Turdus migratorius, a free-living host species of an obligate brood parasite, the brown-headed cowbird Molothrus ater. We then model the coevolutionary trajectory of both the extent of mimicry of host eggs by parasitic eggs and the host’s egg rejection thresholds in response to an increasing role of learning in egg recognition. Critically, with more learning, we see the evolution of both narrower (more discriminating) rejection thresholds in hosts and greater egg mimicry in parasites. Increasing host clutch size (number of eggs/nest) and increasing parasite load (parasitism rate) also have narrowing effects on the egg-rejection threshold. Together, these results suggest that learning from prior experience with egg rejection may play an important role in the coevolution of egg-mimetic lineages of brood parasites and the refined egg rejection defenses of hosts.Egg rejection is an effective and widespread antiparasitic defense to eliminate foreign eggs from the nests of hosts of brood parasitic birds. Several lines of observational and critical experimental evidence support a role for learning by hosts in the recognition of parasitic versus own eggs; specifically, individual hosts that have had prior or current experience with brood parasitism are more likely to reject foreign eggs. Here we confirm experimentally the role of prior experience in altering subsequent egg-rejection decisions in the American robin  Cuculus canorus host-races (or gentes) are best at rejecting foreign eggs from their nests (60–100% rejection rate), whereas hosts of less-mimetic eggs reject fewer foreign eggs (0–40%), leading to a positive coevolutionary pattern between host–parasite egg appearance mimicry and host egg rejection  and then exposed each nest to one of two treatments with model eggs. This meant that the nests were parasitized both in the first and second instance during the incubation stage, instead of the laying stage when brood parasitism typically occurs e.g., . HoweverModel eggs were manufactured as per The first treatment involved nests that received a 3D-printed cowbird egg-sized, -shaped, and -weighted model egg painted a light blue color designed to mimic the robin’s own egg color. Nests in the second treatment group received the same type of 3D-printed model egg but painted quail-beige and speckled with a quail-speckle brown color to resemble a natural cowbird egg. We assessed whether model eggs were rejected 1 day after their deployment, removed any non-ejected eggs, and immediately placed a deep blue model egg in each nest. The rejection of a deep blue egg in the same nest was again assessed 1 day later. Nest abandonment is not a response by robins to experimental parasitism , therefot = −0.25, p = .80) or their clutch sizes . Nonetheless, we also ran a nominal logistic analysis with both date and clutch size included as predictors alongside the treatment, and egg rejection outcome as the bivariate response variable. We initially included all the pairwise interaction terms of the predictors in the model but none of these were significant . For future meta-analytical purposes, we also calculated the odds ratio of each experimental outcome. For both the Fisher’s exact test and the nominal logistic analysis we predicted higher egg rejection rates of the deep blue eggs following the prior exposure to the cowbird-like treatment eggs versus the robin-mimetic blue treatment eggs.Treatments were assigned randomly, and so we primarily analyzed the egg rejection data using Fisher’s exact tests. This was justified as nests between two treatments did not vary in their experimental dates . In  value b . SpecifiWe assume that each host female learns to lower its threshold value where To calculate the selection differential on a trait, the derivation of which is presented in full in the The model involves many multidimensional numerical integrations, causing the numerical simulation of the recursions runs to be exceedingly slow. To approximate the equilibrium value at which the variables will stop changing with time, we run the system for at least 1,000 generations and fit the data of the last 5 generations with the functionwhere p << .0001; odds ratio = 9.19). In addition, as predicted, deep blue model eggs were rejected more often following cowbird-like treatment eggs (18 of 23) versus robin-like treatment eggs   but not of date  or clutch size  overall.As expected, cowbird-like treatment eggs were rejected  from the vast majority of robin nests, and also more so relative to robin-like eggs  . The nomC/H, order of the lines in We found a general lowering of the egg-rejection threshold  with increased learning in our modeled coevolving system of hosts and parasites. The rejection threshold was also lowered with increased host clutch size (order of lines in C/H) is not too high . In this case, the cowbird egg size evolved to be smaller than the host egg size regardless of the amount of learned change in discrimination. These outcomes are likely unrealistic if hosts’ egg-rejection decision mainly relies on egg size in particular, as cowbird eggs in nature are generally larger than host eggs. They may be realistic for other aspects of avian-perceivable egg appearance  of foreign model eggs on the subsequent day . This reWe further demonstrate, using analyses of the equilibrium of the coevolutionary process, that empirical patterns of more egg rejection by hosts of better mimics result from coevolutionary processes in a more pronounced fashion when incorporating learning into the models as a mechanism to refine egg-rejection thresholds at the level of individually impacted hosts. Specifically, moderate levels of changes in rejection through learning resulted in lower (more discriminatory) evolved thresholds, irrespective of host clutch sizes and parasite loads. Coevolution with the cowbird parasites also resulted in greater egg mimicry, provided that parasitism rates were not too high. Exceptions to these patterns were seen at high levels of learning and high parasitism rates, where egg phenotypes are such that self versus other discrimination breaks down and the hosts likely engage in recognition errors to mistakenly reject their own eggs disproportionately more often. Such recognition errors have been detected in empirical systems, including both in the contexts of ontogenetic experience dependence . For example, the hue of a cowbird egg may change from being greater than that of a host egg to briefly mimicking that of the host egg for a short period of evolutionary time, but then may continue to evolve to be less than that of a host egg. However, in our model (where hosts reject the larger of the two largest eggs), the evolutionary outcome of “overshooting” occurs because it is extremely detrimental to the cowbird to have an egg that is unusually large in particular, as opposed to being unusual in size in general. Continuing to use size as an example, we expect that the evolutionary outcome of “overshooting” would induce selection pressure for hosts to change their criteria for acceptance of an egg such that they would discard a usually small egg instead of an unusually large one. Depending on the evolutionary flexibility in such criteria, we would expect anything from the system evolving worsening mimicry (if evolution of the criterion is constrained), through fluctuating between increasing and decreasing levels of mimicry , to remaining near a minimum level of mimicry .Overall, we found that variation in the extent of the role of learning about the host’s own eggs may modulate the outcome of coevolutionary interactions with respect to both shifting (lowering) egg-rejection thresholds and altering  the extent of host–parasite mimicry. Accordingly, these patterns have now been repeatedly detected along different axes of host–parasite mimicry empirically. However, the results predict that the recognition of foreign eggs will depend more extensively on first experience with their own eggs in hosts of the more mimetic brood parasites, a prediction that remains to be assessed in most host species of avian brood parasitic lineages.qrad041_suppl_Supplementary_MaterialClick here for additional data file.


