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Background: The microgravity environment has a direct impact on the cardiovascular system due to the fluid shift and weightlessness that results in cardiac dysfunction, vascular remodeling, and altered Cardiovascular autonomic modulation (CAM), deconditioning and poor performance on space activities, ultimately endangering the health of astronauts. Objective: This study aimed to identify the acute and chronic effects of microgravity and Earth analogues on cardiovascular anatomy and function and CAM. Methods: CINAHL, Cochrane Library, Scopus, Science Direct, PubMed, and Web of Science databases were searched. Outcomes were grouped into cardiovascular anatomic, functional, and autonomic alterations, and vascular remodeling. Studies were categorized as Spaceflight (SF), Chronic Simulation (CS), or Acute Simulation (AS) based on the weightlessness conditions. Meta-analysis was performed for the most frequent outcomes. Weightlessness and control groups were compared. Results: 62 articles were included with a total of 963 participants involved. The meta-analysis showed that heart rate increased in SF [Mean difference (MD) = 3.44; p = 0.01] and in CS (MD = 4.98; p < 0.0001), whereas cardiac output and stroke volume decreased in CS (MD = -0.49; p = 0.03; and MD = -12.95; p < 0.0001, respectively), and systolic arterial pressure decreased in AS (MD = -5.20; p = 0.03). According to the qualitative synthesis, jugular vein cross-sectional area (CSA) and volume were greater in all conditions, and SF had increased carotid artery CSA. Heart rate variability and baroreflex sensitivity, in general, decreased in SF and CS, whereas both increased in AS. Conclusion: This review indicates that weightlessness impairs the health of astronauts during and after spaceflight, similarly to the effects of aging and immobility, potentially increasing the risk of cardiovascular diseases. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42020215515.

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The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

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In this paper we examine several definitions of vaccine efficacy (VE) that we found in the literature, for diseases that express themselves in outbreaks, that is, when the force of infection grows in time, reaches a maximum and then vanishes. The fact that the disease occurs in outbreaks results in several problems that we analyse. We propose a mathematical model that allows the calculation of VE for several scenarios. Vaccine trials usually needs a large number of volunteers that must be enrolled. Ideally, all volunteers should be enrolled in approximately the same time, but this is generally impossible for logistic reasons and they are enrolled in a fashion that can be replaced by a continuous density function (for example, a Gaussian function). The outbreak can also be replaced by a continuous density function, and the use of these density functions simplifies the calculations. Assuming, for example Gaussian functions, one of the problems one can immediately notice is that the peak of the two curves do not occur at the same time. The model allows us to conclude: First, the calculated vaccine efficacy decreases when the force of infection increases; Second, the calculated vaccine efficacy decreases when the gap between the peak in the force of infection and the peak in the enrollment rate increases; Third, different trial protocols can be simulated with this model; different vaccine efficacy definitions can be calculated and in our simulations, all result are approximately the same. The final, and perhaps most important conclusion of our model, is that vaccine efficacy calculated during outbreaks must be carefully examined and the best way we can suggest to overcome this problem is to stratify the enrolled volunteer's in a cohort-by-cohort basis and do the survival analysis for each cohort, or apply the Cox proportional hazards model for each cohort.

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The Biswas-Chatterjee-Sen (BChS) model of opinion dynamics has been studied on three-dimensional Solomon networks by means of extensive Monte Carlo simulations. Finite-size scaling relations for different lattice sizes have been used in order to obtain the relevant quantities of the system in the thermodynamic limit. From the simulation data it is clear that the BChS model undergoes a second-order phase transition. At the transition point, the critical exponents describing the behavior of the order parameter, the corresponding order parameter susceptibility, and the correlation length, have been evaluated. From the values obtained for these critical exponents one can confidently conclude that the BChS model in three dimensions is in a different universality class to the respective model defined on one- and two-dimensional Solomon networks, as well as in a different universality class as the usual Ising model on the same networks.

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Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.

