Sensitive and error-tolerant annotation of protein-coding DNA with BATH.

Summary: We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes. Availability and implementation: The software is available at https://github.com/TravisWheelerLab/BATH.

Multiplexed spatial profiling of Hodgkin Reed-Sternberg cell neighborhoods in classic Hodgkin lymphoma.

PURPOSE: Classic Hodgkin lymphoma (cHL) is a B cell lymphoma that occurs primarily in young adults and, less frequently, in elderly individuals. A hallmark of cHL is the exceptional scarcity (1-5%) of the malignant Hodgkin Reed-Sternberg (HRS) cells within a network of non-malignant immune cells. Molecular determinants governing the relationship between HRS cells and their proximal microenvironment remain largely unknown. EXPERIMENTAL DESIGN: We performed spatially resolved multiplexed protein imaging and transcriptomic sequencing to characterize HRS cell states, cellular neighborhoods, and gene expression signatures of 23.6 million cells from 36 newly diagnosed Epstein-Barr virus (EBV) positive and EBV-negative cHL tumors. RESULTS: We show that MHC-I expression on HRS cells is associated with immune inflamed neighborhoods containing CD8+ T cells, MHC-II+ macrophages, and immune checkpoint expression (i.e., PD-1 and VISTA). We identified spatial clustering of HRS cells, consistent with the syncytial variant of cHL, and its association with T cell excluded neighborhoods in a subset of EBV-negative tumors. Finally, a subset of both EBV-positive and EBV-negative tumors contained regulatory T cells high neighborhoods harboring HRS cells with augmented proliferative capacity. CONCLUSIONS: Our study links HRS cell properties with distinct immunophenotypes and potential immune escape mechanisms in cHL.

Phollow: Visualizing Gut Bacteriophage Transmission within Microbial Communities and Living Animals.

Bacterial viruses (known as "phages") shape the ecology and evolution of microbial communities, making them promising targets for microbiome engineering. However, knowledge of phage biology is constrained because it remains difficult to study phage transmission dynamics within multi-member communities and living animal hosts. We therefore created "Phollow": a live imaging-based approach for tracking phage replication and spread in situ with single-virion resolution. Combining Phollow with optically transparent zebrafish enabled us to directly visualize phage outbreaks within the vertebrate gut. We observed that virions can be rapidly taken up by intestinal tissues, including by enteroendocrine cells, and quickly disseminate to extraintestinal sites, including the liver and brain. Moreover, antibiotics trigger waves of interbacterial transmission leading to sudden shifts in spatial organization and composition of defined gut communities. Phollow ultimately empowers multiscale investigations connecting phage transmission to transkingdom interactions that have the potential to open new avenues for viral-based microbiome therapies.

ULTRA-Effective Labeling of Repetitive Genomic Sequence.

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It released under an open license, and available for download at https://github.com/TravisWheelerLab/ULTRA.

Lack of Consensus on the Management of Medial Collateral Ligament Tears in the Setting of Concomitant Anterior Cruciate Ligament Injury: A Critical Analysis.

¼ There is no clear agreement on the optimal timing or superior type of fixation for medial collateral ligament (MCL) tears in the setting of anterior cruciate ligament (ACL) injury.¼ Anatomic healing of medial knee structures is critical to maintain native knee kinematics, supported by biomechanical studies that demonstrate increased graft laxity and residual valgus rotational instability after ACL reconstruction (ACLR) alone in the setting of concomitant ACL/MCL injury.¼ Historically, most surgeons have favored treating acute combined ACL/MCL tears conservatively with MCL rehabilitation, followed by stress radiographs at 6 weeks after injury to assess for persistent valgus laxity before performing delayed ACLR to allow for full knee range of motion, and reduce the risk of postoperative stiffness and arthrofibrosis.¼ However, with the advancement of early mobilization and aggressive physical therapy protocols, acute surgical management of MCL tears in the setting of ACL injury can have benefits of avoiding residual laxity and further intra-articular damage, as well as earlier return to sport.¼ Residual valgus laxity from incomplete MCL healing at the time of ACLR should be addressed surgically, as this can lead to an increased risk of ACLR graft failure.¼ The treatment of combined ACL/MCL injuries requires an individualized approach, including athlete-specific factors such as level and position of play, timing of injury related to in-season play, contact vs. noncontact sport, and anticipated longevity, as well as consideration of the tear pattern, acuity of injury, tissue quality, and surgeon familiarity with the available techniques.