In emergency colorectal surgery, a primary anastomosis may be unwise or require protection, and so the formation of a stoma can be a lifesaving maneuver. Despite the proven value of preoperative stoma site planning, urgency and patient condition often result in the operation proceeding without it occurring. However, a poorly sited stoma can result in a life of misery from ongoing stomal complications.In this issue of the journal, Watanabe et al.’s paper looks at the impact of preoperative stoma marking on early outcomes following emergency colorectal surgery . This isThere has been much work on the risk factors associated with stomal specific complications including stomal marking. A meta-analysis of over 10,000 patients reported a pooled incidence of stomal complications in 35%, with obesity, laparoscopic stoma formation and emergency surgery being the most prominent risk factors . Other pThere are a few specific challenges to preoperative stomal siting prior to urgent surgery. The first being time of day and the availability of a stomal therapist to mark the patient. In the absence of a stomal therapist, few surgeons or surgical trainees have been formally trained to site a stoma . There iSiting a stoma away from skin creases is important to allow for proper attachment and sealing of pouching systems. A recent meta-analysis by Hsu et al. found that preoperative siting was related to a reduced incidence of skin complications, such as dermatitis and ulceration, with some series showing a twofold decease in complications . VisibilThe most dreaded late peristomal complication is the parastomal hernia, which occurs in a third of patients with a stoma, regardless of surgical urgency or indication. They develop months or years after the stoma-forming surgery. Their incidence was not captured in Watanabe’s series, which was based on the in-patient admission. Hsu et al.’s  meta-anaA poorly formed stoma affects quality of life and can have detrimental long-term social and psychological impacts. Complications can also increase the health care costs resulting in further surgery, hospital readmissions, increased length of stay, increased consumption of medical supplies and need for community nursing . FurtherAlthough one cannot preoperatively perform stoma site marking for all emergency colorectal surgery patients, the value of this should not be forgotten and every effort made to do it when possible. Surgical education and training should include more formal assessment of stoma site marking, particularly in the emergency setting . Stoma f


Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in “wet lab” settings . These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers. Technologies continue to advance, allowing for increasingly high-dimensional measures; though we remain limited by the number of samples  that can be feasibly evaluated in such investigations. As a result, the field of toxicology is currently plagued with the following data analysis problems (p) is larger than the number of samples/subjects (n) (p >> n). However, it can also refer to p and n both being substantially large or even p << n  draw attention to the current need to improve data analyses for large datasets, ii) highlight some example methods of organizing and analyzing high-dimensional data, and iii) describe challenges that now need to be addressed to improve data analyses in toxicology research, all of which represent issues of current discussion between wet lab and dry lab scientists. Examples are relevant to high-dimensional data issues that are prevalent in data produced through toxicology research. The contents of this perspective originated from discussions held in the University of North Carolina’s newly formed Environmental Bioinformatics Research Group (EBioRG) , represeThis perspective is organized to first provide a high-level overview of current data analysis methods for large datasets, including data preparations and downstream machine learning (ML) methods discussed in the context of recent toxicology-relevant studies. The second part of the perspective then discusses current limitations, biases, and a push for the inclusion of these methods in toxicology. Questions are posed in the final section to encourage continued discussion on this evolving subject.n be less prone to skew and therefore minimizes bias in downstream analyses. This is particularly important when dealing with high-dimensional data that suffers from a limited number of observations. Data imputation is a commonly used technique to generate these missing values based on the statistical distribution of present/detected experimental values. Prior to imputation, background filters can be implemented to filter poorly measured or highly missing variables and observations to ensure that enough data is present for the imputed data to be pulled from a data-informed distribution. After these filters are implemented, an evaluation should be carried out by the analysts to consider why data are missing. Current categorizations of missing data include missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR)  with missing data helps m (MNAR) . Data arm (MNAR) . This com (MNAR) . For traThe type of missing data imputation approach should be informed by the reasoning as to why data may be missing. As an example, a metabolomics dataset was analyzed in a previous toxicology-relevant study, and missing data were imputed using eight different imputation methods spanning assumptions of MCAR, MAR, and MNAR . One of n), which may not be feasible for all toxicological experiments. Note, there is not a “magic formula” for determining the optimal n to perform supervised ML, however it is contingent upon the number of features (p) and the number of classes of a labeled outcome being predicted. Nevertheless, supervised ML is particularly useful for large datasets in toxicology given its ability to predict a toxicological outcome from a multitude of features .Data produced through wet lab experimentation are often analyzed using traditional statistics based on two-group comparisons; though if given the tools and guidance, these data could be analyzed using machine learning (ML) to unravel undiscovered biological patterns. ML automates the building of analytical models from parameters tuned by the researcher based on the idea that it can learn from data, identify patterns, and make decisions utilizing multiple independent variables . SuperviIn vitro high-throughput screening data have also been used in RF models to predict multiple in vivo outcomes, such as liver pathway responses , as well as ongoing discussions between wet and dry lab scientists, several questions can be posed for further discussion:It should be mentioned that any computational models we build are dependent upon the quality of the data, which often includes biases. Biases can occur when selecting which variables to collect and which features to include in the final model. It was once believed (and probably still believed) that algorithms inherently help address equity; however, data are often collected based on protocols/systems that have biases that are then perpetuated in any findings produced through high-dimensional data analyses. This belief was not likely held by researchers working specifically in social justice fields; however, the incorporation of a social justice and equity lens should be more consistently incorporated in toxicology research . Artific• Research group: is there sufficient diversity of ideas, experience, and background in the research group and the people specifically building the algorithms?• Research questions: as toxicologists can we measure the impact of those social determinants of health by measuring epigenetic outcomes instead?• Data collection: when gathering human data, what groups  are over or underrepresented?