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The lotic and lentic ecosystem of Pakistan is a rich source of biodiversity, notably including important fish species. Earlier genetic studies pertinent to the family Cyprinidae are limited to some extent, this study aimed to provide comprehensive insights into the species inhabiting these water bodies. We identified 36 specimens nine genera and nine species viz: Labeo rohita, Crossocheilus latius, Ptychobarbus conirostris, Cirrhinus mrigala, Ctenopharyngodon idella, Salmostoma bacaila, Systomus sarana, Cyprinus carpio, Schizopygopsis stoliczkai within the Cyprinidae family. Utilizing key taxonomic features and DNA barcode analysis, we delineated intrinsic genetic variability among these species. An Fst analysis, conducted via R studio script revealed a variability range of 0.03-0.44. Additionally, to provide robust species identification QR codes and barcodes for each species were generated using an online code generator. This scannable QR code technique was adopted for the first time for cyprinids. The genetic distances based on kimura 2-parameter (K2P) model indicated differences of 0.16% within species and 23.09% within family. Notably, this study documents the COI sequence for Ptychobarbus conirostris into the Genbank database, a first time inclusion. Based on the outcomes of this study we conclude that the innovative utilization of DNA barcoding and QR codes provides explicit insights into the genetic variability among Cyprinidae fishes, suggesting their efficiency in the identification and characterization of cryptic species within these vital aquatic systems. [ABSTRACT FROM AUTHOR]

Medicinal plants are being used for the treatment of various livestock ailments by the local peoples since earliest times. It is a recognized fact that plants are an important source of ethnoveterinary medicines. From the last decade, ethnoveterinary practices have gained tremendous importance due to the discovery of some effective ethnoveterinary products. Ethnoveterinary practices are more common in developing countries including Pakistan due to different socioeconomic factors. The studies showed that many medicinal plants used for the treatment of Lumpy skin disease (LSD). Ethno-pharmacological content was obtained from 200 people with knowledge and experience of using plants as medicines for this disease in different areas of Sargodha and Khushab, Punjab, Pakistan. The field study was carried out from January-2023 to June-2023. All the data was collected by evaluation and interviews by calculating Use value (UV). The result from this study shows that 20 medicinal plants that belong to fifteen families greatly represented by Family Meliaceae were significant in treating LSD. This study indicated that the ethno-medicinal practices and knowledge are still used in District Sargodha and Khushab plants that support health care and help in the improvement of alternative system of medicines. These results gave commencing information on the significance use of medicinal plants. It can be tested for use in the future that leading to new discoveries of medicines in the treatment of LSD and other diseases of cattle. [ABSTRACT FROM AUTHOR]

Nigella sativa, a medicinal plant, known for its diverse bioactive compounds, including antioxidants and phytohormones, have shown potential in mitigating salt stress in various plant species. Amongst naturally occurring plant growth stimulants, it has attained enormous attention being rich in thymoquinineand carvacrol in seeds for scavenging free radicals. This research aimed to investigate the effect of Nigella sativa seed extract (NSE) as foliar spray (0, 50, 100 and 150g seeds per liter each) on wheat growth under salt stress (0mM, 75mM and 150mM). Results revealed that salinity decreased growth attributes and accumulation of photosynthetic pigments. On the other hand, salinity stress boosted the contents of malondialdehyde, hydrogen peroxide, glycinebetaine, leaf free proline, Na+ and Cl-. Foliar application of NSEameliorated the negative effects of salinity to considerable extent by enhancing growth traits, chlorophyll contents, glycinebetaine and prolineand decreased Na+, Cl-, malondialdehyde andhydrogen peroxide. This research provides valuable insights into the potential use of NSE as natural and sustainable solution to alleviate salt stress in wheat crop. These findings contribute to the development of eco-friendly strategies for enhancing crop resilience in saline environments, ultimately addressing the global challenge of food security in the face of increasing soil salinity. [ABSTRACT FROM AUTHOR]

Predator-prey interactions in plant-associated ecosystems play a crucial role in shaping ecosystem dynamics and stability. This study provides a comprehensive overview of the implications of these interactions, highlighting the intricate web of relationships among predators, herbivores, and plants. The findings underscore the importance of maintaining predator-prey interactions for ecosystem stability and functioning. Trophic cascades initiated by predator-prey interactions have been shown to regulate herbivore populations, indirectly benefiting plant communities. However, human activities can significantly impact predator populations and trophic cascades, emphasizing the need for conservation efforts to preserve these important ecological dynamics. The integration of molecular techniques and modeling approaches can enhance our understanding of trophic cascades in plant-associated ecosystems. Conservation strategies aimed at promoting predator diversity and enhancing plant fitness are essential for maintaining ecosystem stability and promoting sustainable management of plantassociated ecosystems. Further research is needed to investigate the complex dynamics of predator-prey interactions and trophic cascades, as well as to develop effective conservation strategies to preserve these important ecological dynamics. [ABSTRACT FROM AUTHOR]

