Nanocrystal Assemblies: Current Advances and Open Problems.

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

Estimating Position-Dependent and Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories: Existing Methods and Future Outlook.

Confinement can substantially alter the physicochemical properties of materials by breaking translational isotropy and rendering all physical properties position-dependent. Molecular dynamics (MD) simulations have proven instrumental in characterizing such spatial heterogeneities and probing the impact of confinement on materials' properties. For static properties, this is a straightforward task and can be achieved via simple spatial binning. Such an approach, however, cannot be readily applied to transport coefficients due to lack of natural extensions of autocorrelations used for their calculation in the bulk. The prime example of this challenge is diffusivity, which, in the bulk, can be readily estimated from the particles' mobility statistics, which satisfy the Fokker-Planck equation. Under confinement, however, such statistics will follow the Smoluchowski equation, which lacks a closed-form analytical solution. This brief review explores the rich history of estimating profiles of the diffusivity tensor from MD simulations and discusses various approximate methods and algorithms developed for this purpose. Besides discussing heuristic extensions of bulk methods, we overview more rigorous algorithms, including kernel-based methods, Bayesian approaches, and operator discretization techniques. Additionally, we outline methods based on applying biasing potentials or imposing constraints on tracer particles. Finally, we discuss approaches that estimate diffusivity from mean first passage time or committor probability profiles, a conceptual framework originally developed in the context of collective variable spaces describing rare events in computational chemistry and biology. In summary, this paper offers a concise survey of diverse approaches for estimating diffusivity from MD trajectories, highlighting challenges and opportunities in this area.

Using a non-precious metal catalyst for long-term enhancement of methane production in a zero-gap microbial electrosynthesis cell.

Microbial electrosynthesis (MES) cells exploit the ability of microbes to convert CO2 into valuable chemical products such as methane and acetate, but high rates of chemical production may need to be mediated by hydrogen and thus require a catalyst for the hydrogen evolution reaction (HER). To avoid the usage of precious metal catalysts and examine the impact of the catalyst on the rate of methane generation by microbes on the electrode, we used a carbon felt cathode coated with NiMo/C and compared performance to a bare carbon felt or a Pt/C-deposited cathode. A zero-gap configuration containing a cation exchange membrane was developed to produce a low internal resistance, limit pH changes, and enhance direct transport of H2 to microorganisms on the biocathode. At a fixed cathode potential of -1 V vs Ag/AgCl, the NiMo/C biocathode enabled a current density of 23 ± 4 A/m2 and a high methane production rate of 4.7 ± 1.0 L/L-d. This performance was comparable to that using a precious metal catalyst (Pt/C, 23 ± 6 A/m2, 5.4 ± 2.8 L/L-d), and 3-5 times higher than plain carbon cathodes (8 ± 3 A/m2, 1.0 ± 0.4 L/L-d). The NiMo/C biocathode was operated for over 120 days without observable decay or severe cathode catalyst leaching, reaching an average columbic efficiency of 53 ± 9 % based on methane production under steady state conditions. Analysis of microbial community on the biocathode revealed the dominance of the hydrogenotrophic genus Methanobacterium (∼40 %), with no significant difference found for biocathodes with different materials. These results demonstrated that HER catalysts improved rates of methane generation through facilitating hydrogen gas evolution to an attached biofilm, and that the long-term enhancement of methane production in MES was feasible using a non-precious metal catalyst and a zero-gap cell design.

Effect of Pressure on the Conformational Landscape of Human γD-Crystallin from Replica Exchange Molecular Dynamics Simulations.

Human γD-crystallin belongs to a crucial family of proteins known as crystallins located in the fiber cells of the human lens. Since crystallins do not undergo any turnover after birth, they need to possess remarkable thermodynamic stability. However, their sporadic misfolding and aggregation, triggered by environmental perturbations or genetic mutations, constitute the molecular basis of cataracts, which is the primary cause of blindness in the globe according to the World Health Organization. Here, we investigate the impact of high pressure on the conformational landscape of wild-type HγD-crystallin using replica exchange molecular dynamics simulations augmented with principal component analysis. We find pressure to have a modest impact on global measures of protein stability, such as root-mean-square displacement and radius of gyration. Upon projecting our trajectories along the first two principal components from principal component analysis, however, we observe the emergence of distinct free energy basins at high pressures. By screening local order parameters previously shown or hypothesized as markers of HγD-crystallin stability, we establish correlations between a tyrosine-tyrosine aromatic contact within the N-terminal domain and the protein's end-to-end distance with projections along the first and second principal components, respectively. Furthermore, we observe the simultaneous contraction of the hydrophobic core and its intrusion by water molecules. This exploration sheds light on the intricate responses of HγD-crystallin to elevated pressures, offering insights into potential mechanisms underlying its stability and susceptibility to environmental perturbations, crucial for understanding cataract formation.

