understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.

Theory of nanostructured sensors integrated in/on microneedles for diagnostics and therapy

Efficient real-time diagnostics and on-demand drug delivery are essential components in modern healthcare, especially for managing chronic diseases. The lack of a rapid and effective sensing and therapeutic system can result in analyte level deviations, leading to severe complications. Minimally invasive microneedle (MN)-based patches integrating nanostructures (NSs) in their volume or on their surface have emerged as a biocompatible technology for delay-free analyte sensing and therapy. However, a quantitative relationship for the signal response in NS-assisted reactions remains elusive. Existing generalized formalisms are derived for in-vitro applications, raising questions about their direct applicability to in-situ wearable sensors. In this study, we apply the reaction-diffusion theory to establish a generalized physics-guided framework for NS-in-MN platforms in wearable applications. The model relates the signal response to analyte concentration, incorporating geometric, physical, and catalytic platform properties. Approximating the model under NS (binding or catalytic) and environmental (mass transport) limitations, we validate it against numerical simulations and various experimental results from diverse conditions - analyte sensing (glucose, lactic acid, pyocyanin, miRNA, etc.) in artificial and in-vivo environments (humans, mice, pigs, plants, etc.) through electrochemical and optical/colorimetric, enzymatic and non-enzymatic platforms. The results plotted in the scaled response show that (a) NS-limited platforms exhibit a linear dependence, (b) Mass transport-limited platforms saturate to 1, (c) a one-to-one mapping against traditional sensitivity plots unifies the scattered data points reported in literature. The universality of the model provides insightful perspectives for the design and optimization of MN-based sensing technologies, with potential extensions to dissolvable MNs as part of analyte-responsive closed-loop therapeutic applications.

Selective leaching of indium from spent LCD screens by siderophore desferrioxamine E

Given the criticality of indium (In) in high-tech applications, spent LCD screens can represent a viable secondary In resource. In this work, an innovative and alternative technology to selectively leach In from spent LCD screens using a microbial chelating agent, desferrioxamine E (DFOE), was developed. Indium was concentrated from spent LCD screens by implementing an adapted pre-treatment procedure, allowing the isolation of an indium-rich glassy fraction. During leaching, the competition between aluminum (Al) and In for complexation with DFOE leads to the precipitation of In(OH)3 at low DFOE concentrations (12-240 µM). After adjusting the optimal conditions (fraction size: 0-36 μM, pH: 5.5, S/L ratio: 1 g/L, 25 °C), the In leaching yield reached 32%, ten times higher than Al over 90 days with 5 mM DFOE. Thus, achieving high In recovery is possible through i) prolonging leaching durations, ii) selective leaching, and iii) minimizing Al interference. This is the first attempt to selectively leach In using a selected siderophore from end-of-life products with high concentrations of non-targeted elements (i.e. Al, Si, and Ca). This study demonstrates the potential of generating indium-rich leachates, which can be subsequently processed through the GaLIophore technology for In refining.

Four modeling approaches to study restrictions on everyday life and social activities due to chronic diseases with consequences of suicidal behavior

The purpose of this study was to discover the association between disability in everyday life and social activities due to chronic diseases and suicidal ideation (SI), suicidal plan (SP), and suicidal attempt (SA) from the Korea National Health and Nutrition Examination Survey (KNHANES), considering the cross-sectional design of this study, 2016-2018 dataset. Variables for finding the associated factors of SI, SP, and SA were confirmed through random forest (RF), decision tree, generalized linear model (GLM), and support vector machine (SVM), and the performance of each model is listed. A total of 17,323 (males: 7,530, females: 9793) responders from the KNHANES from 2016 to 2018 were employed for the study. The relationship between restrictions on daily life, social activities, and three stages of suicidal behaviors due to diseases were analyzed using the R function (R version 4.2.0), randomForest, ctree, glm, and ksvm. The F1-score is a measure used to evaluate the accuracy of the performance of a model, in the binary classification. The score of 1 indicates good performance, whereas a score of 0 signifies poor performance. Due to chronic diseases, disability in everyday life and social activities lead to suicide behaviors. In our study, we examined the impact of limitations in daily living and social activities on suicidal behaviors among participants. Our findings revealed that for those experiencing such limitations, the odds ratios (ORs) for SIs were 6.10 (95% CI: 3.99-9.34) for males and 2.61 (1.79-3.81) for females. SPs were 3.69 (2.36-5.78) for males and 3.94 (2.70-5.75) for females. Similarly, the odds ratios for SAs were 5.04 (2.51-10.13) for males and 2.71 (1.48-4.98) for females, indicating a significant association between these limitations and increased suicidal behaviors, with variances observed between genders. These results underscore the necessity of addressing daily living and social activity restrictions when considering mental health interventions and suicide prevention strategies. In RF, GLM, and SVM, F1-score were 0.8192, 0.6887, and 0.9687 in SA, respectively. Among the patients with chronic disease, those with sequelae, low incomes, and low levels of education had limitations in daily activities and social activities, which increased the likelihood of suicidal thoughts, planning, and attempts.

Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials

In silico clinical trials (ISCT) can contribute to demonstrating a device's performance via credible computational models applied on virtual cohorts. Our purpose was to establish the credibility of a model for assessing the risk of humeral stem loosening in total shoulder arthroplasty, based on a twofold validation scheme involving both benchtop and clinical validation activities, for ISCT applications. A finite element model computing bone-implant micromotion (benchtop model) was quantitatively compared to a bone foam micromotion test (benchtop comparator) to ensure that the physics of the system was captured correctly. The model was expanded to a population-based approach (clinical model) and qualitatively evaluated based on its ability to replicate findings from a published clinical study (clinical comparator), namely that grit-blasted stems are at a significantly higher risk of loosening than porous-coated stems, to ensure that clinical performance of the stem can be predicted appropriately. Model form sensitivities pertaining to surgical variation and implant design were evaluated. The model replicated benchtop micromotion measurements (52.1 ± 4.3 µm), without a significant impact of the press-fit ("Press-fit": 54.0 ± 8.5 µm, "No press-fit": 56.0 ± 12.0 µm). Applied to a virtual population, the grit-blasted stems (227 ± 78µm) experienced significantly larger micromotions than porous-coated stems (162 ± 69µm), in accordance with the findings of the clinical comparator. This work provides a concrete example for evaluating the credibility of an ISCT study. By validating the modeling approach against both benchtop and clinical data, model credibility is established for an ISCT application aiming to enrich clinical data in a regulatory submission.

