Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change

Ecosystem response to climate change is complex. In order to forecast ecosystem dynamics, we need high-quality data on changes in past species abundance that can inform process-based models. Sedimentary ancient DNA (sedaDNA) has revolutionised our ability to document past ecosystems' dynamics. It provides time series of increased taxonomic resolution compared to microfossils (pollen, spores), and can often give species-level information, especially for past vascular plant and mammal abundances. Time series are much richer in information than contemporary spatial distribution information, which have been traditionally used to train models for predicting biodiversity and ecosystem responses to climate change. Here, we outline the potential contribution of sedaDNA to forecast ecosystem changes. We showcase how species-level time series may allow quantification of the effect of biotic interactions in ecosystem dynamics, and be used to estimate dispersal rates when a dense network of sites is available. By combining palaeo-time series, process-based models, and inverse modelling, we can recover the biotic and abiotic processes underlying ecosystem dynamics, which are traditionally very challenging to characterise. Dynamic models informed by sedaDNA can further be used to extrapolate beyond current dynamics and provide robust forecasts of ecosystem responses to future climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Competitive performance as a discriminator of doping status in elite athletes

As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.

Improving olaparib exposure to optimize adverse effects management

Background

Olaparib is an inhibitor of the human poly-(ADP-ribose)-polymerase enzymes (PARP1/2) needed to repair single-strand DNA breaks. It is used in breast, ovarian, prostate and pancreatic cancer.

Objectives

This work aimed to describe the pharmacokinetics/pharmacodynamics (PK/PD) relationship between olaparib plasma concentrations and common adverse effects (i.e. anaemia and hypercreatininaemia), in a real-life setting, to propose a target concentration for therapeutic drug monitoring.

Methods

Two PK/PD models describing the evolution of haemoglobinaemia and creatininaemia as a function of time were developed, based on data from, respectively, 38 and 37 patients receiving olaparib. The final model estimates were used to calculate the incidence of anaemia and creatinine increase according to plasma trough concentrations for 1000 virtual subjects to define target exposure.

Results

The final models correctly described the temporal evolution of haemoglobinaemia and creatininaemia for all patients. The haemoglobinaemia PK/PD model is inspired by Friberg's model, and the creatininaemia PK/PD model is an indirect response model. Model parameters were in agreement with physiological values and close to literature values for similar models. The mean (population) plasma haemoglobin concentration at treatment initiation, as estimated by the model, was 11.62 g/dL, while creatinine concentration was 71.91 µmol/L. Using simulations, we have identified a target trough concentration of 3500-4000 ng/mL, above which more than 20% of patients would report grade ≥3 anaemia.

Conclusion

Based on real-world data, we were able to properly describe the time course of haemoglobinaemia and plasma creatininaemia during olaparib treatment.

Modelling Inductive Sensors for Arc Fault Detection in Aviation

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.

Small-scale medical oxygen production unit using PSA technology: modeling and sensitivity analysis

This study presents a solid approach for small-scale medical oxygen production unit using pressure swing adsorption (PSA) technology. The objective of this research is to develop a mathematical model and conduct a sensitivity analysis to optimise the design and operating parameters of the PSA system. Based on the simulation results, an optimal set of operational parameter values has been obtained for the PSA beds. The result shows that the binary system produced oxygen with a purity of 94%, at the adsorption pressure 1 bar and temperature of 308K. The findings demonstrate the effectiveness of the proposed small-scale PSA system for medical oxygen production, highlighting the impact of key parameters and emphasising the need for careful optimisation. The findings serve as a guide for the design and operation of small-scale PSA systems, enabling healthcare facilities to produce their own medical oxygen, thereby improving accessibility and addressing critical shortages during emergencies. Future research may explore the integration of large scale PSA units in hospitals in Morocco.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

The poorly-explored stomatal response to temperature at constant evaporative demand

Changes in leaf temperature are known to drive stomatal responses, because the leaf-to-air water vapour gradient (Δw) increases with temperature if ambient vapour pressure is held constant, and stomata respond to changes in Δw. However, the direct response of stomata to temperature (DRST; the response when Δw is held constant by adjusting ambient humidity) has been examined far less extensively. Though the meagre available data suggest the response is usually positive, results differ widely and defy broad generalisation. As a result, little is known about the DRST. This review discusses the current state of knowledge about the DRST, including numerous hypothesised biophysical mechanisms, potential implications of the response for plant adaptation, and possible impacts of the DRST on plant-atmosphere carbon and water exchange in a changing climate.

Ageing as a two-phase process: theoretical framework

Human ageing, along with the ageing of conventional model organisms, is depicted as a continuous and progressive decline of biological capabilities accompanied by an exponentially increasing mortality risk. However, not all organisms experience ageing identically and our understanding of the phenomenon is coloured by human-centric views. Ageing is multifaceted and influences a diverse range of species in varying ways. Some undergo swift declines post-reproduction, while others exhibit insubstantial changes throughout their existence. This vast array renders defining universally applicable "ageing attributes" a daunting task. It is nonetheless essential to recognize that not all ageing features are organism-specific. These common attributes have paved the way for identifying "hallmarks of ageing," processes that are intertwined with age, amplified during accelerated ageing, and manipulations of which can potentially modulate or even reverse the ageing process. Yet, a glaring observation is that individuals within a single population age at varying rates. To address this, demographers have coined the term 'frailty'. Concurrently, scientific advancements have ushered in the era of molecular clocks. These innovations enable a distinction between an individual's chronological age (time since birth) and biological age (physiological status and mortality risk). In 2011, the "Smurf" phenotype was unveiled in Drosophila, delineating an age-linked escalation in intestinal permeability that presages imminent mortality. It not only acts as a predictor of natural death but identifies individuals exhibiting traits normally described as age-related. Subsequent studies have revealed the phenotype in organisms like nematodes, zebrafish, and mice, invariably acting as a death predictor. Collectively, these findings have steered our conception of ageing towards a framework where ageing is not linear and continuous but marked by two distinct, necessary phases, discernible in vivo, courtesy of the Smurf phenotype. This framework includes a mathematical enunciation of longevity trends based on three experimentally measurable parameters. It facilitates a fresh perspective on the evolution of ageing as a function. In this article, we aim to delineate and explore the foundational principles of this innovative framework, emphasising its potential to reshape our understanding of ageing, challenge its conventional definitions, and recalibrate our comprehension of its evolutionary trajectory.

