APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.lin

Phylogenetic and Evolutionary Patterns in Microbial Carotenoid Biosynthesis Are Revealed by Comparative Genomics

BACKGROUND: Carotenoids are multifunctional, taxonomically widespread and biotechnologically important pigments. Their biosynthesis serves as a model system for understanding the evolution of secondary metabolism. Microbial carotenoid diversity and evolution has hitherto been analyzed primarily from structural and biosynthetic perspectives, with the few phylogenetic analyses of microbial carotenoid biosynthetic proteins using either used limited datasets or lacking methodological rigor. Given the recent accumulation of microbial genome sequences, a reappraisal of microbial carotenoid biosynthetic diversity and evolution from the perspective of comparative genomics is warranted to validate and complement models of microbial carotenoid diversity and evolution based upon structural and biosynthetic data. METHODOLOGY/PRINCIPAL FINDINGS: Comparative genomics were used to identify and analyze in silico microbial carotenoid biosynthetic pathways. Four major phylogenetic lineages of carotenoid biosynthesis are suggested composed of: (i) Proteobacteria; (ii) Firmicutes; (iii) Chlorobi, Cyanobacteria and photosynthetic eukaryotes; and (iv) Archaea, Bacteroidetes and two separate sub-lineages of Actinobacteria. Using this phylogenetic framework, specific evolutionary mechanisms are proposed for carotenoid desaturase CrtI-family enzymes and carotenoid cyclases. Several phylogenetic lineage-specific evolutionary mechanisms are also suggested, including: (i) horizontal gene transfer; (ii) gene acquisition followed by differential gene loss; (iii) co-evolution with other biochemical structures such as proteorhodopsins; and (iv) positive selection. CONCLUSIONS/SIGNIFICANCE: Comparative genomics analyses of microbial carotenoid biosynthetic proteins indicate a much greater taxonomic diversity then that identified based on structural and biosynthetic data, and divides microbial carotenoid biosynthesis into several, well-supported phylogenetic lineages not evident previously. This phylogenetic framework is applicable to understanding the evolution of specific carotenoid biosynthetic proteins or the unique characteristics of carotenoid biosynthetic evolution in a specific phylogenetic lineage. Together, these analyses suggest a "bramble" model for microbial carotenoid biosynthesis whereby later biosynthetic steps exhibit greater evolutionary plasticity and reticulation compared to those closer to the biosynthetic "root". Structural diversification may be constrained ("trimmed") where selection is strong, but less so where selection is weaker. These analyses also highlight likely productive avenues for future research and bioprospecting by identifying both gaps in current knowledge and taxa which may particularly facilitate carotenoid diversification

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

Investigation of Computer Vision and Deep Learning on Thoracic CT for Assessment and Evaluation of Coronary Artery Calcium, Emphysema, and COVID-19

Over the past several years, new advances in computing hardware and artificial intelligence techniques have allowed deep learning to rapidly develop as a key tool in a broad range of fields. In medical imaging, significant attention has been devoted to exploring how these technologies can improve radiological workflow, including more efficient and more accurate image reading and serving as a rapid, objective reader acting concurrently with human radiologists. However, several challenges exist in applying typical deep learning technologies to CT scans. In this dissertation research, we consider three thoracic CT use cases and evaluate novel deep learning techniques to improve clinical utility. The first aim of this dissertation was to develop a deep learning algorithm to evaluate coronary artery calcification (CAC) on low-dose thoracic CT (LDCT) scans. Coronary heart disease is the leading cause of death globally and CAC scores serve as a strong predictor for adverse events related to coronary heart disease. To automatically score LDCT scans, we developed a novel image segmentation network, CACU-Net, which identifies CAC on LDCT scans and classifies lesions based on the coronary artery branch. CACU-Net was able to identify which LDCT scans and individual arteries contain CAC and classify scans into clinically relevant categories based on severity of CAC, outperforming similar segmentation approaches. A second algorithm was developed to detect emphysema on LDCT scans using a transfer attention-based multiple instance learning (TAMIL) approach. This novel technique evaluates slices individually using a transfer learning feature extraction algorithm that requires no additional network training. The slice features are then aggregated through a learned attention-based pooling method that both improves performance and provides interpretable information which a radiologist can utilize to understand model decision-making and identify cases in which the model may fail to perform. The TAMIL and CACU-Net pipelines have the potential to be added to the screening clinical workflow for a rapid, objective augmentation of radiologist findings. When the COVID-19 pandemic began in 2019 and CT served as a potential method of evaluation for severe COVID-19 patients, the techniques developed here were adjusted for COVID-19 evaluation. Thus, the final aim of this dissertation was to develop a multi-modal model which could aid clinicians in identifying when patients should undergo corticosteroid administration during their course of treatment. This algorithm included 1) a novel segmentation architecture, 2) an investigation of an improved TAMIL algorithm, and 3) comorbidity data. The proposed model demonstrates comparable classification performance compared to the unimodal variants with added interpretability. This technique could improve patient care during future waves of COVID-19, particularly in those patients that are immunocompromised and may require more aggressive treatment. The research provided in these three aims has the potential to improve thoracic CT evaluation by providing more flexible, modality-appropriate models that may augment human readers at various stages of the clinical workflow. The application of such deep learning algorithms has significant potential to enhance clinical efficiency and to ultimately improve patient outcomes

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning

Abstract In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valuable. However, manual assessment for each scan is tedious and often subjective, thus we have developed an automatic, rapid computer-aided diagnosis system for emphysema using attention-based multiple instance deep learning and 865 LDCTs. In the task of determining if a CT scan presented with emphysema or not, our novel Transfer AMIL approach yielded an area under the ROC curve of 0.94 ± 0.04, which was a statistically significant improvement compared to other methods evaluated in our study following the Delong Test with correction for multiple comparisons. Further, from our novel attention weight curves, we found that the upper lung demonstrated a stronger influence in all scan classes, indicating that the model prioritized upper lobe information. Overall, our novel Transfer AMIL method yielded high performance and provided interpretable information by identifying slices that were most influential to the classification decision, thus demonstrating strong potential for clinical implementation