Rational and Multidimensional Optimization of Nanozyme Catalytic Performance.

The body tends to maintain a relatively constant number of peripheral T cells, a phenomenon termed T cell homeostasis. Homeostasis is controlled by the coordinated activity of extrinsic regulation, most notably through cytokines of the common gamma chain (cgammaC) family and intrinsic regulation by transcription factors. Whereas the former mechanism has been extensively studied and is relatively well characterized, the transcription factors that govern the homeostasis of late-stage effector and memory T cells have been less well defined but include regulators such as T-bet, Eomes, Bcl6, and Id2. The transcriptional repressor, Blimp-1 is well known as a master regulator of the terminal differentiation of B cells into antibody secreting plasma cells. Recent experiments have now revealed that Blimp-1 is also a key regulator of T cell differentiation. Blimp-1 is expressed in differentiated effector T cells and controls their homeostasis. Interestingly, Blimp-1 expression is controlled by the same cgammaC cytokines that regulate T cell homeostasis suggesting a direct link between the extrinsic and intrinsic arms of the process.

Background and Aims. CAVIAR (Comparing glycaemic benefits of Active Versus passive lifestyle Intervention in kidney Allograft Recipients) was a single-centre, randomised, controlled trial that recruited stable kidney transplant recipients without diabetes between 3-24 months’ post-transplantation. Participants were randomly assigned to active (personalised renal dietetic-led sessions using evidence-based behaviour change techniques) versus passive lifestyle intervention (current standard of care) for a six-month intervention period. Our findings showed encouraging clinical improvements including reduction in weight (mean difference -2.47kg [-.401 to -0.92], p=0.002), fat mass (-1.537 [-2.947 to -0.127], p=0.033) and rates of post-transplantation diabetes (7.6% versus 15.6% respectively, p=0.123). However, evidence from the general population suggests lifestyle intervention behaviour change techniques must be sustained to achieve the greatest benefit. It is unclear whether the same is true for kidney transplant recipients. Therefore, the aim of this study was to conduct a post-hoc analysis of the CAVIAR study to explore 1-year study outcomes (6-months after completion of the study interventions) to explore how clinical outcomes have evolved. Methods. The methodology of the CAVIAR study has already been summarised above and main findings published (Kuningas et al. Transplantation 2019; early online). Assessment of 1-year study outcomes was prespecified in the CAVIAR trial protocol to understand post-intervention outcomes. Between August 2015 and December 2017, 130 individuals were recruited to the trial and final study visits were conducted in February 2018. Electronic patient records were analysed for all study participants and 1-year outcome data (up to August 2019) were extracted in relation to cardio-metabolic risk outcomes where available. The trial is registered on clinicaltrials.org (identifier: NCT02233491). Results. While CAVIAR participants randomised to active versus passive lifestyle intervention demonstrated beneficial weight change in the six-month study period with a negative mean difference, in the six-month period after study completion we observed the opposite effect with a positive mean difference (+1.23 kg [-0.05 to 2.51], p=0.060). Two participants from both active and passive intervention arms subsequently developed post-transplantation diabetes in the six-months after study closure, although difference in post-transplantation diabetes rates remained clinically significant at 1-year (10.6% versus 18.8% respectively, p=0.143). There was no significant correlation between study baseline Situational Motivation Score components (intrinsic motivation, intrinsic regulation, extrinsic regulation or amotivation) and weight change either during or after CAVIAR study completion Conclusion. Our post-hoc analysis of the CAVIAR study suggest active lifestyle intervention using renal dietitian-led behaviour change techniques must be a continuous rather than a one-off strategy to sustain long-term clinical benefits and is unrelated to baseline motivation. This has implications for service delivery and workforce planning for established kidney transplant programs across Europe.

