Biology subjects

Zare, A.

Publications and source records attributed to Zare, A..

At least 19 recordsLinked to original sources

bioRxiv2025

Remote sensing of endogenous pigmentation by inducible synthetic circuits in grasses

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built, and tested in plants to demonstrate proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterization method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pathways, stimulating plants to overproduce secondary metabolites with value to human health. Applying inducible pigmentation coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems. SummaryO_ST_ABSSynbio tools for C4 grass modelC_ST_ABS[bullet] Advantage of synthetic switches as tools for biopharming and functional genomics [bullet]Our workflow to optimize the gene circuits from a transient system to a stable transgenic [bullet]Testing and taming golden gate elements in monocot system, S. viridis

plant biology↗

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

Individual tree crown maps for the National Ecological Observatory Network

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

ecology↗

bioRxiv2023

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species - peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRIs hyperspectral and spatial information improves semantic segmentation of target objects.

plant biology↗

bioRxiv2023

Prevention of tau accumulation through inhibition of hnRNP R-dependent axonal Mapt mRNA localization

Deposition of neurofibrillary tangles composed of hyperphosphorylated tau in the brain is a pathological hallmark and closely correlates with onset and course of Alzheime[r]s disease. While tau reduction is being pursued as therapeutic strategy, prolonged lowering of total tau might lead to adverse effects, necessitating the development of more targeted approaches. We report that the RNA-binding protein hnRNP R facilitates the axonal localization of the Mapt mRNA encoding tau. Depletion of hnRNP R reduces tau in axons but not neuronal cell bodies. Brains of Alzheimers disease mice deficient for hnRNP R contain less tau tangles and amyloid-{beta} plaques. Neurons treated with blocking antisense oligonucleotides to mask hnRNP R binding sites of Mapt mRNA show reduced axonal tau levels, similar to hnRNP R-deficient neurons. Lowering of tau levels selectively in axons, a major subcellular site of tangle formation and spreading, thus represents a novel therapeutic perspective for treatment of Alzheimers disease.

neuroscience↗

bioRxiv2022

Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model

Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground-based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, the majority of classification models only include the most abundant species, leading to biased predictions at broad scales. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. In addition, large landscapes often require multiple acquisition events, leading to significant within-species variation in reflectance spectra. Using a multi-temporal hierarchical model, we demonstrate the ability to include species predicted at less than 1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670,000 individual trees at the Ordway Swisher Biological Station within the National Ecological Observatory Network. We estimate the relative abundance of the species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. These maps provide the first estimates of canopy tree diversity within NEON sites to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales.

ecology↗

bioRxiv2022

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell- free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for production and testing of bioactive peptides within less than 24 hours and <10$ per screen.

synthetic biology↗

bioRxiv2022

Computer vision for assessing species color pattern variation from web-based community science images

Openly available community science digital vouchers provide a wealth of data to study phenotypic change across space and time. However, extracting phenotypic data from these resources requires significant human effort. Here, we demonstrate a workflow and computer vision model for automatically categorizing species color pattern from community science images. Our work is focused on documenting the striped/unstriped color polymorphism in the Eastern Red-backed Salamander (Plethodon cinereus). We used an ensemble convolutional neural network model to analyze this polymorphism in 20,318 iNaturalist images. Our model was highly accurate ([~]98%) despite image heterogeneity. We used the resulting annotations to document extensive niche overlap between morphs, but wider niche breadth for striped morphs at the range-wide scale. Our work showcases key design principles for using machine learning with heterogeneous community science image data to address questions at an unprecedented scale.

bioinformatics↗

bioRxiv2021

Continental-scale Hyperspectral tree species classification in the National Ecological Observatory Network

Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species). On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.

ecology↗

bioRxiv2021

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

ObjectiveWe quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact StatementTo our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. IntroductionWhen designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we implant silk fibroin biomaterials in a rodent subcutaneous injury model. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis. MethodsWe used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Massons Trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, Attention UNET, as well as to versions of the models with a supplemental "attention"-inspired mechanism (JOSHUA+ and UNET+). ResultsThe inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation. ConclusionOur results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code for our experiments are publicly available.

