Paving the Way for Pass Disturb-Free Vertical NAND Storage via a Dedicated and String-Compatible Pass Gate.

In this work, we propose a dual-port cell design to address the pass disturbance in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that (i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-VTH (HVT) state and the screening of the applied field by the channel at the low-VTH (LVT) state; (ii) combined simulations and experimental demonstrations of dual-port design verify the disturb-free operation in a NAND string, overcoming a key challenge in single-port designs; (iii) the proposed design can be incorporated into a highly scaled vertical NAND FeFET string, and the pass gate can be incorporated into the existing three-dimensional (3D) NAND with the negligible overhead of the pass gate interconnection through a global bottom pass gate contact in the substrate.

Combination of Lactiplantibacillus Plantarum ELF051 and Astragalus Polysaccharides Improves Intestinal Barrier Function and Gut Microbiota Profiles in Mice with Antibiotic-Associated Diarrhea.

The purpose of this study was to investigate the improvement of the intestinal barrier and gut microbiota in mice with antibiotic-associated diarrhea (AAD) using Lactiplantibacillus plantarum ELF051 combined with Astragalus polysaccharides. The amoxicillin, clindamycin, and streptomycin triple-mixed antibiotic-induced AAD models were administered with L. plantarum ELF051 or Astragalus polysaccharides or L. plantarum ELF051 + Astragalus polysaccharides for 14 days. Our findings revealed that the combination of L. plantarum ELF051 and Astragalus polysaccharides elevated the number of goblet cells and enhanced the proportion of mucous within the colon tissue. Furthermore, the expression of sIgA and IgG were upregulated, while the levels of IL-17A, IL-4, DAO, D-LA, LPS, and TGF-ß1 were downregulated. L. plantarum ELF051 combined with Astragalus polysaccharides elevated the expression of tight junction (TJ) proteins, facilitating intestinal mucosal repair via Smad signaling nodes. Furthermore, their combination effectively increased the relative abundance of lactic acid bacteria (LAB) and Allobaculum, and decreased the relative abundance of Bacteroides and Blautia. Spearman rank correlation analysis demonstrated that LAB were closely related to permeability factors, immune factors, and indicators of intestinal barrier function. In summary, the effect of combining L. plantarum ELF051 and Astragalus polysaccharides on AAD mice was achieved by enhancing intestinal barrier function and regulating the composition of the gut microbiota.

Monitoring the detrimental impact of congested training periods on the strength levels and landing forces of young female aerobic gymnastics.

Identifying indicators of non-functional overreaching during periods of increased training volume and/or intensity is particularly relevant for understanding the detrimental impacts incurred, as well as how these factors contribute to heightened injury risks among exposed athletes. This study aimed to compare the effects of a congested training period versus a standard training period on the strength levels and landing forces of female young aerobic gymnastics athletes. A prospective cohort study design was implemented, spanning four weeks. Fifty athletes (aged 16.2 ± 1.1 years old) at a trained/developmental level, competing at the regional level, were observed throughout the study. During two of these weeks (specifically weeks 2 and 3), half of the group was subjected to a congested training period consisting of six sessions per week (HTF), while the other half continued with their regular four sessions per week (STF). During each week of observation, participants underwent evaluation using the countermovement jump test (CMJ), squat jump test (SJ), and the leg land and hold test (LHT), with measurements taken on a force platform. The main outcomes repeatedly observed over the four weeks were CMJ peak landing force, CMJ peak power, SJ peak power, SJ maximum negative displacement, LHT time to stabilization, and LHT peak drop landing force. Significant interactions (time*group) were observed in CMJ peak power (p < 0.001), CMJ peak landing force (p < 0.001), SJ peak power (p < 0.001), SJ maximum negative displacement (p < 0.001), LHT time to stabilization (p < 0.001), and LHT peak drop landing force (p < 0.001). Furthermore, the results of the final assessment revealed significantly lower CMJ peak power (p = 0.008) and SJ peak power (p = 0.002) in the HTF group compared to the STF group. Additionally, significantly higher values of CMJ peak landing force (p = 0.041), SJ maximum negative displacement (p = 0.015), and LHT peak drop landing force (p = 0.047) were observed in the HTF group compared to the STF group. In conclusion, the increase in training frequency over two weeks significantly contributed to declines in neuromuscular power performance and peak landing forces. This indicates that intensified training periods may acutely expose athletes not only to performance drops but also to an increased risk of injury due to reduced capacity to absorb landing forces.

