Enhanced Storage Capacity via Anion Substitution for Advanced Delayed X-ray Detection.

X-ray radiation information storage, characterized by its ability to detect radiation with delayed readings, shows great promise in enabling reliable and readily accessible X-ray imaging and dosimetry in situations where conventional detectors may not be feasible. However, the lack of specific strategies to enhance the memory capability dramatically hampers its further development. Here, we present an effective anion substitution strategy to enhance the storage capability of NaLuF4:Tb3+ nanocrystals attributed to the increased concentration of trapping centers under X-ray irradiation. The stored radiation information can be read out as optical brightness via thermal, 980 nm laser, or mechanical stimulation, avoiding real-time measurement under ionizing radiation. Moreover, the radiation information can be maintained for more than 13 days, and the imaging resolution reaches 14.3 lp mm-1. These results demonstrate that anion substitution methods can effectively achieve high storage capability and broaden the application scope of X-ray information storage.

Temperature-Dependent Color-Tunable Afterglow in Zirconium-Doped CsCdCl3 Perovskite for Advanced Anti-Counterfeiting and Thermal Distribution Detection.

Persistent luminescence (PersL) materials exhibit thermal-favored optical behavior, enabling their unique applications in security night vision signage, in vivo bioimaging, and optical anti-counterfeiting. Therefore, developing efficient and color-tunable PersL materials is significantly crucial in promoting advanced practical use. In this study, hexagonal Zr4+ -doped CsCdCl3 perovskite is synthesized via a hydrothermal reaction with a tunable photoluminescent (PL) behavior through heterovalent substitution. Moreover, the incorporation of Zr4+ ions result in an extra blue emission band, originating from the enhanced excitonic recombination in D3d octahedrons. Furthermore, the afterglow performances of the samples are dramatically improved, along with the noticeable temperature-dependent PersL as well as the thermo-luminescence with tunable color output. Detailed analysis reveals that the unique temperature-dependent PersL and thermo-luminescence color change are attributed to the presence of multiple luminous centers and abundant traps. Overall, this work facilitates the development of optical intelligence platforms and novel thermal distribution probes with the as-developed halides perovskite for its superior explored PersL characteristic.

Crystal Field-Engineered Cr3+-Doped Gd3(MgxGa5-2xGex)O12 Phosphors for Near-Infrared LEDs and X-ray Imaging Applications.

Inorganic materials doped with chromium (Cr) ions generate remarkable and adjustable broadband near-infrared (NIR) light, offering promising applications in the fields of imaging and night vision technology. However, achieving high efficiency and thermal stability in these broadband NIR phosphors poses a significant challenge for their practical application. Here, we employ crystal field engineering to modulate the NIR characteristics of Cr3+-doped Gd3Ga5O12 (GGG). The Gd3MgxGa5-2xGexO12 (GMGG):7.5% Cr3+ (x = 0, 0.05, 0.15, 0.20, and 0.40) phosphors with NIR emission are developed through the cosubstitution of Mg2+ and Ge4+ for Ga3+ sites. This cosubstitution strategy also effectively reduces the crystal field strength around Cr3+ ions, which results in a significant enhancement of the photoluminescence (PL) full width at half-maximum (fwhm) from 97 to 165 nm, alongside a red shift in the PL peak and an enhancement of the PL intensity up to 2.3 times. Notably, the thermal stability of the PL behaviors is also improved. The developed phosphors demonstrate significant potential in biological tissue penetration and night vision, as well as an exceptional scintillation performance for NIR scintillator imaging. This research paves a new perspective on the development of high-performance NIR technology in light-emitting diodes (LEDs) and X-ray imaging applications.

A serial mediating effect of perceived family support on psychological well-being.

