Design of Spring Coupling for High-Q High-Frequency MEMS Filters for Wireless Applications

A second-order microelectromechanical systems (MEMS) filter with high selectivity and sharp rolloff is required in wireless transceivers used in dense wireless sensor networks (WSNs). These sensors are expected to replace existing wired sensors used in industrial-plant management and environmental monitoring. These filters, together with MEMS-based oscillators and mixers, are expected to replace off-chip components and enable the development of a single-chip transceiver. Such a transceiver will leverage the integrated MEMS componentsÕ characteristics to operate at lower power and, hence, longer battery life, making autonomous WSNs more feasible in a wider range of applications. As a result, this paper presents the design and optimization of the coupling beam of wineglass-mode micromechanical disk filters using simulated annealing. The filter under consideration consists of two identical wineglass-mode disk resonators, mechanically coupled by a flexural-mode beam. The coupled two-resonator system exhibits two mechanical-resonance modes with closely spaced frequencies that define the filter passband. A constraint is added on the beam length to eliminate the effect of the coupling-beammass on the filterÕs resonant frequency. A new process flow is proposed to realize self-aligned overhanging coupling beams designed in this paper.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87260/4/Saitou6.pd

Bilosomes as a promising nanoplatform for oral delivery of an alkaloid nutraceutical:improved pharmacokinetic profile and snowballed hypoglycemic effect in diabetic rats

Diabetes mellitus is a life-threatening metabolic disease. At the moment, there is no effective treatment available to combat it. In this study, we aimed to develop berberine-loaded bilosomes (BER-BLS) to boost the oral bioavailability and therapeutic efficacy of berberine, a natural antidiabetic medication. The BER-BLS was fabricated using a thin-film hydration strategy and optimized using a central composite design (face-centered). The average vesicle size, entrapment efficiency, and surface charge of the optimized BER-BLS preparation were 196.5 nm, 89.7%, (−) 36.4 mV, respectively. In addition, it exhibited higher stability and better-sustained release of berberine than the berberine solution (BER-SOL). BER-BLS and BER-SOL were administered to streptozocin-induced diabetic rats. The optimized BER-BLS formulation had a significant hypoglycemic impact, with a maximum blood glucose decrease of 41%, whereas BER-SOL only reduced blood glucose by 19%. Furthermore, the pharmacological effect of oral BER-BLS and BER-SOL corresponded to 99.3% and 31.7%, respectively, when compared to subcutaneous insulin (1 IU). A pharmacokinetic analysis found a 6.4-fold rise in the relative bioavailability of berberine in BER-BLS when compared to BER-SOL at a dosage of 100 mg/kg body weight. Histopathological investigation revealed that BER-BLS is suitable for oral administration. Our data demonstrate that BLS is a potential nanocarrier for berberine administration, enhancing its oral bioavailability and antidiabetic activity

Variation Analysis of Three Dimensional non-rigid Assemblies

Variation analysis methods are, currently, more widely used during new product development to greatly reduce downstream rework and/or design changes. This is significantly important when considering large, built up sheet or thin plate flexible" assemblies as those currently used in automotive or aerospace industries. Whereas methods to take flexibility into account can be found in literature, there are few addressing detailed process for three-dimensional assembly of industrial complexity. This paper presents a streamlined procedure for variation analysis of a complex assembly that integrates Datum Flow Chain analysis, a commercial three-dimensional variation analysis and FEA. The procedure is applied to a realistic industry case, a commercial airplane's wing-box assembly to determine the effect of part variation and flexibility on the assembly's variation. The case study shows that a structural enclosure such as the wing-box assembly is robust against pull-up forces applied during assembly operations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87278/4/Saitou64.pd

STAR-loc: Dataset for STereo And Range-based localization

This document contains a detailed description of the STAR-loc dataset. For a quick starting guide please refer to the associated Github repository (https://github.com/utiasASRL/starloc). The dataset consists of stereo camera data (rectified/raw images and inertial measurement unit measurements) and ultra-wideband (UWB) data (range measurements) collected on a sensor rig in a Vicon motion capture arena. The UWB anchors and visual landmarks (Apriltags) are of known position, so the dataset can be used for both localization and Simultaneous Localization and Mapping (SLAM).Comment: 15 pages, 15 figure

Effect of Aromatase Inhibitor Letrozole on the Placenta of Adult Albino Rats: A Histopathological, Immunohistochemical, and Biochemical Study

Background: Letrozole, an aromatase inhibitor, has recently been introduced as the preferred treatment option for ectopic pregnancy. To date, no study has investigated the effect of letrozole alone on placental tissue. The present study aimed to evaluate the effect of different doses of letrozole on the placenta of rats and to clarify the underlying mechanism. Methods: Sixty pregnant female rats were equally divided into three groups, namely the control group (GI), low-dose (0.5 mg/Kg/day) letrozole group (GII), which is equivalent to the human daily dose (HED) of 5 mg, and high-dose (1 mg/Kg/day) letrozole group (GIII), equivalent to the HED of 10 mg. Letrozole was administered by oral gavage daily from day 6 to 16 of gestation. Data were analyzed using a one-way analysis of variance followed by Tukey’s post hoc test and Chi square test. P<0.05 was considered statistically significant.Results: Compared to the GI and GII groups, high-dose letrozole significantly increased embryonic mortality with a high post-implantation loss rate (P<0.001) and significantly reduced the number of viable fetuses (P<0.001) and placental weight (P<0.001) of pregnant rats. Moreover, it significantly reduced placental estrogen receptor (ER) and progesterone receptor (PR) (P<0.001) and the expression of vascular endothelial growth factor (P<0.001), while increasing the apoptotic index of cleaved caspase-3 (P<0.001).Conclusion: Letrozole inhibited the expression of ER and PR in rat placenta. It interrupted stimulatory vascular signals causing significant apoptosis and placental vascular dysfunction. Letrozole in an equivalent human daily dose of 10 mg caused a high post-implantation loss rate without imposing severe side effects

