High Accuracy Research Articles

Melting temperature mapping method using imperfect-match linear long probes

Identifying pathogenic microorganisms as early as possible is critical for selecting the appropriate antimicrobial therapy in infected patients. We previously reported the development of the Tm mapping method for identifying a broad range of pathogenic bacteria within 3 h of blood collection. However, the Tm mapping identification requires an analytical instrument with a tube-to-tube variation of no more than 0.1 °C, so we can only use a few instruments that have such high thermal accuracy. To address the problem, we developed the improved Tm mapping method using imperfect-match linear long quenching probes (IMLL Q-probes). Using IMLL Q-probes, almost all commercially available analytical instruments can be used for the Tm mapping method. Some bacterial species cannot be narrowed down to one species, but they can at least be narrowed down to the genus level. The Tm mapping method using IMLL Q-probes is useful for deciding on antimicrobial therapy in infected patients.

Secondary Conformational Checkpoint in CRISPR-Cas9.

A specific checkpoint between target DNA binding and cleavage primarily governs the precision of Cas9 gene editing. Although various CRISPR-Cas9 variants have been developed to improve DNA cleavage accuracy, we still lack a comprehensive understanding of how they work at the molecular level. Herein, we have focused on studying the late-stage conformational transitions of Cas9 and an evolved Cas9 mutant (evoCas9) that start from the precleavage state. Our submilliseconds of dynamic simulations reveal that the presence of base mismatches leads the HNH nuclease domain of Cas9 to alter its principal functional modes of motion, thereby impairing its conformational activation. This observation suggests the existence of a secondary conformational checkpoint that fine-tunes the final DNA cleavage activation. Remarkably, evoCas9 is prone to deviating from the normal activation pathway with base mismatches. This is characterized by a noticeable shift in the positioning of the HNH domain and a significantly perturbed allosteric communication network within the enzyme. Therefore, the mutations evolved in evoCas9 also reinforce the secondary checkpoint in addition to the previously identified primary checkpoint, collectively ensuring this variant's high gene-editing accuracy. This mechanism should also apply to other Cas9-guide RNA variants with enhanced fidelity.

Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks

Compared to traditional industrial materials, composites have higher durability and compressive strength. However, some components may have flaws due to the manufacturing process. Traditional defect detection methods have low accuracy and cannot adapt to complex shooting environments. Aiming to address the issues of high computational requirements in traditional detection models and the lack of lightweight detection capabilities, the Ghost module is used instead of convolutional arithmetic to construct a lightweight model. To reduce the computational complexity of the feature extraction module, we have incorporated an improved Efficient Channel Attention mechanism to improve the model’s feature extraction capabilities. A rapid defect classification method is implemented to determine whether there are defects in the image or not by comparing the performance and running speed of models such as AlexNet, VGGNet, and ResNet. And ablation experiments are conducted for each model. The results show that the Ghost module model, which incorporates the improved Efficient Channel Attention mechanism, has a significant optimization effect on the convolutional neural network model. It can achieve a high accuracy rate when constructing lightweight models. It improves the running speed of the model, making it more efficient to use and deploy.

Construction of a 3D Quantum Dot Nanoassembly with Two-Step FRET for One-Step Sensing of Human Telomerase RNA in Breast Cancer Cells and Tissues.

Telomerase is an important biomarker for early diagnosis of cancers, but current telomerase assays usually rely on measuring the extension products of telomerase substrates, which increases the assay complexity. More evidence indicates that human telomerase RNA (hTR), as a core component of telomerase, is positively correlated with the telomerase activity. Herein, we demonstrate the development of a duplex-specific nuclease (DSN)-propelled 3D quantum dot (QD) nanoassembly with two-step Föster resonance energy transfer (FRET) for the one-step sensing of hTR in breast cancer cells and tissues. This assay involves only one hairpin probe modified with a Cy5 at the sixth base from the 5'-biotin end and a BHQ2 at the 3'-terminus, which integrates three functions of target recognition, target recycling amplification, and signal readout. The anchoring of the hairpin probe on the 605QD surface results in the formation of a 3D 605QD-Cy5-probe-BHQ2 nanoassembly in which two-step FRET occurs among the 605QD, Cy5, and BHQ2 quencher. Notably, the formation of 605QD-Cy5-probe-BHQ2 nanoassembly facilitates the reduction of background signal and the increase of signal-to-background ratio due to its dense, highly oriented nucleic acid shell-induced steric hindrance effect. This assay can achieve one-step and rapid detection of hTR with a detection limit of 2.10 fM, which is the simplest and most rapid hTR assay reported so far. Moreover, this assay can efficiently distinguish single-base mismatched sequences, and it can discriminate the hTR level between breast cancer patients and healthy donors with a high accuracy of 100%, with great prospects for early diagnosis of cancers.

