Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1

Background

Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.

Methods

The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models.

Results

Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.

Conclusions

Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Improving human cardiac organoid design using transcriptomics

Cardiovascular disease (CVD) is the leading cause of death worldwide. To this end, human cardiac organoids (hCOs) have been developed for improved organotypic CVD modeling over conventional in vivo animal models. Utilizing human cells, hCOs hold great promise to bridge key gaps in CVD research pertaining to human-specific conditions. hCOs are multicellular 3D models which resemble heart structure and function. Varying hCOs fabrication techniques leads to functional and phenotypic differences. To investigate heterogeneity across hCO platforms, we performed a transcriptomic analysis utilizing bulk RNA-sequencing from four previously published unique hCO studies. We further compared selected hCOs to 2D and 3D hiPSC-derived cardiomyocytes (hiPSC-CMs), as well as fetal and adult human myocardium bulk RNA-sequencing samples. Upon investigation utilizing Principal Component Analysis, K-means clustering analysis of key genes, and further downstream analyses such as Gene Set Enrichment (GSEA), Gene Set Variation (GSVA), and GO term enrichment, we found that hCO fabrication method influences maturity and cellular heterogeneity across models. Thus, we propose that adjustment of fabrication method will result in an hCO with a defined maturity and transcriptomic profile to facilitate its specified applications, in turn maximizing its modeling potential.

"Miffy eats the rainbow!" - A colorful modeling- and reward-based intervention to increase willingness to taste fruit and vegetables in 3-7-year-old children

Diets rich in fruit and vegetables (F&Vs) improve cognitive functioning and reduce the risk of non-communicable diseases in children. Nevertheless, 59% of Dutch children do not meet recommended intake levels. Given the importance of color in children's food choices, the concept of "eat the rainbow" presents a promising approach. This project aimed to evaluate the effects of a modeling- and reward-based intervention to stimulate the consumption of colorful foods to increase willingness to taste different F&Vs among children aged 3-7 years. 164 children from Dutch elementary schools participated in a nested cluster randomized multi-arm parallel design study. During two morning school breaks, children were invited to choose from ten F&Vs in five different colors. Their willingness to taste and ad libitum intake were recorded. The first session served as a baseline with no intervention, while the second session involved either the Miffy intervention (modeling- and reward-based), a reward-only intervention (reward-based), or a control session. In the Miffy intervention, children listened to a story about Miffy eating the rainbow before tasting F&Vs and they received colored stickers (e.g., red sticker for tomatoes, green sticker for celery) upon tasting them. In the reward-only intervention, children received a sticker upon tasting a food. The Miffy group showed a higher probability (P(tasted) = 0.39) of tasting a food product compared to the control group (P(tasted) = 0.29; OR = 0.63, p = .04). No significant differences were observed between the Miffy and reward-only groups or between the reward-only and control groups. Both interventions did not significantly impact intake or liking of the foods. The Miffy-themed intervention demonstrates potential to enhance children's willingness to taste F&Vs, primarily due to the use of non-food incentives.

How effective is score-based data quality assessment? An illustration with fish BCF data

Increasingly rigorous data quality (DQ) evaluations and/or screening practices are being applied to environmental and ecotoxicological datasets. DQ is predominantly evaluated by scoring given data against preselected criteria. This study provides the first examination on the effectiveness of score-based DQ evaluation in providing statistically meaningful differentiation of measurements using fish bioconcentration factor (BCF) dataset as an illustration. This is achieved by inspecting how log BCF differs with the built-in overall-DQ and specific-DQ evaluations, and how it is influenced by interactive effects and hierarchy of DQ criteria. Approximately 80-90% of analyzable chemicals show no statistical difference in log BCF between low-quality (LQ) and high-quality (HQ) measurements in overall evaluation (n = 183) or in individual evaluation of 6 DQ criteria (n = 53 to 101). Further examination shows that log BCF may/may not change with different combinations or total number of criteria violations. Tree analysis and nodal structures of deviation in log BCF also reveal the absence of common structural dependence on the criteria violated. Finally, simple averaging of all measurements without DQ differentiation yields comparable log BCFs as those derived using strictly HQ data with ≤0.5 log unit difference in over 93% of the chemicals (n = 158) and no dependence on number of measurements, fraction of LQ measurements, or bioaccumulation potential of the chemicals. For accurate log BCF, DQ appears no more important than having more independent measurements irrespective of their individual DQ statuses. This work concludes by calling for: (i) re-documentation of experimental details in legacy environmental and ecotoxicological datasets, (ii) examination of other DQ-categorized datasets using the tests and tools applied here, and (ii) a thorough and systematic reflection on how DQ should be assessed for modeling, benchmarking, and other data-based analyses or applications.

Feather carotenoids of the American Redstart (Setophaga ruticilla) across age and sex classes and the reliability of standard color metrics to capture pigment variation

Plumage ornaments act as important sexual signals, though the extent to which these ornaments act as honest signals-and the physiological mechanisms that maintain honesty-remain poorly understood. We studied the pigmentary basis of tail color in the American Redstart (Setophaga ruticilla), a species of songbird with sexual dichromatism and delayed plumage maturation; younger males resemble females, only replacing their yellow feathers for bright orange ones after the first breeding season. The yellow rectrices of females and young males and the orange feathers of older males largely contain the same pigments, but in vastly different proportions. Whereas the feathers of females and young males contain primarily lutein, 3'-dehydro-lutein and canary-xanthophylls, those of older males contain primarily 4-keto-carotenoids. The presence of lutein and the predominance of α-doradexanthin as 4-keto-carotenoid, a pigment with a shortened chain of conjugated double bonds compared to keto-carotenoids commonly found in red feathers, in the feathers of older males contribute to their uncommon orange hue. Since the orange coloration of the tail in the American redstart results from the combination of yellow, orange, and red pigments, this is a system where slight adjustments in the types of carotenoids deposited could significantly alter hue. Factors either work against achieving the most oxidized state in this species or the hue is maintained through stabilizing selection for a favored color. The color metrics of Carotenoid Chroma, Visible Hue, λR50 and tetrahedral θ best captured differences in pigment concentration and make-up, and are recommended in future spectrophotometric studies of carotenoid-based traits.

Improved Conductance Blockage Modeling of Cylindrical Nanopores, from 2D to Thick Membranes

The ionic current blockage from a nanopore sensor is a fundamental metric for characterizing its dimensions and identifying molecules translocating through it. Yet, most analytical models predicting the conductance of a nanopore in both open and obstructed states remain inaccurate. Here, using an oblate spheroidal coordinate framework to study the electrical response of nanopore access regions, we reveal that the widely used model from Kowalczyk et al. significantly overestimates access region contributions when blocked by a cylindrical object, like DNA. To address this, we present an improved analytical model for the obstructed access resistance, which we establish as highly accurate through finite-element simulations, especially for ultrathin membranes and long narrow channels. Equipped with an improved nanopore conductance model, this work provides tools for more accurate calculation of the pore size and for the expected blockade from DNA, of high practical value for many biosensing applications.

Deciphering simplified regional anticoagulation with citrate in intermittent hemodialysis: a clinical and computational study

Regional citrate anticoagulation use in intermittent hemodialysis is limited by the increased risk of metabolic complications due to faster solute exchanges than with continuous renal replacement therapies. Several simplifications have been proposed. The objective of this study was to validate a mathematical model of hemodialysis anticoagulated with citrate that was then used to evaluate different prescription scenarios on anticoagulant effectiveness (free calcium concentration in dialysis filter) and calcium balance. A study was conducted in hemodialyzed patients with a citrate infusion into the arterial line and a 1.25 mmol/L calcium dialysate. Calcium and citrate concentrations were measured upstream and downstream of the citrate infusion site and in the venous line. The values measured in the venous lines were compared with those predicted by the model using Bland and Altman diagrams. The model was then used with 22 patients to make simulations. The model can predict the concentration of free calcium, bound to citrate or albumin, accurately. Irrespective of the prescription scenario a decrease in free calcium below 0.4 mmol/L was obtained only in a fraction of the dialysis filter. A zero or slightly negative calcium balance was observed, and should be taken into account in case of prolonged use.

Modeling Rare Disease Datasets with ART-DECOR

This paper presents ongoing work on the modeling of different datasets using the ART-DECOR modeling tool, with a focus on adherence to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The successful modeling of the French minimal dataset for rare diseases (Set de donnees minimal des maladies rares (SDM-MR.fr)) should provide inspiration for the development of the German minimal dataset for rare diseases (Minimalbasisdatensatz fur Seltene Erkrankungen (MBDS-SE.de)).

Morphological, sterical, and localized thermodynamics in the adsorption of CO2 by activated biocarbon from the white rot fungi Trametes gibbosa

The capture of CO2 by biochar has recently become one of the cornerstones of circular economy models for a sustainable society. In this work, we synthesized an activated biocarbon using Trametes gibbosa (BioACTG) in a one-step synthesis. We investigated CO2 adsorption mechanisms under five different temperatures using a statistical physics approach. The data was better represented by the multilayer model with two distinguished energies, providing more accurate values for the estimated parameters. According to the number of carbon dioxide molecules per site (n) and the densities of the receptor sites (Dzif), the tendency to form a second layer increased as the temperature increased. The adsorption of CO2 on BioACTG was exothermic (the values of Qasat = 15.5 mmol/g at 273 K decrease to 10.5 mmol/g at 353 K), and the temperature influenced CO2 as well as the morphological features of the process. A computational approach was used to investigate the electronic properties of the adsorbate, showing that its lowest unoccupied orbital (LUMO) heavily contributed to the high efficiency of the process which was ruled by pore diffusion mechanisms driven by energetic fluctuations. Other molecules present in CO2-rich mixtures were also investigated, showing that their concentration limited their competitiveness with CO2.

Modeling Metformin and Dapagliflozin Pharmacokinetics in Chronic Kidney Disease

Chronic kidney disease (CKD) is a complication of diabetes that affects circulating drug concentrations and elimination of drugs from the body. Multiple drugs may be prescribed for treatment of diabetes and co-morbidities, and CKD complicates the pharmacotherapy selection and dosing regimen. Characterizing variations in renal drug clearance using models requires large clinical datasets that are costly and time-consuming to collect. We propose a flexible approach to incorporate impaired renal clearance in pharmacokinetic (PK) models using descriptive statistics and secondary data with mechanistic models and PK first principles. Probability density functions were generated for various drug clearance mechanisms based on the degree of renal impairment and used to estimate the total clearance starting from glomerular filtration for metformin (MET) and dapagliflozin (DAPA). These estimates were integrated with PK models of MET and DAPA for simulations. MET renal clearance decreased proportionally with a reduction in estimated glomerular filtration rate (eGFR) and estimated net tubular transport rates. DAPA total clearance varied little with renal impairment and decreased proportionally to reported non-renal clearance rates. Net tubular transport rates were negative to partially account for low renal clearance compared with eGFR. The estimated clearance values and trends were consistent with MET and DAPA PK characteristics in the literature. Dose adjustment based on reduced clearance levels estimated correspondingly lower doses for MET and DAPA while maintaining desired dose exposure. Estimation of drug clearance rates using descriptive statistics and secondary data with mechanistic models and PK first principles improves modeling of CKD in diabetes and can guide treatment selection.

Impact of air quality model settings for the evaluation of emission reduction strategies to curb air pollution

For air quality management, while numerical tools are mainly evaluated to assess their performances on absolute concentrations, this study assesses the impact of their settings on the robustness of model responses to emission reduction strategies for the main criteria pollutants. The effect of the spatial resolution and chemistry schemes is investigated. We show that whereas the spatial resolution is not a crucial setting (except for NO2), the chemistry scheme has more impact, particularly when assessing hourly values of the absolute potential of concentrations. The analysis of model responses under the various configurations triggered an analysis of the impact of using online models, like WRF-chem or WRF-CHIMERE, which accounts for the impact of aerosol concentrations on meteorology. This study informs the air quality modeling community on what extent some model settings can affect the expected model responses to emission changes. We suggest to not activate online effects when analyzing the effect of an emission reduction strategy to avoid any confusion in the interpretation of results even if an online simulation should represent better the reality.

Validation of the implementation of phased-array heating systems in Plan2Heat

BACKGROUND

Hyperthermia treatment planning can be supportive to ensure treatment quality, provided reliable prediction of the heating characteristics (i.e., focus size and effects of phase-amplitude and frequency steering) of the device concerned is possible. This study validates the predictions made by the treatment planning system Plan2Heat for various clinically used phased-array systems.

METHODS

The evaluated heating systems were AMC-2, AMC-4/ALBA-4D (Med-Logix srl, Rome, Italy), BSD Sigma-30, and Sigma-60 (Pyrexar Medical, Salt Lake City, UT, USA). Plan2Heat was used for specific absorption rate (SAR) simulations in phantoms representing measurement set-ups reported in the literature. SAR profiles from published measurement data based on E‑field or temperature rise were used to compare the device-specific heating characteristics predicted by Plan2Heat.

RESULTS

Plan2Heat is able to predict the correct location and size of the SAR focus, as determined by phase-amplitude settings and operating frequency. Measured effects of phase-amplitude steering on focus shifts (i.e., local SAR minima or maxima) were also correctly reflected in treatment planning predictions. Deviations between measurements and simulations were typically < 10-20%, which is within the range of experimental uncertainty for such phased-array measurements.

CONCLUSION

Plan2Heat is capable of adequately predicting the heating characteristics of the AMC‑2, AMC-4/ALBA-4D, BSD Sigma-30, and Sigma-60 phased-array systems routinely used in clinical hyperthermia.

Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model

BACKGROUND

Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network-Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures.

METHODS

Using ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop the CNN-LSTM model. Additionally, we utilized R and Python to develop four other models commonly used for predicting infectious diseases. After constructing the models, we employed these models to make retrospective predictions, and compared each model's prediction performance using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and other evaluation metrics.

RESULTS

Based on historical ILI% data from 28 national sentinel hospitals in Hebei Province, the Seasonal Auto-Regressive Indagate Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Convolution Neural Network (CNN), Long Short Term Memory neural network (LSTM) models were constructed. On the testing set, all models effectively predicted the ILI% trends. Subsequently, these models were used to forecast over different time spans. Across various forecasting periods, the CNN-LSTM model demonstrated the best predictive performance, followed by the XGBoost model, LSTM model, CNN model, and SARIMA model, which exhibited the least favorable performance.

CONCLUSION

The hybrid CNN-LSTM model had better prediction performances than the SARIMA model, CNN model, LSTM model, and XGBoost model. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.

The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Process Optimization for Biosurfactant Production by Bacillus aryabhattai SPS1001 using Taguchi Method

This study employs Taguchi design of experiments (DOE) to optimize biosurfactant yield by analyzing the impact of various input parameters. Signal-to-noise ratio analysis was utilized for optimization, corroborated by ANOVA findings. Regression equations depicted response behaviour and are validated through a confirmation test. Taguchi methodology identified optimal conditions for maximum biosurfactant yield: agitation (180 rpm), inoculum size (2%), beef extract (5 g/L), diesel (20 ml/L), peptone (5 g/L), NaCl (7 g/L), incubation time (4 days), pH (7.9), and yeast extract (6 g/L). This yielded an 8.33% increase to 1.53 g/L, with initial optimum parameters projecting 1.41 g/L. ANOVA ranked and quantified control factor contributions, revealing agitation's significant (31.41%) impact on yield. The study underscores the viability of Taguchi's optimal conditions for substantial yield improvement within specific ranges. The strong alignment between expected and experimental yields affirmed the reliability of developed models for optimal yield selection. This study underscores the power of statistical techniques like Taguchi DOE and ANOVA in systematically enhancing biosurfactant production by Bacillus aryabhattai SPS1001 and paves the way for future advancements in bioprocess optimization.

The public health impact of COVID-19 variants of concern on the effectiveness of contact tracing in Vermont, United States

Case investigation and contact tracing (CICT) are public health measures that aim to break the chain of pathogen transmission. Changes in viral characteristics of COVID-19 variants have likely affected the effectiveness of CICT programs. We estimated and compared the cases averted in Vermont when the original COVID-19 strain circulated (Nov. 25, 2020-Jan. 19, 2021) with two periods when the Delta strain dominated (Aug. 1-Sept. 25, 2021, and Sept. 26-Nov. 20, 2021). When the original strain circulated, we estimated that CICT prevented 7180 cases (55% reduction in disease burden), compared to 1437 (15% reduction) and 9970 cases (40% reduction) when the Delta strain circulated. Despite the Delta variant being more infectious and having a shorter latency period, CICT remained an effective tool to slow spread of COVID-19; while these viral characteristics did diminish CICT effectiveness, non-viral characteristics had a much greater impact on CICT effectiveness.

Prediction of g-C3N4-based photocatalysts in tetracycline degradation based on machine learning

Investigating the effects of g-C3N4-based photocatalysts on experimental parameters during tetracycline (TC) degradation can be helpful in discovering the optimal parameter combinations to improve the degradation efficiencies in general. Machine learning methods can avoid the problems of high cost, time-consuming and possible instrumental errors in experimental methods, which have been proven to be an effective alternative for evaluating the entire experimental process. Eight typical machine learning models were explored for their effectiveness in predicting the TC degradation efficiencies of g-C3N4 based photocatalysts. XGBoost (XGB) was the most reliable model with R2, RMSE and MAE values of 0.985, 4.167 and 2.900, respectively. In addition, XGB's feature importance and SHAP method were used to rank the importance of features to provide interpretability to the results. This study provided a new idea for developing g-C3N4-based photocatalysts for TC degradation and intelligent algorithms for predicting the photocatalytic activity of g-C3N4-based photocatalysts.

Role of comammox bacteria in granular bioreactor for nitrogen removal via partial nitritation/anammox

In this study, two bioprocess models were first constructed with the newly-discovered comammox process described as one-step and two-step nitrification and evaluated against relevant experimental data. The validated models were then applied to reveal the potential effect of comammox bacteria on the granular bioreactor particularly suitable for undertaking partial nitritation/anammox (PN/A) under different operating conditions of bulk dissolved oxygen (DO) and influent NH4+. The results showed although comammox bacteria-based PN/A could achieve > 80.0 % total nitrogen (TN) removal over a relatively wider range of bulk DO and influent NH4+ (i.e., 0.25-0.40 g-O2/m3 and 470-870 g-N/m3, respectively) without significant nitrous oxide (N2O) production (< 0.1 %), the bulk DO should be finely controlled based on the influent NH4+ to avoid the undesired full nitrification by comammox bacteria. Comparatively, conventional ammonium-oxidizing bacteria (AOB)-based PN/A not only required higher bulk DO to achieve > 80.0 % TN removal but also suffered from 1.7 %∼2.8 % N2O production.

Leveraging the trend analysis for modeling of the greenhouse gas emissions associated with coal combustion

In this paper, it is aimed, for the first time, at deriving simple models, leveraging the trend analysis in order to estimate the future greenhouse gas emissions associated with coal combustion. Due to the expectations of becoming the center of global economic development in the future, BRICS-T (Brazil, the Russian Federation, India, China, South Africa, and Turkiye) countries are adopted as cases in the study. Following the models' derivation, their statistical validations and estimating accuracies are also tested through various metrics. In addition, the future greenhouse gas emissions associated with coal combustion are estimated by the derived models. The results demonstrate that the derived models can be successfully used as a tool for estimating the greenhouse gas emissions associated with coal combustions with accuracy ranges from at least 90% to almost 98%. Moreover, the estimating results show that the total amount of greenhouse gas emissions associated with coal combustions in the relevant countries and in the world will increase to 14 BtCO2eq and 19 BtCO2eq by 2035, with an annual growth of 2.39% and 1.71%, respectively. In summary, the current study's findings affirm the usefulness of trend analysis in deriving models to estimate greenhouse gas emissions associated with coal combustion.

Smoking patterns by birth cohort in Argentina: an age-period-cohort population-based modeling study

Background

Argentina's smoking rates remain high. We aim to estimate Argentina age-specific histories of smoking initiation, cessation, prevalence, and intensity by birth-cohort to inform policy interventions.

Methods

Modeling study. Data from three Argentinian nationally representative surveys conducted from 2004 to 2018 (n = 268,193) were used to generate smoking histories. The Cancer Intervention and Surveillance Modeling (CISNET) Network Lung Working Group age, period, and cohort modeling approach was used to calculate smoking initiation and cessation probabilities, ever and current smoking prevalence, and intensity (cigarettes per day, CPD) by age, sex, and birth cohort from 1950 to 2018.

Findings

Ever smoking prevalence increases with age up to 25 and decreases with birth cohorts after 1990. Smoking initiation peaks between 15 and 18 years of age. Among females, initiation probabilities increased until the 1955 cohort, reaching a second peak in 1980-85 cohorts and declining thereafter. Males have higher initiation probabilities than females. Among males, initiation has decreased since the 1950 birth cohort, with a slight increase around the 1985 cohort. Current smoking prevalence has been decreasing since the 1960 birth cohort, except for a peak in 1980-85 cohorts. Cessation increases with age. Mean CPD increases with age and peaks around age 40, appearing flat in females since the 1985 cohort.

Interpretation

Recent birth cohorts seem to be experiencing lower rates of initiation, stable rates of quitting and lower current and ever smoking prevalence. The stabilization of cessation probabilities and mean CPD indicate the need to strengthen existing tobacco control measures and advance new ones.

Funding

NIH/NCI U01CA253858 grant.

Online monitoring lignocellulosic particles by focus beam reflectance measurement for efficient bioprocessing

Lignocellulose presents a promising alternative to fossil fuels. Monitoring the mass and size changes of lignocellulosic particles without disrupting the process can assist in adjusting pretreatment and enzymatic hydrolysis, where conventional sieving methods fall short. A method utilizing focused beam reflectance measurement (FBRM) was developed to establish mathematical correlations between FBRM chord information (chord length and count) and particle characteristics (weight and size) quantified through sieving. Results indicate particle size exhibits a linear correlation with the square weighted median chord length (Lsqr) with R2 at 0.93. Further, real-time bulk particle mass can be predicted using Lsqr and chord count (R2 0.98). These correlations are applicable in range 53 μm to 358.5 μm. Real-time monitoring of enzymatic hydrolysis of corn stalks has demonstrated the practical applicability of FBRM. This study introduces a novel approach for online characterization of lignocellulosic particles, thereby enhancing lignocellulosic biorefineries.

Molecular dynamics simulations shed light into the donor substrate specificity of vertebrate poly-alpha-2,8-sialyltransferases ST8Sia IV

BACKGROUND

Sialic acids are essential monosaccharides influencing several biological processes and disease states. The sialyltransferases catalyze the transfer of Sia residues to glycoconjugates playing critical roles in cellular recognition and signaling. Despite their importance, the molecular mechanisms underlying their substrate specificity, especially between different organisms, remain poorly understood. Recently, the human ST8Sia IV, a key enzyme in the synthesis of polysialic acids, was found to accept only CMP-Neu5Ac as a sugar-donor, whereas the whitefish Coregonus maraena enzyme showed a wider donor substrate specificity, accepting CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn. However, what causes these differences in donor substrate specificity is unknown.