Prostatic adenocarcinoma: molecular underpinnings and treatment-related options.

Prostate cancer is heterogeneous with varied pathologic features and presents with a wide spectrum of clinical manifestations from indolent to advanced cancer. Interrogation of the molecular landscape of prostate cancer has unveiled the complex genomic alterations in these tumors, which significantly impacts tumor biology. The documented array of chromosomal alterations, gene fusions, and epigenetic changes not only play a crucial role in oncogenesis and disease progression, but also impacts response and resistance to various therapeutic modalities. Various gene expression assays have been developed and are currently recommended in aiding clinical decision making in these clinically and molecularly heterogeneous cancer. In this review, we provide an overview of the molecular underpinnings of prostate cancer, and briefly review the current status of molecular testing and therapeutic options in the management of these tumors.

Dental service utilization and the COVID-19 pandemic, a micro-data analysis.

BACKGROUND: Global crises and disease pandemics, such as COVID-19, negatively affect dental care utilization by several factors, such as infection anxiety, disrupted supply chains, economic contraction, and household income reduction. Exploring the pattern of this effect can help policy makers to be prepared for future crises. The present study aimed to investigate the financial impact of COVID-19 disruptions on dental service utilization. METHODS: Data on the number of dental services offered in Dental School Clinics of Tehran University of Medical Sciences was collected over a period of two years, before and after the initial COVID-19 outbreak in Iran. School of Dentistry operates two clinics; one with competitive service fees and one with subsidies. Regression analyses were performed to determine the effect of the pandemic on the number of dental services divided by dental treatment groups and these clinics. The analyses were adjusted for seasonal patterns and the capacity of the clinics. RESULTS: There was a significant drop in dental services offered in both clinics across all dental groups in the post-COVID period (on average, 77 (39.44%) fewer services per day). The majority of the procedure loss happened in the Private clinic. Adjusting for seasonal patterns and the service capacity, regression results documented 54% and 12% service loss in Private and Subsidized clinics following the pandemic, respectively. Difference-in-difference analysis documented that the Subsidized clinic performed 40% more treatments than the Private clinic in the post-COVID period. CONCLUSIONS: Pandemic -reduction in dental care utilization could have long-term ramifications for the oral health of the population, and policymakers need to provide supportive packages to the affected segments of the economy to reverse this trend.

Ideal conductor/dielectric model (ICDM): A generalized technique to correct for finite-size effects in molecular simulations of hindered ion transport.

Molecular simulations serve as indispensable tools for investigating the kinetics and elucidating the mechanism of hindered ion transport across nanoporous membranes. In particular, recent advancements in advanced sampling techniques have made it possible to access translocation timescales spanning several orders of magnitude. In our prior study [Shoemaker et al., J. Chem. Theory Comput. 18, 7142 (2022)], we identified significant finite size artifacts in simulations of pressure-driven hindered ion transport through nanoporous graphitic membranes. We introduced the ideal conductor model, which effectively corrects for such artifacts by assuming the feed to be an ideal conductor. In the present work, we introduce the ideal conductor dielectric model (Icdm), a generalization of our earlier model, which accounts for the dielectric properties of both the membrane and the filtrate. Using the Icdm model substantially enhances the agreement among corrected free energy profiles obtained from systems of varying sizes, with notable improvements observed in regions proximate to the pore exit. Moreover, the model has the capability to consider secondary ion passage events, including the transport of a co-ion subsequent to the traversal of a counter-ion, a feature that is absent in our original model. We also investigate the sensitivity of the new model to various implementation details. The Icdm model offers a universally applicable framework for addressing finite size artifacts in molecular simulations of ion transport. It stands as a significant advancement in our quest to use molecular simulations to comprehensively understand and manipulate ion transport processes through nanoporous membranes.

A data-driven approach for timescale decomposition of biochemical reaction networks.

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.

Divergence among Local Structure, Dynamics, and Nucleation Outcome in Heterogeneous Nucleation of Close-Packed Crystals.

Heterogeneous crystal nucleation is the dominant mechanism of crystallization in most systems, yet its underlying physics remains an enigma. While emergent interfacial crystalline order precedes heterogeneous nucleation, its importance in the nucleation mechanism is unclear. Here, we use path sampling simulations of two model systems to demonstrate that crystalline order in its traditional sense is not predictive of the outcome of the heterogeneous nucleation of close-packed crystals. Consequently, structure-based collective variables (CVs) that reliably describe homogeneous nucleation can be poor descriptors of heterogeneous nucleation. This divergence between structure and nucleation outcome is accompanied by an intriguing dynamical anomaly, wherein low-coordinated crystalline particles outpace their liquid-like counterparts. We use committor analysis, high-throughput screening, and machine learning to devise CV optimization strategies and present suitable structural heuristics within the metastable fluid for CV prescreening. Employing such optimized CVs is pivotal for properly characterizing the mechanism of heterogeneous nucleation in metallic and colloidal systems.

