Quantifying mangrove carbon assimilation rates using UAV imagery.

Mangrove forests are recognized as one of the most effective ecosystems for storing carbon. In drylands, mangroves operate at the extremes of environmental gradients and, in many instances, offer one of the few opportunities for vegetation-based sequestering of carbon. Developing accurate and reproducible methods to map carbon assimilation in mangroves not only serves to inform efforts related to natural capital accounting, but can help to motivate their protection and preservation. Remote sensing offers a means to retrieve numerous vegetation traits, many of which can be related to plant biophysical or biochemical responses. The leaf area index (LAI) is routinely employed as a biophysical indicator of health and condition. Here, we apply a linear regression model to UAV-derived multispectral data to retrieve LAI across three mangrove sites located along the coastline of the Red Sea, with estimates producing an R2 of 0.72 when compared against ground-sampled LiCOR LAI-2200C LAI data. To explore the potential of monitoring carbon assimilation within these mangrove stands, the UAV-derived LAI estimates were combined with field-measured net photosynthesis rates from a LiCOR 6400/XT, providing a first estimate of carbon assimilation in dryland mangrove systems of approximately 3000 ton C km-2 yr-1. Overall, these results advance our understanding of carbon assimilation in dryland mangroves and provide a mechanism to quantify the carbon mitigation potential of mangrove reforestation efforts.

Whole blood transcriptome analysis in dairy calves experimentally challenged with bovine herpesvirus 1 (BoHV-1) and comparison to a bovine respiratory syncytial virus (BRSV) challenge.

Bovine herpesvirus 1 (BoHV-1), is associated with several clinical syndromes in cattle, among which bovine respiratory disease (BRD) is of particular significance. Despite the importance of the disease, there is a lack of information on the molecular response to infection via experimental challenge with BoHV-1. The objective of this study was to investigate the whole-blood transcriptome of dairy calves experimentally challenged with BoHV-1. A secondary objective was to compare the gene expression results between two separate BRD pathogens using data from a similar challenge study with BRSV. Holstein-Friesian calves (mean age (SD) = 149.2 (23.8) days; mean weight (SD) = 174.6 (21.3) kg) were either administered BoHV-1 inoculate (1 × 107/mL × 8.5 mL) (n = 12) or were mock challenged with sterile phosphate buffered saline (n = 6). Clinical signs were recorded daily from day (d) -1 to d 6 (post-challenge), and whole blood was collected in Tempus RNA tubes on d six post-challenge for RNA-sequencing. There were 488 differentially expressed (DE) genes (p < 0.05, False Discovery rate (FDR) < 0.10, fold change ≥2) between the two treatments. Enriched KEGG pathways (p < 0.05, FDR <0.05); included Influenza A, Cytokine-cytokine receptor interaction and NOD-like receptor signalling. Significant gene ontology terms (p < 0.05, FDR <0.05) included defence response to virus and inflammatory response. Genes that are highly DE in key pathways are potential therapeutic targets for the treatment of BoHV-1 infection. A comparison to data from a similar study with BRSV identified both similarities and differences in the immune response to differing BRD pathogens.

PlantACT! - how to tackle the climate crisis.

Greenhouse gas (GHG) emissions have created a global climate crisis which requires immediate interventions to mitigate the negative effects on all aspects of life on this planet. As current agriculture and land use contributes up to 25% of total GHG emissions, plant scientists take center stage in finding possible solutions for a transition to sustainable agriculture and land use. In this article, the PlantACT! (Plants for climate ACTion!) initiative of plant scientists lays out a road map of how and in which areas plant scientists can contribute to finding immediate, mid-term, and long-term solutions, and what changes are necessary to implement these solutions at the personal, institutional, and funding levels.

Genome Sequence of an Ungulate Tetraparvovirus 1 from a Nasal Swab of an Irish Beef-Suckler Calf.

Here, we report the genome sequence of strain UTPV1/AB belonging to the species Ungulate tetraparvovirus 1 (UTPV1). UTPV1/AB was isolated in the east of Ireland, directly from a nasal swab of a beef-suckler calf diagnosed with bovine respiratory disease on a farm in County Meath (longitude, 6°65'W; latitude, 53°52'N).

