Biology subjects

Harley, J.

Publications and source records attributed to Harley, J..

5 recordsLinked to original sources

bioRxiv2024

Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided machine learning approach

Sandy soils are susceptible to excessive nitrogen (N) leaching under intensive crop production which is linked with the soils low nutrient holding capacity and high-water infiltration rate. Estimating soil mineral nitrogen (SMN) at the daily time-step is crucial in providing fertilizer recommendations balancing plant nitrogen use efficiency (NUE) and N losses to the environment. Crop models [e.g., Decision Support System for Agrotechnology Transfer (DSSAT)] can simulate the trend of SMN in varied fertilizer rates and timing of application but are unable to replicate its magnitude due to the inability to capture high-water table conditions in a sub-irrigated soil. As an alternative to such physics-based model, time-series deep learning (DL) models based on a long short-term memory (LSTM) are promising in understanding nonlinearity among complex variables. Yet, purely data-driven DL models for crops are difficult to obtain due to the insufficient amount of data available and the excessive costs with producing more data. To address this challenge, a hybrid model (hybrid-LSTM) was developed by leveraging both the DSSAT and LSTM models to estimate daily SMN primarily using daily weather, applied fertilizer rates-timings, and the SMN sparse observations. This study used the observations from field trials conducted between 2010-2014 in Hastings, FL. The first step was to calibrate the DSSAT-SUBSTOR-Potato model to produce reliable SMN of the topsoil for treatments with varied N applied fertilizer rates split among the pre-planting, emergence, and tuber-initiation stages of the potato crop. Thereafter, the hybrid-LSTM model was trained on the calibrated DSSAT simulated SMN time-series and fine-tuned its predictions using the observed SMN to improve DSSAT simulated SMN. The hybrid-LSTM model was then tested on both calibrated and uncalibrated DSSAT SMN simulations where it outperformed the DSSAT model (range of improvement ranged [~]18-30% on comparing the normalized root mean squared error) in providing reliable estimates of SMN across most of the farms and years. This novel hybrid modeling approach could guide stakeholders and farmers to build sustainable N management with improved crop NUE and yield and help in minimizing environmental losses.

physiology↗

bioRxiv2023

m6a methylation orchestrates IMP1 regulation of microtubules during human neuronal differentiation

Neuronal differentiation requires building a complex intracellular architecture, and therefore the coordinated regulation of defined sets of genes. RNA-binding proteins (RBPs) play a key role in this regulation. However, while their action on individual mRNAs has been explored in depth, the mechanisms used to coordinate expression of the gene programs shaping neuronal morphology are poorly understood. To address this, we analysed how the paradigmatic RBP IMP1 (IGF2BP1), an essential developmental factor, selects and regulates its RNA targets during the differentiation of human neurons. We performed a combination of system-wide and molecular analyses, revealing that IMP1 developmentally transitions to and directly regulates the expression of mRNAs encoding essential regulators of the microtubule network, a key component of neuronal morphology. Furthermore, we showed that m6A methylation drives the selection of specific IMP1 mRNA targets and their protein expression during the developmental transition from neural precursors to neurons, providing a molecular principle for the onset of target selectivity.

molecular biology↗

bioRxiv2021

Image-based deep learning reveals the responses of human motor neurons to stress and ALS

Although morphological attributes of cells and their substructures are recognized readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research. In this study we integrate multichannel fluorescence high-content microscopy data with deep-learning imaging methods to reveal - directly from unsegmented images - novel neurite-associated morphological perturbations associated with (ALS-causing) VCP-mutant human motor neurons (MNs). Surprisingly, we reveal that previously unrecognized disease-relevant information is withheld in broadly used and often considered generic biological markers of nuclei (DAPI) and neurons ({beta}III-tubulin). Additionally, we identify changes within the information content of ALS-related RNA binding protein (RBP) immunofluorescence imaging that is captured in VCP-mutant MN cultures. Furthermore, by analyzing MN cultures exposed to different extrinsic stressors, we show that heat stress recapitulates key aspects of ALS. Our study therefore reveals disease-relevant information contained in a range of both generic and more specific fluorescent markers, and establishes the use of image-based deep learning methods for rapid, automated and unbiased testing of biological hypotheses.

neuroscience↗

bioRxiv2020

Temporal Scaling of Human Scalp-Recorded Potentials During Interval Estimation

Much of human behaviour is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single unit recordings in animals have revealed neural activity scales to span different durations during behaviours demanding flexible timing. Here, we employed a general linear modelling approach using a novel combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent EEG datasets, including interval perception, production, prediction and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behaviour. Our results provide a general approach for studying flexibly timed behaviour in the human brain. Significance StatementNeural activity is traditionally thought to occur over fixed time scales. However, recent animal work has suggested that some neural responses occur over varying timescales. We extended this animal result to humans by detecting temporally scaled signals non-invasively at the scalp in four different tasks. Our results suggest that temporal scaling is an important feature of cognitive processes known to unfold over varying timescales.

neuroscience↗

bioRxiv2018

Ddx3x regulates B-cell development and light chain recombination in mice

The X chromosome gene, DDX3X, is an ATP-dependent RNA helicase with roles in transcription, splicing, nuclear export, and translation. Loss of function mutations in DDX3X are linked to a variety of neoplasms, including B-cell lymphoma. We find that conditional homozygous deletion (Mb1-Cre) of Ddx3x in developing mouse B cells in female mice results in a complete absence of mature peripheral B cells associated with an absolute block at the pro-B cell stage of development in the bone marrow. In male mice with Vav1-Cre or Mb1-Cre mediated hemizygous deletion of Ddx3x, there are less severe reductions in peripheral B-cell frequencies with skewing towards the marginal zone lineage, suggesting that the Y chromosome homolog Ddx3y or other male factors may partially compensate for loss of Ddx3x. Loss of Ddx3x in male mice is associated with perturbations at developmental time points linked to cell cycle arrest and immunoglobulin chain rearrangement. Mechanistically, loss of Ddx3x in pre-B cells is associated with reduced expression of the histone reader Brwd1, failure to curtail proliferation, and defective Igk rearrangement, which skews the peripheral B cell receptor repertoire toward lambda light chain usage. These data reveal that Ddx3x plays an essential role in B-cell development by supporting proliferative and epigenetic changes necessary for rearrangement of immunoglobulin genes.

immunology↗