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Quantum comparators and modular arithmetic are fundamental in many quantum algorithms. Current research mainly focuses on operations between two quantum states. However, various applications, such as integer factorization, optimization, and financial risk analysis, commonly require one of the inputs to be classical. It requires many ancillary qubits, especially when subsequent computations are involved. In this paper, we propose a quantum–classical comparator based on the quantum Fourier transform. Then we extend it to compare two quantum integers and modular arithmetic. Proposed operators only require up to one ancilla qubit, which is optimal for qubit resources. We analyze limitations in the current modular addition circuit and develop it to process arbitrary quantum states in the entire n -qubit space. The proposed algorithms reduce computing resources and make them valuable for noisy intermediate-scale quantum computers.

In order to elucidate whether deep freezing could maintain the quality of snowflake beef, three different deep freezing temperatures (−18 °C, −40 °C, and −60 °C) were used in order to evaluate the changes in tissue structures, quality characteristics and spoilage indexes, and their comparative effects on the quality of snowflake beef. Compared to samples frozen at −18 °C, those stored at −40 °C and −60 °C took a shorter time to exceed the maximum ice crystallization zone (significantly reduced by 2–6 h). In terms of short-term storage, samples frozen at −40 °C and −60 °C had better tissue structure and lower drip loss rate than those frozen at −18 °C; significant differences between groups in drip loss were observed between −18 °C and −60 °C. Moreover, a better bright red color and lower shear force were maintained at −40 °C and −60 °C, with significant differences in shear force between the −18 °C group and the other two groups on day 60. Although there were significant effects on the inhibition of lipid and protein oxidation at −40 °C and −60 °C; no significant variation was observed between these two groups throughout storage. A similar phenomenon was found in flavor, with 1-pentanol identified as an important potential indicator of flavor change in snowflake beef during storage. This study demonstrated that −40 °C and −60 °C had favorable impacts on the quality maintenance of snowflake beef compared to −18 °C. These findings provide a theoretical basis for effective stability of snowflake beef quality during frozen storage.

For adult patients with acute lymphoblastic leukemia (ALL), allogeneic hematopoietic stem cell transplantation (allo-HSCT) from HLA-matched related donors (MSD) is recommended for standard and high-risk patients. The role of unrelated donor transplantation (URD) in first remission has not been fully determined. We sought to compare directly the outcome of URD allo-HSCT and chemotherapy in patients with high-risk ALL. In this single-center retrospective analysis, we included 74 consecutive adult patients with high-risk ALL in first complete remission (CR) and without a sibling donor, in which 32 patients received URD allo-HSCT in CR1 with busulfan-cyclophosphamide preparation regimen and in vivo T-cell depletion with anti-T-lymphoglobulin (ATG). The remaining 42 patients received chemotherapy consolidation and maintenance only in first remission. With median follow-up of 18 months, in the URD allo-HSCT group, the relapse rate (RR) was 30.6 ± 11.4 % which was significantly lower than that of the chemotherapy group (80.5 ± 10.1 %, p  < 0.001), while non-relapse mortality (NRM) was higher (16.4 ± 6.7 % vs. 0, p  = 0.028). Overall, 3-year leukemia-free survival (LFS) was superior in the URD allo-HSCT group compared to chemotherapy group (57.8 ± 10.6 vs. 19.5 ± 10.5 %, p  = 0.002), as was 3-year overall survival (OS, 63.5 ± 13.3 vs. 31.6 ± 10.6 %, p  = 0.016). URD HSCT was the only factor associated with improved OS, LFS and reduced RR in multivariate analysis. Based on our data, URD allo-HSCT significantly reduced the relapse in high-risk ALL and the benefit translated into improvement in both LFS and OS. Prospective studies based on availability of HLA-matched URD are warranted to evaluate the precise role of URD transplantation in adult ALL.

Electronic (E-) nose and gas chromatography-ion mobility spectrometry (GC-IMS) were combined to investigate the effect of blown pack spoilage (BPS) on the volatile profile of meatball groups (Fresh, Spoilage, and BPS) stored for 4 d at 15 °C (temperature abuse). The TVB-N and TBARS were significantly higher in BPS group (P < 0.05), but no variation in TVB-N was detected between fresh and spoilage groups (P > 0.05). The flavor profiles were mostly linked to S5, S7 and S8 E-nose sensors. Specifically, fresh group responded higher to S5 (hydrocarbons and aromatic compounds) and S7 sensors (sulfides and terpenes), while spoilage group demonstrated the lowest in both sensors but intermediary in S8 (alcohols and some aromatic compounds) and highest in BPS group. A total of 25 volatile organic compounds (VOCs) were identified via GC-IMS analysis. Principal Component Analysis loading plot of the data obtained through the GC-IMS and E-nose methods provided insightful differences in the VOCs in the groups. The results showed that the contents of n-hexanol, 1-propanol, 1-octanol, 2-butanone, butanal, and ethyl acetate were significantly higher (P < 0.05) in BPS group than in other groups and were separated in the load diagram, indicating that the compounds may have induced the overall flavor profile of the meatballs. Furthermore, there was a high correlation between the E-nose and GC-IMS results (P < 0.05). This study provides theoretical information in elucidating BPS phenomenon and its adverse effect on the flavor profile of meatballs.