Determination of angle of impact and directionality of drip stains on various fabrics.

Bloodstain pattern analysis (BPA) on absorbent surfaces, such as fabrics, is far more complex compared to its application on smooth, hard, non-porous surfaces. Angle of impact and directionality are commonly interpreted from bloodstains but may be adversely affected by porous surfaces. In fact, there is a lack of evidence that traditional approaches to BPA are even applicable when blood impacts absorbent materials such as clothing and other fabrics. Hence, there is a critical need for research focusing on the validity and reliability of methods for bloodstain pattern analysis on textiles. Here, human blood drops were deposited on six different fabric types (cotton, satin polyester, rayon, blended polyester/spandex, blended nylon/spandex, and blended modal/polyester/spandex) at two known impact angles: 30° and 10°. Bloodstain morphology was found to be unique for each fabric. Calculated angles of impact for cotton and satin polyester were not statistically different from the known angle of impact while blended polyester/spandex, blended nylon/spandex, and blended modal/polyester/spandex significantly underestimated the known angle of impact. Even when stain morphology on fabric resembled those on a glass control, the angle of impact significantly underestimated the known. The ability to assign directionality based upon bloodstain morphology was dependent on the fabric type. These findings support the need for further research and the development of guidelines for bloodstain pattern interpretation on fabric materials.

Ion channel inhibition by targeted recruitment of NEDD4-2 with divalent nanobodies.

Targeted recruitment of E3 ubiquitin ligases to degrade traditionally undruggable proteins is a disruptive paradigm for developing new therapeutics. Two salient limitations are that <2% of the ~600 E3 ligases in the human genome have been exploited to produce proteolysis targeting chimeras (PROTACs), and the efficacy of the approach has not been demonstrated for a vital class of complex multi-subunit membrane proteins- ion channels. NEDD4-1 and NEDD4-2 are physiological regulators of myriad ion channels, and belong to the 28-member HECT (homologous to E6AP C-terminus) family of E3 ligases with widespread roles in cell/developmental biology and diverse diseases including various cancers, immunological and neurological disorders, and chronic pain. The potential efficacy of HECT E3 ligases for targeted protein degradation is unexplored, constrained by a lack of appropriate binders, and uncertain due to their complex regulation by layered intra-molecular and posttranslational mechanisms. Here, we identified a nanobody that binds with high affinity and specificity to a unique site on the N-lobe of the NEDD4-2 HECT domain at a location physically separate from sites critical for catalysis- the E2 binding site, the catalytic cysteine, and the ubiquitin exosite- as revealed by a 3.1 Å cryo-electron microscopy reconstruction. Recruiting endogenous NEDD4-2 to diverse ion channel proteins (KCNQ1, ENaC, and CaV2.2) using a divalent (DiVa) nanobody format strongly reduced their functional expression with minimal off-target effects as assessed by global proteomics, compared to simple NEDD4-2 overexpression. The results establish utility of a HECT E3 ligase for targeted protein downregulation, validate a class of complex multi-subunit membrane proteins as susceptible to this modality, and introduce endogenous E3 ligase recruitment with DiVa nanobodies as a general method to generate novel genetically-encoded ion channel inhibitors.

Development of a novel precision applicator for spot treatment of granular agrochemical in wild blueberry.

While spot spraying has gained increasing popularity in recent years, spot application of granule agrochemical has seen little development. Despite the potential for the technology, there currently exists no commercially available granular applicators capable of spot application. Therefore, the goal of this study was to design, build, and lab evaluate a precision applicator for spot applying granular agrochemical in wild blueberry. The design incorporated a John Deere RC2000 with a custom control box, recirculation system, and electrically actuated valves. All components were modified to fit a Valmar 1255 Twin-Roller. The system receives inputs from a predeveloped prescription map and can actuate each of the twelve valves separately to provide individual orifice control. Casoron® G4 was used as the testing agrochemical and in cycling the product pneumatically for 1 hour incurred no significant product degradation (p = 0.110). In lab evaluations, the applicator encountered zero errors in reading prescription maps and actuating the correct valves accordingly. Further, the granule recycling system had zero instances where product built up in the lines or jammed the valves. In all, this project represents the first successful development of a precision granular spot applicator for any cropping system.