bioengineering↗

bioRxiv2021

Spatial and Texture Analysis of Root System Architecture with Earth Mover's Distance

PurposeRoot system architectures are complex and challenging to characterize effectively for agronomic and ecological discovery. MethodsWe propose a new method, Spatial and Texture Analysis of Root SystEm distribution with Earth movers Distance (STARSEED), for comparing root system distributions that incorporates spatial information through a novel application of the Earth Movers Distance (EMD). ResultsWe illustrate that the approach captures the response of sesame root systems for different genotypes and soil moisture levels. STARSEED provides quantitative and visual insights into changes that occur in root architectures across experimental treatments. ConclusionSTARSEED can be generalized to other plants and provides insight into root system architecture development and response to varying growth conditions not captured by existing root architecture metrics and models. The code and data for our experiments are publicly available: https://github.com/GatorSense/STARSEED.

plant biology↗

bioRxiv2021

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for delineation of individual crowns and classification to determine species identity. This competition included data from multiple sites to assess the methods ability to generalize learning across multiple sites simultaneously, and transfer learning to novel sites where the methods were not trained. Six teams, representing 4 countries and 9 individual participants, submitted predictions. Methods from a previous competition were also applied and used as the baseline to understand whether the methods are changing and improving over time. The best delineation method was based on an instance segmentation pipeline, closely followed by a Faster R-CNN pipeline, both of which outperformed the baseline method. However, the baseline (based on a growing region algorithm) still performed well as did the Faster R-CNN. All delineation methods generalized well and transferred to novel forests effectively. The best species classification method was based on a two-stage fully connected neural network, which significantly outperformed the baseline (a random forest and Gradient boosting ensemble). The classification methods generalized well, with all teams training their models using multiple sites simultaneously, but the predictions from these trained models generally failed to transfer effectively to a novel site. Classification performance was strongly influenced by the number of field-based species IDs available for training the models, with most methods predicting common species well at the training sites. Classification errors (i.e., species misidentification) were most common between similar species in the same genus and different species that occur in the same habitat. The best methods handled class imbalance well and learned unique spectral features even with limited data. Most methods performed better than baseline in detecting new (untrained) species, especially in the site with no training data. Our experience further shows that data science competitions are useful for comparing different methods through the use of a standardized dataset and set of evaluation criteria, which highlights promising approaches and common challenges, and therefore advances the ecological and remote sensing field as a whole.

ecology↗

bioRxiv2021

FIBRIN POLYMER ON THE SURFACE OF BIOMATERIAL IMPLANTS DRIVES THE FOREIGN BODY REACTION

ABSTRACTImplantation of biomaterials and medical devices in the body triggers the foreign body reaction (FBR) which is characterized by macrophage fusion at the implant surface leading to the formation of foreign body giant cells and the development of the fibrous capsule enveloping the implant. While adhesion of macrophages to the surface is an essential step in macrophage fusion and implanted biomaterials are known to rapidly acquire a layer of host proteins, a biological substrate that is responsible for this process in vivo is unknown. Here we show that mice with genetically-imposed fibrinogen deficiency display a dramatic reduction of macrophage fusion on implanted biomaterials and are protected from the formation of fibrin-containing granulation tissue, a precursor of the fibrous capsule. Furthermore, macrophage fusion on biomaterials implanted in FibAEK mice that express a mutated form of fibrinogen incapable of thrombin-mediated polymerization was strongly reduced. Surprisingly, despite the lack of fibrin, the capsule was formed in FibAEK mice, although it had a different composition and distinct mechanical properties than that in wild-type mice. Specifically, while mononuclear -SMA-expressing macrophages embedded in the capsule of both strains of mice secreted collagen, the amount of collagen and its density in the tissue of FibAEK mice was reduced. These data identify fibrin polymer as a key biological substrate driving the development of the FBR.