Comparative Fitting of Mathematical Models to Carvedilol Release Profiles Obtained from Hypromellose Matrix Tablets

The mathematical models available in DDSolver were applied to experimental dissolution data obtained by analysing carvedilol release from hypromellose (HPMC)-based matrix tablets. Different carvedilol release profiles were generated by varying a comprehensive selection of fillers and carvedilol release modifiers in the formulation. Model fitting was conducted for the entire relevant dissolution data, as determined by using a paired t-test, and independently for dissolution data up to approximately 60% of carvedilol released. The best models were selected based on the residual sum of squares (RSS) results used as a general measure of goodness of fit, along with the utilization of various criteria for visual assessment of model fit and determination of the acceptability of estimated model parameters indicating burst release or lag time concerning experimental dissolution results and previous research. In addition, a model-dependent analysis of carvedilol release mechanisms was carried out.

A Dynamic Model of Opioid Overdose Deaths in Canada during the Co-Occurring Opioid Overdose Crisis and COVID-19 Pandemic

With over 40,000 opioid-related overdose deaths between January 2016 and June 2023, the opioid-overdose crisis is a significant public health concern for Canada. The opioid crisis arose from a complex system involving prescription opioid use, the use of prescription opioids not as prescribed, and non-medical opioid use. The increasing presence of fentanyl and its analogues in the illegal drugs supply has been an important driver of the crisis. In response to the overdose crisis, governments at the municipal, provincial/territorial, and federal levels have increased actions to address opioid-related harms. At the onset of the COVID-19 pandemic, concerns emerged over how the pandemic context may impact the opioid overdose crisis. Using evidence from a number of sources, we developed a dynamic mathematical model of opioid overdose death to simulate possible trajectories of overdose deaths during the COVID-19 pandemic. This model incorporates information on prescription opioid use, opioid use not as prescribed, non-medical opioid use, the level of fentanyl in the drug supply, and a measure of the proportion deaths preventable by new interventions. The simulated scenarios provided decision makers with insight into possible trajectories of the opioid crisis in Canada during the COVID-19 pandemic, highlighting the potential of the crisis to take a turn for the worse under certain assumptions, and thus, informing planning during a period when surveillance data were not yet available. This model provides a starting point for future models, and through its development, we have identified important data and evidence gaps that need to be filled in order to inform future action.

A Framework for Assessing the Impact of Outbreak Response Immunization Programs

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

On the mechanism for winter stem pressure build-up in walnut trees

Xylem embolism is a significant factor in tree mortality. Restoration of hydraulic conductivity after massive embolization of the vascular system requires the application of positive pressure to the vessels and/or the creation of new conductive elements. Some species generate positive pressure from the root system to propagate pressure in distal, aboveground organs in spring, whereas other species generate positive pressure locally at the stem level during winter. We provide a mechanistic explanation for winter stem pressure build-up in the walnut tree. We have developed a physical model that accounts for temperature fluctuations and phase transitions. This model is based on the exchange of water and sugars between living cells and vessels. Our computations demonstrate that vessel pressurization can be attributed to the transfer of water between vessels across the parenchyma rays, which is facilitated by a radial imbalance in sugar concentration. The ability to dispose of soluble sugars in living cells, and to transport them between living cells and up to the vessels, is identified as the main drivers of stem pressure build-up in the walnut tree.

Development of a generalized pharmacokinetic model to characterize clinical pharmacokinetics of monomethyl auristatin E-based antibody-drug conjugates

AIMS

This study aims to develop a generalized pharmacokinetic (PK) model for monomethyl auristatin E (MMAE)-based antibody-drug conjugates (ADCs) that can simultaneously capture the PK of multiple ADC analytes commonly measured in the clinic.

METHODS

A comprehensive literature review was conducted to collect PK data on MMAE-based ADCs from clinical trials. From each study, PK profiles of total antibody, the ADC, conjugated MMAE, and unconjugated MMAE, were extracted. These data were pooled and dose-normalized to evaluate the generalizability of PK across various ADCs and dose levels. Upon confirming PK generalizability, a generalized PK model for MMAE-based ADCs was developed using the entire dataset. Furthermore, exposure metrics (C max $$ {\mathrm{C}}_{\mathrm{max}} $$ and AUC) reported across the range of doses were combined to establish linear relationships between dose and exposure metrics for MMAE-based ADCs.

RESULTS

A total of 109 PK profiles from 18 distinct MMAE-based ADCs were gathered. The dose-normalized PK profiles supported the generalizability of PK for MMAE-based ADCs. A generalized PK model was developed, which enabled capturing the PK data for 4 ADC analytes across all collected MMAE-based ADCs. A linear relationship between dose and PK exposure metrics was established, enabling the prediction of typical exposure values across different doses for MMAE-based ADCs.

CONCLUSIONS

This study comprehensively analysed clinical PK data from different valine-citrulline (vc)-MMAE-based ADCs. The generalized PK model developed here serves as an important tool for a priori prediction of the PK for multiple ADC analytes in clinical settings and lays the foundation for establishing generalized exposure-response and exposure-toxicity correlations for MMAE-based ADCs.