Expression Of Glucocorticoid Receptor Beta (GCR Β) In Asthmatic Patients And Its Correlation With Clinical Severity And Pulmonary Functions

N e w Y o r k S c i e n c e J o u r n a l 2 0 1 0 ; 3 Expression Of Glucocorticoid Receptor Beta (GCR Β) In Asthmatic Patients And Its Correlation With Clinical Severity And Pulmonary Functions Engy Yousry Elsayed , Enas M Foda, khaled AH Mohammed, Hassan Shalaby, Amal Z. Abd El-Halem* and Eman Ramzy** Internal Medicine, Clinical Pathology* and Chest** Departments Faculty Of Medicine, Ain Shams University, cairo, Egypt. [email protected] ABSTRACT Background: Glucocorticoids are the gold standard treatment of bronchial asthma. Although the majority of patients with asthma respond favorably to inhaled and systemic steroid therapy, a subset of asthmatics failed to demonstrate a satisfactory response even to systemic glucocorticoid therapy. GCR β (glucocorticoid receptor beta) is a hormone binding deficit isoform of GCR (glucocorticoid receptor) which has been isolated in humans and when over expressed, it may function as a dominant negative modulator of GCR. Aim of the work: This study was designed to determine the percentage of expression of GCRβ on PBMCs: (peripheral blood mononuclear cells )of asthmatic patients and to correlate it with the clinical severity and pulmonary functions. Subjects and Methods: 60 asthmatic patients (41 males, 19 females) and 20 healthy controls were enrolled in this study. Asthmatics were classified according to GINA guidelines (2002) into mild, moderate and severe asthma. They were subdivided into asthmatic on inhaled corticosteroid (ICS) (n=35) and those not on ICS (n=25). For all studied groups, spirometeric pulmonary functions and immunohisto-chemistry staining of PBMC S were performed to analyze percentage of expression of GCRβ on PBMCs. Results: It showed that the percentage of expression of GCRβ on PBMC S were statistically higher in all asthmatic patient groups compared to control, with higher % of expression in those not on ICS. Also a statistical significant higher % of expression of GCR β in severe asthmatics compared to both mild and moderate groups was detected. In conclusion: This study highlights the importance of glucocorticoid receptor beta isoform in pathogenesis of bronchial asthma and this may be directly linked to asthma severity and can affect the response to medications especially ICS

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

Drought stress-induced modification of morpho-anatomical and yield attributes of mung bean associated with the application of silicon and Moringa leaf extract

Mung bean (Vigna radiata) is the rich source of fiber and essential nutrients. They play a vital role in sustainable agriculture due to their ability to fix nitrogen in the soil and enhance soil fertility. Drought is characterized by limited water resources and severe arid climatic conditions, notably impair crop growth and yield. In the current experiment, two genotypes, Azri-M 2006 and NM-92, were studied against drought stress that was applied as 2 days and 4 days irrigation gap per week. Foliar application of magnesium-silicate (20 ppm and 30 ppm concentrations) and Moringa leaf extract (30% v/v solution) was applied as treatments. The results from the experiment morphology anatomical and yield components were recorded according to the prescribed methods. The result revealed that drought stress reduced the growth of plant. Foliar application of 30 ppm silicon against drought stress showed a highly significant (p&lt;0.001) result compared with control group. Morphology parameters, including shoot and root length, shoot and root fresh weight, root dry weight, leaf area, leaf number, the anatomical structure included (stem epidermis, cortex, and stem vascular bundles,) and also yield components (pod length, and seed numbers). In contrast, MLE (30%) showed a significant impact (p&lt;0.01) on leaf lamina thickness (Leaf anatomical parameters; midrib xylem and phloem, number of stomata on the adaxial and abaxial surface) and yield components included (100-grain weight, grains weight per plant, and numbers of pods,). The overall impact of 30 ppm Si was 39.9% more positive on Azri-M2006 than the NM-92 against the drought stress. The 30-ppm silicon and 30% MLE showed 90% similar results in all studied parameters. This study confirms that 30% MLE could be recommended to farmers to improve productivity under arid conditions than the silicon

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Whole Exome Sequencing Identifies Potential Candidate Genes for Spina Bifida Derived From Mouse Models

Spina bifida (SB) is the second most common nonlethal congenital malformation. The existence of monogenic SB mouse models and human monogenic syndromes with SB features indicate that human SB may be caused by monogenic genes. We hypothesized that whole exome sequencing (WES) allows identification of potential candidate genes by (i) generating a list of 136 candidate genes for SB, and (ii) by unbiased exome-wide analysis. We generated a list of 136 potential candidate genes from three categories and evaluated WES data of 50 unrelated SB cases for likely deleterious variants in 136 potential candidate genes, and for potential SB candidate genes exome-wide. We identified 6 likely deleterious variants in 6 of the 136 potential SB candidate genes in 6 of the 50 SB cases, whereof 4 genes were derived from mouse models, 1 gene was derived from human nonsyndromic SB, and 1 gene was derived from candidate genes known to cause human syndromic SB. In addition, by unbiased exome-wide analysis, we identified 12 genes as potential candidates for SB. Identification of these 18 potential candidate genes in larger SB cohorts will help decide which ones can be considered as novel monogenic causes of human SB