Fractional order model of MRSA bacterial infection with real data fitting: Computational Analysis and Modeling

Bacterial infections in the health-care sector and social environments have been linked to the Methicillin-Resistant Staphylococcus aureus (MRSA) infection, a type of bacteria that has remained an international health risk since the 1960s. From mild colonization to a deadly invasive disease with an elevated mortality rate, the illness can present in many different forms. A fractional-order dynamic model of MRSA infection developed using real data for computational and modeling analysis on the north side of Cyprus is presented in this paper. Initially, we tested that the suggested model had a positively invariant region, bounded solutions, and uniqueness for the biological feasibility of the model. We study the equilibria of the model and assess the expression for the most significant threshold parameter, called the basic reproduction number (ℛ0). The reproductive number's parameters are also subjected to sensitivity analysis through mathematical methods and simulations. Additionally, utilizing the power law kernel and the fixed-point approach, the existence, uniqueness, and generalized Ulam-Hyers-Rassias stability are presented. Chaos Control was used to regulate the linear responses approach to bring the system to stabilize according to its points of equilibrium, taking into account a fractional-order system with a managed design where solutions are bound in the feasible domain. Finally, numerical simulations demonstrating the effects of different parameters on MRSA infection are used to investigate the impact of the fractional operator on the generalized form of the power law kernel through a two-step Newton polynomial method. The impact of fractional orders is emphasized in the study so that the numerical solutions support the importance of these orders on MRSA infection. With the application of fractional order, the significance of cognizant antibiotic usage for MRSA infection is verified.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Spatiotemporal regulation of plant cell division

Plant morphogenesis largely depends on the orientation and rate of cell division and elongation, and their coordination at all levels of organization. Despite recent progresses in the comprehension of pathways controlling division plane determination in plant cells, many pieces are missing to the puzzle. For example, we have a partial comprehension of formation, function and evolutionary significance of the preprophase band, a plant-specific cytoskeletal array involved in premitotic setup of the division plane, as well as the role of the nucleus and its connection to the preprophase band of microtubules. Likewise, several modeling studies point to a strong relationship between cell shape and division geometry, but the emergence of such geometric rules from the molecular and cellular pathways at play are still obscure. Yet, recent imaging technologies and genetic tools hold a lot of promise to tackle these challenges and to revisit old questions with unprecedented resolution in space and time.

Aspergillus niger as an eco-friendly agent for potassium release from K- bearing minerals: Isolation, screening and culture medium optimization using Plackett-Burman design and response surface methodology

The potential of Aspergillus niger, to enhance non-exchangeable potassium (K+) release from mineral structures were investigated as a cost-effective and environmentally friendly alternative to traditional chemical fertilizers. Optimizing the culture medium for maximum K+ release, alongside identifying potential mechanisms of action of the A. niger including the production of various organic acids and pH reduction in the minerals feldspar and phlogopite, were among the primary objectives of the present study. K+ dissolution from feldspar and phlogopite in the presence of Aspergillus niger were examined through a two-step experiment; impact of different carbon sources (glucose, sucrose, and fructose) on K+ release using the Plackett-Burman design (PBD) with 12 experimental runs and effect of other independent variables including pH (ranging from 5 to 10), carbon concentration (3-12.3 g l-1), and incubation time (5-18 days) on K+ release using the central composite design (CCD). Our results indicated that the PBD demonstrated a strong predictive capacity (RMSE = 0.012-0.018 g l-1 and R2 = 0.85-0.89) for K+ release. According to the CCD model, pH exerted a significant positive influence on increasing soluble K+ release (P < 0.001). The highest levels of K+ release (157.8 and 175.3 mg l-1 in feldspar and phlogopite, respectively) were observed at the central levels (0) of time and carbon source, and at the +α level (+1.68) of pH. Furthermore, based on the CCD model, the optimal conditions for achieving high K+ release from feldspar and phlogopite in a medium were pHs of 10.36 and 10.31, sucrose concentrations of 11.23 and 11.32 g l-1, and incubation times of 15 and 18 days, respectively. The determination coefficients of the CCD model indicated that 89.5% and 92.6% of the changes in soluble K+ for feldspar and phlogopite, could be explained by this model, respectively. In the current study, the production of organic acids and the resulting pH reduction, along with the reduction in mineral particle size in feldspar and phlogopite, were identified as potential mechanisms influencing the enhancement of potassium solubility. The predominant acids in both feldspar and phlogopite were lactic acid (70.9 and 69.15 mg l-1) and citric acid (40.48 and 22.93 mg l-1), although the production levels of organic acids differed in the two minerals. Overall, our findings highlight the potential of A. niger to proficiently release non-exchangeable potassium from mineral matrices, indicating its promising potential in agricultural applications.

Equilibrium and kinetic modeling of Cr(VI) removal by novel tolerant bacteria species along with zero-valent iron nanoparticles

This work describes the study of the removal of a refractory contaminant, i.e., Hexavalent chromium (Cr(VI)) from aqueous systems by a novel adsorbent comprising Cr(VI) tolerant bacteria and zero valent iron nanoparticle (nZVI). A gram-positive, rod-shaped bacteria used in the study were isolated from wastewater (WW) received from the effluent of leather industries. The adsorbents were prepared with bacteria, nZVI alone, and a combination of both. The adsorbent comprising both elements was found to remove Cr(VI) with a higher percentage (93%) and higher capacities (0.58 mg/g) as compared to adsorbent with bacteria (Cr(VI) removal = 63%, qe = 0.163 mg/g) or nanoparticles (Cr(VI) removal = 80%, qe = 0.45 mg/g) alone. The adsorbent worked best at neutral pH, and the removal became saturated after 90 min of incubation. Equilibrium studies with isotherm modeling suggested that the adsorption process follows sips isotherm (R2 = 0.9955), which is expected to bean intra-particle diffusion process before the actual adsorption. Process kinetics was modeled with pseudo-first order, pseudo-second order, and Vermeulen model. The diffusion coefficient determined by fitting the kinetic data to Vermeulen model was found to be 0.0000314 cm2/s. The adsorbent can be tested further for continuous flow processes to find more insights about the usage on a large scale.

A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO$_2$ injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach.

Estimated public health impact of concurrent mask mandate and vaccinate-or-test requirement in Illinois, October to December 2021

BACKGROUND

Facing a surge of COVID-19 cases in late August 2021, the U.S. state of Illinois re-enacted its COVID-19 mask mandate for the general public and issued a requirement for workers in certain professions to be vaccinated against COVID-19 or undergo weekly testing. The mask mandate required any individual, regardless of their vaccination status, to wear a well-fitting mask in an indoor setting.

METHODS

We used Illinois Department of Public Health's COVID-19 confirmed case and vaccination data and investigated scenarios where masking and vaccination would have been reduced to mimic what would have happened had the mask mandate or vaccine requirement not been put in place. The study examined a range of potential reductions in masking and vaccination mimicking potential scenarios had the mask mandate or vaccine requirement not been enacted. We estimated COVID-19 cases and hospitalizations averted by changes in masking and vaccination during the period covering October 20 to December 20, 2021.

RESULTS

We find that the announcement and implementation of a mask mandate are likely to correlate with a strong protective effect at reducing COVID-19 burden and the announcement of a vaccinate-or-test requirement among frontline professionals is likely to correlate with a more modest protective effect at reducing COVID-19 burden. In our most conservative scenario, we estimated that from the period of October 20 to December 20, 2021, the mask mandate likely prevented approximately 58,000 cases and 1,175 hospitalizations, while the vaccinate-or-test requirement may have prevented at most approximately 24,000 cases and 475 hospitalizations.

CONCLUSION

Our results indicate that mask mandates and vaccine-or-test requirements are vital in mitigating the burden of COVID-19 during surges of the virus.

