PRMT1 Modulates Alternative Splicing to Enhance HPV18 mRNA Stability and Promote the Establishment of Infection.

Only persistent HPV infections lead to the development of cancer. Thus, understanding the virus-host interplay that influences the establishment of viral infection has important implications for HPV biology and human cancers. The ability of papillomaviruses to establish in cells requires the strict temporal regulation of viral gene expression in sync with cellular differentiation. This control primarily happens at the level of RNA splicing and polyadenylation. However, the details of how this spatio-temporal regulation is achieved still need to be fully understood. Until recently, it has been challenging to study the early events of the HPV lifecycle following infection. We used a single-cell genomics approach to identify cellular factors involved in viral infection and establishment. We identify protein arginine N-methyltransferase 1 (PRMT1) as an important factor in viral infection of primary human cervical cells. PRMT1 is the main cellular enzyme responsible for asymmetric dimethylation of cellular proteins. PRMT1 is an enzyme responsible for catalyzing the methylation of arginine residues on various proteins, which influences processes such as RNA processing, transcriptional regulation, and signal transduction. In this study, we show that HPV18 infection leads to increased PRMT1 levels across the viral lifecycle. PRMT1 is critical for the establishment of a persistent infection in primary cells. Mechanistically, PRMT1 inhibition leads to a highly dysregulated viral splicing pattern. Specifically, reduced PRMT1 activity leads to intron retention and a change in the E6 and E7 expression ratio. In the absence of PRMT1, viral transcripts are destabilized and subject to degradation via the nonsense-mediated decay (NMD) pathway. These findings highlight PRMT1 as a critical regulator of the HPV18 lifecycle, particularly in RNA processing, and position it as a potential therapeutic target for persistent HPV18 infections.

GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Spiders manipulate and exploit bioluminescent signals of fireflies.

Predators often search for prey while moving through the environment, but there are important exceptions, including the way sedentary predators sometimes rely on signals for drawing prey to within striking distance1,2. Some spiders, for instance, leave the remnants of previously-captured prey in their webs where they function as static lures that effectively attract a diverse array of additional prey3456. However, important questions remain concerning how specific the targeted prey may be and how dynamic, instead of static, signalling might be. With these questions as our rationale, we initiated research on Araneus ventricosus (L. Koch, 1878), an orb-weaving spider, as the predator and the firefly Abscondita terminalis males as the prey (Figure 1A-C). Using two lanterns situated on their abdomen (Figure 1D,F), A. terminalis males make female-attracting multi-pulse flash trains (Figure 1J), whereas sedentary females attract males by making single-pulse signals (Figure 1C,K) with a single lantern (Figure 1E,G). Drawing from extensive field observations, we propose that A. ventricosus practices deceptive interspecific communication by first ensnaring firefly males in its web and then predisposing the entrapped male fireflies to broadcast bioluminescent signals that deviate from female-attracting signals typically made by A. terminalis males and instead mimic the male-attracting signals typically made by females. The outcome is that the entrapped male fireflies broadcast false signals that lure more male fireflies into the web.

Deep learning for detecting and characterizing oil and gas well pads in satellite imagery.

Methane emissions from the oil and gas sector are a large contributor to climate change. Robust emission quantification and source attribution are needed for mitigating methane emissions, requiring a transparent, comprehensive, and accurate geospatial database of oil and gas infrastructure. Realizing such a database is hindered by data gaps nationally and globally. To fill these gaps, we present a deep learning approach on freely available, high-resolution satellite imagery for automatically mapping well pads and storage tanks. We validate the results in the Permian and Denver-Julesburg basins, two high-producing basins in the United States. Our approach achieves high performance on expert-curated datasets of well pads (Precision = 0.955, Recall = 0.904) and storage tanks (Precision = 0.962, Recall = 0.968). When deployed across the entire basins, the approach captures a majority of well pads in existing datasets (79.5%) and detects a substantial number (>70,000) of well pads not present in those datasets. Furthermore, we detect storage tanks (>169,000) on well pads, which were not mapped in existing datasets. We identify remaining challenges with the approach, which, when solved, should enable a globally scalable and public framework for mapping well pads, storage tanks, and other oil and gas infrastructure.

Coevolutionary analysis of Pseudomonas syringae-phage interactions to help with rational design of phage treatments.

