransferase and O‐acetylserine O‐acetylhomoserine sulphydrylase, respectively. The met2 mutant was shown to be a methionine auxotroph in contrast to met15 which was not. Interestingly, met2 and met15 mutants formed brown colonies when cultured on lead‐containing medium, contrary to the wild‐type strain, which develop as white colonies on this medium. The MET2 wild‐type allele was successfully used to transfer a yellow fluorescent protein (YFP) gene‐expressing vector into the met2 recipient strain. In addition, we showed that the loss of the MET2‐containing YFP‐expressing plasmid can be easily observed on lead‐containing medium. The MET2 wild‐type allele, flanked by two short repeated sequences, was then used to disrupt the LYS2 gene (encoding the α‐aminoadipate reductase) in the C. guilliermondii met2 recipient strain. The resulting lys2 mutants displayed, as expected, auxotrophy for lysine. Unfortunately, all our attempts to pop‐out the MET2 marker (following the recombination of the bordering repeat sequences) from a target lys2 locus were unsuccessful using white/brown colony colour screening. Nevertheless, this MET2 transformation/disruption system represents a new versatile genetic tool for C. guilliermondii. Copyright © 2014 John Wiley & Sons, Ltd.     

Saffron (Crocus sativus L.) increases glucose uptake and insulin sensitivity in muscle cells via multipathway mechanisms

Saffron (Crocus sativus Linn.) has been an important subject of research in the past two decades because of its various biological properties, including anti-cancer, anti-inflammatory, and anti-atherosclerotic activities. On the other hand, the molecular bases of its actions have been scarcely understood. Here, we elucidated the mechanism of the hypoglycemic actions of saffron through investigating its signaling pathways associated with glucose metabolism in C₂C₁₂ skeletal muscle cells. Saffron strongly enhanced glucose uptake and the phosphorylation of AMPK (AMP-activated protein kinase)/ACC (acetyl-CoA carboxylase) and MAPKs (mitogen-activated protein kinases), but not PI 3-kinase (Phosphatidylinositol 3-kinase)/Akt. Interestingly, the co-treatment of saffron and insulin further improved the insulin sensitivity via both insulin-independent (AMPK/ACC and MAPKs) and insulin-dependent (PI 3-kinase/Akt and mTOR) pathways. It also suggested that there is a crosstalk between the two signaling pathways of glucose metabolism in skeletal muscle cells. These results could be confirmed from the findings of GLUT4 translocation. Taken together, AMPK plays a major role in the effects of saffron on glucose uptake and insulin sensitivity in skeletal muscle cells. Our study provides important insights for the possible mechanism of action of saffron and its potential as a therapeutic agent in diabetic patients.     

Single-view-based 3D facial reconstruction method robust against pose variations

The 3D Morphable Model (3DMM) and the Structure from Motion (SfM) methods are widely used for 3D facial reconstruction from 2D single-view or multiple-view images. However, model-based methods suffer from disadvantages such as high computational costs and vulnerability to local minima and head pose variations. The SfM-based methods require multiple facial images in various poses. To overcome these disadvantages, we propose a single-view-based 3D facial reconstruction method that is person-specific and robust to pose variations. Our proposed method combines the simplified 3DMM and the SfM methods. First, 2D initial frontal Facial Feature Points (FFPs) are estimated from a preliminary 3D facial image that is reconstructed by the simplified 3DMM. Second, a bilateral symmetric facial image and its corresponding FFPs are obtained from the original side-view image and corresponding FFPs by using the mirroring technique. Finally, a more accurate the 3D facial shape is reconstructed by the SfM using the frontal, original, and bilateral symmetric FFPs. We evaluated the proposed method using facial images in 35 different poses. The reconstructed facial images and the ground-truth 3D facial shapes obtained from the scanner were compared. The proposed method proved more robust to pose variations than 3DMM. The average 3D Root Mean Square Error (RMSE) between the reconstructed and ground-truth 3D faces was less than 2.6 mm when 2D FFPs were manually annotated, and less than 3.5 mm when automatically annotated.     

