bluuebunny/crossref_metadata_2025 huggingface dataset:
A continuum soft robotic trout with embedded HASEL actuators: design, fabrication, and swimming kinematics
Authors: Hess Isabel , Musgrave Patrick
Published: September 2024

Autonomous underwater vehicles with greater maneuverability, efficiency, and resiliency are needed to meet the challenges of exploring and monitoring the underwater world, so we look to underwater creatures to uncover what makes them such excellent swimmers. Bio-inspired, soft robots can combine the performance of biological swimming with the robustness of soft construction, where the ideal robot has a jointless, flexible body with embedded muscles just like real fish. In this paper, we present a continuously deformable robotic trout with embedded electro-hydraulic hydraulically amplified, self-healing electrostatic artificial muscles, experimentally characterize its swimming kinematics, and report a reduced order numerical model which predicts the robot fish’s natural frequencies and mode shapes. We characterized the robot’s 3D full body swimming kinematics while submerged in water with digital image correlation. The soft robot undergoes whole body bending in response to internal muscle actuation and yields kinematics comparable to biological trout. Tail beat velocity was measured at the first three observed natural frequencies with a maximum of 69 mm s−1 corresponding to a caudal fin trailing edge displacement of ±10 mm. We derive a beam-based fluid structure interaction (FSI) model which predicts swimming kinematics in response to embedded muscle forces and includes the effects of nonlinear vortex and convective forces on the robot’s body. The nonlinear FSI model predicted the first three damped natural frequencies within 5% error and mode shapes which correlated with the experimental data. This paper contributes the design, fabrication, and characterization of a solid-state robotic trout featuring whole-body flexibility and embedded actuation through numerical modeling and experimental analysis.
Harnessing ZnO morphologies in energy application and sustainable development
Authors: Phogat Peeyush , Shreya  , Jha Ranjana , Singh Sukhvir
Published: September 2024

Zinc oxide (ZnO) is a remarkably versatile material, with diverse tailored morphologies for a variety of applications. This chapter delves into the exploration of various ZnO morphologies, including nanoflowers, nanorods, nanospheres, nanocubes, nanotubes, nanowires, quantum dots, hollow spheres, and core–shell structures, along with their respective practical applications. Nanoflowers, distinguished by intricate petal-like structures, excel as catalysts in chemical reactions, advancing catalysis and fine chemical synthesis. ZnO nanorods, characterized by their high aspect ratio, enhance energy conversion in solar cells, increasing light absorption and facilitating electron transport. ZnO nanospheres, spherical nanoparticles with extensive surface area, play a pivotal role in photocatalysis, purifying wastewater and air by decomposing organic pollutants. ZnO quantum dots, with size-dependent quantum confinement effects, underpin progress in optoelectronics, enhancing solar cells, LEDs, and sensors with tunable bandgaps and exceptional optical properties. Hollow ZnO spheres, with their unique void-centered structure, find use in controlled drug delivery, enabling targeted release with minimal side effects. They also serve as versatile templates for synthesizing various nanomaterials. core–shell structures, where ZnO cores are encapsulated by other materials, provide sensitivity and protection in sensors and drug delivery systems, tailored to environmental sensing or precise control over drug release rates and durations. The versatility of ZnO, embodied through its various morphologies, spans diverse applications, from catalysis and photovoltaics to environmental remediation, drug delivery, and personal care products. This chapter provides a comprehensively study of distinct ZnO morphologies and new possibilities of those structures across scientific and industrial realms, leaving an enduring impact on modern technology and improving everyday life.
Effect of staggered sublattice potential on electronic and transport properties of Bi(111) pristine and hydrogen-passivated nanoribbons
Authors: Munna Mahfuzur Rahman , Khan Md Niloy , Alam Mahbub
Published: September 2024

We investigate the impact of staggered sublattice potential (SSP) on the electronic and transport properties of Bi(111) bilayer and nanoribbons through first-principle calculations and the nonequilibrium Green's function method. We find that the topological phase transition of Bi(111) bilayer from topologically nontrivial (Z
2 = 1) to topologically trivial (Z
2 = 0) occurs at Δ = 1.77 eV SSP. Our study also reveals that energy bandgap opens for both pristine zigzag and armchair nanoribbon as the strength of the SSP (Δ > 1.50 eV for armchair nanoribbons and Δ > 1.90 eV for zigzag nanoribbons) increases, transitioning from non-trivial metallic edge states to insulating edge states. Furthermore, we explore the influence of SSP on edge-passivated zigzag nanoribbon. Through edge passivation, the dangling bonds are eliminated. As a result, it requires 0.4 eV less SSP to open an energy gap in edge-passivated nanoribbons compared to pristine nanoribbons. These findings hold promise for the advancement of Bi(111) nanoribbon-based field-effect transistors and spintronic devices.
Wideband CPW fed four port MIMO antenna based microwave sensor for deep brain temperature measurement
Authors: Singh Tilakdhari , Mishra Piyush Kumar , Pal Aditya , Tripathi Vijay Shanker
Published: September 2024