Treating plant bacterial diseases is notoriously difficult because of the lack of available antimicrobials. Pseudomonas syringae pathovar syringae (Pss) is a major pathogen of cherry (Prunus avium) causing bacterial canker of the stem, leaf and fruit, impacting productivity and leading to a loss of trees. In an attempt to find a treatment for this disease, naturally occurring bacteriophage (phage) that specifically target Pss is being investigated as a biocontrol strategy. However, before using them as a biocontrol treatment, it is important to both understand their efficacy in reducing the bacterial population and determine if the bacterial pathogens can evolve resistance to evade phage infection. To investigate this, killing curve assays of five MR phages targeting Pss showed that phage resistance rapidly emerges in vitro, even when using a cocktail of the five phages together. To gain insight to the changes occurring, Pss colonies were collected three times during a 66-h killing curve assay and separately, Pss and phage were also coevolved over 10 generations, enabling the measurement of genomic and fitness changes in bacterial populations. Pss evolved resistance to phages through modifications in lipopolysaccharide (LPS) synthesis pathways. Bacterial fitness (growth) and virulence were affected in only a few mutants. Deletion of LPS-associated genes suggested that LPS was the main target receptor for all five MR phages. Later generations of coevolved phages from the coevolution experiment were more potent at reducing the bacterial density and when used with wild-type phages could reduce the emergence of phage-resistant mutants. This study shows that understanding the genetic mechanisms of bacterial pathogen resistance to phages is important for helping to design a more effective approach to kill the bacteria while minimizing the opportunity for phage resistance to manifest.

Reverse triggering ? a novel or previously missed phenomenon?

BACKGROUND: Reverse triggering (RT) was described in 2013 as a form of patient-ventilator asynchrony, where patient's respiratory effort follows mechanical insufflation. Diagnosis requires esophageal pressure (Pes) or diaphragmatic electrical activity (EAdi), but RT can also be diagnosed using standard ventilator waveforms. HYPOTHESIS: We wondered (1) how frequently RT would be present but undetected in the figures from literature, especially before 2013; (2) whether it would be more prevalent in the era of small tidal volumes after 2000. METHODS: We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, from 1950 to 2017, with key words related to asynchrony to identify papers with figures including ventilator waveforms expected to display RT if present. Experts labelled waveforms. 'Definite' RT was identified when Pes or EAdi were in the tracing, and 'possible' RT when only flow and pressure waveforms were present. Expert assessment was compared to the author's descriptions of waveforms. RESULTS: We found 65 appropriate papers published from 1977 to now, containing 181 ventilator waveforms. 21 cases of 'possible' RT and 25 cases of 'definite' RT were identified by the experts. 18.8% of waveforms prior to 2013 had evidence of RT. Most cases were published after 2000 (1 before vs. 45 after, p = 0.03). 54% of RT cases were attributed to different phenomena. A few cases of identified RT were already described prior to 2013 using different terminology (earliest in 1997). While RT cases attributed to different phenomena decreased after 2013, 60% of 'possible' RT remained missed. CONCLUSION: RT has been present in the literature as early as 1997, but most cases were found after the introduction of low tidal volume ventilation in 2000. Following 2013, the number of undetected cases decreased, but RT are still commonly missed. Reverse Triggering, A Missed Phenomenon in the Literature. Critical Care Canada Forum 2019 Abstracts. Can J Anesth/J Can Anesth 67 (Suppl 1), 1-162 (2020). https://doi-org.myaccess.library.utoronto.ca/ https://doi.org/10.1007/s12630-019-01552-z .

Nitrogen dioxide exposure, health outcomes, and associated demographic disparities due to gas and propane combustion by U.S. stoves.

Gas and propane stoves emit nitrogen dioxide (NO2) pollution indoors, but the exposures of different U.S. demographic groups are unknown. We estimate NO2 exposure and health consequences using emissions and concentration measurements from >100 homes, a room-specific indoor air quality model, epidemiological risk parameters, and statistical sampling of housing characteristics and occupant behavior. Gas and propane stoves increase long-term NO2 exposure 4.0 parts per billion volume on average across the United States, 75% of the World Health Organization's exposure guideline. This increased exposure likely causes ~50,000 cases of current pediatric asthma from long-term NO2 exposure alone. Short-term NO2 exposure from typical gas stove use frequently exceeds both World Health Organization and U.S. Environmental Protection Agency benchmarks. People living in residences <800 ft2 in size incur four times more long-term NO2 exposure than people in residences >3000 ft2 in size; American Indian/Alaska Native and Black and Hispanic/Latino households incur 60 and 20% more NO2 exposure, respectively, than the national average.