In recent years, quantum computation has been rapidly advancing, driving a technological revolution with significant potential across various sectors, particularly in finance. Despite this, the insurance industry, an essential tool for mitigating unforeseen risks and losses, has received limited attention. This paper provides an initial exploration into the realm of quantum computational insurance and actuarial science. After introducing key insurance models and challenges, we discuss quantum algorithms that can address insurance problems based on their mathematical nature. Our study includes experimental and numerical demonstrations of quantum applications in non-life insurance, life insurance, and reinsurance. Additionally, we explore the timeline for quantum insurance, the development of quantum-enhanced insurance products, and the challenges posed by quantum computational advancements. This work systematically constructs the connection between quantum computation and the insurance industry, enhancing the development of insurance while promoting the application of quantum computation to more realistic problems.

Magnetic particle imaging (MPI) enables real-time, sensitive and quantitative visualization of magnetic tracers' spatial distribution, augmenting the capability of in vivo imaging technologies. Previous tracer studies in MPI have primarily focused on superparamagnetic nanoparticles; however, their non-ideal sigmoidal magnetization response limits the spatial resolution. Here we demonstrate the utilization of magnetically induced magnetosome chain (MAGiC) as a novel superferromagnetic MPI tracer, exhibiting a 25-fold improvement in resolution and a 91-fold enhancement in signal intensity compared to the commercial tracer VivoTrax+. The spatial resolution of MPI was pushed to an unprecedented 80 μm under a 4 T/m gradient field. Additionally, MAGiC can be precisely controlled using magnetic fields, enabling it to function as a MPI trackable microrobot. We provided a theoretical model elucidating MAGiC's unique properties, and validated its imaging and actuation performance through phantom studies and in vivo experiments. As a high-performance MPI tracer and magnetic microrobot with exceptional capabilities, MAGiC holds tremendous potential for diverse applications including cell tracking, targeted drug delivery as well as therapeutic interventions.Competing Interest StatementThe authors have declared no competing interest.

Great progress has been made in quantum computing in recent years, providing opportunities to overcome computation resource poverty in many scientific computations like computational fluid dynamics (CFD). In this work, efforts are made to exploit quantum potentialities in CFD, and a hybrid classical and quantum computing CFD framework is proposed to release the power of current quantum computing. In this framework, the traditional CFD solvers are coupled with quantum linear algebra libraries in weak form to achieve collaborative computation between classical and quantum computing. The quantum linear solver provides high-precision solutions and scalable problem sizes for linear systems and is designed to be easily callable for solving linear algebra systems similar to classical linear libraries, thus enabling seamless integration into existing CFD solvers. Some typical cases are performed to validate the feasibility of the proposed framework and the correctness of quantum linear algorithms in CFD.

Oxidized alkaline loofah sponge (OAL) was given by treating alkaline loofah sponge (AL) with H2O2, four classic adsorption kinetics models which were Lagergren’s pseudo-first-order, Pseudo-second-order equation, the Elovich equation and Intraparticle diffusion were applied to study how the different oxidation degrees of loofah sponge impacted on the values of fitting goodness of the above models and to determine which kind of model was the best fit of the adsorption process and adsorption mechanism. The results showed that Pseudo-second-order equation’s fitting degree was proposed to be best and adsorption mechanism was chemical adsorption by ionic bonding, at the same time, as hydrogen bonds increased, water molecules formed a barrier to impede MB diffusing into the micro-structure.

Relapsed/refractory multiple myeloma (RRMM) with extramedullary disease (EMD) represents a challenging condition, with limited treatment options and poor prognosis. We conducted a phase 1 clinical trial to evaluate the safety and effectiveness of a novel bispecific chimeric antigen receptor (CAR) T-cell therapy targeting two antigens, B-cell maturation antigen and G protein-coupled receptor class C group 5 member D (BCMA/GPRC5D), in this high-risk population. A total of 12 patients were enrolled, of whom 3 were excluded due to disease progression or death before CAR T-cell infusion, despite meeting the inclusion criteria, leaving 9 for analysis. The median follow-up was 6.08 months (Interquartile Range [IQR]: 0.9–16.5). All patients received BCMA/GPRC5D bispecific CAR T-cell therapy after bridging therapy with localized radiotherapy or Elranatamab. Efficacy assessments revealed that 100% of patients achieved partial response (PR) or better, with 44.4% achieving complete response (CR). Common adverse events included hematological toxicities such as anemia, leukopenia, and thrombocytopenia. Cytokine release syndrome (CRS) occurred in 66.7% of patients, all of which were grade 1–2, and no neurotoxicity (ICANS) was observed. The 1-year overall survival (OS) and progression-free survival (PFS) rates were 60% and 63%, respectively. Median OS and PFS were not reached. Collectively, these findings highlight a potential therapeutic strategy involving BCMA/GPRC5D dual-targeted CAR T-cell therapy for patients with aggressive forms of multiple myeloma, particularly those with extramedullary disease, and support the need for further exploration and validation in larger, multi-center clinical studies.

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method \"Iterative-QLS\" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than \\(0.2\\%\\), and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science.