Modeling Approach Research Articles

Heparosan biosynthesis in recombinant Bacillus megaterium: Influence of N-acetylglucosamine supplementation and kinetic modeling.

Heparosan, an unsulfated polysaccharide, plays a pivotal role as a primary precursor in the biosynthesis of heparin-an influential anticoagulant with diverse therapeutic applications. To enhance heparosan production, the utilization of metabolic engineering in nonpathogenic microbial strains is emerging as a secure and promising strategy. In the investigation of heparosan production by recombinant Bacillus megaterium, a kinetic modeling approach was employed to explore the impact of initial substrate concentration and the supplementation of precursor sugars. The adapted logistic model was utilized to thoroughly analyze three vital parameters: the B. megaterium growth dynamics, sucrose utilization, and heparosan formation. It was noted that at an initial sucrose concentration of 30gL-1 (S1), it caused an inhibitory effect on both cell growth and substrate utilization. Intriguingly, the inclusion of N-acetylglucosamine (S2) resulted in a significant 1.6-fold enhancement in heparosan concentration. In addressing the complexities of the dual substrate system involving S1 and S2, a multi-substrate kinetic models, specifically the double Andrew's model was employed. This approach not only delved into the intricacies of dual substrate kinetics but also effectively described the relationships among the primary state variables. Consequently, these models not only provide a nuanced understanding of the system's behavior but also serve as a roadmap for optimizing the design and management of the heparosan production method.

Root plasticity improves maize nitrogen use when nitrogen is limiting - an analysis using 3D plant modelling.

Plant phenotypic plasticity plays an important role in nitrogen (N) acquisition and use under nitrogen-limited conditions. However, this role has never been quantified as a function of N availability, leaving it unclear whether plastic responses should be considered as potential targets for selection. A combined modelling and experimentation approach was adopted to quantify the role of plasticity on N uptake and plant yield. Based on a greenhouse experiment we considered plasticity in two maize traits: root-to-leaf biomass allocation ratio and emergence rate of axial roots. In a simulation experiment we individually enabled or disabled both plastic responses for maize stands grown across six N levels. Both plastic responses contributed to maintaining a higher N uptake and plant productivity as N-availability declined, compared to stands in which plastic responses were disabled. We conclude that plastic responses quantified in this study may be a potential target trait in breeding programs for greater N uptake across N levels while it may only be important for the internal use of N under N-limited conditions in maize. Given the complexity of breeding for plastic responses, an a priori model analysis is useful to identify which plastic traits to target for enhanced plant performance.

An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease.

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo.

Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.

Flash Flood Potential Analysis and Hazard Mapping of Wadi Mujib Using GIS and Hydrological Modelling Approach

Flash Flood Potential Analysis and Hazard Mapping of Wadi Mujib Using GIS and Hydrological Modelling Approach

Nexus between Innovation and Ecological Impact: A Moderated Mediation Investigation through Structural Equation Modeling Approach

Nexus between Innovation and Ecological Impact: A Moderated Mediation Investigation through Structural Equation Modeling Approach

Tailored antimicrobial PHBV-based packaging for extended shelf life of processed cheese

A coupled experimental and modelling approach was used to develop an adapted PHBV active film with Lauroyl Arginate Ethyl (LAE®) as an antimicrobial compound incorporated in the bulk or as a coating to prolong the shelf life of cheese. The minimum inhibitory concentration (MIC) of LAE® was evaluated in the range of 25–100 ppm against spoilage and pathogenic microorganisms, such as Micrococcus luteus, Penicillium roqueforti, Salmonella enteritidis, and Pseudomonas putida. Film production induced a loss of 50 % of LAE® when introduced into the bulk, whereas only 4 % was lost in the case of incorporation by coating. When in contact with food simulant D1 (50 % ethanol), a release of 43 % and 34 % of LAE® was achieved in 10 days at 20 °C for LAE® incorporated as a coating and in the bulk, respectively, while a lower release level of 34 % and 14 % respectively was observed in food simulant C (20 % ethanol), confirming the interest to use active coating for fatty foods. The developed packaging was well adapted to reach the MIC while remaining below the admissible daily intake. The results also showed that the PHBV film activated with LAE® incorporated as a coating was as effective as modified atmosphere packaging (MAP) to preserve cheese against microorganism growth.