Multiple toxins and a protease contribute to the aphid-killing ability of Pseudomonas fluorescens PpR24.

Aphids are globally important pests causing damage to a broad range of crops. Due to insecticide resistance, there is an urgent need to develop alternative control strategies. In our previous work, we found Pseudomonas fluorescens PpR24 can orally infect and kill the insecticide-resistant green-peach aphid (Myzus persicae). However, the genetic basis of the insecticidal capability of PpR24 remains unclear. Genome sequencing of PpR24 confirmed the presence of various insecticidal toxins such as Tc (toxin complexes), Rhs (rearrangement hotspot) elements, and other insect-killing proteases. Upon aphids infection with PpR24, RNA-Seq analysis revealed 193 aphid genes were differentially expressed with down-regulation of 16 detoxification genes. In addition, 1325 PpR24 genes (542 were upregulated and 783 downregulated) were subject to differential expression, including genes responsible for secondary metabolite biosynthesis, the iron-restriction response, oxidative stress resistance, and virulence factors. Single and double deletion of candidate virulence genes encoding a secreted protease (AprX) and four toxin components (two TcA-like; one TcB-like; one TcC-like insecticidal toxins) showed that all five genes contribute significantly to aphid killing, particularly AprX. This comprehensive host-pathogen transcriptomic analysis provides novel insight into the molecular basis of bacteria-mediated aphid mortality and the potential of PpR24 as an effective biocontrol agent.

Genetic dissection of the tissue-specific roles of type III effectors and phytotoxins in the pathogenicity of Pseudomonas syringae pv. syringae to cherry.

When compared with other phylogroups (PGs) of the Pseudomonas syringae species complex, P. syringae pv. syringae (Pss) strains within PG2 have a reduced repertoire of type III effectors (T3Es) but produce several phytotoxins. Effectors within the cherry pathogen Pss 9644 were grouped based on their frequency in strains from Prunus as the conserved effector locus (CEL) common to most P. syringae pathogens; a core of effectors common to PG2; a set of PRUNUS effectors common to cherry pathogens; and a FLEXIBLE set of T3Es. Pss 9644 also contains gene clusters for biosynthesis of toxins syringomycin, syringopeptin and syringolin A. After confirmation of virulence gene expression, mutants with a sequential series of T3E and toxin deletions were pathogenicity tested on wood, leaves and fruits of sweet cherry (Prunus avium) and leaves of ornamental cherry (Prunus incisa). The toxins had a key role in disease development in fruits but were less important in leaves and wood. An effectorless mutant retained some pathogenicity to fruit but not wood or leaves. Striking redundancy was observed amongst effector groups. The CEL effectors have important roles during the early stages of leaf infection and possibly acted synergistically with toxins in all tissues. Deletion of separate groups of T3Es had more effect in P. incisa than in P. avium. Mixed inocula were used to complement the toxin mutations in trans and indicated that strain mixtures may be important in the field. Our results highlight the niche-specific role of toxins in P. avium tissues and the complexity of effector redundancy in the pathogen Pss 9644.

Boreal-Arctic wetland methane emissions modulated by warming and vegetation activity.

Wetland methane (CH4) emissions over the Boreal-Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002-2021) of Boreal-Arctic wetland CH4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH4 emissions (22.3 Tg CH4 yr-1) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH4 emission driven by amplified Boreal-Arctic warming and greening.

A new winter wheat genetic resource harbors untapped diversity from synthetic hexaploid wheat.