Recommendations for quantitative uncertainty consideration in ecology and evolution

Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.

The resilience of the pork supply chain to a food safety outbreak: The case of dioxins

Food supply chains are constantly challenged by food safety hazards entering the chain. The ability of the supply chain to provide safe food within a reasonable time after such a food safety threat or shock can be investigated with the concept of resilience using food safety as an indicator. Resilience is then defined as the food safety performance deviation due to the shock and takes both the severity of the shock as well as the time to fully recover or reach a new equilibrium into account. This study developed a stochastic simulation model to evaluate the resilience of the Dutch pork supply chain to dioxin contamination in the feed. The resilience of the supply chain as well as the potential costs associated with the contamination are compared between several monitoring strategies with the aim to determine the optimal control points for dioxin monitoring. Model results show that collecting and analyzing samples at more than one control point along the pork supply chain, in particular at feed mills and fat melting facilities, resulted in the highest resilience and the lowest costs after a shock. This model and these results can be used by public and private decision makers to make proactive and informed decisions on the monitoring strategies to control dioxins in the pork supply chain that result in optimal resilience to a dioxin crises.

Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants

Freshwaters are highly threatened ecosystems that are vulnerable to chemical pollution and climate change. Freshwater taxa vary in their sensitivity to chemicals and changes in species composition can potentially affect the sensitivity of assemblages to chemical exposure. Here we explore the potential consequences of future climate change on the composition and sensitivity of freshwater macroinvertebrate assemblages to chemical stressors using the UK as a case study. Macroinvertebrate assemblages under end of century (2080-2100) and baseline (1980-2000) climate conditions were predicted for 608 UK sites for four climate scenarios corresponding to mean temperature changes of 1.28 to 3.78°C. Freshwater macroinvertebrate toxicity data were collated for 19 chemicals and the hierarchical species sensitivity distribution model was used to predict the sensitivity of untested taxa using relatedness within a Bayesian approach. All four future climate scenarios shifted assemblage compositions, increasing the prevalence of Mollusca, Crustacea and Oligochaeta species, and the insect taxa of Odonata, Chironomidae, and Baetidae species. Contrastingly, decreases were projected for Plecoptera, Ephemeroptera (except for Baetidae) and Coleoptera species. Shifts in taxonomic composition were associated with changes in the percentage of species at risk from chemical exposure. For the 3.78°C climate scenario, 76% of all assemblages became more sensitive to chemicals and for 18 of the 19 chemicals, the percentage of species at risk increased. Climate warming-induced increases in sensitivity were greatest for assemblages exposed to metals and were dependent on baseline assemblage composition, which varied spatially. Climate warming is predicted to result in changes in the use, environmental exposure and toxicity of chemicals. Here we show that, even in the absence of these climate-chemical interactions, shifts in species composition due to climate warming will increase chemical risk and that the impact of chemical pollution on freshwater macroinvertebrate biodiversity may double or quadruple by the end of the 21st century.

Roles for leakiness and O2 evolution in explaining lower-than-theoretical quantum yields of photosynthesis in the PEP-CK subtype of C4 plants

Theoretically, the PEP-CK C4 subtype has a higher quantum yield of CO2 assimilation (Φ CO 2 ) than NADP-ME or NAD-ME subtypes because ATP required for operating the CO2-concentrating mechanism is believed to mostly come from the mitochondrial electron transport chain (mETC). However, reportedΦ CO 2 is not higher in PEP-CK than in the other subtypes. We hypothesise, more photorespiration, associated with higher leakiness and O2 evolution in bundle-sheath (BS) cells, cancels out energetic advantages in PEP-CK species. Nine species (two to four species per subtype) were evaluated by gas exchange, chlorophyll fluorescence, and two-photon microscopy to estimate the BS conductance (gbs) and leakiness using a biochemical model. Average gbs estimates were 2.9, 4.8, and 5.0 mmol m-2 s-1 bar-1, and leakiness values were 0.129, 0.179, and 0.180, in NADP-ME, NAD-ME, and PEP-CK species, respectively. The BS CO2 level was somewhat higher, O2 level was marginally lower, and thus, photorespiratory loss was slightly lower, in NADP-ME than in NAD-ME and PEP-CK species. Differences in these parameters existed among species within a subtype, and gbs was co-determined by biochemical decarboxylating sites and anatomical characteristics. Our hypothesis and results partially explain variations in observedΦ CO 2 , but suggest that PEP-CK species probably use less ATP from mETC than classically defined PEP-CK mechanisms.

Modelling a two-stream emergency department segregation and admission system from COVID-19 early rapid antigen testing: A pilot study

OBJECTIVE

Many factors influence patient flow through an ED, including streaming, treatment spaces and staff resources. This pilot study explored and compared real time patient flow using a single-stream system versus varying configurations of possible two-stream systems using computer simulation.

METHODS

Simulation modelling was used to assess the delay in treatment of a rapid-antigen-tested-based, two-stream model for patient flow through ED during the peak phase of the COVID pandemic.

RESULTS

Modelling two-stream configuration for all patients (minimum time to be seen for both COVID-positive and COVID-negative patients) showed that in the case study ED, a two-stream system and linked changes in bed configuration for managing the risks of infection can impact delays in treatment.

CONCLUSIONS

Data-driven modelling within specific clinical settings can inform the (in)efficiency of patient flow processes and help clinicians and managers make evidence-based decisions about patient transition through EDs. This can assist with reconfiguration of ED patient streaming particularly during periods of unique need, such as the recent COVID-19 pandemic.