An investigation of relationships between aberrant driving behavior and crash risk among long-haul truck drivers traveling across India: A Structural Equation Modeling approach

IntroductionRoad Traffic Crashes (RTC) are showing an increasing trend around the world. Empirical evidence suggests that the majority of the crashes are attributed to unsafe or dangerous driving behaviors among drivers. However, little is known about the crash risk among professional drivers such as Long-Haul Truck Drivers (LHTDs) considering aberrant driving behaviors in developing countries like India. Objectives: This study aims to investigate the direct effects of characteristics of LHTDs including socio-demographic, work and vehicle, health-related lifestyle on crash risk. This study also attempts to investigate the indirect impacts of socio-demographic characteristics on crash involvement among LHTDs mediated through aberrant driving behaviors. MethodsUsing cross sectional study design, face-to-face interviews were conducted among LHTDs in Salem city, Tamil Nadu, India. A Driver Behavior Questionnaire (DBQ) consisting of 23 items was utilized to measure aberrant driving behaviors among LHTDs and information related to background variables was also collected. A total of 756 valid samples were used for analysis purpose. Exploratory factor analysis confirmed a four-factor solution including errors, lapses, ordinary, and aggressive violations and the identified structure was validated using confirmatory factor analysis. The deployment of Structural Equation Model (SEM) enabled identification of interrelationships among identified latent and observed variables. Results: The developed SEM model showed that all the identified four latent constructs were positively associated with crash involvement among LHTDs. The results further revealed that age and marital status were found to exhibit an indirect impact on crash involvement in addition to direct effects. ConclusionThe findings from this study could serve road safety authorities and Indian trucking industries to target risky driving behaviors in order to develop engineering and interventional countermeasures.

Spatio-temporal modeling of lake's ecosystem and dynamism in response to changing environment: a case study of L. Ol Bolossat in Kenya.

Lakes' ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Ol Bolossat ecosystem in Kenya to changing environment between 1992 to 2022 and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope, and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake's extents between 1992 and 2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992 and 2002, the lake extent shrunk by about 18%; between 2002 and 2012, the lake extent increased by about 13.58%; and between 2012 and 2022, the lake expanded by about 26%. The spatial-temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, K standard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the Lake Ol Bolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning

Locomotion gait control of snake robots based on a novel unified CPG network model composed of Hopf oscillators

Snake robots with limbless structure and rich locomotion gaits have been designed and built for wide application in various fields including military reconnaissance, pipeline operation, disaster search and rescue, etc. However, the problem how to flexibly and smoothly control switch and change of different locomotion gaits is still facing enormous challenges. A novel unified design rule of the CPG network model composed of improved Hopf oscillators is proposed, based on which a variety of different network structures can be created by designing connection distances and coupling weights among all oscillator units. Through the relationships between the control parameters of the Hopf oscillator, decoupling of the bifurcation parameters is achieved to solve inconsistent output waveform amplitude when the bifurcation parameters are not completely equal. Furthermore, five typical movement modes of biological snake are designed and smooth switch between different locomotion gaits is realized. A control system is constructed based on the Robot Operating System (ROS) and a prototype of snake robot is built, and the effectiveness of the proposed CPG model in controlling locomotion gaits was verified through simulations and experiments. The CPG modeling approach has important theoretical significance and practical instructive value for motion planning and gait control of snake robots in complex environments.

Neuropsychiatric drugs and a neurophysiological marker as predictors of health-related quality of life in patients with phantom limb pain.