Recalibrating single-study effect sizes using hierarchical Bayesian models.

Introduction: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. Results: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Discussion: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Genome Sequence of Bovine Coronavirus Variants from the Nasal Virome of Irish Beef Suckler and Pre-weaned Dairy Calves Clinically Diagnosed with Bovine Respiratory Disease.

We report 24 bovine coronavirus (BCoV) genome sequences from Ireland. BCoV was sequenced directly from nasal swabs that had been collected during a bovine respiratory disease (BRD) outbreak among recently purchased beef suckler and pre-weaned dairy calves.

Assessment of Rapid MinION Nanopore DNA Virus Meta-Genomics Using Calves Experimentally Infected with Bovine Herpes Virus-1.

Bovine respiratory disease (BRD), which is the leading cause of morbidity and mortality in cattle, is caused by numerous known and unknown viruses and is responsible for the widespread use of broad-spectrum antibiotics despite the use of polymicrobial BRD vaccines. Viral metagenomics sequencing on the portable, inexpensive Oxford Nanopore Technologies MinION sequencer and sequence analysis with its associated user-friendly point-and-click Epi2ME cloud-based pathogen identification software has the potential for point-of-care/same-day/sample-to-result metagenomic sequence diagnostics of known and unknown BRD pathogens to inform a rapid response and vaccine design. We assessed this potential using in vitro viral cell cultures and nasal swabs taken from calves that were experimentally challenged with a single known BRD-associated DNA virus, namely, bovine herpes virus 1. Extensive optimisation of the standard Oxford Nanopore library preparation protocols, particularly a reduction in the PCR bias of library amplification, was required before BoHV-1 could be identified as the main virus in the in vitro cell cultures and nasal swab samples within approximately 7 h from sample to result. In addition, we observed incorrect assignment of the bovine sequence to bacterial and viral taxa due to the presence of poor-quality bacterial and viral genome assemblies in the RefSeq database used by the EpiME Fastq WIMP pathogen identification software.

Mangrove distribution and afforestation potential in the Red Sea.

Mangrove ecosystems represent one of the most effective natural environments for fixing and storing carbon (C). Mangroves also offer significant co-benefits, serving as nurseries for marine species, providing nutrients and food to support marine ecosystems, and stabilizing coastlines from erosion and extreme events. Given these considerations, mangrove afforestation and associated C sequestration has gained considerable attention as a nature-based solution to climate adaptation (e.g., protect against more frequent storm surges) and mitigation (e.g. offsetting other C-producing activities). To advance our understanding and description of these important ecosystems, we leverage Landsat-8 and Sentinel-2 satellite data to provide a current assessment of mangrove extent within the Red Sea region and also explore the effect of spatial resolution on mapping accuracy. We establish that Sentinel-2 provides a more precise spatial record of extent and subsequently use these data together with a maximum entropy (MaxEnt) modeling approach to: i) map the distribution of Red Sea mangrove systems, and ii) identify potential areas for future afforestation. From these current and potential mangrove distribution maps, we then estimate the carbon sequestration rate for the Red Sea (as well as for each bordering country) using a meta-analysis of sequestration values surveyed from the available literature. For the mangrove classification, we obtained mapping accuracies of 98 %, with a total Red Sea mangrove extent estimated at approximately 175 km2. Based on the MaxEnt approach, which used soil physical and environmental variables to identify the key factors limiting mangrove growth and distribution, an area of nearly 410 km2 was identified for potential mangrove afforestation expansion. The factors constraining the potential distribution of mangroves were related to soil physical properties, likely reflecting the low sediment load and limited nutrient input of the Red Sea. The current rate of carbon sequestration was calculated as 1034.09 ± 180.53 Mg C yr-1, and the potential sequestration rate as 2424.49 ± 423.26 Mg C yr-1. While our results confirm the maintenance of a positive trend in mangrove growth over the last few decades, they also provide the upper bounds on above ground carbon sequestration potential for the Red Sea mangroves.