METHODS

Computational approaches were used to investigate the structural and biochemical determinants of the donor substrate specificity in ST8Sia IV. Accurate structural models of the human and fish ST8Sia IV catalytic domains and their complexes with three sialic acid donors (CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn) were generated. Subsequently, molecular dynamics simulations were conducted to analyze the stability and interactions within these complexes and identify differences in complex stability and substrate binding sites between the two ST8Sia IV.

RESULTS

Our MD simulations revealed that the human enzyme effectively stabilizes CMP-Neu5Ac, whereas CMP-Neu5Gc and CMP-Kdn are unstable and explore different conformations. In contrast, the fish ST8Sia IV stabilizes all three donor substrates. Based on these data, we identified the key interacting residues for the different Sias parts of the substrate donors.

GENERAL SIGNIFICANCE

This work advances our knowledge of the enzymatic mechanisms governing sialic acid transfer, shedding light on the evolutionary adaptations of sialyltransferases.

Modeling key intermediates during anaerobic digestion of lipid rich kitchen waste with an extended ADM1

Quantitative dynamics of the key intermediates, gases and carbohydrates during anaerobic digestion of different lipid rich kitchen waste and lipid rich model kitchen waste were modeled. Six batch reactors loaded with 25 gVS  l- 1 ( ∼ 39  g O 2  l- 1 ) kitchen waste and model kitchen waste during a batch experiment were considered in simulation. Observed dynamics of carbohydrates, volatile organic acids and gases were described by an extended benchmark simulation model no. 2 (BSM2). In this study the extended BSM2 included a more detailed β -oxidation for prediction of caproic acid. Furthermore, the extensions included carbohydrate digestion with an additional intermediate before propionic acid was released. In addition, a novel simplification approach for initial pH estimation was successfully applied. For parameter estimation a Markov Chain Monte Carlo method was used to obtain parameter distributions. With the presented model it was possible even with no calibrated data to predict point of times of intermediates maxima and propionic acid with relative stable concentration over several days for kitchen waste.

Estimation of differential pathlength factor from NIRS measurement in skeletal muscle

The utilization of continuous wave (CW) near-infrared spectroscopy (NIRS) device to measure non-invasively muscle oxygenation in healthy and disease states is limited by the uncertainties related to the differential path length factor (DPF). DPF value is required to quantify oxygenated and deoxygenated heme groups' concentration changes from measurement of optical densities by NIRS. An integrated approach that combines animal and computational models of oxygen transport and utilization was used to estimate the DPF value in situ. The canine model of muscle oxidative metabolism allowed measurement of both venous oxygen content and tissue oxygenation by CW NIRS under different oxygen delivery conditions. The experimental data obtained from the animal model were integrated in a computational model of O2 transport and utilization and combined with Beer-Lambert law to estimate DPF value in contracting skeletal muscle. A 2.1 value was found for DPF by fitting the mathematical model to the experimental data obtained in contracting muscle (T3) (Med.Sci.Sports.Exerc.48(10):2013-2020,2016). With the estimated value of DPF, model simulations well predicted the optical density measured by NIRS on the same animal model but with different blood flow, arterial oxygen contents and contraction rate (J.Appl.Physiol.108:1169-1176, 2010 and 112:9-19,2013) and demonstrated the robustness of the approach proposed in estimating DPF value. The approach used can overcome the semi-quantitative nature of the NIRS and estimate non-invasively DPF to obtain an accurate concentration change of oxygenated and deoxygenated hemo groups by CW NIRS measurements in contracting skeletal muscle under different oxygen delivery and contraction rate.

Stand age diversity (and more than climate change) affects forests' resilience and stability, although unevenly

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Unfortunately, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a state-of-the-science biogeochemical, biophysical, validated process-based model on historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions (i.e., forests are left to develop undisturbed). Such a model, forced by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario to disentangle the effect of climate change, spanned several age classes as representative of the current European forests' context, for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with longstanding ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO2 and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age class diversity -lacking in most, if not all, the current Global Vegetation Models - for reliable and robust assessments of the impacts of climate change on future forests' stability and resilience capacity. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.