Asymmetric coordination structures in single-atom catalysts (SACs) represent a frontier in electrocatalysis, offering tunable electronic environments and enhanced catalytic performance beyond traditional symmetric M–N<sub>4</sub> motifs. This review first categorizes asymmetric SACs into four structural families: (1) single-metal asymmetric coordination, achieved by heteroatom substitution or axial ligand incorporation; (2) non-contact multi-metal sites, where adjacent but unbonded metal atoms synergize electronically; (3) directly bimetallic-bonded asymmetric coordination structures; and (4) bridged multi-metal constructs connected via non-metal linkers (e.g., O, N, S). Key synthetic strategies, including metal–organic framework confinement, defect engineering, dual-solvent loading, and macrocyclic precursor mediation, are examined in detail. Then we summarize applications in oxygen reduction reaction and CO<sub>2</sub> reduction reaction catalysis, and highlight how asymmetric coordination tunes intermediate adsorption energies, breaks scaling relations, and enables tandem catalysis to improve activity, selectivity, and stability. Advanced characterization techniques - aberration-corrected scanning transmission electron microscopy with electron energy loss spectroscopy, synchrotron X-ray absorption spectroscopy, and time-of-flight secondary ion mass spectrometry - are discussed for their roles in resolving atomic dispersion, coordination environment, oxidation states, and dynamic evolution under operando conditions. Finally, challenges and future directions are outlined, including precise low-temperature assembly of heteronuclear sites, scalability, long-term stability under harsh reaction conditions, selective pathway control, and the integration of operando analyses with theoretical modeling to guide rational catalyst design.

This paper presents the Pixel Super Resolution with Axial Scanning (PSR-AS) technique to enhance the resolution of Lensless Digital In-line Holographic Microscopy (LDHM). By utilizing multiple holograms captured at different axial distances, PSR allows for the recovery of high-resolution information, improving the image clarity and information content. Experimental results demonstrate a twofold lateral resolution enhancement, achieving the resolution of 1.23 μm in amplitude imaging and significantly augmenting phase reconstruction quality. The integration of PSR-AS into LDHM systems leads to notable reductions in noise and artifacts, offering a promising approach for high-resolution imaging in applications such as biomedical diagnostics and environmental monitoring. Full Text: PDF References Y. Park, C. Depeursinge, and G. Popescu, "Quantitative phase imaging in biomedicine," Nature Photon, 12(10), 578 (2018). CrossRef D. Gabor, "A New Microscopic Principle," Nature, 161(4098), 777 (1948). CrossRef K. Matsushima and T. Shimobaba, "Band-Limited Angular Spectrum Method for Numerical Simulation of Free-Space Propagation in Far and Near Fields," Opt. Express, 17(22), 19662 (2009). CrossRef Y. Wu and A. Ozcan, "Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring," Methods, 136, 4 (2018). CrossRef J. Zhang, J. Sun, Q. Chen, and C. Zuo, "Resolution Analysis in a Lens-Free On-Chip Digital Holographic Microscope," IEEE Transactions on Computational Imaging, 6, 697 (2020). CrossRef Gerchberg, R. W.. “A practical algorithm for the determination of phase from image and diffraction plane pictures.” Optik 35, 237 (1972). CrossRef H. Lee, J. Kim, J. Kim, P. Jeon, S. A. Lee, and D. Kim, "Noniterative sub-pixel shifting super-resolution lensless digital holography," Opt. Express,29(19), 29996 (2021). CrossRef A. Greenbaum and A. Ozcan, "Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy," Opt. Express, 20(3), 3129 (2012). CrossRef J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, "Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy," Sci Rep, 7(1), 11777 (2017). CrossRef L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D: Nonlinear Phenomena, 60(1), 259 (1992). CrossRef

We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or to request wireless energy transfer. Second, multiple mobile nodes can cooperate and form coalitions to help one another in the delivery of packets from their associated data sources. However, this cooperation may not be always beneficial due to the limited buffer and energy resources. Moreover, some mobile nodes may secretly decide to deviate from a given coalition, thus taking advantage of the other innocent mobile nodes. To address these two issues, this paper introduces a performance analysis and optimization framework, which is based on a joint optimization and game-theoretic framework. The optimization model is used to obtain the packet delivery policy of each individual mobile node. Then, a novel game-theoretic model, namely a repeated coalition formation game , is developed to analyze the cooperation strategies of multiple mobile nodes.

Resident adult epithelial stem cells maintain tissue homeostasis by balancing self-renewal and differentiation. The stem cell potential of human epidermal keratinocytes is retained in vitro but lost over time suggesting extrinsic and intrinsic regulation. Transcription factor-controlled regulatory circuitries govern cell identity, are sufficient to induce pluripotency and transdifferentiate cells. We investigate whether transcriptional circuitry also governs phenotypic changes within a given cell type by comparing human primary keratinocytes with intrinsically high versus low stem cell potential. Using integrated chromatin and transcriptional profiling, we implicate IRF2 as antagonistic to stemness and show that it binds and regulates active cis-regulatory elements at interferon response and antigen presentation genes. CRISPR-KD of IRF2 in keratinocytes with low stem cell potential increases self-renewal, migration and epidermis formation. These data demonstrate that transcription factor regulatory circuitries, in addition to maintaining cell identity, control plasticity within cell types and offer potential for therapeutic modulation of cell function.

