Scanning Photo-Induced Impedance Microscopy - Resolution studies and polymer characterization

Scanning Photo-Induced Impedance Microscopy (SPIM) is an impedance imaging technique that is based on photocurrent measurements at field-effect structures. The material under investigation is deposited onto a semiconductor-insulator substrate. A thin metal film or an electrolyte solution with an immersed electrode serves as the gate contact. A modulated light beam focused into the space charge region of the semiconductor produces a photocurrent, which is directly related to the local impedance of the material. The absolute impedance of a polymer film can be measured by calibrating photocurrents using a known impedance in series with the sample. Depending on the wavelength of light used, charge carriers are not only generated in the focus but also throughout the bulk of the semiconductor. This can have adverse effects on the lateral resolution. Two-photon experiments were carried out to confine charge carrier generation to the spacecharge layer. The lateral resolution of SPIM is also limited by the lateral diffusion of charge carriers in the semiconductor. This problem can be solved by using thin silicon layers as semiconductor substrates. A resolution of better than 1 mu m was achieved using silicon on sapphire (SOS) substrates with a I l.Lm thick silicon layer

Construction of a Plasmodium falciparum Rab-interactome identifies CK1 and PKA as Rab-effector kinases in malaria parasites

Background information The pathology causing stages of the human malaria parasite Plasmodium falciparum reside within red blood cells that are devoid of any regulated transport system. The parasite, therefore, is entirely responsible for mediating vesicular transport within itself and in the infected erythrocyte cytoplasm, and it does so in part via its family of 11 Rab GTPases. Putative functions have been ascribed to Plasmodium Rabs due to their homology with Rabs of yeast, particularly with Saccharomyces that has an equivalent number of rab/ypt genes and where analyses of Ypt function is well characterized. Results Rabs are important regulators of vesicular traffic due to their capacity to recruit specific effectors. In order to identify P. falciparum Rab (PfRab) effectors, we first built a Ypt-interactome by exploiting genetic and physical binding data available at the Saccharomyces genome database (SGD). We then constructed a PfRab-interactome using putative parasite Rab-effectors identified by homology to Ypt-effectors. We demonstrate its potential by wet-bench testing three predictions; that casein kinase-1 (PfCK1) is a specific Rab5B interacting protein and that the catalytic subunit of cAMP-dependent protein kinase A (PfPKA-C) is a PfRab5A and PfRab7 effector. Conclusions The establishment of a shared set of physical Ypt/PfRab-effector proteins sheds light on a core set Plasmodium Rab-interactants shared with yeast. The PfRab-interactome should benefit vesicular trafficking studies in malaria parasites. The recruitment of PfCK1 to PfRab5B+ and PfPKA-C to PfRab5A+ and PfRab7+ vesicles, respectively, suggests that PfRab-recruited kinases potentially play a role in early and late endosome function in malaria parasites

Emotion recognition from speech: tools and challenges

Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more “natural” than the acted datasets where the subjects attempt to suppress all but one emotion. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Do acute elevations of serum creatinine in primary care engender an increased mortality risk?

Background: The significant impact Acute Kidney Injury (AKI) has on patient morbidity and mortality emphasizes the need for early recognition and effective treatment. AKI presenting to or occurring during hospitalisation has been widely studied but little is known about the incidence and outcomes of patients experiencing acute elevations in serum creatinine in the primary care setting where people are not subsequently admitted to hospital. The aim of this study was to define this incidence and explore its impact on mortality. Methods: The study cohort was identified by using hospital data bases over a six month period. Inclusion criteria: People with a serum creatinine request during the study period, 18 or over and not on renal replacement therapy. The patients were stratified by a rise in serum creatinine corresponding to the Acute Kidney Injury Network (AKIN) criteria for comparison purposes. Descriptive and survival data were then analysed. Ethical approval was granted from National Research Ethics Service (NRES) Committee South East Coast and from the National Information Governance Board. Results: The total study population was 61,432. 57,300 subjects with ‘no AKI’, mean age 64.The number (mean age) of acute serum creatinine rises overall were, ‘AKI 1’ 3,798 (72), ‘AKI 2’ 232 (73), and ‘AKI 3’ 102 (68) which equates to an overall incidence of 14,192 pmp/year (adult). Unadjusted 30 day survival was 99.9% in subjects with ‘no AKI’, compared to 98.6%, 90.1% and 82.3% in those with ‘AKI 1’, ‘AKI 2’ and ‘AKI 3’ respectively. After multivariable analysis adjusting for age, gender, baseline kidney function and co-morbidity the odds ratio of 30 day mortality was 5.3 (95% CI 3.6, 7.7), 36.8 (95% CI 21.6, 62.7) and 123 (95% CI 64.8, 235) respectively, compared to those without acute serum creatinine rises as defined. Conclusions: People who develop acute elevations of serum creatinine in primary care without being admitted to hospital have significantly worse outcomes than those with stable kidney function

Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine

The recognition of the poetry meter in spoken lines is a natural language processing application that aims to identify a stressed and unstressed syllabic pattern in a line of a poem. Stateof-the-art studies include few works on the automatic recognition of Arud meters, all of which are text-based models, and none is voice based. Poetry meter recognition is not easy for an ordinary reader, it is very difficult for the listener and it is usually performed manually by experts. This paper proposes a model to detect the poetry meter from a single spoken line (“Bayt”) of an Arabic poem. Data of 230 samples collected from 10 poems of Arabic poetry, including three meters read by two speakers, are used in this work. The work adopts the extraction of linear prediction cepstrum coefficient and Mel frequency cepstral coefficient (MFCC) features, as a time series input to the proposed long short-term memory (LSTM) classifier, in addition to a global feature set that is computed using some statistics of the features across all of the frames to feed the support vector machine (SVM) classifier. The results show that the SVM model achieves the highest accuracy in the speakerdependent approach. It improves results by 3%, as compared to the state-of-the-art studies, whereas for the speaker-independent approach, the MFCC feature using LSTM exceeds the other proposed models

Enhancing physical layer security of cognitive radio transceiver via chaotic OFDM

Due to the enormous potential of improving the spectral utilization by using Cognitive Radio (CR), designing adaptive access system and addressing its physical layer security are the most important and challenging issues in CR networks. Since CR transceivers need to transmit over multiple non-contiguous frequency holes, multi-carrier based system is one of the best candidates for CR's physical layer design. In this paper, we propose a combined chaotic scrambling (CS) and chaotic shift keying (CSK) scheme in Orthogonal Frequency Division Multiplexing (OFDM) based CR to enhance its physical layer security. By employing chaos based third order Chebyshev map which allows optimum bit error rate (BER) performance of CSK modulation, the proposed combined scheme outperforms the traditional OFDM system in overlay scenario with Rayleigh fading channel. Importantly, with two layers of encryption based on chaotic scrambling and CSK modulation, large key size can be generated to resist any brute-force attack, leading to a significantly improved level of security

A Remark On Proper Left H* — Algebras

W. Ambrose gave the theory of proper H* -algebras and M. Smiley in (2) gave an example of a left H* -algebra which is not a two-sided H* -algebra. Then he modified some of the arguments of Ambrose which yield the structure of proper right H*-algebras. In fact he proved that a proper right H*-algebra is merely a proper H*-algebra in which the norm has been changed to a certain equivalent norm in each of the simple components. In this short paper, we define proper left H*-algebras and give two lemmas for these classes. Then we prove the main result that every proper left H*-algebra is a proper H*-algebra. Thus, in this paper, we prove that the following are equivalent: (i) Proper left H*-algebras. (ii) Proper right H*-algebras. (iii) Proper H*-algebras

Enhancing secrecy rate in cognitive radio networks via multilevel Stackelberg game

In this letter, physical layer (PHY) security is investigated for both primary and secondary transmissions of a cognitive radio network (CRN) that is in danger of malicious attempt by an eavesdropper (ED). In our proposed system, the secondary transmitter (ST) is acted as a trusted relay (TR) for primary transmission and the PHY security is facilitated by the cooperation between the primary transmitter (PT) and the ST using the multilevel Stackelberg game. In particular, we formulate and solve the optimization problem of maximizing secrecy rates in different phases of primary and secondary transmissions. Finally, numerical examples are provided to demonstrate that the spectrum leasing based on trading secondary access for cooperation is a promising framework for enhancing secrecy rate in CRNs