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.

Temperature-driven dynamics: unraveling the impact of climate change on cryptic species interactions within the Litoditis marina complex.

Anthropogenic climate change and the associated increase in sea temperatures are projected to greatly impact marine ecosystems. Temperature variation can influence the interactions between species, leading to cascading effects on the abundance, diversity and composition of communities. Such changes in community structure can have consequences on ecosystem stability, processes and the services it provides. Therefore, it is important to better understand the role of species interactions in the development of communities and how they are influenced by environmental factors like temperature. The coexistence of closely related cryptic species, with significant biological and ecological differences, makes this even more complex. This study investigated the effect of temperature on species growth and both intra- and interspecific interactions of three species within the free-living nematode Litoditis marina complex. To achieve this, closed microcosm experiments were conducted on the L. marina species Pm I, Pm III and Pm IV in monoculture and combined cultures at two temperature treatments of 15 °C and 20 °C. A population model was constructed to elucidate and quantify the effects of intra- and interspecific interactions on nematode populations. The relative competitive abilities of the investigated species were quantified using the Modern Coexistence Theory (MCT) framework. Temperature had strong and disparate effects on the population growth of the distinct L. marina species. This indicates temperature could play an important role in the distribution of these cryptic species. Both competitive and facilitative interactions were observed in the experiments. Temperature affected both the type and the strength of the species interactions, suggesting a change in temperature could impact the coexistence of these closely related species, alter community dynamics and consequently affect ecosystem processes and services.

Cooperative interactions between invader and resident microbial community members weaken the negative diversity-invasion relationship.

The negative diversity-invasion relationship observed in microbial invasion studies is commonly explained by competition between the invader and resident populations. However, whether this relationship is affected by invader-resident cooperative interactions is unknown. Using ecological and mathematical approaches, we examined the survival and functionality of Aminobacter niigataensis MSH1 to mineralize 2,6-dichlorobenzamide (BAM), a groundwater micropollutant affecting drinking water production, in sand microcosms when inoculated together with synthetic assemblies of resident bacteria. The assemblies varied in richness and in strains that interacted pairwise with MSH1, including cooperative and competitive interactions. While overall, the negative diversity-invasion relationship was retained, residents engaging in cooperative interactions with the invader had a positive impact on MSH1 survival and functionality, highlighting the dependency of invasion success on community composition. No correlation existed between community richness and the delay in BAM mineralization by MSH1. The findings suggest that the presence of cooperative residents can alleviate the negative diversity-invasion relationship.

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning.

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

A comparison of embedding aggregation strategies in drug-target interaction prediction.

The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.

When driving becomes risky: Micro-scale variants of the lane-changing maneuver in highway traffic.

Objective: Vehicular lane-changing is one of the riskiest driving maneuvers. Since vehicular automation is quickly becoming a reality, it is crucial to be able to identify when such a maneuver can turn into a risky situation. Recently, it has been shown that a qualitative approach: the Point Descriptor Precedence (PDP) representation, is able to do so. Therefore, this study aims to investigate whether the PDP representation can detect hazardous micro movements during lane-changing maneuvers in a situation of structural congestion in the morning and/or evening.Method: The approach involves analyzing a large real-world traffic dataset using the PDP representation and adding safety distance points to distinguish subtle movement patterns.Results: Based on these subtleties, we label four out of seven and five out of nine lane-change maneuvers as risky during the selected peak and the off-peak traffic hours respectively.Conclusions: The results show that the approach can identify risky movement patterns in traffic. The PDP representation can be used to check whether certain adjustments (e.g., changing the maximum speed) have a significant impact on the number of dangerous behaviors, which is important for improving road safety. This approach has practical applications in penalizing traffic violations, improving traffic flow, and providing valuable information for policymakers and transport experts. It can also be used to train autonomous vehicles in risky driving situations.

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.

Oracle selection provides insight into how far off practice is from Utopia in plant breeding.

Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution.

Response of phytoplankton functional types to Hurricane Fabian (2003) in the Sargasso Sea.

Understanding how tropical cyclones affect phytoplankton communities is important for studies on ecological variability. Most studies assessing the post-storm phytoplankton response rely on satellite observations of chlorophyll a concentration, which inform on the ocean surface conditions and the whole phytoplankton community. In this work, we assess the potential of the Massachusetts Institute of Technology marine ecosystem model to account for the response of individual phytoplankton functional types (PFTs, coccolithophores, diatoms, diazotrophs, mixotrophic dinoflagellates, picoeukaryotes, Prochlorococcus and Synechococcus) in the euphotic zone to the passage of Hurricane Fabian (2003) across the tropical and subtropical Sargasso Sea. Fabian induced a significant mean concentration increase (t-test, p < 0.05) of all PFTs in the tropical waters (except for Prochlorococcus), which was driven by the mean nutrient concentration increase and by a limited zooplankton grazing pressure. More specifically, the post-storm nutrient enrichment increased the contribution of fast-growing PFTs (e.g. diatoms and coccolithophores) to the total phytoplankton concentration and decreased the contribution of slow-growing dominant groups (e.g. picoeukaryotes, Prochlorococcus and Synechococcus), which lead to a significant increase (t-test, p < 0.05) of the Shannon diversity index values. Overall, the model captured the causal relationship between nutrient and PFT concentration increases in the tropical waters, although it only reproduced the most pronounced PFT responses such as those in the deep euphotic zone. In contrast, the model did not capture the oceanic perturbations induced by Fabian as observed in satellite imagery in the subtropical waters, probably due to its limited performance in this complex oceanographic area.