Semiconductor materials form the technological backbone of modern microelectronics and optoelectronics, enabling continuous advancements in computing, communication, sensing, and energy conversion. As device dimensions approach atomic scales and performance demands intensify, conventional silicon-based technologies face fundamental physical and material limitations. Consequently, current research has shifted toward the exploration of novel semiconductor materials, advanced fabrication techniques, and heterostructure engineering to sustain progress in device efficiency, speed, and integration density. Emerging materials such as wide-bandgap semiconductors, compound III-V materials, two-dimensional layered semiconductors, and perovskite-based systems have demonstrated significant potential for next-generation electronic and photonic applications. These materials offer superior electrical mobility, tunable bandgaps, enhanced thermal stability, and strong light-matter interactions, making them suitable for high-frequency transistors, power electronics, light-emitting devices, and photodetectors. In parallel, advancements in nanofabrication, epitaxial growth, and defect engineering have enabled precise control over material properties at the nanoscale, further expanding application possibilities. However, challenges related to material integration, scalability, interface stability, and long-term reliability continue to impede large-scale commercialization. Current research trends increasingly emphasize material compatibility with existing manufacturing infrastructure, sustainable processing methods, and performance optimization through computational modeling and machine learning approaches. This article provides a focused overview of recent research trends in semiconductor materials for microelectronics and optoelectronics, highlighting key material systems, technological drivers, and unresolved challenges. By synthesizing recent developments, the research aims to clarify the evolving research landscape and identify directions that may enable future breakthroughs in semiconductor device technologies and integrated optoelectronic systems across industrial and scientific domains.

Polysaccharide-based molecularly imprinted polymers: Design, synthesis, sustainability, characterization, and applications.

Smart-PAD: A multi-channel mechanized approach for high-throughput paper-based colorimetric analysis: Determination of ammonia via the Berthelot reaction.

Surface-Enhanced Raman Scattering (SERS) integrated with optical waveguide sensing offers a transformative approach to overcoming the limitations of conventional SERS techniques, such as complex alignment requirements and limited signal collection efficiency. By leveraging the unique properties of optical waveguides, this integration significantly enhances detection sensitivity, simplifies sensor design, and enables the analysis of ultra-low concentration analytes in trace-volume samples. This review explores the latest advancements in combining diverse optical waveguide architectures with SERS technology, focusing on strategies to optimize the sensing interface and SERS substrate design for maximal Raman signal enhancement. By enabling efficient analyte excitation and enhanced scattered signal collection through waveguide-mediated light-matter interactions, this approach unlocks new possibilities for high-sensitivity Raman detection. Furthermore, we discuss the potential of this integration to drive breakthroughs in fields such as biomedical diagnostics, environmental monitoring, and chemical sensing, paving the way for next-generation, portable and ultra-sensitive sensing platforms.

In this study, we report the design and synthesis of two luminescent polypyridyl iridium(III) complexes, [Ir(C^N)2(N^N)]Cl (Ir1 and Ir2), for the simultaneous detection of Al3⁺ and F- ions. In these complexes, C^N represents a cyclometalated 2-phenylbenzimidazole (Ph-benz) ligand, while N^N corresponds to 2,2'-bipyridine derivatives, specifically, 2,2'-bipyridine-4,4'-dicarboxylic acid in Ir1 and diethyl[2,2'-bipyridine]-4,4'-dicarboxylate in Ir2. The interaction of the ─NH group of Ph-benz and the ─COOH group of the bipyridine ligand with F- and Al3⁺ ion induces distinct photoluminescence changes, enabling selective and sensitive detection of both analytes. Ir1 and Ir2 exhibit detection limits of 0.09 µM and 0.44 µM for F-, and 0.008 µM and 0.06 µM for Al3⁺, respectively. Mechanistic investigations, supported by computational analysis, reveal that F--induced deprotonation of the Ph-benz ─NH and bipyridine ─COOH groups modulates the lowest-energy electronic transitions, thereby altering the photophysical properties. Additionally, Al3⁺ binds at distinct coordination sites on Ir1 and Ir2, leading to divergent photoluminescence responses. Both probes exhibit excellent luminescence reversibility upon sequential addition of F- and Al3⁺, underscoring their reusability and cost-effectiveness. Notably, Ir1 displays visible luminescence switching observable by the naked eye. These findings establish Ir1 as promising dual-ion luminescent sensors for potential applications in environmental monitoring and biomedical diagnostics.