Exploring the nexus between MYH9 and tumors: novel insights and new therapeutic opportunities.

The myosin heavy chain 9 (MYH9) gene, located on human chromosome 22, encodes non-muscle myosin heavy chain IIA (NM IIA). This protein is essential to various cellular events, such as generating intracellular chemomechanical force and facilitating the movement of the actin cytoskeleton. Mutations associated with thrombocytopenia in autosomal dominant diseases first highlighted the significance of the MYH9 gene. In recent years, numerous studies have demonstrated the pivotal roles of MYH9 in various cancers. However, its effects on cancer are intricate and not fully comprehended. Furthermore, the elevated expression of MYH9 in certain malignancies suggests its potential as a target for tumor therapy. Nonetheless, there is a paucity of literature summarizing MYH9's role in tumors and the therapeutic strategies centered on it, necessitating a systematic analysis. This paper comprehensively reviews and analyzes the pertinent literature in this domain, elucidating the fundamental structural characteristics, biological functions, and the nexus between MYH9 and tumors. The mechanisms through which MYH9 contributes to tumor development and its multifaceted roles in the tumorigenic process are also explored. Additionally, we discuss the relationship between MYH9-related diseases (MYH9-RD) and tumors and also summarize tumor therapeutic approaches targeting MYH9. The potential clinical applications of studying the MYH9 gene include improving early diagnosis, clinical staging, and prognosis of tumors. This paper is anticipated to provide novel insights for tumor therapy.

Rapid discriminant analysis for the origin of specialty yam based on multispectral data fusion strategies.

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.

Recent progress on triterpenoid derivatives and their anticancer potential.

Cancer poses a significant global public health challenge, with commonly used adjuvant or neoadjuvant chemotherapy often leading to adverse side effects and drug resistance. Therefore, advancing cancer treatment necessitates the ongoing development of novel anticancer agents with diverse structures and mechanisms of action. Natural products remain crucial in the process of drug discovery, serving as a primary source for pharmaceutical leads and therapeutic advancements. Triterpenoids are particularly compelling due to their complex structures and wide array of biological activities. Recent research has demonstrated that naturally occurring triterpenes and their derivatives have the potential to serve as promising candidates for new drug development. This review aims to comprehensively explore the anticancer properties of triterpenoids and their synthetic analogs, with a focus on recent advancements. Various aspects, such as synthesis, phytochemistry, and molecular simulation for structure-activity relationship analyses, are summarized. It is anticipated that triterpenoid derivatives will emerge as notable anticancer agents following further investigation into their mechanisms of action and in vivo studies.

Conversion of notoginsenoside R1 to 3ß,12ß-dihydroxydammar-(E)-20(22),24-diene-6-O-ß-D-xylopyranosyl-(1→2)-ß-D-glucopyranoside by Lactiplantibacillus plantarum S165 enhanced protective effects of LPS-induced intestinal epithelial barrier injury in Caco-2 cells.

AIMS: Microbial transformation to modify saponins and enhance their biological activities has received increasing attention in recent years. This study aimed to screen the strain that can biotransform notoginsenoside R1, identify the product and study its biological activity. METHODS AND RESULTS: A lactic acid bacteria strain S165 with glycosidase-producing activity was isolated from traditional Chinese fermented foods, which was identified and grouped according to API 50 CHL kit and 16S rDNA sequence analysis. Subsequently, notoginsenoside R1 underwent a 30-day fermentation period by the strain S165, and the resulting products were analyzed using High-performance liquid chromatography (HPLC), Ultra-performance liquid chromatography (UPLC)-mass spectrometry (MS)/MS, and 13C-Nuclear magnetic resonance (NMR) techniques. Employing a model of Lipopolysaccharide (LPS)-induced damage to Caco-2 cells, the damage of Caco-2 cells was detected by Hoechst 33 258 staining, and the activity of notoginsenoside R1 biotransformation product was investigated by CCK-8 and western blotting assay. The strain S165 was identified as Lactiplantibacillus plantarum and was used to biotransform notoginsenoside R1. Through a 30-day biotransformation, L. plantarum S165 predominantly converts notoginsenoside R1 into 3ß,12ß-dihydroxydammar-(E)-20(22),24-diene-6-O-ß-D-xylopyranosyl-(1→2)-ß-D-glucopyranoside, temporarily named notoginsenoside T6 (NGT6) according to HPLC, UPLC-MS/MS, and 13C-NMR analysis. Results from CCK-8 and Hoechst 33258 staining indicated that the ability notoginsenoside T6 to alleviate the intestinal injury induced by LPS in the Caco-2 cell was stronger than that of notoginsenoside R1. In addition, Western blotting result showed that notoginsenoside T6 could prevent intestinal injury by protecting tight junction proteins (Claudin-1, Occludin, and ZO-1). CONCLUSION: Notoginsenoside R1 was biotransformed into the notoginsenoside T6 by L. plantarum S165, and the biotransformed product showed an enhanced intestinal protective effect in vitro.

