Phenotypic characterization of mycobacteria isolates from tuberculosis patients in Kaduna State, Nigeria

Background: Tuberculosis (TB) remains one of the leading public health challenges in Nigeria and the burden is still high. There is hence a need for continuous characterization of mycobacteria to obtain current data that will aid the ongoing TB prevention and control programme. The aim of this study was to phenotypically characterize mycobacteria isolates recovered from clinical specimens of patients with tuberculosis in Kaduna State, Nigeria.Methods: Two thousand, two hundred and twelve (2212) sputum samples were collected from patients clinically suspected to have TB in three different zones of Kaduna State, Nigeria, between May 2017 and October, 2018. Samples were processed by decontaminating with NaOH-Citrate N-acetyl-L-Cystein method for Ziehl Neelsen (ZN) AFB microscopy and culture on Lowenstein Jensen (LJ) slants which were incubated at 37ᵒC for 8 weeks. Positive LJ cultures were further analyzed with a rapid TB antigen assay (SD-Bioline) to differentiate Mycobacterium tuberculosis complex (MTBC) from Non Tuberculous Mycobacteria (NTM).Results: Out of the 2212 patients with suspected TB, 300 (13.6%) were positive for AFB by microscopy with Zone A (Kaduna North) having the highest AFB positive cases of 169 (15.2%). Of the 300 AFB positive samples, 272 (91.0%) were culture positive on LJ medium, 18 (6.0%) were culture negative and 10 (3.0%) were culture contaminated. Result of the distribution of mycobacteria among infected patients within the study area revealed that 219 (80.5%) were infected with MTBC, 42 (15.4%) with NTM and 11 (4.0%) with both MTBC and NTM.Conclusion: A relatively high number of TB in the study area was caused by NTM. There is need for advanced diagnostic tools that can differentiate MTBC and NTM strains among TB patients in all TB Reference Laboratories in Nigeria.Keywords: Phenotypic, Characterization, Tuberculosis, Mycobacteri

Dynalog: An Automated Dynamic Analysis Framework for Characterizing Android Applications

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications

Recoil Polarization for Delta Excitation in Pion Electroproduction

We measured angular distributions of recoil-polarization response functions for neutral pion electroproduction for W=1.23 GeV at Q^2=1.0 (GeV/c)^2, obtaining 14 separated response functions plus 2 Rosenbluth combinations; of these, 12 have been observed for the first time. Dynamical models do not describe quantities governed by imaginary parts of interference products well, indicating the need for adjusting magnitudes and phases for nonresonant amplitudes. We performed a nearly model-independent multipole analysis and obtained values for Re(S1+/M1+)=-(6.84+/-0.15)% and Re(E1+/M1+)=-(2.91+/-0.19)% that are distinctly different from those from the traditional Legendre analysis based upon M1+ dominance and sp truncation.Comment: 5 pages, 2 figures, for PR

Virtual Compton Scattering and Neutral Pion Electroproduction in the Resonance Region up to the Deep Inelastic Region at Backward Angles

We have made the first measurements of the virtual Compton scattering (VCS) process via the H$(e,e'p)\gamma$ exclusive reaction in the nucleon resonance region, at backward angles. Results are presented for the $W$-dependence at fixed $Q^2=1$ GeV$^2$, and for the $Q^2$-dependence at fixed $W$ near 1.5 GeV. The VCS data show resonant structures in the first and second resonance regions. The observed $Q^2$-dependence is smooth. The measured ratio of H$(e,e'p)\gamma$ to H$(e,e'p)\pi^0$ cross sections emphasizes the different sensitivity of these two reactions to the various nucleon resonances. Finally, when compared to Real Compton Scattering (RCS) at high energy and large angles, our VCS data at the highest $W$ (1.8-1.9 GeV) show a striking $Q^2$- independence, which may suggest a transition to a perturbative scattering mechanism at the quark level.Comment: 20 pages, 8 figures. To appear in Phys.Rev.

A connectome of the adult drosophila central brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain

Rotavirus group : a genotype circulation patterns across Kenya before and after nationwide vaccine introduction, 2010-2018

Background Kenya introduced the monovalent G1P [8] Rotarix® vaccine into the infant immunization schedule in July 2014. We examined trends in rotavirus group A (RVA) genotype distribution pre- (January 2010–June 2014) and post- (July 2014–December 2018) RVA vaccine introduction. Methods Stool samples were collected from children aged < 13 years from four surveillance sites across Kenya: Kilifi County Hospital, Tabitha Clinic Nairobi, Lwak Mission Hospital, and Siaya County Referral Hospital (children aged < 5 years only). Samples were screened for RVA using enzyme linked immunosorbent assay (ELISA) and VP7 and VP4 genes sequenced to infer genotypes. Results We genotyped 614 samples in pre-vaccine and 261 in post-vaccine introduction periods. During the pre-vaccine introduction period, the most frequent RVA genotypes were G1P [8] (45.8%), G8P [4] (15.8%), G9P [8] (13.2%), G2P [4] (7.0%) and G3P [6] (3.1%). In the post-vaccine introduction period, the most frequent genotypes were G1P [8] (52.1%), G2P [4] (20.7%) and G3P [8] (16.1%). Predominant genotypes varied by year and site in both pre and post-vaccine periods. Temporal genotype patterns showed an increase in prevalence of vaccine heterotypic genotypes, such as the commonly DS-1-like G2P [4] (7.0 to 20.7%, P < .001) and G3P [8] (1.3 to 16.1%, P < .001) genotypes in the post-vaccine introduction period. Additionally, we observed a decline in prevalence of genotypes G8P [4] (15.8 to 0.4%, P < .001) and G9P [8] (13.2 to 5.4%, P < .001) in the post-vaccine introduction period. Phylogenetic analysis of genotype G1P [8], revealed circulation of strains of lineages G1-I, G1-II and P [8]-1, P [8]-III and P [8]-IV. Considerable genetic diversity was observed between the pre and post-vaccine strains, evidenced by distinct clusters. Conclusion Genotype prevalence varied from before to after vaccine introduction. Such observations emphasize the need for long-term surveillance to monitor vaccine impact. These changes may represent natural secular variation or possible immuno-epidemiological changes arising from the introduction of the vaccine. Full genome sequencing could provide insights into post-vaccine evolutionary pressures and antigenic diversity

Dynamics of the 16^{16}O(e,e'p) cross section at high missing energies

We measured the cross section and response functions (R_L, R_T, and R_LT) for the 16O(e,e'p) reaction in quasielastic kinematics for missing energies 25 60 MeV and P_miss > 200 MeV/c, the cross section is relatively constant. Calculations which include contributions from pion exchange currents, isobar currents and short-range correlations account for the shape and the transversity but only for half of the magnitude of the measured cross section