Does type 2 diabetes duration influence the effectiveness of an aerobic exercise intervention: Results from the INTENSITY study.

BACKGROUND: Studies suggest that longer durations of T2DM increase the risk of T2DM complications and premature mortality. However, whether T2DM duration impacts the efficacy of an aerobic exercise intervention is unclear. OBJECTIVE: The purpose of this study was: 1) to compare changes in body composition, cardiorespiratory fitness, and glycemia between individuals with short- and long-duration T2DM after aerobic exercise and 2) to determine whether these changes were associated with changes in glycemia by T2DM duration. METHODS: A secondary analysis of the INTENSITY study (NCT03787836), including thirty-four adults (≥19 years) with T2DM who participated in 28 weeks of aerobic exercise training for 150 minutes per week at a moderate-to-vigorous intensity (4.5 to 6.0 metabolic equivalents (METs)). Using pre-established cut-points, participants were categorized into two groups 1) short-duration T2DM (<5 years) or 2) long-duration T2DM (≥5 years). Glycemia was measured by glycated hemoglobin (HbA1c), body composition by BodPod, and cardiorespiratory fitness by a measure of peak oxygen consumption (VO2peak). All measurements were performed at baseline, 16 weeks, and 28 weeks. RESULTS: Participants in the short-duration T2DM group experienced decreases in fat mass (kg) (p = 0.03), HbA1c (p = 0.05), and an increased relative VO2peak (p = 0.01). Those with long-duration T2DM experienced decreases in fat mass (kg) (p = 0.02) and HbA1c (p <0.001) and increased fat-free mass (p = 0.02). No significant differences were observed between groups in any outcomes. Changes in fat mass (r = 0.54, p = 0.02), and body fat percentage (r = 0.50, p = 0.02) were significantly associated with the change in HbA1c in those with a long-duration T2DM only. CONCLUSION: Our results suggest T2DM duration did not differently impact the efficacy of a 28-week aerobic exercise intervention. However, changes in body composition were associated with better glycemia in individuals with longer T2DM duration only.

Automated classification of cellular expression in multiplexed imaging data with Nimbus.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

The effect of a direct-fed microbial (10-G) on live animal performance, carcass characteristics, and Salmonella prevalence of fed beef heifers.

The objective of this study was to determine the efficacy of the direct-fed microbial 10-G upon cattle growth performance, liver and lung health, carcass quality, and yield outcomes, as well as prevalence and enumeration of Salmonella in feces and lymph nodes. Fed beef heifers (N = 1,400; initial shrunk body weight [BW] 343.3 ±â€…36.2 kg) were blocked by the day of arrival and randomly allocated to one of two treatments (0 [negative control, CON] or 2 g of a direct-fed microbial [10-G] that provided 1 billion CFUs per animal per day of Lactobacillus acidophilus, Enterococcus faecium, Pediococcus pentosaceus, L. brevis, and L. plantarum) with 10 pens per treatment. Recto-anal mucosal fecal samples (RAMs; n = 477) and subiliac lymph nodes (SLNs; n = 479) were collected longitudinally at harvest from 23 to 25 heifers per pen. Data were analyzed using mixed models; pen served as the experimental unit; block and harvest date were random effects. No differences were detected in dry matter intake (P = 0.78), final BW (P = 0.64), average daily gain (P = 0.51), gain to feed (P = 0.71), hot carcass weight (P = 0.54), dressed carcass yield (P = 0.52), 12th rib fat depth (P = 0.13), longissimus muscle area (P = 0.62), calculated empty body fat (P = 0.26), or marbling score (P = 0.82). Distributions of liver scores (P ≥ 0.34), quality grades (P ≥ 0.23), and yield grades (P ≥ 0.11) were also not different between treatments. A tendency was detected for more normal lungs (P = 0.08; 10-G = 65.96%, CON = 61.12%) and fewer inflated lungs at harvest for cattle fed 10-G (P = 0.10; 10-G = 0.29%, CON = 1.16%); other lung outcomes did not differ (P ≥ 0.54). Salmonella prevalence did not differ for RAM samples (P = 0.41; 10-G = 97.74%, CON = 96.82%) or SLN (P = 0.22; 10-G = 17.92%, CON = 13.66%). Salmonella concentration of RAM samples (P = 0.25; 10-G = 3.87 log CFU/g, CON = 3.32 log CFU/g) or SLN (P = 0.37; 10-G = 1.46 log CFU/g, CON = 1.14 log CFU/g) also did not differ between treatments at harvest. These results do not demonstrate any difference in live animal performance, carcass characteristics, or Salmonella carriage for heifers fed 10-G.

