AIMC Topic: Animals

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An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods.

Scientific reports
Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtain...

A deep-learning-based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating Pongo and Homo upper molars.

American journal of physical anthropology
OBJECTIVES: Convolutional neural network (CNN) is a state-of-art deep learning (DL) method with superior performance in image classification. Here, a CNN-based workflow is proposed to discriminate hominid teeth. Our hope is that this method could hel...

Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge.

Sensors (Basel, Switzerland)
Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that compe...

Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut.

eNeuro
Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without a...

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data.

Journal of dairy science
Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as...

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Nature biomedical engineering
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Nature communications
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources ...

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

European radiology
OBJECTIVES: Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images.

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.

Infectious diseases of poverty
BACKGROUND: As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health concern with a heavy disease burden. In China, especially in the epidemic pastoral communities on the ...

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Molecular diversity
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design a...