Thoracoscopic treatment of bronchogenic cyst

A central bronchogenic cyst was excised thoracoscopically from a 44-year-old woman in whom a tumor had been pointed out in the left upper posterior mediastinum at a screening examination. Since the tumor was diagnosed to be benign, only conservative follow-up was undertaken, but the patient consulted our department desiring active therapy. On the basis of the chest CT and MRI findings a bronchogenic cyst was diagnosed. Under general anesthesia and mechanical ventilation of one lung, the thoracoscope was inserted into the thoracic cavity revealing in the left upper posterior mediastinum a cyst which was excised thoracoscopically. While coagulation was performed gingerly with an electric scalpel, the tumor was detached sharply and bluntly with a pair of scissors. The postoperative course was uneventful with little wound pain or scar formation. Hitherto bronchogenic cysts have been treated by resection after thoracotomy. Although this is an easy procedure, a relatively large operative scar is left and considerable wound pain may develop. In contrast, thoracoscopic treatment is characterized by minimal surgical invasiveness, little postoperative wound pain, and small scars. These advantages suggest that this technique may be indicated for benign mediastinal tumors, particularly cysts.     

Low voltage current mirrors with enhanced bandwidth

This paper presents an improved low voltage cascode and flipped voltage follower (FVF) based current mirror with the enhancement of the bandwidth obtained by using a compensation resistor between the gates of the primary transistor pair. In this technique a carefully determined resistance is used in the diode connected MOS transistor of the current mirror for enhancing the bandwidth. Active realization of the compensation resistance using MOS operating in the triode region has also been applied to both the cascode and FVF based current mirror circuits. The proposed circuits have been simulated using PSpice for 0.25 μm CMOS technology and the obtained results are compared with their uncompensated topologies to show their effectiveness.     

Photosensitization of TiO2 Nanostructures with CdS Quantum Dots: Particulate versus Tubular Support Architectures

TiO₂ nanotube arrays and particulate films are modified with CdS quantum dots with an aim to tune the response of the photoelectrochemical cell in the visible region. The method of successive ionic layer adsorption and reaction facilitates size control of CdS quantum dots. These CdS nanocrystals, upon excitation with visible light, inject electrons into the TiO₂ nanotubes and particles and thus enable their use as photosensitive electrodes. Maximum incident photon to charge carrier efficiency (IPCE) values of 55% and 26% are observed for CdS sensitized TiO₂ nanotube and nanoparticulate architectures respectively. The nearly doubling of IPCE observed with the TiO₂ nanotube architecture is attributed to the increased efficiency of charge separation and transport of electrons.     

Synthesis and Biological Characterization of (3R,4R)‐4‐(2‐(Benzhydryloxy)ethyl)‐1‐((R)‐2‐hydroxy‐2‐phenylethyl)‐piperidin‐3‐ol and Its Stereoisomers for Activity toward Monoamine Transporters

A novel series of optically active molecules based on a 4‐(2‐(benzhydryloxy)ethyl)‐1‐((R)‐2‐hydroxy‐2‐phenylethyl)‐piperidin‐3‐ol template were developed. Depending on stereochemistry, the compounds exhibit various degrees of affinity for three dopamine, serotonin, and norepinephrine transporters. These molecules have the potential for treating several neurological disorders such as drug abuse, depression, and attention deficit hyperactivity disorder.

     

Stress relaxation behavior of glass fiber‐reinforced polyester composites prepared by the newly proposed rubber pressure molding