New zeolite made from Tunisian raw clay: study and modeling for C3H6 breakthrough dynamic adsorption onto zeolite material

In this study, Tunisian raw clay (RC) was utilized as a cheap source of silicium and aluminum for the preparation of faujasite zeolite (FAUsyn) using the alkaline fusion technique. The zeolite's structural analysis was carried out using the XRD, nitrogen adsorption-desorption, and SEM-EDX techniques. The data collected demonstrate that the produced zeolite only included one homogeneous faujasite phase. Textural analysis shows that the FAUsyn prepared from RC has a hierarchical porosity (micro-, meso-, and macropores). The total porosity was found to be 0.33 cm3/g as well as the BET area was equal to 360 m2/g. Adsorption experiments for propene capture were performed using the FAUsyn as adsorbent material. The performance of the column was examined in relation to various parameter impacts, including flow rate (50, 100, and 150 mL/min), input concentration (4, 8, and 12 mg/L), and bed depth (10, 14, and 18 cm). Finally, experimental and theoretical studies were investigated to predict adsorption capacities and kinetics parameters. To clarify and estimate column inputs, a model that incorporates axial dispersion, Langmuir equation, and migration within the adsorbent's pore was improved. COMSOL Multiphysics software was used to execute the model and resolve it computationally. The results of the experiments and the expected breakthrough curves were very well agreed. Modeling obtained results can be extrapolated to industrial level.

Timely Questions Emerging in Chronobiology: The Circadian Clock Keeps on Ticking

Chronobiology investigations have revealed much about cellular and physiological clockworks but we are far from having a complete mechanistic understanding of the physiological and ecological implications. Here we present some unresolved questions in circadian biology research as posed by the editorial staff and guest contributors to the Journal of Circadian Rhythms. This collection of ideas is not meant to be comprehensive but does reveal the breadth of our observations on emerging trends in chronobiology and circadian biology. It is amazing what could be achieved with various expected innovations in technologies, techniques, and mathematical tools that are being developed. We fully expect strengthening mechanistic work will be linked to health care and environmental understandings of circadian function. Now that most clock genes are known, linking these to physiological, metabolic, and developmental traits requires investigations from the single molecule to the terrestrial ecological scales. Real answers are expected for these questions over the next decade. Where are the circadian clocks at a cellular level? How are clocks coupled cellularly to generate organism level outcomes? How do communities of circadian organisms rhythmically interact with each other? In what way does the natural genetic variation in populations sculpt community behaviors? How will methods development for circadian research be used in disparate academic and commercial endeavors? These and other questions make it a very exciting time to be working as a chronobiologist.

Clinical pharmacology strategies to accelerate the development of polatuzumab vedotin and summary of key findings

The favorable benefit-risk profile of polatuzumab vedotin, as demonstrated in a pivotal Phase Ib/II randomized study (GO29365; NCT02257567), coupled with the need for effective therapies in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), prompted the need to accelerate polatuzumab vedotin development. An integrated, fit-for-purpose clinical pharmacology package was designed to support regulatory approval. To address key clinical pharmacology questions without dedicated clinical pharmacology studies, we leveraged non-clinical and clinical data for polatuzumab vedotin, published clinical data for brentuximab vedotin, a similar antibody-drug conjugate, and physiologically based pharmacokinetic and population pharmacokinetic modeling approaches. We review strategies and model-informed outcomes that contributed to regulatory approval of polatuzumab vedotin plus bendamustine and rituximab in R/R DLBCL. These strategies made polatuzumab vedotin available to patients earlier than previously possible; depending on the strength of available data and the regulatory/competitive environment, they may also prove useful in accelerating the development of other agents.

Photo-production of excited triplet-state of dissolved organic matters in inland freshwater and coastal seawater

The excited triplet-state of dissolved organic matter (3DOM*) is a major reactive intermediate in sunlit waters. Its quantum yield is important in understanding the fate of organic micropollutants. The degradation efficiency of its chemical probe, 2,4,6-trimeythlphenol (fTMP), is generally used as a proxy of the quantum yield. However, fTMP has been described and modelled only for freshwater systems. Therefore, this study quantified fTMP in inland freshwater and coastal seawater sampled in Japan by conducting steady-state photochemical experiments. Optical properties of water were then used to model fTMP. Results indicated that the inland freshwater DOM originated mainly from terrestrial sources, while the coastal seawater DOM were microbial-dominated. On average, inland freshwater exhibited lower fTMP (61.2 M-1) than coastal seawater (79.7 M-1) and the coastal seawater exhibited significant variations in the proportion of high-energy 3DOM* (> 250 kJ/mol). In addition, E2:E3 (ratio of absorbance at 254 to 365 nm) was positively correlated with fTMP of inland freshwater, coastal seawater, and the overall dataset. Catchment conditions such as forest coverage also influenced the production of 3DOM* and high-energy 3DOM* in inland freshwater. Furthermore, the developed models estimated fTMP based on the optical properties of both freshwater and seawater, providing valuable insights about 3DOM* photochemistry in the aquatic environment.

Multiscale insights into postnatal aortic development

Despite its vital importance for establishing proper cardiovascular function, the process through which the vasculature develops and matures postnatally remains poorly understood. From a clinical perspective, an ability to mechanistically model the developmental time course in arteries and veins, as well as to predict how various pathologies and therapeutic interventions alter the affected vessels, promises to improve treatment strategies and long-term clinical outcomes, particularly in pediatric patients suffering from congenital heart defects. In the present study, we conducted a multiscale investigation into the postnatal development of the murine thoracic aorta, examining key allometric relations as well as relationships between in vivo mechanical stresses, collagen and elastin expression, and the gradual accumulation of load-bearing constituents within the aortic wall. Our findings suggest that the production of fibrillar collagens in the developing aorta associates strongly with the ratio of circumferential stresses between systole and diastole, hence emphasizing the importance of a pulsatile mechanobiological stimulus. Moreover, rates of collagen turnover and elastic fiber compaction can be inferred directly by synthesizing transcriptional data and quantitative histological measurements of evolving collagen and elastin content. Consistent with previous studies, we also observed that wall shear stresses acting on the aorta are similar at birth and in maturity, supporting the hypothesis that at least some stress targets are established early in development and maintained thereafter, thus providing a possible homeostatic basis to guide future experiments and inform future predictive modeling.

Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning

Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron-astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron-astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron-glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.

Systems genetics of influenza A virus-infected mice identifies TRIM21 as a critical regulator of pulmonary innate immune response

Tripartite motif 21 (TRIM21) is a cytosolic Fc receptor that targets antibody-bound, internalized pathogens for destruction. Apart from this intrinsic defense role, TRIM21 is implicated in autoimmune diseases, inflammation, and autophagy. Whether TRIM21 participates in host interactions with influenza A virus (IAV), however, is unknown. By computational modeling of body weight and lung transcriptome data from the BXD parents (C57BL/6 J (B6) and DBA/2 J (D2)) and 41 BXD mouse strains challenged by IAV, we reveal that a Trim21-associated gene network modulates the early host responses to IAV infection. Trim21 transcripts were significantly upregulated in infected mice of both B6 and D2 backgrounds. Its expression was significantly higher in infected D2 than in infected B6 early after infection and significantly correlated with body weight loss. We identified significant trans-eQTL on chromosome 14 that regulates Trim21 expression. Nr1d2 and Il3ra were among the strongest candidate genes. Pathway analysis found Trim21 to be involved in inflammation and immunity related pathways, such as inflammation signaling pathways (TNF, IL-17, and NF-κB), viral detection signaling pathways (NOD-like and RIG-I-like), influenza, and other respiratory viral infections. Knockdown of TRIM21 in human lung epithelial A549 cells significantly augmented IAV-induced expression of IFNB1, IFNL1, CCL5, CXCL10, and IFN-stimulated genes including DDX58 and IFIH1, among others. Our data suggest that a TRIM21-associated gene network is involved in several aspects of inflammation and viral detection mechanisms during IAV infection. We identify and validate TRIM21 as a critical regulator of innate immune responses to IAV in human lung epithelial cells.

