Abstract: The disclosure provides a method of treating, preventing, and/or controlling plant diseases by applying the agricultural composition comprising a bacterial isolate belonging to Paenibacillus or Bacillus. Also, the disclosure provides a method of enhancing disease resistance of a plant seed by applying the agricultural composition to the plant seed, and an agricultural composition comprising the bacterial isolate.

Abstract: The disclosure provides a method of treating, preventing, and/or controlling plant diseases by applying the agricultural composition comprising a bacterial isolate belonging to Paenibacillus or Bacillus. Also, the disclosure provides a method of enhancing disease resistance of a plant seed by applying the agricultural composition to the plant seed, and an agricultural composition comprising the bacterial isolate.

Abstract: The disclosure provides a method of treating, preventing, and/or controlling plant diseases by applying the agricultural composition comprising a bacterial isolate belonging to Paenibacillus or Bacillus. Also, the disclosure provides a method of enhancing disease resistance of a plant seed by applying the agricultural composition to the plant seed, and an agricultural composition comprising the bacterial isolate.

Abstract: Methods are provided for treating an infectious disease of a subject with a composition which comprises an active ingredient that can be produced by bacteria of Paenibacillus or Bacillus. The disclosure also provides a composition or a pharmaceutical composition which comprises, or alternatively consists essentially of, or yet further consists of an active ingredient which can be produced by bacteria of Paenibacillus or Bacillus.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: An embodiment of the invention improves upon the International Telecommunication Union's ITU-T G.729 Annex B comfort noise generation algorithm by reducing the computational complexity of the comfort noise generation algorithm. The computational complexity is reduced by reusing pre-computed random Gaussian noise samples for each non active voice frame versus calculating new random Gaussian noise samples for each non active voice frame as described by Annex B.