This review paper provides an overview of the advancements and applications of genetic engineering techniques in livestock, providing an in-depth look at how genetic engineering has improved animal health, productivity, and sustainability in agriculture. In the introduction, genetic engineering is presented as an important tool for improving animal health, productivity, and sustainability in agriculture. Several genetic engineering tools and methods are discussed in the paper, including transgenic technology and genome editing techniques such as CRISPR-Cas9. A special focus is placed on the application of genetic engineering to livestock, specifically disease resistance and control, improved productivity, and environmental sustainability. Additionally, the review discusses the role of genetic engineering in improving animal health by introducing disease-resistant genes and eliminating diseasecausing genes. Genetically engineered vaccines and enhanced biosecurity measures are also discussed in detail as well. A further examination of how genetic engineering can enhance livestock productivity is also included in the review, including the introduction of growth-promoting genes and the manipulation of metabolic pathways. As part of the program, nutritional content and allergenicity can also be enhanced in milk and meat products. As well as highlighting the importance of genetic engineering in promoting sustainable agriculture, the review also discusses how genetic engineering can reduce environmental impacts on agriculture. It is intended to reduce methane emissions and nitrogen and phosphorus waste, as well as optimize resources and land use through improved feed conversion efficiency and disease resistance in order to reduce the need for antibiotics. In this article, ethical and regulatory issues are discussed, including ethical concerns, current regulations, and guidelines for genetic engineering in livestock. At the conclusion of the review, future directions and challenges are discussed, emphasizing the need for continued research and responsible implementation in order to improve animal health, productivity, and sustainability in agriculture. [ABSTRACT FROM AUTHOR]

Sentiment Analysis deals with consumer reviews available on blogs, discussion forums, E-commerce websites, and App Store. These online reviews about products are also becoming essential for consumers and companies as well. Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services. These reviews are also a very precious source of information for requirement engineers. But companies and consumers are not very satisfied with the overall sentiment; they like fine-grained knowledge about consumer reviews. Owing to this, many researchers have developed approaches for aspect-based sentiment analysis. Most existing approaches concentrate on explicit aspects to analyze the sentiment, and only a few studies rely on capturing implicit aspects. This paper proposes a Keywords-Based Aspect Extraction method, which captures both explicit and implicit aspects. It also captures opinion words and classifies the sentiment about each aspect. We applied semantic similarity-based Word Net and Senti Word Net lexicon to improve aspect extraction. We used different collections of customer reviews for experiment purposes, consisting of eight datasets over seven domains. We compared our approach with other state-of-the-art approaches, including Rule Selection using Greedy Algorithm (RSG), Conditional Random Fields (CRF), Rule-based Extraction (RubE), and Double Propagation (DP). Our results have shown better performance than all of these approaches. [ABSTRACT FROM AUTHOR]

Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents a three tier contextual text mining framework through ontologies for judicial corpora. This framework comprises on the judicial corpus, text mining processing resources and ontologies for mining contextual text from corpora to make text and data mining more reliable and fast. A top-down ontology construction approach has been adopted in this paper. The judicial corpus has been selected with a sufficient dataset to process and evaluate the results. The experimental results and evaluations show significant improvements in comparison with the available techniques. [ABSTRACT FROM AUTHOR]

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based onmachine learning with shallowframeworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed models is amino acid sequences which were derived from CulledPDB dataset. Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models. The proposed models enables effective processing of amino acids and attain approximately 87.05% and 87.47% Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models, respectively. [ABSTRACT FROM AUTHOR]

Geneticists employ wide range of simulation programs for the analysis of genomic dataset. Main difficulties are to deal which include complex user interface, limited hardware capacity, complex software design, and analyzing diverse genomes. Forward time population genetics is powerful technique to identify gene underlying traits or mutations of interest. In this work, population genetics simulators Mendel’s Accountant and Nemo are integrated in a cloud computing environment. Wolbachia was used for platform testing. Computational analysis of Wolbachia was performed to highlight the factors that associated with prevention of arboviral diseases. [ABSTRACT FROM AUTHOR]