Prediction of Klebsiella phage-host specificity at the strain level

Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

The role of Helicobacter suis, Fusobacterium gastrosuis, and the pars oesophageal microbiota in gastric ulceration in slaughter pigs receiving meal or pelleted feed

This study investigated the role of causative infectious agents in ulceration of the non-glandular part of the porcine stomach (pars oesophagea). In total, 150 stomachs from slaughter pigs were included, 75 from pigs that received a meal feed, 75 from pigs that received an equivalent pelleted feed with a smaller particle size. The pars oesophagea was macroscopically examined after slaughter. (q)PCR assays for H. suis, F. gastrosuis and H. pylori-like organisms were performed, as well as 16S rRNA sequencing for pars oesophagea microbiome analyses. All 150 pig stomachs showed lesions. F. gastrosuis was detected in 115 cases (77%) and H. suis in 117 cases (78%), with 92 cases (61%) of co-infection; H. pylori-like organisms were detected in one case. Higher infectious loads of H. suis increased the odds of severe gastric lesions (OR = 1.14, p = 0.038), while the presence of H. suis infection in the pyloric gland zone increased the probability of pars oesophageal erosions [16.4% (95% CI 0.6-32.2%)]. The causal effect of H. suis was mediated by decreased pars oesophageal microbiome diversity [-1.9% (95% CI - 5.0-1.2%)], increased abundances of Veillonella and Campylobacter spp., and decreased abundances of Lactobacillus, Escherichia-Shigella, and Enterobacteriaceae spp. Higher infectious loads of F. gastrosuis in the pars oesophagea decreased the odds of severe gastric lesions (OR = 0.8, p = 0.0014). Feed pelleting had no significant impact on the prevalence of severe gastric lesions (OR = 1.72, p = 0.28). H. suis infections are a risk factor for ulceration of the porcine pars oesophagea, probably mediated through alterations in pars oesophageal microbiome diversity and composition.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Plant science in the age of simulation intelligence

Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.

Requirements for dose calculation on an active scanned proton beamline for small, shallow fields

INTRODUCTION

A growing interest in using proton pencil beam scanning in combination with collimators for the treatment of small, shallow targets, such as ocular melanoma or pre-clinical research emerged recently. This study aims at demonstrating that the dose of a synchrotron-based PBS system with a dedicated small, shallow field nozzle can be accurately predicted by a commercial treatment planning system (TPS) following appropriate tuning of both, nozzle and TPS.

MATERIALS

A removable extension to the clinical nozzle was developed to modify the beam shape passively. Five circular apertures with diameters between 5 to 34mm, mounted 72cm downstream of a range shifter were used. For each collimator treatment plans with spread-out Bragg peaks (SOBP) with a modulation of 3 to 30mm were measured and calculated with GATE/Geant4 and the research TPS RayStation (RS11B-R). The dose grid, multiple coulomb scattering and block discretization resolution were varied to find the optimal balance between accuracy and performance.

RESULTS

For SOBPs deeper than 10mm, the dose in the target agreed within 1% between RS11B-R, GATE/Geant4 and measurements for aperture diameters between 8 to 34mm, but deviated up to 5% for smaller apertures. A plastic taper was introduced reducing scatter contributions to the patient (from the pipe) and improving the dose calculation accuracy of the TPS to a 5% level in the entrance region for large apertures.

CONCLUSION

The commercial TPS and GATE/Geant4 can accurately calculate the dose for shallow, small proton fields using a collimator and pencil beam scanning.

Plant impedance spectroscopy: a review of modeling approaches and applications

Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.

Novel unconventional radiotherapy techniques: Current status and future perspectives - Report from the 2nd international radiation oncology online seminar

•Improvement of therapeutic ratio by novel unconventional radiotherapy approaches.•Immunomodulation using high-dose spatially fractionated radiotherapy.•Boosting radiation anti-tumor effects by adding an immune-mediated cell killing.

