Advanced Approaches Research Articles

Navigating the Landscape of Precision Horticulture: Sustainable Agriculture in the Digital Age

Automation, autonomy, and precision play vital roles in modern technology and procedures, especially in response to the rising global population and the need for improved fruit production technologies. The quality criteria for fruit yield are becoming increasingly complex, necessitating fundamental upgrades. Sensor-based technologies pave the way for more rational and efficient soil usage. Precision farming, an advanced agricultural approach, employs software technologies and principles to optimize various aspects of horticultural production. Its goal is to enhance crop yields and promote environmental sustainability by managing spatial and temporal variations. Precision horticulture, a cutting-edge farming method, focuses on increasing crop yields and quality while minimizing environmental impact. This approach leverages advanced technology and data-driven decision-making. By utilizing sensors, robots, drones, and other innovative tools for yield mapping, irrigation control, robotic harvesting, nutrient and pesticide application, soil sensing, and crop growth analysis, growers can monitor plant growth and health, identify potential issues, and make informed crop management decisions. Tailoring practices to specific plant needs can lead to higher yields, improved product quality, and reduced environmental impact. Overall, precision horticulture holds significant promise for promoting sustainable agricultural practices while meeting the increasing demand for food production.

HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search.

Recent neural architecture search (NAS)-based approaches have made great progress in the hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, increasing searching time, and restricting model performances. To tackle these issues, in this article, different from previous methods that extra define structural parameters, we propose to directly generate structural parameters by utilizing the specifically designed hyper kernels, ingeniously converting the original complex dual optimization problem into easily implemented one-tier optimizations, and greatly shrinking searching costs. Then, we develop a hierarchical multimodule search space whose candidate operations only contain convolutions, and these operations can be integrated into unified kernels. Using the above searching strategy and searching space, we obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. In addition, by combining the proposed hyper kernel searching scheme with the 3-D convolution decomposition mechanism, we obtain diverse architectures to simulate 3-D convolutions, greatly improving network flexibilities. A series of quantitative and qualitative experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results compared with other advanced NAS-based HSI classification approaches.

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

The urban heat island effect is a prevalent urban concern, and studies on its influencing factors are essential to mitigate its negative effects and promote sustainable urban development. Although many studies have revealed the impact of land cover change on land surface temperature (LST) during urbanization, most have focused only on built-up areas without considering the relationship between urban internal functional structure and LST. To clarify the influence of changes in urban functional zones (UFZs) on LST, focusing on the downtown area of Nanjing City, China, this study proposed an effective framework using multi-source geospatial data for qualitative and quantitative analysis. Specifically, very high spatial resolution images and point of interest data were jointly used to generate UFZ maps using an advanced deep learning approach, and LST distribution maps were retrieved from Landsat 8 images using the radiative transfer equation. The UFZ and LST maps were then overlaid and analyzed to reveal the impact of UFZ change on LST. The results showed that in 2013, 2018, and 2022, the residential region constituted the largest proportion of area, comprising 23.52%, 24.52%, and 27.28%, respectively. The areas of unused region and transport region experienced the most rapid decrease and increase of 6.08% and 4.05% from 2013 to 2022, respectively. Furthermore, the area of sub-high temperature zone decreased the most, by 3.47%, whereas that of the high-temperature zone increased the most, by 6.05%. In the study area, water and green space maintained relatively low LSTs, whereas residential region and unused region exhibited higher LSTs. From 2013 to 2022, UFZ changes contributed to a 0.71°C increase in LST. Transfers from green space, water, commercial region, transport region, and industrial region mostly resulted in a higher LST, which was associated with a decrease in fractional vegetation cover (FVC). Transfers from unused region, residential region, and public service region mostly contributed to reducing LST, which was caused by the increase in FVC. The findings and the proposed framework can provide reliable references for urban managers to optimize urban spatial layout and promote sustainable urban development.