Expert system for scheduling in an airline gate allocation

Scheduling is an important technique encompassing a wide application area. Because of the complex interrelations among the resources, knowledge, and various other constraints, scheduling has many difficulties. Artificial Intelligence technology has been applied to solve the scheduling problem. As AI techniques are efficient in representing knowledge and dealing with heuristics, it is an adequate approach to model and to solve scheduling problems. We have implemented the ramp scheduling system, called RACES (Ramp Activity Coordination Expert System), to solve complex and dynamic aircraft parking problems. RACES was developed from the domain knowledge and experience which were acquired from the domain experts. Domain knowledge and experience are important factors in controlling the scheduling procedure. RACES divides the problem into sub-problems and experimental heuristics in the knowledge acquisition process. The system independently processes scheduling for the divided sub-problems and shares variables and domains. During the scheduling, the system selects or confines the search space with domain filtering techniques by exploiting the characteristics of various constraints and knowledge. RACES produces a user-driven near-optimal solution by means of a trade-off scheduling method using heuristics between the size of aircraft and the best-fit time. For 400 daily flights, RACES made parking schedules for aircraft in about 20 s compared with 4–5 h by human experts.     

The teacher's responsibility in whole‐class debriefing of mathematical activity

Abstract

Prompted by recent moves in the United Kingdom to guide teachers’ practices in whole‐class, direct interactive teaching, in this article, we offer an opportunity for North American mathematics educators to reflect on possibilities for whole‐class teaching of mathematics. We focus particularly on the plenary aspect of lessons—what might be considered the debriefing of mathematical activity—and specifically on the teacher's responsibility during those sessions, both to his or her students and to the authenticity of the discipline of mathematics. Drawing on data from a Grade‐3 classroom and invoking complexity science as a theoretical lens to explore the classroom as a complex learning system, we present implications for teaching in whole‐class debriefings of mathematical activity.     

Output feedback of Markov jump linear systems with no mode observation: An automotive throttle application

The note presents an output feedback control strategy for Markov jump linear systems with no mode observation. Based on minimizing a finite‐time quadratic cost, we derive an algorithm that generates output feedback gains that satisfy a necessary optimality condition. These gains can be computed off‐line relying only on the initial condition of the system. This result expands a previous one from the literature that considered state‐feedback only. To illustrate the usefulness of the approach, real‐time laboratory experiments were performed to control an automotive electronic throttle valve subject to Markov‐driven voltage fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

The characteristics of Li(CoxNi1 − x)O2 cathode powders formed from the fine-sized Co3O4/NiO precursor powders

Li(Co_xNi_1 − x)O₂ (0 ≤ x ≤ 1) cathode powders were prepared by solid state reaction method using Co₃O₄/NiO precursor powders obtained by spray pyrolysis. The effect of the ratios of cobalt and nickel components on the characteristics of Co₃O₄/NiO precursor and Li(Co_xNi_1 − x)O₂ cathode powders were investigated. The Co₃O₄/NiO precursor powders with the ratios of cobalt and nickel components as 1/0, 0.75/0.25 and 0.5/0.5 had submicron size and regular morphologies. On the other hand, the Co₃O₄/NiO powders with the high contents of nickel component had aggregated morphologies of submicron size primary powders. The fine-sized precursor powders formed the fine-sized LiCoO₂ and Li(Co_0.75Ni_0.25)O₂ cathode powders by solid state reaction with LiOH powders. However, the high contents of the nickel component of the Co₃O₄/NiO precursor powders formed the Li(Co_xNi_1 − x)O₂ (0 ≤ x ≤ 0.5) cathode powders with aggregated morphologies and large sizes. The discharge capacities of the powders increased with increasing the nickel content into the Li(Co_xNi_1 − x)O₂ cathode powders up to 188 mAh/g.     

Grain-Refined AZ92 Alloy with Superior Strength and Ductility

Grain-refined AZ92 (GR-AZ92) alloy with superior tensile properties is developed by adding 1 wt% Zn and a very small amount of SiC (0.17 wt%) to commercial AZ91 alloy for enhancing the solid-solution strengthening effect and refining the crystal grains, respectively. The homogenized GR-AZ92 alloy with an average grain size of 91 μm exhibits a tensile yield strength (TYS) of 125 MPa, ultimate tensile strength (UTS) of 281 MPa, and elongation of 12.1%, which are significantly higher than those of AZ91 alloy with a grain size of 420 μm (TYS of 94 MPa, UTS of 192 MPa, and elongation of 7.0%). The peak-aging time of GR-AZ92 alloy (8 h) is significantly shorter than that of AZ91 alloy (32 h) owing to a larger amount of grain boundaries in the former, which serve as nucleation sites of Mg₁₇Al₁₂ precipitates. A short-aging treatment for less than 1 h of the GR-AZ92 alloy causes an effective improvement in its strength without a significant reduction in its ductility. The 30-min-aged GR-AZ92 alloy has an excellent combination of strength and ductility, with a TYS of 142 MPa, UTS of 304 MPa, and elongation of 8.0%.