Family has a significant impact on individual mental health. Based on social support theory, family system theory and the Mental Health Continuum Short Form (MHC-SF), this research constructed a model of the pathway of perceived family support on psychological well-being and the results empirically clarified that perceived family support has a significant positive relationship with emotional well-being, social well-being, and psychological well-being (P < 0.001). Emotional well-being positively influences social well-being and psychological well-being (P < 0.001). Social well-being positively affects psychological well-being (P < 0.001). There were direct mediating effects of emotional well-being (13.45%), direct mediating effects of social well-being (32.82%) and a serial mediating effect (28.07%) between perceived family support and psychological well-being (P < 0.001).

Visualized Stress-Temperature Sensor with the Zinc Sulfide and Perovskite Glass Ceramics Composite.

The visualized dual-modal stress-temperature sensing refers to the ability of a sensor to provide real-time and visible information about both stress and temperature and has indeed attracted significant interest in various fields. However, the development of convenient methods for achieving this capability remains a challenge. In this work, a dual-modal stress-temperature sensor is successfully fabricated using a ZnS/Cu@CsPbBr1.2I1.8 glass ceramics (GCs)/polydimethylsiloxane (PDMS) (ZCP) composite film. The tunable ML color is achieved by modulating the concentration of CsPbBr1.2I1.8 GCs in the ZCP composite films based on the light conversion process from ZnS/Cu to CsPbBr1.2I1.8 GCs. Additionally, the stress and temperature can be visualized simultaneously by integrating the ML intensity and ML color of the ZCP composite film. This feature allows for the real-time monitoring of automotive tire temperature by embedding the ZCP composite film on the tire surface, enabling a strong and stable response to both stress and temperature changes. Overall, this work offers a convenient, efficient, and repeatable approach for achieving visualized dual-modal stress-temperature sensing in the fields of mechanical engineering, structural health monitoring, and intelligent devices.

Multimode-Responsive Luminescence of Er3+ Single-Activated CaF2 Phosphor for Advanced Information Encryption.

The current optical anticounterfeit strategies that rely on multimode luminescence in response to the photon or thermal stimuli have significant importance in the field of anticounterfeiting and information encryption. However, the dependence on light and heat sources might limit their flexibility in practical applications. In this work, Er3+ single-doped CaF2 phosphors that show multistimuli-responsive luminescence have been successfully prepared. The as-obtained CaF2:Er3+ phosphor exhibits green photoluminescence (PL) and color-tunable up-conversation (UC) luminescence from red to green due to the cross-relaxation of Er3+ ions. Additionally, as-obtained CaF2:Er3+ phosphors also display green mechano-luminescence behavior, which is induced by the contact electrification between the CaF2 particles and PDMS polymers, enabling the phosphor to flexibly respond to mechanical stimuli. Moreover, feasible anticounterfeiting schemes with the capability of multistimuli-responsive and flexible decryption have been constructed, further expanding the application of optical materials in the field of advanced anticounterfeiting and information encryption.

MBX: A many-body energy and force calculator for data-driven many-body simulations.

Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet+ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet+ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

Discontinuous Subatmospheric Pressure Interface Reduces the Gas Flow Effects on Miniature CAPI Mass Spectrometer.

In the range of miniature mass spectrometers, the miniature ion trap mass spectrometer with continuous atmospheric pressure interface (CAPI) shows good performance potential and advantages due to its excellent sensitivity and analysis speed. However, in previous cases, placing the ion trap directly near the skimmer aperture means it will suffer high gas shock, which may affect performance. In this study, an improved miniature CAPI ion trap mass spectrometer was developed by gas flow optimization. According to the experimental results, excessive gas flow affects stability and resolution. The impact of the gas flow can be effectively reduced by reducing the inner diameter of the skimmer and adding an additional lens element to move the ion trap away from the skimmer aperture. However, this method will affect the sensitivity of the instrument to some extent, so a discontinuous subatmospheric pressure interface (DSPI) was developed to reduce the gas flow effects and improve the comprehensive performance. When using the DSPI system with a 0.4 mm skimmer and entrance lens, the resolution for roxithromycin was up to 2800 at a scanning speed of 1015 Th/s, which was 3.4-fold higher that without DSPI. The dynamic range of concentration reached 4 orders of magnitude and the detection limit for repaglinide was as low as 1 ng/mL. This study offers a new approach to develop better miniature ion trap mass spectrometers and to extend their practical application.