Equal Treatment, Unequal Outcomes? Debunking the Racial Disparity in Renin Angiotensin Aldosterone System Inhibitor Associated Reduction in Heart Failure Hospitalizations

BackgroundRenin angiotensin aldosterone system inhibitors (RAASi) are a mainstay treatment in patients with heart failure with reduced ejection fraction (HFrEF) in part to prevent hospitalizations. However, whether RAAS inhibitors reduce the risk of hospitalization in Black patients is not entirely clear because enrollment of Black patients in previous clinical trials was low, and a previous meta-analysis showed a significant racial disparity: reduction in hospitalizations with an RAAS inhibitor in White patients but not Black patients. Previous studies relied on the use of self-identified race instead of genomic ancestry. Therefore, this study aimed to investigate the role of self-identified race and genomic ancestry in the racial disparity in RAAS inhibitor associated reductions in HFrEF hospitalizations. MethodsThe primary outcome was time to first heart failure hospitalization. A (de-identified) heart failure patient registry and data from the GUIDE-IT multi-center randomized control trial were analyzed with Cox proportional hazards models un/adjusted for clinical risk factors, death as a competing risk, and time-varying RAAS inhibitor exposure. The proportion of Yoruba African ancestry was quantified.. Analysis of self – identified race were performed in both the registry and GUIDE-IT. Analysis of genomic ancestry was only performed in the registry since this information was not available in GUIDE-IT. A fixed effect meta-analysis combined results of both the registry and GUIDE-IT for race. ResultsThe registry had 1010 total HFrEF patients (Black = 509 and White = 501) with 852 having ancestry quantification (>80% Yoruba African Ancestry = 381 and <5% Yoruba African Ancestry = 471). GUIDE-IT had 810 HFrEF patients (Black = 322 and White = 488). There was no significant difference in the association of RAAS inhibitor exposure with heart failure hospitalization by race (meta-analysis p-value for race*RAAS inhibitor exposure interaction = 0.49; Black patients HR [95% CI] for RAAS inhibitor exposure = 0.89 [0.64-1.23)] P = 0.47; White patients = 1.20 (0.83-1.75) P = 0.34). Results were similar when analyzed by ancestry (p-value for ancestry*RAAS inhibitor exposure interaction = 0.57; >80% Yoruba African Ancestry = 0.93 [0.51-1.69] P = 0.80; <5% Yoruba African Ancestry = 1.29 [0.57-2.92] P = 0.54). ConclusionsIn contrast to a previous meta-analysis, this more contemporary analysis of 2 HFrEF patient datasets demonstrates the absence of a racial disparity in RAAS inhibitor associated reductions in heart failure hospitalizations. The difference in this racial disparity over time may be due to improvements in background heart failure therapies, racial differences in healthcare usage, and the use of more advanced statistical approaches.

Advanced approach for active and durable proton exchange membrane fuel cells: Coupling synergistic effects of MNC nanocomposites

AbstractAtomically dispersed metal and nitrogen co‐doped carbon (MNC) is a promising oxygen reduction reaction (ORR) catalyst for electrochemical energy storage and conversion applications but typically suffers from low durability and activity under the acidic conditions of practical polymer electrolyte exchange membrane fuel cells (PEMFCs). Recently, the performance of MNC nanocomposites under acidic ORR conditions has been enhanced by exploiting the synergistic coupling effects of their constituents (single‐atom sites, nanoclusters, and nanoparticles). The unique geometric structures formed by the coupling of diverse sites in these nanocomposites provide optimal electronic structures and efficient reaction pathways, thus resulting in high activity and long‐term durability. This work provides an overview of MNC nanocomposites as ORR electrocatalysts under practical PEMFC conditions, focusing on activity and durability enhancement methods and highlighting the strategies used to prepare electrocatalytically efficient MNC nanocomposites containing no or low amounts of platinum group metals. Progress in the development of advanced MNC nanocomposites as acidic ORR catalysts is discussed, and the pivotal role of synergistic effects resulting from the coupling sites within the nanocomposites is explored together with the characterization methods used to elucidate these effects. Finally, the challenges and prospects of developing MNC nanocomposites as next‐generation electrocatalysts are presented.image

New Technologies in the Treatment of Base of Thumb Osteoarthritis.

Symptomatic osteoarthritis (OA) of the first carpometacarpal (CMC) joint is prevalent and debilitating, commonly affecting the elderly and postmenopausal population. This review highlights the latest advancements in the treatment of thumb CMC OA, which historically includes a range of nonsurgical and surgical options without a consensus benchmark. We will focus on innovative and emerging technologies. Nonsurgical treatments typically comprise custom braces and corticosteroid injections. In addition, this review explores advanced approaches such as 3D printed braces, which have improved patient satisfaction, and novel intra-articular injectables such as autologous fat, optimized by ultrasonography to enhance treatment precision and outcomes. Although standard surgical treatments include trapeziectomy, with or without ligament reconstruction and tendon interposition, more recent implant arthroplasty designs show promising long-term survival. Newer interventions include patient-specific instrumentation for metacarpal osteotomies, selective joint denervation, and innovative suspensionplasty devices, all marked by their increased precision and personalized care. However, it is important to note that these novel technologies are not yet established as superior to standard treatments of thumb CMC OA.