JS09.6.A Low incidence of radiation-induced brain lesions and stable QoL following proton irradiation for CNS and Skull Base tumors- results from the prospective MedAustron register REGI-MA-002015

Background

Irradiation of intracranial tumors may induce endothelial damage in the surrounding normal brain tissues, resulting in an increase of capillary permeability. These changes can be depicted on magnetic resonance imaging (MRI) as a new contrast medium uptake - not associated with tumor. Radiation-induced brain lesions (RIBL) occur after photon as well as proton irradiation. This study evaluated the incidence of RIBL after proton irradiation and their impact on Quality of Life (QoL).

Material and Methods

421 patients treated between 01/2017 and 06/2021 were included. All patients participated in a prospective registry study (ClinicalTrials.gov Identifier: NCT03049072). Follow-up evaluations including MRIs were at 3,6,12 months after treatment completion and annually thereafter. QoL parameters were assessed by EORTC-CTC30 and BN20 questionnaires.

Results

48.9% (n=206) patients received therapy for intracranial non-CNS tumors (meningioma, pituitary adenoma, and other), 26.8% (n=113) for head and neck cancer with skull base involvement, 14.5% (n=61) for primary CNS tumors and 9.7% (n=41) for skull base tumor. Median follow-up was 24 months (range 6-54 ), 352 (86%) patients had proton therapy as primary treatment, 59 (14%) had salvage treatment with proton re-irradiation (ReRT). Median prescribed dose was 58.5 Gy (RBE) (range 40-78 Gy (RBE)), median D1% of brain tissue was 54.3 Gy (RBE) (range 30-76 Gy RBE). Local control and overall survival were 91% and 95% at 2 years. The cumulative RIBL incidence was 15.0% (n=63), with significantly lower occurrence in the primary RT group vs. the ReRT group (12.9% vs. 27.1%; p<0.001). According to Grade, the distribution was 10.5% (n=44) Grade I (asymptomatic, MRT finding only), Grade II RIBL, 13 (3.1%) (moderate symptoms) (grade 2) and 1,4% (n=6) developed Grade 3 toxicity. Actuarial 2-year RIBL incidence was 18.2% (95%CI: 14.1-23.2) for the all Grades and the entire, 15.7% (95%CI: 11.6-21) following primary radiation and 34.2% (95%CI: 21.9-50.9) after ReRT. All RIBL developed outside the residual tumor, but inside the Planning Target Volume (PTV), median D1% was 60.3Gy (RBE) (range 46.1- 122.3 Gy(RBE)), median time to development was 11.8 months (range 2.7-37 months) in the total cohort, for primary RT 14.2mo (4.3mo -37.1mo) and 6.0mo (2.7mo -19.3mo) following ReRT. At the time of analysis 26 of the 63 RIBL had resolved (41.3%). General QoL was not compromised. In a matched-pair analysis of 54/50 patients with/without RIBL, only at the 12 month a significant difference in the global health score in favour of non-RIBL patients was observed. At 24 months the score for RIBL patients improved without difference between the groups.

Conclusion

Overall incidence of RIBL after proton radiotherapy is very low - even for skull base tumors requiring high total doses and it had no significant negative impact on long term QoL.

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Covering the Combinatorial Design Space of Multiplex CRISPR/Cas Experiments in Plants

Over the past years, CRISPR/Cas-mediated genome editing has revolutionized plant genetic studies and crop breeding. Specifically, due to its ability to simultaneously target multiple genes, the multiplex CRISPR/Cas system has emerged as a powerful technology for functional analysis of genetic pathways. As such, it holds great potential for application in plant systems to discover genetic interactions and to improve polygenic agronomic traits in crop breeding. However, optimal experimental design regarding coverage of the combinatorial design space in multiplex CRISPR/Cas screens remains largely unexplored. To contribute to well-informed experimental design of such screens in plants, we first establish a representation of the design space at different stages of a multiplex CRISPR/Cas experiment. We provide two independent computational approaches yielding insights into the plant library size guaranteeing full coverage of all relevant multiplex combinations of gene knockouts in a specific multiplex CRISPR/Cas screen. These frameworks take into account several design parameters (e.g., the number of target genes, the number of gRNAs designed per gene, and the number of elements in the combinatorial array) and efficiencies at subsequent stages of a multiplex CRISPR/Cas experiment (e.g., the distribution of gRNA/Cas delivery, gRNA-specific mutation efficiency, and knockout efficiency). With this work, we intend to raise awareness about the limitations regarding the number of target genes and order of genetic interaction that can be realistically analyzed in multiplex CRISPR/Cas experiments with a given number of plants. Finally, we establish guidelines for designing multiplex CRISPR/Cas experiments with an optimal coverage of the combinatorial design space at minimal plant library size.