This study presents a wideband CPW fed four-port multi-input multi-output (MIMO) antenna-based microwave sensor for measuring deep brain temperature inside human head. The range of frequency is from 5.45 to 8.56 GHz at the resonance frequency of 7.25 GHz. Coplanar Waveguide (CPW) fed approach on a low-cost FR4 substrate is used in designing the planar antenna. The presented antenna is compact, having overall dimensions of 40 × 40 × 1.6 mm3. In the operational frequency range, the obtained diversity performance for MIMO antenna shows Envelope Correlation Coefficient (ECC) < 0.05, Diversity Gain > 9.95, total active reflection coefficient (TARC ) < −13 dB and channel capacity loss (CCL) < 0.125 bits/S/Hz. For deep brain temperature measurements, the proposed sensor provides electric field of 3.92 V m−1 and specific absorption rate (SAR) value of 0.16 W kg−1 at the 9 mm distance inside the human head Phantom. The proposed sensor is capable of measuring 0.1 °C change in temperature at 10 W of input power for 60 s when all four ports are active.
Electrical and dynamic mechanical analysis of kenaf fiber reinforced epoxy composites hybridized with acacia concinna powder
Authors: Pachappareddy Chiranjeevi , Padhy Chinmaya Prasad , Pendyala Srinivas
Published: September 2024

The objective of this work is to create and analyze composites made of kenaf fibers and epoxy polymer, which are strengthened by the addition of Acacia concinna pod (ACP) powder. These composites are intended for use in electrical insulation applications. The study investigated the impact of alkali treatment using a 6% NaOH solution on kenaf fiber. The effects were analyzed in relation to the dielectric and dynamic mechanical properties, while also considering the addition of different quantities of ACP powder (0%, 4%, and 8%). The composites were produced using the hand layup method, and the dielectric constant, dissipation factor, storage modulus, loss modulus, and damping factor were assessed. The study demonstrated that NaOH-treated kenaf fiber composites displayed considerably lower dielectric constant values than untreated composites, due to increased fiber crystallinity and decreased moisture absorption. Dynamic Mechanical Analysis (DMA) found that untreated composites had greater storage modulus and glass transition temperature (Tg) due to reduced segmental motion at the fiber-matrix interface. SEM research indicated better fiber-matrix bonding in treated composites, with decreased voids and robust interlocking, notably in those containing 4% Acacia concinna (ACP) filler. These findings show that NaOH treatment substantially enhances the performance of hybrid composites for electrical insulating applications.
Physical properties of La:ZnO thin films prepared at different thicknesses using spray pyrolysis technique
Authors: Alsaiari Norah A , Awad Abanoub A , Ismail Motaz F , Aboud Ahmed A
Published: September 2024

Herein we investigate the impact of film thickness on the physical properties of Lanthanum (La) doped ZnO thin films. The films were fabricated using the spray pyrolysis technique with a consistent La content of 5 weight (wt) % in the initial solution. X-ray diffraction analysis indicated the presence of a hexagonal ZnO phase with preferred orientation along the (002) direction and no other phases were detected. The crystallite sizes were calculated using the Halder-Wagner equation, with a maximum size of 16.1 nm observed for a film thickness of 106 nm. Field-emission scanning electron microscopy (FE-SEM) images revealed the formation of a continuous film with an average grain size that increased as the thickness of the film increased. The grain size ranged from 74.5 to 136 nm as the film thickness varied from 106 to 426 nm. Films with lower thicknesses up to 196 nm exhibited two band gaps at approximately 3.2 and 4 eV, while films with higher thicknesses displayed a single band gap around 3.2 eV. The refractive index dispersion for all films was modeled using the Cauchy model, with parameters showing high dependence on the thickness values.The refractive index at high frequency, as calculated using the Cauchy model, was observed to decrease with increasing film thickness, ranging from 1.87 at 106nm to 1.63 at 426nm. Similar values were obtained by fitting the optical refractive index data with the Wemple-DiDomenico relation. Additionally, the UV sensing performance of the films was evaluated against UV light of a single wavelength (365 nm) at applied voltages of 10, 20, and 30V. The rise and decay times were measured, with the film thickness of 426 nm exhibiting the shortest rise and decay times at a specific applied voltage.
Stable RbCsFAPbI3 perovskite solar cell: numerical modelling and optimisation using SCAPS-1D
Authors: Valeti Naga Jyothi , Singha Monoj Kumar , Tupakula Sreenivasulu
Published: September 2024