• Algorithm training: if a predicted outcome is quantified, what variables have the largest impact on the model? What biases systemically or biologically need to be addressed as a result?• Algorithm interpretation: is this model generalizable or interpretable enough to be useful on a similar dataset or context?Effort is required to not perpetuate biases on all levels, which lead to the following questions for further discussion:ML and artificial intelligence will continue to become more integrated into toxicological analyses as the prevalence of high-dimensional data continues to soar. We should learn from past mistakes of clinical applications of computational techniques and better design models that offer opportunities to extract insights while minimizing biases. First and foremost, as public health investigators, special attention and mention should be given to “the why” behind the models we build and their positive or negative implications on the communities we hope to serve.Advances in high-dimensional data analyses in toxicology will fail to evolve and advance over time if methods are not passed along to future leaders in the toxicology field. Hence, there is now an urgent need to generate and disseminate training materials surrounding high-dimensional data analyses. Our team has contributed to this need by developing the inTelligence And ML (TAME) Toolkit, which was organized to promote trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies . Dissemi• How can we more effectively train the next-generation of toxicologists on these methods?• What are the best methods for reviewing trainee-driven analyses?• Where are we still limited in data analysis training efforts in toxicology?• How can we improve upon these methods as a concerted effort across toxicology groups?Given the current status of training resources for emerging scientists, we posed the following questions for further discussion:in vivo in conjunction with traditional methods that isolate a limited number of variables in analyses. These computational techniques, like data imputation and ML, help maximize the number of observations, predict exposure or disease outcomes, identify features that are most influential on an outcome, and reveal possible relationships between variables. Despite challenges regarding interpretability and health equity, effective data analysis approaches have the potential to yield novel toxicological results. These analyses will significantly advance the field of toxicology when emerging from continued interactions between wet and dry lab scientists.As high-dimensional data become increasingly ubiquitous, the adoption of rigorous, standardized, high-throughput computing is imperative to extract novel insights. The advancement within the field of toxicology hinges upon leveraging computational techniques to better simulate what may potentially occur


This phenomenon demonstrates that proxy spatial data is a useful tool to classify catchments with similar DIN regimes. Catchments lacking similarity with gauged ones are identified as priority monitoring areas to gain observed data for all DIN regimes in catchments that flow to the Great Barrier Reef, Australia.Transfer of processed data and parameters to ungauged catchments from the most similar gauged counterpart is a common technique in water quality modelling. But catchment similarities for Dissolved Inorganic Nitrogen (DIN) are ill posed, which affects the predictive capability of models reliant on such methods for simulating DIN. Spatial data proxies to classify catchments for most similar DIN responses are a demonstrated solution, yet their applicability to ungauged catchments is unexplored. We adopted a neural network pattern recognition model (ANN-PR) and explainable artificial intelligence approach (SHAP-XAI) to match all ungauged catchments that flow to the Great Barrier Reef to gauged ones based on proxy spatial data. Catchment match suitability was verified using a neural network water quality (ANN-WQ) simulator trained on gauged catchment datasets, tested by simulating DIN for matched catchments in unsupervised learning scenarios. We show that discriminating training data to DIN regime benefits ANN-WQ simulation performance in unsupervised scenarios (  Model design and development, referred in here as customisation, is achieved by using observed water quality data from gauging stations for design and verification of models3. However, many water catchments globally are ungauged, and a lesser proportion of those have corresponding water quality data to inform model customisation. Techniques to overcome such data voids in ungauged areas are necessary5. Methods to simulate flows in ungauged areas are well researched7, however, refinement of methods that simulate nutrients in ungauged areas remained unresolved. This knowledge gap in water quality modelling needs addressing to best inform anthropogenic nitrogen management, and to demonstrate progress to the 2030 UN Nations Sustainable Development Goals commitment to reduce land-based nutrients that enter the oceans8. This has relevance for the Great Barrier Reef World Heritage Area where over ~ 20% of the terrestrial drainage area is ungauged, and nutrient balances are critical for the reef’s health10. Logical explainability in nutrient models for ungauged areas can support communications and enable more responsive water quality improvement investments12.Communicating catchment influences towards the ecology of the receiving environment is enhanced by water quality simulation tools. Customising water quality simulation models to the catchment they represent is essential for limiting uncertainty in results and maintaining trust in land use decisions they aim to inform13. Data driven water quality models are useful to forecast water quality output, but water flows and water quality must be known a priori to develop covariates15. In contrast, process-based models use physical and empirical principles and can be established for catchments lacking observed water quality data. In ungauged areas, data is donated to ungauged catchments from the most similar gauged ones17. Alongside these traditional water quality modelling approaches, deep learning, particularly in the revised forms of Artificial Neural Networks, has been relatively successful in simulating water quality, including nitrogen, without the need for prior established principles19. As a subsector of Deep Learning, Artificial Neural Networks have demonstrated the ability to recognise patterns in input datasets, classify them, and establish algorithms to match target data. The merits of ANN are demonstrated to forecast and extend non-linear water quality data within respective catchment datasets20, but their application to inform scenario simulation, and hence land management decisions, which is the benefit of process-based models, is lacking21.For both data-driven and process-based models that simulate water quality, observed water quality and quantity data, as well as a comprehensive understanding of catchment characteristics are required23. However, machine learning models that incorporate process considerations for water quality modelling are disproportionally underrepresented in many research articles23. Additionally, where applied to ungauged areas, low landscape heterogeneity between drivers for the constituent being simulated is necessary24. While variations in patterns of nutrients are observed across gauged catchments that drain to the Great Barrier Reef26, methods for classifying those catchments to the most similar ungauged catchments that drain to the Great Barrier Reef, based on similarity of nitrogen drivers, are unexplored.To exploit the benefits and overcome the drawbacks of each data driven vs process based model approach, the coupling of machine learning models such as