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Confirmatory diagnosis of invasive aspergillosis is paramount to ensure proper treatment and effective management of the disease in food and companion animals. Suspected invasive aspergilllosis in chickens was encountered at post-mortem. Morphological and molecular methods were employed to identify Aspergillus from samples collected from dead chickens at post-mortem. Morphologically, two species of Aspergillus were identified, namely Aspergillus fumigatus and Aspergillus flavus. Molecular identification based on polymerase chain reaction (PCR) and sequence analysis of the partial 5.8 S rRNA, complete internal transcribed spacer-2 and partial 28S rRNA sequences bolstered morphological identification to arrive at the confirmatory diagnosis of the disease. Various hotspots that differentiate A. flavus from A. fumigatus and from other Aspergillus species were identified based on multiple sequence analysis. Maximum likelihood phylogenetic tree showed that isolates from the same species were grouped in the same clade. It is important to correctly identify the Aspergillus species in order to efficiently manage the disease. Key words: Aspergillosis, chicken, morphology, molecular characterization.

With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important. Underwater acoustic communication has been widely used but greatly impaired due to the complicated nature of the underwater environment. In a bid to better understand the underwater acoustic channel so as to help in the design and improvement of underwater communication systems, attempts have been made to model the underwater acoustic channel using mathematical equations and approximations under some assumptions. In this paper, we explore the capability of machine learning and deep learning methods to learn and accurately model the underwater acoustic channel using real underwater data collected from a water tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network (DNN) and Long Short Term Memory (LSTM) are applied to model the underwater acoustic channel. Experimental results show that these models are able to model the underwater acoustic communication channel well and that deep learning models, especially LSTM are better models in terms of mean absolute percentage error., Comment: 8-Page Conference Paper

Report on epizootic lymphangitis, a chronic contagious zoonosis of Equidae caused by Histoplasma capsulatum var. farciminosum characterized clinically by pyogranulomatous multifocal dermatitis with lymphadenitis, is rare in donkeys. This case reports a 6-year-old, 200-kg local male donkey at the University of Ibadan zoological garden with wounds on its dorsum which developed into ulcerative lesions on the left cranial thoracic region. The lesions were also along the lymphatic drainage of the left infrascapular area down to the girth with extensive nodular lesions on the skin, characteristic focal ulcers and fenestrations along the left flank. The ulcers were recurrent with blockage of the left lymphatic drainage causing ventral edema of the sternum extending to the prepuce and the left forelimb. The wound was managed for about 1 year but remained persistent with episodes of apparent healing, scarification and recurrence. Tentative diagnosis of epizootic lymphangitis was made. Blood sample, pus swab and aspirated exudate were collected for confirmatory diagnosis using light microscopy and polymerase chain reaction (PCR) to detect and amplify a fragment of the internal transcribed spacer-2 (ITS-2) gene. These techniques detected Histoplasma capsulatum var. farciminosum, which informed the use of appropriate therapeutic agents, providone iodine wash, and griseofulvin cream and tablets for 3 weeks to treat the disease condition.

Intelligent capabilities are of utmost importance in future wireless communication systems. For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems. We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios. Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution. We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain. The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed. From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice.

An adult green monkey (Chlorocebus sabaeus) donated to the zoological garden after history of attack on the owner about a year earlier was found dead a week later with history of sluggish reflexes. At necropsy, there was severe adhesive pleuritis, multifocal to coalescing broncho-interstitial granulomatous pneumonia, diffuse granulomatous splenitis, unilateral multifocal granulomatous epididymitis, and orchitis with moderately congested meningeal blood vessels and shrunken sulci. Histological examination of all tissues stained with routine Hematoxylin and Eosin, Periodic Acid Schiff, and Grocott’s methenamine silver stains showed numerous intrahistiocytic (macrophages and giant cells) and extracellular round (10–30 μm) yeast with double-contoured refractile wall. The yeasts exhibited broad-based budding in some tissues. Molecular detection of the 18S rDNA gene of the fungus was amplified from the extracted DNA using polymerase chain reaction and the gene products were partially sequenced. The gross and histologic lesions and PCR 18S rDNA gene detection were consistent with that of Histoplasma capsulatum var. duboisii as well as Blastomyces dermatitidis. However, the multiple sequence alignments of the nucleotides showed three mutations that confirmed H. capsulatum. Therefore, there is need for closer examination and monitoring of wild life for mycotic zoonoses.

Background:Lead exposure is reported to cause impaired male reproductive tract functions often due to elevated oxidative species. F. exasperata leaf extract contains potent phyto-antioxidants with beneficial endothelial functions. Objectives: This study investigates the effect of ethanolic extract of F. exasperata (EEFE) treatment on the corpus cavernosal contractile activity in lead-exposed adult male Wistar rats. Materials & Methods: Corpus cavernosum strips (CS) were excised from control, EEFE only treated (400 mg/kg), lead + EEFE treated and lead only treated (100 mg/kg) male Wistar rats. Cumulative contractile responses of CS to acetylcholine, potassium chloride, and calcium influx were determined. Furthermore, cumulative dose responses to acetylcholine after incubation in LNAME, indomethacin, nifedipine, and acetovanillone were also determined by measuring isometric contractions using Ugo Basile data capsule acquisition system. Results: Relaxation(%) response to cumulative doses of acetylcholine (10-9–10-5 M) was enhanced in EEFE only treated (59%) compared to lead only (45%). Contractile response to KCl (10-60 M) was restrained in control and EEFE cotreated when compared to lead only treated. Preincubation of the CS with nifedipine (10-4 M) suppressed the relaxation(%) in the lead + EEFE co-treated. Preincubation of the CS in indomethacin improved the relaxant response(%) of the CS in EEFE only and lead + EEFE co-treated (53.53% ±2.80 & 54.65%± 2.95 respectively) compared with the lead only treated (45.40%±1.79). Preincubation with L-NAME significantly reduced relaxation response(%) in EEFE only treated group. Relaxation response(%) of the CS was enhanced in the lead + EEFE group after preincubation with acetovanillone. Conclusion: The study suggests that L-type and voltage-gated calcium channels, NO, and potent antioxidant activity, are important in EEFE amelioration of lead-induced contractile impairments.

