Case report: BCR-ABL-positive acute lymphoblastic leukemia with bone destruction: a treatment dilemma

Although bone destruction and hypercalcemia without acute peripheral blast BCR-ABL-positive acute lymphoblastic leukemia (ALL) have been reported in children, they are rare in adults. Herein, we describe a case of BCR-ABL positive ALL with a triploid karyotype, WT1, and CDKN2A mutations with hypercalcemia and bone destruction as the first manifestations. Complete remission (CR) was achieved by induction chemotherapy. BCR-ABL turned negative after treatment with dasatinib. However, computed tomography and whole-body bone scan showed extensive bone destruction. Additionally, bone biopsy showed leukemic infiltration. After treatment with dasatinib and VMCP, leukemia recurred with positive BCR-ABL. The T315I mutation occurred. The patient was surgically diagnosed with calculous cholecystitis and achieved CR2 by postoperative orebatinib and VP regimens. Later, the patient died due to a severe pulmonary infection. BCR-ABL-positive ALL with bone destruction is rare and difficult to control using tyrosine kinase inhibitor chemotherapy alone. Therefore, further exploration of more effective treatments is needed

Analysis of the Willingness and Factors Influencing the Residents to Choose Between Chinese Medicine and Western Medicine under the New Coronavirus Pandemic: A Study in Zhejiang Province Community Health Service Center

Objective: To understand the willingness of Chinese residents to choose between Chinese and Western medicine in the face of sudden outbreak, this study aims to investigate and analyze the willingness and factors influencing Chinese residents (taking Zhejiang Province as an example) to choose between Chinese and Western medicine under the new coronavirus pandemic. Methods: The present study performed a large-scale cross-sectional online survey among 666 random residents in Zhejiang Province. We used questionnaires to investigate the feedback form from residents seeking medical care. In addition, a multivariate logistic regression model was used to analyze the influence of gender, education, medical reimbursement, and age on the choice of Chinese and Western medicine. Results: Among the patients with mild disease, 55.9% patients chose traditional Chinese medicine, while 44.1% chose Western medicine. Moreover, the proportion of patients with severe diseases who chose traditional Chinese medicine was 7.0%, while the rate of Western medicine was 93.0%. Among the patients suffering from mild diseases, the proportion of men who chose traditional Chinese medicine (46.2%) was lower than that of women (53.8%). The usage of Chinese medicine was preferred among residents of all ages, income levels, and educational backgrounds. A total of 93.0% of patients who chose Western medicine for treatment were severely ill, and the residents with severe diseases preferred Western medicine to Chinese medicine. People with high education and young were more inclined toward Western medicine for treatment compared with Chinese medicine. It was noted that people paid most attention to the medical insurance reimbursement ratio, followed by the distance between the medical institution and the place of residence. Conclusion: The acceptance of Chinese medicine among patients has generally increased; however, gender, educational background, and income still exert a great influence on the choice between Chinese and Western medicine

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance between style and content, whereas ignoring the significant demand for flexible and customized stylization results and thereby limiting their practical application. To address this critical issue, a novel AST approach namely HiCAST is proposed, which is capable of explicitly customizing the stylization results according to various source of semantic clues. In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM. It is characterized by introducing of \textit{Style Adapter}, which allows user to flexibly manipulate the output results by aligning multi-level style information and intrinsic knowledge in LDM. Lastly, we further extend our model to perform video AST. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency in the premise of maintaining stylization strength. Qualitative and quantitative comparisons as well as comprehensive user studies demonstrate that our HiCAST outperforms the existing SoTA methods in generating visually plausible stylization results

Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this paper, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of 91.2$\times$ in latency and 671$\times$ in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.Comment: arXiv admin note: text overlap with arXiv:2205.0356

Production of human blood group B antigen epitope conjugated protein in Escherichia coli and utilization of the adsorption blood group B antibody

Additional file 1: Table S1. List of constructed plasmids, strains and primers used in the study. Figure S1. MALDI-TOF detection of MBPmut (a) and MBPmut-OPS (b)

Random resistive memory-based deep extreme point learning machine for unified visual processing

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. We show the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems. Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks

Pruning random resistive memory for optimizing analogue AI

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware

A novel true random number generator based on a stochastic diffusive memristor

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator (TRNG) utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity and power consumption. The random bits generated by the diffusive memristor TRNG passed all 15 NIST randomness tests without any post-processing, a first for memristive-switching TRNGs. Based on nanoparticle dynamic simulation and analytical estimates, we attributed the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of Internet of Things (IoT)