Propargylic Dialkyl Effect for Cyclobutene Formation through Ir(III)-Catalyzed Cycloisomerization of 1,6-Enynes.

The propargylic dialkyl effect (PDAE) has a significant impact on the cyclization reaction of enynes, partly reflected in changing the types of products. Herein, we described the influence of the propargylic dialkyl effect on the Ir(III)-catalyzed cycloisomerization of 1,6-enynes to provide strained cyclobutenes. A series of substituted 1,6-enynes were proved to be excellent substrate candidates in the presence of [Cp*IrCl2]2 in toluene. Mechanistic investigation, based on deuterium labeling experiments and control experiments, indicated that the propargylic dialkyl effect might boost C(sp)-H activation by preventing the coordination of active iridium species to the C(sp)≡C(sp) bond of enynes. This finding contributes to the fundamental understanding of enyne cyclization reactions and offers valuable insight into the propargylic dialkyl effect.

High-Resolution X-ray Image from Copper-Based Perovskite Hybrid Polymer.

Cs3Cu2I5 nanocrystals (NCs) are considered to be promising materials due to their high photoluminescence efficiency, lack of lead toxicity, and X-ray responsiveness. However, during the crystallization process, NCs are prone to agglomeration and exhibit uneven size distribution, resulting in several light scattering that severely affect their imaging resolution. Herein, we successfully developed a high-resolution scintillator film by growing copper-based perovskite NCs within a hybrid polymer matrix. By leveraging the ingenious integration of polyvinylidene fluoride (PVDF) and polymethyl methacrylate (PMMA), the size and distribution uniformity of Cs3Cu2I5 NCs can be effectively controlled. Consequently, a high spatial resolution of 14.3 lp mm-1 and a low detection limit of 105 nGy s-1 are achieved, and the scintillator film has excellent flexibility and stability. These results highlight the promising application of Cs3Cu2I5 scintillator films in low-cost, flexible, and high-performance medical imaging.

Nonstoichiometry-Induced Self-Activated Phosphors for Dynamic Anti-counterfeiting Applications.

Optical signals with distinctive properties, such as contactless, fast response, and high identification, are harnessed to realize advanced anti-counterfeiting. However, the simultaneous attainment of multi-color, -temporal, and -modal luminescence performance remains a compelling and imperative pursuit. In our work, a temperature/photon-responded dynamic self-activated luminescence originating from nonstoichiometric Zn2GeO4 is developed with the modulation of intrinsic defects. The increased concentration of oxygen vacancies (VO••) contributes to an enhanced recombination of ZnGe″-VO••, ultimately improving the self-activated luminescence performance. Additionally, the photoluminescence (PL) color of the representative Zn2.2GeO4 sample changes from green to blue-white with the increased ultraviolet (UV) irradiation time. Concurrently, the emission color undergoes a variation from blue to green as the ambient temperature raises from 280 to 420 K. Remarkably, green long persistent luminescence (LPL) and photostimulated luminescence (PSL) behaviors are observed. Herein, this study elucidates a sophisticated anti-counterfeiting approach grounded in the dynamic luminescent attributes of nonstoichiometric Zn2GeO4, presenting a promising frontier for the evolution of anti-counterfeiting technologies.

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.

INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the field induced by these pathological susceptibility sources. METHOD: This study proposes a new deep learning-based method, BFRnet, to remove the background field in healthy and hemorrhagic subjects. The network is built with the dual-frequency octave convolutions on the U-net architecture, trained with synthetic field maps containing significant susceptibility sources. The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects. Robustness against acquisition field-of-view (FOV) orientation and brain masking are also investigated. RESULTS: For both simulation and in vivo experiments, BFRnet led to the best visually appealing results in the local field and QSM results with the minimum contrast loss and the most accurate hemorrhage susceptibility measurements among all five methods. In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and further brain edge erosions. It is also observed that BFRnet performed the best among all BFR methods for acquisition FOVs oblique to the main magnetic field. CONCLUSION: The proposed BFRnet improved the accuracy of local field reconstruction in the hemorrhagic subjects compared with conventional BFR algorithms. The BFRnet method was effective for acquisitions of tilted orientations and retained whole brains without edge erosion as often required by traditional BFR methods.

MB-pol(2023): Sub-chemical Accuracy for Water Simulations from the Gas to the Liquid Phase.

We use the MB-pol theoretical/computational framework to introduce a new family of data-driven many-body potential energy functions (PEFs) for water, named MB-pol(2023). By employing larger 2-body and 3-body training sets, including an explicit machine-learned representation of 4-body energies, and adopting more sophisticated machine-learned representations of 2-body and 3-body energies, we demonstrate that the MB-pol(2023) PEFs achieve sub-chemical accuracy in modeling the energetics of the hexamer isomers, outperforming both the original MB-pol and q-AQUA PEFs, which currently provide the most accurate description of water clusters in the gas phase. Importantly, the MB-pol(2023) PEFs provide remarkable agreement with the experimental results for various properties of liquid water, improving upon the original MB-pol PEF and effectively closing the gap with experimental measurements.

Pyridinium-Masked Enol as a Precursor for Constructing Alpha-Fluoromethyl Ketones.

We present herein a pyridinium-masked enol as a versatile platform to produce ketones bearing tri-, di-, and monofluoromethyl in the presence of [Ir(dF(Me)ppy)]2(dtbbpy)]PF6 under blue light (455 nm) irradiation. By simply changing the F-source, α-trifluoromethyl ketones, α-difluoromethyl ketones, and α-monofluoromethyl ketones could be easily prepared in moderate to excellent yields in one step, making it a practical tool for the synthesis of fluorine-containing ketones. In addition, the pyridinium-masked enol could also be extended to the synthesis of sulfonyl ketones. The findings of the present protocol contribute to the arsenal of fluorine chemistry and might have potential applications in the pharmaceutical and agrochemical industries.

Dynamic anti-counterfeiting and information encryption of Sr3Y2Ge3O12: Tb3+, Er3+ phosphor via carriers filling and release processes.

Complex and high-security-level anti-counterfeiting strategies with multiple luminescent modes are extremely critical for meeting the requirement of constantly developing information storage and information security. In this work, Tb3+ ions doped Sr3Y2Ge3O12 (SYGO) and Tb3+/Er3+ co-doped SYGO phosphors are successfully fabricated and are unitized for anti-counterfeiting and information encoding under distinct stimuli sources. The green photoluminescence (PL), long persistent luminescence (LPL), mechano-luminescence (ML), and photo-stimulated luminescence (PSL) behaviors are respectively observed under the stimuli of ultraviolet (UV), thermal disturbance, stress, and 980 nm diode laser. Based on the time-dependence of the filling and releasing rate of the carriers from the shallow traps, the dynamic information encryption strategy is proposed by simply changing the UV pre-irradiation time or shut-off time. Moreover, a tunable color from green to red is realized by prolonging the 980 nm laser irradiation time, which is attributed to the elaborate cooperation of the PSL and upconversion (UC) behaviors. The anti-counterfeiting method based on SYGO: Tb3+ and SYGO: Tb3+, Er3+ phosphors herein possess an extremely high-security level with attractive performance for designing advanced anti-counterfeiting technology.

Effects of Glucose Metabolism, Lipid Metabolism, and Glutamine Metabolism on Tumor Microenvironment and Clinical Implications.