The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba.

To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.

No bacterial-mediated alleviation of thermal stress in a brown seaweed suggests the absence of ecological bacterial rescue effects.

While microbiome alterations are increasingly proposed as a rapid mechanism to buffer organisms under changing environmental conditions, studies of these processes in the marine realm are lagging far behind their terrestrial counterparts. Here, we used a controlled laboratory experiment to examine whether the thermal tolerance of the brown seaweed Dictyota dichotoma, a common species in European coastal ecosystems, could be enhanced by the repeated addition of bacteria from its natural environment. Juvenile algae from three genotypes were subjected for two weeks to a temperature gradient, spanning almost the entire thermal range that can be tolerated by the species (11-30 °C). At the start of the experiment and again in the middle of the experiment, the algae were inoculated with bacteria from their natural environment or left untouched as a control. Relative growth rate was measured over the two-week period, and we assessed bacterial community composition prior to and at the end of the experiment. Since the growth of D. dichotoma over the full thermal gradient was not affected by supplementing bacteria, our results indicate no scope for bacterial-mediated stress alleviation. The minimal changes in the bacterial communities linked to bacterial addition, particularly at temperatures above the thermal optimum (22-23 °C), suggest the existence of a barrier to bacterial recruitment. These findings indicate that ecological bacterial rescue is unlikely to play a role in mitigating the effects of ocean warming on this brown seaweed.

Seven-state rotation-symmetric number-conserving cellular automaton that is not isomorphic to any septenary one.

We consider two-dimensional cellular automata with the von Neumann neighborhood that satisfy two properties of interest from a modeling viewpoint: rotation symmetry (i.e., the local rule is invariant under rotation of the neighborhood by 90^{∘}) and number conservation (i.e., the sum of all the cell states is conserved upon every update). It is known that if the number of states k is smaller than or equal to six, then each rotation-symmetric number-conserving cellular automaton is isomorphic to some k-ary one, i.e., one with state set {0,1,...,k-1}. In this paper, we exhibit an example of a seven-state rotation-symmetric number-conserving cellular automaton that is not isomorphic to any septenary one. This example strongly supports our plea that research into multistate cellular automata should not only focus on those that have {0,1,...,k-1} as a state set.

Food biodiversity: Quantifying the unquantifiable in human diets.

Dietary diversity is an established public health principle, and its measurement is essential for studies of diet quality and food security. However, conventional between food group scores fail to capture the nutritional variability and ecosystem services delivered by dietary richness and dissimilarity within food groups, or the relative distribution (i.e., evenness or moderation) of e.g., species or varieties across whole diets. Summarizing food biodiversity in an all-encompassing index is problematic. Therefore, various diversity indices have been proposed in ecology, yet these require methodological adaption for integration in dietary assessments. In this narrative review, we summarize the key conceptual issues underlying the measurement of food biodiversity at an edible species level, assess the ecological diversity indices previously applied to food consumption and food supply data, discuss their relative suitability, and potential amendments for use in (quantitative) dietary intake studies. Ecological diversity indices are often used without justification through the lens of nutrition. To illustrate: (i) dietary species richness fails to account for the distribution of foods across the diet or their functional traits; (ii) evenness indices, such as the Gini-Simpson index, require widely accepted relative abundance units (e.g., kcal, g, cups) and evidence-based moderation weighting factors; and (iii) functional dissimilarity indices are constructed based on an arbitrary selection of distance measures, cutoff criteria, and number of phylogenetic, nutritional, and morphological traits. Disregard for these limitations can lead to counterintuitive results and ambiguous or incorrect conclusions about the food biodiversity within diets or food systems. To ensure comparability and robustness of future research, we advocate food biodiversity indices that: (i) satisfy key axioms; (ii) can be extended to account for disparity between edible species; and (iii) are used in combination, rather than in isolation.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2022.2051163 .

Moisture Dynamics of Wood-Based Panels and Wood Fibre Insulation Materials.