Nivolumab and Cabozantinib Immunotherapy Induced Thyroid Dysfunction Detected on 18 F-FDG PET/CT in Renal Cell Carcinoma.

Targeted immunotherapy became the most advanced approach for cancer treatment. Programmed death-1 (PD-1) expressed on activated T cells can reverse immune suppression and cause T-cell activation. Nivolumab, a PD-1 immune checkpoint inhibitor antibody that is a fully human immunoglobulin G4, blocks PD-1 and promotes antitumor immunity. Cabozantinib (tyrosine kinase inhibitor) inhibits the tyrosine kinase activity of vascular endothelial growth factor receptors 1, 2, and 3. As a result of enhancing immune response in normal tissues, immune-related adverse events can occur. Thyroid dysfunction is a common form of immune-related adverse event and seen on 18 F-FDG PET/CT scans post therapy.

Simultaneous removal of E1, E2, EE2 and levonorgestrel from water using TiO2 catalyst anchored on activated carbon: Processes optimization, materials characterization, and assessment of the estrogenicity reduction

Removal of estrogen hormones from water matrices is crucial owing to its adverse effects on aquatic ecosystems and human health. The present investigation applies an advanced approach to assess the effectiveness of combined processes (adsorption and visible-light-driven photo-degradation) for simultaneous removal of estrone (E1), 17β-estradiol (E2), 17α-ethinylestradiol (EE2), and levonorgestrel (LEVO) in water, applying TiO2-activated carbon composite selected through design of experiment (DoE) for process optimization. Based on a central composite rotatable design (CCRD), composites were synthesized with percentages of activated carbon (AC) ranging from 2.93% to 17.07% (wt.) and calcination temperatures between 259 and 541 °C. The composite with the best performance TiO2-AC15%-541 (15% wt. AC, calcined at 541 °C), achieved a total removal of 98.3 ± 1.15% for E1, 99.0 ± 1% for E2, 99.3 ± 1.15% for EE2, and 96.0 ± 2.65% for LEVO at the initial concentration of 100 μg L−1 under simulated solar irradiation. Further optimization using once more CCRD involved three independent variables: pH; hormone concentration/TiO2-AC15%-541 loading ratio; and the intensity of simulated solar irradiation. Under optimized conditions (pH 2.64, hormone concentration/TiO2-AC15%-541 loading ratio of 3.8 mg g−1, and irradiation intensity of 41 W m−2 UV-A), the TiO2-AC15%-541 composite removed 99.8% of E1, 99.8% of E2, 99.0% of EE2, and 92.1% of LEVO. Furthermore, the process achieved a 99.9% reduction in estrogenic activity, assessed with yeast estrogen screen (YES) assay. These results demonstrate that TiO2-AC15%-541 is an efficient and cost-effective remediation agent for treating mixed estrogen compounds in water, with significant potential for commercial applications.

Integrating small data and shape prior knowledge with gradient-enhanced Kriging through adaptive knowledge sampling

Integrating prior knowledge into surrogate models constitutes an advanced approach to tackling the challenges posed by small data. The existing gradient-enhanced Kriging (GEK) method can enhance accuracy by incorporating gradient points of shape prior knowledge. Nonetheless, our findings indicate that the strategy employed for setting gradient points significantly impacts accuracy, even when the number of gradient points remains constant. To enhance the efficient utilization of gradient information, we propose a novel Adaptive Knowledge Sampling (AKS) strategy with an innovative acquisition function that balances local exploitation and global exploration, effectively extracting gradient points from shape prior knowledge. The acquisition function consists of three parts: (1) The Local Information Filling Criterion (LIFC), designed to mitigate model error; (2) The Global Information Filling Criterion (GIFC), aimed at reducing model uncertainty in data-sparse areas; and (3) The Minimum Distance Constraint (MDC), implemented to prevent excessive clustering of gradient points. To strike a balance between model accuracy and computational cost, an adaptive stopping condition is introduced to regulate the number of gradient points sampled. The impact of hyperparameters in the AKS strategy was analyzed in detail, and the method’s performance was compared to existing approaches using six benchmark functions and a missile aerodynamic prediction case. The results demonstrate that AKS-GEK offers higher prediction accuracy and robustness when integrating small data with shape prior knowledge.