Dynamic performance of functionally graded composite structures with viscoelastic polymers

The functionally graded composite structures with viscoelastic polymers inherits the excellent performance of functionally graded composites and also possesses large damping performance, which has broad application prospects in the aerospace and mechanical engineering fields. However, due to the complexity of the structure itself, there is limited literature available on its theoretical modeling for efficient solutions. To predict its dynamic performance, a simplified dynamic model of the functionally graded composite structures with viscoelastic polymers is established. This model takes into account the displacement transfer relationship between the functional graded composite layer and the viscoelastic polymer layer. The governing differential equations are obtained by applying the Navier method and complex modulus theory. These equations are then solved using the Rayleigh-Ritz method. The validity of the theoretical model is confirmed by comparing it with existing literature and the results obtained from ANSYS software. Additionally, the model that has been developed is used to analyze how the graded index and elastic modulus of the structure, as well as its geometric parameters, affect its vibration and damping characteristics.

Pharmacokinetic models for first-in-human dose selection of immune-activating products in oncology

Pharmacokinetic (PK) models are increasingly submitted to the FDA to support first-in-human (FIH) dose selection of immune-oncology products. To examine whether a simple PK modeling (SPM) using clearance for scaling was acceptable for dose estimation, FIH(SPM) doses were computed and compared to doses that were safely administered to patients. We concluded that the SPM approach is acceptable in FIH dose estimation, but the variables should be carefully selected for CD3 constructs. For CD3 constructs, use of 60 kg BWh, a clearance exponent of 0.75, and a targeted plasma concentration based on relevant and/or sensitive activity assays was an acceptable approach for FIH dose selection; use of 0.85 as the scaling factor is questionable at this time as it resulted in a FIH dose that was too close to the AHD for one product (7%). Immune activating mAbs were not sensitive to changes in the clearance exponent (0.75-0.85) or body weight (60-70 kg). For PD-1/PD-L1 mAbs, using products' in vitro EC50 in the model resulted in suboptimal FIH doses and clinical data of closely related products informed FIH dose selection. PK models submitted by sponsors were diverse in methods, assumptions, and variables, and the resulting FIH doses were not always optimal.

Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068-0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

How nutrient retention and TN:TP ratios depend on ecosystem state in thousands of Chinese lakes

Worldwide, anthropogenic activities threaten surface water quality by aggravating eutrophication and increasing total nitrogen to total phosphorus (TN:TP) ratios. In hydrologically connected systems, water quality management may benefit from in-ecosystem nutrient retention by preventing nutrient transport to downstream systems. However, nutrient retention may also alter TN:TP ratios with unforeseen consequences for downstream water quality. Here, we aim to increase understanding of how nutrient retention may influence nutrient transport to downstream systems to improve long-term water quality management. We analyzed lake ecosystem state, in-lake nutrient retention, and nutrient transport (ratios) for 3482 Chinese lakes using the lake process-based ecosystem model PCLake+. We compared a low climate change and sustainability-, and a high climate change and economy-focused scenario for 2050 against 2012. In both scenarios, the effect of nutrient input reduction outweighs that of temperature rise, resulting in more lakes with good ecological water quality (i.e., macrophyte-dominated) than in 2012. Generally, the sustainability-focused scenario shows a more promising future for water quality than the economy-focused scenario. Nevertheless, most lakes remain phytoplankton-dominated. The shift to more macrophyte-dominated lakes in 2050 is accompanied by higher nutrient retention fractions and less nutrient transport to downstream waterbodies. In-lake nutrient retention also alters the water's TN:TP ratio, depending on the inflow TN:TP ratio and the ecosystem state. In 2050 higher TN:TP ratios are expected in the outflows of lakes than in 2012, especially for the sustainability-focused scenario with strong TP loading reduction. However, the downstream impact of increased TN:TP ratios depends on actual nutrient loadings and the limiting nutrient in the receiving system. We conclude that nutrient input reductions, improved water quality, higher in-lake nutrient retention fractions, and lower nutrient transport to downstream waterbodies go hand in hand. Therefore, water quality management could benefit even more from nutrient pollution reduction than one would expect at first sight.

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.

Estimated Incidence of Hospitalizations and Deaths Attributable to Respiratory Syncytial Virus Infections Among Adults in Germany Between 2015 and 2019

INTRODUCTION

Respiratory syncytial virus (RSV) burden in adults is underestimated mainly due to unspecific symptoms and limited standard-of-care testing. We estimated the population-based incidence of hospitalization and mortality attributable to RSV among adults with and without risk factors in Germany.

METHODS

Weekly counts of hospitalizations and deaths for respiratory, cardiovascular, and cardiorespiratory diseases were obtained (Statutory Health Insurance database, 2015-2019). A quasi-Poisson regression model was fitted to estimate the number of hospitalizations and deaths attributable to RSV as a function of periodic and aperiodic time trends, and viral activity while allowing for potential overdispersion. Weekly counts of RSV and influenza hospitalizations in children < 2 years and adults ≥ 60 years, respectively, were used as viral activity indicators. Models were stratified by age group and risk status (defined as presence of selected comorbidities).

RESULTS

Population-based RSV-attributable hospitalization incidence rates were high among adults ≥ 60 years: respiratory hospitalizations (236-363 per 100,000 person-years) and cardiorespiratory hospitalizations (584-912 per 100,000 person-years). RSV accounted for 2-3% of all cardiorespiratory hospitalizations in this age group. The increase in cardiorespiratory hospitalization risk associated with underlying risk factors was greater in 18-44 year old persons (five to sixfold higher) than in ≥ 75 year old persons (two to threefold higher).

CONCLUSIONS

This is a first model-based study to comprehensively assess adult RSV burden in Germany. Estimated cardiorespiratory RSV hospitalization rates increased with age and were substantially higher in people with risk factors compared to those without risk factors. Our study indicates that RSV, like other respiratory viruses, contributes to both respiratory and cardiovascular hospitalizations. Effective prevention strategies are needed, especially among older adults ≥ 60 years and among adults with underlying risk factors.

Atmospheric fate and impacts of HFO-1234yf from mobile air conditioners in East Asia

HFO-1234yf (2,3,3,3-tetrafluoropropene) is being used as refrigerant to replace HFC-134a (1,1,1,2-tetrafluoroethane), a potent greenhouse gas, in mobile air conditioners. However, the environmental impacts of HFO-1234yf, which is quickly and almost completely transformed to the persistent and phytotoxic trifluoroacetic acid (TFA), is of great concern. Here, we used the nested-grid chemical transport model, GEOS-Chem, to assess the fate and environmental impacts of HFO-1234yf emissions from mobile air conditioners in East Asia. With total emissions of 30.3 Gg yr-1, the annual mean concentrations of HFO-1234yf in China, Japan, and South Korea were 4.00, 3.23, and 5.54 pptv (parts per trillion volume), respectively, and the annual deposition fluxes (dry plus wet) of TFA in these regions were 0.35, 0.48, and 0.53 kg km-2 yr-1, dominated by wet deposition. About 14 %, 13 % and 11 % of HFO-1234yf emissions were deposited as TFA in China, Japan and South Korea, respectively, i.e. a large portion of TFA was deposited in areas outside of the emission boundary regions. The TFA characteristics in Japan and South Korea was significantly influenced by emission from China, which contributions ranged from 43 % to 94 % for the TFA concentrations and 44 % to 98 % for the TFA depositions across the four seasons. This suggests that the influence of neighboring emission sources cannot be ignored when assessing the impact of HFO-1234yf emissions in individual countries.

Heavy metal (Cu2+) removal from wastewater by metal-organic framework composite adsorbent: Simulation-based- artificial neural network and response surface methodology

Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.

Recent advances in the role of dissolved organic matter during antibiotics photodegradation in the aquatic environment

The presence of residual antibiotics in the environment is a prominent issue. Photodegradation behavior is an important way of antibiotics reduction, which is closely related to dissolved organic matter (DOM) in water. The review provides an overview of the latest advancements in the field. Classification, characterization of DOM, and the dominant mechanisms for antibiotic photodegradation were discussed. Furthermore, it summarized and compared the effects of DOM on different antibiotics photodegradation. Moreover, the review comprehensively considered the factors influencing the photodegradation of antibiotics in the aquatic environment, including the characteristics of light, temperature, dosage of DOM, concentration of antibiotics, solution pH, and the presence of coexisting ions. Finally, potential directions were proposed for the development of predictive models for the photodegradation of antibiotics. Based on the review of existing literature, this paper also considered several pathways for the future study of antibiotic photodegradation. This study allows for a better understanding of the DOM's environmental role and provides important new insights into the photochemical fate of antibiotics in the aquatic environment.

Both local stability and dispersal contribute to metacommunity sensitivity to asynchronous habitat availability

The stability of isolated communities depends on the complexity of their foodwebs. However, it remains unclear how local stability interacts with dispersal in multitrophic metacommunities to shape biodiversity patterns. This lack of understanding is deeper in the more realistic frame of landscapes that exhibit non-trivial and time-varying structures. Therefore, in this study, we aim to evaluate the influence of local stabilizing factors versus dispersal in determining the sensitivity of metacommunity biodiversity to increasing asynchrony of site availability. Additionally, we assess the role of foodweb complexity and landscape structure as modulating factors. To accomplish our goals we developed a model based on random matrices for local communities, which are linked by stochastic dispersal over explicit dynamic landscapes. We ran numerical simulations and computed the effect sizes of foodweb temperature, self-limitation, dispersal ability, and all pairwise combinations, on the sensitivity of biodiversity to landscape asynchrony. In our experiments we explored gradients of species richness, foodweb connectance, number of sites, and landscape modularity. Our results showed that asynchrony among site availability periods reduced α -diversity and increased β -diversity. Asynchrony increased γ -diversity at high dispersal rates. Both local and regional stabilizing factors determined the sensitivity of metacommunities to landscape asynchrony. Local factors were more influential in landscapes with fewer sites and lower modularity, as well as in metacommunities composed of complex foodwebs. This research offers insights into the dynamics of metacommunities in dynamic landscapes, providing valuable knowledge about the interplay between local and regional factors in shaping ecological stability and species persistence.

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions

The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.

Fate-and-transport modeling of SARS-CoV-2 for rural wastewater-based epidemiology application benefit

Wastewater-based epidemiology (WBE) for the detection of agents of concern such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been prevalent in literature since 2020. The majority of reported research focuses on large urban centers with few references to rural communities. In this research the EPA-Storm Water Management Model (EPA-SWMM) software was used to describe a small sewershed and identify the effects of temperature, temperature-affected decay rate, flow rate, flush time, fecal shedding rate, and historical infection rates during the spread of the Omicron variant of the SARS-CoV-2 virus within the sewershed. Due to the sewershed's relative isolation from the rest of the city, its wastewater quality behavior is similar to a rural sewershed. The model was used to assess city wastewater sampling campaigns to best appropriate field and or lab equipment when sampling wastewater. An important aspect of the assessment was the comparison of SARS-CoV-2 quantification methods with specifically between a traditional microbiological lab (practical quantitation limit, PQL, 1 GC/mL) versus what can be known from a field method (PQL 10 GC/mL). Understanding these monitoring choices will help rural communities make decisions on how to best implement the collection and testing for WBE agents of concern. An important outcome of this work is the knowledge that it is possible to simulate a WBE agent of concern with reasonable precision, if uncertainties are incorporated into model sensitivity. These ideas could form the basis for future mixed monitoring-modeling studies that will enhance its application and therefore adoption of WBE techniques in communities of many sizes and financial means.

Leveraging the immunoinformatics approach for designing the SARS-CoV-2 omicron-specific antigenic cassette of mRNA vaccine

Emergence of SARS-CoV-2 Omicron variant has presented a significant challenge to global health, demanding rapid development of mRNA-based vaccines. The mRNA-guided vaccine platforms offer various advantages over traditional vaccine platforms. The mRNA by nature is a short-lived molecule that guides the cells to manufacture antigenic proteins. In the present work, we have created an omicron spike antigenic protein sequence characterized by base composition analysis, modeling, and docking with the ACE-2 receptor. Further, we predicted the B-cell and T-cell epitopes followed by antigenicity, toxicity, and allergenicity. Finally, the protein was reverse translated, codon-optimized, and encoding mRNA sequence was checked for its stability by predicting the secondary structures. A comprehensive examination of in-silico data revealed 628.2 as a potent antigenic candidate that was finally used in Gemcovac®-OM, a heterologous booster mRNA vaccine for COVID-19.

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.

Effect of Membrane Permeance and System Parameters on the Removal of Protein-Bound Uremic Toxins in Hemodialysis

Inadequate clearance of protein-bound uremic toxins (PBUTs) during dialysis is associated with morbidities in chronic kidney disease patients. The development of high-permeance membranes made from materials such as graphene raises the question whether they could enable the design of dialyzers with improved PBUT clearance. Here, we develop device-level and multi-compartment (body) system-level models that account for PBUT-albumin binding (specifically indoxyl sulfate and p-cresyl sulfate) and diffusive and convective transport of toxins to investigate how the overall membrane permeance (or area) and system parameters including flow rates and ultrafiltration affect PBUT clearance in hemodialysis. Our simulation results indicate that, in contrast to urea clearance, PBUT clearance in current dialyzers is mass-transfer limited: Assuming that the membrane resistance is dominant, raising PBUT permeance from 3 × 10-6 to 10-5 m s-1 (or equivalently, 3.3 × increase in membrane area from ~ 2 to ~ 6 m2) increases PBUT removal by 48% (from 22 to 33%, i.e., ~ 0.15 to ~ 0.22 g per session), whereas increasing dialysate flow rates or adding adsorptive species have no substantial impact on PBUT removal unless permeance is above ~ 10-5 m s-1. Our results guide the future development of membranes, dialyzers, and operational parameters that could enhance PBUT clearance and improve patient outcomes.

Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022

Objectives

Following the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali.

Methods

Cohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria.

Results

A total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season.

Conclusion

Determining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.

Revealing the interface chemistry of polyaniline grafted biomass via statistical modeling of multi-component dye systems: optimization, kinetics, thermodynamics, and adsorption mechanism

The growing need to examine the adsorption capabilities of innovative materials in real-world water samples has encouraged a shift from single to multicomponent adsorption systems. In this study, a novel composite, PANI-g-SM was synthesized by covalently grafting a lignocellulosic biomass, Saccharum munja (SM) with polyaniline (PANI). The as-synthesized composite was investigated for the simultaneous adsorption of cationic (Methylene Blue (MB); Crystal Violet (CV)) and anionic dyes (Reactive Red 35 (RR); Fast Green FCF (FG)) from four single components and two binary systems, MB + RR and CV + FG. Further, the effect and interaction of pH (2-11), dosage (0.01-0.04 g/10 mL), and initial concentration (0.0313 to 0.1563 mmol/L) on the elimination of dyes by PANI-g-SM were studied through a novel design of Box-Behnken of Response Surface Methodology (RSM) technique which was found to be highly useful for revealing the chemistry of interfaces in multi-component systems. The extended Langmuir model for the binary system indicated the presence of synergism, as result the maximum monolayer adsorption capacity increased by 44.44%, 645.83%, 67.88%, and 441.07% for MB, RR, CV, and FG dye, respectively. Further, the adsorption process mainly followed a pseudo-second-order kinetic model, and the thermodynamic studies revealed the exothermic nature of adsorption for RR and FG dye while endothermic for MB and CV dye, respectively with Δ G varying from - 1.68 to - 6.12 kJ/mol indicating the spontaneity of the process. Importantly, the efficacy of the composite was evaluated for the treatment of textile industry effluent highlighting its potential as an adsorbent for wastewater treatment.

Optimized design of an enthesis-mimicking suture anchor-tendon hybrid graft for mechanically robust bone-tendon repair

Repair of functionally graded biological interfaces requires joining dissimilar materials such as hard bone to soft tendon/ligament, with re-injuries/re-tears expected to be minimized by incorporating biomimicking, stress-reducing features within grafts. At bone-tendon interfaces (entheses), stress can be reduced via angled insertion, geometric flaring, mechanical gradation, and interdigitation of tissues. Here, we incorporated enthesis attributes into 3D in silico and physical models of a unique suture anchor-tendon hybrid graft (SATHG) and investigated their effects on stress reduction via finite element analyses (FEA) studies. Over 20 different simulations altering SATHG angulation, flaring, mechanical gradation, and interdigitation identified an optimal design, which included 90° angulation, 25° flaring, and a compliant (ascending then descending) mechanical gradient in SATHG's bone-to-tendon-like transitional region. This design reduced peak stress concentration factor (SCF) by 43.6 % relative to an ascending-only mechanical gradient typically used in hard-to-soft tissue engineering. To verify FEA results, SATHG models were fabricated using a photocrosslinkable bone-tendon-like polyurethane (QHM polymer) for ex vivo tensile assessment. Tensile testing showed that ultimate load (132.9 N), displacement-at-failure (1.78 mm), stiffness (135.4 N/mm), and total work-to-failure (422.1 × 10-3 J) were highest in the optimized design. Furthermore, to assess envisioned usage, SATHG pull-out testing and 6-week in vivo implantation into large, 0.5-cm segmental supraspinatus tendon defects was performed. SATHG pull-out testing showed secure bone attachment while histological assessment such as hematoxylin and eosin (H&E) together with Safranin-O staining showed biocompatibility including enthesis regeneration. This work demonstrates that engineering biomaterials with FEA-optimized, enthesis-like attributes shows potential for enhancing hard-to-soft tissue repair. STATEMENT OF SIGNIFICANCE: Successful repair of hard-to-soft tissue injuries is challenging due to high stress concentrations within bone-tendon/ligament grafts that mechanically compromise repair strength. While stress-reducing gradient biomaterials have been reported, little-to-no attention has focused on other bone-tendon/ligament interface (enthesis) features. To this end, a unique bone-tendon graft (SATHG) was developed by combining two common orthopaedic devices along with biomimetic incorporation of four enthesis-like features to reduce stress and encourage widespread clinician adoption. Notably, utilizing designs based on natural stress dissipation principles such as anchor insertion angle, geometric flaring, and mechanical gradation reduced stress by 43.6 % in silico, which was confirmed ex vivo, while in vivo studies showed SATHG's ability to support native enthesis regeneration. Thus, SATHG shows promise for hard-to-soft tissue repairs.

Flexural rigidity of pressurized model notochords in regular packing patterns

The biomechanics of embryonic notochords are studied using an elastic membrane model. An initial study varying internal pressure and stiffness ratio determines tension and geometric ratios as a function of internal pressure, membrane stiffness ratio, and cell packing pattern. A subsequent three-point bending study determines flexural rigidity as a function of internal pressure, configuration, and orientation. Flexural rigidity is found to be independent of membrane stiffness ratio. Controlling for number and volume of cells and their internal pressure, the eccentric staircase pattern of cell packing has more than double the flexural rigidity of the radially symmetric bamboo pattern. Moreover, the eccentric staircase pattern is found to be more than twice as stiff in lateral bending than in dorsoventral bending. This suggests a mechanical advantage to the eccentric WT staircase pattern of the embryonic notochord, over patterns with round cross-section.

Exploring the interplay between climate change and schistosomiasis transmission dynamics

Schistosomiasis, a neglected tropical disease caused by parasitic worms, poses a major public health challenge in economically disadvantaged regions, especially in Sub-Saharan Africa. Climate factors, such as temperature and rainfall patterns, play a crucial role in the transmission dynamics of the disease. This study presents a deterministic model that aims to evaluate the temporal and seasonal transmission dynamics of schistosomiasis by examining the influence of temperature and rainfall over time. Equilibrium states are examined to ascertain their existence and stability employing the center manifold theory, while the basic reproduction number is calculated using the next-generation technique. To validate the model's applicability, demographic and climatological data from Uganda, Kenya, and Tanzania, which are endemic East African countries situated in the tropical region, are utilized as a case study region. The findings of this study provide evidence that the transmission of schistosomiasis in human populations is significantly influenced by seasonal and monthly variations, with incidence rates varying across countries depending on the frequency of temperature and rainfall. Consequently, the region is marked by both schistosomiasis emergencies and re-emergences. Specifically, it is observed that monthly mean temperatures within the range of 22-27 °C create favorable conditions for the development of schistosomiasis and have a positive impact on the reproduction numbers. On the other hand, monthly maximum temperatures ranging from 27 to 33 °C have an adverse effect on transmission. Furthermore, through sensitivity analysis, it is projected that by the year 2050, factors such as the recruitment rate of snails, the presence of parasite egg-containing stools, and the rate of miracidia shedding per parasite egg will contribute significantly to the occurrence and control of schistosomiasis infections. This study highlights the significant influence of seasonal and monthly variations, driven by temperature and rainfall patterns, on the transmission dynamics of schistosomiasis. These findings underscore the importance of considering climate factors in the control and prevention strategies of schistosomiasis. Additionally, the projected impact of various factors on schistosomiasis infections by 2050 emphasizes the need for proactive measures to mitigate the disease's impact on vulnerable populations. Overall, this research provides valuable insights to anticipate future challenges and devise adaptive measures to address schistosomiasis transmission patterns.

Trophic transfer and biomagnification of microplastics through food webs in coastal waters: A new perspective from a mass balance model

Since the 1950s, plastic pollution and its risk have been recognized as irreversible and nonnegligible problems as global plastic production has increased. In recent years, the transport and trophic transfer of microplastics (MPs) in biotic and abiotic environment have attracted extensive attention from researchers. In this study, based on the Ecotracer module from Ecopath with Ecosim (EwE) model, the marine ranching area of Haizhou Bay, Jiangsu Province, China, was taken as a case study by linking the environmental plastic inflow with MPs in organisms to simulate the variation of MPs in the marine food web for 20 years, as well as its potential trophic transfer and biomagnification. We found that the concentration of MPs in top consumers first increased when the concentration of MPs in the environment increased, while that in primary consumers first decreased when the concentration of MPs in the environment decreased. Moreover, high TL consumers had a stronger ability to accumulate MPs, and pelagic prey fishes was the opposite. From the perspective of the food web, all functional groups showed significant trophic magnification along with the trophic level and no biodilution. Generally, there is a direct relationship between the MPs in marine organisms and environmental inflow. If the pollutants flowing into the environment can be reduced, the MP pollution problem in coastal waters will be effectively alleviated. Our research can further provide a scientific basis for ecological risk assessment and management of MPs and biodiversity protection in marine ecosystems.