Moisture performance is an important factor determining the resistance of wood-based building materials against fungal decay. Understanding how material porosity and chemistry affect moisture performance is necessary for their efficient use, as well as for product optimisation. In this study, three complementary techniques (X-ray computed tomography, infrared and low-field NMR spectroscopy) are applied to elucidate the influence of additives, manufacturing process and material structure on the liquid water absorption and desorption behaviour of a selection of wood-based panels, thermally modified wood and wood fibre insulation materials. Hydrophobic properties achieved by thermal treatment or hydrophobic additives such as paraffin and bitumen, had a major influence on water absorption and desorption rates. When hydrophobic additives did not play a role, pore distributions and manufacturing process had a decisive influence on the amount and rate of absorption and desorption. In that case, a higher porosity resulted in a higher water absorption rate. Our results show that there is a clear potential for tailoring materials towards specific moisture performance by better understanding the influence of different material characteristics. This is useful both for achieving desired moisture buffering as well as to increase service life of wood-based materials. From a sustainability perspective, fit-for-purpose moisture performance is often easier to achieve and preferred than wood protection by biocide preservative treatments.

Qualitative Team Formation Analysis in Football: A Case Study of the 2018 FIFA World Cup.

In this paper, we explore the use of the Static Qualitative Trajectory Calculus (QTCS), a qualitative spatiotemporal method based on the QTC, for the analysis of team formations in football. While methods for team formation analysis in sports are predominantly quantitative in nature, QTCS enables the comparison of team formations by describing the relative positions between players in a qualitative manner, which is more related to the way players position themselves on the field. QTCS has the potential to allow to monitor to what extent a football team plays according to a coach's predetermined formation. When applied to multiple matches of one team, the method can contribute to the definition of the playing style of a team. We present an experiment aimed at identifying the team formation played by Belgian national football team during the 2018 FIFA World Cup held in France.

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.

Improved wood species identification based on multi-view imagery of the three anatomical planes.

BACKGROUND: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. RESULTS: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. CONCLUSIONS: Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.

Identification of Phage Receptor-Binding Protein Sequences with Hidden Markov Models and an Extreme Gradient Boosting Classifier.

Receptor-binding proteins (RBPs) of bacteriophages initiate the infection of their corresponding bacterial host and act as the primary determinant for host specificity. The ever-increasing amount of sequence data enables the development of predictive models for the automated identification of RBP sequences. However, the development of such models is challenged by the inconsistent or missing annotation of many phage proteins. Recently developed tools have started to bridge this gap but are not specifically focused on RBP sequences, for which many different annotations are available. We have developed two parallel approaches to alleviate the complex identification of RBP sequences in phage genomic data. The first combines known RBP-related hidden Markov models (HMMs) from the Pfam database with custom-built HMMs to identify phage RBPs based on protein domains. The second approach consists of training an extreme gradient boosting classifier that can accurately discriminate between RBPs and other phage proteins. We explained how these complementary approaches can reinforce each other in identifying RBP sequences. In addition, we benchmarked our methods against the recently developed PhANNs tool. Our best performing model reached a precision-recall area-under-the-curve of 93.8% and outperformed PhANNs on an independent test set, reaching an F1-score of 84.0% compared to 69.8%.

Machine Learning to Assist in Large-Scale, Activity-Based Synthetic Cannabinoid Receptor Agonist Screening of Serum Samples.

BACKGROUND: Synthetic cannabinoid receptor agonists (SCRAs) are amongst the largest groups of new psychoactive substances (NPS). Their often high activity at the CB1 cannabinoid receptor frequently results in intoxication, imposing serious health risks. Hence, continuous monitoring of these compounds is important, but challenged by the rapid emergence of novel analogues that are missed by traditional targeted detection strategies. We addressed this need by performing an activity-based, universal screening on a large set (n = 968) of serum samples from patients presenting to the emergency department with acute recreational drug or NPS toxicity. METHODS: We assessed the performance of an activity-based method in detecting newly circulating SCRAs compared with liquid chromatography coupled to high-resolution mass spectrometry. Additionally, we developed and evaluated machine learning models to reduce the screening workload by automating interpretation of the activity-based screening output. RESULTS: Activity-based screening delivered outstanding performance, with a sensitivity of 94.6% and a specificity of 98.5%. Furthermore, the developed machine learning models allowed accurate distinction between positive and negative patient samples in an automatic manner, closely matching the manual scoring of samples. The performance of the model depended on the predefined threshold, e.g., at a threshold of 0.055, sensitivity and specificity were both 94.0%. CONCLUSION: The activity-based bioassay is an ideal candidate for untargeted screening of novel SCRAs. The combination of this universal screening assay and a machine learning approach for automated sample scoring is a promising complement to conventional analytical methods in clinical practice.

Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems.

Mathematical modelling is increasingly used to improve the design, understanding, and operation of water systems. Two modelling paradigms, i.e., mechanistic and data-driven modelling, are dominant in the water sector, both with their advantages and drawbacks. Hybrid modelling aims to combine the strengths of both paradigms. Here, we introduce a novel framework that incorporates a data-driven component into an existing activated sludge model of a water resource recovery facility. In contrast to previous efforts, we tightly integrate both models by incorporating a neural differential equation into an existing mechanistic ODE model. This machine learning component fills in the knowledge gaps of the mechanistic model. We show that this approach improves the predictive capabilities of the mechanistic model and is able to extrapolate to unseen conditions, a problematic task for data-driven models. This approach holds tremendous potential for systems that are difficult to model using the mechanistic paradigm only.