To explore the relationship between sociodemographic, clinical, and neurophysiological variables and health-related quality of life (HR-QOL) of patients with phantom limb pain. This is a cross-sectional analysis of a previous clinical trial. Univariate and multivariate linear and logistic regression analyses were used to model the predictors of HR-QOL. We utilized a sequential modeling approach with increasing adjustment levels, controlling for age and sex, and other relevant clinical variables (time since amputation, level of amputation, and pain). HR-QOL was assessed by the SF-36 Health Survey and its eight subdomains. We analyzed baseline data from 92 patients with lower-limb amputations. They were mostly male (63%), 45.2 ± 15.6 years, with a mean time since amputation of 82.7 ± 122.4 months, and an overall SF-36 score of 55.9 ± 21.5. We found an association between intracortical facilitation in the affected hemisphere (ICF), gabapentin usage, and HR-QOL. ICF is a predictor of better HRQOL, whereas gabapentin usage was associated with a poorer HR-QOL, with the main model explaining 13.4% of the variance in the outcome. For the SF-36 subdomains, ICF was also a positive predictor for social functioning, bodily pain, and vitality, while medication usage was associated with lower scores in mental health, general health perception, bodily pain, and vitality. We found firsthand two new independent predictors of HR-QOL in individuals with PLP, namely, the neurophysiological metric ICF and gabapentin usage. These results highlight the role of the motor cortex excitability in the HR-QOL and stress the need for treatments that favor the neuroplastic adaptation after amputation, for which ICF may be used as a possible marker.

The relationship between semantics, phonology, and naming performance in aphasia: a structural equation modeling approach

ABSTRACT The exploration of naming error patterns in aphasia provides insights into the cognitive processes underlying naming performance. We investigated how semantic and phonological abilities correlate and how they influence naming performance in aphasia. Data from 296 individuals with aphasia, drawn from the Moss Aphasia Psycholinguistics Project Database, were analyzed using a structural equation model. The model incorporated latent variables for semantics and phonology and manifest variables for naming accuracy and error patterns. There was a moderate positive correlation between semantics and phonology after controlling for overall aphasia severity. Both semantic and phonological abilities influenced naming accuracy. Semantic abilities are negatively related to semantic, mixed, unrelated errors and no responses. Interestingly, phonology positively affected semantic errors. Additionally, phonological abilities are negatively related to each of the phonological and neologism errors. These results highlight the role of semantic and phonological skills on naming performance in aphasia and reveal a relationship between these cognitive processes.

Electromagnetic informed data model considerations for near-field DOA and range estimates

Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

P-660 Finding the optimal starting dose of gonadotropins for an ovarian stimulation treatment through Machine Learning