Polymer-based photonic sensors are emerging as cost-effective, scalable alternatives to conventional silicon and glass photonic platforms, offering unique advantages in flexibility, functionality, and manufacturability. This review provides a comprehensive assessment of recent advances in polymer photonic sensing technologies, focusing on material systems, fabrication techniques, device architectures, and application domains. Key polymer materials, including PMMA, SU-8, polyimides, COC, and PDMS, are evaluated for their optical properties, processability, and suitability for integration into sensing platforms. High-throughput fabrication methods such as nanoimprint lithography, soft lithography, roll-to-roll processing, and additive manufacturing are examined for their role in enabling large-area, low-cost device production. Various photonic structures, including planar waveguides, Bragg gratings, photonic crystal slabs, microresonators, and interferometric configurations, are discussed concerning their sensing mechanisms and performance metrics. Practical applications are highlighted in environmental monitoring, biomedical diagnostics, and structural health monitoring. Challenges such as environmental stability, integration with electronic systems, and reproducibility in mass production are critically analyzed. This review also explores future opportunities in hybrid material systems, printable photonics, and wearable sensor arrays. Collectively, these developments position polymer photonic sensors as promising platforms for widespread deployment in smart, connected sensing environments.

Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, with applications in environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, along with a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying the spectral bands and thus the molecules involved in the process. This work describes a significantly innovative approach to deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes.

In this study, we present the green synthesis and multifunctional potential of amino-functionalized carbon quantum dots (ACQDs) derived from Withania somnifera (ashwagandha) for the sensitive detection of Rhodamine B (RhB), a toxic dye commonly found in adulterated food and cosmetic products. The ACQDs exhibited quasi-spherical morphology with a uniform size distribution (2.82 ± 0.07 nm), abundant surface functional groups (-OH, -COOH, -NH₂), and strong photoluminescence. Structural and optical analyses confirmed their excellent stability across a range of environmental conditions, including pH, UV exposure, and saline media. A dual-mode sensing of RhB was achieved via visible colorimetric change and fluorescence quenching, with a low quantification limit (LOQ) of 1.9 μgL-1. Mechanistic studies revealed that both static and dynamic quenching, involving electrostatic interactions, hydrogen bonding, and π-π stacking, contributed to the sensing process. The ACQDs also demonstrated notable antioxidant activity (IC50 = 0.168 mg mL-1) and low cytotoxicity (< 90 μg mL-1) in NIH-3 T3 cells, confirming their biocompatibility. Furthermore, their efficient internalization into plant tissues highlights their suitability for plant imaging. These results establish ACQDs as a promising nanomaterial for applications in environmental monitoring, food safety, biomedical diagnostics, and agricultural research.

Recent developments in biosensors based on digital platforms have primarily focused on enhancing rapid detection, flexibility, and selectivity through the utilization of nanomaterials. Despite these advances, the complexity of image colorimetric measurements continues to be a subject of interest. This study focused on the development of a new digital image colorimetric biosensor for real-time quantification of hydrogen peroxide (H2O2). The designed nanostructure-based sensor showed excellent selectivity and sensitivity, utilizing polyethylene glycol/Silver/Silver(II) oxide nanoparticles obtained from tangerine leaf extract (TLE/PEG/Ag/AgO NPs). The sensor's performance was validated using Ag/AgO NPs derived from tangerine leaf extract (TLE), demonstrating remarkable selectivity and sensitivity using a Red-Green-Blue (RGB)--based approach. Based on digital image colorimetric measurements of TLE/PEG/Ag/AgO NPs, a system for determining H2O2 was established in a linear range of 2.0–100.0 μmol/L with a low limit of detection (LOD) of 1.82 μmol/L. This study not only presented a facile strategy for the design of the digital image colorimetric TLE/PEG/Ag/AgO NPs-based biosensor but also shed light on the remarkable potential of smartphone sensing devices based on nanosensor technology. These sensors offer fresh perspectives and multidisciplinary approaches to visually sensitive sensing in a range of applications, such as biomedical diagnostics, security screening, and environmental monitoring.