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Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

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Due to their physical properties including high thermal stability, very low vapor pressure, and high microwave absorption, ionic liquids have attracted great attention as solvents for the synthesis of nanomaterials, being considered as greener alternatives to traditional solvents. While usual solvents often need additives like surfactants, polymers, or other ligands to avoid nanoparticle coalescence, some ionic liquids can stabilize nanoparticles in dispersion without any additive. In order to quantify how the ionic liquids can affect both the aggregation thermodynamics and kinetics, molecular dynamics simulations were performed to simulate the evolution of concentrated dispersions and to compute the potential of mean force between nanoparticles of both hydrophilic and hydrophobic natures in two imidazolium-based ionic liquids, which differ from each other by the length of the cation alkyl group. Depending on the nature of the nanoparticle, structured layers of the polar and apolar regions of the ionic liquid can be formed close to its surface, and those layers lead to activation barriers for dispersed particles to get in contact. If the alkyl group of the ionic liquid is long enough to lead to domain segregation between the ionic and apolar portions of the solvent, the layered structure around the particle becomes more structured and propagates several nanometers away from its surface. This leads to stronger barriers close to the contact and also multiple barriers at larger distances that result from the unfavorable superposition of solvent layers of opposing nature when the nanoparticles approach each other. Those long-range solvent-mediated forces not only provide kinetic stability to dispersions but also affect their dynamics and lead to a long-range ordering between dispersed particles that can be explored as a template for the synthesis of complex materials.

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Herein, graphene oxide was used as the highly efficient phenazopyridine adsorbent from aqueous medium, synthetic, and human urine. The nanoadsorbent was characterized by different instrumental techniques. The adsorption capacity (1253.17 mg g-1) was reached at pH 5.0, using an adsorbent dosage of 0.125 g L-1 at 298 K. The Sips and Langmuir described the equilibrium data well. At the same time, the pseudo-second order was more suitable for fitting the kinetic data. Thermodynamic parameters revealed the exothermic nature of adsorption with an increase in randomness at the solid-liquid interface. The magnitude of the enthalpy variation value indicates that the process involves the physisorption phenomenon. At the same time, ab initio molecular dynamics data corroborated with the thermodynamic results, indicating that adsorbent and adsorbate establish hydrogen bonds through the amine groups (adsorbate) and hydroxyl groups on the adsorbent surface (weak interactions). Electrostatic interactions are also involved. Additionally, the adsorption assays conducted in simulated medium and human urine showed the excellent performance of adsorbent material to remove the drug in real concentrations excreted by the kidneys (removal values higher than 60%).

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INTRODUCTION: The relationship between immediate symptom control, reliever medication use and exacerbation risk on treatment response and factors that modify it have not been assessed in an integrated manner. Here we apply simulation scenarios to evaluate the effect of individual baseline characteristics on treatment response in patients with moderate-severe asthma on regular maintenance dosing monotherapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR). METHODS: Reduction in reliever medication use (puffs/24 h), change in symptom control scores (ACQ-5), and annualised exacerbation rate over 12 months were simulated in a cohort of patients with different baseline characteristics (e.g. time since diagnosis, asthma control questionnaire (ACQ-5) symptom score, smoking status, body mass index (BMI) and sex) using drug-disease models derived from large phase III/IV clinical studies. RESULTS: Simulation scenarios show that being a smoker, having higher baseline ACQ-5 and BMI, and long asthma history is associated with increased reliever medication use (p < 0.01). This increase correlates with a higher exacerbation risk and higher ACQ-5 scores over the course of treatment, irrespective of the underlying maintenance therapy. Switching non-responders to ICS monotherapy to combination therapy after 3 months resulted in immediate reduction in reliever medication use (i.e. 1.3 vs. 1.0 puffs/24 h for FP/SAL and BUD/FOR, respectively). In addition, switching patients with ACQ-5 > 1.5 at baseline to FP/SAL resulted in 34% less exacerbations than those receiving regular dosing BUD/FOR (p < 0.01). CONCLUSIONS: We have identified baseline characteristics of patients with moderate to severe asthma that are associated with greater reliever medication use, poor symptom control and higher exacerbation risk. Moreover, the effects of different inhaled corticosteroid (ICS)/long-acting beta agonist (LABA) combinations vary significantly when considering long-term treatment performance. These factors should be considered in clinical practice as a basis for personalised management of patients with moderate-severe asthma symptoms.