Correlations in Charged Multipore Systems: Implications for Enhancing Selectivity and Permeability in Nanoporous Membranes.

Nanoporous membranes have emerged as powerful tools for diverse applications, including gas separation and water desalination. Achieving high permeability for desired molecules alongside exceptional rejection of other species presents a significant design challenge. One potential strategy involves optimizing the chemistry and geometry of isolated nanopores to enhance permeability and selectivity while maximizing their density within a membrane. However, the impact of the pore proximity on membrane performance remains an open question. Through path sampling simulations of model graphitic membranes with multiple subnanometer pores, we reveal that nanoscale proximity between pores detrimentally affects water permeability and salt rejection. Specifically, counterion transport is decelerated, while co-ion transport is accelerated, due to direct interactions among water molecules, salt ions, and the dipoles within neighboring pores. Notably, the observed ionic transport time scales significantly deviate from established theories such as the access resistance model but are well explained using the simple phenomenological model that we develop in this work. We use this model to prescreen and optimize pore arrangements that elicit minimal correlations at a target pore density. These findings deepen our understanding of multipore systems, informing the rational design of nanoporous membranes for enhanced separation processes such as water desalination. They also shed light on the physiology of biological cells that employ ion channel proteins to modulate ion transport and reversal potentials.

Inferred regulons are consistent with regulator binding sequences in E. coli.

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery.

The Impact of the COVID-19 Pandemic and Lockdown on Adult Foot and Ankle Fractures Presenting to the Largest Trust in the United Kingdom.

Background The COVID-19 pandemic induced unprecedented changes in medical practices, prompting a reassessment of their impact on adult foot and ankle fractures within the National Health Service (NHS). This study employs a retrospective observational approach, leveraging the Pathpoint™ eTrauma platform for a comprehensive analysis of prospectively collected data. Methods Data encompassing weekly fracture incidence, weekly surgical procedures, patient demographics, and mean wait time from injury presentation to surgery were systematically evaluated. The study population included all adults (18+) admitted during five distinct periods: pre-pandemic, national lockdown 1, post-lockdown, national lockdown 2, and national lockdown 3. Results An analysis of 434 foot and ankle fractures revealed that national lockdown 1 exhibited the lowest fracture incidence (4.97 per week) and surgeries performed (4.77 per week), reflecting a notable reduction in trauma cases and elective procedures. Conversely, post-lockdown displayed the highest fracture incidence (7.46 per week) and surgeries performed (6.31 per week), suggesting a resurgence in both trauma and elective surgical activities. The pre-pandemic cohort, characterized by the highest mean age (51.98 years) and mean wait time (8.74 days), served as a temporal baseline. Conclusion While the incidence of fractures decreased during all three national lockdowns compared to pre-pandemic or post-lockdown periods, a gradual increase was observed in subsequent lockdowns. Notably, mean wait times showed a significant reduction, reaching the lowest point (5.79 days) during national lockdown 3. These findings underscore the complex interplay between pandemic-related disruptions, evolving guidelines, and adaptive measures within the healthcare system, influencing the dynamics of foot and ankle fracture management.

Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories.

Confinement breaks translational and rotational symmetry in materials and makes all physical properties functions of position. Such spatial variations are key to modulating material properties at the nanoscale, and characterizing them accurately is therefore an intense area of research in the molecular simulations community. This is relatively easy to accomplish for basic mechanical observables. Determining spatial profiles of transport properties, such as diffusivity, is, however, much more challenging, as it requires calculating position-dependent autocorrelations of mechanical observables. In our previous paper (Domingues, T.S.; Coifman, R.; Haji-Akbari, A. J. Phys. Chem. B 2023, 127, 5273 10.1021/acs.jpcb.3c00670), we analytically derive and numerically validate a set of filtered covariance estimators (FCEs) for quantifying spatial variations of the diffusivity tensor from stochastic trajectories. In this work, we adapt these estimators to extract diffusivity profiles from MD trajectories and validate them by applying them to a Lennard-Jones fluid within a slit pore. We find our MD-adapted estimator to exhibit the same qualitative features as its stochastic counterpart, as it accurately estimates the lateral diffusivity across the pore while systematically underestimating the normal diffusivity close to hard boundaries. We introduce a conceptually simple and numerically efficient correction scheme based on simulated annealing and diffusion maps to resolve the latter artifact and obtain normal diffusivity profiles that are consistent with the self-part of the van Hove correlation functions. Our findings demonstrate the potential of this MD-adapted estimator in accurately characterizing spatial variations of diffusivity in confined materials.

A data-driven approach for timescale decomposition of biochemical reaction networks.

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.

Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT).

Background: Intraoperative hypotension is associated with organ injury. Current intraoperative arterial pressure management is mainly reactive. Predictive haemodynamic monitoring may help clinicians reduce intraoperative hypotension. The Acumen™ Hypotension Prediction Index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) was developed to predict hypotension. We built up the European multicentre, prospective, observational EU HYPROTECT Registry to describe the incidence, duration, and severity of intraoperative hypotension when using HPI-software monitoring in patients having noncardiac surgery. Methods: We enrolled 749 patients having elective major noncardiac surgery in 12 medical centres in five European countries. Patients were monitored using the HPI-software. We quantified hypotension using the time-weighted average MAP <65 mm Hg (primary endpoint), the proportion of patients with at least one ≥1 min episode of a MAP <65 mm Hg, the number of ≥1 min episodes of a MAP <65 mm Hg, and duration patients spent below a MAP of 65 mm Hg. Results: We included 702 patients in the final analysis. The median time-weighted average MAP <65 mm Hg was 0.03 (0.00-0.20) mm Hg. In addition, 285 patients (41%) had no ≥1 min episode of a MAP <65 mm Hg; 417 patients (59%) had at least one. The median number of ≥1 min episodes of a MAP <65 mm Hg was 1 (0-3). Patients spent a median of 2 (0-9) min below a MAP of 65 mm Hg. Conclusions: The median time-weighted average MAP <65 mm Hg was very low in patients in this registry. This suggests that using HPI-software monitoring may help reduce the duration and severity of intraoperative hypotension in patients having noncardiac surgery.

Influence of different modes of exercise training on inflammatory markers in older adults with and without chronic diseases: A systematic review and meta-analysis.

INTRODUCTION: Ageing can be accompanied by increased inflammation, which contributes to the development of sarcopenia. Exercise training could be effective for preventing sarcopenia and mitigate inflammation and thus a viable intervention in ageing. Therefore, we performed a systematic review and meta-analysis to investigate the effects of exercise training on markers of inflammation including interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) in older adults (≥65 years). Exercise-based interventions are most successful in preventing the decline in skeletal muscle mass and in preserving or ameliorating functional capacities with increasing age. METHOD: PubMed and Web of Science were searched through to December 2021 using "exercise", "inflammatory markers", "elderly", and "randomized controlled trial" to identify randomized trials evaluating the effects of exercise training versus control groups on IL-6, TNF-α, and CRP in older adults with mean ages ≥ 65 yrs. Standardized mean differences (SMD) and 95% confidence intervals (95% CIs) were determined using random effects models. RESULTS: Forty studies involving 49 trials and 1,898 older adults were included in the meta-analysis. Overall, exercise training reduced IL-6 [-0.17 (95% CI -0.32 to -0.02), p = 0.02], TNF-α [-0.30 (95% CI -0.46 to -0.13), p = 0.001], and CRP [-0.45 (95% CI -0.61 to -0.29), p = 0.001]. Subgroup analyses showed that IL-6 was reduced significantly by combined training, TNF-α by aerobic training, and CRP by aerobic, resistance, and combined training. In addition, exercise training reduced IL-6 and TNF-α in older adults with chronic diseases, and CRP in older adults with and without chronic diseases. CONCLUSION: The current results highlight that exercise training, regardless of exercise type, has small to moderate beneficial effects on markers of inflammation in older adults, particularly in those with chronic diseases.

Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Stochastic Trajectories.

Materials under confinement can possess properties that deviate considerably from their bulk counterparts. Indeed, confinement makes all physical properties position-dependent and possibly anisotropic, and characterizing such spatial variations and directionality has been an intense area of focus in experimental and computational studies of confined matter. While this task is fairly straightforward for simple mechanical observables, it is far more daunting for transport properties such as diffusivity that can only be estimated from autocorrelations of mechanical observables. For instance, there are well established methods for estimating diffusivity from experimentally observed or computationally generated trajectories in bulk systems. No rigorous generalizations of such methods, however, exist for confined systems. In this work, we present two filtered covariance estimators for computing anisotropic and position-dependent diffusivity tensors and validate them by applying them to stochastic trajectories generated according to known diffusivity profiles. These estimators can accurately capture spatial variations that span over several orders of magnitude and that assume different functional forms. Our kernel-based approach is also very robust to implementation details such as the localization function and time discretization and performs significantly better than estimators that are solely based on local covariance. Moreover, the kernel function does not have to be localized and can instead belong to a dictionary of orthogonal functions. Therefore, the proposed estimator can be readily used to obtain functional estimates of diffusivity rather than a tabulated collection of pointwise estimates. Nonetheless, the susceptibility of the proposed estimators to time discretization is higher at the immediate vicinity of hard boundaries. We demonstrate this heightened susceptibility to be common among all covariance-based estimators.