The construction of urban cultural and creative industries using deep learning and information management.

The ongoing transition within the Chinese economy assumes a pivotal role in shaping the landscape of the cultural and creative industries (CCI). Renowned for its environmentally sustainable attributes, coupled with high productivity, CCI has garnered considerable attention across diverse societal strata. This study endeavors to delineate the determinants influencing the developmental trajectory of CCI, with a focal point on City A as the primary subject of investigation, juxtaposed against Cities G, D, B, H, and X for comparative analysis, leveraging developmental data from the year 2021. Initially, the study elucidates the conceptual framework underpinning CCI and its intrinsic significance in facilitating urban metamorphosis. Subsequently, the positive impact of CCI through deep learning and information management technology is emphasized, and a cultural and creative recommendation model based on LSTM algorithm is constructed. Through performance evaluation, the recommendation accuracy for cultural and creative projects reaches 94.74 %. A robust developmental assessment model for CCI is then constructed via meticulous factor analysis of pertinent influencers. Employing factor analysis techniques, the study identifies two primary determinants exerting sway over CCI development: sustainable profitability factors and cultural influence factors. Noteworthy among the factors influencing CCI development within City A are fixed asset investment, cultural industry financing, the proliferation of university-based research institutions, and per capita cultural expenditure by residents. Of these, fixed asset investment, cultural industry financing, and the density of university research institutions prominently impinge upon sustainable profitability, with a discernible impact weight of 0.738 in the evaluative framework of CCI development, thus significantly shaping its trajectory. Moreover, consumer psychological factors, particularly market consumption patterns, are observed to exert a discernible influence on CCI evolution. This study augurs fresh insights into the realm of CCI development, infusing it with renewed vigor and vitality. Moreover, it underscores the inherent interdependence and positive correlation among the various research factors, offering novel perspectives and methodologies germane to the advancement of urban CCI.

Deep random forest with ferroelectric analog content addressable memory.

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

Jumping Interval Training: An Effective Training Method for Enhancing Anaerobic, Aerobic, and Jumping Performances in Aerobic Gymnastics.

The aim of this study was to compare the effects of jumping interval training (JIT) and running high-intensity interval training (HIIT) on the aerobic, anaerobic and jumping performances of youth female aerobic gymnasts. A randomized controlled study was conducted over an 8-week period, involving 73 youth female athletes (16.2 ± 1.3 years old) of aerobic gymnastics. The study comprised two experimental groups (JIT and HIIT) and a control group. Participants in the experimental groups engaged in two additional training sessions per week alongside their regular training regimen, while the control group followed their usual training routine. Before and after the intervention period, gymnasts were assessed for their performance in the countermovement jump test (CMJ), the specific aerobic gymnastics anaerobic test (SAGAT) and the 20-m multistage fitness test. Significant interactions time × group were found in SAGAT (p < 0.001; = 0.495), CMJ (p < 0.001; = 0.338) and 20-m multistage fitness test (p < 0.001; = 0.500). The time × group analysis post-intervention revealed significantly lower scores in SAGAT for the control group compared to the JIT (p = 0.003) and HIIT (p = 0.034). Additionally, significantly higher scores were observed for the JIT group in the CMJ test compared to the HIIT (p = 0.020) and control (p = 0.028) groups following the intervention. Finally, the 20 m multistage fitness test post-intervention revealed significantly lower scores for the control group compared to JIT (p < 0.001) and HIIT (p < 0.001). Both JIT and HIIT are recommended training strategies to adopt in aerobic gymnastics for significantly improving the aerobic and anaerobic performances of athletes. However, JIT may be particularly relevant to use as it offers additional benefits in improving vertical jumping performances.