The studies concerning solar cell technology has consistently been captivating and inspiring, largely because of its environmentally friendly and sustainable characteristics. The outstanding electronic, optical, mechanical, and electrical characteristics of perovskite materials make them crucial for the development of the photovoltaic industry. In order to model the mixed cation Rb0.05Cs0.1FA0.85PbI3 perovskite solar cells, the SCAPS-1D tool was used. The main feature of RbCsFAPbI3 perovskite is its remarkable stability, and wide bandgap. Rubidium (Rb) and cesium (Cs) cations improve the optoelectronic characteristics of the material, resulting in less non-radiative recombination and improved charge transfer. In this work, the effects of different hole transport layers (CuSCN, CuSbS2,Cu2O) and back metal contacts (Ag, Fe, C-Cu, Au, Ni, Pt) on solar cell performance were investigated. The maximum efficiency of the solar cell has been achieved by studying various parameters like temperature, series resistance, shunt resistance, defect density, and absorber layer thickness. With FF = 84.12%, Jsc = 24.52 mA cm−2, Voc = 1.19 V, and the configuration of FTO/TiO2/RbCsFAPbI3/Cu2O/Au, the optimised device obtains a PCE of 24.64%. The impressive enhancements in performance parameters observed in the structure of the device make it highly suitable for applications in solar energy harvesting systems.
A New Parameterization for Finding Solutions for Microlensing Exoplanet Light Curves
Authors: Hall Kylie E. , Yee Jennifer C. , Shin In-Gu , Yang Hongjing , Zhang Jiyuan
Published: September 2024

The gravitational microlensing method of discovering exoplanets and multi-star systems can produce degenerate solutions, some of which require in-depth analysis to uncover. We propose a new parameter space that can be used to sample potential solutions more efficiently and is more robust at finding all degenerate solutions for the “central-resonant” caustic degeneracy. We identified two new parameters, k and h, that can be sampled in place of the mass ratios and separations of the systems under analysis to identify degenerate solutions. The parameter k is related to the size of the central caustic, Δξ

c
, while h is related to the distance of a point along the k contour from log(s) = 0, where s is the projected planet-host separation. In this work, we present the characteristics of these parameters and the tests we conducted to prove their efficacy.
Ultra-low frequency calibration of broadband seismometers using a tilt table
Authors: Bruns Th , Klaus L , Yan N , Gonzalez M A
Published: September 2024

The calibration of current broadband seismometers with the aim to provide traceability to the International System of Units (SI) is an active topic in the vibration metrology community. As it turns out, this exercise has its specific challenges at very low frequencies. A major problem faced at that end of the applicable frequency range, is the influence of tilt as a disturbance on the measured motion component of the sensor. In the presented work, the roles of the quantities are swapped. Tilt is used as a means of excitation of a seismometer and the rectilinear motion is considered as a disturbance, which is, however, well defined by the facilitated set-up. As it is demonstrated, this approach can be very beneficial for very low frequency calibration, and if applied correctly, it can provide a reliable link to base units of the SI.
Hydrogen isotopic ratio by residual gas analysis during the JET DT campaigns
Authors: Gervasini G. , Laguardia L. , Douai D. , Matveev D. , Borodin D. , Borodkina I. , Pawelec E. , Jepu I. , Widdowson A. ,   JET Contributors
Published: September 2024