ANN with process-based approaches can be performed to provide benefits of transfer learning28. The fluxing nature of these biotic processes mean catchment similarities for drivers of DIN differ from the abiotic drivers of flow have therefore been complicated to quantify29. Variability in drivers of DIN affect the consistency of water quality modelling of ungauged areas25. This disparity between biotic and abiotic influence on nitrogen drivers means classical classification approaches that only use physical similarities miss the influence of all biologically influenced differences that may exist between catchments. Spatio-temporal variability in nutrient drivers can be represented in catchment models by the natural physical drivers of geology, aspect, topography, climate etc., as well as land use to represent anthropogenic impacts, including standard fertiliser application rates which affect DIN25. Our earlier studies found Original Vegetation is a proxy dataset for the residual biotic responses to these, and any other unaccounted-for drivers that can parsimoniously classify catchments for DIN, and identify the classification drivers using explainable artificial intelligence, (XAI)31.In terrestrial landscapes, Dissolved Inorganic Nitrogen molecules are influenced by decomposers, vegetation uptake, nitrogen fixing bacteria etc., which change depending on a unique combination of physical and biological influences at each location32. Established process-based models instead can be customised to respective catchments using regionalised parameter data, enabling trials of different land management scenarios18. This technique has been effective for water quality constituents driven by abiotic processes, which result in consistent performance6, and so is pragmatic for the purpose of informing land management decisions. Despite the acceptable track record for process-based models, the suitability of parameter transfer for constituents with biotic process drivers is still lacking, and studies regarding the spatio-temporal scales are necessary35. We found earlier that original vegetation can be a proxy for matching gauged catchments with dynamic DIN patterns26. However, no approach has yet been developed that matches ungauged to gauged catchments for DIN similarities, which would be beneficial for models that transfer data across catchments with similar processes.Explainable artificial intelligence, (XAI) has outstanding capabilities to highlight the influential variables in machine learning algorithms, however, performance criteria for the corresponding ANN models are likely to vary unpredictably with changes to model architecture and scenarios36 approaches to match the currently ungauged to gauged catchments that flow to the Great Barrier Reef using mapped spatial data as a proxy for DIN. Mapped spatial data is useful because it provides data for all areas of the Great Barrier Reef catchments where water quality data is lacking26. In this study we verify the classification results by building and applying an ANN-WQ simulator to compare changes in simulation performance criteria for a case study catchment, under various dataset arrangement scenarios. Our earlier studies found dominant original vegetation data features may provide guidance to the part of the hydrograph that is relevant to consider for matched catchments and that it is a useful proxy to group gauged catchments that flow to the Great Barrier Reef to three DIN response categories30. In this study we evaluate whether our previous method is extendable to all ungauged catchments that flow to the Great Barrier Reef, and undertake a case study to verify its suitability as a proxy for DIN classification. Our verification case study aims to confirm catchments classified together based on original landscape variables also have transferrable water quality responses that can be exploited to simulate DIN.This study extends our previous XAI-SHAP30 can be used to match gauged catchments to ungauged catchments that also flow to the Great Barrier Reef. (2) An ANN-WQ simulator trained using predictor variables of original vegetation, coupled with flow data characterised to match with DIN targets will achieve superior performance compared to an ANN-WQ simulator trained to simulate DIN using non-categorised flow data only.The hypotheses investigated here are: (1) Original vegetation spatial features found to be a proxy for DIN discharge from gauged catchments in our previous studies(3) The trained ANN-WQ simulator can simulate DIN in an unsupervised scenario for a pseudo-ungauged case study catchment matched based on the spatial proxy data and achieve satisfactory performance criteria to verify the suitability of the catchment match approach. For this study, pseudo-ungauged means a gauged catchment, with the same data collection method as the other gauged catchments, but intentionally omitted from previous research that informed this study. DIN data for the pseudo ungauged catchment is used here for hypothesis validation purposes only.The present study therefore aims to create an XAI approach for considering original vegetation data classification as the proxy for spatio-temporal nitrogen patterns in ungauged catchment flows for the specific case of the Great Barrier Reef in Australia.Apart from the Mary Catchment, the results show that ungauged portions of gauged catchments do not necessarily classify together, and catchments do not necessarily classify with their nearest neighbours Fig. . Catchme30 revealed that every catchment had a unique combination and weighting of deviated features. The same combinations of top XAI-SHAP 10% floristic structure variables did however match the most similar gauged catchment and group them to Categories based on the combination of deviated variables. It also revealed catchments that did not share the same combinations of deviated variables. Grouping the deviated variables by landform and vegetation descriptors in the Original Vegetation dataset allowed for 20 of the 37 pseudo/ungauged catchments to be matched to individual gauged ones, while 9 catchments were not matched to another ungauged or gauged catchment or spatio-temporal category. Of those, unable to match to gauged catchments XAI-SHAP results facilitated four closely matched groups to be identified.In this study, matching variable deviations using the XAI-SHAP approach methodVariable combinations only occurring in ungauged catchments and not in the gauged ones include: hilly alluvial with basalt, health land with sandplains and coast, or mangrove landform structures, as well as additional combinations of vineforest with woodland drainage, or open forests combined with grassland and open woodlands Fig. .Figure 2The most notable observation was that the combination of catchments included in the training datasets influenced the unsupervised performance of the ANN-WQ simulator Table . When thIn contrast, training using data grouped from multiple catchments generated non flatline results for all scenarios. Satisfactory to very good performance for all metrics were achieved for all spatial dataset combinations grouped and discriminated in spatio-temporal Category 2 and 3. Except for their unsatisfactory Nash Sutcliffe Efficiency (NSE) performance, datasets grouped and discriminated to spatio-temporal Category 1 also achieved satisfactory to very good performance , where spatial data was omitted  scenario also varied where the dataset was first discriminated to spatio-temporal categories Table . Simulatme Table . Benefit30, prior to loading to the DIN simulator resulted in improved performance criteria . Interestingly, for Category 2 flow datasets the control scenarios, which did not contain spatial variables achieved superior performance for MSE, R2, NSE and Wilmott’s d compared to the other Category 2 scenarios that