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Background: Pigeon pea (Cajanus cajan, Mayer) is the member of Asian Cajanus crop and also reffered as Tur and Arhar and commonly grown in Kharif session in Indian subcontinent.). In India, around 250 known species of insect pests have been identified to affect pigeon pea crops at different growth stages. The leaf webber and leaf roller are notable insect pests that pose a significant threat to pigeonpea, particularly during its vegetative stage. The leaf webber and leaf roller, among various insect pests, are notably visible and pose a threat to pigeonpea. These insects target the pigeonpea plant during its vegetative stage. Specifically, the leaf webber, identified as Grapholita (Pammene) critica, Meyrick (Lepidoptera: Tortricidae), has been acknowledged as a significant constraint affecting pigeonpea production. The aim of this investigation is to assess the relationship between the incidence of leaf roller and leaf webber and various abiotic factors, including temperature, relative humidity, wind velocity and rainfall. Methods: The research, spanning Kharif 2021-22 and 2022-23, at Entomological Research Block in BUAT, Banda. It aimed to observe leaf roller and leaf webber incidence in pigeon pea alongside weather conditions. 'IPA-203' pigeon pea variety was manually sown in 200 m2 plots on July 12, 2021 (first year) and July 18, 2022 (second year). Standard agronomic practices were adhered to and no insecticides were used on the crop. Result: Over the two-year observation period, the larval population of G. critica on pigeon pea plants commenced during the 34th SMW, peaking at 4.90 larvae plant-1 in the 45th SMW. Simultaneously, C. soyella infestation initiated in the 34th SMW, steadily increasing until the 42nd SMW, reaching its peak larval count at 3.03. [ABSTRACT FROM AUTHOR]

In this study, Wingless-type MMTV (mouse mammary tumor virus) integration site family member (WNT10B) gene was sequence characterized in the Indian water buffalo. Sequence analysis revealed an open reading frame of 1176 nucleotides in buffalo, encoding 391 amino acids long protein. Nineteen nucleotide variations were observed between cattle and buffalo resulting in six amino acid changes. Phylogenetic analysis showed the clustering of ruminant species together. Real-time expression analysis of WNT10B in tissues collected from different organs of fetal and adult buffalo, revealed, the gene being abundantly expressed in the rumen and liver of the fetus. The fetal ovary, heart, kidney, lung, testis and mammary gland showed moderate expression, while in adult tissues, expression was high in the ovary, testis, brain, kidney, small intestine and liver, whereas lower expression was observed in the adult rumen. Significant differences in WNT10B expression levels were found for the brain, small intestine, testes, kidney, heart, rumen, and ovary when adult and fetal tissues were compared. A moderate level of genetic variation was found between cattle and buffalo WNT10B and expression patterns in a variety of tissues in adult buffalo implies that in addition to possible roles in adipogenesis and hematopoiesis, the WNT10B gene might be playing a significant role in other regulatory pathways as well. [ABSTRACT FROM AUTHOR]

Disaster is a big issue that seriously disrupts and affects the community or society. The impact of a disaster causes a short and long period of time. To analyze the impacts of disasters there are lots of available related datasets. Data analytics methods have the potential to assess the impacts of different types of disasters. Collectively data analytics and machine learning techniques play an important role in transforming and being able to make decisions about our social, economic, mental, and psychological things. The objective of this paper is to assess the impacts of disasters from immediate term to long-term, provide crucial help to the emergency management workforce, and policy decisions making based on the latest available datasets. With the help of the various data agencies, extraction of information and activities carried out, we can determine the effects on disaster victims, their community and impacts on society in general. The analysis provides the statistics that can guide our emergency service about the status of facilities that can further support the survivors, and other related information. Detailed assessment i.e., structural survey and hazard mapping provide specific information about reconstruction and mitigation to monitor the situation, needs of the victims, and supporting entities. The assessment is based on the type of disaster that happened and its impact after a few years. In the current technological advancement of data analytics and machine learning algorithms, the prediction of the long-term effects of a disaster can be performed. Analyzing the impacts over a long period of time is also dependent on the growth of actively cared datasets gathering bodies like agencies, government, NGOs, media, etc., where prediction of short-term and long-term impacts is dependent on the available datasets. Available datasets are preprocessed using data analytics tools and implementation of training and testing for the purpose of predictions and recommendations. As a huge amount of data sets are available through different sources the classification of the datasets can also be performed resulting fast and accurate processing. Model validation techniques play an important role to check the validation, test result, and related outcomes. In this paper advanced machine learning and data analytics tools i.e., XG boost, modified SVM, and modified RF are used for better prediction. The analysis of the short-term effects of disasters has already been suggested and recommended by the various conventional approaches. Here the focus is to analyze and detect the long-term effects of a disaster along with recommendations and models preparing for good decision-making. Therefore, planning should be focused on assessing the impacts from short-term to long-term. The findings of the paper would be helpful to the agencies, local & national authorities, and the government by recommending action plans and their future effects for a longer period in case of disaster. [ABSTRACT FROM AUTHOR]