The analysis of hydrogen isotope content is crucial for understanding the operation of fusion devices. Hydrogen isotopic analysis in the core and plasma edge is conducted through neutron and spectroscopic diagnostics. In the case of exhaust gas, mass spectrometry is employed using residual gas analyzers (RGAs), along with optical gas analysis (OGA) utilizing an optical Penning gauge. The use of traditional quadrupole mass spectrometers for RGA encounters challenges during discharges with tritium gas due to signal overlap of different hydrogen molecules at the same mass number. This paper introduces a novel technique to address this issue through a cross-related analysis of RGA data with OGA results. Another consideration in mass spectrometry analysis is the instrument’s varying sensitivity concerning the gas mass number. The paper includes gas calibration data results for all the quadrupoles used in the JET gas analysis. Hydrogen isotopic ratios are calculated from RGA detected currents using simple formulas. The results of this procedure are presented for selected DT JET discharges in relation to the JET pulse time, and they are compared with corresponding optical data. Time-averaged hydrogen isotopic ratio values are computed for numerous discharges during the DT and T campaigns.
Simultaneous comparison of the chemical composition and attributable source of PM2.5 during 2014–2018 in major metropolitan cities in South Korea: impacts of policy interventions
Authors: Kim Sangcheol , Yi Seung-Muk , Park Jung Min , Song In Ho , Jeon Kwonho , Park Jieun
Published: September 2024

Ambient fine particulate matter (PM2.5) is one of the most concerning pollutants, characterized by its diverse chemical composition. Although various studies have revealed PM2.5 chemical components, there is limited knowledge on how to reduce PM2.5 concentrations through administrative policies focusing on source management. We compared PM2.5 compositional characteristics and performed source apportionment using the positive matrix factorization in four metropolitan cities (Seoul, Daejeon, Gwangju, and Ulsan) in South Korea from 2014 to 2018. The annual average of PM2.5 concentrations exceeded the annual national ambient air quality standard of 15 μg m−3 in all areas while secondary inorganic aerosols constituted the largest fraction of PM2.5. This implies that secondary formation from gaseous precursors in the atmosphere was the main factor contributing to ambient PM2.5. However, the concentrations of PM2.5 constituents varied significantly across cities, suggesting that PM2.5 is a heterogeneous pollutant considerably influenced by region-specific conditions. In line with the chemical composition, secondary nitrate, secondary sulfate, and mobile-related sources were found to be significant contributors of PM2.5. Additionally, the extent of contribution from each source varied across the study regions. We also evaluated the impacts of policy interventions by comparing the PM2.5 composition and source apportionment before and after the policies for improving air quality. Intensive programs focused on the vehicle sector in Seoul led to a considerable decrease in the concentrations of carbonaceous compounds and mobile-related sources. In addition, strengthened regulations on coal-fired power plants (CFPPs) since 2016 have influenced the contributions of coal combustion sources in two cities adjacent to the west coastal area, where approximately half of the CFPPs of the country are densely distributed. Overall, the study’s findings indicate that region-specific PM2.5 chemical constituents and source contributions should be considered for establishing PM2.5-related policies, considering the high heterogeneity of PM2.5.
Blue in green: forestation turns blue water green, mitigating heat at the expense of water availability
Authors: Asselin Olivier , Leduc Martin , Paquin Dominique , de Noblet-Ducoudré Nathalie , Rechid Diana , Ludwig Ralf
Published: September 2024

In order to meet a stringent carbon budget, shared socioeconomic pathways (SSPs) aligned with the Paris Agreement typically require substantial land-use changes (LUC), such as large-scale forestation and bioenergy crop plantations. What if such a low-emission, intense-LUC scenario actually materialized? This paper quantifies the biophysical effects of LUC under SSP1-2.6 using an ensemble of regional climate simulations over Europe. We find that LUC projected over the 21st century, primarily broadleaf-tree forestation at the expense of grasslands, reduce summertime heat extremes significantly over large swaths of continental Europe. In fact, cooling from LUC trumps warming by greenhouse gas (GHG) emissions, resulting in milder heat extremes by 2100 for about half of the European population. Forestation brings heat relief by shifting the partition of turbulent energy fluxes away from sensible and towards latent heat fluxes. Impacts on the water cycle are then assessed. Forestation enhances precipitation recycling over continental Europe, but not enough to match the boost of evapotranspiration (green water flux). Run-off (blue water flux) is reduced as a consequence. Some regions experience severe drying in response. In other words, forestation turns blue water green, bringing heat relief but compromising water availability in some already-dry regions.
A safe operating space for the major rivers in the Bangladesh Delta
Authors: Kabir Alamgir , Hasan Md Mahmudul , Rokonuzzaman Sheikh , Abdullah Hasan Muhammad , Hossain Md Sarwar
Published: September 2024

The contributions of water to all Sustainable Development Goals (SDGs) are critical to achieving SDGs in the context of climate change. This poses a major challenge as nearly 40% of the global population lives under water scarcity, including areas such as Bangladesh, which is one of the largest, most populous and climate-vulnerable deltas (Ganges–Brahmaputra–Meghna: GBM) in the world. Here, we aim (first attempt) to analyse the historical dynamics (spatial and temporal) of river flows in 10 major rivers and provide policy implications using a safe operating space (SOS) concept for the Bangladesh delta. In general, the space just before the unsustainable state is defined as an SOS, within which humanity can safely thrive and beyond which is dangerous to humanity. Time series analysis highlights that all seasonal river flow shows a decreasing trend in the last three decades except in the winter season. The hydrological alteration using range of variability approach confirms that the majority of the river flow has been altered high to severely including three major rivers (Ganges, Jamuna, and Old Brahmaputra) in the Bangladesh delta. Our findings show that four out of ten rivers (Ganges (dry season), Gorai, Halda and Old Brahmaputra) exceeded the SOS, with the rest of the six rivers given cautious status considering the hydrological alteration (moderate to severe) in the Bangladesh delta. Our assessment provides scientific evidence to inform science and policy related to transboundary water disputes and achieving SDGs in Bangladesh and South Asia.
Laplacian operator and its square lattice discretization: Green function vs. Lattice Green function for the flat 2-torus and other related 2D manifolds
Authors: Mamode Malik
Published: September 2024

The paper investigates the truncation error between the Green function and the lattice Green function (LGF) for the Laplacian operator defined on the 2-torus and its discretization on a regular square lattice. Extensions to the cylinder and the rectangular domain with free (or Neumann) boundary conditions are also proposed. In each of these instances, the Green function and its discrete analog are given in exact analytical closed-form allowing to infer accurate estimates as the lattice spacing tends to zero. As expected, it is shown that the continuum limit of the LGF coincides well with the Green function in every case. In particular, the issue of logarithmic singularity regularization of the Green function by the lattice discretization is addressed through two related application examples regarding the rectangular domain, and devoted to the computation of corner-to-corner resistance of an electrical conducting square and the mean first-passage time between the diagonally opposite vertices of a square for a standard Brownian motion, both derived considering the continuum limit.
Tradeoff relations in open quantum dynamics via Robertson, Maccone–Pati, and Robertson–Schrödinger uncertainty relations
Authors: Nishiyama Tomohiro , Hasegawa Yoshihiko
Published: September 2024

The Heisenberg uncertainty relation, together with Robertson’s generalisation, serves as a fundamental concept in quantum mechanics, showing that noncommutative pairs of observables cannot be measured precisely. In this study, we explore the Robertson-type uncertainty relations to demonstrate their effectiveness in establishing a series of thermodynamic uncertainty relations and quantum speed limits in open quantum dynamics. The derivation utilises a scaled continuous matrix product state representation that maps the time evolution of the quantum continuous measurement to the time evolution of the system and field. Specifically, we consider the Maccone–Pati uncertainty relation, a refinement of the Robertson uncertainty relation, to derive thermodynamic uncertainty relations and quantum speed limits. These newly derived relations, which use a state orthogonal to the initial state, yield bounds that are tighter than previously known bounds. Moreover, we consider the Robertson–Schrödinger uncertainty, which extends the Robertson uncertainty relation. Our findings not only reinforce the significance of the Robertson-type uncertainty relations, but also expand its applicability in identifying uncertainty relations in open quantum dynamics.
Discovery and analysis of the relationship between organic components in exhaled breath and bronchiectasis
Authors: Fan Lichao , Chen Yan , Chen Yang , Wang Ling , Liang Shuo , Cheng Kebin , Pei Yue , Feng Yong , Li Qingyun , He Mengqi , Jiang Ping , Chen Haibin , Xu Jin-fu
Published: September 2024

The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy (ACC) of breath biomarkers (BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning (ML) method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without nontuberculous mycobacterium (NTM), P. aeruginosa (P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. The top ten BBs based ML model achieved an area under the curve of 0.940, sensitivity of 90.7%, specificity of 85%, and ACC of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection, P.a isolation, the number of involved lobes, and three lung functional parameters in BE patients. BBs-based BE detection model showed good ACC for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for BE screening and personalized management.
Innovative 3D bioprinting approaches for advancing brain science and medicine: a literature review
Authors: Bocheng Xu , França Rodrigo
Published: September 2024

