Use of sonic tomography to detect and quantify wood decay in living trees.

Premise of the studyField methodology and image analysis protocols using acoustic tomography were developed and evaluated as a tool to estimate the amount of internal decay and damage of living trees, with special attention to tropical rainforest trees with irregular trunk shapes.Methods and resultsLiving trunks of a diversity of tree species in tropical rainforests in the Republic of Panama were scanned using an Argus Electronic PiCUS 3 Sonic Tomograph and evaluated for the amount and patterns of internal decay. A protocol using ImageJ analysis software was used to quantify the proportions of intact and compromised wood. The protocols provide replicable estimates of internal decay and cavities for trees of varying shapes, wood density, and bark thickness.ConclusionsSonic tomography, coupled with image analysis, provides an efficient, noninvasive approach to evaluate decay patterns and structural integrity of even irregularly shaped living trees

CARB-ES-19 Multicenter Study of Carbapenemase-Producing Klebsiella pneumoniae and Escherichia coli From All Spanish Provinces Reveals Interregional Spread of High-Risk Clones Such as ST307/OXA-48 and ST512/KPC-3

ObjectivesCARB-ES-19 is a comprehensive, multicenter, nationwide study integrating whole-genome sequencing (WGS) in the surveillance of carbapenemase-producing K. pneumoniae (CP-Kpn) and E. coli (CP-Eco) to determine their incidence, geographical distribution, phylogeny, and resistance mechanisms in Spain.MethodsIn total, 71 hospitals, representing all 50 Spanish provinces, collected the first 10 isolates per hospital (February to May 2019); CPE isolates were first identified according to EUCAST (meropenem MIC > 0.12 mg/L with immunochromatography, colorimetric tests, carbapenem inactivation, or carbapenem hydrolysis with MALDI-TOF). Prevalence and incidence were calculated according to population denominators. Antibiotic susceptibility testing was performed using the microdilution method (EUCAST). All 403 isolates collected were sequenced for high-resolution single-nucleotide polymorphism (SNP) typing, core genome multilocus sequence typing (cgMLST), and resistome analysis.ResultsIn total, 377 (93.5%) CP-Kpn and 26 (6.5%) CP-Eco isolates were collected from 62 (87.3%) hospitals in 46 (92%) provinces. CP-Kpn was more prevalent in the blood (5.8%, 50/853) than in the urine (1.4%, 201/14,464). The cumulative incidence for both CP-Kpn and CP-Eco was 0.05 per 100 admitted patients. The main carbapenemase genes identified in CP-Kpn were blaOXA–48 (263/377), blaKPC–3 (62/377), blaVIM–1 (28/377), and blaNDM–1 (12/377). All isolates were susceptible to at least two antibiotics. Interregional dissemination of eight high-risk CP-Kpn clones was detected, mainly ST307/OXA-48 (16.4%), ST11/OXA-48 (16.4%), and ST512-ST258/KPC (13.8%). ST512/KPC and ST15/OXA-48 were the most frequent bacteremia-causative clones. The average number of acquired resistance genes was higher in CP-Kpn (7.9) than in CP-Eco (5.5).ConclusionThis study serves as a first step toward WGS integration in the surveillance of carbapenemase-producing Enterobacterales in Spain. We detected important epidemiological changes, including increased CP-Kpn and CP-Eco prevalence and incidence compared to previous studies, wide interregional dissemination, and increased dissemination of high-risk clones, such as ST307/OXA-48 and ST512/KPC-3

Vision, challenges and opportunities for a Plant Cell Atlas

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.</jats:p

Unambiguous Identification of Glucose-Induced Glycation in mAbs and other Proteins by NMR Spectroscopy.

Glycation is a non-enzymatic and spontaneous post-translational modification (PTM) generated by the reaction between reducing sugars and primary amine groups within proteins. Because glycation can alter the properties of proteins, it is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) and should therefore be carefully monitored. The most abundant product of glycation is formed by glucose and lysine side chains resulting in fructoselysine after Amadori rearrangement. In proteomics, which routinely uses a combination of chromatography and mass spectrometry to analyze PTMs, there is no straight-forward way to distinguish between glycation products of a reducing monosaccharide and an additional hexose within a glycan, since both lead to a mass difference of 162 Da.To verify that the observed mass change is indeed a glycation product, we developed an approach based on 2D NMR spectroscopy spectroscopy and full-length protein samples denatured using high concentrations of deuterated urea.The dominating β-pyranose form of the Amadori product shows a characteristic chemical shift correlation pattern in 1H-13C HSQC spectra suited to identify glucose-induced glycation. The same pattern was observed in spectra of a variety of artificially glycated proteins, including two mAbs, as well as natural proteins.Based on this unique correlation pattern, 2D NMR spectroscopy can be used to unambiguously identify glucose-induced glycation in any protein of interest. We provide a robust method that is orthogonal to MS-based methods and can also be used for cross-validation

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

Background: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. Methods: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists' decisions. Results: In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p&lt;0.001). Improvements were somewhat lower but highly significant (p&lt;0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion: A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone

Use of sonic tomography to detect and quantify wood decay in living trees

PREMISE OF THE STUDY: Field methodology and image analysis protocols using acoustic tomography were developed and evaluated as a tool to estimate the amount of internal decay and damage of living trees, with special attention to tropical rainforest trees with irregular trunk shapes. METHODS AND RESULTS: Living trunks of a diversity of tree species in tropical rainforests in the Republic of Panama were scanned using an Argus Electronic PiCUS 3 Sonic Tomograph and evaluated for the amount and patterns of internal decay. A protocol using ImageJ analysis software was used to quantify the proportions of intact and compromised wood. The protocols provide replicable estimates of internal decay and cavities for trees of varying shapes, wood density, and bark thickness. CONCLUSIONS: Sonic tomography, coupled with image analysis, provides an efficient, noninvasive approach to evaluate decay patterns and structural integrity of even irregularly shaped living trees

Use of sonic tomography to detect and quantify wood decay in living trees.

Field methodology and image analysis protocols using acoustic tomography were developed and evaluated as a tool to estimate the amount of internal decay and damage of living trees, with special attention to tropical rainforest trees with irregular trunk shapes. Living trunks of a diversity of tree species in tropical rainforests in the Republic of Panama were scanned using an Argus Electronic PiCUS 3 Sonic Tomograph and evaluated for the amount and patterns of internal decay. A protocol using ImageJ analysis software was used to quantify the proportions of intact and compromised wood. The protocols provide replicable estimates of internal decay and cavities for trees of varying shapes, wood density, and bark thickness. Sonic tomography, coupled with image analysis, provides an efficient, noninvasive approach to evaluate decay patterns and structural integrity of even irregularly shaped living trees

Genome Scan Meta-Analysis of Schizophrenia and Bipolar Disorder, Part II: Schizophrenia

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size ([Formula: see text]). A permutation test was used to compute the probability of observing, by chance, each bin’s average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (P(AvgRnk)<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations