Mechanistic Parameters Research Articles

Air-cooling process modelling for cord steel based on fusion approach of mechanism and artificial neural network

In this study, a modelling approach is introduced that fuses the mechanistic model with an artificial neural network (ANN) during the cord steel manufacturing. This involves replacing certain mechanistic calculations with ANN representations, optimizing mechanistic parameters through ANN techniques, and implanting mechanistic equations into the ANN framework to achieve more accurate and efficient calculations of the cooling process. The mechanistic parameters come from the models including Natural Convection Heat Transfer, Forced Convection Heat Transfer, Radiation Heat Transfer and Phase Transition during the air-cooling process. The model demonstrated an average error of 8.6 °C and 5.4 °C, compared to measurement values from two temperature sensors on the air-cooling line, and an average error of 3.3 °C relative to manually obtained temperature.

Emerging ZnO Semiconductors for Photocatalytic CO2 Reduction to Methanol.

Carbon recycling is poised to emerge as a prominent trend for mitigating severe climate change and meeting the rising demand for energy. Converting carbon dioxide (CO2) into green energy and valuable feedstocks through photocatalytic CO2 reduction (PCCR) offers a promising solution to global warming and energy needs. Among all semiconductors, zinc oxide (ZnO) has garnered considerable interest due to its ecofriendly nature, biocompatibility, abundance, exceptional semiconducting and optical properties, cost-effectiveness, easy synthesis, and durability. This review thoroughly discusses recent advances in mechanistic insights, fundamental principles, experimental parameters, and modulation of ZnO catalysts for direct PCCR to C1 products (methanol). Various ZnO modification techniques are explored, including atomic size regulation, synthesis strategies, morphology manipulation, doping with cocatalysts, defect engineering, incorporation of plasmonic metals, and single atom modulation to boost its photocatalytic performance. Additionally, the review highlights the importance of photoreactor design, reactor types, geometries, operating modes, and phases. Future research endeavors should prioritize the development of cost-effective catalyst immobilization methods for solid-liquid separation and catalyst recycling, while emphasizing the use of abundant and non-toxic materials to ensure environmental sustainability and economic viability. Finally, the review outlines key challenges and proposes novel directions for further enhancing ZnO-based photocatalytic CO2 conversion processes.

Toxicity of Dibutyl phthalate (DBP) toward isolated human blood lymphocytes: Apoptosis initiated from intracellular calcium enhancement and mitochondrial/lysosomal cross talk

Dibutyl phthalate (DBP) is a phthalate ester with wide application in industrial products, so human exposure can happen in workplaces and environment. Conflicting results have been acquired in researches which measured the influences of phthalates contact on immune responses in laboratory animals. Nevertheless, the straight influence of DBP on human lymphocytes and entire mechanisms of its effect against these cells continue to be unexplored. The major purpose of present research was to evaluate the mechanisms which lead to the DBP toxicity on human lymphocytes using accelerated cytotoxicity mechanisms screening (ACMS) technique. Cell viability was determined following12h incubation of lymphocytes with 0.05–1 mM DBP, and mechanistic parameters were assessed after 2, 4 and 6 h of lymphocyte treatment with ½ the IC5012h (0.3 mM), the IC5012h (0.6 mM) and twice the IC5012h (1.2 mM) of DBP. The IC5012 h of a chemical/toxicant is defined as concentration that kills 50 % of cells after 12 h of exposure. The results indicate that DBP exerts toxic effects on isolated human lymphocytes, probably through mitochondrial and lysosomal damage induced by glutathione depletion and oxidative stress. In this study, suppression of cytokines (IL2, INF-gamma and TNF-alpha) production and increase in intracellular calcium were also related to DBP induced lymphocyte toxicity.

Acoustic deep brain modulation: Enhancing neuronal activation and neurogenesis

BackgroundNon-invasive deep brain modulation (DBM) stands as a promising therapeutic avenue to treat brain diseases. Acoustic DBM represents an innovative and targeted approach to modulate the deep brain, employing techniques such as focused ultrasound and shock waves. Despite its potential, the optimal mechanistic parameters, the effect in the brain and behavioral outcomes of acoustic DBM remains poorly understood. ObjectiveTo establish a robust protocol for the shock wave DBM by optimizing its mechanistic profile of external stimulation, and to assess its efficacy in preclinical settings. MethodsWe used shockwaves due to their capacity to leverage a broader spectrum of peak intensity (10–127 W/mm2) in contrast to ultrasound (0.1–5.0 W/mm2), thereby enabling a more extensive range of neuromodulation effects. We established various types of shockwave pressure profiles of DBM and compared neural and behavioral responses. To ascertain the anticipated cause of the heightened neural activity response, numerical analysis was employed to examine the mechanical dynamics within the brain. ResultsAn optimized profile led to an enhancement in neuronal activity within the hypothalamus of mouse models. The optimized profile in the hippocampus elicited a marked increase in neurogenesis without neuronal damage. Behavioral analyses uncovered a noteworthy reduction in locomotion without significant effects on spatial memory function. ConclusionsThe present study provides an optimized shock wave stimulation protocol for non-invasive DBM. Our optimized stimulation profile selectively triggers neural functions in the deep brain. Our protocol paves the way for new non-invasive DBM devices to treat brain diseases.

Restoration of Radiosensitivity by Soya Isoflavone Genistein is Accomplished by Facilitating DNA Damage Response in Radioresistant Cervical Cancer in vitro

ABSTRACT Context: Enhancing radiotherapeutic efficacy in tumor cells and sparing the normal tissues are major clinical concerns for the betterment of cancer therapy. Genistein (GEN) being a radiosensitizer ameliorates the effectiveness of radiation-induced cell killing by inducing DNA damage. This molecule is accountable for minimizing radiation-related toxicity and protecting healthy cells. However, the explicit mechanism of action of such molecules needs exploration. Aims: The objective of this study is to investigate the mechanistic action of GEN in cervical cancer cell radiosensitization. Settings and Design: Cervical squamous carcinoma cell SiHa and a radioresistant subline SiHa/RR (developed and isolated from SiHa) were taken for this study. The experiments were performed by pretreating the cells with IC30 dose of GEN, followed by acute irradiation to detect the impact of GEN in imparting radiosensitivity. Subjects and Methods: Optimal dose selection of GEN was performed by MTT assay, and radiosensitizing potency was determined by pretreating the cells with IC30 dose of GEN, followed by challenging with acute incremental doses of radiation. Mechanistic parameters were checked by clonogenic assay, cell cycle analysis, DNA damage estimation, apoptosis, and wound healing-sphere-forming assay. Statistical Analysis Used: Statistical analysis was performed in GraphPad software by performing the Student’s t-test. Results: Results depicted decreased numbers of colonies, increased frequency of DNA damage and apoptotic cells, and suppressed wound healing ability along with restrained sphere-forming ability upon the intervention of cells with GEN before radiation exposure. Such observations implied that GEN pretreatment renders improved radiosensitivity in cervical cancer by increased DNA damage-mediated G2/M arrest with subsequent apoptosis. Conclusions: GEN by inducing DNA damage stimulates radiation-induced cell killing in vitro.

Two-parameter C-history method: A fast and accurate method for determining the characteristic parameters of formaldehyde/VOC early-stage emissions from building materials

With the worsening indoor air quality in developing countries, more and more attention is being paid to indoor air pollution, especially formaldehyde and volatile organic compounds (VOCs) emitted from indoor building materials. A series of methods, such as the C-history method, have been proposed to determine the mechanistic parameters of formaldehyde and other VOC emissions. However, these methods require a relatively long test duration (at least 3 days) and may yield a multi-solution problem for these parameters. Therefore, we have developed a novel method, the two-parameter C-history method, to overcome these limitations by measuring the two early-stage emission characteristic parameters for formaldehyde/VOCs. The experimental results validate the accuracy of this method for different building materials and showed that the test duration can be substantially shortened to within 12 h. Based on this, we propose a new method to quickly predict the two emission characteristic parameters at different temperatures. We optimize the experimental parameters and discuss their influence to further improve accuracy. This method will be useful in engineering applications.

DNA lesion bypass and the stochastic dynamics of transcription-coupled repair

DNA base damage is a major source of oncogenic mutations and disruption to gene expression. The stalling of RNA polymerase II (RNAP) at sites of DNA damage and the subsequent triggering of repair processes have major roles in shaping the genome-wide distribution of mutations, clearing barriers to transcription, and minimizing the production of miscoded gene products. Despite its importance for genetic integrity, key mechanistic features of this transcription-coupled repair (TCR) process are controversial or unknown. Here, we exploited a well-powered in vivo mammalian model system to explore the mechanistic properties and parameters of TCR for alkylation damage at fine spatial resolution and with discrimination of the damaged DNA strand. For rigorous interpretation, a generalizable mathematical model of DNA damage and TCR was developed. Fitting experimental data to the model and simulation revealed that RNA polymerases frequently bypass lesions without triggering repair, indicating that small alkylation adducts are unlikely to be an efficient barrier to gene expression. Following a burst of damage, the efficiency of transcription-coupled repair gradually decays through gene bodies with implications for the occurrence and accurate inference of driver mutations in cancer. The reinitation of transcription from the repair site is not a general feature of transcription-coupled repair, and the observed data is consistent with reinitiation never taking place. Collectively, these results reveal how the directional but stochastic activity of TCR shapes the distribution of mutations following DNA damage.

Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age

Abstract Aging is associated with impaired signaling between brain regions when measured using resting-state functional magnetic resonance imaging (fMRI). This age-related destabilization and desynchronization of brain networks reverses itself when the brain switches from metabolizing glucose to ketones. Here, we probe the mechanistic basis for these effects. First, we confirmed their robustness across measurement modalities using two datasets acquired from resting-state EEG (Lifespan: standard diet, 20–80 years, N = 201; Metabolic: individually weight-dosed and calorically-matched glucose and ketone ester challenge, μage = 26.9 ±11.2 years, N = 36). Then, using a multiscale conductance-based neural mass model, we identified the unique set of mechanistic parameters consistent with our clinical data. Together, our results implicate potassium (K+) gradient dysregulation as a mechanism for age-related neural desynchronization and its reversal with ketosis, the latter finding of which is consistent with direct measurement of ion channels. As such, the approach facilitates the connection between macroscopic brain activity and cellular-level mechanisms.

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning

Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

Sketching feasibility of additively manufactured different size gradient conventional hollow capsular shells (HCSs) by selective laser sintering (SLS): From design to applications

Additive manufacturing (AM) is widely used to fabricate 3D printed objects from Computer-aided Design (CAD) prepared using the SolidWorks CAD modelling software. Different printing techniques are used to fabricate desired 3D objects; among all these techniques, it is widely accepted that SLS is one of the most effective methods of 3D printing for fabricating drug-loaded solid oral dosage forms (SODFs) in bulk quantities using the single-step process. Different SODFs, such as pills, miniprintlets, dual miniprintlets, and tablets, were fabricated with different sizes and shapes. In this study, for the first time, we introduce SLS-mediated hollow capsular shells (HCSs) with the help of the SLS 3D printing technique. This work aimed to explore the sinterability and feasibility of sketching HCSs using the SLS-mediated sintering technique with different marketed sizes of capsules ranging from 000 to 5. Here, we have utilized Kolliphor P 188 (KP 188) and Kollidon SR (KSR) in a 1:1 ratio as a matrix-forming agent and 1% charcoal as a laser absorption-enhancing material. In accordance with the CAD models, we have fabricated the gradient conventional different sizes of HCSs ranging from 000 to 5 using the constant printing parameters and composition. Fabricated all biobased HCSs were subjected to the assessment of mechanistic and physicochemical parameters using varied analytical tools. In the current study, tartrazine dye is used to assess the release pattern from HCSs, which resulted in the modified release pattern. The adapted approach will be the futuristic approach to replace animal-based gelatin capsules with pharmaceutical-grade polymer-based HCSs with a modified release with optimum mechanical strength.

Catalytic acceptorless dehydrogenative borylation of styrenes enabled by a molecularly defined manganese complex.

In this study, we employed a 3d metal complex as a catalyst to synthesize alkenyl boronate esters through the dehydrogenative coupling of styrenes and pinacolborane. The process generates hydrogen gas as the sole byproduct without requiring an acceptor, rendering it environmentally friendly and atom-efficient. This methodology demonstrated exceptional selectivity for dehydrogenative borylation over direct hydroboration. Additionally, it exhibited a preference for borylating aromatic alkenes over aliphatic ones. Notably, derivatives of natural products and bioactive molecules successfully underwent diversification using this approach. The alkenyl boronate esters served as precursors for the synthesis of various pharmaceuticals and potential anticancer agents. Our research involved comprehensive experimental and computational studies to elucidate the reaction pathway, highlighting the B-H bond cleavage as the rate-determining step. The catalyst's success was attributed to the hemilability and metal-ligand bifunctionality of the ligand backbone.

A data-driven workflow for prediction of fracturing parameters with machine learning

In the realm of unconventional reservoir hydraulic fracturing design, the conventional optimization of mechanistic model parameters is a time-consuming process that impedes its responsiveness to the swift demands of on-site development. This study, rooted in Xinjiang oilfield data, delves into the utilization of machine learning methods for extensive field data. The research systematically elucidates the training and optimization procedures of a production forecasting model, achieving effective optimization of hydraulic fracturing design parameters. By employing polynomial feature cross-construction generate composite features, feature filtering is performed using the maximal information coefficient. Subsequently, wrapper-style feature selection techniques, including ridge regression and decision trees, are applied to ascertain the optimal combinations of model input parameters. The integration of stacking during model training enhances performance, while stratified K-fold cross-validation is implemented to mitigate the risk of overfitting. The ultimate optimization of hydraulic fracturing design parameters is realized through a competitive learning particle swarm algorithm. Results indicate that the accuracy of the data-driven production forecasting model can reach 85%. This model proficiently learns patterns from mature blocks and effectively applies them to optimize new blocks. Furthermore, expert validation confirms that the optimization results align closely with actual field conditions.