Monitoring coastal water flow dynamics using sub-daily high-resolution SkySat satellite and UAV-based imagery.

Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73-16.05%) and commission errors (0.32-2.77%) and model dye concentration (coefficient of determination = 0.73; root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems.

Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors.

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this spatio-temporal sampling constraint. Here, we evaluate a customized machine learning (ML) workflow to retrieve multi-temporal leaf-Chl levels, combining sub-centimeter resolution UAV-hyperspectral imagery (400-1,000 nm) with leaf-level reflectance spectra and SPAD measurements, capturing temporal correlations, selecting relevant predictors, and retrieving accurate results under different conditions. The study is performed within a phenotyping experiment to monitor wild tomato plants' development. Several analyses were conducted to evaluate multiple ML strategies, including: (1) exploring sequential versus retraining learning; (2) comparing insights gained from using 272 spectral bands versus 60 pigment-based vegetation indices (VIs); and (3) assessing six regression methods (linear, partial-least-square regression; PLSR, decision trees, support vector, ensemble trees, and Gaussian process; GPR). Goodness-of-fit (R 2) and accuracy metrics (MAE, RMSE) were determined using training/testing and validation data subsets to assess the models' performance. Overall, while equally good performance was obtained using either PLSR, GPR, or random forest, results show: (1) the retraining strategy improved the ability of most of the approaches to model SPAD-based Chl dynamics; (2) comparative analysis between retrievals and validation data distributions informed the models' ability to capture Chl dynamics through SPAD levels; (3) VI predictors slightly improved R 2 (e.g., from 0.59 to 0.74 units for GPR) and accuracy (e.g., MAE and RMSE differences of up to 2 SPAD units) in specific algorithms; (4) feature importance examined through these methods, revealed strong overlaps between relevant bands and VI predictors, highlighting a few decisive spectral ranges and indices useful for retrieving leaf-Chl levels. The proposed ML framework allows the retrieval of high-quality spatially distributed and multi-temporal SPAD-based chlorophyll maps at an ultra-high pixel resolution (e.g., 7 mm).

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals.

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles.

Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serve as ideal platforms from which to capture such dynamic responses. Here, we provide a UAV-based approach for tracing coastal water flows using object-based detection of dye plume extent coupled with a regression approach for mapping dye concentration. From hovering UAV images and nine subsequent flight surveys covering the duration of an ebbing tide in the Red Sea, our results show that dye plume extent can be mapped with low omission and commission errors when assessed against manual delineations. Our results also demonstrated that the interaction term of two UAV-derived indices may be employed to accurately map dye concentration (coefficient of determination = 0.96, root mean square error = 7.78 ppb), providing insights into vertical and horizontal transportation and dilution of materials in the water column. We showcase the capabilities of high-frequency UAV-derived data and demonstrate how field-based dye concentration measurements can be integrated with UAV data for future studies of coastal water flow dynamics.

Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle.

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green-red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East.

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in "mega" cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO2 in Amman, Beirut, and Jeddah decreased by -56.6%, -43.4%, and -32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO2 levels in megacities like Tehran (-0.9%) and Cairo (-3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions.

Earth observation has traditionally required a compromise in data collection. That is, one could sense the Earth with high spatial resolution occasionally; or with lower spatial fidelity regularly. For many applications, both frequency and detail are required. Precision agriculture is one such example, with sub-10 m spatial, and daily or sub-daily retrieval representing a key goal. Towards this objective, we produced the first cloud-free 3 m daily evaporation product ever retrieved from space, leveraging recently launched nano-satellite constellations to showcase this emerging potential. Focusing on three agricultural fields located in Nebraska, USA, high-resolution crop water use estimates are delivered via CubeSat-based evaporation modeling. Results indicate good model agreement (r2 of 0.86-0.89; mean absolute error between 0.06 and 0.08 mm/h) when evaluated against corrected flux tower data. CubeSat technologies are revolutionizing Earth observation, delivering novel insights and new agricultural informatics that will enhance food and water security efforts, and enable rapid and informed in-field decision making.

