Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

Classification of Parkinson's Disease in Patch-Based MRI of Substantia Nigra.

Parkinson's disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson's Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression.

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection.

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.

Infectious clone construction and pathogenicity confirmation of Cotton leaf curl Multan virus (CLCuMuV), Ramie mosaic virus (RamV) and Corchorus yellow vein Vietnam virus (CoYVV) by southern blot analysis.

Geminiviruses are insect-transmissible, economically vital group of plant viruses, which cause significant losses to crop production and ornamental plants across the world. During this study, infectious clones of three devastating begomoviruses, i.e., Cotton leaf curl Multan virus (CLCuMuV), Ramie mosaic virus (RamV) and Corchorus yellow vein Vietnam virus (CoYVV) were constructed by following novel protocol. All infectious clones were confirmed by cloning and sequencing. All of the infectious clones were agro-inoculated in Agrobacterium. After the agro-infiltrations, all clones were injected into Nicotiana benthamiana and jute plants under controlled condition. After 28 days of inoculation, plants exhibited typical symptoms of their corresponding viruses. All the symptomatic and asymptomatic leaves were collected from inoculated plants for further analysis. The southern blot analysis was used to confirm the infection of studied begomoviruses. At the end, all the products were sequenced and analyzed.

LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19.

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike the coronavirus (Covid-19) outbreak, a remote Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework that enables wireless communication of physiological signals to data processing hub where long short-term memory (LSTM)-based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions that enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In the proposed IoT protocols (TS-MAC and R-MAC), ultralow latency of 1 ms is achieved. R-MAC also offers improved reliability in comparison to state of the art. In addition, the proposed deep learning scheme offers high performance ([Formula: see text]-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support, and general wellbeing.

Characterization of mycotoxins from entomopathogenic fungi (Cordyceps fumosorosea) and their toxic effects to the development of asian citrus psyllid reared on healthy and diseased citrus plants.

Entomopathogenic fungi (EPF) produce multiple mycotoxins, which play an essential role in improving fungal pathogenesis and virulence. To characterize various mycotoxins from the crude methanol extract of Cordyceps fumosorosea, a major EPF against various insect pests, we performed ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometer (UPLC-QTOF MS) technique, and all compounds were identified through molecular mass and formulae. Bassianolide was assessed against the nymphs and adults of Diaphorina citri reared on healthy and Huánglóngbìng (HLB)-diseased Citrus spp. Plants under laboratory conditions. Overall, 17 compounds were identified from the fungal extract and categorized into three groups, i.e. (1) alkaloids (Isariotins A-C), (2) peptides (Bassianolide, Beauverolides, Beauvericin A, Isaridins and Destruxin E) and (3) polyketide (Tenuipyrone). The detected beauverolides (B, C, F, I, Ja) from C. fumosorosea were novel mycotoxins, and their detection intensity was the highest in the fungal extract. Furthermore, bassianolide caused more than 70% and 80% mortality of D. citri nymphs and adults after two days of application, respectively. After three days of chemical application, all nymphal and adult populations of D. citri were killed by bassianolide. However, the mortality rates of both populations, nymphs and adults, were higher on HLB-diseased plants as compared to healthy plants.

Molecular characterization and RSV Co-infection of Nicotiana benthamiana with three distinct begomoviruses.

Geminiviruses constitute a family of plant viruses with characteristic twinned quasi-icosahedral virions and a small circular DNA genome. Geminiviruses, especially begomoviruses, cause substantial economic losses in tropical and subtropical regions globally. Geminiviruses use the host's transcriptional mechanisms to synthesize their mRNAs. They are considered as an attractive model to understand the transcription mechanism of their host plants. Experiments were conducted to identify transcriptional start sites (TSSs) of the three begomoviruses, i.e., Cotton leaf curl Multan virus (CLCuMuV), Corchorus yellow vein virus (CoYVV), and Ramie mosaic virus (RamV). We first rub-inoculated Rice stripe tenuivirus (RSV), a segmented negative-sense RNA virus that uses cap-snatching to produce capped viral mRNAs, into N. benthamiana. After the inoculation, RSV-infected N. benthamiana were super-infected by CoYVV, CLCuMuV, or RamV, respectively. The capped-RNA leaders snatched by RSV were obtained by determining the 5'-ends of RSV mRNA with high throughput sequencing. Afterwards, snatched capped-RNA leaders of RSV were mapped onto the genome of each begomovirus and those matching the begomoviral genome were considered to come from the 5' ends of assumed begomoviral mRNAs. In this way, TSSs of begomoviruses were obtained. After mapping these TSSs onto the genome of the respective begomovirus, it was found very commonly that a begomovirus can use many different TSSs to transcribe the same gene, producing many different mRNA isoforms containing the corresponding open reading frames (ORFs).

Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network.

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments.

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.

Endophytic entomopathogenic fungi enhance the growth of Phaseolus vulgaris L. (Fabaceae) and negatively affect the development and reproduction of Tetranychus urticae Koch (Acari: Tetranychidae).

Entomopathogenic fungi (EPF) have primarily been applied as an inundative approach to manage pests. However, in recent decade multifunctional role of EPF have been documented which provide multiple benefits to host plants when colonized as an endophyte. In this study five fungal isolates from the genus Beauveria (three), Isaria (one) and Lecanicillium (one) were evaluated for their ability to colonize common bean, Phaseolus vulgaris and to assess their effects in planta on plant growth promotion and possible negative effects on the two-spotted spider mites, Tetranychus urticae. All the tested isolates in this study were able to endophytically colonize root, stem and even leaves of inoculated plants examined at 7 and 14 days post inoculation, indicating the systemic colonization of EPF. Colonized plants showed increased plant heights, fresh shoot and root weights compared to plants without inoculation. Survivorship of T. urticae significantly differed among the treatments with higher survival probability in control plants. Significant reduction in larval development, adult longevity and female fecundity of spider mites were observed when fed on treated plants compared to control plants. The negative effects were found to be carried over the second generation fed on fresh plants. Overall, our results show (i) the positive effects of fungal endophytes on plant growth, (ii) reduction in population growth rate and (iii) negative effects of endophytes on growth and reproduction of spider mites in successive generations. The study presents reports on the endophytic management of plant-feeding mites and highlights the possibility of utilizing entomopathogenic fungal endophytes in the integrated pest management program.

Recent progress on gene silencing/suppression by virus-derived small interfering RNAs in rice viruses especially Rice grassy stunt virus.

Noncoding RNAs play essential functions during epigenetic regulation of gene expression and development in numerous organisms. Three type of small noncoding RNAs found in eukaryotes, which are small interfering RNAs (siRNAs), microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Small RNAs (sRNAs) originated from infecting viruses are known as virus-derived small interfering RNAs (vsiRNAs), are responsible for RNA silencing in plants. However, Virus-induced gene silencing (VIGS) is mainly dependent on RNA silencing (RNAi). Interestingly, RNA silencing happens in plants and insects during viral infections. VsiRNAs originate from dsRNA molecules which further require hosts Dicer-like (DCL) proteins, RNA dependent RNA polymerase (RdRP) proteins, and Argonaute (AGO) proteins. RdRP uses ssRNA for complete RNA amplification process as well as DCL dependent secondary vsiRNA formation. Viral Suppressors of RNA silencing (VSRs) interfere with the movement of signals during silencing mechanism. Moreover, intercellular movement of viruses is facilitated by virus-encoded movement proteins. Proteomic and Transcriptomic mechanisms regulated by specific factors like microRNAs, which has become an essential factor of gene regulation. RNAi is also involved in gene suppression by regulating the transcriptional and post-transcriptional gene expression in many eukaryotes. Rice grassy stunt virus (RGSV) is a member of genus Tenuivirus. Although, there is no much work done on RGSV, but this virus has become very potent and destructive, and effects rice crop in many Asian countries, particularly in China. In this review, we have highlighted the rice viruses' biology and silencing suppressors. This work will be helpful for plant virologists in understanding the role of vsiRNAs mechanism in rice viruses especially RGSV.