Alginate-Moroccan Clay, New Bio-Nanocomposite for Removal of H2PO4-, HPO42-, and NO3- Ions from Aqueous Solutions.

The aim of this work is to synthesize and characterize alginate-Moroccan clay bio-composite in order to improve our understanding of the adsorption of inorganic pollutants found in textile effluents. Characterization of the bio-composite used was carried out using a variety of techniques (IR-TF, SEM, DRX, and pHZPC). The influence of the medium's physico-chemical parameters (temperature, pH, initial concentration, etc.) on the retention of inorganic pollutants was also studied. Studies of adsorption and inorganic pollutants such as orthophosphate (H2PO4- and HPO42-) and nitrate (NO3-) ions were carried out, using simple solutions from the laboratory, in a batch system. This study explored the impact of adsorbent dose, contact time, solution pH, and temperature on the adsorption process. Various kinetic models, including pseudo-first-order, pseudo-second-order, intra-particle diffusion, and Elovich models, were tested and evaluated, to illustrate the adsorption kinetics. This study's findings demonstrated that the adsorption process follows second-order kinetics, with associated rate constants successfully determined. The correlation coefficient for the pseudo-second-order kinetic model is nearly equal to 1 (>0.98), and the value of theoretical adsorption capacity (qe,the) is comparable to the experimental one (qe,the = 58.14 mg/g for H2PO4-, qe,the = 54.64 mg/g for HPO42-, and qe,the = 52.63 mg/g for NO3-). Additionally, the adsorption equilibrium was investigated through the application of various mathematical models, including the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models, to assess the mechanistic parameters associated with the adsorption process. Among these models, the Langmuir isotherm emerged as the most suitable one for characterizing the adsorption of H2PO4-, HPO42-, and NO3- ions using bio-nanocomposite beads. The maximum adsorbed amounts of metal ions by the bio-nanocomposite used were 625 mg/g for H2PO4-, 909.09 mg/g for HPO42-, and 588.23 mg/g for NO3- from the batch system. The endothermic and physical nature of the adsorption is suggested by the positive values of ΔH°, which is consistent with experimental findings. The adsorption process is spontaneous, as evidenced by the negative ΔG° values. Positive ΔS° values indicate increased randomness at the solid/liquid interface during adsorption of ion-organic ions onto the engineered bio-nanocomposite. The obtained results demonstrated that, from a scientific perspective, alginate-Moroccan clay bio-nanocomposites exhibit a highly significant adsorption capability for the removal of oxyanions in aqueous environments.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

Objective:Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods:A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results:The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. Conclusion:The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Mechanistic modeling projections of antibody persistence after homologous booster regimens of COVID-19 vaccine Ad26.COV2.S in humans.

Mechanistic model-based simulations can be deployed to project the persistence of humoral immune response following vaccination. We used this approach to project the antibody persistence through 24 months from the data pooled across five clinical trials in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-seronegative participants following vaccination with Ad26.COV2.S (5 × 1010 viral particles), given either as a single-dose or a homologous booster regimen at an interval of 2, 3, or 6 months. Antibody persistence was quantified as the percentage of participants with detectable anti-spike binding and wild-type virus neutralizing antibodies. The projected overall 24-month persistence after single-dose Ad26.COV2.S was 70.5% for binding antibodies and 55.2% for neutralizing antibodies, and increased after any homologous booster regimen to greater than or equal to 89.9% for binding and greater than or equal to 80.0% for neutralizing antibodies. The estimated model parameters quantifying the rates of antibody production attributed to short-lived and long-lived plasma cells decreased with increasing age, whereas the rate of antibody production mediated by long-lived plasma cells was higher in women relative to men. Accordingly, a more pronounced waning of antibody responses was predicted in men aged greater than or equal to 60 years and was markedly attenuated following any homologous boosting regimen. The findings suggest that homologous boosting might be a viable strategy for maintaining protective effects of Ad26.COV2.S for up to 24 months following prime vaccination. The estimation of mechanistic modeling parameters identified the long-lived plasma cell pathway as a key contributor mediating antibody persistence following single-dose and homologous booster vaccination with Ad26.COV2.S in different subgroups of recipients stratified by age and sex.