Notoginsenoside R1 Ameliorate High-Fat-Diet and Vitamin D3-Induced Atherosclerosis via Alleviating Inflammatory Response, Inhibiting Endothelial Dysfunction, and Regulating Gut Microbiota.

Aim: Natural medicines possess significant research and application value in the field of atherosclerosis (AS) treatment. The study was performed to investigate the impacts of a natural drug component, notoginsenoside R1, on the development of atherosclerosis (AS) and the potential mechanisms. Methods: Rats induced with AS by a high-fat-diet and vitamin D3 were treated with notoginsenoside R1 for six weeks. The ameliorative effect of NR1 on AS rats was assessed by detecting pathological changes in the abdominal aorta, biochemical indices in serum and protein expression in the abdominal aorta, as well as by analysing the gut microbiota. Results: The NR1 group exhibited a noticeable reduction in plaque pathology. Notoginsenoside R1 can significantly improve serum lipid profiles, encompassing TG, TC, LDL, ox-LDL, and HDL. Simultaneously, IL-6, IL-33, TNF-α, and IL-1ß levels are decreased by notoginsenoside R1 in lowering inflammatory elements. Notoginsenoside R1 can suppress the secretion of VCAM-1 and ICAM-1, as well as enhance the levels of plasma NO and eNOS. Furthermore, notoginsenoside R1 inhibits the NLRP3/Cleaved Caspase-1/IL-1ß inflammatory pathway and reduces the expression of the JNK2/P38 MAPK/VEGF endothelial damage pathway. Fecal analysis showed that notoginsenoside R1 remodeled the gut microbiota of AS rats by decreasing the count of pathogenic bacteria (such as Firmicutes and Proteobacteria) and increasing the quantity of probiotic bacteria (such as Bacteroidetes). Conclusion: Notoginsenoside R1, due to its unique anti-inflammatory properties, may potentially prevent the progression of atherosclerosis. This mechanism helps protect the vascular endothelium from damage, while also regulating the imbalance of intestinal microbiota, thereby maintaining the overall health of the body.

Plastically Deformed Tubes Subjected to Mechanical Expansion Processes.

In engineering, the stress state of expanded tubes is crucial for ensuring structural integrity and preventing stress corrosion cracking. The analysis of stresses and strains in tubes subjected to mechanical expansion using an ogive bullet is essential, yet existing theoretical methods for estimating the stress distributions, especially with spherical and ogive shapes, are sparse. This study explores the expansion of 3/8 inch copper and stainless-steel tubes using an expanding bullet, where tangential and longitudinal strains are measured. A novel analytical approach is introduced to evaluate the stresses and strains, segmenting the tube into three zones, each analyzed with a distinct theory. Validation is achieved through an axisymmetric finite element model that employs a multi-linear kinematic hardening material behavior. The analytical model also estimates the expanding mandrel's push force, which is then compared with the results from numerical simulations and experimental data, showing good agreement across methods.

Electroacupuncture improves gastrointestinal motility through a central-cholinergic pathway-mediated GDNF releasing from intestinal glial cells to protect intestinal neurons in Parkinson's disease rats.

Constipation symptoms of Parkinson's disease (PD) seriously reduce the quality of life of patients and aggravate the development of the disease, but current treatment options still cannot alleviate the progress of constipation. Electroacupuncture (EA) is a new method for the treatment of constipation, which can effectively treat the symptoms of constipation in PD patients. However, the specific regulatory mechanisms of EA in the treatment of constipation symptoms in PD remain unclear. The aim of this study is to investigate the therapeutic effect of EA on PD constipation rats and its regulatory mechanism. A rotenone (ROT)-induced gastrointestinal motility disorder model was used to simulate the pathological process of constipation in PD. The results showed that EA could effectively promote gastrointestinal peristalsis, reduce α-synuclein accumulation in substantia nigra and colon and colonic injury in rats after ROT administration. Mechanistically, EA activation of the central-cholinergic pathway increases acetylcholine release in the colon. At the same time, EA up-regulated the co-expression of enteric glial cells (EGCs) and α7 nicotinic acetylcholine receptor (α7nAChR). EA increased the expression of choline acetyltransferase (ChAT), neuronal nitric oxide synthase (nNOS), and tyrosine hydroxylase (TH) in the colon of PD rats. Further mechanistic studies showed that EA increased the expression of glial cell-derived neurotrophic factor (GDNF), GFRa1 and p-AKT in colon tissues. The present study confirmed that EA upregulates α7nAChR through a central-cholinergic mechanism to promote GDNF release from EGCs, thereby protecting intestinal neurons and thereby improving gastrointestinal motility.