The Clinical Pulmonary Infection Score Combined with Procalcitonin and Lung Ultrasound (CPIS-PLUS), a Good Tool for Ventilator Associated Pneumonia Early Diagnosis in Pediatrics

The Clinical Pulmonary Infection Score Combined with Procalcitonin and Lung Ultrasound (CPIS-PLUS), a Good Tool for Ventilator Associated Pneumonia Early Diagnosis in Pediatrics

Jarvis-Virtual Voice Assistant

Although voice recognition technology has advanced significantly, there are still obstacles in the way of obtaining high accuracy, especially in a variety of environmental settings. This research uses deep learning models to increase the robustness and accuracy of voice recognition systems. Implementing different deep learning architectures, such as recurrent and convolutional neural networks (RNNs), and training them on a wide range of datasets with different noise levels and accents was part of the technique. Important results show a 15% improvement in recognition accuracy over current systems for the suggested models, which perform especially well in noisy contexts and with accented speech. These outcomes demonstrate the effectiveness of deep learning techniques in resolving issues that traditional voice recognition systems encounter. The results of this study have important significance for real-world applications, since they may enable smooth communication in a variety of linguistic and environmental contexts. Context: Since voice recognition technology has come a long way, it still has problems with accuracy, particularly in noisy situations and different locations with different accents. Even with advancements, traditional systems find it difficult to function consistently and dependably under these circumstances. This lays the groundwork for the need to investigate more durable ways to raise voice recognition's accuracy and dependability.

Application of Data Mining Techniques in Biopsy Interpretation and Staging of Carcinoma Cancer Disease: A Case Study of Northeastern Nigeria

This paper introduces an innovative framework tailored for carcinoma cancer staging in northeastern Nigeria, employing data mining techniques and machine learning algorithms, with analysis conducted using the WEKA toolkit. Given the challenges in healthcare systems, especially in cancer diagnosis and treatment, this study aims to enhance diagnostic precision thorough analysis of biopsy reports. By assessing Support Vector Machine (SVM) and decision tree algorithms using datasets from six tertiary health institutions, the framework demonstrates promising outcomes in accurately determining cancer malignancy stage. SVM exhibits high accuracies in staging breast and prostate cancer, while decision tree algorithms show notable accuracy rates in staging skin cancer. These results highlight the potential of machine learning in advancing cancer diagnosis and treatment planning, particularly in resource-constrained settings, and pave the way for the development of computer-aided diagnostic tools tailored for carcinoma cancer staging in northeastern Nigeria.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Stepping-Stone CBH: Benchmark and Application of a Multilayered Isodesmic-Based Correction Scheme.

We present a generalization of the connectivity-based hierarchy (CBH) of isodesmic-based correction schemes to a multilayered fragmentation platform for overall cost reduction while retaining high accuracy. The newly developed multilayered CBH approach, called stepping-stone CBH (SSCBH), is benchmarked on a diverse set of 959 medium-sized organic molecules. Applying SSCBH corrections to the PBEh-D3 density functional resulted in an average error of 0.76 kcal/mol for the full test set compared to accurate CCSD(T)-quality enthalpies and an even lower error of 0.44 kcal/mol on a subset containing only acyclic molecules. These results rival the traditional CBH-3 approach at a greatly reduced cost, allowing larger fragment corrections to be made at the MP2 level of theory rather than with G4. Our SSCBH approach will enable more widespread applications of CBH methods to a broader range of organic and biomolecular systems.