Elucidation of the Host Bronchial Lymph Node miRNA Transcriptome Response to Bovine Respiratory Syncytial Virus.

Bovine respiratory disease (BRD) causes substantial morbidity and mortality, affecting cattle of all ages. One of the main causes of BRD is an initial inflammatory response to bovine respiratory syncytial virus (BRSV). MicroRNAs are novel and emerging non-coding small RNAs that regulate many biological processes and are implicated in various inflammatory diseases. The objective of the present study was to elucidate the changes in the bovine bronchial lymph node miRNA transcriptome in response to BRSV following an experimental viral challenge. Holstein-Friesian calves were either administered a challenge dose of BRSV (103.5 TCID50/ml × 15 ml) (n = 12) or were mock inoculated with sterile phosphate buffered saline (n = 6). Daily scoring of clinical signs was performed and calves were euthanized at day 7 post-challenge. Bronchial lymph nodes were collected for subsequent RNA extraction and sequencing (75 bp). Read counts for known miRNAs were generated using the miRDeep2 package using the UMD3.1 reference genome and the bovine mature miRNA sequences from the miRBase database (release 22). EdgeR was used for differential expression analysis and Targetscan was used to identify target genes for the differentially expressed (DE) miRNAs. Target genes were examined for enriched pathways and gene ontologies using Ingenuity Pathway Analysis (Qiagen). Multi-dimensional scaling (MDS) based on miRNA gene expression changes, revealed a clearly defined separation between the BRSV challenged and control calves, although the clinical manifestation of disease was only mild. One hundred and nineteen DE miRNAs (P < 0.05, FDR < 0.1, fold change > 1.5) were detected between the BRSV challenged and control calves. The DE miRNAs were predicted to target 465 genes which were previously found to be DE in bronchial lymph node tissue, between these BRSV challenged and control calves. Of the DE predicted target genes, 455 had fold changes that were inverse to the corresponding DE miRNAs. There were eight enriched pathways among the DE predicted target genes with inverse fold changes to their corresponding DE miRNA including: granulocyte and agranulocyte adhesion and diapedesis, interferon signalling and role of pathogen recognition receptors in recognition of bacteria and viruses. Functions predicted to be increased included: T cell response, apoptosis of leukocytes, immune response of cells and stimulation of cells. Pathogen recognition and proliferation of cytotoxic T cells are vital for the recognition of the virus and its subsequent elimination.

Messenger RNA biomarkers of Bovine Respiratory Syncytial Virus infection in the whole blood of dairy calves.

Bovine Respiratory Syncytial Virus (BRSV) is a primary viral cause of Bovine Respiratory Disease (BRD) in young calves, which is responsible for substantial morbidity and mortality. Infection with BRSV induces global gene expression changes in respiratory tissues. If these changes are observed in tissues which are more accessible in live animals, such as whole blood, they may be used as biomarkers for diagnosis of the disease. Therefore, the objective of the current study was to elucidate the whole blood transcriptomic response of dairy calves to an experimental challenge with BRSV. Calves (Holstein-Friesian) were either administered BRSV inoculate (103.5 TCID50/ml × 15 ml) (n = 12) or sterile phosphate buffered saline (n = 6). Clinical signs were scored daily and whole blood was collected in Tempus RNA tubes immediately prior to euthanasia, at day 7 post-challenge. RNA was extracted from blood and sequenced (150 bp paired-end). The sequence reads were aligned to the bovine reference genome (UMD3.1) and EdgeR was subsequently employed for differential gene expression analysis. Multidimensional scaling showed that samples from BRSV challenged and control calves segregated based on whole blood gene expression changes, despite the BRSV challenged calves only displaying mild clinical symptoms of the disease. There were 281 differentially expressed (DE) genes (p < 0.05, FDR < 0.1, fold change > 2) between the BRSV challenged and control calves. The top enriched KEGG pathways and gene ontology terms were associated with viral infection and included "Influenza A", "defense response to virus", "regulation of viral life cycle" and "innate immune response". Highly DE genes involved in these pathways may be beneficial for the diagnosis of subclinical BRD from blood samples.