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.

Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.

Comparative efficacy of different exercise types on inflammatory markers in women with overweight and obesity: A systematic review and network meta-analysis of randomized controlled trials.

OBJECTIVES: This study aimed to compare and rank the effectiveness of aerobic exercise, resistance training, combined aerobic and resistance exercise, and high-intensity interval training on inflammatory marker levels in women with overweight and obesity by using network meta-analysis. DESIGN: Systematic review with network meta-analysis and Grading Recommendations Assessment, Development, and Evaluation of the evidence. METHODS: Literature as of April 2023 was searched from databases such as Cochrane, Embase, Pubmed, Web of Science, and EBSCO, and English-language randomized controlled trials that meet the inclusion criteria were selected. A random-effects network meta-analysis was performed within a frequentist framework. RESULTS: A total of 75 articles and 4048 participants were included. Resistance training was the most recommended type of exercise to decrease C-reactive protein levels (surface under cumulative ranking = 90.1; standardized mean difference = -0.79, 95 % confidence interval: -1.17, -0.42); aerobic exercise was the most effective exercise type to reduce tumor necrosis factor-α levels (surface under cumulative ranking = 87.9; standardized mean difference = -0.79, 95 % confidence interval: -1.19, -0.39); combined aerobic and resistance exercise was the most effective type of exercise to reduce interleukin-6 levels (surface under cumulative ranking = 75.8; standardized mean difference = -0.77, 95 % confidence interval: -1.38, -0.16) and leptin levels (surface under cumulative ranking = 77.1; standardized mean difference = -0.96, 95 % confidence interval: -1.72, -0.20), and high-intensity interval training was the type of exercise that was well suited to increase adiponectin levels (surface under cumulative ranking = 87.2; standardized mean difference = 0.99, 95 % confidence interval: 0.27, 1.71). CONCLUSIONS: This network meta-analysis based on randomized controlled trials confirmed that different exercise types have different efficacies on inflammation indicators among women with overweight and obesity. The findings may provide clinicians and healthcare professionals with insights into the implementation of exercise programs for women struggling with overweight and obesity.

SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics.

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow.

Ferroelectric compute-in-memory annealer for combinatorial optimization problems.

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

A highly integrated digital PCR system with on-chip heating for accurate DNA quantitative analysis.

Digital polymerase chain reaction (dPCR) is extensively used for highly sensitive disease diagnosis due to its single-molecule detection ability. However, current dPCR systems require intricate DNA sample distribution, rely on cumbersome external heaters, and exhibit sluggish thermal cycling, hampering efficiency and speed of the dPCR process. Herein, we presented the development of a microwell array based dPCR system featuring an integrated self-heating dPCR chip. By utilizing hydrodynamic and electrothermal simulations, the chip's structure is optimized, resulting in improved partitioning within microwells and uniform thermal distribution. Through strategic hydrophilic/hydrophobic modifications on the chip's surface, we effectively secured the compartmentalization of sample within the microwells by employing an overlaying oil phase, which renders homogeneity and independence of samples in the microwells. To achieve precise, stable, uniform, and rapid self-heating of the chip, the ITO heating layer and the temperature control algorithm are deliberately designed. With a capacity of 22,500 microwells that can be easily expanded, the system successfully quantified EGFR plasmid solutions, exhibiting a dynamic linear range of 105 and a detection limit of 10 copies per reaction. To further validate its performance, we employed the dPCR platform for quantitative detection of BCR-ABL1 mutation gene fragments, where its performance was compared against the QuantStudio 3D, and the self-heating dPCR system demonstrated similar analytical accuracy to the commercial dPCR system. Notably, the individual chip is produced on a semiconductor manufacturing line, benefiting from mass production capabilities, so the chips are cost-effective and conducive to widespread adoption and accessibility.

Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines.

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)-based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.