The Integration of Gold Nanoparticles into Dental Biomaterials as a Novel Approach for Clinical Advancement: A Narrative Review.

Gold nanoparticles (AuNPs) have gained significant attention in the biomedical field owing to their versatile properties. AuNPs can be customized by modifying their size, shape and surface characteristics. In recent years, extensive research has explored the integration of AuNPs into various dental materials, including titanium, polymethylmethacrylate (PMMA) and resin composites. This review aims to summarize the advancements in the application of modified AuNPs in dental materials and to assess their effects on related cellular processes in the dental field. Relevant articles published in English on AuNPs in association with dental materials were identified through a systematic search of the PubMed/MEDLINE, Embase, Scopus and ScienceDirect databases from January 2014 to April 2024. Future prospects for the utilization of AuNPs in the field of dentistry are surveyed.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model’s accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model’s robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

Melatonin can mitigate H2O2-induced atrophy and promote muscle fiber hypertrophy in morphological level in mouse myoblast cell line

Objective: It is known that oxidative stress is the main factor in the formation of disuse muscle atrophy which is the most common type of muscle atrophy. The utilization of antioxidants as supplements, particularly the category known as mitochondrial targeting antioxidants (MTA), such as melatonin, have demonstrated significant potential as an advanced therapeutic approach. In this study, we aimed to investigate the effect of melatonin application on the cellular morphology of the C2C12 cell line. Materials and Methods: In our experiment, we induced oxidative stress using hydrogen peroxide (H2O2) to create a model of skeletal muscle atrophy. We established four distinct groups of C2C12 cells, all exposed to the same conditions. These groups included Control (C), Melatonin (M), H2O2 (H), and Melatonin + H2O2 (MH). The aim was to examine morphological features, specifically myotube diameters, to assess atrophy. Results: The analysis revealed significant differences in diameters among the groups (p &lt; 0.05). The melatonin treatment group not only showed a mitigation of diameter change due to atrophy but also exhibited a significant increase in diameter. Conclusion: The results suggest that H2O2 induces muscle atrophy, and melatonin plays a dual role in maintaining muscle health, protecting against atrophy and promoting hypertrophy, particularly at the morphological level. However, additional research is needed to figure out the details of underlying mechanisms.

Theoretical design and synthesis of Co30Ni60/CC for high selective methanol oxidation assisted energy-saving hydrogen production

The integration of methanol oxidation reaction (MOR) with hydrogen evolution reaction (HER) represents an advanced approach to hydrogen production technology. Nonetheless, the rational design and synthesis of bifunctional catalysts for both MOR and HER with exceptional activity, stability and selectivity present formidable challenges. In this work, firstly, density functional theory (DFT) was utilized to design and evaluate material models with high performance for both MOR and HER. Secondly, guided by DFT, Co30Ni60/CC (CC, carbon cloth) composites with a leaf-like nanosheet structure were successfully fabricated via electrodeposition. In the MOR process, Ni acts as the predominant active center, while Co amplifies the electrochemically active surface area (ECSA) and enhances the selectivity of methanol oxidation. Conversely, in the HER process, Co serves as the primary active center, with Ni augmenting the charge transfer rate. The electrochemical results demonstrate that Co30Ni60/CC exhibits exceptional performance in both MOR and HER at a current density (j) of 10 mA cm−2, with peak potentials of 1.323 V and −95 mV, respectively. Additionally, it shows remarkable selectivity for the oxidiation of methanol to high value-added formic acid. Thirdly, following a 100 h chronopotentiometry (CP) test, the required potential demonstrates an increase of 4.9 % (MOR) and 8.1 % (HER), signifying the superior stability of Co30Ni60/CC compared to those reported in the literature. The exceptional performance of Co30Ni60/CC can be primarily attributed to that the leaf-like nanosheets structure not only exposes a plethora of active sites but also facilitates electrolyte diffusion, the monolithic structure prepared by electrodeposition enhances its stability, and the transfer of electrons from Co to Ni regulates its electronic structure, as corroborated by X-ray photoelectron spectroscopy (XPS) and density of states (DOS) analyses. Finally, at the same j, the voltage required by the Co30Ni60/CC||Co30Ni60/CC electrolytic cell, powered by an electrochemical workstation, is 198 mV lower than that required for alkaline water-splitting. Meanwhile, at higher j (100 mA cm−2), the electrolytic cell exhibits sustained and stable operation for 150 h, enabling high-efficiency hydrogen production and the synthesis of high value-added formic acid.

