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Fabrication and characterizations of 3D printed GelMA-Gel/bioactive glass scaffolds containing cerium for bone damage repair
Models for sustainable management of livestock waste based on neural network architectures
Dynamics and vibrational spectroscopy of quasi-one dimensional water wires inside carbon nanotubes of different diameter and chirality
Abstract Water strongly confined in nanostructures such as carbon nanotubes (CNTs) exhibits structural, dielectric, transport, dynamical and thermodynamical properties vastly different from bulk water, due to a strong modification of the (three-dimensional) hydrogen bond network. In this work, we mainly address the following aspects of extremely confined, quasi-one dimensional water chains in CNTs which have have not been emphasized much so far: The effect of chirality of the CNT, strong interactions with the hydrophobic walls and the (altered) vibrational response of confined water. Specifically, we have studied the (i) translation / diffusion, (ii) rotation / reorientation and (iii) vibrations of water chains confined within narrow carbon nanotubes (CNTs) with chirality indices (6,2), (6,4) and (6,6) using ab initio molecular dynamics. Special emphasis is on vibrational spectra, notably in the OH stretch region, obtained from fluctuations in the local OH stretching modes which were further employed to obtain two-dimensional infrared spectra and frequency-frequency correlation functions. We find that the vibrational distribution of water molecules under confinement is overall blue-shifted in comparison to bulk water, due to a breakdown of the three-dimensional hydrogen bond network. Further, the vibrational dynamics were found to dependent strongly upon the chirality and diameter of the CNTs, the latter causing stronger hydrophobic interactions with the walls of the nanotube. With respect to translational and rotational motion, the CNT-confined water molecules exhibit slower translational diffusion and faster reorientational motion compared to bulk liquid water for all cases simulated in this work.
Intraarticular injection of the stromal vascular fraction for the treatment of knee osteoarthritis a prospective randomized controlled clinical trial
Green finance and environmental decentralization drive OECD low carbon transitions
Development of Epstein Barr virus biosensor by mimicking its infection mechanism for early detection of multiple sclerosis
Abstract Given the established link between Epstein-Barr virus (EBV) and multiple sclerosis, the early and asymptomatic detection of EBV has become increasingly important. In this study, we developed an impedimetric EBV biosensor designed for practical and early screening, based on mimicking the virus’s natural infection mechanism. To achieve this, the B cell surface protein CD21—serving as the host receptor—was immobilized onto a carbon screen-printed electrode. The interaction between CD21 and the EBV surface glycoprotein gp350 was monitored using electrochemical impedance spectroscopy. Following the optimization of experimental parameters, the biosensor’s analytical performance was evaluated. It exhibited a linear detection range from 5 to 100 ng/mL, with a limit of detection of 0.0074 ng/mL, a limit of quantification of 0.022 ng/mL, and a relative standard deviation of 1.34%. The optimized biosensor was then tested using 1:100 diluted saliva samples collected from healthy individuals, yielding highly promising results that support its potential for real-world applications.
Elucidation of crystal growth, structural characterization, thermal properties, and molecular dynamics using NMR near phase transition temperature of [N(CH3)4]2CoCl4
IC2Bert: masked gene expression pretraining and supervised fine tuning for robust immune checkpoint blockade (ICB) response prediction
Abstract Bulk RNA-seq-based prediction of immune checkpoint blockade (ICB) responses has been extensively studied to distinguish responders from non-responders. However, cohort heterogeneity remains a major challenge, hindering the robustness and generalizability of predictive models across diverse RNA-seq datasets. In this study, we present IC2Bert, a novel model that employs masked gene expression pretraining combined with domain-specific supervised fine-tuning to enhance predictive robustness across heterogeneous ICB response cohorts. To ensure an objective evaluation, we assessed the model’s performance using a Leave-One-Dataset-Out Cross-Validation (LODOCV) approach. IC2Bert demonstrated significantly improved predictive accuracy and robustness compared to existing methods, effectively addressing the challenges posed by cohort heterogeneity. The IC2Bert model and its source code are publicly available on GitHub: https://github.com/data2intelligence/ic2bert.
Driven degenerate Λ -type three-level laser with squeezed vacuum reservoir
The molecular mechanism of the protective effect of tianeptine against ovarian ischemia-reperfusion injury
Impact of asymptomatic malaria infection on children’s growth in rural Malawi
Abstract Asymptomatic malaria infections are common in endemic regions, yet their impact on children’s growth remains inadequately understood. This study investigates the association between asymptomatic malaria and 6-18-month-old children’s growth indices in rural Malawi. Dried blood spots from 840 participants in Lungwena Child Nutrition Intervention 5 (LCNI-5) clinical trial were analysed at the baseline (N = 697) and every 3 months for a year. The associations between asymptomatic Plasmodium falciparum (determined by real-time PCR), and growth indices (length-for-age Z score (LAZ), weight-for-age Z score (WAZ), and weight-for-length Z score (WLZ)) were examined. Across all ages (6 to 18 months), malaria-positive children had lower mean WAZ (-1.03 vs. -0.87, 95% CI -0.17 − -0.04) and WLZ (-0.03 vs. -0.13, 95% CI -0.22 − -0.06) compared to those uninfected peers, whereas LAZ showed no significant association. However, no significant impact was observed at individual time points, except at 12 months of age. After adjusting for confounders, malaria infection remained associated with poorer children growth outcome. Asymptomatic malaria is linked to impaired growth outcomes among young children in rural Malawi. Targeted interventions aimed at managing asymptomatic malaria could mitigate growth faltering and improve child health in malaria-endemic settings.