Accurate modelling of the scroll expander via a mechanism-incorporated data-driven method

Accurate modelling of the scroll expander is essential for efficiency analysis and optimal control. In this study, we propose a mechanism-incorporated adaptive-network-based fuzzy inference system (MI+ANFIS) to establish the scroll expander model. In this method, to fully utilize the mechanism characteristics and improve the prediction performance, we firstly identify the mechanistic model parameters based on the least squares method. Then, the ANFIS is adopted to construct residual prediction model according to the residual errors from the mechanistic model. The final forecasting outputs of the MI+ANFIS model are obtained by combining the mechanistic model and the ANFIS model. Experiments on forecasting the volume flow rate and torque of the scroll expander are taken separately. To demonstrate the superiorities of the proposed MI+ANFIS, it is compared with several other popular models, including the ANFIS, the extreme learning machine (ELM), the back-propagation neural network (BPNN), and the support vector regression (SVR), while with the MI+ELM, the MI+BPNN and the MI+SVR. Experimental results indicated that the proposed MI+ANFIS exhibits higher accuracy and greater robustness due to the consideration of the mechanistic properties.

Bacterial Replication Initiation as Precision Control by Protein Counting.

Balanced biosynthesis is the hallmark of bacterial cell physiology, where the concentrations of stable proteins remain steady. However, this poses a conceptual challenge to modeling the cell-cycle and cell-size controls in bacteria, as prevailing concentration-based eukaryote models are not directly applicable. In this study, we revisit and significantly extend the initiator-titration model, proposed 30 years ago, and we explain how bacteria precisely and robustly control replication initiation based on the mechanism of protein copy-number sensing. Using a mean-field approach, we first derive an analytical expression of the cell size at initiation based on three biological mechanistic control parameters for an extended initiator-titration model. We also study the stability of our model analytically and show that initiation can become unstable in multifork replication conditions. Using simulations, we further show that the presence of the conversion between active and inactive initiator protein forms significantly represses initiation instability. Importantly, the two-step Poisson process set by the initiator titration step results in significantly improved initiation synchrony with scaling rather than the standard scaling in the Poisson process, where is the total number of initiators required for initiation. Our results answer two long-standing questions in replication initiation: (i) Why do bacteria produce almost two orders of magnitude more DnaA, the master initiator proteins, than required for initiation? (ii) Why does DnaA exist in active (DnaA-ATP) and inactive (DnaA-ADP) forms if only the active form is competent for initiation? The mechanism presented in this work provides a satisfying general solution to how the cell can achieve precision control without sensing protein concentrations, with broad implications from evolution to the design of synthetic cells.

Mechanistic Model Describing the Time Course of Humoral Immunity Following Ad26.COV2.S Vaccination in Non-Human Primates.

Mechanistic modeling can be used to describe the time course of vaccine-induced humoral immunity and to identify key biologic drivers in antibody production. We used a six-compartment mechanistic model to describe a 20-week time course of humoral immune responses in 56 non-human primates (NHPs) elicited by vaccination with Ad26.COV2.S according to either a single-dose regimen (1 × 1011 or 5 × 1010 viral particles [vp]) or a two-dose homologous regimen (5 × 1010 vp) given in an interval of 4 or 8 weeks. Humoral immune responses were quantified by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-specific binding antibody concentrations as determined by spike protein-enzyme-linked immunosorbent assay. The mechanistic model adequately described the central tendency and variability of binding antibody concentrations through 20 weeks in all vaccination arms. The estimation of mechanistic modeling parameters revealed greater contribution of the antibody production mediated by short-lived cells as compared with long-lived cells in driving the peak response, especially post second dose when a more rapid peak response was observed. The antibody production mediated by long-lived cells was identified as relevant for generating the first peak and for contributing to the long-term time course of sustained antibody concentrations in all vaccination arms. The findings contribute evidence on the key biologic components responsible for the observed time course of vaccine-induced humoral immunity in NHPs and constitute a step toward defining immune biomarkers of protection against SARS-CoV-2 that might translate across species. SIGNIFICANCE STATEMENT: We demonstrate the adequacy of a mechanistic modeling approach describing the time course of binding antibody concentrations in non-human primates (NHPs) elicited by different dose levels and regimens of Ad26.COV2.S. The findings are relevant for informing the mechanism-based accounts of vaccine-induced humoral immunity in NHPs and translational research efforts aimed at identifying immune biomarkers of protection against SARS-CoV-2 infection.

Suicide prevention and ketamine: insights from computational modeling.

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.