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

BackgroundGout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout.MethodsUsing datasets from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model.ResultsAn initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798–0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model.ConclusionThe interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity

Data-Driven Initial Gap Identification of Piecewise-Linear Systems Using Sparse Regression and Universal Approximation Theorem

Abstract This paper proposes a method for identifying an initial gap in piecewise-linear (PWL) systems from data. Piecewise-linear systems appear in many engineered systems such as degraded mechanical systems and infrastructures and are known to show strong nonlinearities. To analyze the behavior of such piecewise-linear systems, it is necessary to identify the initial gap, at which the system behavior switches. The proposed method identifies the initial gap by discovering the governing equations using sparse regression and calculating the gap based on the universal approximation theorem. A key step to achieve this is to approximate a piecewise-linear function by a finite sum of piecewise-linear functions in sparse regression. The equivalent gap is then calculated from the coefficients of the multiple piecewise-linear functions and their respective switching points in the obtained equation. The proposed method is first applied to a numerical model to confirm its applicability to piecewise-linear systems. Experimental validation of the proposed method has then been conducted with a simple mass-spring-hopping system, where the method successfully identifies the initial gap in the system with high accuracy.

Adaptive Physics-encoded Graph Neural Network for Health Stage Assessment of Liquid-propellant Rocket Engines

Abstract The improvement of reliable health monitoring system (HMS) for liquid-propellant rocket engines (LREs) is a crucial part for reusable launch vehicle, which contributes to providing competitive and cost-effective propulsion systems. Thus, it accentuates the need for reliable and quick health stage assessment of system and follow-up damage-mitigating control. In this paper, we propose a novel adaptive physics-encoded graph neural network (APGNN) for health stage assessment of LREs. Our approach embeds the relations of different sensors obtained through expert experience, which contributes to constructing a physical graph layer. To better capture the information contained in all the sensor data, a novel convolutional layer of adaptive auto-regressive moving average (ARMA) filters is designed, which considers the personalized information propagation needs of each neural network layer. The performance of the proposed method is quantified with data obtained from physics simulations and real-world engineering systems. The results show that our model has potential applicability for the health stage assessment of LREs with high accuracy.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

Pest target identification in agricultural production environments is challenging due to the dense distribution, small size, and high density of pests. Additionally, changeable environmental lighting and complex backgrounds further complicate the detection process. This study focuses on enhancing the recognition performance of tea pests by introducing a lightweight pest image recognition model based on the improved YOLOv8 architecture. First, slicing-aided fine-tuning and slicing-aided hyper inference (SAHI) are proposed to partition input images for enhanced model performance on low-resolution images and small-target detection. Then, based on an ELAN, a generalized efficient layer aggregation network (GELAN) is designed to replace the C2f module in the backbone network, enhance its feature extraction ability, and construct a lightweight model. Additionally, the MS structure is integrated into the neck network of YOLOv8 for feature fusion, enhancing the extraction of fine-grained and coarse-grained semantic information. Furthermore, the BiFormer attention mechanism, based on the Transformer architecture, is introduced to amplify target characteristics of tea pests. Finally, the inner-MPDIoU, based on auxiliary borders, is utilized as a replacement for the original loss function to enhance its learning capacity for complex pest samples. Our experimental results demonstrate that the enhanced YOLOv8 model achieves a precision of 96.32% and a recall of 97.95%, surpassing those of the original YOLOv8 model. Moreover, it attains an mAP@50 score of 98.17%. Compared to Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8, its average accuracy is 17.04, 11.23, 5.78, 3.75, and 2.71 percentage points higher, respectively. The overall performance of YOLOv8 outperforms that of current mainstream detection models, with a detection speed of 95 FPS. This model effectively balances lightweight design with high accuracy and speed in detecting small targets such as tea pests. It can serve as a valuable reference for the identification and classification of various insect pests in tea gardens within complex production environments, effectively addressing practical application needs and offering guidance for the future monitoring and scientific control of tea insect pests.

On-axis complex-amplitude modulation for the generation of super-stable vector modes

We propose a technique to generate complex vector beams with high quality and stability. Our approach relies on the combination of complex amplitude modulation (CAM) and on-axis modulation, two techniques that seem incompatible at first glance. The first one produces scalar structured light fields in phase and amplitude with high accuracy, while the second one is preferred for generating vector beams of great stability although of reduced quality. Specifically, the idea behind our technique is to send the shaped light produced by CAM co-axially with the zeroth order, rather than diffracted to the first order, as it is commonly done. We first describe our technique, explaining the generation of the hologram and experimental setup to isolate the desired vector mode, and then present experimental results that corroborate our approach. We first address the quality of the generated beams using Stokes polarimetry to reconstruct their transverse polarisation distribution, and then compare their stability against the same mode produced using a Sagnac interferometric method. Our vector beams are of good quality and remarkably stable, two qualities that we expect will appeal to the community working with vector modes.