Machine learning in cardiac surgery: a narrative review.

Background and Objective: Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support. Methods: We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery". Key Content and Findings: ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited. Conclusions: Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.

Manufacturing Autoclave-Grade Thermoset Carbon Fiber-Reinforced Polymer Aerospace Composites without an Autoclave Using Nanoporous Materials.

Composite laminates utilizing autoclave-grade carbon fiber-reinforced plastic (CFRP) prepreg were manufactured using a polymer nanoporous network (NPN) interlayer that generates capillary pressure in lieu of pressure from an autoclave. The polymer nanofiber NPN film is integrated into the interlaminar region and is shown to eliminate voids in a vacuum-bag-only (VBO) curing process. After a preliminary investigation of the effect of NPN thickness on the interlaminar region and performance, an 8 µm thick polymer NPN was selected for a scaled manufacturing demonstration. Combining the polymer NPN with "out-of-oven" (OoO) electrothermal heating of a carbon nanotube (CNT)-heated tool, a 0.6 × 0.6 m void-free plate is successfully manufactured. OoO cure enables an accelerated cure cycle, which reduces the cure time by 35% compared to the manufacturer-recommended cure cycle (MRCC). X-ray microcomputed tomography (µ-CT) reveals that the laminates are void-free and of identical quality to autoclave-cured specimens. An array of mechanical tests including tension, compression, open-hole compression (OHC), tension-bearing (bolt-bearing), and compression after impact, reveal that the accelerated NPN-cured composites were broadly equivalent, with some instances of improved properties, relative to the autoclave-cured parts, e.g., OHC strength increased by 5%. With reduced capital costs, energy consumption, and increased throughput, the facile polymer NPN-enabled out-of-autoclave (OoA) fabrication method is shown to be a practical and attractive alternative to conventional autoclave fabrication.

Diviner uncovers hundreds of novel human (and other) exons though comparative analysis of proteins.

Background: Eukaryotic genes are often composed of multiple exons that are stitched together by splicing out the intervening introns. These exons may be conditionally joined in different combinations to produce a collection of related, but distinct, mRNA transcripts. For protein-coding genes, these products of alternative splicing lead to production of related protein variants (isoforms) of a gene. Complete labeling of the protein-coding content of a eukaryotic genome requires discovery of mRNA encoding all isoforms, but it is impractical to enumerate all possible combinations of tissue, developmental stage, and environmental context; as a result, many true exons go unlabeled in genome annotations. Results: One way to address the combinatoric challenge of finding all isoforms in a single organism A is to leverage sequencing efforts for other organisms - each time a new organism is sequenced, it may be under a new combination of conditions, so that a previously unobserved isoform may be sequenced. We present Diviner, a software tool that identifies previously undocumented exons in organisms by comparing isoforms across species. We demonstrate Diviner's utility by locating hundreds of novel exons in the genomes of human, mouse, and rat, as well as in the ferret genome. Further, we provide analyses supporting the notion that most of the new exons reported by Diviner are likely to be part of a true (but unobserved) isoform of the containing species.

WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences.