Single Atom‐Engineered 2D Catalytic Nanosheets for Sonocatalytic Suppression of Orthotopic Clear Cell Renal Cell Carcinoma

AbstractClear cell renal cell carcinoma (ccRCC), a prevalent malignant tumor, exhibits resistance to chemotherapy, necessitating advanced therapeutic approaches. Sonocatalytic therapy has emerged as a promising non‐invasive treatment for solid tumors due to its ability to penetrate deep tissue. Herein, the development of single atomic Au‐doped 2D TiO2 nanosheets are presented with oxygen vacancies (Au‐SA/Def‐TiO2, ASDT) for sonocatalytic treatment of ccRCC. Surface defects on TiO2 nanosheets effectively stabilize Au single atomic sites via the Ti‐Au‐Ti structure, enhancing catalytic properties and facilitating reactive oxygen species (ROS) generation by reducing energy barriers and improving electron (e−) and hole (h+) separation. Additionally, ASDT exhibits peroxidase‐like activities, further augmenting ROS production, leading to significant inhibition of tumor growth in subcutaneous and orthotopic ccRCC models under ultrasound irradiation. This study underscores the efficacy of defect engineering for stabilizing single atoms to boost ROS production and achieve effective tumor eradication by the enhanced sonocatalytic effect.

An Innovative Approach for Oxidative Desulfurization Advancement through High Shear Mixing: An Optimization Study on the Application of Benzothiophene.

Alternative fuels are being explored to mitigate the effects of petroleum-based fuels. Pyrolysis oil from waste tires is a promising alternative fuel; however, it contains very high concentrations of benzothiophene (BT) which are beyond the allowable sulfur limits of Taiwan and the Philippines. Mixing-assisted oxidative desulfurization (MAOD) is a method that removes sulfur from fuel oils by utilizing high-shear mixing and oxidants. In this paper, the oxidation of BT in a model fuel was studied to determine optimal process conditions. Crude Fe(VI) prepared from sludge was used as the oxidant. Using the Box-Behnken design under response surface methodology, the significance of the following independent variables was studied: mixing speed (4400-10 800 rpm), phase transfer agent (PTA) amount (100 to 300 mg), Fe(VI) concentration (400-6000 ppm), and mixing temperature (40 to 60 °C). The results from a comprehensive statistical analysis showed the increase of sulfur conversion with high levels of Fe(VI) concentrations and PTA amounts together with low levels of agitation speeds and temperatures. The BT to BT sulfone conversions from experimental runs ranged from 17% to 64%. The optimum sulfur conversion of 88% for the BT model fuel was reached at the maximum levels of Fe(VI) concentration and mixing speed, along with the minimum levels of PTA concentration and temperature. The optimal MAOD variables were applied to a high-sulfur pyrolysis oil sample, which resulted in a sulfur reduction of 55%. The produced fuel oil meets the sulfur requirements of Taiwan and the Philippines for industrial heating oils. Therefore, the findings of the study support the effectiveness of sludge-derived Fe(VI) in the MAOD of BT in the model fuel and pyrolysis oil under mild process conditions.

Combining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study

ObjectivesTo assess the feasibility of assessing long-term outcomes of peripheral vascular intervention (PVI) by linking data from a clinical registry to electronic health records (EHR) data from a clinical research...

TCR3d 2.0: expanding the T cell receptor structure database with new structures, toolsand interactions.

Recognition of antigens by T cell receptors (TCRs) is a key component of adaptive immunity. Understanding the structures of these TCR interactions provides major insights into immune protection and diseases, and enables design of therapeutics, vaccinesand predictive modeling algorithms. Previously, we released TCR3d, a database and resource for structures of TCRs and their recognition. Due to the growth of available structures and categories of complexes, the content of TCR3d has expanded substantially in the past 5 years. This expansion includes new tables dedicated to TCR mimic antibody complex structures, TCR-CD3 complexesand annotated Class I and II peptide-MHC complexes. Additionally, tools are available for users to calculate docking geometries for input TCR and TCR mimic complex structures. The core tables of TCR-peptide-MHC complexes have grown by 50%, and include binding affinity data for experimentally determined structures. These major content and feature updates enhance TCR3d as a resource for immunology, therapeuticsand structural biology research, and enable advanced approaches for predictive TCR modeling and design. TCR3d is available at: https://tcr3d.ibbr.umd.edu.