Abstract Study question Can machine learning algorithms provide an individualized starting dose of gonadotropins to maximize the number of oocytes retrieved in patients undergoing ovarian stimulation ? Summary answer Methods of Policy Learning based on Causal Inference and Machine Learning optimize starting doses of gonadotropins with a substantial gain of oocytes under appropriate treatment. What is known already Despite attempts toward standardization, there has been no consensus about establishing the optimal starting dose of gonadotropins to be used in ovarian stimulation. In the real world, medical practice differs considerably across countries and even among fertility clinics nationally. The machine learning community has proposed a variety of models for doing this, but with limited comparisons, causal inference can provide the statistical tools to enable the best model to be chosen and allow its explicability. Study design, size, duration In a retrospective single-center study, we reviewed the data of 11,436 cycles of ovarian stimulation for IVF between 2012 and 2023. In order to estimate the causal effect of the starting dose of gonadotropins on the number of oocytes retrieved, we selected a relevant subset of confounding covariates – including age, Antral Follicle Count (AFC), Anti-Müllerian Hormone (AMH) and oestradiol (E2) rates. Participants/materials, setting, methods We used five different Machine Learning models : Linear Regression, Regression Forest, Super Learner (SL), Multi-Arm Causal Forest (MACF) and a model based on the Targeted Maximum Likelihood Estimation (TMLE). We employed these models for Policy Learning through an Outcome Regression Modeling approach. We compared the gain of oocytes obtained through the optimal policy of our different models and analyzed the variables which played a crucial role in determining the optimal dose. Main results and the role of chance We use two approaches to compare our models, the first one gives the expected average gain of oocytes under the optimal policy : in this case, the Linear Regression is the best with an average gain of 0.90 oocytes, followed by the MACF (0.80), the SL (0.58), the TMLE (0.44) and the Regression Forest (0.18). Considering the lack of heterogeneity of our database we use a second approach, an Augmented Inverse Propensity Score based value, to compare with more robustness the performance of the optimal policy of each model. In this case the MACF (an explainable model specifically designed for causal inference) is the best with a gain of 0.50 oocytes, followed by the SL (0.42), the Linear Regression (0.37), the TMLE (0.23) and the Regression Forest (0.18). In general, our models recommend lower doses, for instance 20% of the patients are recommended by the MACF to reduce their initial doses of gonadotropins by on average 100 IU. The most significant covariates in the models are the AFC, the BMI, and the rate of AMH, with p-values < 0.001 in the Linear Regression that has a R-squared of 0.418. Limitations, reasons for caution Full IVF history was not included in patients who had previous IVF attempts, but only data from the last previous attempt was considered. Also, the study is monocentric and more heterogeneity will be present considering other centers. Wider implications of the findings This study needs to be completed by data from different centers in France and around the world to bring diversity to the patients’ data and to the treatment prescriptions in order to improve external validity. Algorithm recommendations and their adherence need to be tested prospectively. Trial registration number not applicable

Joint flexural-density modeling of the Taltal, Copiapó, and Iquique hotspot ridges and the surrounding oceanic plate, offshore Chile

Based on gravity and bathymetric data and using a novel two-dimensional joint flexural-density modeling approach, this work studies the physical properties of the oceanic Nazca plate around the Taltal, Copiapó, and Iquique hotspot ridges offshore northern Chile. The area is located westward of the Chilean Trench where the Taltal and Copiapó Ridges collide with the continental margin. The results show that the variability in density structure at different scales is a key factor in explaining the observed gravity signal, playing an important role in the lithospheric flexure and hence the elastic properties of the Nazca plate in this setting. The results can be interpreted as evidence of spatial and temporal heterogeneities in the plate-weakening process at the hotspots, magmatic underplating, and crustal and upper mantle fracturing and/or hydration. These processes might be relevant for the ascent of magma pathways of later (secondary) volcanism and influence the mechanical segmentation of the oceanic plate. The latter is critical in explaining the active seismogenic contact between the oceanic Nazca and overriding South America plates.

An agent-based modelling approach to wave-like diversification of language families

Abstract In contrast to phylogenetic tree inference, wave model approaches are often regarded as difficult to computationally implement for inference of language relatedness. This paper proposes a basic framework for the computational modelling of wave-like diversification in language families and explains the model type of agent-based models for linguistic data. The approach is based on agent-based simulations which allow for the detailed simulation of speaker interactions within speech communities. The proposed framework operates by simulating a large number of possible diversification situations with different parameter settings and selecting those runs that yield a good fit to the linguistic data of the geographical spread of different languages. The model can be fed with the geographical extent of languages and their known innovations in order to computationally reconstruct the most likely diversification scenarios of these languages under the wave model.