In recent years, an increasingly more in depth understanding of tumor metabolism in tumorigenesis, tumor growth, metastasis, and prognosis has been achieved. The broad heterogeneity in tumor tissue is the critical factor affecting the outcome of tumor treatment. Metabolic heterogeneity is not only found in tumor cells but also in their surrounding immune and stromal cells; for example, many suppressor cells, such as tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and tumor-associated T-lymphocytes. Abnormalities in metabolism often lead to short survival or resistance to antitumor therapy, e.g., chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Using the metabolic characteristics of the tumor microenvironment to identify and treat cancer has become a great research hotspot. This review systematically addresses the impacts of metabolism on tumor cells and effector cells and represents recent research advances of metabolic effects on other cells in the tumor microenvironment. Finally, we introduce some applications of metabolic features in clinical oncology.

Manipulating Metabolic Alterations and their Consequences to Unleash the Potential of Antitumor Immunotherapy.

Cellular metabolic reprogramming driven by oncogenic mutations is considered as a hallmark in the development of malignant cells, and has been a focus over the past decade. A common theme emerging from these metabolic alterations is that tumor cells can acquire necessary nutrients from a nutrient-limited microenvironment and utilize them to sustain growth and unrestrained cellular division. However, this significant metabolic flexibility and the hostile microenvironment caused by the insufficient vascular exchange, depletion of nutrients, hypoxia, and accumulation of waste products, can inhibit the metabolism and immune activity of tumor-infiltrating lymphocytes and impose barriers to effective antitumor immunotherapies. In this perspective, we review the classical alterations in tumorigenesis- associated metabolic reprogramming and examine the functional contribution of these aberrant metabolisms to the establishment and maintenance of an immunosuppressive microenvironment. Furthermore, we explore the possible approaches to targeting on these metabolic pathways to achieve antitumor immunotherapy, as well as some hypothetical or ongoing combination therapeutic strategies that could, to a certain extent, biologically rationalize and broaden the utility of immune checkpoint inhibitors. Ultimately, we elucidate some dietary modifications that can limit tumor-specific nutritional requirements and maximize the cytotoxicity of other antineoplastic drugs.

Accurate Modeling of Bromide and Iodide Hydration with Data-Driven Many-Body Potentials.

Ion-water interactions play a central role in determining the properties of aqueous systems in a wide range of environments. However, a quantitative understanding of how the hydration properties of ions evolve from small aqueous clusters to bulk solutions and interfaces remains elusive. Here, we introduce the second generation of data-driven many-body energy (MB-nrg) potential energy functions (PEFs) representing bromide-water and iodide-water interactions. The MB-nrg PEFs use permutationally invariant polynomials to reproduce two-body and three-body energies calculated at the coupled cluster level of theory, and implicitly represent all higher-body energies using classical many-body polarization. A systematic analysis of the hydration structure of small Br-(H2O)n and I-(H2O)n clusters demonstrates that the MB-nrg PEFs predict interaction energies in quantitative agreement with "gold standard" coupled cluster reference values. Importantly, when used in molecular dynamics simulations carried out in the isothermal-isobaric ensemble for single bromide and iodide ions in liquid water, the MB-nrg PEFs predict extended X-ray absorption fine structure (EXAFS) spectra that accurately reproduce the experimental spectra, which thus allows for characterizing the hydration structure of the two ions with a high level of confidence.

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.

INTRODUCTION: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation. METHOD: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. The xQSM method is compared with two conventional dipole inversion methods using simulated and in vivo experiments from 4 healthy subjects at 3T. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages, ranging from 100% (∼32mm) to 400% (∼128mm) of the actual DGM physical size. RESULTS: The proposed xQSM network led to the lowest DGM contrast loss in both simulated and in vivo subjects, with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages, as compared to conventional methods. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48mm axial slabs using the xQSM network, while a minimum of 112mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion. CONCLUSION: The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.