Biology subjects

French, A.

Publications and source records attributed to French, A..

4 recordsLinked to original sources

bioRxiv2026

Divergence in sleep architecture associated with interspecific shifts in memory investment

Sleep plays a critical role in stabilising memory. However, whether the architecture of sleep co-evolves with investment in learning and memory is unknown. To address this, we exploit the extreme levels of neural and behavioural divergence among Heliconiini butterflies. Compared to other Heliconiini, Heliconius have massively expanded mushroom bodies, the insect centres of learning and memory, which are associated with specific enhancements in visual learning and memory. Using a purpose-built video system, machine learning-facilitated behavioural tracking, and in-depth analysis of micro-behaviours during nighttime quiescence, we describe multi-state sleep in Heliconius melpomene and an outgroup Heliconiini, Dryas iulia. In both species, sleep is predominantly composed of quiet sleep, however, Heliconius and Dryas deviate in specific features. In Dryas iulia, quiet sleep is characterised by highly stereotyped, rhythmic antennal oscillations. Conversely, the antennae of Heliconius melpomene remain largely motionless but occasionally perform large movements which interrupt quiet sleep. These antennal deflections are associated with bouts of activity that include increased overall antennal displacement, isolated head twitching, proboscis movements and less frequently, wing movements. While these episodes were also seen in Dryas iulia, they were considerably less frequent. We further demonstrate that the behavioural profile of these active sleep bouts is constituently different from bouts of induced wakefulness, providing experimental support for multi-state sleep in Lepidoptera. This work shows that Heliconius melpomene, a species distinguished by its expanded mushroom bodies, exhibits more frequent bouts of active sleep than the outgroup species Dryas iulia. These findings raise the possibility that increased investment in learning and memory, as well as the structures that support it, may be associated with changes in sleep architecture.

animal behavior and cognition↗

bioRxiv2020

Evaluation of the Abbott ARCHITECT HIV Ag/Ab Combo Assay for Determining Recent HIV-1 Infection

BackgroundGiven the challenges and costs associated with implementing HIV-1 incidence assay testing, there is great interest in evaluating the use of commercial HIV diagnostic tests for determining recent HIV infection. A diagnostic test with the capability of providing reliable data for the determination of recent HIV infection without substantial modifications to the test protocol would have a significant impact on HIV surveillance. The Abbott ARCHITECT HIV Ag/Ab Combo Assay is an antigen/antibody immunoassay, which meets the criteria as the first screening test in the recommended HIV laboratory diagnostic algorithm for the United States. MethodsIn this study, we evaluated the performance characteristics of the ARCHITECT HIV Ag/Ab Combo signal-to-cutoff ratio (S/Co) for determining recent infection, including estimation of the mean duration of recent infection (MDRI) and false recent rate (FRR), and selection of recency cutoffs. ResultsThe MDRI estimates for the S/Co recency cutoff of 400 is within the 4 to 12 months range recommended for HIV incidence assays, and the FRR rate for this cutoff was 1.5%. Additionally, ARCHITECT Combo S/Co values were compared relative to diagnostic test results from two prior prospective HIV-1 diagnostic studies in order to validate the use of the S/Co for both diagnostic and recency determination. ConclusionDual-use of the ARCHITECT Combo assay data for diagnostic and incidence purposes would reduce the need for separate HIV incidence testing and allow for monitoring of recent infection for incidence estimation and other public health applications.

microbiology↗

bioRxiv2020

Pre-existing immunity provides a barrier to airborne transmission of influenza viruses

Human-to-human transmission of influenza viruses is a serious public health threat, yet the precise role of immunity from previous infections on the susceptibility to airborne viruses is still unknown. Using human seasonal influenza viruses in a ferret model, we examined the roles of exposure duration and heterosubtypic immunity on influenza transmission. We found that airborne transmission of seasonal influenza strains is abrogated in recipient animals with pre-existing non-neutralizing immunity, indicating that transmissibility of a given influenza virus strain should be examined in the context of ferrets that are not immunologically naive.

microbiology↗

bioRxiv2020

RootNet: A Convolutional Neural Networks for Complex Plant Root Phenotyping from High-Definition Datasets

Plant phenotyping using machine learning and computer vision approaches is a challenging task. Deep learning-based systems for plant phenotyping is more efficient for measuring different plant traits for diverse genetic discoveries compared to the traditional image-based phenotyping approaches. Plant biologists have recently demanded more reliable and accurate image-based phenotyping systems for assessing various features of plants and crops. The core of these image-based phenotyping systems is structural classification and features segmentation. Deep learning-based systems, however, have shown outstanding results in extracting very complicated features and structures of above-ground plants. Nevertheless, the below-ground part of the plant is usually more complicated to analyze due to its complex arrangement and distorted appearance. We proposed a deep convolutional neural networks (CNN) model named "RootNet" that detects and pixel-wise segments plant roots features. The feature of the proposed method is detection and segmentation of very thin (1-3 pixels wide roots). The proposed approach segment high definition images without significantly sacrificing pixel density, it leads to more accurate root type detection and segmentation results. It is hard to train CNNs with high definition images due to GPU memory limitations. The proposed patch-based CNN training setup makes use of the entire image (with maximum pixel desisity) to recognize and segment give root system efficiently. We have used wheat (Triticum aestivum L.) seedlings dataset, which consists of wheat roots grown in visible pouches. The proposed system segments are given root systems and save it to the Root System Markup Language (RSML) for future analysis. RootNet trained on the dataset mentioned above along with popular semantic segmentation architectures, and it achieved a benchmark accuracy.

plant biology↗