Background: Software for labeling biological sequences typically produces a theory-based statistic for each match (the E-value) that indicates the likelihood of seeing that match's score by chance. E-values accurately predict false match rate for comparisons of random (shuffled) sequences, and thus provide a reasoned mechanism for setting score thresholds that enable high sensitivity with low expected false match rate. This threshold-setting strategy is challenged by real biological sequences, which contain regions of local repetition and low sequence complexity that cause excess matches between non-homologous sequences. Knowing this, tool developers often develop benchmarks that use realistic-seeming decoy sequences to explore empirical tradeoffs between sensitivity and false match rate. A recent trend has been to employ reversed biological sequences as realistic decoys, because these preserve the distribution of letters and the existence of local repeats, while disrupting the original sequence's functional properties. However, we and others have observed that sequences appear to produce high scoring alignments to their reversals with surprising frequency, leading to overstatement of false match risk that may negatively affect downstream analysis. Results: We demonstrate that an alignment between a sequence S and its (possibly mutated) reversal tends to produce higher scores than alignment between truly unrelated sequences, even when S is a shuffled string with no notable repetitive or low-complexity regions. This phenomenon is due to the unintuitive fact that (even randomly shuffled) sequences contain palindromes that are on average longer than the longest common substrings (LCS) shared between permuted variants of the same sequence. Though the expected palindrome length is only slightly larger than the expected LCS, the distribution of alignment scores involving reversed sequences is strongly right-shifted, leading to greatly increased frequency of high-scoring alignments to reversed sequences. Impact: Overestimates of false match risk can motivate unnecessarily high score thresholds, leading to potentially reduced true match sensitivity. Also, when tool sensitivity is only reported up to the score of the first matched decoy sequence, a large decoy set consisting of reversed sequences can obscure sensitivity differences between tools. As a result of these observations, we advise that reversed biological sequences be used as decoys only when care is taken to remove positive matches in the original (un-reversed) sequences, or when overstatement of false labeling is not a concern. Though the primary focus of the analysis is on sequence annotation, we also demonstrate that the prevalence of internal palindromes may lead to an overstatement of the rate of false labels in protein identification with mass spectrometry.

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data.

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia's dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Needle bacterial community structure across the species range of limber pine.

Bacteria on and inside leaves can influence forest tree health and resilience. The distribution and limits of a tree species' range can be influenced by various factors, with biological interactions among the most significant. We investigated the processes shaping the bacterial needle community across the species distribution of limber pine, a widespread Western conifer inhabiting a range of extreme habitats. We tested four hypotheses: (i) Needle community structure varies across sites, with site-specific factors more important to microbial assembly than host species selection; (ii) dispersal limitation structures foliar communities across the range of limber pine; (iii) the relative significance of dispersal and selection differs across sites in the tree species range; and (iv) needle age structures bacterial communities. We characterized needle communities from the needle surface and tissue of limber pine and co-occurring conifers across 16 sites in the limber pine distribution. Our findings confirmed that site characteristics shape the assembly of bacterial communities across the host species range and showed that these patterns are not driven by dispersal limitation. Furthermore, the strength of selection by the host varied by site, possibly due to differences in available microbes. Our study, by focusing on trees in their natural setting, reveals real needle bacterial dynamics in forests, which is key to understanding the balance between stochastic and deterministic processes in shaping forest tree-microbe interactions. Such understanding will be necessary to predict or manipulate these interactions to support forest ecosystem productivity or assist plant migration and adaptation in the face of global change.

Diagnostic efficacy and clinical impact of image-guided core needle biopsy of suspected vertebral osteomyelitis.

OBJECTIVES: The diagnostic yield and clinical impact of image-guided core needle biopsy (ICNB) of suspected vertebral osteomyelitis in adults is heterogenous in published studies owing to small sample sizes, indicating the need for large cohort studies. METHODS: A retrospective analysis of ICNBs was performed from 2010 to 2021 for patients with imaging findings consistent with vertebral osteomyelitis. For each biopsy, a series of factors were analyzed, as well as if histopathology was diagnostic of osteomyelitis and if microbiological cultures were positive. In addition, it was recorded in what way biopsy influenced clinical management regarding antimicrobial treatment. A multivariate statistical analysis was performed to evaluate the factors associated with yield. RESULTS: A total of 570 biopsies performed on 527 patients were included. A histopathologic diagnosis of osteomyelitis was made in 68.4% (359 of 525) of biopsies, and microbiological cultures were positive in 29.6% (169 of 570). Elevated erythrocyte sedimentation rate was positively associated with a histopathologic diagnosis of osteomyelitis (odds ratio [OR] =1.96, P = 0.007) and positive cultures from bone cores (OR = 1.02, P ≤0.001) and aspirate (OR = 1.02, P ≤0.001). Increased total core length was positively associated with a histopathologic diagnosis of osteomyelitis (OR = 1.81, P = 0.013) and positive cultures from bone cores (OR = 1.65, P = 0.049). Clinical management was affected by ICNB in 37.5% (214 of 570) of cases. CONCLUSIONS: In this large cohort, ICNB yielded approximately 30% positive cultures and changed clinical management in over one-third of the patients.