29 research outputs found
Open Science: From the crisis of science to science for times of crisis?
No full textPeter Murray-Rust - #openNoteBookScience
Shweata Hegde & Ambreen Hamadani #openVirus - https://github.com/petermr/openVirus
Simon Worthington #openClimateKnowledge - https://github.com/OCKProjectA joint presentatio
Artificial intelligence algorithm comparison and ranking for weight prediction in sheep
No full textAbstract In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Artificial Intelligence, with its capacity to handle big data is rapidly becoming popular. In addition, it can also handle non-linear, noisy data and is not limited by the conditions required for conventional data analysis. This study was therefore undertaken to compare the most popular machine learning (ML) algorithms and rank them as per their ability to make predictions on sheep farm data spanning 11 years. Data was cleaned and prepared was done before analysis. Winsorization was done for outlier removal. Principal component analysis (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA (wherein only PCA was used), PCA+ FS (both techniques used for dimensionality reduction), and FS (only feature selection used) bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991, 0.991, 0.991, 0.99, 0.99, 0.984, 0.984, 0.957, 0.949, 0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus help in drawing inferences and making futuristic predictions on farms for their economic prosperity, performance improvement and subsequently food security
Characterization of spliceosome assembly in cyanidioschyzon merolae.
No full textPre-mRNA splicing is the removal of intervening sequences from pre-messenger RNA in a reaction catalyzed by the spliceosome, which contains five small nuclear RNAs (snRNAs) and more than 100 proteins. Assembly of the spliceosome occurs in a highly ordered manner, making the spliceosome a very complex and dynamic particle. The spliceosome has been studied in yeast and humans but a simpler system would simplify splicing studies. Cyanidioschyzon merolae (Cm) has been shown to have a simpler spliceosome. The goal of this study was to characterize the Cm spliceosome beginning with the question of how large it is. To measure the size of the Cm spliceosome I used glycerol gradient centrifugation and assembly gels to study the assembly pathways. Lastly an attempt was made to study the components of Cm spliceosome by developing an assay in yeast (Saccharomyces cerevisiae) where small molecule inhibitors were used to stall the spliceososme, which could then be purified and its composition studied. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b197654
Artificial neural networks for data mining in animal sciences
No full textAbstract Background With the advancement in technology the amount of data generated, in almost every sphere of life, is increasing exponentially. This enormous amount of data needs new powerful tools for analysis and inference drawing. One such process is data mining which is the automated extraction of hidden, previously unknown, and useful knowledge from big data. Data mining is crucial as conventional strategies cannot keep up with the analysis of rapidly accumulating data and they are also inflexible in the wake of new challenges. Animal sciences are no exception to the changing scenario, especially when animal farms are quickly becoming more data intensive. Main body of the abstract The amount of data generated on the farms is also growing exponentially as farms become more intensive and mechanized. There is thus a need to utilize the knowledge of multidisciplinary fields like advanced statistics, artificial intelligence, machine learning, and database management, for revamping animal sciences. Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. Neural networks are being applied successfully for a myriad of supervised and unsupervised learning applications to draw useful hitherto unknown inferences, patterns, and relationships. Neural networks have been used successfully for pattern recognition, clustering, forecasting, prediction, and classification in animal sciences due to their capacity to learn from data, their nonparametric nature, and their ability to generalize well. Today ANN computing is a major element within any data mining tool kit. Popular methods used for neural network computing include feed-forward networks, feedback networks, and self-organization networks. ANN also offers powerful and distributed computing architecture, especially under a scenario where the data are readily available in significant quantity. Short conclusion This paper gives an overview of ANN and their applications in animal sciences and reviews major research conducted in this new and exciting area of artificial intelligence. Research in many aspects of ANN in Animal Sciences has been conducted globally although there is scope for more research in aspects of animal health, monitoring, breeding as well as nutrition
Laughing with an Iranian American Woman: Firoozeh Dumas\u27s Memoirs and the (Cross-) Cultural Work of Humor