BioCCP.jl: collecting coupons in combinatorial biotechnology

SUMMARY

In combinatorial biotechnology, it is crucial for screening experiments to sufficiently cover the design space. In the BioCCP.jl package (Julia), we provide functions for minimum sample size determination based on the mathematical framework coined the Coupon Collector Problem.

AVAILABILITY AND IMPLEMENTATION

BioCCP.jl, including source code, documentation and Pluto notebooks, is available at https://github.com/kirstvh/BioCCP.jl.

ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli

A major goal in synthetic biology is the engineering of synthetic gene circuits with a predictable, controlled and designed outcome. This creates a need for building blocks that can modulate gene expression without interference with the native cell system. A tool allowing forward engineering of promoters with predictable transcription initiation frequency is still lacking. Promoter libraries specific for σ⁷⁰ to ensure the orthogonality of gene expression were built in Escherichia coli and labeled using fluorescence-activated cell sorting to obtain high-throughput DNA sequencing data to train a convolutional neural network. We were able to confirm in vivo that the model is able to predict the promoter transcription initiation frequency (TIF) of new promoter sequences. Here, we provide an online tool for promoter design (ProD) in E. coli, which can be used to tailor output sequences of desired promoter TIF or predict the TIF of a custom sequence.

Digital phagograms: predicting phage infectivity through a multilayer machine learning approach

Machine learning has been broadly implemented to investigate biological systems. In this regard, the field of phage biology has embraced machine learning to elucidate and predict phage-host interactions, based on receptor-binding proteins, (anti-)defense systems, prophage detection, and life cycle recognition. Here, we highlight the enormous potential of integrating information from omics data with insights from systems biology to better understand phage-host interactions. We conceptualize and discuss the potential of a multilayer model that mirrors the phage infection process, integrating adsorption, bacterial pan-immune components and hijacking of the bacterial metabolism to predict phage infectivity. In the future, this model can offer insights into the underlying mechanisms of the infection process, and digital phagograms can support phage cocktail design and phage engineering.

Disentangling the Information in Species Interaction Networks

Shannon's entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.

Optimal transportation theory for species interaction networks

Observed biotic interactions between species, such as in pollination, predation, and competition, are determined by combinations of population densities, matching in functional traits and phenology among the organisms, and stochastic events (neutral effects).We propose optimal transportation theory as a unified view for modeling species interaction networks with different intensities of interactions. We pose the coupling of two distributions as a constrained optimization problem, maximizing both the system's average utility and its global entropy, that is, randomness. Our model follows naturally from applying the MaxEnt principle to this problem setting.This approach allows for simulating changes in species relative densities as well as to disentangle the impact of trait matching and neutral forces.We provide a framework for estimating the pairwise species utilities from data. Experimentally, we show how to use this framework to perform trait matching and predict the coupling in pollination and host-parasite networks.

Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

Nowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

Predictive design of sigma factor-specific promoters

To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.