Modelling the cost-effectiveness of a newborn hearing screening programme; usability and pitfalls

OBJECTIVE

The EUSCREEN project concerns the study of European vision and hearing screening programmes. Part of the project was the development of a cost-effectiveness model to analyse such programmes. We describe the development and usability of an online tool to enable stakeholders to design, analyse or modify a newborn hearing screening (NHS) programme.

DESIGN

Data from literature, from existing NHS programmes, and observations by users were used to develop and refine the tool. Required inputs include prevalence of the hearing impairment, test sequence and its timing, attendance, sensitivity, and specificity of each screening step. Outputs include the number of cases detected and the costs of screening and diagnostics.

STUDY SAMPLE

Eleven NHS programmes with reliable data.

RESULTS

Three analyses are presented, exploring the effect of low attendance, number of screening steps, testing in the maternity ward, or screening at a later age, on the benefits and costs of the programme. Knowledge of the epidemiology of a staged screening programme is crucial when using the tool.

CONCLUSIONS

This study presents a tool intended to aid stakeholders to design a new or analyse an existing hearing screening programme in terms of benefits and costs.

Influence of ultrasound pretreatment on drying characteristics of cocoyam (Xanthosoma sagittifolium) slices during convective hot air drying

BACKGROUND

Convective hot air drying of cocoyam is risk-free and inexpensive to a significant level. However, hot air drying causes negative changes to the color, texture, flavor and nutritional content of cocoyam as a result of the prolonged drying. Recently, the innovative technology of ultrasound pretreatment has been applied in food processing to reduce the processing time, conserve energy and preserve the quality of the food product. Thus, there is need to investigate the effect of ultrasound pretreatment with distilled water (UDW) and ultrasound with osmotic dehydration (UOD) for different ultrasonic times (10-30 min) on the drying kinetics of cocoyam slices during convective hot air drying. Ultrasound pretreatment was applied at a frequency of 20 kHz and an output power of 600 W for UDW and UOD. The ultrasound-pretreated samples were further dried in a convective hot-air drying oven at 70 °C.

RESULTS

UDW and UOD samples, respectively, had a 25% and 46% reduction in drying time compared to untreated samples. The UOD samples had the lowest activation energy (10.697 × 10 3 kJ), as well as the highest moisture diffusivity (3.782  × 10-10  m2  s-1 ) and mass transfer coefficient (2.006  × 10-8  m s-1 ), among the untreated and UDW samples. Wang and Singh, Page and Peleg models were found to be the most fitted models with respect to the drying characteristics of cocoyam for untreated, UDW and UOD samples, respectively.

CONCLUSION

Ultrasound pretreatment technology is a potential non-thermal process that can be incorporated as a pretreatment method in the convective drying of cocoyam to reduce processing time, conserve energy and enhance cocoyam product shelf life. © 2023 Society of Chemical Industry.

Modelling tomato pericarp microstructure as force control reference for harvesting robot

BACKGROUND

The harvest of fruit can be significantly advanced with the thriving development of intelligent and automated robot technologies. Nevertheless, the picking success rate of tomato fruit still requires improvement as some fruits are unexpectedly damaged inside, which is imperceptible by machine vision. Herein, a modelling method based on modified Voronoi algorithm is proposed to reconstruct the cellular structure of tomato pericarp.

RESULTS

Based on the reconstructed micro-model, the compression physical behaviour of the pericarp cells is simulated to observe internal local stress and potential damage. It is revealed that the simulation result for pericarps of tomatoes with different ripeness is highly consistent to the experimental tests, which has well validated the feasibility of this modelling and simulation method.

CONCLUSION

A Voronoi-based modelling method is proposed for micro-reconstruction of tomato pericarp, and the corresponding compression simulation results agree well with the experimental tests. Such result can be utilized as reference to improve the grasping force control for harvesting robot to avoid invisible damage induced by accident overload issue. With the predicting result, superior success rate can be achieved to enhance robot performance. © 2024 Society of Chemical Industry.

Contributions of early detection and cancer prevention to colorectal cancer mortality reduction by screening colonoscopy: a validated modelling study

BACKGROUND AND AIMS

Screening colonoscopy, recommended every ten years, reduces mortality from colorectal cancer (CRC) by early detection of prevalent but undiagnosed CRC, as well as by prevention of CRC by removal of precursor lesions. The aim of this study was to assess the relative contribution of both components to total CRC mortality reduction over time.

METHODS

Using a validated multistate Markov model, we simulated hypothetical cohorts of 100,000 individuals aged 55-64 with and without use of screening at baseline. Main outcomes included proportions of prevented CRC deaths arising from (asymptomatic) CRC already prevalent at baseline and from newly developed CRC during 15-years of follow-up, and mortality rate ratios of screened versus unscreened groups over time.

RESULTS

Early detection of prevalent cases accounted for 52%, 30% and 18% of deaths prevented by screening colonoscopy within 5, 10 and 15 years, respectively. Relative reduction of mortality was estimated to be much larger for mortality from incident cancers than for mortality from cancers that were already present and early detected at screening endoscopy and for total CRC mortality (i.e., 88% versus 67% and 79% within 10 years from screening).

CONCLUSIONS

Reduction of CRC mortality mainly arises from early detection of prevalent cancers during the early years after screening colonoscopy, but prevention of incident cases accounts for the majority of prevented deaths in the longer run. Prevention of incident cases leads to sustained strong reduction of colorectal cancer mortality, possibly warranting an extension of screening intervals.

Development of a Prediction Model for Significant Adverse Outcome After Spine Surgery

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

Development, validation, and decision curve analysis of a novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery.