Genetic diversity of the families Aeshnidae, Gomphidae and Libellulidae through COI gene from South China.

Adult dragonflies (Anisoptera) were collected from different localities of South China covering eight provinces. Representative sequences were sixty-one, including 16 species, 11 genera and three families (Aeshnidae, Gomphidae and Libellulidae), under cytochrome oxidase subunit I (COI) gene. After alignment of sequences by BioEdit v6, genetic interaction and divergence were computed by MEGA 7 whereas all the indices of genetic diversity were calculated by DnaSP v5 software. Phylogenetic trees were constructed through Neighbor-Joining method under Jukes-Cantor model, and all species of respective families were assembled with each other into individual groups. Maximum divergence was observed by Trithemis genus (18.69%), followed by Orthetrum genus (18.16%), whereas a minimum value of divergence was noted for Pantala genus (0.31%). On the other hand, maximum genetic diversity was recorded for Orthetrum genus up to 142 mutations, followed by Trithemis genus (126 mutations), while the minimum value (two mutations) was observed for Pantala genus. Genetic diversity for overall and Libellulidae family sequences was much higher, up to 404 mutations and 344 mutations, respectively. Current results suggest a high diversity of odonates in the South China region and results are valuable in gaining a total obligation of the diversity of Asian odonates and conservation measures of this insect group.

Complete genome sequences of three novel cycloviruses identified in a dragonfly (Odonata: Anisoptera) from China.

Three cycloviruses (genus Cyclovirus, family Circoviridae) were recovered from a dragonfly (Odonata: Anisoptera) captured in Fuzhou, China. The three cycloviruses, named dragonfly associated cyclovirus 9, 10 and 11 (DfCyV-9, -10, -11), respectively, show 56.1-79.6% genome-wide identity to known cycloviruses and 61.6-65.1% among themselves. Thus, according to the current species demarcation criteria, they represent three novel cycloviruses. Notably, DfCyV-10 has a predicted replication-associated protein (Rep) that is most similar to that of bat associated cyclovirus 2 (BatACyV-2), a cyclovirus discovered in China, with 79.4% amino acid sequence identity, but a putative capsid protein (Cp) most similar to that of BatACyV-10, a cyclovirus discovered in Brazil, with 71.7% amino acid sequence identity. These data are useful for understanding the diversity and evolution of cycloviruses, especially those found in insects.

A nation-wide genetic survey revealed a complex population structure of Bemisia tabaci in Pakistan.

The whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a cryptic species complex distributed worldwide. In Pakistan, B. tabaci poses a serious threat to agriculture production. To understand its diversity in Pakistan, a large-scale sampling was conducted from various locations of all four provinces of the country and Mitochondrial cytochrome oxidase I (mtCOI) gene sequencing was used to determine the whiteflies genetically. The study revealed the presence of five different cryptic species in Pakistan namely Asia II-1, Asia II-5, Asia II-7, Asia II-8 and MEAM-1, respectively. Among them, Asia II-1, which was previously reported from a few areas in the country, had been found now to be prevalent all over the country covering 88.7% of all the sequenced samples. Based on the mtCOI sequences and genetic distance analyses, the diversity of Asia II-1 was much greater than all other cryptic species, which exist only in small patches.

Temperature-dependent development of Asian citrus psyllid on various hosts, and mortality by two strains of Isaria.