did include information on spatial variables. In contrast, the Original Vegetation scenario discriminated to Category 3 records had the smallest pde score meaning that the inclusion of Original Vegetation variables improved the ability of the DIN simulator under extremes in the data for Category 3 catchments . Visualisation of simulated vs true data demonstrates that these scenarios’ pre-discriminated spatio-temporal flows result in simulations that include all the peaks in the observed dataset. On the other hand, training data discriminated to include all catchments in the corresponding Category 1, but using all flow and season records, with no spatial data failed to simulate half the peaks . We adopt an explainable AI approach referred to as XAI-SHAP to provide a deeper understanding of the modelled classification results. In accordance with earlier research works, our satisfactory performance metrics show classification of the pseudo-ungauged area to the most similar gauged ones is validated and works well where data for proxy drivers of DIN are included because they facilitate grouping of catchments by the DIN regime. Evaluation of DIN simulation performances using transfer learning in an Artificial Neural network environment allowed us to demonstrate the variability in DIN patterns depending on the spatio-temporal regime of the ungauged catchments, as exposed by original vegetation data Additionally, the XAI-SHAP method allowed for ungauged catchments with insufficient similarity to the gauged ones to be identified, regardless of being classified by brute force using ANN-PR techniques.40. Likely explanations include hidden neuron complexity was low in the development trials and relationships between flow, spatial data and DIN response was not adequately formed to facilitate simulations in the unsupervised scenario. The contrasting ability of Wet Tropics catchment datasets to overcome possible lack of dataset complexity in the training dataset is explained by the different DIN and flow dynamics in wet tropics catchments compared to the others27. We previously demonstrated that DIN remains elevated in retreating flows for Wet Tropics catchments only26. One explanation for the contrast with Wet Tropics catchments could be a more consistent relationship between flows and DIN releases throughout the hydrograph which the ANN-WQ simulator was trained to simulate for42. This phenomenon demonstrates that consistency of DIN to flow relationships influence the performance of defined algorithm based models developed to use transferred data.Development and verification of the ANN-WQ simulator to establish DIN response similarities in datasets between pseudo-ungauged catchments with the gauged ones found dataset complexity and representative flow patterns were influential. This highlighted caution in direct application without prior understanding of the DIN to flow dynamics of the catchment. Flatline simulations that resulted in the unsupervised scenario are a known symptom of inadequate complexity in the dataset43. The significant differences in performance criteria of DIN simulations (p = 0.003–0.045) depending on the data discrimination for catchment categories suggests that DIN dynamics differ between those categories. This finding of significant variation in nitrogen regimes through the Great Barrier Reef catchments, as demonstrated by the ANN-WQ simulator training dataset predictive performance, regardless of anthropogenic influence is consistent with proceeding research45. Our research shows variability in DIN regimes is an influential consideration for data transfer purposes in water quality models. The improved performance criteria where information on proxy drivers of DIN was considered supports our application of original vegetation spatial datasets used in this study to discriminate differences in DIN regimes for each catchment30.For catchments with inconsistent DIN to flow relationships, our results found training data arrangements that group catchments using prior knowledge of spatio-temporal similarities, i.e. either by prior discrimination , or within the model training datasets  improved the performance. This approach to remove heteroskedasticity where seasonal differences for Nitrate are considered has already been shown to benefit model development47, and also influenced by connectivity to stream network48. The superior performance of the control scenario for Category 2, compared to Category 2 scenarios that included spatial data, indicate this category is flow limited and abundant in DIN. It is demonstrated that soils higher in total organic carbon, consistent with rainforest soils, have higher supplies of nitrogen created by the residual soil biology50. The abundance of nitrogen generation in the soils, coupled with abundant flows, in the Wet Tropics catchments can result in consistent nitrogen to flow patterns and is a logical explanation for the ability of the ANN-WQ simulator to generate results in the development trials, where catchments from other categories flatlined. This supports our previous suggestions30 that the timing of data collection is important in Category 1 and 3 catchments, while Category 2 catchments could classify regardless of the season or flow phase. While our research is not designed to interrogate reasons for drivers of DIN in each category per-se, this is one of many possible explanations for how categorising datasets by vegetation removes noise associated with different combinations of biotic responses in each location51. The findings, therefore, support the second hypothesis that prior grouping of catchments by categories of Original Vegetation, as a proxy for the DIN to flow regime, is a necessary first step for identifying catchments that share similar DIN patterns.Spatio-temporal category differences for DIN simulation performance may be explained via the wide body of literature that demonstrate nitrogen is either flow or production limited52. Once heteroskedasticity in the training dataset was removed, the influence of spatial variables as drivers to the DIN patterns became less relevant. Separately, the research also found that training the ANN-WQ simulator using data from all catchments improved where all EULUOV variables were included. This could be attributed to the ANN-WQ simulator discriminating datasets within the algorithms, as opposed to prior discrimination provided by classification, and further demonstrates the benefit of the spatial datasets to expose the drivers of the DIN patterns.Training dataset discrimination and variable combinations separately influenced the performance of the ANN-WQ simulations of DIN for the pseudo ungauged catchment. The ANN-WQ simulator achieved the best DIN simulation performance metrics for the pseudo-ungauged catchment when trained on data only from the classified catchment and therefore highlights that data transfer with the classified catchment achieved the best results. Concurrently, discriminating the dataset by the respective flow regime of wet season increasing flows had a greater influence on simulation performance than inclusion of spatial data variables. In contrast, training datasets using data from multiple catchments from the same flow regime, i.e. Category 1, achieved equivalent performance only where the training data was first discriminated to increasing above average flow regime, hence removing hetroskedacity of DIN in the retreating and below average flows. Both these findings are consistent with the ANN-WQ development phase and demonstrate that prior discrimination of the training dataset to flow regime reduces heteroscedasticity in DIN patterns to flow53. Varied performance for each scenario trialled in the ANN-WQ simulator development phase demonstrated that training data from catchments with the most similar proxy drivers of DIN