DNA Barcoding: Amplification and sequence analysis of rbcl and matK genome regions in three divergent plant species

Background: DNA barcoding is a novel method of species identification based on nucleotide diversity of conserved sequences. The establishment and refining of plant DNA barcoding systems is more challenging due to high genetic diversity among different species. Therefore, targeting the conserved nuclear transcribed regions would be more reliable for plant scientists to reveal genetic diversity, species discrimination and phylogeny.Methods: In this study, we amplified and sequenced the chloroplast DNA regions (matk+rbcl) of Solanum nigrum, Euphorbia helioscopia and Dalbergia sissoo to study the functional annotation, homology modeling and sequence analysis to allow a more efficient utilization of these sequences among different plant species. These three species represent three families; Solanaceae, Euphorbiaceae and Fabaceae respectively. Biological sequence homology and divergence of amplified sequences was studied using Basic Local Alignment Tool (BLAST).Results: Both primers (matk+rbcl) showed good amplification in three species. The sequenced regions reveled conserved genome information for future identification of different medicinal plants belonging to these species. The amplified conserved barcodes revealed different levels of biological homology after sequence analysis. The results clearly showed that the use of these conserved DNA sequences as barcode primers would be an accurate way for species identification and discrimination.Conclusion: The amplification and sequencing of conserved genome regions identified a novel sequence of matK in native species of Solanum nigrum. The findings of the study would be applicable in medicinal industry to establish DNA based identification of different medicinal plant species to monitor adulteration

Impact of Internal Physical Environment on Academicians' Productivity in Pakistan: Higher Education Institutes Perspectives

This study empirically examines the impact of indoor physical environment on academicians' productivity in different higher education institutes of Khyber Pakhtoonkhawa (KPK) province of Pakistan. The study is based on primary data collected from one hundred and forty four educationists' of various institutes in Pakistan. A structured questionnaire was used for data collection. The data was analyzed using the techniques of rank correlation coefficient and multiple regression analysis. All the findings were tested at 0.01 and 0.05 level of significance. The finding of this study shows that office design is very important in terms of increasing employee's productivity. The study opines that comfortable and contented office design motivates and energized the employees to increase their performance. Keywords: Ergonomics, Productivity, Office design, Higher education institutes, Correlation, Regression, Pakistan

Impact of Internal Physical Environment on Academicians' Productivity in Pakistan: Higher Education Institutes Perspectives

This study empirically examines the impact of indoor physical environment on academicians' productivity in different higher education institutes of Khyber Pakhtoonkhawa (KPK) province of Pakistan. The study is based on primary data collected from one hundred and forty four educationists' of various institutes in Pakistan. A structured questionnaire was used for data collection. The data was analyzed using the techniques of rank correlation coefficient and multiple regression analysis. All the findings were tested at 0.01 and 0.05 level of significance. The finding of this study shows that office design is very important in terms of increasing employee's productivity. The study opines that comfortable and contented office design motivates and energized the employees to increase their performance. Keywords: Ergonomics, Productivity, Office design, Higher education institutes, Correlation, Regression, Pakistan

Functional outcome of anorectal malformations and associated anomalies in era of krickenbeck classification

Abstract OBJECTIVE: To describe the management and functional outcome of anorectal malformations and associated anomalies according to Krickenbeck classification. STUDY DESIGN: Case series. PLACE AND DURATION OF STUDY: The Aga Khan University Hospital, Karachi, from January 2002 to December 2012. METHODOLOGY: Anorectal anomalies were classified according to Krickenbeck classification. Data was collected and proforma used regarding the primary disease associated anomalies, its management and functional outcome, according to Krickenbeck classification. Cases included were: all those children with imperforate anus managed during the study period. Qualitative variables like gender and functional outcome were reported as frequencies and percentages. Quantitative variables like age were reported as medians with interquartile ranges. RESULTS: There were 84 children in study group. Most common associated anomaly was cardiac (38%), followed by urological anomaly (33%). All children were treated by Posterior Sagittal Anorectoplasty (PSARP). Fistula was present in 64 out of 84 (76%) cases. The most common fistula was rectourethral (33%), followed by recto vestibular (31%). According to Krickenbeck classification, continence was achieved in 62% children; however 27% children were constipated, followed by 12% children having fecal soiling. CONCLUSION: Functional outcome of anorectal malformation depends upon severity of disease. A thorough evaluation of all infants with ARM should be done with particular focus on cardiovascular (38%) and genitourinary abnormalities (33%)

