10 results about "Biological interaction" patented technology

The present disclosure relates to systems, non-transitory computer-readable media, and methods that a implement a framework for active learning to discover pairwise interactions via representation learning. Indeed, in one or more implementations, the disclosed systems generate a first individual perturbation embedding from a first representation of a first cell exposed to a first perturbation and a second individual perturbation embedding, from a second representation of a second cell exposed to a second perturbation. For instance, the disclosed systems combine the first individual perturbation embedding and the second individual perturbation embedding to determine a predicted pairwise embedding. Moreover, in some instances, the disclosed systems generate a pairwise embedding from a representation of a cell exposed to both the first and second perturbation. Additionally, from comparing the predicted pairwise embedding with the pairwise embedding, the disclosed systems generate a measure of biological interaction of the first and second perturbation.

The present application relates to the technical field of ecological environment, in particular to a water ecological system evaluation system and method based on a water habitat coupling mechanism model. The technical problem is to integrate key ecological mechanisms while maintaining high computational efficiency. The system includes: an input layer that receives measured data related to the water environment of the target water habitat; a configuration layer that selects several indicators from the geographic indicators, hydrological indicators, hydrodynamic indicators and meteorological indicators input by the input layer, and configures multiple different environmental factors; key ecological processes are integrated through selective mechanism coupling, and interspecific competition factors are configured for algae and biological interaction factors are configured for fish; the simulation layer uses a differential evolution algorithm to automatically calibrate and optimize the parameters configured by the configuration layer; obtain the spatial and temporal changes of aquatic plants, algae and fish; the output layer outputs the spatial distribution results of the three species of aquatic plants, algae and fish, and provides suggestions for scenario analysis results and environmental economic balance decision-making.

The invention relates to a method of producing nitrogen-enhanced vermicomposting through the incorporation of nitrogen-rich Gliricidia sepium leaves into a vermicomposting substrate. Organic waste materials including animal manure, crop residues, and food waste are blended with pre-dried and shredded Gliricidia sepium foliage in optimized proportions and decomposed by epigeic earthworms (Eisenia fetida) under mesophilic conditions (20–35 °C). The biological interaction between earthworms and microorganisms results in a finely divided, porous, humus-like vermicompost characterized by increased nitrogen, phosphorus, and potassium (NPK) levels and measurable nitrogen content.

The application provides a method for evaluating ecological risk of microplastics based on population and community indexes, and belongs to the technical field of pollutant risk evaluation. The application studies the influence of microplastic concentration and particle size on the filter-feeding speed of Daphnia magna, analyzes the relationship between microplastic intake and algal morphology, and studies the influence of microplastics on the movement ability and filter-feeding of Daphnia magna after the microplastics are taken into the body, so as to evaluate the ecological risk of microplastics at a concentration far below the lethal concentration from the perspective of population and community. It is found for the first time that Daphnia magna will mistake microplastics similar in size to algal cells as food, and after the microplastics are taken into the body, the behavior characteristics of zooplankton will be affected, so that the harm of microplastics to zooplankton is comprehensively evaluated from the perspective of behavior and interspecific relationship. The application provides a new idea for evaluating ecological risk of microplastics from the perspective of population and community, and provides a basis for correctly formulating pollutant emission standards.

The present disclosure relates to a liquid handling device, methods of operating a liquid handling device, a method of performing a diagnostic test, a computer program and a system. In particular, the liquid handling device is capable of controllable bi-directional or multi-directional flow of reagents across one or more reaction zones allowing rapid, precise and controllable quenching of reactions and / or biological interactions.

The application discloses a benthic organism sample identification analysis method and system based on data mining. The method first acquires standard image data of benthic organisms at different growth stages, extracts appearance features and constructs a feature database; an identification model is established based on the database, three-dimensional structure image data of target samples and communities are collected by using underwater robots, initial identification is carried out; if the result is not unique, interspecific relationships are mined, a community relationship network is constructed by combining a graph algorithm, the reliability of the identification result is calculated, secondary identification is carried out, and the final result is obtained. The application improves the identification accuracy and reliability, and is suitable for marine ecological monitoring and biodiversity research.

The invention relates to the technical field of abandoned mine recovery evaluation, in particular to an abandoned mine natural recovery evaluation method based on ecological element collaborative characteristics, and the method comprises the steps: obtaining data; performing primary judgment; performing secondary judgment; and finally carrying out collaborative judgment. And adjusting a threshold value and generating an evaluation report. According to the method, the vegetation coverage is rapidly and preliminarily identified; secondly, evaluating the internal recovery potential of the starting area from the dimensions of provenance, microorganism, hydrological connectivity and the like; and finally, for the potential-sufficient region, accurately judging a system which is still fragile in function and a health system which has formed stable self-maintenance capability by quantifying water and soil vegetation process coupling, a biological interaction network and anti-interference capability, and dynamically adjusting an initial threshold value based on the time sequence stability of a classification result of each region. The whole evaluation system has a data-driven adaptive capability, and the problems of low evaluation result accuracy and response speed lag caused by single evaluation dimension are effectively solved.

An improved biofilter design and related methods of utilizing shaped drain assemblies for directing fluid flow though a biofilter media to promote biological interaction. A shaped drain assembly can include a plurality of elongated drain structures having a permeable upper surface, a. lower surface and a drain interior defining a drain cross-section. When configured as a shaped underdrain assembly, each elongated drain structures can be in proximity to a biofilter tank floor such that the lower surface is in fluid communication with one or more flow channels below the floor and with the permeable upper surface supporting the biofilter media. The biofilter can include a shaped drain assembly positioned above the shaped underdrain assembly and located within or above the biofilter media. The shaped drain assemblies can control the flow and mixing of two or more flow species into the biofilter media.

This invention relates to a machine learning-based method and system for predicting interspecific toxicity. The method acquires toxicity data of the same chemical to both alternative and predicted species, calculates its molecular descriptors, and preprocesses them to select descriptors suitable for modeling. Combining the toxicity data of the alternative species with the selected descriptors as independent variables and the toxicity data of the predicted species as the dependent variable, a machine learning algorithm is used to construct an interspecific toxicity relationship prediction model. The model's performance is then evaluated and validated through cross-validation. For a chemical to be predicted, only its toxicity data to an alternative species and related molecular descriptors need to be acquired. The validated final interspecific relationship prediction model is then used to predict the toxicity of the chemical to the predicted species. Compared to existing technologies, this invention utilizes a reliable model to efficiently and accurately predict the potential toxicity of a chemical to a target predicted species, effectively reducing the cost and time of traditional cross-species toxicity testing.

The invention discloses a mine restoration effect prediction method and system based on soil data analysis, and relates to the field of mine ecological restoration, and the method comprises the steps: S1, deploying a bionic sensor network with a self-adaptive triggering capability in a restoration region, obtaining soil multi-modal data before, during and after restoration, and synchronously obtaining background data of a reference ecological region; and the bionic sensor network adaptively switches a monitoring mode and a sampling frequency according to the soil temperature and humidity, a biological disturbance signal or a preset repair event. According to the method, the bionic adaptive sensor network is introduced to actively capture soil-biological interaction key signals, a seed bank-soil-microorganism collaborative response model is constructed to reveal an ecological restoration internal mechanism, and the problems that in the prior art, data and the ecological process are disjointed, the model generalization ability is weak, and prediction lags are effectively solved.