The rapid advancements in 3D printing technology have revolutionized the field of tissue engineering, particularly in the development of neural tissues for the treatment of nervous system diseases. Brain neural tissue, composed of neurons and glial cells, plays a crucial role in the functioning of the brain, spinal cord, and peripheral nervous system by transmitting nerve impulses and processing information. By leveraging 3D bioprinting and bioinks, researchers can create intricate neural scaffolds that facilitate the proliferation and differentiation of nerve cells, thereby promoting the repair and regeneration of damaged neural tissues. This technology allows for the precise spatial arrangement of various cell types and scaffold materials, enabling the construction of complex neural tissue models that closely mimic the natural architecture of the brain. Human-induced pluripotent stem cells (hiPSCs) have emerged as a groundbreaking tool in neuroscience research and the potential treatment of neurological diseases. These cells can differentiate into diverse cell types within the nervous system, including neurons, astrocytes, microglia, oligodendrocytes, and Schwann cells, providing a versatile platform for studying neural networks, neurodevelopment, and neurodegenerative disorders. The use of hiPSCs also opens new avenues for personalized medicine, allowing researchers to model diseases and develop targeted therapies based on individual patient profiles. Despite the promise of direct hiPSC injections for therapeutic purposes, challenges such as poor localization and limited integration have led to the exploration of biomaterial scaffolds as supportive platforms for cell delivery and tissue regeneration. This paper reviews the integration of 3D bioprinting technologies and bioink materials in neuroscience applications, offering a unique platform to create complex brain and tissue architectures that mimic the mechanical, architectural, and biochemical properties of native tissues. These advancements provide robust tools for modelling, repair, and drug screening applications. The review highlights current research, identifies research gaps, and offers recommendations for future studies on 3D bioprinting in neuroscience. The investigation demonstrates the significant potential of 3D bioprinting to fabricate brain-like tissue constructs, which holds great promise for regenerative medicine and drug testing models. This approach offers new avenues for studying brain diseases and potential treatments.
Assessing anticancer properties of PEGylated platinum nanoparticles on human breast cancer cell lines using in-vitro assays
Authors: W Felicia Aswathy , Jose Jiya , E I Anila
Published: September 2024

This study describes the in-vitro cytotoxic effects of PEG-400 (Polyethylene glycol-400)-capped platinum nanoparticles (PEGylated Pt NPs) on both normal and cancer cell lines. Structural characterization was carried out using x-ray diffraction and Raman spectroscopy with an average crystallite size 5.7 nm, and morphological assessment using Scanning electron microscopy (SEM) revealed the presence of spherical platinum nanoparticles. The results of energy-dispersive x-ray spectroscopy (EDX) showed a higher percentage fraction of platinum content by weight, along with carbon and oxygen, which are expected from the capping agent, confirming the purity of the platinum sample. The dynamic light scattering experiment revealed an average hydrodynamic diameter of 353.6 nm for the PEGylated Pt NPs. The cytotoxicity profile of PEGylated Pt NPs was assessed on a normal cell line (L929) and a breast cancer cell line (MCF-7) using the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results revealed an IC50 of 79.18 μg ml−1 on the cancer cell line and non-toxic behaviour on the normal cell line. In the dual staining apoptosis assay, it was observed that the mortality of cells cultured in conjunction with platinum nanoparticles intensified and the proliferative activity of MCF-7 cells gradually diminished over time in correlation with the increasing concentration of the PEGylated Pt NPs sample. The in vitro DCFH-DA assay for oxidative stress assessment in nanoparticle-treated cells unveiled the mechanistic background of the anticancer activity of PEGylated platinum nanoparticles as ROS-assisted mitochondrial dysfunction.
Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks
Authors: Rouhollahi Amir , Rismanian Milad , Ebrahimi Amin , Ilegbusi Olusegun J , Nezami Farhad R
Published: September 2024

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study’s major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.
Ag enhanced CuS nanoflower catalyst coupling dielectric barrier discharge plasma for disinfection performance and mechanism
Authors: WANG 王 Run 润 , WANG 王 Man 蔓 , YANG 杨 Zhouling 周铃 , WANG 王 Mingyu 明宇 , LIU 刘 Leyi 乐怡 , ZHOU 周 Lu 鹭 , ZHANG 张 Yi 轶 , SONG 宋 Yingqi 英琦
Published: September 2024