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Infantile haemangioendothelioma of the parotid gland: Case report and review of literature

Haemangioendotheliomas (HAE), although rare but are the most common parotid gland tumours in children. We report a 4-month-old girl who presented with a progressively enlarging right sided facial swelling overlying the angle of the mandible. An Ultrasound of the lesion and a computed tomography (CT) scan of the head and neck was carried out which revealed a large lesion within the right parotid gland. CT scan further demonstrated a direct communication with the right external carotid artery and external jugular vein. Considering the clinical course and radiological findings, there was sufficient evidence to avoid any invasive testing. Due to the self-limiting nature of the disease, patient was managed expectantly

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset

Can Different Salt Formulations Revert the Depressing Effect of Salinity on Maize by Modulating Plant Biochemical Attributes and Activating Stress Regulators through Improved N Supply?

Salinity is a major constraint in improving agricultural productivity due to its adverse impact on various physiological and biochemical attributes of plants, and its effect on reducing nitrogen (N) use efficiency due to ion toxicity. To understand the relationship between sodium chloride (NaCl) and increased N application rates, a pot study was performed in which the ammonical (NH4+) form of N was applied as urea to maize crops at different rates (control, 160, 186, 240, 267, 293, and 320 kg N ha−1) using two salinity levels (control and 10 dS m−1 NaCl). The results indicate that all biochemical and physiological attributes of the maize plant improved with increased concentration of N up to 293 kg ha−1, compared to those in the control treatment. Similarly, the optimal N concentration regulated the activities of antioxidant enzymes, i.e., catalase activity (CAT), peroxidase activity (POD), and superoxide dismutases (SOD), and also increased the N use efficiencies of the maize crop up to 293 kg N ha−1. Overall, our results show that the optimum level of N (293 kg ha−1) improved the salinity tolerance in the maize plant by activating stress coping physiological and biochemical mechanisms. This may have been due to the major role of N in the metabolic activity of plants and N assimilation enzymes activity such as nitrate reductase (NR) and nitrite reductase (NiR)

Characterization of Sodium and Potassium Nitrate Contaminated Polyaniline-Poly (Ethylene Oxide) Composites Synthesized via Facile Solution Casting Technique

Fabrication of composites by developing simple techniques can be an effective way to modify some properties of individual materials. The present study relates to facile synthesis of sodium nitrate (NaNO3) and potassium nitrate (KNO3) contaminated polyaniline (PANI) and poly (ethylene oxide) (PEO) composites without using any additives, plasticizers, or fibers. The physic-chemical and rheological properties of synthesized composites were analyzed. The composites showed enhancement in both storage and loss modules in comparison with the polymer matrices. The dynamic viscosity of the synthesized materials has inverse relation with that of temperature and shear stress. Rheological analysis reveals a continuous drop off in viscosity by increasing shear stress. The flow behavior was affected little by temperature. However, the overall results showed a shear thinning effect suggesting that polymer composites show non-Newtonian behavior. The addition of NaNO3 and KNO3 had a profound effect on shear viscosity of the materials, although the overall shear thinning behavior prevails. The PANI-PEO composite follows, as the first approximation models, both Bingham and modified Bingham models, while the salt contaminated system follows only the Bingham model. Both show shear stress values. The greater values of storage (G') and loss (G″) modulus of composites than PANI-PEO blend suggests excellent elasticity, better stiffness, and good mechanical strength of the composites. Furthermore, the composites were more thermally stable than pure polymers