In this study we looked at how different factors affect the response to asthma treatment in people with moderate to severe asthma who are taking regular medication. Specifically, we wanted to quantify how much asthma duration, differences in the degree of symptom control and lung function, as well as smoking habit, body weight, and sex influence how well someone responds to regular maintenance therapy. Using computer simulations based on models obtained from data in a large patient population with moderate­severe asthma, we explored scenarios that reflect real-life management of patients undergoing treatment with inhaled corticosteroids alone or in combination with long-acting beta agonists over a 12-month period. We looked at how much reliever inhaler they use, how well they rate their asthma control, and how often they have asthma attacks. By considering these results together, we evaluated how well the treatments work on ongoing symptoms and/or reduce the risk of future asthma attacks. Our simulations showed that smokers, people with higher asthma symptom scores, who are obese, and have a longer history of asthma tend to use their reliever inhalers more often. This was linked to a higher risk of having asthma attacks and worse symptom control. Switching those patients who do not respond well to their initial treatment with corticosteroid to combination therapy reduced how much reliever inhaler they need. Also, the effects of fluticasone propionate/salmeterol combination therapy were greater than budesonide/formoterol. In conclusion, our study found that certain patient characteristics can predict how well someone responds to asthma treatment.

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The MC1R protein is a receptor found in melanocytes that plays a role in melanin synthesis. Mutations in this protein can impact hair color, skin tone, tanning ability, and increase the risk of skin cancer. The MC1R protein is activated by the alpha-melanocyte-stimulating hormone (α-MSH). Previous studies have shown that mutations affect the interaction between MC1R and α-MSH; however, the mechanism behind this process is poorly understood. Our study aims to shed light on this mechanism using molecular dynamics (MD) simulations to analyze the Asp84Glu and Asp294His variants. We simulated both the wild-type (WT) protein and the mutants with and without ligand. Our results reveal that mutations induce unique conformations during state transitions, hindering the switch between active and inactive states and decreasing cellular levels of cAMP. Interestingly, Asp294His showed increased ligand affinity but decreased protein activity, highlighting that tighter binding does not always lead to increased activation. Our study provides insights into the molecular mechanisms underlying the impact of MC1R mutations on protein activity.

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Due to the low temperature, the Antarctic marine environment is challenging for protein functioning. Cold-adapted organisms have evolved proteins endowed with higher flexibility and lower stability in comparison to their thermophilic homologs, resulting in enhanced reaction rates at low temperatures. The Antarctic bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) genome is one of the few examples of coexistence of multiple hemoglobin genes encoding, among others, two constitutively transcribed 2/2 hemoglobins (2/2Hbs), also named truncated Hbs (TrHbs), belonging to the Group II (or O), annotated as PSHAa0030 and PSHAa2217. In this work, we describe the ligand binding kinetics and their interrelationship with the dynamical properties of globin Ph-2/2HbO-2217 by combining experimental and computational approaches and implementing a new computational method to retrieve information from molecular dynamic trajectories. We show that our approach allows us to identify docking sites within the protein matrix that are potentially able to transiently accommodate ligands and migration pathways connecting them. Consistently with ligand rebinding studies, our modeling suggests that the distal heme pocket is connected to the solvent through a low energy barrier, while inner cavities play only a minor role in modulating rebinding kinetics.