dynamics, was more suitable for data transfer compared to training data from all catchments lumped together54. This demonstrates that rationalising training data to the most similar responding catchments reduces heteroskedasticity in the training dataset and benefits DIN simulation accuracy for the classified catchment. XAI-SHAP provided insight to identify catchments grouped by known DIN to flow proxy drivers. While classification using nearest neighbour catchments has historically been supported for their influence towards flow similarities55, our finding demonstrates that catchments with the most similar drivers of DIN, in addition to flow, are not necessarily located as the neighbouring catchment, and are influential towards DIN simulation performance.Catchments matched using ANN-PR were not always the same as the catchments recommended to be matched by the XAI-SHAP deviation approach for variable independence. One reason could be that only 10% of the most influential variables were considered in the XAI-SHAP approach, in contrast the less deviated variables contributed to the ANN-PR matches. Catchment classification informed by the match options in both ANN-PR and XAI-SHAP approaches provide foundational guidance to rationalise catchments to evaluate in future data transfer investigations or models for DIN simulations to the Great Barrier ReefThis study established Original Vegetation as a suitable proxy for DIN dynamics for the benefit of water quality modelling. Therefore, the 20 ungauged catchments that matched to gauged ones, based off Original Vegetation similarity have justification to receive data from the corresponding gauged catchment. The remaining 21 ungauged catchments had combinations of original vegetation unique from the gauged catchments and therefore did not support the hypothesis that they share similar DIN drivers with gauged catchments. Consequently, this study found that only 20 of the 41 ungauged catchments were suitable to consider for data transfer with existing gauged catchments for satisfactory water quality modelling purposes.30 While data transfer from existing gauged catchments to the four coastal catchments is not supported by our study, our method can instead be used to inform where new water quality monitoring and gauging sites could have the greatest value to represent all DIN regimes58. New monitoring and gauging sites are recommended in each of the four coastal catchment groups to collect data representative of all DIN regimes, which could facilitate data transfer for modelled DIN predictions across all ungauged Great Barrier Reef catchments.Of the ungauged catchments that failed to match to gauged ones, 4 groups shared unique combinations of deviated spatial variables. Deviated original vegetation floristic structure and landform descriptors shown by the XAI-SHAP deviations, were grassland, heathland and mangrove. These are all coastal ecosystems and differ from vineforest, open woodlands and forest shown to be proxy indicators of DIN dynamics for gauged catchments59 and in our evaluation, models trained using Mary data were never exposed to high concentration peaks observed in the Herbert catchment. Limitation for simulating extremes not included in the training data could be addressed with differing model techniques, the ANN-WQ simulator was intended only as a coarse method to verify whether similarity in DIN drivers exists between catchments matched using the ANN-PR method, and this was demonstrated.It is well known that performance of neural networks deteriorates when the unsupervised scenario includes extremes outside the range of the training dataset43.For the case study, the matched catchment was a Category 1 catchment. Collectively, Category 1 catchments showed the poorest performance in the ANN-WQ development phase. The fact the case study trial achieved satisfactory performance criteria for the poorest performing category in the development phase, it is expected that better results can be achieved for Category 2 and Category 3 catchments which have less heteroskedastic DIN to flow relationships. Further studies to refine the ANN-WQ simulator performance, along with a full comparison of all training dataset options, i.e. discrimination of data to Category 2 and 3 flows to evaluate difference in performance and facilitate year round classification is recommended. Regardless of these limitations, we encourage results from this study to be applied in established models that will benefit from data transfer from the most similar catchments for purpose of DIN modelling, and intentionally developed for superior performanceThis study matched all ungauged catchments that drain to the Great Barrier Reef to the gauged ones using ANN-PR coupled with Land use and Original Vegetation datasets. While ANN-PR enabled matching using proxy datasets for drivers of DIN, XAI-SHAP method explained similarities between catchments based on feature deviations as well as concurrently allowing grouping of catchments to known spatio-temporal categories. Prior knowledge of spatio-temporal DIN response categories within training datasets improved performance of the ANN-WQ simulator developed to verify catchment matches.While all catchments matched to a gauged one using ANN-PR, consistent with hypothesis 1, the additional interrogation by XAI-SHAP deviations found 17 catchments did not share deviated feature similarity with a spatio-temporal category. The XAI-SHAP method instead provides justification to prioritise gauging and monitoring efforts in those unmatched catchments to better understand the spatial temporal dynamics of DIN in coastal areas that those unmatched catchments were located in. For the ungauged catchments that did match to gauged ones using the XAI-SHAP method, the subsequent ANN-WQ simulator development and case study to test the second hypothesis, found prior discrimination of data included in the training dataset, based on the spatio-temporal category of the ungauged catchment, improved performance of the ANN-WQ simulator in all scenarios tested. It was, however, an unexpected finding that, after the spatio-temporal discrimination by category was first applied, inclusion of Original Vegetation, Ecounit or Land Use variables had insignificant influence on results. Findings that emerged throughout this study therefore built nuance to our expected hypothesis 3 whereby although a trained ANN-WQ simulator successfully simulated DIN in the unsupervised scenario, it was the knowledge provided by original vegetation data to pre-process the training datasets into categories that mattered. Implications of these findings are that XAI-SHAP coupled with Original Vegetation data has demonstrated merit for customising catchment matching to the portion of water quality datasets most likely to share similar DIN to flow regimes between gauged and ungauged catchments.This study includes all catchments that flow to the Great Barrier Reef, in north-eastern Australia. Each of those catchments is referred to herein as gauged, ungauged and pseudo-ungauged as shown in Fig. The objective of this study is to establish whether patterns in the flow and spatial variable datasets contain sufficient information to simulate Dissolved Inorganic Nitrogen (DIN), and whether forecasting capabilities can extend to new catchments, referred to in this study as pseudo-ungauged. Because the influence of every variable input and their interrelationships to overall DIN response are unknown a priori, a dense fully connected Artificial Neural Network (ANN) algorithm was developed to trial the proof of concept approach. Algorithms were trained for a number of dataset arrangements and their performance metrics were compared to quantify the viability of the novel forecasting/data transfer concept within the Artificial Intelligence modelling environment. A