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The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant's platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.

Haplogroup H dominates present-day Western European mitochondrial DNA variability (>40%), yet was less common (~19%) among Early Neolithic farmers (~5450 BC) and virtually absent in Mesolithic hunter-gatherers. Here we investigate this major component of the maternal population history of modern Europeans and sequence 39 complete haplogroup H mitochondrial genomes from ancient human remains. We then compare this ‘real-time’ genetic data with cultural changes taking place between the Early Neolithic (~5450 BC) and Bronze Age (~2200 BC) in Central Europe. Our results reveal that the current diversity and distribution of haplogroup H were largely established by the Mid Neolithic (~4000 BC), but with substantial genetic contributions from subsequent pan-European cultures such as the Bell Beakers expanding out of Iberia in the Late Neolithic (~2800 BC). Dated haplogroup H genomes allow us to reconstruct the recent evolutionary history of haplogroup H and reveal a mutation rate 45% higher than current estimates for human mitochondria. Here, Brotherton and colleagues sequence 39 mitochondrial genomes from ancient human remains. They track population changes across Central Europe and find that the foundations of the European mitochondrial DNA pool were formed during the Neolithic rather than the post-glacial period.

Automated leaf segmentation pipelines must balance accuracy, scalability, and usability to be readily adopted in plant research. We present an end-to-end deep learning pipeline designed for practical use in plant phenotyping, which we developed and evaluated during a real-world plant growth experiment using Atriplex lentiformis. The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). We quantitatively compare the fine-tuned Mask R-CNN model to Meta AI’s Segment Anything Model (SAM) and evaluate natural language prompts using Grounded SAM and the Leaf-Only SAM post-processing pipeline for refining segmentation outputs. Our findings highlight that transfer learning on a specialized data set can still outperform a large foundation model in domain-specific tasks. In addition, we integrate QR codes for automated sample identification and benchmark multiple QR code decoding libraries, evaluating their robustness under real-world imaging conditions like distortion and lighting variation. To ensure accessibility, we deploy the pipeline as a user-friendly Streamlit web application, allowing researchers to analyze images without deep learning expertise. By focusing on practical deployment in addition to model performance, this study provides an open-source, scalable framework for plant science applications and addresses real-world challenges in automation and usability by the end-researcher.

Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. To tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results. The outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI. We also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps.

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.

Cheap and ubiquitous sensing has made collecting large agricultural datasets relatively straightforward. These large datasets (for instance, citizen science data curation platforms like iNaturalist) can pave the way for developing powerful artificial intelligence (AI) models for detection and counting. However, traditional supervised learning methods require labeled data, and manual annotation of these raw datasets with useful labels (such as bounding boxes or segmentation masks) can be extremely laborious, expensive, and error‐prone. In this paper, we demonstrate the power of zero‐shot computer vision methods—a new family of approaches that require (almost) no manual supervision—for plant phenomics applications. Focusing on insect detection as the primary use case, we show that our models enable highly accurate detection of insects in a variety of challenging imaging environments. Our technical contributions are two‐fold: (a) We curate the Insecta rank class of iNaturalist to form a new benchmark dataset of approximately 6 million images consisting of 2526 agriculturally and ecologically important species, including pests and beneficial insects. (b) Using a vision‐language object detection method coupled with weak language supervision, we are able to automatically annotate images in this dataset with bounding box information localizing the insect within each image. Our method succeeds in detecting diverse insect species present in a wide variety of backgrounds, producing high‐quality bounding boxes in a zero‐shot manner with no additional training cost. This open dataset can serve as a use‐inspired benchmark for the AI community. We demonstrate that our method can also be used for other applications in plant phenomics, such as fruit detection in images of strawberry and apple trees. Overall, our framework highlights the promise of zero‐shot approaches to make high‐throughput plant phenotyping more affordable. Core Ideas Annotation is the bottleneck to machine learning‐based phenotyping. We show a way to get bounding boxes using a zero‐shot method. Advances in coupled vision language models allow very accurate zero‐shot annotation. A vision‐language object detection method coupled with weak language supervision is used to identify insects. A benchmark annotated (bounding box and segmentation masks) dataset of 6 million images is created. This approach is widely applicable and can produce very affordable approaches for phenotyping.

Docker has become a very popular container-based virtualization platform for software distribution that has revolutionized the way in which scientific software and software dependencies (software stacks) can be packaged, distributed, and deployed. Docker makes the complex and time-consuming installation procedures needed for scientific software a one-time process. Because it enables platform-independent installation, versioning of software environments, and easy redeployment and reproducibility, Docker is an ideal candidate for the deployment of identical software stacks on different compute environments such as XSEDE and Amazon AWS. CyVerse's Discovery Environment also uses Docker for integrating its powerful, community-recommended software tools into CyVerse's production environment for public use. This paper will help users bring their tools into CyVerse Discovery Environment (DE) which will not only allows users to integrate their tools with relative ease compared to the earlier method of tool deployment in DE but will also help users to share their apps with collaborators and release them for public use.

[...]Rule 10 focuses on the importance of continuously evaluating and learning from past workshops to inform future pedagogy. The breadth of knowledge required and cognitive biases of a single person could derail a workshop entirely. [...]the organization and planning of such workshops necessitate collaboration, as the perspective of different people typically allows to develop a more inclusive training material. Connecting learning objectives from different training modules taught by different instructors requires a large effort in coordination and communication. [...]a person should be designated as the overall coordinator to ensure learning objectives from individual training modules are connected. [...]the workshop coordinator should ensure each session and learning objectives build toward the overall vision and goals of the workshop (Rule 1).

SARS-CoV-2, the cause of COVID19, has caused a pandemic that has infected more than 80 M and killed more than 1.6 M persons worldwide. In the US as of December 2020, it has infected more than 32 M people while causing more than 570,000 deaths. As the pandemic persists, there has been a public demand to reopen schools and university campuses. To consider these demands, it is necessary to rapidly identify those individuals infected with the virus and isolate them so that disease transmission can be stopped. In the present study, we examined the sensitivity of the Quidel Rapid Antigen test for use in screening both symptomatic and asymptomatic individuals at the University of Arizona from June to August 2020. A total of 885 symptomatic and 1551 asymptomatic subjects were assessed by antigen testing and real-time PCR testing. The sensitivity of the test for both symptomatic and asymptomatic persons was between 82 and 90%, with some caveats.