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The oxidative phase of the pentose phosphate pathway (PPP) involving the enzymes glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconolactonase (6PGL), and 6-phosphogluconate dehydrogenase (6PGDH), is critical to NADPH generation within cells, with these enzymes catalyzing the conversion of glucose-6-phosphate (G6P) into ribulose-5-phosphate (Ribu5-P). We have previously studied peroxyl radical (ROO•) mediated oxidative inactivation of E. coli G6PDH, 6PGL, and 6PGDH. However, these data were obtained from experiments where each enzyme was independently exposed to ROO•, a condition not reflecting biological reality. In this work we investigated how NADPH production is modulated when these enzymes are jointly exposed to ROO•. Enzyme mixtures (1:1:1 ratio) were exposed to ROO• produced from thermolysis of 100 mM 2,2'-azobis(2-methylpropionamidine) dihydrochloride (AAPH). NADPH was quantified at 340 nm, and protein oxidation analyzed by liquid chromatography with mass spectrometric detection (LC-MS). The data obtained were rationalized using a mathematical model. The mixture of non-oxidized enzymes, G6P and NADP+ generated ∼175 µM NADPH. Computational simulations showed a constant decrease of G6P associated with NADPH formation, consistent with experimental data. When the enzyme mixture was exposed to AAPH (3 h, 37 °C), lower levels of NADPH were detected (∼100 µM) which also fitted with computational simulations. LC-MS analyses indicated modifications at Tyr, Trp, and Met residues but at lower concentrations than detected for the isolated enzymes. Quantification of NADPH generation showed that the pathway activity was not altered during the initial stages of the oxidations, consistent with a buffering role of G6PDH towards inactivation of the oxidative phase of the pathway.

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In this work we report magnetic properties of the orthorhombic perovskite Lu0.9Sr0.1Cr0.5Fe0.5O3synthesized by a wet chemical method. As in LuCr0.5Fe0.5O3the compound with Sr shows the magnetization reversal phenomenon, but the magnetic order and the compensation temperature occur at higher temperatures. Interestingly, in M vs H curves a hysteresis loop is observed when Cr4+and Cr3+ions coexist as a consequence of the aliovalent substitution of Lu3+by Sr2+in the B sites of the perovskite. To explain this behavior, we performed numerical simulations with a magnetic model for Lu1-xSrxCr0.5Fe0.5O3perovskites withx= 0 andx= 0.1. We found that the ferromagnetic coupling of Fe3+and Cr4+through superexchange interactions (according the empiric Goodenough-Kanamori-Anderson rules) increases the magnetization at high fields and that the presence of ferromagnetic clusters explains the hysteretic behavior found in simulations.

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We study a five-species cyclic system wherein individuals of one species strategically adapt their movements to enhance their performance in the spatial rock-paper-scissors game. Environmental cues enable the awareness of the presence of organisms targeted for elimination in the cyclic game. If the local density of target organisms is sufficiently high, individuals move towards concentrated areas for direct attack; otherwise, they employ an ambush tactic, maximising the chances of success by targeting regions likely to be dominated by opponents. Running stochastic simulations, we discover that the ambush strategy enhances the likelihood of individual success compared to direct attacks alone, leading to uneven spatial patterns characterised by spiral waves. We compute the autocorrelation function and measure how the ambush tactic unbalances the organisms' spatial organisation by calculating the characteristic length scale of typical spatial domains of each species. We demonstrate that the threshold for local species density influences the ambush strategy's effectiveness, while the neighbourhood perception range significantly impacts decision-making accuracy. The outcomes show that long-range perception improves performance by over 60%, although there is potential interference in decision-making under high attack triggers. Understanding how organisms' adaptation their environment enhances their performance may be helpful not only for ecologists, but also for data scientists, aiming to improve artificial intelligence systems.

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A monolayer consisting of two types of particles, with energetically favored alternating stripes of the two components, is studied by Monte Carlo simulations and within a mesoscopic theory. We consider a triangular lattice model and assume short-range attraction and long-range repulsion between particles of the same kind, as well as short-range repulsion and long-range attraction for the cross-interaction. The structural evolution of the model upon increasing temperature is studied for equal chemical potentials of the two species. We determine the structure factor, the chemical potential-density isotherms, the specific heat, and the compressibility, and show how these thermodynamic functions are associated with the spontaneous formation of stripes with varying degrees of order.

