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Honeybees (Apis mellifera) play a significant role in the pollination of various food crops and plants. In the past decades, honeybee management has been challenged with increased pathogen and environmental pressure associating with increased beekeeping costs, having a marked economic impact on the beekeeping industry. Pathogens have been identified as a contributing cause of colony losses. Evidence suggested a possible route of pathogen transmission among bees via oral-oral contacts through trophallaxis. Here we propose a model that describes the transmission of an infection within a colony when bee members engage in the trophallactic activity to distribute nectar. In addition, we examine two important features of social immunity, defined as collective disease defenses organized by honeybee society. First, our model considers the social segregation of worker bees. The segregation limits foragers, which are highly exposed to pathogens during foraging outside the nest, from interacting with bees residing in the inner parts of the nest. Second, our model includes a hygienic response, by which healthy nurse bees exterminate infected bees to mitigate horizontal transmission of the infection to other bee members. We propose that the social segregation forms the first line of defense in reducing the uptake of pathogens into the colony. If the first line of defense fails, the hygienic behavior provides a second mechanism in preventing disease spread. Our study identifies the rate of egg-laying as a critical factor in maintaining the colony's health against an infection. We propose that winter conditions which cease or reduce the egg-laying activity combined with an infection in early spring can compromise the social immunity defenses and potentially cause colony losses. [ABSTRACT FROM AUTHOR]

The effects of surface stress on the lattice thermal conductivity are investigated for a silicon nanowire. A phonon dispersion relation is derived based on a continuum approach for a nanowire under surface stress. The phonon Boltzmann equation and the relaxation time are employed to calculate the lattice thermal conductivity. Surface stress, which has a significant influence on the phonon dispersion and thus the Debye temperature, decreases the lattice thermal conductivity. The conductivity varies with changing surface stress, e.g., due to adsorption layers and material coatings. This suggests a phonon engineering approach to tune the conductivity of nanomaterials. [ABSTRACT FROM AUTHOR]

Magnetic fluid hyperthermia (MFH) is a noninvasive treatment that destroys cancer cells by heating a ferrofluid-impregnated malignant tissue with an ac magnetic field while causing minimal damage to the surrounding healthy tissue. The strength of the magnetic field must be sufficient to induce hyperthermia but it is also limited by the human ability to safely withstand it. The ferrofluid material used for hyperthermia should be one that is readily produced and is nontoxic while providing sufficient heating. We examine six materials that have been considered as candidates for MFH use. Examining the heating produced by nanoparticles of these materials, barium-ferrite and cobalt-ferrite are unable to produce sufficient MFH heating, that from iron-cobalt occurs at a far too rapid rate to be safe, while fcc iron-platinum, magnetite, and maghemite are all capable of producing stable controlled heating. We simulate the heating of ferrofluid-loaded tumors containing nanoparticles of the latter three materials to determine their effects on tumor tissue. These materials are viable MFH candidates since they can produce significant heating at the tumor center yet maintain the surrounding healthy tissue interface at a relatively safe temperature. [ABSTRACT FROM AUTHOR]

The interplay between vacant graphene and silicon (Si) provides a viable way to tune the properties for successful implementation as a promising anode material of lithium-ion batteries. Therefore, understanding the interaction of such defects when coupled with silicon clusters is of particular importance. Using first-principles methods, we investigate the atomic adsorption on graphene with a single vacancy and a double vacancy, as well as the structural and electrical properties. The presence of defects strongly enhances the interaction between the defective graphene and Si clusters, dependent on Si type. We observe that SiO strongly adsorbed on the divacancy graphene with the adsorption energy of -4.49 eV could provide extra intercalation places for Li atoms, augmenting the adsorption energy of Li from -1.18 eV to -4.15 eV compared to the pristine graphene. Moreover, it exhibits a threefold increase in the lithiation /de-lithiation potential and the superior Li storage capacity. The enrichment of Li adsorption and uptake is also observed in the hybrid Si 6 /single-vacancy graphene. Thus, the interface engineering via deposition of various Si clusters on the vacant graphene could be a new strategy to achieve a promising anode material for Li-ion batteries. [Display omitted] [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation. [ABSTRACT FROM AUTHOR]