Preliminary sensitivity study for a gravitational redshift measurement with China's Lunar Exploration Project

Abstract General relativity (GR) is a highly successful theory that describes gravity as a geometric phenomenon. The gravitational redshift, a classic test of GR, can potentially be violated in alternative gravity theories, and experimental tests on this effect are crucial for our understanding of gravity. In this paper, considering the space-ground clock comparisons with free-space links, we discuss a high-precision Doppler cancellation-based measurement model for testing gravitational redshift. This model can effectively reduce various sources of error and noise, reducing the influences of the first-order Doppler effect, atmospheric delay, Shapiro delay, etc. China's Lunar Exploration Project (CLEP) is proposed to equip the deep-space H maser with a daily stability of $2\times10^{-15}$, which provides an approach for testing gravitational redshift. Based on the simulation, we analyze the space-ground clock comparison experiments of the CLEP experiment, and simulation analysis demonstrates that under ideal condition of high-precision measurement of the onboard H-maser frequency offset and drift, the CLEP experiment may reach the uncertainty of $3.7\times10^{-6}$ after a measurement session of 60 days. Our results demonstrate that if the issue of frequency offset and drift is solved, CLEP missions have a potential of testing the gravitational redshift with high accuracy.

Diagnosis of Serous Effusion with Intelligent Imaging Flow Cytometry

AbstractA serous effusion is a buildup of extra fluid in the serous cavities including pleural, peritoneal, and pericardial cavities. It is important to distinguish benign reactive effusions from effusions caused by malignant proliferation in cytopathology since different diagnoses can lead to completely different disease staging and therapeutic choices. The conventional cytopathology procedure has the disadvantages of low throughput and low objectivity. To enhance the efficiency and accuracy of malignant serous effusion diagnosis, in this paper, an imaging flow cytometry, called optofluidic time‐stretch microscopy is first employed, to image the cells in the serous effusion at an event rate of 100 000 events per second and with a spatial resolution better than 1 µm. The acquired cellular images are then analyzed using a convolutional neural network, by which the malignant cells are accurately detected. The performance of the method is validated with 18 clinical samples, including 14 malignant and 4 benign ones. The results show that the method can detect malignant cells at an accuracy of 90.53%. The high throughput, high accuracy, and high convenience of the method make it a potential solution for malignant serous effusion diagnosis in various scenarios.

Development of a time-resolved immunochromatographic test strip for rapid and quantitative determination of GFAP in serum.

Glial fibrillary acidic protein (GFAP) in serum has been shown as a biomarker of traumatic brain injury (TBI) which is a significant global public health concern. Accurate and rapid detection of serum GFAP is critical for TBI diagnosis. In this study, a time-resolved fluorescence immunochromatographic test strip (TRFIS) was proposed for the quantitative detection of serum GFAP. This TRFIS possessed excellent linearity ranging from 0.05 to 2.5 ng/mL for the detection of serum GFAP and displayed good linearity (Y = 598723X + 797198, R2 = 0.99), with the lowest detection limit of 16 pg/mL. This TRFIS allowed for quantitative detection of serum GFAP within 15 min and showed high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both < 4.0%. Additionally, this TRFIS was applied to detect GFAP in the serum samples from healthy donors and patients with cerebral hemorrhage, and the results of TRFIS could efficiently discern the patients with cerebral hemorrhage from the healthy donors. Our developed TRFIS has the characteristics of high sensitivity, high accuracy, and a wide linear range and is suitable for rapid and quantitative determination of serum GFAP on-site.

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modeling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of high-temperature superconducting devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning (ML) methods for estimating the n-value has been carried out. The results demonstrated that cascade forward neural network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 root mean squared error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). In contrast, the rigid regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, random forest, boosting methods, and simple feed forward neural network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modeling of superconductors but also pave the way for applications and further research on ML plug-and-play codes for superconducting studies including modeling of superconducting devices.