Temporal prediction and feedforward control in cerebellar ataxia during spontaneous, instructed, and adaptive auditory-motor coupling while walking
The role of cognitive load in automatic integration of emotional information from face and body
Prevalence, characterization and antibiogram of Staphylococcus aureus isolates from bovine and swine population in Bareilly, Uttar Pradesh, India
Nanoprobe synchrotron X-ray fluorescence microscopy reveals selenium-rich spherical structure in mouse retinal pigment epithelium
Abstract High-resolution nano-focus X-ray fluorescence microscopy using hard X-rays at the European Synchrotron Radiation Facility (ESRF) IDB16 beamline detected endogenous barium, bromine, calcium, chlorine, copper, iron, manganese, potassium, phosphorus, rubidium, sulphur, selenium, strontium and zinc, at tissue, cellular and subcellular level in the outer retinal complex of light adapted, 3-week-old, male C57BL6 mice. Fresh snap-frozen (20 μm) cryosections dried at room temperature were scanned at 1 μm, 300 nm and 50 nm spatial resolution by incident X-ray photons from the synchrotron beam. Analysis of 2D maps and 3D surface plots by PyMCA and ImageJ revealed elevated zinc concentrations in the choriocapillaris (CC) (mean 45, range 28–77 ppm), retinal pigment epithelium (RPE) layer (mean 47, range 20–76 ppm), photoreceptor inner segments (RIS) ellipsoid zone, outer limiting membrane (OLM) (mean 32, range < 1–44 ppm) and outer nuclear layer (ONL) in between photoreceptor cell bodies. Mūller cells processes in ONL and their interdigitations in RIS ellipsoid zone seem to contain zinc in the cell membrane. Iron was found at elevated amounts in RIS myoid zone (mean 38, range 14–68 ppm), RPE layer (52, range 24–143 ppm), and choroid (60, range 36–172 ppm). Copper was also detected in the CC (4.3, range 1.9–9.7 ppm), RPE layer (4.5, range 1.6–20.8 ppm), and RIS myoid zone (4.9, range 1.25–10.2 ppm). Calcium was found with granular/punctate distribution in OLM (159, range 49–962 ppm), RIS myoid zone (245, range 36-1370 ppm), RPE layer (1134, range 257–2503 ppm), and CC (1101, range 323–2090 ppm). The metalloid selenium was present in the CC (1.8, range < 1-4.7 ppm] and across the RPE (basal, central, apical) (2.4, range < 1-8.5 ppm). High resolution maps of the interface photoreceptor outer segments (ROS) and the RPE apical side revealed selenium-rich spherical structures (appr. 1 μm diameter) (mean 5.6, range 2.2–8.1 ppm), associated with calcium (mean 1057, range 619–1755 ppm), phosphorus (9924, range 6118–15058 ppm), and manganese (0.7, < 1–24 ppm), surrounded by a zinc-containing layer. This study presents the first nanoprobe X-ray fluorescence microscopy image analysis of adult mouse light adapted outer retinal complex from the whole tissue to subcellular structures. The high spatial resolution (location) and high sensitivity (metal quantity) findings, together with the information on biometals available in the literature, allowed us to propose a schematic model of possible selenium biological processes and their role in physiological activities in the outer retinal complex. We hypothesise there is a dedicated selenium-rich spherical structure with the ability to cross RPE cell membranes (i.e. the outer blood retinal barrier) and with potential roles in certain biological function(s) (e.g. ROS phagocytosis by RPE cell microvilli, trans-RPE transport).
Climate-driven physiological changes in Mahseer (Tor tambroides) juveniles
A novel data-driven NLMPC strategy for techno-economic microgrid management with battery energy storage under uncertainty
Abstract As renewable energy sources become more widespread and energy consumption continues to grow, there is an urgent requirement for smarter, more flexible control methods to manage microgrids (MGs) effectively. This study proposes a data-driven nonlinear model predictive control (NLMPC) framework for optimized MG operation, emphasizing energy storage system (ESS) integration. Effective MG management is crucial given increasing renewable penetration and energy demands. This framework coordinates distributed generation (DG) units, including rotating and non-rotating resources, with a battery ESS in a dynamic MG environment. Leveraging Gaussian Process Regression (GPR), the framework accurately models the complex dynamics of both DG units and the ESS. Unlike traditional model-based approaches, GPR learns system behavior from operational data, enabling precise performance prediction under varying conditions. This accuracy is crucial for optimized resource dispatch and efficient MG operation. GPR models capture ESS charging/discharging characteristics, efficiency, and state-of-charge (SOC) dynamics for informed ESS utilization. To address renewable energy uncertainties, Monte Carlo simulations are incorporated. This allows robust evaluation of the control strategy under various scenarios, ensuring MG stability and reliability despite fluctuating renewable generation. By considering these uncertainties, the NLMPC controller proactively manages DG and ESS dispatch, mitigating forecast errors and maximizing renewable energy use. The framework aims to achieve optimal power flow, balancing supply and demand while respecting operational constraints. This includes constraints on DG units, the ESS (SOC limits, charge/discharge rates), and overall MG operation (voltage and frequency stability). The NLMPC controller dynamically adjusts DG and ESS setpoints to minimize costs, maximize renewable energy use, and ensure MG stability and reliability. Simulation results demonstrate the framework’s effectiveness. Significant cost savings (approximately 39.2% compared to Conventional MPC and 41.5% compared to Adaptive MPC) and voltage stability improvements (28.57% and 52.38% respectively) are achieved. These improvements stem from accurate system dynamics modeling, robust uncertainty handling, and coordinated DG and ESS control.