METHODS

Data was gathered retrospectively from medical records of patients who underwent spine surgery at a single tertiary centre between January 2019 and December 2020 (n = 488). Postoperative adverse events were classified objectively using the Comprehensive Complication Index (CCI). The model was derived using backward stepwise logistic regression. Validation was undertaken using bootstrap resampling. Discrimination was determined by calculating the area under the receiver operating characteristic (AUC). Calibration was assessed graphically. Clinical utility of the model was assessed using decision curve analysis (DCA). Performance measures were compared to an existing tool, SpineSage.

RESULTS

Overall complication rate was 34%. Modelling showed higher age, surgical invasiveness and preoperative anemia were most strongly predictive of any complication (OR = 1.03, 1.09, 2.1 respectively, P < .001), whereas the occurrence of a major complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, P < .001). At validation, the model showed good discrimination with an AUC of .73 (.71 - .75) and excellent calibration. SpineSage had an AUC of .71, while DCA showed the novel model had greater expected benefit at all risk thresholds.

CONCLUSION

NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their perioperative risk.

Predicting nitrogen excretion of cattle kept under tropical and subtropical conditions using semimechanistic models

The present study aims at evaluating whether current semimechanistic models developed for temperate cattle systems can be adopted for cattle under (sub-) tropical husbandry systems to adequately (accurately and precisely) predict total nitrogen (TN), urine nitrogen (UN), faecal nitrogen (FN) excretion and its partition into different FN fractions. Selected models were built based on the feeding recommendations for ruminants of the British (Model A), German (Model G) and French (INRA; Model I) system. Model evaluation was conducted using eight nitrogen balance studies performed in El Salvador, Kenya and Peru (n = 392 individual observations including lactating cows, heifers and steers). Concordance correlation coefficient, root mean square errors (RMSE), and mean biases were estimated to evaluate the models' adequacy in predicting nitrogen excretion. Input variables causing greatest variation in nitrogen excretion prediction were identified by a sensitivity analysis and adjusted. Model G was able to adequately (i.e., RMSE of <25% of observed mean, systematic error of <5% of the mean square error) predict TN excretion through a compensation between overestimation of UN excretion and underestimation of FN excretion. None of the models were able to adequately predict UN, FN, and different FN fractions. Model I adequately predicted FN (RMSE = 18%) when duodenal microbial crude protein flow was increased, and the intercept used to predict FN excretion was reduced from 4.30 to 3.82 g of nitrogen per kilogram of dry matter intake. These adjustments, however, were not sufficient to predict adequately UN excretion (RMSE = 38%), individual FN fractions (RMSE > 56%), and TN (RMSE = 22%) excretion, by Model I.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Comparative reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data

Objectives

Africa has experienced fewer COVID-19 cases and deaths than other regions, with a contrasting epidemiological situation between countries, raising questions regarding the determinants of disease spread in Africa.

Methods

We built a susceptible-exposed-infected-recovered model including COVID-19 mortality data where recovery class is structured by specific immunization and modeled by a partial differential equation considering the opposed effects of immunity decline and immunization. This model was applied to Tunisia, Senegal, and Madagascar.

Results

Senegal and Tunisia experienced two epidemic phases. Initially, infections emerged in naive individuals and were limited by social distancing. Variants of concern (VOCs) were also introduced. The second phase was characterized by successive epidemic waves driven by new VOCs that escaped host immunity. Meanwhile, Madagascar demonstrated a different profile, characterized by longer intervals between epidemic waves, increasing the pool of susceptible individuals who had lost their protective immunity. The impact of vaccination on model parameters in Tunisia and Senegal was evaluated.

Conclusions

Loss of immunity and vaccination-induced immunity have played crucial role in controlling the African pandemic. SARS-CoV-2 has become endemic now and will continue to circulate in African populations. However, previous infections provide significant protection against severe diseases, thus providing a basis for future vaccination strategies.

Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis

The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.

Excitatory action of low frequency depolarizing GABA/glycine synaptic inputs is prevalent in prenatal spinal SOD1G93A motoneurons

Amyotrophic lateral sclerosis (ALS) is a fatal adult-onset neurodegenerative disease characterized by progressive motor neuron degeneration and muscle paralysis. Recent evidence suggests the dysfunction of inhibitory signalling in ALS motor neurons. We have shown that embryonic day (E)17.5 spinal motoneurons (MNs) of the SOD1G93A mouse model of ALS exhibit an altered chloride homeostasis. At this prenatal stage, inhibition of spinal motoneurons (MNs) is mediated by depolarizing GABAergic/glycinergic postsynaptic potentials (dGPSPs). Here, using an ex vivo preparation and patch clamp recording from MNs with a chloride equilibrium set below spike threshold, we report that low input resistance (Rin ) E17.5 MNs from the SOD1G93A ALS mouse model do not correctly integrate dGPSPs evoked by electrical stimulations of GABA/glycine inputs at different frequencies. Indeed, firing activity of most wild-type (WT) MNs with low Rin was inhibited by incoming dGPSPs, whereas low Rin SOD1G93A MNs were excited or exhibited a dual response (excited by low frequency dGPSPs and inhibited by high frequency dGPSPs). Simulation highlighted the importance of the GABA/glycine input density and showed that pure excitation could be obtained in SOD-like MNs by moving GABA/glycine input away from the cell body to dendrites. This was in agreement with confocal imaging showing a lack of peri-somatic inhibitory terminals in SOD1G93A MNs compared to WT littermates. Putative fast ALS-vulnerable MNs with low Rin are therefore lacking functional inhibition at the near-term prenatal stage. KEY POINTS: We analysed the integration of GABAergic/glycinergic synaptic events by embryonic spinal motoneurons (MNs) in a mouse model of the amyotrophic lateral sclerosis (ALS) neurodegenerative disease. We found that GABAergic/glycinergic synaptic events do not properly inhibit ALS MNs with low input resistance, most probably corresponding to future vulnerable MNs. We used a neuron model to highlight the importance of the GABA/glycine terminal location and density in the integration of the GABAergic/glycinergic synaptic events. Confocal imaging showed a lack of GABA/glycine terminals on the cell body of ALS MNs. The present study suggests that putative ALS vulnerable MNs with low Rin lack functional inhibition at the near-term stage.