Time-resolved dosimetry for validation of 4D dose calculation in PBS proton therapy

Four-dimensional dose calculation (4D-DC) is crucial for predicting the dosimetric outcome in the presence of intra-fractional organ motion. Time-resolved dosimetry can provide significant insights into 4D pencil beam scanning dose accumulation and is therefore irreplaceable for benchmarking 4D-DC. In this study a novel approach of time-resolved dosimetry using five PinPoint ionization chambers (ICs) embedded in an anthropomorphic dynamic phantom was employed and validated against beam delivery details. Beam intensity variations as well as the beam delivery time structure were well reflected with an accuracy comparable to the temporal resolution of the IC measurements. The 4D dosimetry approach was further applied for benchmarking the 4D-DC implemented in the RayStation 6.99 treatment planning system. Agreement between computed values and measurements was investigated for (i) partial doses based on individual breathing phases, and (ii) temporally distributed cumulative doses. For varied beam delivery and patient-related parameters the average unsigned dose difference for (i) was 0.04 ± 0.03 Gy over all considered IC measurement values, while the prescribed physical dose was 2 Gy. By implementing (ii), a strong effect of the dose gradient on measurement accuracy was observed. The gradient originated from scanned beam energy modulation and target motion transversal to the beam. Excluding measurements in the high gradient the relative dose difference between measurements and 4D-DCs for a given treatment plan at the end of delivery was 3.5% on average and 6.6% at maximum over measurement points inside the target. Overall, the agreement between 4D dose measurements in the moving phantom and retrospective 4D-DC was found to be comparable to the static dose differences for all delivery scenarios. The presented 4D-DC has been proven to be suitable for simulating treatment deliveries with various beam- as well as patient-specific parameters and can therefore be employed for dosimetric validation of different motion mitigation techniques.

Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.

Predicting Pharmaceutical Particle Size Distributions Using Kernel Mean Embedding

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGma TM -25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physical phenomena leading to changes in this particle size distribution is lacking. It is essential to understand how the particle size distribution changes as a function of the unit operation's process settings, as it has a profound effect on the behavior of the fluid bed dryer. Therefore, we suggest a data-driven modeling framework that links the machine settings of the wet granulation unit operation and the output distribution of granules. We do this without making any assumptions on the nature of the distributions under study. A simulation of the granule size distribution could act as a soft sensor when in-line measurements are challenging to perform. The method of this work is a two-step procedure: first, the measured distributions are transformed into a high-dimensional feature space, where the relation between the machine settings and the distributions can be learnt. Second, the inverse transformation is performed, allowing an interpretation of the results in the original measurement space. Further, a comparison is made with previous work, which employs a more mechanistic framework for describing the granules. A reliable prediction of the granule size is vital in the assurance of quality in the production line, and is needed in the assessment of upstream (feeding) and downstream (drying, milling, and tableting) issues. Now that a validated data-driven framework for predicting pharmaceutical particle size distributions is available, it can be applied in settings such as model-based experimental design and, due to its fast computation, there is potential in real-time model predictive control.

Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is time-consuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.

Dynamic lung phantom commissioning for 4D dose assessment in proton therapy

Anthropomorphic phantoms mimicking organ and tumor motion of patients are essential for end-to-end testing of motion mitigation techniques in ion beam therapy. In this work a commissioning procedure developed with the in-house designed respiratory phantom ARDOS (Advanced Radiation DOSimetry system) is presented. The phantom was tested and benchmarked for 4D dose verification in proton therapy, which included: characterization of the tissue equivalent materials from computed tomography (CT) imaging, assessment of dose calculation accuracy in critical structures of the phantom, and testing various detectors for proton dosimetry in the ARDOS phantom. To prove the validity of the CT calibration curve, measured relative stopping powers (RSP) of the ARDOS materials were compared with values from CTs: original and overwritten with known material parameters. Override of rib- and soft-tissue phantom components improved RSP accuracy while inhomogeneous lung tissue, represented by the balsa wood, was better modelled by the CT Hounsfield units. Monte Carlo (MC) dose calculations were benchmarked against measurements with a reference Farmer chamber embedded in ARDOS material samples showing less than 3% relative dose difference. Differences between MC calculated dose distributions and those calculated by analytical algorithms for the ARDOS geometry were higher than 20% of the prescribed dose, depending on the position in the phantom. Pinpoint ionization chambers and thermoluminescence dosimeters showed differences of up to 5.5% compared to MC dose calculations for all lung setups in the static phantom. They were also able to detect dose distortions due to motion. EBT3 film dosimetry was shown to be suitable for 2D relative dose characterization, which could provide extended information on dose distributions in the penumbra area. The presented methodology and results can be used for drafting general recommendations for dynamic phantom commissioning, which is an essential step towards end-to-end evaluation of motion mitigation techniques in ion beam therapy.