Strategic Approaches to Design Teams for Construction Quality Management and Green Building Performance

Buildings exert a profound influence on the environment, with the design phase recognized as the pivotal determinant of a building’s overall performance. Green building design, in particular, introduces heightened complexity, where the attributes of the design team play a pivotal role in shaping performance outcomes. Consequently, the characteristics of the design team emerge as crucial factors in the enhancement of both green building design performance and client attributes. This study aims to empirically examine a model formulated to gauge the extent to which Effective Design Team Attributes contribute to the enhancement of performance in designing green buildings and influencing client attributes. To achieve this objective, a comprehensive questionnaire survey was administered to professionals within the architecture and engineering domains actively engaged in the design and consulting sectors of the building industry. The collected data underwent meticulous scrutiny for authenticity and dependability using the WINSTEPS 5.2.5 software before undergoing subsequent analysis. Statistical analyses were conducted using SPSS version 19, with Principal Components Analysis (PCA) and the Structural Equation Modeling (SEM) approach implemented through Amos version 18 to derive the most robust model. The findings underscore the pivotal role of an adeptly managed design team in significantly improving both the performance of green building designs and the qualities of clients. Rasch’s analysis confirmed the validity of our 5-point Likert scale for Design Green Building Performance (DGBP), Effective Design Team Attributes (EDTA), and Client Qualities (CQ). All items demonstrated excellent reliability, separation, and discrimination, ensuring robust data quality. Dimensionality tests revealed the appropriateness of response categories, indicating satisfactory scale performance. The Effective Design Team Model, validated through Principal Components Analysis (PCA), exhibited a satisfactory fit, supported by significant chi-square statistics, high goodness-of-fit indices, and acceptable root mean square residual values. Client attributes displayed a strong association with effective design team management, validating key model elements. The intricacies inherent in the design process can be mitigated by adopting the green design charrette approach. Consequently, the establishment of an effective design team, coupled with green design leadership, active participation, and clarity in roles and responsibilities, emerges as a potent strategy for elevating the performance level of green building designs.

Predictive noise annoyance and noise-induced health effects models for road traffic noise in NCT of Delhi, India

Abstract The present study analyzed the effect of noise annoyance due to road traffic based on a comprehensive socio-acoustics survey at a large number of sites in the NCT of Delhi. This study presents the development of exposure–effect relationships and noise annoyance models using the Structural Equation Modeling (SEM) approach. The study assesses the traffic noise annoyance caused by road traffic at various sites in NCT of Delhi, India. Exposure–effect relationships correlate the noise annoyance and traffic noise levels. The Structural Equation Model depicts the relationship between noise annoyance and various psychological factors. The effect of noise annoyance and awareness level was determined using a socio-acoustic questionnaire survey among the community of NCT of Delhi. The various parameters undertaken were demographic, honking, health impacts, and psychological perception. The developed PLS (Partial Least Square)–SEM model predicted that noise sensitivity and road traffic noise remarkably affected the noise-induced annoyance.

Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach.

Cardiovascular disease is a chronic inflammatory disease with high mortality rates. TNF-alpha is pro-inflammatory and associated with the disease, but current medications have adverse effects. Therefore, efficient inhibitors are urgently needed as alternatives. This study represents a structural-activity relationship investigation of TNF-alpha, curated from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of different bioactivity groups. The extracted molecules were subjected to PubChem and SubStructure fingerprints, and a QSAR-based Random Forest (QSAR-RF) model was generated using the WEKA tool. The QSAR random Forest model was built based on the SubStructure fingerprint with a correlation coefficient of 0.992 and 0.716 as the respective tenfold cross-validation scores. The variance important plot (VIP) method was used to extract the important features for TNF-alpha inhibition. The Substructure-based QSAR-RF (SS-QSAR-RF) model was validated using molecules from PubChem and ZINC databases. The generated model also predicts the pIC50 value of the molecules selected from the docking study followed by molecular dynamic simulation with the time step of 100ns. Through virtual reverse pharmacology, we determined the main drug targets from the top four hit compounds obtained via molecular docking study. Our analysis included an integrated bioinformatics approach to pinpoint crucial targets like EGRF, HSP900A1, STAT3, PSEN1, AKT1, and MDM2. Further, GO and KEGG pathways analysis identified relevant cardiovascular disease-related pathways for the hub gene involved. However, this study provides valuable insights, it is important to note that it lacks experimental application. Future research may benefit from conducting in-vitro and in-vivo studies.