KEY MESSAGE: The NIAB_WW_SHW_NAM population, a large nested association mapping panel, is a useful resource for mapping QTL from synthetic hexaploid wheat that can improve modern elite wheat cultivars. The allelic richness harbored in progenitors of hexaploid bread wheat (Triticum aestivum L.) is a useful resource for addressing the genetic diversity bottleneck in modern cultivars. Synthetic hexaploid wheat (SHW) is created through resynthesis of the hybridisation events between the tetraploid (Triticum turgidum subsp. durum Desf.) and diploid (Aegilops tauschii Coss.) bread wheat progenitors. We developed a large and diverse winter wheat nested association mapping (NAM) population (termed the NIAB_WW_SHW_NAM) consisting of 3241 genotypes derived from 54 nested back-cross 1 (BC1) populations, each formed via back-crossing a different primary SHW into the UK winter wheat cultivar 'Robigus'. The primary SHW lines were created using 15 T. durum donors and 47 Ae. tauschii accessions that spanned the lineages and geographical range of the species. Primary SHW parents were typically earlier flowering, taller and showed better resistance to yellow rust infection (Yr) than 'Robigus'. The NIAB_WW_SHW_NAM population was genotyped using a single nucleotide polymorphism (SNP) array and 27 quantitative trait loci (QTLs) were detected for flowering time, plant height and Yr resistance. Across multiple field trials, a QTL for Yr resistance was found on chromosome 4D that corresponded to the Yr28 resistance gene previously reported in other SHW lines. These results demonstrate the value of the NIAB_WW_SHW_NAM population for genetic mapping and provide the first evidence of Yr28 working in current UK environments and genetic backgrounds. These examples, coupled with the evidence of commercial wheat breeders selecting promising genotypes, highlight the potential value of the NIAB_WW_SHW_NAM to variety improvement.

Correction: Plasmids manipulate bacterial behaviour through translational regulatory crosstalk.

[This corrects the article DOI: 10.1371/journal.pbio.3001988.].

Global distribution of surface soil organic carbon in urban greenspaces.

Urban greenspaces continue to grow with global urbanization. The global distribution and stock of soil organic carbon (SOC) in urban greenspaces remain largely undescribed and missing in global carbon (C) budgets. Here, we synthesize data of 420 observations from 257 cities in 52 countries to evaluate the global pattern of surface SOC density (0-20 cm depth) in urban greenspaces. Surface SOC density in urban greenspaces increases significantly at higher latitudes and decreases significantly with higher mean annual temperature, stronger temperature and precipitation seasonality, as well as lower urban greenness index. By mapping surface SOC density using a random forest model, we estimate an average SOC density of 55.2 (51.9-58.6) Mg C ha-1 and a SOC stock of 1.46 (1.37-1.54) Pg C in global urban greenspaces. Our findings present a comprehensive assessment of SOC in global urban greenspaces and provide a baseline for future urban soil C assessment under continuing urbanization.

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems.

Climate warming is expected to increase global methane (CH4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4 ) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4 , with increases in FCH4 of ca. 10% or higher found in the fall from 1981-1989 to 2010-2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10-40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Growth hormone-releasing hormone receptor antagonist MIA-602 attenuates cardiopulmonary injury induced by BSL-2 rVSV-SARS-CoV-2 in hACE2 mice.

COVID-19 pneumonia causes acute lung injury and acute respiratory distress syndrome (ALI/ARDS) characterized by early pulmonary endothelial and epithelial injuries with altered pulmonary diffusing capacity and obstructive or restrictive physiology. Growth hormone-releasing hormone receptor (GHRH-R) is expressed in the lung and heart. GHRH-R antagonist, MIA-602, has been reported to modulate immune responses to bleomycin lung injury and inflammation in granulomatous sarcoidosis. We hypothesized that MIA-602 would attenuate rVSV-SARS-CoV-2-induced pulmonary dysfunction and heart injury in a BSL-2 mouse model. Male and female K18-hACE2tg mice were inoculated with SARS-CoV-2/USA-WA1/2020, BSL-2-compliant recombinant VSV-eGFP-SARS-CoV-2-Spike (rVSV-SARS-CoV-2), or PBS, and lung viral load, weight loss, histopathology, and gene expression were compared. K18-hACE2tg mice infected with rVSV-SARS-CoV-2 were treated daily with subcutaneous MIA-602 or vehicle and conscious, unrestrained plethysmography performed on days 0, 3, and 5 (n = 7 to 8). Five days after infection mice were killed, and blood and tissues collected for histopathology and protein/gene expression. Both native SARS-CoV-2 and rVSV-SARS-CoV-2 presented similar patterns of weight loss, infectivity (~60%), and histopathologic changes. Daily treatment with MIA-602 conferred weight recovery, reduced lung perivascular inflammation/pneumonia, and decreased lung/heart ICAM-1 expression compared to vehicle. MIA-602 rescued altered respiratory rate, increased expiratory parameters (Te, PEF, EEP), and normalized airflow parameters (Penh and Rpef) compared to vehicle, consistent with decreased airway inflammation. RNASeq followed by protein analysis revealed heightened levels of inflammation and end-stage necroptosis markers, including ZBP1 and pMLKL induced by rVSV-SARS-CoV-2, that were normalized by MIA-602 treatment, consistent with an anti-inflammatory and pro-survival mechanism of action in this preclinical model of COVID-19 pneumonia.