workflow conceptualising the research approach is shown in Fig. 26. The spatial extent of gauged areas for catchments evaluated in this study are consistent with Khan et al.13. DIN records were collected at irregular frequencies depending on flows for each gauged catchment as detailed in Supplementary Material Table S60. The water quality and flow datasets were duplicated then partitioned as outlined in O’Sullivan et al.30 for wet season/increasing flows, dry season/retreating flows, and all flows/seasons to capture spatio-temporal influences.Observed water quality data and flow records for those gauged and pseudo-ungauged catchments are from locations listed in Supplementary Material Table S26. The three separate spatial datasets were created as the proxy drivers of DIN. These included: Land use to represent human biotic influence which included 6 variables61, Original Vegetation consisting of 38 variables31 intended as a parsimonious biotic response proxy for natural DIN responses across the catchment62, and Ecounit which was a created via a combination of Land use and Original Vegetation and resulted in 179 variables. The area of variables for each catchment was established via clipping the spatial datasets to the catchment boundaries and extracting corresponding data tables from ArcGIS. The area of the gauged catchments extended only to the gauged monitoring point, the ungauged portion was created as a sub-catchment polygon for all areas that drain to the catchment’s waterway downstream of the gauged monitoring point, or for fully ungauged catchments. For each catchment, the spatial dataset was duplicated to match the number of data rows to the same number of DIN records in each catchment dataset. For the pseudo-ungauged datasets, the number of spatial dataset rows were duplicated to match to the number of daily average flow records available.Spatial data for all gauged and ungauged portions of catchments in the study area, were extracted from Queensland Government Q-Spatial mapping platform, as per the methods described in O'Sullivan et. al.A master dataset was created by joining the preceding flow and spatial dataset to create the training variables dataset, and the corresponding DIN data allocated as the target dataset. All data in each dataset was then normalised. Scenario datasets were then created by extracting subsets of data from the master dataset as detailed in Table 30. XAI evaluations of those datasets provide explainability to the corroborating ANN-PR results for both spatial data and water quality classification30. Here, we explore, for the first time, extending that classification approach beyond the gauged portion of the study area to classify catchments of the ungauged and pseudo-ungauged areas to gauged catchments. The method is therefore extending the ANN-PR approaches of our previous studies to now evaluate which gauged catchments the pseudo-ungauged and ungauged catchments classify to, and evaluate whether XAI explainability applies to the ANN-PR matches. To accomplish this, we apply a combination of the ANN-PR approach used in our previous studies and XAI explainability coupled with SHAP36 (XAI-SHAP) to evaluate the similarities between the catchments. This allows classification of catchments that have not been gauged, based on the similarities between the gauged catchments, and provide a better understanding of the underlying similarities between the catchments. Importantly, inclusion of the XAI-SHAP method demonstrates whether the sufficient underlying similarity is likely to exist between the proxy drivers of DIN in the gauged and ungauged catchments for the purpose of data transfer.The novel aspect of this research is establishing whether pseudo-ungauged, and ungauged catchments share spatial data similarities suitable for classifying to gauged catchment classifiers, and for water quality classification data transfer purposes. Our previous studies used ANN-PR to classify only the gauged catchments together using the same spatial variables used in this studyMATLAB 2020a  Deep Learning toolbox . The code used is a two-layer feedforward network, with sigmoid transfer function in the hidden layer, and softmax transfer function in the output layer (The MathWorks Inc. 2020)”63. For the spatial datasets, heuristics and previous knowledge for the gauged data spatial dataset meant that an architecture of 3 hidden neurons were used to set the classification training architecture for this model. Data were split within the coding architecture to 70% for network training, 15% network validation and 15% network testing. In the training phase, the network is designed to match spatial data variables for each row in the dataset to one of the 11 the gauged catchment categories the spatial data is sourced from. The network architecture is set such that training continues towards minimisation of cross entropy and stops once mean square error elevates above its minimum pivot point at which point the ANN-PR algorithm achieves optimal performance64. Optimal performance is for each of the 100 replicates of spatial data to allocate to the catchment category the data belonged to in the validation and testing phase.This step used a similar approach explained in detail in our previous studies, however this time we trained the ANN-PR tool on all 11 gauged catchments, and introduced the spatial variable data for the ungauged catchments in the unsupervised environment, to force a match to one of the 11 gauged catchments. A 100-fold duplicate of each spatial variable in each gauged catchment was used to estimate the percentage match between the ungauged catchments and the gauged catchments. We then trained the ANN-PR classification tool in a supervised environment by applying the gauged catchment classification training datasets to standard codes extracted from “box Fig. . The codTesting datasets were separately introduced in an unsupervised environment to the optimised classification algorithm trained to match spatial data to only one of the 11 gauged catchments. Spatial variables for each ungauged or pseudo-ungauged catchment were duplicated 100 times so that the catchment the ungauged or gauged spatial dataset was classified to was based on 100 replicates. The algorithm forces each of the 100 rows of spatial data variables the ungauged or pseudo-ungauged catchment to match to one of the 11 classifiers in the trained environment. This approach was repeated for the Land use, Original Vegetation and Ecounit spatial datasets for all 41 ungauged and pseudo-ungauged catchments. The gauged catchment with allocations of more than half the records for each gauged or ungauged catchment was deemed classified for the respective dataset.30 to spatial variables for all catchments, shown in Eq. , S30 it was used directly in this study. Geology and landform has also been demonstrated as a fundamental driver of nitrate in hydrological processes48, therefore in this study we also further scrutinised influences of the original vegetation dataset by breaking each variable down into its separate landform type and floristic structure descriptor as described by the data authority31 to better visualise hydrological drivers of results.Variables in the top 10%deviated from the mean were then graphed and visually compared for similarities between the deviated variables for gauged and ungauged catchments sharing similar combinations of deviations were categorised together. Because the Original Vegetation dataset had previously been shown to explain the ANN-PR matches between the gauged catchmentsAn Artificial Neural Network water quality (ANN-WQ) simulator was developed to facilitate a rapid assessment of the similarity of matched catchments for DIN. The ANN-WQ simulator was intended for rapid comparison purposes only, and therefore method optimisation was outside