Morphoelastic models discriminate between different mechanisms of left-right asymmetric stomach morphogenesis

The mechanisms by which the vertebrate stomach undergoes its evolutionarily conserved leftward bending remain incompletely understood. Although the left and right sides of the organ are known to possess different gene expression patterns and undergo distinct morphogenetic events, the physical mechanisms by which these differences generate morphological asymmetry remain unclear. Here, we develop a continuum model of asymmetric stomach morphogenesis. Using a morphoelastic framework, we investigate the morphogenetic implications of a variety of hypothetical, tissue-level growth differences between the left and right sides of a simplified tubular organ. Simulations reveal that, of the various differential growth mechanisms tested, only one category is consistent with the leftward stomach curvature observed in wild-type embryos: equal left and right volumetric growth rates, coupled with transversely isotropic tissue thinning on the left side. Simulating this mechanism in a defined region of the model over a longer period of growth leads to mature stomach-like curvatures.

A simulation-based phantom model for generating synthetic mitral valve image data-application to MRI acquisition planning

PURPOSE

Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve.

METHODS

We present a pipeline for synthetic image generation based on the combination of individual anatomical 3D models with a position-based dynamics simulation of the mitral valve closure. The corresponding images are generated using modality-specific intensity models and spatiotemporal sampling concepts. We test the applicability in the context of MRI imaging strategies for the assessment of the mitral valve. Synthetic images are generated with different strategies regarding image orientation (SAX and rLAX) and spatial sampling density.

RESULTS

The suitability of the imaging strategy is evaluated by comparing MV segmentations against ground truth annotations. The generated synthetic images were compared to ones acquired with similar parameters, and the result is promising. The quantitative analysis of annotation results suggests that the rLAX sampling strategy is preferable for MV assessment, reaching accuracy values that are comparable to or even outperform literature values.

CONCLUSION

The proposed approach provides a valuable tool for the evaluation and optimization of cardiac valve image acquisition. Its application to the use case identifies the radial image sampling strategy as the most suitable for MV assessment through MRI.

A comparison of minimum segment models for the estimation of centre of mass position and velocity for slip recovery during a bathtub transfer task

BACKGROUND

Exploring the use of minimum marker sets is important for balancing the technical quality of motion capture with challenging data collection environments and protocols. While minimum marker sets have been demonstrated to be appropriate for evaluation of some motion patterns, there is limited evidence to support model choices for abrupt, asymmetrical, non-cyclic motion such as balance disturbance during a bathtub exit task.

RESEARCH QUESTION

How effective are six models of reduced complexity for the estimation of centre of mass (COM) displacement and velocity, relative to a full-body model.

METHODS

Eight participants completed a bathtub exit task. Participants received a balance perturbation as they crossed the bathtub rim, stepping from a soapy wet bathtub to a dry floor. Six reduced models were developed from the full, 72-marker, 12 segment 3D kinematic data set. Peak displacement and velocity of the body COM, and RMSE (relative to the full-body model) for displacement and velocity of the body COM were determined for each model.

RESULTS

Main effects were observed for peak right, left, anterior, posterior, upwards and downwards motion, and peak left, anterior, posterior, upwards and downwards velocity. Time-varying (RMSE) was smaller for models including the thighs than models not containing the thighs. In contrast, inclusion of upper arm, forearm, and hand segments did not improve model performance. The model containing the sacrum marker only consistently performed the worst across peak and RMSE metrics.

SIGNIFICANCE

Findings suggest a simplified centre of mass model may adequately capture abrupt, asymmetrical, non-cyclic tasks, such as balance disturbance recovery during obstacle crossing. A reduced kinematic model should include the thighs, trunk and pelvis segments, although models that are more complex are recommended, depending on the metrics of interest.

A predictive model of motor deficit in civilian ballistic lumbosacral spine injuries

BACKGROUND CONTEXT

Civilian gunshot wounds to the spine are an increasingly common injury in the USA. A majority of the available research is focused on a military population suffering high energy missile injury. Minimal research has focused on civilian ballistic injuries to the lumbosacral spine as the available studies focus on the entire spine due to limited numbers.

PURPOSE

Characterize ballistic injuries to the lumbosacral spine and develop a model to predict the presence of neurological deficit based upon a patients presenting fracture morphology.

STUDY DESIGN

Retrospective chart review.

PATIENT SAMPLE

One hundred forty-eight consecutive patients that were presented to an urban level 1 trauma center with ballistic injures to the spine involving the levels L1-S2.

OUTCOMES MEASURES

Neurological status at presentation and final follow up using the American Spinal Injury Association (ASIA) Impairment Scale.

METHODS

IRB approval was obtained, and retrospective chart review was performed. Extracted data included patient demographics, neurological status on presentation and final follow-up, fracture morphology, assessment of stability, other associated injuries, and surgical procedures performed. Proportional analysis was performed to characterize the fractures and their associated neurological injuries. Chi-square testing was done to identify fracture characteristics associated with neurologic injury. A multiple logistical regression was performed using fracture characteristics highly associated with neurological deficit to develop a model to predict neurologic deficit. The model was then validated with a receiver operator curve.

RESULTS

Of the 148 patients, 14 patients underwent spinal surgery with the most common indication being decompression and foreign body removal. There was a high incidence of intra-abdominal injury (73.6%). Fractures were characterized by level, affected vertebral component, and spinal canal involvement. Neurological injury was classified using the ASIA scale at presentation and final follow up. Odds-ratios of vertebral fracture characteristics showed neurologic deficit was highly associated with pedicle fractures (OR=9.07 [4.14-21.54] - 95% CI), lamina fractures (OR=6.42 [3.16-13.62] - 95% CI), facet fractures (OR=5.95 [2.90-12.79] - 95% CI), intra-canal bone (OR=12.79 [5.98-29.05] - 95% CI), and an intra-canal trajectory (OR=1078 [4.48-28.98]) - 95% CI. Multiple logistic regression was performed to construct a predictive model of neurologic deficit which showed that intra-canal trajectory, pedicle fracture and facet fracture are associated with neurologic deficit. An ROC curve was made with an area of 0.849 ([0.7853 to 0.9128 - 95% CI], p<.0001) demonstrating a good model fit.

CONCLUSIONS

Ballistic injuries to the lumbosacral spine involve complex injury patterns in an often polytraumatized patient. Fractures involving the pedicle, lamina, and facet are highly associated with neurologic injury, as is a trans-canal trajectory. A patients fracture morphology can be used to predict if a neurologic deficit is present.

Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.