The Challenge of Water Competition in Physical Adsorption of CO2 by Porous Solids for Carbon Capture Applications - A Short Perspective

With ever-increasing efforts to design sorbent materials to capture carbon dioxide from flue gas and air, this perspective article is provided based on nearly a decade of collaboration across science, engineering, and industry partners. A key point learned is that a holistic view of the carbon capture problem is critical. While researchers can be inclined to value their own fields and associated metrics, often, key parameters are those that enable synergy between materials and processes. While the role of water in the chemisorption of CO2 is well-studied, in this perspective, it is hoped to highlight the often-overlooked but critical role of water in assessing the potential of a physical adsorbent for CO2 capture. This is a challenge that requires interdisciplinarity. As such, this document is written for a general audience rather than experts in any specific discipline.

Bioenergetic modelling of a marine top predator's responses to changes in prey structure

Determining how animals allocate energy, and how external factors influence this allocation, is crucial to understand species' life history requirements and response to disturbance. This response is driven in part by individuals' energy balance, prey characteristics, foraging behaviour and energy required for essential functions. We developed a bioenergetic model to estimate minimum foraging success rate (FSR), that is, the lowest possible prey capture rate for individuals to obtain the minimum energy intake needed to meet daily metabolic requirements, for female sperm whale (Physeter macrocephalus). The model was based on whales' theoretical energetic requirements using foraging and prey characteristics from animal-borne tags and stomach contents, respectively. We used this model to simulate two prey structure change scenarios: (1) decrease in mean prey size, thus lower prey energy content and (2) decrease in prey size variability, reducing the variability in prey energy content. We estimate the whales need minimum of ~14% FSR to meet their energetic requirements, and energy intake is more sensitive to energy content changes than a decrease in energy variability. To estimate vulnerability to prey structure changes, we evaluated the compensation level required to meet bioenergetic demands. Considering a minimum 14% FSR, whales would need to increase energy intake by 21% (5-35%) and 49% (27-67%) to compensate for a 15% and 30% decrease in energy content, respectively. For a 30% and 50% decrease in energy variability, whales would need to increase energy intake by 13% (0-23%) and 24% (10-35%) to meet energetic demands, respectively. Our model demonstrates how foraging and prey characteristics can be used to estimate impact of changing prey structure in top predator energetics, which can help inform bottom-up effects on marine ecosystems. We showed the importance of considering different FSR in bioenergetics models, as it can have decisive implications on estimates of energy acquired and affect the conclusions about top predator's vulnerability to possible environmental fluctuations.

Impact of climate change on the water resources of the Atbara River using novel hydrological models

Rivers respond directly to climate change, as well as incorporating the effects of climate-driven changes occurring within their watersheds. In this research, climate change's impact on the Atbara River, one of the main tributaries of the Nile River, was studied. Various statistical methods of analysis were applied to study the basic characteristics of the climatic parameters that affect the discharge of the Atbara River. The three hydrological gauging stations on the Atbara River, namely, the Upper Atbara and Setit reservoirs, Khashm el-Girba reservoir, and Atbara Kilo 3 station, were included in the study. The correlation between the meteorological parameters and the hydrology of the Atbara River and the prediction of the future hydrology of the Atbara River Basin was determined. Many hydrological models were developed and tested to predict the hydrology of the river. Finally, forecasting for river hydrology was built. No significant trend was found in the precipitation in the study area. The developed model simulates the observed data with a high coefficient of determination ranging from 0.7 to 0.91 for the three hydrological gauging stations studied. Results predicted a slight decrease in river discharge in future years.

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Population abundance estimates in conservation and biodiversity research

Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring.

Establishing an EU-wide front-of-pack nutrition label: Review of options and model-based evaluation

This paper reviews the effectiveness of four types of front-of-pack nutrition labels (FoPLs) in influencing calorie purchases. The four FoPL types are poised for unified implementation across European countries. Further, this study extends its analysis to evaluate the impacts of the voluntary adoption of these FoPLs within 27 EU nations. Nutri-Score displays higher potential for yielding positive health and economic outcomes, compared with other FoPLs. Across EU countries, Nutri-Score is projected to avert nearly two million cases of non-communicable diseases, in total, between 2023 and 2050. Keyhole demonstrates effects of a similar magnitude but with no statistical significance. Nutri-Repere shows smaller impacts, while Nutri-Couleurs has non-significant effects. Nutri-Score is projected to significantly lower annual healthcare spending by 0.05%, whereas the other labels have negligible impacts. By reducing cases of disease, FoPLs have the potential to improve employment and work productivity. Nutri-Score surpasses the other labels with an estimated annual gain of 10.6 full-time equivalent workers per 100,000 individuals of working age across EU countries. In all, mandatory implementation of any of the four labels would lead to greater effects than those obtained with a voluntary implementation, providing evidence to inform legislation proposal for an EU-wide nutrition labelling system.