The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Psyllidae) is a devastating pest of Citrus spp. The aim of present study was to investigate the development and mortality of ACP on citrus (Citrus sinensis) (healthy and Huánglóngbìng- (HLB) diseased) and jasmine (Murraya paniculata) plants at various temperatures. Two new Isaria strains were collected from citrus orchards of Fuzhou (China), and HLB-diseased plants were verified by running PCR for 16S gene of Candidatus Liberibacter asiaticus (CLas). Development observations were recorded for egg, nymph and adult stages on all plants and three different temperatures (20, 25 and 30 °C) whereas mortality observations were recorded for the nymph (fifth instar) and adults on all plants at 25 °C. Field collected Isaria strains were belonged to previously reported Chinese strains under Maximum Parsimony (MP) and Maximum Likelihood methods, as well as, CLas isolates were belonged to previously reported Chinese isolates under MP and Neighbor-Joining methods. The fastest development and mortality was observed on HLB-diseased plants whereas longest time was taken by development and mortality completion on jasmine plants at all temperatures. The fastest developmental times of egg, nymph (first to fourth and fifth instar) and adult stages were ranged from 3.02 to 3.72 d, to 7.63-9.3 d, 5.35-5.65 d and 24.46-28.47 d on HLB-diseased plants at 30-20 °C, respectively. On the other hand, I. javanica caused the fastest mortality of nymphs and adults (32.21 ± 4.47% and 19.33 ± 4.51%) on HLB-diseased plants with the concentration of 1 × 108 conidia.mL-1 after 3 d and 7 d, respectively. It is concluded that there is a need for extensive molecular work to understand the extra-development and mortality of ACP on diseased plants, because, CLas bacterium can be supportive to uptake more sap from plant phloem.

A novel bacterial symbiont association in the hispid beetle, Octodonta nipae (Coleoptera: Chrysomelidae), their dynamics and phylogeny.

The hispid leaf beetle, Octodonta nipae (Maulik), (Coleoptera: Chrysomelidae), is a devastating pest of palm cultivation worldwide. Endosymbiotic bacteria in the genus Wolbachia are arguably one of the most abundant bacterial group associated with arthropods. Owing to its critical effects on host reproduction, Wolbachia has garnered much attention as a prospective future tool for insect pest management. However, their association, infection dynamics, and functionality remain unknown in this insect pest. Here, we diagnosis for the first time, the infection prevalence, and occurrence of Wolbachia in O. nipae. Experimental evidence by the exploration of wsp gene vindicate that O. nipae is naturally infected with bacterial symbiont of genus Wolbachia, showing a complete maternal inheritance with shared a common Wolbachia strain (wNip). Moreover, MLST (gatB, fbpA, coxA, ftsZ, and hcpA) analysis enabled the detections of new sequence type (ST-484), suggesting a particular genotypic association of O. nipae and Wolbachia. Subsequently, quantitative real-time PCR (qPCR) assay demonstrated variable infection density across different life stages (eggs, larvae, pupae and adult male and female), body parts (head, thorax, abdomen), and tissues (ovaries, testes, and guts). Infection density was higher in egg and female adult stage, as well as abdomen and reproductive tissues as compared to other samples. Interestingly, Wolbachia harbored dominantly in a female than the male adult, while, no significant differences were observed between male and female body parts and tissues. Phylogeny of Wolbachia infection associated with O. nipae rectified from all tested life stages were unique and fall within the same monophyletic supergroup-A of Wolbachia clades. The infection density of symbiont is among the valuable tool to understand their biological influence on hosts, and this latest discovery would facilitate the future investigations to understand the host-symbiont complications and its prospective role as a microbiological agent to reduce pest populations.

Genetic diversity of begomoviruses in Pakistan captured through a vector based survey.

Begomoviruses (Geminiviridea), transmitted by whiteflies, constitute one of the most dangerous groups of plant viruses posing a severe threat to economically important crops in tropical and sub-tropical areas. In this study, whiteflies were collected from various locations all over Pakistan. The begomoviruses carried by these whiteflies were detected by PCR with the degenerative primers pair AV94/Dep3. Analysis of the 177 sequences obtained in our study, revealed 14 distinct begomovirus species, including five which were not previously reported in this country. Putative novel strains of Corchorus yellow vein virus (CoYVV) and Chilli leaf curl virus (ChiLCV) showing less than 90% identity with the previously available taxa were also identified. The greatest number of begomoviruses per single site was detected in Sindh province, where up to five different begomovirus species were identified from the same cropping field. Moreover, Cotton leaf curl Multan virus - Rajasthan (CLCuMuV-Ra) was found prevalent in all the cotton growing areas. The data reported here may be useful in the development of control measures against begomoviruses.