The Azotobacter vinelandii mannuronan C-5-epimerase AlgE1 consists of two separate catalytic domains

The Azotobacter vinelandii enzyme AlgE1 is a member of a family of secreted mannuronan C-5-epimerases. These enzymes convert beta-D-mannuronic acid residues (M) to alpha-L-guluronic acid residues (G) at the polymer level in the industrially important polysaccharide alginate, leading to altered physical and immunological properties of the polymer. The reaction product of AlgE1 was found to be a mixture of blocks of continuous G residues (G-blocks) and blocks containing alternating M and G residues (MG-blocks). The enzyme is dependent on Ca2+ for activity, and only Sr2+ of those tested was able to replace Ca2+. Zn2+ blocked the activity even at low concentrations. algE1 has been divided into two parts based on the modular type of structure previously reported to be a characteristic of the secreted epimerases, and each part has been expressed in Escherichia coli. These experiments showed that AlgE1 contains two catalytic domains, AlgE1-1, which introduces both G-blocks and MG-blocks, and AlgE1-2, which only introduces MG-blocks. AlgE1-1 has a much lower specific activity than both AlgE1-2 and AlgE1. However, the two halves of AlgE1 seem to cooperate in such a way that they contribute approximately equally to the overall epimerization reaction.     

Application of an Additional Excitation in Inverter-Fed Induction Motors for Air-Gap Eccentricity Diagnosis

In this paper, the application of an additional excitation in induction motor (IM) drives for static, dynamic, and mixed eccentricity diagnosis is proposed. The additional excitation consists in a predefined inverter-switching pattern that is applied on the motor for a short time, while the fundamental excitation is canceled. This excitation was used previously to implement a position estimation strategy. The strategy obtains information about the rotor position from the motor saliencies effects over the zero-sequence voltage. The air-gap eccentricity is a kind of saliency that affects the zero-sequence voltage and allows the use of the additional excitation for eccentricity diagnosis. For the evaluation of the feasibility of this proposal, a multiple-coupled circuit model of the IM is used. The effects of series and series-parallel stator winding connections on the diagnosis signals are shown. Experimental results to validate the proposal are also given. These results show that it is possible to use the diagnosis strategy in a self-commissioning scheme     

Assisted reproduction and hepatitis C virus infection

Medically assisted procreation poses a difficult problem when one or both partners of couple are infected with HCV. Epidemiologic and fundamental works show a low risk of HCV sexual transmission and no pregnancy complications or fetal abnormalities have been reported. However, the outcome of HCV infected children is unknown. These contrasting findings suggest that medically assisted procreation using sperm of spouse needs to be cautious. Before medically assisted procreation, testing of couples for HCV antibodies must be done and interferon therapy is required for patients with histological chronic active hepatitis and for HCV positive mothers or infected couples. In the absence of specific legislation or consensual recommendations, detailed informations must be given to the couples in order to obtain an informed consent.     

Torsion of the adnexa in children: the value of laparoscopy

Experience with adnexal torsion in neonates and children is often disappointing. Delay between the first symptoms and operation is important, and adnexal loss the rule. The authors reviewed their experience and the literature to assess the appropriate diagnostic and therapeutic approach. Diagnostic procedures (standard ultrasonography [US], color Doppler US, computed tomography, magnetic resonance imaging, endorectal US, and diagnostic laparoscopy) are discussed; for neonates and premenarcheal girls a specific surgical approach is proposed. Twenty-seven adnexal torsions treated between 1985 and 1995 in the same institution were retrospectively reviewed. Neonatal (7) and premenarcheal cases (20) were separated. The neonatal cases (7) were all operated upon: 4 adnexectomies, 2 oophorectomies, and 1 detorsion with cystectomy were performed. In the premenarcheal group (20) 8 adnexectomies, 6 oophorectomies, 5 detorsions with cystectomy, and 1 salpingectomy were performed. There were only 6 salvaged adnexa in this series. In the neonatal group, US seemed accurate in predicting complicated cases. Prenatal puncture of large (>40 mm) ovarian cysts is possible. The authors advocate a laparoscopic approach in the first days of life of all uncomplicated cysts independent of size in order to increase the percentage salvaged. In ultrasonic complicated cases a delayed operation is proposed in the premenarcheal group, endorectal US will probably become the diagnostic method of choice for complicated ovaries; other methods were disappointing. In order to increase adnexal salvage, the authors recommend a laparoscopic approach in the emergency situation if a clinical examination is positive as well as better medical (pediatricians, gynecologists) and general (girls, parents) information. They suggest controlateral oophoropexy in cases of torsion of a normal adnexum.     

Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.     

Why‐ and how‐ to integrate Verilog‐A compact models in SPICE simulators

This article presents a fast and accurate way to integrate and validate Verilog‐A compact models in SPICE‐like simulators. Modifications in the models' Verilog‐A source code may be required prior to their conversion into low‐level C language by a code generator. The most common of these modifications is discussed. The generated C code is then directly compiled in the target simulator resulting in an equivalent SPICE model. The comparison between Verilog‐A and SPICE models in the same simulation environment, for simple and complex circuits, validates the procedure. Performance tests for demanding designs are carried out for both models. Results highlight the higher simulation speed and lower memory consumption of SPICE models. Copyright © 2012 John Wiley & Sons, Ltd.     

Disturbance and nonlinear Luenberger observers for estimating mechanical variables in permanent magnet synchronous motors under mechanical parameters uncertainties

This paper deals with rotor position and speed estimation of permanent-magnet synchronous motors. Two reduced-order observers, a disturbance observer and a nonlinear one, are considered. It is shown that under exact model assumption the nonlinear observer converges in an exponential way, while a residual estimation error appears when the disturbance observer is used. Therefore, uncertainties in the mechanical submodel are taken into account. Estimation error arises due to the model mismatch; bounds for this error are calculated in both observers. Finally, a comparison of the performance of each observer is presented.     

A rotor position and speed observer for permanent-magnet motors with nonsinusoidal EMF waveform

A new nonlinear reduced-order observer to estimate the rotor speed and position for permanent-magnet motors, with arbitrary electromotive force (EMF) waveform, is presented. The proposed observer is suitable for the realization of a torque control with minimum torque ripple. In order to implement the observer, the EMF generated by the motor is first obtained experimentally offline. After that, it is approximated by a Fourier series in order to develop the model to be used in the online estimation. From the estimated EMF, rotor position and speed are calculated using the relationship between the EMF and the rotor variables. The proposal is validated with experimental results.     

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