Venous Thromboembolism: Review of Clinical Challenges, Biology, Assessment, Treatment, and Modeling

Venous thromboembolism (VTE) is a massive clinical challenge, annually affecting millions of patients globally. VTE is a particularly consequential pathology, as incidence is correlated with extremely common risk factors, and a large cohort of patients experience recurrent VTE after initial intervention. Altered hemodynamics, hypercoagulability, and damaged vascular tissue cause deep-vein thrombosis and pulmonary embolism, the two permutations of VTE. Venous valves have been identified as likely locations for initial blood clot formation, but the exact pathway by which thrombosis occurs in this environment is not entirely clear. Several risk factors are known to increase the likelihood of VTE, particularly those that increase inflammation and coagulability, increase venous resistance, and damage the endothelial lining. While these risk factors are useful as predictive tools, VTE diagnosis prior to presentation of outward symptoms is difficult, chiefly due to challenges in successfully imaging deep-vein thrombi. Clinically, VTE can be managed by anticoagulants or mechanical intervention. Recently, direct oral anticoagulants and catheter-directed thrombolysis have emerged as leading tools in resolution of venous thrombosis. While a satisfactory VTE model has yet to be developed, recent strides have been made in advancing in silico models of venous hemodynamics, hemorheology, fluid-structure interaction, and clot growth. These models are often guided by imaging-informed boundary conditions or inspired by benchtop animal models. These gaps in knowledge are critical targets to address necessary improvements in prediction and diagnosis, clinical management, and VTE experimental and computational models.

The role of vision in sensory integration models for predicting motion perception and sickness

Users of automated vehicles will engage in other activities and take their eyes off the road, making them prone to motion sickness. To resolve this, the current paper validates models predicting sickness in response to motion and visual conditions. We validate published models of vestibular and visual sensory integration that have been used for predicting motion sickness through sensory conflict. We use naturalistic driving data and laboratory motion (and vection) paradigms, such as sinusoidal translation and rotation at different frequencies, Earth-Vertical Axis Rotation, Off-Vertical Axis Rotation, Centrifugation, Somatogravic Illusion, and Pseudo-Coriolis, to evaluate different models for both motion perception and motion sickness. We investigate the effects of visual motion perception in terms of rotational velocity (visual flow) and verticality. According to our findings, the SVCI model, a 6DOF model based on the Subjective Vertical Conflict (SVC) theory, with visual rotational velocity input is effective at estimating motion sickness. However, it does not correctly replicate motion perception in paradigms such as roll-tilt perception during centrifuge, pitch perception during somatogravic illusion, and pitch perception during pseudo-Coriolis motions. On the other hand, the Multi-Sensory Observer Model (MSOM) accurately models motion perception in all considered paradigms, but does not effectively capture the frequency sensitivity of motion sickness, and the effects of vision on sickness. For both models (SVCI and MSOM), the visual perception of rotational velocity strongly affects sickness and perception. Visual verticality perception does not (yet) contribute to sickness prediction, and contributes to perception prediction only for the somatogravic illusion. In conclusion, the SVCI model with visual rotation velocity feedback is the current preferred option to design vehicle control algorithms for motion sickness reduction, while the MSOM best predicts perception. A unified model that jointly captures perception and motion sickness remains to be developed.

Predicting current and future climate suitability for arecanut (Areca catechu L.) in India using ensemble model

Climate change has the potential to influence plant development, physiology, and distribution. Arecanut (Areca catechu L.), with its long life span of 60-70 years, thrives in a tropical habitat remains exposed to various abiotic and biotic factors. It is pertinent to comprehend the adaptation strategies of this crop towards climate change over time. The Biomod2 ensemble platform for species distribution modeling was utilized to predict the potential impact of climate change on the adaptability of the crop. The extracted study region of India was used for prediction, and the final run of 6 models ensemble includes 894 occurrence points and 9 climate variables with 80%-20% of training and validation sets. The model's outputs had area under curve (AUC) values of 0.943 and true skills statistics (TSS) of 0.741, which are regarded as accurate. The research area was categorized into five groups: very high, high, moderate, low, and very low. The examination involved assessing the shift in each category from the present to two prospective scenarios (shared socio-economic pathways; SSP 2-4.5 and SSP 5-8.5) projected for the 2050s and 2070s. A shift in the climate suitability area from 'very high' and 'high' categories to 'moderate' or 'very low' categories was observed suggesting the need for adaptive strategies to sustain the current yield levels. Amongst the regions, Karnataka state, which at present has more than 50% area under cultivation, is highly vulnerable and more area is coming under 'very low' and 'low' categories from eastern side. Meanwhile, in north eastern part of the country a shift in high suitable region from northwest to southwest is observed. Overall, the model prediction suggests that some parts of west and south interior regions of the country warrant immediate consideration in order to adapt to future climate change, whereas some part of north east can be considered for future cultivation.

A general-purpose tool for modeling multifunctional thin porous media (POREnet): From pore network to effective property tensors

POREnet, a novel approach to model effective properties of thin porous media, TPM, is presented. The methodology allows the extraction of local effective property tensors by volume averaging from discrete pore networks, PNs, built on the tessellated continuum space of a TPM. The gradient theorem is used to describe 3D transport in bulk tessellated space, providing an appropriate metric to normalize network fluxes. Implemented effective transport properties include diffusivity, permeability, solid-phase conductivity, and entry capillary pressure and contact angle under two-phase conditions, considering multi-component materials with several solid phases and local contact resistances. Calculated property tensors can be saved on 3D image stacks, where interfacial and sub-CV scale features can be added before exporting data to CFD meshes for simulation. Overall, POREnet provides a general-purpose, versatile methodology for modeling TPM in an ample range of conditions within a single CFD framework. Among other advantages, coupling of PN and continuum models at TPM-channel interfaces is simplified, interfacial contact resistances can be included using robin boundary conditions, and transient multiphysics simulations can be implemented more easily using CFD. The code is tested against a miscellaneousness of examples extracted from electrochemical applications.

Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it is not biodegradable and causes environmental issues. One of the most effective ways is recycling those wastes or using them as a replacement for normal aggregate in the concrete mixture, which has high impact resistance and toughness; thus, it will be a good choice. In this study, 135 data were collected from previous literature to develop a model for the prediction of rubberized concrete compressive strength; the database comprised different mixture proportions, the maximum size of the rubber (1-40 mm), and the rubber percentage (0-100%) replacing natural fine and coarse aggregates were among the input parameters in addition to cement content (380-500 kg/m3) water content (129-228 kg/m3), fine aggregate content (0-925 kg/m3), coarse aggregate content (0-1303 kg/m3), and curing time of the samples (1-96 Days); then the collected data were used in developing Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) Models for predicting compressive strength (CS) of rubberized concrete. The parametric analysis reveals that as the maximum rubber size increases, the reduction in compressive strength becomes more pronounced. Notably, this strength decline is more significant when rubber replaces coarse aggregate than its replacement of fine aggregate. Among the input parameters considered, it is evident that the fine aggregate content exerts the most substantial influence on the compressive strength of rubberized concrete. Its impact on predicting compressive strength surpasses other factors, with the concrete samples' curing time ranking second in importance. According to the assessment tools, the ANN model performed better than other developed models, with high R2 and lower RMSE, MAE, SI, and MAPE. Additionally, ANN and MARS models predicted the CS of different sizes better than MEP and NLR models. Subsequently, we employed the collected data to develop predictive models using Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) techniques to forecast the compressive strength (CS) of rubberized concrete. The statistical analysis tools assessed the performance of these developed models through various evaluation criteria, including the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), and Mean Absolute Percentage Error (MAPE). In summary, our study underscores the efficacy of recycling rubber materials in concrete production. It presents a powerful predictive model for assessing the compressive strength of rubberized concrete, with the ANN model standing out as the most accurate and reliable choice for this purpose.