the catchment classification scope intended for this research. Similarity between catchments was evaluated by the comparative accuracy of DIN simulations generated for catchments depending on the dataset scenario included in the ANN-WQ simulator training phase.66.For each gauged catchment dataset, a Dense Deep Learning feed forward network was created in Matlab. The dense fully connected learning approach was selected to facilitate for all data relationships to be considered, to maximise the pattern recognition ability within the dataset, timesteps of data are still captured in variables as the corresponding time-date number. This architecture was resource intensive and therefore a ReLU hidden layer activation was included due to its superior ability to deal with weights and bias over large intensity variations, as could be expected in the dataset67. Trialling up to 1000 hidden layers on big data creates heavy computing demands, therefore, Adam optimiser was selected for its minimal memory usage benefits whilst also addressing sparse gradients and non-stationary objectives68. The model performance metrics comprised of RMSE, MSE, Nash Sutcliffe Efficiency, Peak Deviation and Correlation as R2 were recorded for each of the hidden layer trials, and the algorithm with the best performance metrics evaluated for the optimal hidden neuron and for pass or fail of satisfactory performance criteria. The performance metrics equations in Table 73.Training datasets involved a data set split of 80% Training, 10% Verification and 10% Testing. Development of the Dense Deep Learning feed forward network began with a trial and error phase to scope for functionality at the default hidden neurons (< 10). To overcome inadequate complexity and dimensionality within datasets, trials of 1 to 1000 hidden layers were then undertaken for each dataset to identify the best performing hidden layer network suited to the training datasetTo compare model DIN forecasts against observed DIN forecasts, the algorithm was rerun with the optimal number of hidden layers for every dataset. We normalized all data for graphing so we could compare forecasting potential across the different datasets.The trained algorithms that met minimum satisfactory performance criteria as well as demonstrating a simulation ability in the supervised environment were then used in an unsupervised environment to simulate DIN for their respective classified pseudo-ungauged catchment datasets based on flow inputs. For the data available, only the pseudo-ungauged Herbert was suitable for evaluation for the study and is evaluated as a case study within this article as proof of concept. For this study, scenario datasets evaluated included the ANN-WQ simulation results for the pseudo-ungauged catchment trained on the matched gauged catchment, ANN-WQ simulator trained using all gauged catchment data, and ANN-WQ simulator trained using data from the matching spatio-temporal category. Performance metrics for each scenario were then collated and visualised in a dart plot. To create the dart plot, performance metrics were adjusted using Eq.  to make 2c, NSEc, dc, RMSE, MAE,Pdvc}.where: PC = Performance Criteria, K = unsupervised portion of ANN-WQ simulation scenario, 1….n = a performance criteria adjusted to make zero target i.e.{RWhere:2c = 1 − R2.Rc = 1 − NSE.NSEc = pdeSupplementary Information.


PLOS Biology found a previously undefined group of cells that diverges from the main lineages and undergo apoptosis through the activity of young transposable elements.Our current understanding of early human development is limited. A study in  Upon fertilisation, the human embryo undergoes several rounds of cleavage divisions and morphological changes generating the blastocyst, a cavitated structure that will implant in the uterus 7 days post fertilisation (dpf). Understanding how cell specification takes place in the human embryo has significant implications in improving human reproductive health, in particular in vitro fertilisation (IVF) success rate .Before implantation, the zygote undergoes successive divisions and ultimately is composed of 3 lineages, namely, the trophectoderm (TE), epiblast (EPI), and primitive endoderm (PrE). Single-cell RNA sequencing (scRNA-seq) allowed the generation of expression atlases of early mammalian development, which was pivotal to identify molecular signatures associated with each fate. Nonetheless, the datasets generated are large and the analysis pipelines are constantly improving, giving opportunities for refined analysis of the scRNA-seq datasets. A particular challenge lies in identifying cells that are not clearly aligning with one fate, often dubbed “intermediate”. Careful in-depth analysis of those “intermediate” cells is key to account for distinguishing biological features from technical and biological noise.Here, Singh and colleagues reanalyse multiple independent scRNA-seq datasets –4 of earOf great importance, identification of the REjects permitted clear identification of ICM cells. Moreover, the authors reveal a previously unknown role for transposable elements. REjects are associated with the activity of young transposable elements, which include potentially mutagenic transposons. In contrast, the developing ICM only expresses old (no longer able to transpose) human-specific endogenous retrovirus H (HERVH), which is capable of suppressing active transposable elements by modulating the expression of retroviral inhibitors. Among these transposable elements restricting factors, APOBEC3s seem to be indeed uniquely expressed in the ICM and proposed to suppress young transposable elements activity.The work of Singh and colleagues sheds new light on early human development . The stuSingh and colleagues also suggest that endogenous retroviruses such as HERVH represent a key defence mechanism for the developing embryo against potential DNA damage, linking transposable elements to their known role in pluripotency . It is kFinally, REjects seem to be absent in mice at similar developmental time frames, highlighting interspecies differences that need to be taken in account when translating animal model findings to humans, and emphasising the importance of developing relevant models to study human early development. On this note, it is possible that REjects apoptosis could be an artefact from in vitro culturing of human embryos. However, their reproducibility in different datasets and predicted presence in nonhuman primates would dismiss this hypothesis. To strengthen these findings, it will be important to identify more precisely how HERVH suppresses young transposable elements activity in vivo and the mechanisms that regulate induced apoptosis. It will be also interesting to investigate whether transposable elements activity is a developmentally conserved mechanism of gene regulation, as it could perhaps also apply to cell fate decisions. In other words, in parallel to selecting out REjects, repressing transposable elements might be necessary for further lineage specification, as it has been proposed that ICM markers such as IFI16 could inhibit the burst of transposable elements expressed at the morula stage ,11.PLOS Biology highlights the importance of carefully mining and correctly annotating large omics datasets, and to thoughtfully consider “intermediate” cells to define whether they are representing transitions or aberrant cell fates or states that would undergo apoptosis. Through the identification of REjects, they describe an apoptosis-mediated mechanism for cell selection in the early human embryo correlated with transposable elements activity. This shows that abnormal fates, potentially related to aneuploidies, are extremely frequent in all embryos and hints at the way that aneuploid cells may be selected out.Overall, the work by Singh and colleagues in this issue of