In this study, the hydrothermal method was employed to grow submicron CuS on carbon cloth (CC), and the photoreduction method was used to grow Ag nanoparticles on the CuS submicron flowers, thus forming the Ag/CuS/CC catalytic electrode. The application of Ag/CuS/CC electrode-coupled dielectric barrier discharge (DBD) plasma in the disinfection of pathogenic bacteria in water was studied. The Ag/CuS/CC electrode exhibits strong antibacterial activity, and under an external voltage of 30 V, the degradation efficiency of Bacillus subtilis reaches 99.99% within 15 min without regeneration. After five cycles, the inactivation rate of Bacillus subtilis reached 99.99% within 25 min. The practical applicability of the Ag/CuS/CC-coupled DBD system for treating actual wastewater was evaluated, and the changes in biological toxicity were investigated. The results indicate that the prepared Ag/CuS/CC coupled DBD has great potential for safe disinfection of pathogenic bacteria in water through integrated processes.
Analytical and FE modeling of a bi-directional functionally graded Timoshenko beam under thermomechanical loading
Authors: Omar Omar K. , Al-Huniti Naser S N , Al-Jeradat Amer G
Published: September 2024

Functionally graded materials (FGMs) have variable volume fractions in different directions, making structural analysis challenging due to shear deformation. This study investigates the behavior of a two-directional functionally graded material (TDFGM) Timoshenko beam under thermomechanical loads. Varied material properties in both axial and transverse directions are analyzed using a mathematical approach and a finite element model. The mathematical approach involves transforming the system of two ordinary differential equations to a single fourth-order differential equation to determine deflection and rotation. Additionally, a bilinear quadrilateral element is used to validate the mathematical solution. The results show that increasing grading parameters reduced transverse deflection, with a maximum reduction of 38.97% for CF beams and 39.26% for SS beams when the gradation index

a

z

increased from 0 to 1 under mechanical loading. Under combined mechanical and thermal loading, higher temperatures on the beam’s hot side lead to increased deflection, with notable increases at higher gradation indices. Moreover, increased maximum deflection by 6.29% for CF beams and 4.05% for SS beams when considering temperature-dependent properties. It was observed that when TDFGM Timoshenko beams were heated for thermal loading, the SS beam deflected in the opposite direction of the applied mechanical load. The results agree with previous research and indicate alignment between mathematical and finite element solutions. The impact of boundary conditions on the deflection of Timoshenko beams made of TDFGM is discussed. The introduced methodology enhances TDFGM beam bending analysis, allowing control over behavior through grading parameters and boundary conditions selection.
Building community resilience to extreme heat: Lessons learned from Spokane, WA community conversations
Authors: Reed A , Henning B , Cortes Espinosa M , McKinley H , Busch Isaksen T
Published: September 2024

Heat is the primary cause of weather-related mortality in the United States. The 2021 Northwest heat dome highlighted this susceptibility. In Washington State, 159 excess deaths were attributed to the 7-day period of unprecedented extreme heat between June 26th and July 2nd. This impact was felt even in some of the more acclimatized parts of the state, like Spokane County, where 19 heat-related deaths were reported. As climate change increases the frequency, duration, and intensity of extreme heat events, creating and sustaining heat-resilient communities has become an urgent public health priority. On 6 June 2023, Gonzaga University, in partnership with the University of Washington, hosted the Spokane Extreme Heat Risk Intervention Stakeholder Synthesis Symposium. The goals of the symposium were to debrief from recent heat events, identify extreme heat risk reduction interventions used in the region, and characterize remaining practice-relevant research priorities. The symposium convened 45 stakeholders including representatives from local and state agencies, academia, and community-based and Tribal organizations. Symposium participants engaged in small group discussions using the World Café MethodTM. Notes from each discussion were coded using a content analysis approach. Symposium participants identified strengths, barriers to heat resilience, and solutions to reduce risk throughout the Spokane community. We present these findings by practice topic, including risk communication, intervention, collaboration, policy, and research. Additionally, we utilize the socio-ecological model as a conceptual framework to illustrate the complex interplay of factors that govern an individual’s experience of, and ability to respond to, extreme heat events. Given extreme heat’s impact on global public health, the methods used to increase community resilience in Spokane, WA, USA could be used by other communities worldwide to increase their own heat-resilience.
The GTP-tubulin cap is not the determinant of microtubule end stability in cells
Authors: Cassidy Anna , Farmer Veronica , Arpağ Göker , Zanic Marija
Published: September 2024

