Background Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. Findings We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. Conclusion The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

Background: Metagenomic next generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource limited environments. Findings: We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics which are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences, and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. Conclusion: The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

There is limited understanding of how patella realignment or patellectomy to surgically manage patellofemoral pain (PFP) affects knee biomechanics. By analysing marsupials like kangaroos that lack an ossified patella, actionable biomimetic insight for the management of end-stage PFP could be gained. This study aimed to provide the foundation of a multi-stage approach, by establishing a static biomechanical profile of the kangaroo stifle that informs the inputs and factors requiring consideration for future dynamic analyses. Volumetric CT and MRI sequences were obtained for four hindlimbs from two Macropus giganteus specimens, from which three-dimensional models of the stifles were created. Two limbs were dissected to visualise the insertion points, origins and lines of action of the quadriceps muscles and the knee extensor mechanism. Static measurements were obtained from the three-dimensional models to establish the biomechanical profile. The results confirmed structural differences in the kangaroo stifle with lack of an ossified patella, a prominent tuberosity and a shorter femur, which functionally affect the mechanical advantage and the torque-generating capability of the joint. The data reported in this study can be used to inform the inputs and constraints of future comparative analyses from which important lessons can be learned for the human knee.

The aims of this study were to (1) describe the three-dimensional characteristics and sources of anatomical variability in the geometry of the intercondylar fossa ("notch") in an anterior cruciate ligament (ACL)-injured sample and (2) assess the relationship between patient factors and anatomical variability of the fossa in the context of impingement risk. A retrospective analysis of preoperative magnetic resonance imaging (MRI) for 49 patients with ACL rupture was performed. Scans were examined in the axial plane using an online picture archiving and communication system (PACS) viewer and fossa width and angle assessed at multiple slices, as well as anteroposterior depth, fossa height, and calculated total volume. Principal component analysis was performed to prioritize the sources of variability. A multivariate linear regression was performed to assess relationships between different patient factors, controlling for imaging parameters and principal component loadings. Geometric properties were normally distributed for all but fossa volume, height, and distal angle. Three principal components (PCs) were identified explaining 80% of total variance, shape (PC1), size in the coronal plane (PC2), and size in the sagittal plane (PC3). Patient factors were significantly (P < 0.05) related to PC loadings; however, a substantial amount of variance in each model remained unexplained. Intercondylar fossa characteristics vary considerably within ACL-injury patients with shape and size in coronal and axial planes, explaining most of the variance. Although patient factors are associated with anatomical characteristics, further work is required to identify the correct combination of factors accurately predicting geometry of the fossa for planning ACL reconstruction. Clin. Anat. 33:610-618, 2020.

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