No full textThis essay critically analyzes Firoozeh Dumas\u27s humorous memoirs and situates them in the multiple contexts of post-9/11 Muslim American responses to Islamophobia, women\u27s humor, and Iranian American women\u27s life writing. Drawing on philosophical, feminist, ethnic, and contemporary scientific theories of humor and the methods of literary criticism, the author argues that Dumas employs the beneficial and inclusive (not malign and exclusive) positive mode of humorous personal storytelling to build connection through laughter via the emotional and cognitive shifts structurally central to humor. Dumas addresses multiple audiences and engages in important (cross-) cultural work in a particularly fraught political and cultural climate of anti-Muslim sentiment and tense Iran-U.S. relations
Important genes affecting fibre production in animals: A review
No full textThe realignment of the production profile to respond to demanding market signals is one of the most important challenges that an animal breeders face today. Animal fibre being a significant contributor to the agricultural economy needs special attention. This is especially true for sheep and goats where fibre production can account for as much as 20% of the total gross income. It is therefore necessary to gain a better insight into the genes governing wool traits. Gene mapping studies have identified some chromosomal regions influencing fibre quality and production. These may help in the selection of animals producing better quality wool. These are more efficient and accurate than the conventional techniques. This paper critically reviews various genes governing fibre growth in animals and their importance. Fibre quality and production genes may provide novel insights into our understanding of the science of
genetics and breeding. The discovery of new fibre-related genes and their functions may also help in future studies related to fibre development and in the development of new and advanced techniques for the improvement of fibre production and quality
Leveraging surface-specific analysis and machine learning to model the efficacy of rhamnolipids as bio-decontaminants
No full textFood contact surfaces (FCS) play a crucial role in food processing environments, and maintaining their hygiene is essential for preventing foodborne illnesses. Rhamnolipids, biodegradable and eco-friendly biosurfactants, have gained interest in the food industry for their sustainable antimicrobial properties. In this study, L. monocytogenes demonstrated the highest sensitivity to rhamnolipid treatment, with the lowest minimum inhibitory (1.5625 mu g/ mL) and bactericidal (3.125 mu g/mL) concentrations, compared to Salmonella typhimurium and Escherichia coli. Due to its increased susceptibility, L. monocytogenes was further used to assess the decontamination efficacy of rhamnolipids on stainless steel, wood, and HDPE surfaces, each inoculated to achieve consistent microbial loads. When applied at its MBC level, rhamnolipid exhibited rapid bactericidal action, eliminating L. monocytogenes within 10 min on stainless steel and wood. However, its effectiveness was diminished on HDPE, where high bacterial counts (4.9 log10 CFU) persisted after 40 min. To evaluate bacterial count dynamics, three mixed-effects models, Generalized Estimating Equation (GEE) models, and fixed-effects (OLS) models were developed. We evaluated multiple models, including Random Forest, Gradient Boosting, Bagging, AdaBoost, Linear Regression, Elastic Net, and SVR, under two cross-validation strategies: 5-Fold CV and Leave-One-Out CV (LOOCV). Among all models, Gradient Boosting (LOOCV CV-MSE: 0.98, Test RMSE: 1.30) and Random Forest (LOOCV CV-MSE: 1.10, Test RMSE: 1.18) demonstrated the best predictive performance. Feature importance analysis using Random Forest revealed treatment type (importance = 0.48) and incubation time (importance = 0.41) as the most critical predictors. These results underscore the importance of rhamnolipids for FCS decontamination and highlight the predictive power of machine learning in food safety applications
Colonizing Kashmir: state-building under Indian occupation Colonizing Kashmir: state-building under Indian occupation , by Hafsa Kanjwal, Stanford, Stanford University Press, 2023, xiii + 366 pp., $32, ISBN 978-1-5036-3603-3
No full textKashmiri life is expendable for the Indian state. While the love for the land is close to national imaginaries, the people have been subjected to decades of abuse and violence, and infringement of their basic human rights. In this book, Hafsa Kanjwal delves into the history of Kashmir, tracing the role of two pivotal political figures – Sheikh Abdullah (1947–1953) and Bakshi Ghulam Mohammad (1953–1963) – and their relationship with the Indian project of state-building in Kashmir. The author characterizes this as the ‘politics of life’ (9), where the Indian government and client regimes in Kashmir have normalized occupation with the propagation of ‘development, empowerment and progress’ along with bureaucratic integration and the forging of affective intimate relationships with the people of the state. However, the politics of life and appeals to emotions did not mean that there was an absence of coercive measures used by the Indian state to shape conforming and confronting subjectivities