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SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

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Knowledge of the environmental photochemical fate of pesticides is essential to assess their potential impacts. However, there are few studies in the literature focused on the photochemical attenuation of micropollutants in Brazilian rivers. In this context, this study characterized the surface waters of the Pontal of Paranapanema region (region which concentrates more than 80% of Brazilian sugarcane cultivations), in order to determine its photochemical attenuation potential against micropollutants in different seasons. Thus, the steady-state concentrations of the photochemically produced reactive intermediates (PPRIs) (hydroxyl radical, HO•; singlet oxygen, 1O2, and triplet-excited state chromophoric dissolved organic matter, 3CDOM*), formed in the rivers, were simulated by using the APEX model (Aquatic Photochemistry of Environmentally-occurring Xenobiotics), considering the sunlight irradiance, water chemistry, and depth. Based on our simulations, these concentrations vary between 0.35 × 10-15 and 4.52 × 10-14 mol L-1 for HO•, 1.3 × 10-15 and 1.2 × 10--14 mol L-1 for 3CDOM*, and 2.5 × 10-15 and 2.5 × 10-14 mol L-1 for 1O2. Finally, mathematical simulations were used for predicting persistence of pesticides atrazine (ATZ) and diuron (DIR) in Pontal of Paranapanema surface waters and the half-life times (t1/2) of the pollutants ranged from a few hours to one week.

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One of the challenges in augmentative biological control programs is the definition of releasing strategy for natural enemies, especially when macro-organisms are involved. Important information about the density of insects to be released and frequency of releases usually requires a great number of experiments, which implies time and space that are not always readily available. In order to provide science-based responses for these questions, computational models offer an in silico option to simulate different biocontrol agent releasing scenarios. This allows decision-makers to focus their efforts to more feasible options. The major insect pest in sugarcane crops is the sugarcane borer Diatraea saccharalis, which can be managed using the egg parasitoid Trichogramma galloi. The current strategy consists in releasing 50,000 insects per hectare for each release, in three weekly releases. Here, we present a simulation model to check whether this releasing strategy is optimal against the sugarcane borer. A sensitive analysis revealed that the population of the pest is more affected by the number of releases rather than by the density of parasitoids released. Only the number of releases demonstrated an ability to drive the population curve of the pest towards a negative growth. For example, releasing a total of 600,000 insects per hectare in three releases led to a lower pest control efficacy that releasing only 250,000 insects per hectare in five releases. A higher number of releases covers a wider range of time, increasing the likelihood of releasing parasitoids at the correct time given that the egg stage is short. Based on these results, it is suggested that, if modifications to the releasing strategy are desired, increasing the number of releases from 3 to 5 at weekly intervals is most likely preferable.

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BACKGROUND: Serum albumin is the most abundant protein in the Mammalia blood plasma at where plays a decisive role in the transport wide variety of hydrophobic ligands. BSA undergoes oxidative modifications like the carbonylation by the reactive carbonyl species (RCSs) 4-hydroxy-2-nonenal (HNE), 4 hydroxy-2-hexenal (HHE), malondialdehyde (MDA) and 4-oxo-2-nonenal (ONE), among others. The structural and functional changes induced by protein carbonylation have been associated with the advancement of neurodegenerative, cardiovascular, metabolic and cancer diseases. METHODS: To elucidate structural effects of protein carbonylation with RCSs on BSA, parameters for six new non-standard amino acids were designated and molecular dynamics simulations of its mono­carbonylated-BSA systems were conducted in the AMBER force field. Trajectories were evaluated by RMSD, RMSF, PCA, RoG and SASA analysis. RESULTS: An increase in the conformational instability for all proteins modified with local changes were observed, without significant changes on the BSA global three-dimensional folding. A more relaxed compaction level and major solvent accessible surface area for modified systems was found. Four regions of high molecular fluctuation were identified in all modified systems, being the subdomains IA and IIIB those with the most remarkable local conformational changes. Regarding essential modes of domain movements, it was evidenced that the most representatives were those related to IA subdomain, while IIIB subdomain presented discrete changes. CONCLUSIONS: RCSs induces local structural changes on mono­carbonylated BSA. Also, this study extends our knowledge on how carbonylation by RCSs induce structural effects on proteins.

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We introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials. Applied to single-component superlattices, MOLT-CF predicts fcc-to-bcc Bain transitions for decreasing vapor pressure and for increasing ligand length, both in agreement with experimental results. We explore the stability of other single-component phases and show that the C14 Frank-Kasper phase, which has been reported in experiments, is not a global free-energy minimum. Implications for precise assembly and prediction of multicomponent nanoparticle systems are discussed.