Fish are important components of freshwater ecosystems and are one of the main sources of protein for humans. One of the main problems in fishery resource investigation is the accurate identification of fish species and the accurate discrimination of fish stocks. In the context of large data, machine learning techniques as developing data processing techniques have gradually replaced traditional methods. This study demonstrated an automated study, using machine learning, to classify fish species by morphological features (e.g., body shape, size, and fins etc.). Machine learning-based fish species identification is essential for automated ecological monitoring, biodiversity assessment, and fisheries management. This study used a dataset of 540 samples across 20 distinct fish species, a Random Forest model was trained and evaluated using a 10-fold cross-validation strategy, outperforming alternative comparative models in classification accuracy. The procedure used to prepare the dataset consisted of preprocessing, including categorical to binary encoded categorical data (converting non-numeric data to numbers the machine could interpret). Several machine learning models were examined, with the Random Forest (RF) providing the best results, and the RF became a trained, tested, and saved machine learning model for user applications. Subsequently, a slight implementation interface was additionally developed to demonstrate the practical applicability of the trained classification framework that allowed users to enter fish features and receive predictions. The proposed framework illustrates how machine learning approaches may support species identification in fisheries and ecological studies, increase prediction accuracy, and have potential applications in fish farming and research as well as in aquatic ecosystem protection and conservation.
The ecologically diverse foothills of the Hindu Kush range have remained largely unexplored in taxonomic surveys. The current research aimed to explore the diversity and distribution of the Odonata fauna in the foothills of the Hindu Kush Mountain range within the district of Lower Dir, Khyber Pakhtunkhwa, Pakistan. Field visits were carried out at 35 localities across the study area in 2024. Specimens were collected using aerial nets and identified under a stereoscope using standard taxonomic keys, following the descriptions provided by Fraser (1933, 1934, 1936); Chaudhry (2010); Zia (2010). The fieldwork yielded a sum of 321 Anisoptera specimens, representing 26 species and subspecies across15 genera and 3 families. In addition, 72 Zygoptera specimens were recorded, representing 6 species and subspecies under 5 genera and 4 families. Detailed accounts are provided for every recorded species, including the valid name, sex ratio, locality with coordinates and elevation, collection date, previous records from Pakistan, global distribution, habitat description, and zoogeographic affiliation. The current study provided baseline knowledge regarding the Odonata fauna of the study area and revealed a substantial increase in recorded species richness from 11 to 32 species.
The use of antibiotic growth promoters in chicken broiler production has become increasingly questioned. The study evaluated the effect of including 10% dried Opuntia ficus-indica fruit on performance and serum biochemical parameters in Ross 308 broiler chicken. A total of 180 chicks were allocated to two experimental treatments: a control group and a treated group receiving the supplemented diet. Birds receiving the supplemented diet showed higher body weight (+9.84%) and average daily weight gain (+13.55%) than controls. Feed intake was only slightly increased (+2.73%), while feed conversion ratio decreased from 2.73 to 2.47, indicating improved feed efficiency in the OFI-supplemented group. Blood analysis showed increased total protein, albumin, and gamma-glutamyltransferase (GGT) (p = 0.040). Together with reduced AST and stable ALT, this pattern may reflect higher metabolic activity rather than liver damage, although this finding warrants cautious interpretation. No significant differences were observed for renal and lipid parameters (p > 0.05). Overall, these results indicate that dried Opuntia ficus-indica fruit could be used as a natural feed ingredient in broiler chicken production.
The epithelial cells play a significant role in the developmental and reproductive functions of the mammalian oviduct. Recent innovations in tissue culture methods have enabled long-term in vitro culturing of the bovine oviductal epithelial cells (BOECs). The sensitive phenotype and limited proliferative capacity of BOECs made efficient plasmid delivery challenging. To provide robust experimental platforms for gene-function and mechanistic studies, a reproducible, welldefined primary culture with optimised lipofection protocols has been established. In this study, we isolated primary BOECs from fresh cow oviducts, cultured and passaged them to optimise lipofection conditions and evaluate plasmid delivery and transfection efficiency. LipofectamineTM Stem Transfection Reagent with pX458 and pMax-GFP plasmids was used to perform the transient transfection. We assessed a range of cell seeding densities (0.75-1.5 & times;105 cells/ml/well) and plasmid concentrations (1-2.5 & micro;g per well) to evaluate lipofection efficiency. The cell viability was assessed using the Trypan Blue exclusion assay and morphological evaluation of cultured cells, and GFP expression was monitored qualitatively using fluorescence microscopy. Our findings showed that 2.0 & micro;g of pMax and 1.5 & micro;g of pX458 with 1 & times; 105 cells per well exhibited greater GFP expression with minimal cytotoxicity. Lipofection of BOECs resulted in generally lower transfection efficiency, with pX458 exhibiting markedly lower performance than pMax. However, the successful detection of the GFP expression using pX458. Successful detection of GFP expression from pX458 supports its use as a suitable reporter model for related CRISPR/Cas9 vector systems used in genome-editing studies. A puromycin kill curve was performed to determine the effective antibiotic concentration for bovine oviductal epithelial cells, providing a basis for future CRISPR-based genetic manipulation studies.
The main objective of this study is to estimate goat meat production for 2025-2030 including the five largest producing countries, the total production of all other countries and the world production. Besides this main goal, it also tries to compare the share of world production in the 10-year intervals in the top 5 producing countries with the one in the next 6 years. These objectives were pursued using FAOSTAT data on goat meat production over the 64 years from 1961 to 2024. In the study ARIMA model was used for the data without breaks and the ARIMAX model was used for the data with breaks. The best models were determined by AIC, BIC, model forecast deviations, residual normality, correlation, and white noise controls for the years 2019-2024 to select the best forecasting models. In addition, the Herfindahl-Hirschman Index (HHI) and the 5-country concentration (CR5) were applied to identify the concentration of goat meat production across countries. The market share of the major producers has been steadily increasing. The combined production of the three first countries (China, India, and Pakistan) increased from about 52% in the 1990s to 58.48% in the 2010s and had its peak of 61.75% between 2021 and 2024. In the same period, the top five countries, which included Nigeria and Bangladesh in fourth and fifth place respectively, accounted for 68.84% of the world's production. Moreover, the ARIMA and ARIMAX estimations indicate that the five leading countries will account for about 70% of global goat meat production during 2025-2030, while global production is projected to average 8.37 million tons per yearMajor goat producers in the industry should focus on breeding climate-resilient goats and increasing meat production using modern techniques to be competitive at the global level. Producers can achieve medium-and long-term objectives through comprehensive training and sustainable export strategies. Medium-and long-term targets will be achieved through intensive training and a permanent export strategy.