Use of bioengineered human commensal gut bacteria-derived microvesicles for mucosal plague vaccine delivery and immunization

Plague caused by the Gram‐negative bacterium, Yersinia pestis, is still endemic in parts of the world today. Protection against pneumonic plague is essential to prevent the development and spread of epidemics. Despite this, there are currently no licensed plague vaccines in the western world. Here we describe the means of delivering biologically active plague vaccine antigens directly to mucosal sites of plague infection using highly stable microvesicles (outer membrane vesicles; OMVs) that are naturally produced by the abundant and harmless human commensal gut bacterium Bacteroides thetaiotaomicron (Bt). Bt was engineered to express major plague protective antigens in its OMVs, specifically Fraction 1 (F1) in the outer membrane and LcrV (V antigen) in the lumen, for targeted delivery to the gastrointestinal (GI) and respiratory tracts in a non‐human primate (NHP) host. Our key findings were that Bt OMVs stably expresses F1 and V plague antigens, particularly the V antigen, in the correct, immunogenic form. When delivered intranasally V‐OMVs elicited substantive and specific immune and antibody responses, both in the serum [immunoglobulin (Ig)G] and in the upper and lower respiratory tract (IgA); this included the generation of serum antibodies able to kill plague bacteria. Our results also showed that Bt OMV‐based vaccines had many desirable characteristics, including: biosafety and an absence of any adverse effects, pathology or gross alteration of resident microbial communities (microbiotas); high stability and thermo‐tolerance; needle‐free delivery; intrinsic adjuvanticity; the ability to stimulate both humoral and cell‐mediated immune responses; and targeting of primary sites of plague infection.

X-ray microtomography and linear discriminant analysis enable detection of embolism-related acoustic emissions

BACKGROUND

Acoustic emission (AE) sensing is in use since the late 1960s in drought-induced embolism research as a non-invasive and continuous method. It is very well suited to assess a plant's vulnerability to dehydration. Over the last couple of years, AE sensing has further improved due to progress in AE sensors, data acquisition methods and analysis systems. Despite these recent advances, it is still challenging to detect drought-induced embolism events in the AE sources registered by the sensors during dehydration, which sometimes questions the quantitative potential of AE sensing.

RESULTS

In quest of a method to separate embolism-related AE signals from other dehydration-related signals, a 2-year-old potted Fraxinus excelsior L. tree was subjected to a drought experiment. Embolism formation was acoustically measured with two broadband point-contact AE sensors while simultaneously being visualized by X-ray computed microtomography (µCT). A machine learning method was used to link visually detected embolism formation by µCT with corresponding AE signals. Specifically, applying linear discriminant analysis (LDA) on the six AE waveform parameters amplitude, counts, duration, signal strength, absolute energy and partial power in the range 100-200 kHz resulted in an embolism-related acoustic vulnerability curve (VC_AE-E) better resembling the standard µCT VC (VC_CT), both in time and in absolute number of embolized vessels. Interestingly, the unfiltered acoustic vulnerability curve (VC_AE) also closely resembled VC_CT, indicating that VCs constructed from all registered AE signals did not compromise the quantitative interpretation of the species' vulnerability to drought-induced embolism formation.

CONCLUSION

Although machine learning could detect similar numbers of embolism-related AE as µCT, there still is insufficient model-based evidence to conclusively attribute these signals to embolism events. Future research should therefore focus on similar experiments with more in-depth analysis of acoustic waveforms, as well as explore the possibility of Fast Fourier transformation (FFT) to remove non-embolism-related AE signals.