immunology↗

bioRxiv2020

Evaluation of Postharvest Senescence in Broccoli Via Hyperspectral Imaging

Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining "freshness" of fruits or vegetables limits our capacity to control product quality leading to food loss and waste. It has been hypothesized that certain proteins and compounds such as glucosinolates can be used as an indicator to monitor the freshness of vegetables and fruits. However, it is challenging to "visualize" the proteins and bioactive compounds during the senescence processes. In this work, we propose machine learning hyperspectral image analysis approaches for estimating glucosinolates levels to detect postharvest senescence in broccoli. Therefore, we set out the research to quantify glucosinolates as "freshness-indicators" which aid in the development of an innovative and accessible tool to precisely estimate the freshness of produce. Such a tool would allow for significant advancement in postharvest logistics and supporting the availability for high-quality and nutritious fresh produce.

bioengineering↗

bioRxiv2020

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Networks Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.

ecology↗

bioRxiv2020

NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

Forests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain significant technical and computational challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source dataset of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Networks Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. Tree crowns identified using this technique correspond well with hand-labeled crowns, exhibiting both high levels of overlap and good correspondence in height estimates. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses at scales of ~10,000 ha and cross-region comparisons encompassing forest types from most of the United States.

ecology↗

bioRxiv2020

The effect of noise trauma and high-frequency stimulation on thalamic sensory gating in rodents

BackgroundThe medial geniculate body (MGB) of the thalamus plays a central role in tinnitus pathophysiology. Breakdown of sensory gating in this part of the auditory thalamus is a potential mechanism underlying tinnitus. The alleviation of tinnitus-like behavior by high-frequency stimulation (HFS) of the MGB might mitigate dysfunctional sensory gating. ObjectiveThe study aims at exploring the role of the MGB in sensory gating as a mandatory relay area in auditory processing in noise-exposed and control subjects, and to assess the effect of MGB HFS on this function. MethodsNoise-exposed rats and controls were tested. Continuous auditory sequences were presented to allow assessment of sensory gating effects associated with pitch, binary grouping, and temporal regularity. Evoked potentials (EP) were recorded from the MGB and acquired before and after HFS (100 Hz). ResultsNoise-exposed rats showed differential modulation of MGB EP amplitudes, confirmed by significant main effects of stimulus type, pair position and temporal regularity. Noise-exposure selectively abolished the effect of temporal regularity on EP amplitudes. A significant three-way interaction between HFS phase, temporal regularity and rat condition (noise-exposed, control) revealed that only noise-exposed rats showed significantly reduced EP amplitudes following MGB HFS. ConclusionThis is the first report that shows thalamic filtering of incoming auditory signals based on different sound features. Noise-exposed rats further showed higher EP amplitudes in most conditions and did not differentiate the temporal regularity. Critically, MGB HFS was effective in reducing amplitudes of the EP responses in noise-exposed animals. HighlightsO_LIEP findings indicate sensory gating in the MGB in rats. C_LIO_LINoise exposure alters EP amplitudes in the MGB. C_LIO_LIHFS selectively suppresses EP responses in noise-exposed animals. C_LI

neuroscience↗

bioRxiv2019

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization of proposed approaches, and limits tree detection at broad scales. Using data from the National Ecological Observatory Network we extend a recently developed semi-supervised deep learning algorithm to include data from a range of forest types, determine whether information from one forest can be used for tree detection in other forests, and explore the potential for building a universal tree detection algorithm. We find that the deep learning approach works well for overstory tree detection across forest conditions, outperforming conventional LIDAR-only methods in all forest types. Performance was best in open oak woodlands and worst in alpine forests. When models were fit to one forest type and used to predict another, performance generally decreased, with better performance when forests were more similar in structure. However, when models were pretrained on data from other sites and then fine-tuned using a small amount of hand-labeled data from the evaluation site, they performed similarly to local site models. Most importantly, a universal model fit to data from all sites simultaneously performed as well or better than individual models trained for each local site. This result suggests that RGB tree detection models that can be applied to a wide array of forest types at broad scales should be possible.

ecology↗