Basalt fibers corrosion in concrete: New perspectives originating from computational modelling techniques employment

Most of engineering disciplines or research areas have recently witnessed strategic changes leading to formulation of new challenges and revision of existing activities. Promoting the environmental aspects to the highest priority level was mostly in common. Seeking for energy efficient solutions, concrete society has mentioned basalt as a promising candidate having a potential to replace steel in the future. The chemical disharmony between basalt and alkaline environment of concrete, however, represents the most challenging tasks that obstructs the replacement procedure due to durability issues. Since only 3.3 % of the research priorities in this area deals with the corrosion and degradation problems, while the mainstream (57.0 %) prefers determination of various material properties, this problem seems to be undervalued. Additionally, computational modelling approaches are surprisingly not applied as frequently (6.1 %) as in other disciplines, although they could provide the essential long-term research. Simplified methods therefore still dominate these estimations approaches. This paper therefore brings an overview on basalt fiber corrosion in concrete, while summarizing the most topical gaps slowing down the basalt application progress. Since these gaps originate mostly from the simplifications adopted, specific solutions are proposed respectively, exploiting the unique advantages and possibilities of computational modelling approaches that are omitted. The topicality as well as the potential of the advanced approach is demonstrated on a critical example dealing with corrosion analysis evaluation in a specific wall segment. Highlighting the differences between simplified- and advanced techniques outputs, the main findings are concluded, and the most topical gaps are identified.

Classical Simulations on Quantum Computers: Interface-Driven Peptide Folding on Simulated Membrane Surfaces

Background:Antimicrobial peptides (AMPs) are crucial in the fight against infections and play significant roles in various health contexts, including cancer, autoimmune diseases, and aging. A key aspect of AMP functionality is their selective interaction with pathogen membranes, which often exhibit altered lipid compositions. These interactions are thought to induce a conformational shift in AMPs from random coil to alpha-helical structures, essential for their lytic activity. Traditional computational approaches have faced challenges in accurately modeling these structural changes, especially in membrane environments, thereby opening and opportunity for more advanced approaches. Method:This study extends an existing quantum computing algorithm, initially designed for peptide folding simulations in homogeneous environments, to address the complexities of AMP interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, resembling lipid membranes. The new method was tested on three 10-amino-acid-long peptides, each characterized by distinct hydrophobic, hydrophilic, or amphipathic properties, across different media and at interfaces between solvents of different polarity. Results:The developed method successfully modeled the structure of the peptides without increasing the number of qubits required compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite the current limitations in computational power and qubit availability, the findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly AMP folding at membrane models. Conclusions:This research highlights the promising applications of quantum computing in biomolecular simulations, paving the way for future advancements in the development of novel therapeutic agents. We aim to offer a new perspective on enhancing the accuracy and applicability of biomolecular simulations in the context of AMP interactions with membrane models.

Identification of Aurora A kinase allosteric inhibitors: A comprehensive virtual screening through fingerprint-based similarity search, molecular docking, machine learning and molecular dynamics simulation

The Aurora A kinase (AAK) protein controls spindle assembly and promotes cell divisions in various diseases including cancer. In the present study, allosteric inhibition of AAK protein through different advanced computational screening approaches is employed to target AAK’s allosteric inhibition-modulation. Precisely, extensive computational techniques including allosteric binding sites recognition of AAK protein, fingerprint-based similarity search, multi-step molecular docking through AutoDock Vina and PLANTS, and a 100 ns molecular dynamics (MD) simulations studies were carried out followed by the calculation of binding free energy with MM-GBSA based approaches, has been employed for identification of potential allosteric inhibitors-modulators of AAK protein. The study outcome highlighted that all three identified small molecular chemical entities exhibit strong binding interaction affinity in both the docking analyses at the allosteric domain of AAK protein and also greater interaction stability in comparison to the considered standard compound. In addition, all identified three screened hits also show acceptable pharmacokinetics and medicinal chemistry properties, which certainly dictates their potentiality for becoming good drug-like compounds for inhibiting-modulating the activity of AAK protein binds with similar amino acids of the allosteric domain of AAK protein with selected three compounds. All the selected molecules were found to show an acceptable ADMET profile. Moreover, the MM-GBSA-based binding energy was found to be in the range of −8 to −35 Kcal/mol, which showed a strong association between proposed molecules and AAK protein. Comprehensive computational approach shows that the selected proposed three inhibitors of AAK protein are the best candidates as potential inhibitors.