ATAC-Seq identifies regions of open chromatin in the bronchial lymph nodes of dairy calves experimentally challenged with bovine respiratory syncytial virus.

BACKGROUND: Bovine Respiratory Syncytial Virus (BRSV) is a cause of Bovine Respiratory Disease (BRD). DNA-based biomarkers contributing to BRD resistance are potentially present in non-protein-coding regulatory regions of the genome, which can be determined using ATAC-Seq. The objectives of this study were to: (i) identify regions of open chromatin in DNA extracted from bronchial lymph nodes (BLN) of healthy dairy calves experimentally challenged with BRSV and compare them with those from non-challenged healthy control calves, (ii) elucidate the chromatin regions that were differentially or uniquely open in the BRSV challenged relative to control calves, and (iii) compare the genes found in regions proximal to the differentially open regions to the genes previously found to be differentially expressed in the BLN in response to BRSV and to previously identified BRD susceptibility loci. This was achieved by challenging clinically healthy Holstein-Friesian calves (mean age 143 ± 14 days) with either BRSV inoculum (n = 12) or with sterile phosphate buffered saline (PBS) (n = 6) and preparing and sequencing ATAC-Seq libraries from fresh BLN tissues. RESULTS: Using Diffbind, 9,144 and 5,096 differentially accessible regions (P < 0.05, FDR < 0.05) were identified between BRSV challenged and control calves employing DeSeq2 and EdgeR, respectively. Additionally, 8,791 chromatin regions were found to be uniquely open in BRSV challenged calves. Seventy-six and 150 of the genes that were previously found to be differentially expressed using RNA-Seq, were located within 2 kb downstream of the differentially accessible regions, and of the regions uniquely open in BRSV challenged calves, respectively. Pathway analyses within ClusterProfiler indicated that these genes were involved in immune responses to infection and participated in the Th1 and Th2 pathways, pathogen recognition and the anti-viral response. There were 237 differentially accessible regions positioned within 40 previously identified BRD susceptibility loci. CONCLUSIONS: The identified open chromatin regions are likely to be involved in the regulatory response of gene transcription induced by infection with BRSV. Consequently, they may contain variants which impact resistance to BRD that could be used in breeding programmes to select healthier, more robust cattle.

Altered Fast Synaptic Transmission in a Mouse Model of DNM1-Associated Developmental Epileptic Encephalopathy.

Developmental epileptic encephalopathies (DEEs) are severe seizure disorders that occur in infants and young children, characterized by developmental delay, cognitive decline, and early mortality. Recent efforts have identified a wide variety of genetic variants that cause DEEs. Among these, variants in the DNM1 gene have emerged as definitive causes of DEEs, including infantile spasms and Lennox-Gastaut syndrome. A mouse model of Dnm1-associated DEE, known as "Fitful" (Dnm1Ftfl ), recapitulates key features of the disease, including spontaneous seizures, early lethality, and neuronal degeneration. Previous work showed that DNM1 is a key regulator of synaptic vesicle (SV) endocytosis and synaptic transmission and suggested that inhibitory neurotransmission may be more reliant on DNM1 function than excitatory transmission. The Dnm1Ftfl variant is thought to encode a dominant negative DNM1 protein; however, the effects of the Dnm1Ftfl variant on synaptic transmission are largely unknown. To understand these synaptic effects, we recorded from pairs of cultured mouse cortical neurons and characterized all four major connection types [excitation of excitation (E-E), inhibition of inhibition (I-I), E-I, I-E]. Miniature and spontaneous EPSCs and IPSCs were larger, but less frequent, at all Dnm1Ftfl synaptic types, and Dnm1Ftfl neurons had reduced expression of excitatory and inhibitory SV markers. Baseline evoked transmission, however, was reduced only at inhibitory synapses onto excitatory neurons, because of a smaller pool of releasable SVs. In addition to these synaptic alterations, Dnm1Ftfl neurons degenerated later in development, although their activity levels were reduced, suggesting that Dnm1Ftfl may impair synaptic transmission and neuronal health through distinct mechanisms.