Stress‐relaxation behavior of glass fiber‐reinforced polyester composites, prepared by a recently developed manufacturing method called rubber pressure molding (RPM), is investigated with special reference to the effect of environmental temperature (−70°C to +100°C), fiber volume fraction (30–60%), and initial load level (1–5 kN). It is found that the stress‐relaxation rate decreases with an increase in the applied load of composites and a decrease in temperature. Below glass transition temperature, the rate of stress relaxation increases with an increase in volume fraction of fibers in the composites, whereas above glass transition temperature, it increases with a decrease in the volume fraction of fibers. The experimental results for a given composites are summarized by four values, the slopes of the two straight lines (two separate relaxation processes), and their intercepts upon the stress axis. Both the slopes are dependent upon the applied load, temperature, and volume fraction of fibers in the composites. Relaxation times in both primary and secondary are calculated over the wide range of temperatures, loads, and volume fraction of fibers in the composites. It depends strongly on the temperature, but does not depend strongly on the applied load and volume fraction of fibers. The performances of the composites are also evaluated through conventional compression‐molding process. The rate of stress relaxation is small when the composites are made of newly proposed RPM technique when compared with the conventional process. POLYM. COMPOS., 2008. © 2008 Society of Plastics Engineers     

Ultrastructural analysis of cell wall of drug resistant and sensitive Mycobacterium tuberculosis isolated from cerebrospinal fluid by transmission electron microscope

Drug‐resistant tuberculosis is being increasingly recognized and is one among the leading cause of death worldwide. Remarkable impermeability of cell wall to antituberculous drugs protects the mycobacteria from drug action. The present study analyzed the cell wall thickness among first‐line drug resistant and sensitive Mycobacterium tuberculosis (Mtb) isolated from cerebrospinal fluid by transmission electron microscopy (TEM). The average thickness of the cell wall of sensitive isolates was 13.60 ± 0.98 nm. The maximum difference (26.48%) in the cell wall thickness was seen among multi‐drug resistant (18.50 ± 1.71 nm) isolates and the least difference (4.14%) was shown by streptomycin‐resistant (14.18 ± 1.38 nm) isolates. The ultrastructural study showed evident differences in the cell wall thickness among sensitive and resistant isolates. Preliminary TEM examination of cells indicates that morphological changes occur in the cell wall which might be attributed to the drug resistance. The thickened wall of Mtb appears to help the bacilli to overcome the action of antituberculous drugs.     

Aging model development based on multidisciplinary parameters for lithium-ion batteries

In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters-based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.     

An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flow prediction is based on the dataset generated from the simulations with NS-2.35 for multiple Wireless Sensor Networks (5- to -50 nodes). The performance comparison of different machine learning models with continuous data and discretized data is also presented. The proposed approach considerably reduces the execution time of the machine learning models for training purposes and also enhances the accuracy of prediction. The result analysis shows that the proposed approach is better compared to various machine learning methods. Also, the proposed approach is able to handle both continuous and discrete data. The datasets used in this work are available as a supplement at NDS and DDS link.

     

Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ_e) as high as 2.22 cm² V^?1 s^?1 and a hole/electron mobility ratio (μ_h/μ_e) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.     

Size‐Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature‐Based Differential Targeting in Triple Negative Breast Cancer

Rapid sequestration and prolonged retention of intravenously injected nanoparticles by the liver and spleen (reticuloendothelial system (RES)) presents a major barrier to effective delivery to the target site and hampers clinical translation of nanomedicine. Inspired by biological macromolecular drugs, synthesis of ultrasmall (diameter ≈12–15 nm) porous silica nanoparticles (UPSNs), capable of prolonged plasma half‐life, attenuated RES sequestration, and accelerated hepatobiliary clearance, is reported. The study further investigates the effect of tumor vascularization on uptake and retention of UPSNs in two mouse models of triple negative breast cancer with distinctly different microenvironments. A semimechanistic mathematical model is developed to gain mechanistic insights into the interactions between the UPSNs and the biological entities of interest, specifically the RES. Despite similar systemic pharmacokinetic profiles, UPSNs demonstrate strikingly different tumor responses in the two models. Histopathology confirms the differences in vasculature and stromal status of the two models, and corresponding differences in the microscopic distribution of UPSNs within the tumors. The studies demonstrate the successful application of multidisciplinary and complementary approaches, based on laboratory experimentation and mathematical modeling, to concurrently design optimized nanomaterials, and investigate their complex biological interactions, in order to drive innovation and translation.