Host Use Patterns by the European Woodwasp, Sirex noctilio, in Its Native and Invaded Range

Accelerating introductions of forest insects challenge decision-makers who might or might not respond with surveillance programs, quarantines, eradication efforts, or biological control programs. Comparing ecological controls on indigenous vs. introduced populations could inform responses to new introductions. We studied the European woodwasp, Sirex noctilio, which is not a pest in its native forests, is a serious invasive pest in the southern hemisphere, and now has an uncertain future in North America after its introduction there. Indigenous populations of S. noctilio (in Galicia, Spain) resembled those in New York in that S. noctilio were largely restricted to suppressed trees that were also dying for other reasons, and still only some dying trees showed evidence of S. noctilio: 20–40% and 35–51% in Galicia and New York, respectively. In both areas, P. sylvestris (native to Europe) was the species most likely to have attacks in non-suppressed trees. P. resinosa, native to North America, does not appear dangerously susceptible to S. noctilio . P. radiata, which sustains high damage in the southern hemisphere, is apparently not innately susceptible because in Galicia it was less often used by native S. noctilio than either native pine (P. pinaster and P. sylvestris ). Silvicultural practices in Galicia that maintain basal area at 25–40 m2/ha limit S. noctilio abundance. More than 25 species of other xylophagous insects feed on pine in Galicia, but co-occurrences with S. noctilio were infrequent, so strong interspecific competition seemed unlikely. Evidently, S. noctilio in northeastern North America will be more similar to indigenous populations in Europe, where it is not a pest, than to introduced populations in the southern hemisphere, where it is. However, S. noctilio populations could behave differently when they reach forests of the southeastern U.S., where tree species, soils, climate, ecology, management, and landscape configurations of pine stands are different

Evolved Resistance to a Novel Cationic Peptide Antibiotic Requires High Mutation Supply

Background and Objectives A key strategy for resolving the antibiotic resistance crisis is the development of new drugs with antimicrobial properties. The engineered cationic antimicrobial peptide WLBU2 (also known as PLG0206) is a promising broad-spectrum antimicrobial compound that has completed Phase I clinical studies. It has activity against Gram-negative and Gram-positive bacteria including infections associated with biofilm. No definitive mechanisms of resistance to WLBU2 have been identified. Methodology Here, we used experimental evolution under different levels of mutation supply and whole genome sequencing (WGS) to detect the genetic pathways and probable mechanisms of resistance to this peptide. We propagated populations of wild-type and hypermutator Pseudomonas aeruginosa in the presence of WLBU2 and performed WGS of evolved populations and clones. Results Populations that survived WLBU2 treatment acquired a minimum of two mutations, making the acquisition of resistance more difficult than for most antibiotics, which can be tolerated by mutation of a single target. Major targets of resistance to WLBU2 included the orfN and pmrB genes, previously described to confer resistance to other cationic peptides. More surprisingly, mutations that increase aggregation such as the wsp pathway were also selected despite the ability of WLBU2 to kill cells growing in a biofilm. Conclusions and implications The results show how experimental evolution and WGS can identify genetic targets and actions of new antimicrobial compounds and predict pathways to resistance of new antibiotics in clinical practice

Dalbavancin Sequential Therapy for Gram-Positive Bloodstream Infection: A Multicenter Observational Study

Introduction Long-acting lipoglycopeptides such as dalbavancin may have utility in patients with Gram-positive bloodstream infections (BSI), particularly in those with barriers to discharge or who require prolonged parenteral antibiotic courses. A retrospective cohort study was performed to provide further multicenter real-world evidence on dalbavancin use as a sequential therapy for Gram-positive BSI. Methods One hundred fifteen patients received dalbavancin with Gram-positive BSI, defined as any positive blood culture or diagnosed with infective endocarditis, from 13 centers geographically spread across the United States between July 2015 and July 2021. Results Patients had a mean (SD) age of 48.5 (17.5) years, the majority were male (54%), with many who injected drugs (40%). The most common infection sources (non-exclusive) were primary BSI (89%), skin and soft tissue infection (SSTI) (25%), infective endocarditis (19%), and bone and joint infection (17%). Staphylococcus aureus accounted for 72% of index cultures, coagulase-negative Staphylococcus accounted for 18%, and Streptococcus species in 16%. Dalbavancin started a median (Q1–Q3) of 10 (6–19) days after index culture collection. The most common regimen administered was dalbavancin 1500 mg as one dose for 50% of cases. The primary outcome of composite clinical failure occurred at 12.2%, with 90-day mortality at 7.0% and 90-day BSI recurrence at 3.5%. Conclusions Dalbavancin may serve as a useful tool in facilitating hospital discharge in patients with Gram-positive BSI. Randomized controlled trials are anticipated to validate dalbavancin as a surrogate to current treatment standards

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

The effect of climate change on avian offspring production: A global meta-analysis

Climate change affects timing of reproduction in many bird species, but few studies have investigated its influence on annual reproductive output. Here, we assess changes in the annual production of young by female breeders in 201 populations of 104 bird species (N = 745,962 clutches) covering all continents between 1970 and 2019. Overall, average offspring production has declined in recent decades, but considerable differences were found among species and populations. A total of 56.7% of populations showed a declining trend in offspring production (significant in 17.4%), whereas 43.3% exhibited an increase (significant in 10.4%). The results show that climatic changes affect offspring production through compounded effects on ecological and life history traits of species. Migratory and larger-bodied species experienced reduced offspring production with increasing temperatures during the chick-rearing period, whereas smaller-bodied, sedentary species tended to produce more offspring. Likewise, multi-brooded species showed increased breeding success with increasing temperatures, whereas rising temperatures were unrelated to repro- ductive success in single-brooded species. Our study suggests that rapid declines in size of bird populations reported by many studies from different parts of the world are driven only to a small degree by changes in the production of young

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Study sites with <i>S. noctilio</i> in Finger Lakes National Forest, New York.

a<p>Tree heights (± SD): 1^st two <i>P. resinosa</i> stands  =  23.1±3.1 and 18.1±3.5 m; 1^st two <i>P. sylvestris</i> stands  =  20.1±4.7 and 18.0±1.8 m.</p>b<p>Resin drips from ovipositor stings by <i>S. noctilio</i> and/or emergence holes from siricids.</p>c<p>Only including trees from which there were some siricid emergence holes.</p