DNA (deoxyribonucleic acid) possesses the ability to alter its conformation in response to stresses. In the case that DNA is mistakenly structured or packed, it can lead to diseases or deformities. Additionally, stretching and compressing of DNA can be useful for some applications. Thus the investigation of the DNA elasticity and stability is necessary. We propose to study the mechanics of a DNA molecule under mechanical stress within the framework of Kirchhoff's rod model. The problem is solved by perturbation method to find equilibrium configurations of DNA at different modes that can be excited. The results show that DNA overwinds under tension of 31 pN, and it begins to unwind beyond 31 pN which is in good agreement with the previous literature. It also suggests that the helical structure is stable under compression less than 367 pN, above that it is unstable. Moreover, the critical values of torsion, modes, the number of turns, and forces leading to the instability are reported. This analytical study could be a groundwork providing better understandings on DNA's deformation and relevant biological processes, and critical parameters affecting to the instability with engineering implications in designing a DNA-based functional nanodevice. [ABSTRACT FROM AUTHOR]

Graphene has been extensively explored as an electrode material in supercapacitors because of its large surface area and high electronic conductivity. By designing diverse morphologies and doping graphene with certain elements, the properties of graphene can be efficiently modified. We present the influence of Al, B, N and P doping, monovacancy and multilayer graphene structures on stability, electronic structures and quantum capacitance, by applying density functional theory calculations. The electrode quantum capacitances are substantially modified due to doping, the presence of monovacancy, and interaction between layers occurred in multilayer structures. Our calculations suggest that the monolayer graphene with monovacancy, the monolayer and multilayer graphene structures with nitrogen doped around the monovacancy, and multilayer graphene structure with aluminum doped could provide substantial change of quantum capacitance. However, the structure stability could be challenging. The interaction between layers could lower quantum capacitances compared to those of the monolayer structures with the same dopant elements. Moreover, the association of monovacancy and nitrogen doping of a single layer structure could lead to as high quantum capacitance as ∼80 μF/cm 2 . This work suggests the possibility to enhance quantum capacitance of the graphene-based electrodes using the combination effect of doping, vacancy defect and stacking layers. [ABSTRACT FROM AUTHOR]

In thermoelectrics, the negative response of the electron transport can be observed and can block the flow of electrons leading to the reduced thermal current. As a result, the thermoelectric device’s efficiency decreases accordingly. Because of interesting properties and superior stability of Bi 2 O 2 Se at elevated temperature, it is used in this work to investigate the effects of temperature on thermoelectric properties. In the first part of calculations, density functional theory (DFT) and semiclassical Mott formula are introduced to compute density of states (DOS) and energy dependent thermoelectric properties, respectively. Then the hopping model is employed to investigate temperature-dependent thermoelectric properties, such as, electrical conductivity ( σ ), Seebeck coefficient ( S ), and power factor ( PF = S 2 σ ). The obtained results agree well with the experimental data in literature where the maximum PF = 1.1 × 10 −4 W/(m K 2 ) occurs around 800 K. This investigation can provide basic knowledge and better understandings of the negative response of electron transport to temperature-dependent thermoelectric properties. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design principles of ZnO-graphene based gas sensors were investigated using density functional theory (DFT) calculations. • ZnO/graphene nanocomposites with varying graphene contents were synthesized by chemical method. • The hybrid structure exhibited the high gas response to ethanol gas, which was two-fold of the pristine graphene at the operating temperature. • • Enhanced response and selectivity to ethanol gas were explained. Gas sensing devices are essential in many applications and are widely developed to detect exhaust gases and air quality rapidly and accurately. However, there is still an urgent demand to achieve the sensing material and state-of-the-art sensor device. This work combines the theoretical and experimental studies to design and fabricate a hybrid zinc oxide (ZnO)/graphene to further tailor the materials for better performance than the individual materials. The first-principles calculations were performed to gain fundamental understandings of the gas response of the different hybrid structures. Then, we constructed the different nanocomposite models to include the graphene decorated with ZnO nanoclusters (Zn 12 O 12 /graphene), a hybrid ZnO/graphene layers, compared with bare ZnO and graphene layers. The best candidate matrix, Zn 12 O 12 /graphene, exhibiting excellent gas sensing properties, was selected for chemical synthesis using the reliable and straightforward technique. Hybrid nanocomposites were formed, varying the graphene concentrations in ZnO from 1 to 10 wt%, confirmed by SEM and XRD. The nanohybrid with 5 wt% graphene mixture showed a high gas response of 54.30 upon the ethanol gas of 2,000 ppm, which was more responsive than the graphene or ZnO alone. The proposed study can lead to the design and fabrication of highly sensitive ethanol gas sensors. [ABSTRACT FROM AUTHOR]