Impacts of climate change, population growth, and power sector decarbonization on urban building energy use.

Climate, technologies, and socio-economic changes will influence future building energy use in cities. However, current low-resolution regional and state-level analyses are insufficient to reliably assist city-level decision-making. Here we estimate mid-century hourly building energy consumption in 277 U.S. urban areas using a bottom-up approach. The projected future climate change results in heterogeneous changes in energy use intensity (EUI) among urban areas, particularly under higher warming scenarios, with on average 10.1-37.7% increases in the frequency of peak building electricity EUI but over 110% increases in some cities. For each 1 °C of warming, the mean city-scale space-conditioning EUI experiences an average increase/decrease of ~14%/ ~ 10% for space cooling/heating. Heterogeneous city-scale building source energy use changes are primarily driven by population and power sector changes, on average ranging from -9% to 40% with consistent south-north gradients under different scenarios. Across the scenarios considered here, the changes in city-scale building source energy use, when averaged over all urban areas, are as follows: -2.5% to -2.0% due to climate change, 7.3% to 52.2% due to population growth, and -17.1% to -8.9% due to power sector decarbonization. Our findings underscore the necessity of considering intercity heterogeneity when developing sustainable and resilient urban energy systems.

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM2) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

Characterization of 3D organotypic epithelial tissues reveals tonsil-specific differences in tonic interferon signaling.

Three-dimensional (3D) culturing techniques can recapitulate the stratified nature of multicellular epithelial tissues. Organotypic 3D epithelial tissue culture methods have several applications, including the study of tissue development and function, drug discovery and toxicity testing, host-pathogen interactions, and the development of tissue-engineered constructs for use in regenerative medicine. We grew 3D organotypic epithelial tissues from foreskin, cervix, and tonsil-derived primary cells and characterized the transcriptome of these in vitro tissue equivalents. Using the same 3D culturing method, all three tissues yielded stratified squamous epithelium, validated histologically using basal and superficial epithelial cell markers. The goal of this study was to use RNA-seq to compare gene expression patterns in these three types of epithelial tissues to gain a better understanding of the molecular mechanisms underlying their function and identify potential therapeutic targets for various diseases. Functional profiling by over-representation and gene set enrichment analysis revealed tissue-specific differences: i.e., cutaneous homeostasis and lipid metabolism in foreskin, extracellular matrix remodeling in cervix, and baseline innate immune differences in tonsil. Specifically, tonsillar epithelia may play an active role in shaping the immune microenvironment of the tonsil balancing inflammation and immune responses in the face of constant exposure to microbial insults. Overall, these data serve as a resource, with gene sets made available for the research community to explore, and as a foundation for understanding the epithelial heterogeneity and how it may impact their in vitro use. An online resource is available to investigate these data (https://viz.datascience.arizona.edu/3DEpiEx/).

Pseudomonas syringae isolated in lichens for the first time: Unveiling Peltigera genus as the exclusive host.

Pseudomonas syringae is a bacterial complex that is widespread through a range of environments, typically associated with plants where it can be pathogenic, but also found in non-plant environments such as clouds, precipitation, and surface waters. Understanding its distribution within the environment, and the habitats it occupies, is important for examining its evolution and understanding behaviours. After a recent study found P. syringae living among a range of vascular plant species in Iceland, we questioned whether lichens could harbour P. syringae. Sixteen different species of lichens were sampled all over Iceland, but only one lichen genus, Peltigera, was found to consistently harbour P. syringae. Phylogenetic analyses of P. syringae from 10 sampling points where lichen, tracheophyte, and/or moss were simultaneously collected showed significant differences between sampling points, but not between different plants and lichens from the same point. Furthermore, while there were similarities in the P. syringae population in tracheophytes and Peltigera, the densities in Peltigera thalli were lower than in moss and tracheophyte samples. This discovery suggests P. syringae strains can localize and survive in organisms beyond higher plants, and thus reveals opportunities for studying their influence on P. syringae evolution.