Microtubules are dynamic cytoskeletal polymers essential for cell division, motility, and intracellular transport. Microtubule dynamics are characterized by dynamic instability—the ability of individual microtubules to switch between phases of growth and shrinkage. Dynamic instability can be explained by the GTP-cap model, suggesting that a “cap” of GTP-tubulin subunits at the growing microtubule end has a stabilizing effect, protecting against microtubule catastrophe—the switch from growth to shrinkage. Although the GTP-cap is thought to protect the growing microtubule end, whether the GTP-cap size affects microtubule stability in cells is not known. Notably, microtubule end-binding proteins, EBs, recognize the nucleotide state of tubulin and display comet-like localization at growing microtubule ends, which can be used as a proxy for the GTP-cap. Here, we employ high spatiotemporal resolution imaging to compare the relationship between EB comet size and microtubule dynamics in interphase LLC-PK1 cells to that measured in vitro. Our data reveal that the GTP-cap size in cells scales with the microtubule growth rate in the same way as in vitro. However, we find that microtubule ends in cells can withstand transition to catastrophe even after the EB comet is lost. Thus, our findings suggest that the presence of the GTP-cap is not the determinant of microtubule end stability in cells.
Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies
Authors: Liu Xin , Pang Yu , Shan Junqi , Wang Yunfei , Zheng Yanhua , Xue Yuhang , Zhou Xuerong , Wang Wenjun , Sun Yanlai , Yan Xiaojing , Shi Jiantao , Wang Xiaoxue , Gu Hongcang , Zhang Fan
Published: September 2024

Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.
Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA–gene–disease networks
Authors: Si Yuxuan , Huang Zihan , Fang Zhengqing , Yuan Zhouhang , Huang Zhengxing , Li Yingming , Wei Ying , Wu Fei , Yao Yu-Feng
Published: September 2024

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA–disease associations (MDAs), miRNA–target interactions (MTIs), and disease–gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides
Authors: Wang Qian , Hu Xiaotong , Wei Zhiqiang , Lu Hao , Liu Hao
Published: September 2024

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning’s strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.
BenchStab: a tool for automated querying of web-based stability predictors
Authors: Velecký Jan , Berezný Matej , Musil Milos , Damborsky Jiri , Bednar David , Mazurenko Stanislav
Published: September 2024

Protein design requires information about how mutations affect protein stability. Many web-based predictors are available for this purpose, yet comparing them or using them en masse is difficult. Here, we present BenchStab, a console tool/Python package for easy and quick execution of 19 predictors and result collection on a list of mutants. Moreover, the tool is easily extensible with additional predictors. We created an independent dataset derived from the FireProtDB and evaluated 24 different prediction methods.

Availability and implementation

BenchStab is an open-source Python package available at https://github.com/loschmidt/BenchStab with a detailed README and example usage at https://loschmidt.chemi.muni.cz/benchstab. The BenchStab dataset is available on Zenodo: https://zenodo.org/records/10637728
mgikit: demultiplexing toolkit for MGI fastq files
Authors: Al Bkhetan Ziad , Wang Sen
Published: September 2024

MGI sequencing is reported to be an inexpensive solution to obtain genomics information. There is a growing need for software and tools to analyse MGI’s outputs efficiently. mgikit is a tool collection to demultiplex MGI fastq data, reformat it effectively and produce visual quality reports. mgikit overcomes several limitations of the standard MGI demultiplexer. It is highly customizable to suit different kinds of datasets and is designed to achieve high performance and optimal memory utilization.

Availability and implementation

The tool and its documentation are available at: https://sagc-bioinformatics.github.io/mgikit/.
Dual-energy CT: Impact of detecting bone marrow oedema in occult trauma in the Emergency
Authors: Ahmad Muhammad Israr , Liu Lulu , Sheikh Adnan , Nicolaou Savvas
Published: September 2024

Dual-energy computed tomography (DECT) is an advanced imaging technique that acquires data using two distinct X-ray energy spectra, typically at 80 and 140 kVp, to differentiate materials based on their atomic number and electron density. This capability allows for the enhanced visualisation of various pathologies, including bone marrow oedema (BMO), by providing high-resolution images with notable energy spectral separation while maintaining radiation doses comparable to conventional CT. DECT’s ability to create colour-coded virtual non-calcium (VNCa) images has proven particularly valuable in detecting traumatic bone marrow lesions (BMLs) and subtle fractures, offering a reliable alternative or complement to MRI. DECT has emerged as a significant tool in the detection and characterisation of bone marrow pathologies, especially in traumatic injuries. Its ability to generate high-resolution images and distinguish between different tissue types makes it a valuable asset in clinical diagnostics. With its comparable diagnostic accuracy to MRI and the added advantage of reduced examination time and increased availability, DECT represents a promising advancement in the imaging of BMO and related conditions.