A reduction in phonon thermal conductivity enhances thermoelectric performance. One method to accomplish this is by applying mechanical stress to a nanostructure. We consider an initially twisted 20 nm polyvinyl acetate nanofiber undergoing torsion. Our analytical method uses the continuum approach of Mooney's model. Torsion modifies the phonon dispersion. Angles of twist between 45° and 70° reduce the averaged phonon group velocity and the phonon thermal conductivity but when these angles are increased further, this conductivity increases. This suggests a phonon engineering approach to tune the thermal conductivity of nanomaterials. [ABSTRACT FROM AUTHOR]

The Thomson effect heats or cools a material when a current passes along its length in the presence of a temperature gradient VT. It is typically neglected in analyses of thermoelectric cooling because the Thomson coefficient T(T), which is related to the temperature depen-dence of the Seebeck coefficient S(T), is ignored. We investigated the influence of (1) |S (7")| and |T (7")| on an intrinsic Si nanowire cooler and found that the temperature dependence of both parameters became more significant at lower temperatures and (2) T(T) on the cooling temperature also increased as the current density is raised. These effects caused more heat to be pumped away from the cold side than when T was neglected or assumed constant. Cooling was also compared for two systems, one in which phonons and electrons existed in thermal equilibrium, and the other when there was nonequilibrium between them. A nanomaterial with a smaller carrier cooling length induces a relative decrease in the energy transfer between electrons and phonons, which provides better thermoelectric cooling. [ABSTRACT FROM AUTHOR]

Abstract: Magnetic fluid hyperthermia (MFH) is a cancer treatment that can selectively elevate the tumor temperature without significantly damaging the surrounding healthy tissue. Optimal MFH design requires a fundamental parametric investigation of the heating of soft materials by magnetic fluids. We model the problem of a spherical tumor and its surrounding healthy tissue that are heated by exciting a homogeneous dispersion of magnetic nanoparticles infused only into the tumor with an external AC magnetic field. The key dimensionless parameters influencing thermotherapy are the Péclet, Fourier, and Joule numbers. Analytical solutions for transient and steady hyperthermia provide correlations between these parameters and the portions of tumor and healthy tissue that are subjected to a threshold temperature beyond which they are damaged. Increasing the ratio of the Fourier and Joule numbers also increases the tumor temperature, but doing so can damage the healthy tissue. Higher magnetic heating is required for larger Péclet numbers due to the larger convection heat loss that occurs through blood perfusion. A comparison of the model predictions with previous experimental data for MFH applied to rabbit tumors shows good agreement. The optimal MFH conditions are identified based on two indices, the fraction I T of the tumor volume in which the local temperature is above a threshold temperature and the ratio I N of the damaged normal tissue volume to the tumor tissue volume that also lies above it. The spatial variation in the nanoparticle concentration is also considered. A Gaussian distribution provides efficacy while minimizing the possibility of generating a tumor hot spot. Varying the thermal properties of tumor and normal tissue alters I T and I N but the nature of the temperature distribution remains unchanged. [ABSTRACT FROM AUTHOR]

• Gas sensing properties of the ZnO-based material were investigated using DFT. • The ZnO thin film was modified by Pd doping and decorating on the surface. • The sensing properties of ZnO-based substrate were computed. • The Pd surface decoration was the critical factor affecting the sensitivity. The improved gas-detecting ability of ZnO nanostructures has generated considerable attention for further development based on a unique and practical approach. However, reduced gas sensitivity and selectivity, as well as the sophisticated synthesis requiring high-cost technology restricts its efficient use. In this regard, the enhancement of gas sensing properties can be brought about by incorporating the noble metal palladium (Pd) to modify the ZnO surface. This work applied density functional theory (DFT) calculations to study such sensing materials to the target gases (i.e., hydrogen (H 2) and ethanol (C 2 H 5 OH)), which are extensively applied in energy production, thereby requiring rapid and accurate detection. The modified ZnO thin film-based gas sensor was modeled into Pd-substitutional doped and Pd-decorated ZnO surfaces. The charge transport phenomenon at the atomic scale, binding strength, and other electronic and structural properties were elucidated as the varied number of Pd. Pd-decorated ZnO surfaces enhance the sensitivity of both molecules. The calculation results could provide an atomistic insight and a better understanding of altered electrical and sensing properties of ZnO surface modification by Pd atoms. [ABSTRACT FROM AUTHOR]

Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems. [ABSTRACT FROM AUTHOR]

A two-step patterning process was developed based on nanosphere lithography and plasma etching to fabricate an array of electrodes with two different gold ring structures: the arrays of Au micro-ring electrode (Au-MRE) and Au covered with polystyrene micro-ring electrode (Au-PS-MRE). The Au-MRE structure was fabricated by etching a monolayer of polystyrene (PS) spheres on indium tin oxide (ITO) surface to generate PS rings on ITO glass. PS rings served as a mask in secondary etching for blocking an interaction of oxygen plasma and ITO surface to create a ring-patterned ITO surface. Then, the PS residue was removed and gold was deposited. The site-selective electrodeposition of gold was carried out and an array of a gold ring structure was formed on the ITO glass. The Au-PS-MRE structure was fabricated by keeping the PS residue from second etching before deposition of gold. The Au-PS-MRE microelectrode was studied by using hexacyanoferrate as an electrochemical probe where it displayed steady state current in cyclic voltammetry. The respective calibration plots were acquired at a working potential of 0.31 V and 0.12 V (vs. Ag/AgCl) for oxidation and reduction reaction, respectively. The sensitivity is as high as 163.4–220.7 μA·mM−1·mm−2 which is larger by a factor of 95–132 compared to a conventional gold film macroelectrode. The detection limit (at a signal-to-noise ratio of 3) is 2.2 μM. This approach thus yields relatively effective and low-cost fabrication without resorting to high resolution instruments. Conceivably, the technique may be used to produce microelectrode arrays on a large scale. [ABSTRACT FROM AUTHOR]

The influence of boron (B), nitrogen (N), oxygen (O), and sulfur (S) doping on enhancing quantum capacitance were investigated through a series of the surface-doped trilayer graphene structures by using density functional theory (DFT) calculations. The quantum capacitance of monolayer models was enhanced through a single doping, a triple doping, and a vacancy defect. Our calculations suggested that the layer interactions within the trilayer models decreased the quantum capacitance but increased the stability of the doped structures. Interestingly, in the case of sulfur dopants with significantly larger atomic size than carbon, the stacking layers induced a surface distortion that could avoid the steric clashes with stacking layers and enhanced the stability. In conclusion, this work provided more realistic models of modified carbon-based electrodes for supercapacitors with more accurate information from the combined effects of doping and stacking layers. • This work provided more realistic models of modified carbon-based electrodes considering the effects of stacking layers. • Stacking layers decreased the quantum capacitance as peaks in density of state (DOS) profiles were averaged out. • Distortion of sulfur doped stacking layers enhanced both stability and quantum capacitance. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge. Results: This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects. Conclusions: The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment. [ABSTRACT FROM AUTHOR]

Harvesting waste energy through thermoelectric has widely gained attention to aid green energy production. Current efforts are to take advantages of nanomaterials and nanosystems because of dramatic improvements in the performance. However, its cost-effectiveness in generating a 3D configuration for a large-area use is hindered by high production cost. To overcome the present challenges, we propose a flexible and lightweight thermoelectric developed on a melamine foam using a simple dip-dry technique to self-assemble conductive nanofilms in the scaffold. Different amounts of poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) conductive nanofilms were variedly fabricated in the foam due to altered amounts of sodium dodecyl sulfate (SDS) surfactant from 0 to 5 wt%. Together with experimental results, a theoretical model was constructed to predict thermal and electrical conductivities, indicating the strong influence of SDS to the electrical conductivity. As a result, the highest nanofilm formation in the foam structure is achieved by adding SDS at 3 wt%. The figure of merit (ZT) of thermoelectric foam is about 0.006-0.007. Our first device was also demonstrated with output voltage of 1.1 mV (ΔT = 40 K). The present study could provide the design and optimization of a hybrid thermoelectric that can act as a simultaneous thermal insulator and power generator. [ABSTRACT FROM AUTHOR]