Bio-conversion of whey lactose using enzymatic hydrolysis with β-galactosidase: an experimental and kinetic study

Lactose in cheese whey is increasingly challenging to metabolise under normal conditions. The hydrolysis of whey lactose into glucose and galactose using enzymatic methods has been acclaimed to confer benefits like enhanced substrate availability for better degradation in anaerobic digestion. In the present study, whey lactose was subjected to hydrolysis using the enzyme β-galactosidase derived from Aspergillus oryzae fungus to reduce the difficulty of lipid and fat transformation in anaerobic digestion. The individual and combined effects of hydrolysis parameters, pH, enzyme load, reaction time and temperature were studied using Response Surface Methodology by Central Composite Design. The optimum conditions were determined based on variance analyses and surface plots; pH 4.63, temperature 40.47°C, reaction time 25.96 min and enzyme load 0.49%. Results showed a maximum lactose hydrolysis value of 86.21%, while the predicted value was 87.44%. Indeed, enzyme hydrolysis induced a change of soluble chemical oxygen demand around 24.6% and 75.8% reduction in volatile fatty acid concentration. Upon anaerobic digestion, the pre-hydrolysed whey revealed a 3.6-fold higher bio-methane production than that of raw hey, and a visible decrease in volatile fatty acid concentrations. The resultant data agreed with the Gompertz model, and lag phase times were significantly reduced for hydrolysed whey.

Review on Moringa oleifera, a green adsorbent for contaminants removal: characterization, prediction, modelling and optimization using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Moringa oleifera utilization in water treatment to eliminate emerging pollutants such as heavy metal ions, pesticides, pharmaceuticals, and pigments has been extensively evaluated. The efficacy of Moringa oleifera biosorbent has been investigated in diverse research work using various techniques, including its adsorption capacity kinetic, thermodynamic evaluation, adsorbent modifications, and mechanism behind the adsorption process. The Langmuir isotherm provided the most remarkable experimental data fit for batch adsorption investigations, whereas the best fit was obtained with the pseudo-second order kinetic model. Furthermore, only a few papers that combined batch adsorption with fixed-bed column investigations were examined. In the latter articles, the scientists modified the adsorbent to increase the material's adsorption capacity as determined by analytical methods, including IR spectroscopy, scanning electronic microscope (SEM), and X-ray diffraction (XRD). However, the raw material can show appreciable adsorption capacity values, proving moringa's potency as a biosorbent. Hydrogen bonds, electrostatic interaction, and van der Waals forces were the main processes in the found and reported adsorbent-adsorbate interactions. These mechanisms could change depending on the physiochemical nature of adsorption. Although frequently employed for heavy metal ions and dye adsorption, Moringa oleifera can still be explored in pesticide and medication adsorption investigations due to the few publications in this comprehensive review. This study, therefore, examined different Adsorbents from the Moringa oleifera plant, as well as parameters and models for enhancing the adsorption process.

Dispersion of SO2 emissions in a gas refinery by AERMOD modeling and human health risk: a case study in the Middle East

This study aimed to model the dispersion of emitted SO2 from stacks and flares in one of the largest Gas Refinery Companies in the Middle East . Pollutant emission coefficients and air pollution's various sources contributions were determined based on the collected data after measuring SO2 concentrations in a fixed monitoring station (stack) and across different distances from it for a year. The SO2 release pattern was simulated, and annual pollutant concentrations in average periods of 1-hr and 24-hr were predicted using AERMOD 8.9.0. The maximum simulated ambient SO2 were 27,447 and 4592 µg/m3 in average sampling times of 1-hr and 24-hr, respectively. The hazard quotient of 95% percentile for children, teenagers, and adults due to inhalation of SO2 was more than one.The maximum concentration of SO2 in the 1-hour and 24-hour period in the study area was higher than the amount introduced by Iran's clean air standard and the WHO standard.

Survival Estimation Using Multistate Cormack-Jolly-Seber Models-The Case of the Bearded Vulture Gypaetus barbatus in Spain

The bearded vulture (Gypaetus barbatus) is an endangered species with a specialist osteophagous (bone) diet. We estimated the survival and productivity of this vulture in the Aragonese Pyrenees, where the main population of the species in Europe is found. We used a database covering a period of 33 years (1987-2020). To estimate the probability of survival, we used Cormack-Jolly-Seber models with a Bayesian approach. Our models estimated a survival rate of 0.90 ± 0.08 in juveniles, 0.95 ± 0.04 in subadults and 0.92 ± 0.05 in adults. The survival probability increased over the study period in adults and subadults but not in juveniles. By contrast, productivity decreased over the same period. Our study provides updated information on the status of two demographic parameters of great importance to the species and allows us to identify the most vulnerable age classes and to plan conservation actions to improve the situation of the species in a territory that is a donor of specimens for reintroduction projects. The estimated survival values suggest that more caution should be exercised when planning these feeding points according to the use the species makes of them.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Predicted and Experimental Bending Behaviour of Glulam Bonded by RPF Adhesive

In this study, alder, spruce, and beech woods were used for homogeneous symmetric, inhomogeneous symmetric (combined) and inhomogeneous non-symmetric glued laminated timber (glulam) beams glued with resorcinol phenol formaldehyde (RPF) adhesive. The aim of this paper is to determine and compare the modulus of elasticity of glulam beams using three methods, i.e., analytical calculation, numerical model (FEM) and experimental testing. As an additional characteristic, the bending strength (MOR) of the beams was determined during experimental testing. Analytical calculation was used to calculate the modulus of elasticity (MOE) of glued laminated timber based on the knowledge of the modulus of elasticity of solid wood and to estimate the location of the neutral axis during bending. According to calculations, for symmetrical combinations, the deviation from the real neutral axis does not exceed 5%. In the case of the modulus of elasticity, the deviation is an average of 4.1% from that of the actual measured beams. The numerical model includes finite element modelling, where the deflection of the modelled beams can be calculated with a deviation of up to 10%. The last method was experimental testing of glued beams using four-point bending, in which, among homogeneous beams, beech glulam beams achieved the highest MOE and MOR, while alder glulam beams achieved the lowest. The combination of wood species resulted in an increase in both MOE and MOR compared to homogeneous spruce and alder beams.

Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019

We developed mathematical models to analyze a large dengue virus (DENV) epidemic in Reunion Island in 2018-2019. Our models captured major drivers of uncertainty including the complex relationship between climate and DENV transmission, temperature trends, and underreporting. Early assessment correctly concluded that persistence of DENV transmission during the austral winter 2018 was likely and that the second epidemic wave would be larger than the first one. From November 2018, the detection probability was estimated at 10%-20% and, for this range of values, our projections were found to be remarkably accurate. Overall, we estimated that 8% and 18% of the population were infected during the first and second wave, respectively. Out of the 3 models considered, the best-fitting one was calibrated to laboratory entomological data, and accounted for temperature but not precipitation. This study showcases the contribution of modeling to strengthen risk assessments and planning of national and local authorities.

Using queueing models as a decision support tool in allocating point-of-care HIV viral load testing machines in Kisumu County, Kenya

Point-of-care (POC) technologies-including HIV viral load (VL) monitoring-are expanding globally, including in resource-limited settings. Modelling could allow decision-makers to consider the optimal strategy(ies) to maximize coverage and access, minimize turnaround time (TAT) and minimize cost with limited machines. Informed by formative qualitative focus group discussions with stakeholders focused on model inputs, outputs and format, we created an optimization model incorporating queueing theory and solved it using integer programming methods to reflect HIV VL monitoring in Kisumu County, Kenya. We modelled three scenarios for sample processing: (1) centralized laboratories only, (2) centralized labs with 7 existing POC 'hub' facilities and (3) centralized labs with 7 existing and 1-7 new 'hub' facilities. We calculated total TAT using the existing referral network for scenario 1 and solved for the optimal referral network by minimizing TAT for scenarios 2 and 3. We conducted one-way sensitivity analyses, including distributional fairness in each sub-county. Through two focus groups, stakeholders endorsed the provisionally selected model inputs, outputs and format with modifications incorporated during model-building. In all three scenarios, the largest component of TAT was time spent at a facility awaiting sample batching and transport (scenarios 1-3: 78.7%, 89.9%, 91.8%) and waiting time at the testing site (18.7%, 8.7%, 7.5%); transportation time contributed minimally to overall time (2.6%, 1.3%, 0.7%). In scenario 1, the average TAT was 39.8 h (SD: 2.9), with 1077 h that samples spent cumulatively in the VL processing system. In scenario 2, the average TAT decreased to 33.8 h (SD: 4.8), totalling 430 h. In scenario 3, the average TAT decreased nearly monotonically with each new machine to 31.1 h (SD: 8.4) and 346 total hours. Frequency of sample batching and processing rate most impacted TAT, and inclusion of distributional fairness minimally impacted TAT. In conclusion, a stakeholder-informed resource allocation model identified optimal POC VL hub allocations and referral networks. Using existing-and adding new-POC machines could markedly decrease TAT, as could operational changes.

The Constructive Black Queen hypothesis: new functions can evolve under conditions favouring gene loss

Duplication is a major route for the emergence of new gene functions. However, the emergence of new gene functions via this route may be reduced in prokaryotes, as redundant genes are often rapidly purged. In lineages with compact, streamlined genomes, it thus appears challenging for novel function to emerge via duplication and divergence. A further pressure contributing to gene loss occurs under Black Queen dynamics, as cheaters that lose the capacity to produce a public good can instead acquire it from neighbouring producers. We propose that Black Queen dynamics can favour the emergence of new function because, under an emerging Black Queen dynamic, there is high gene redundancy spread across a community of interacting cells. Using computational modelling, we demonstrate that new gene functions can emerge under Black Queen dynamics. This result holds even if there is deletion bias due to low duplication rates and selection against redundant gene copies resulting from the high cost associated with carrying a locus. However, when the public good production costs are high, Black Queen dynamics impede the fixation of new functions. Our results expand the mechanisms by which new gene functions can emerge in prokaryotic systems.

Functional selectivity of Receptor Tyrosine Kinases regulates distinct cellular outputs

Functional selectivity refers to the activation of differential signalling and cellular outputs downstream of the same membrane-bound receptor when activated by two or more different ligands. Functional selectivity has been described and extensively studied for G-protein Coupled Receptors (GPCRs), leading to specific therapeutic options for dysregulated GPCRs functions. However, studies regarding the functional selectivity of Receptor Tyrosine Kinases (RTKs) remain sparse. Here, we will summarize recent data about RTK functional selectivity focusing on how the nature and the amount of RTK ligands and the crosstalk of RTKs with other membrane proteins regulate the specificity of RTK signalling. In addition, we will discuss how structural changes in RTKs upon ligand binding affects selective signalling pathways. Much remains to be known about the integration of different signals affecting RTK signalling specificity to orchestrate long-term cellular outcomes. Recent advancements in omics, specifically quantitative phosphoproteomics, and in systems biology methods to study, model and integrate different types of large-scale omics data have increased our ability to compare several signals affecting RTK functional selectivity in a global, system-wide fashion. We will discuss how such methods facilitate the exploration of important signalling hubs and enable data-driven predictions aiming at improving the efficacy of therapeutics for diseases like cancer, where redundant RTK signalling pathways often compromise treatment efficacy.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects

PREMISE

Bryophytes and lichens have important functional roles in many ecosystems. Insight into their CO2 -exchange responses to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 -exchange rates of these poikilohydric organisms change through time as a function of weather conditions.

METHODS

PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes were net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2 -exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) were used to parameterize the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects.

RESULTS

Diel patterns were reproduced well, and the modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.

CONCLUSIONS

Reproducing weather-dependent variation in diel carbon balances is a clear improvement compared to simply extrapolating short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding conservation strategies for species.