Canine parvovirus (CPV) the causative agent of canine parvovirus enteritis is an intractable pathogen of dogs characterised by mutations, evolutionary changes and eventual vaccine failure. The disease is a serious problem in dogs with limited studies conducted in Nigeria. Therefore, this study was designed to characterise the subtypes of CPV isolates in six commonly used vaccines and 157 clinical samples collected from seven states in Nigeria from June 2016 to March 2018. Faecal samples collected from the clinical cases were subjected to in-clinic immunoassay to detect viral antigens. Polymerase chain reaction (PCR) was used to amplify viral VP2 gene in the samples and commonly used vaccines in Nigeria. Thereafter, PCR products were sequenced and analysed. The result showed that 93.0% of the dogs tested positive for CPV in both assays; 72.8% were puppies less than six months old, with 58.3% of them vaccinated. Partial VP2 gene sequence and phylogenetic analysis of 11 random clinical samples showed that CPV-2c 7(63.6%) and CPV-2a 4(36.4%) were the predominant subtypes in Nigeria; with genetic signatures that are 98.7% to 99.9% closely related to Asian and European strains, respectively. No CPV-2b was detected. Amino acid mutation analysis divulged some imperative transmutation sites: D305Y, Y324I, Q370R, N375D, T440A, Y444S, I447M and Y451C in the isolates. The viruses in the vaccines were characterised as the wild-type CPV. The genetic variability, viral population heterogeneity and phylogenetic linkage with isolates from other countries probably suggest transboundary migrations and local differentiations are contributing to continuous CPV evolution and vaccine failure in Nigeria.

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML for next-generation wireless communications are discussed., Comment: 17 pages, 3 figures

Localization of aircraft is important to control air traffic safely and effectively. Although the Automatic Dependent Surveillance Broadcast (ADS-B) has many advantages, the transfer of control over the reported location to the aircraft brings a number of safety and security issues. In order to mitigate these issues and determine the locations of the aircraft which do not have position reporting capabilities or may report wrong locations, complementary or redundant localization methods that are independent of the aircraft are needed. The goal of this paper is to study the feasibility to localize aircraft (estimate the longitude, latitude, and altitude of an aircraft) based on crowdsourced air traffic control communication data, specifically time of arrival and signal strength measurements reported by many different sensors. Specifically, we design and test a deep neural network model for aircraft location prediction using realworld data from OpenSky Network, a crowd-sourced receiver network that obtains volumes of air traffic data from thousands of sensors. It is demonstrated that the proposed deep neural network outperforms the time difference of arrival (TDOA) and support vector regressor (SVR) in terms of the mean absolute percentage error (MAPE), and the proposed deep learning based method using crowdsourced air traffic control communication data is an effective solution for accurate aircraft location prediction that are independent of the aircraft.

Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed extensively. To ensure secure operations of these systems with wireless transmission capabilities, Radio Frequency (RF) surveillance is important to monitor their activities in RF spectrum and detect unauthorized IoT devices. Specifically, in order to prevent an adversary from impersonating legitimate users using identical devices from the same manufacturer, unique “signatures” must be obtained for every individual device in order to uniquely identify each device. In this study, a novel intrusion detection method is proposed to detect unauthorized IoT devices using deep learning. The proposed method is based on RF fingerprinting since physical layer based features are device specific and more difficult to impersonate. RF traces are collected from six “identical” ZigBee devices via a USRP based test bed. The traces span a range of Signal-to-Noise Ratio, to ensure robustness of the model. A convolutional neural network is used to extract features from the RF traces, and dimension reduction and de-correlation are performed on the extracted features. The reduced features are then clustered to identify IoT devices. We show that the proposed method is able to identify devices that are not observed during training. The results not only highlight the benefit of deep learning based feature extraction, but also show promising prospects for being able to distinguish new devices (classes) that are not observed during training.

Radio Frequency (RF) fingerprinting as a physical layer authentication method could be used to distinguish legitimate wireless devices from adversarial ones. In this paper, we present a wireless device identification platform to improve Internet of things (IoT) security using deep learning techniques. Deep learning is a promising method for obtaining the characteristics of the different RF devices through learning from their RF data. Specifically, three different deep learning models, namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are considered here to identify wireless devices and distinguish among wireless devices from the same manufacture. As a case study, large data sets of RF traces from six “identical” ZigBee devices are collected using a USRP based test bed. We captured RF data across a wide range of Signal-to-Noise Ratio (SNR) levels to guarantee the resilience of our proposed models to variety of wireless channel conditions in practical scenarios. Experimental results demonstrate high accuracy of deep learning methods for wireless device identification that potentially could enhance IoT security.