Detection Efficiency Research Articles

Rapid and In-Field Sensing of Hydrogen Peroxide in Plant by Hydrogel Microneedle Patch.

The rapidly changing climate is exacerbating the environmental stress that negatively impacts crop health and yield. Timely sensing of plant response to stress is beneficial to timely adjust planting conditions, promoting the healthy growth of plants, and improving plant productivity. Hydrogen peroxide (H2O2) is an importantmolecule of signal transduction in plants. However, the common methods for detecting H2O2in plants are associated with certain drawbacks, such as long extraction time, cumbersome steps, dependence on large instruments, and difficulty in realizing in-field sensing. Therefore, it is urgent to establish more efficient detection methods to realize the rapid detection of H2O2 content in plants. In this research, poly (methyl vinyl ether-alt-maleic acid) (PMVE/MA) hydrogel microneedle (MN) patch for rapid extraction of leaf sap are prepared, and the extraction mechanism of PEG-crosslinked PMVE/MA hydrogel MN patch isstudied. A method of rapid detection of H2O2 content in plants based on MN patch with optical detection technology is constructed. The hydrogel MN patch can be usedfor timely H2O2 analysis. This application enables new opportunities in plant engineering, and can be extended to the safety and healthmonitoring of other plants and animals.

Evaluating the utility of multi-gene, multi-disease population-based panel testing accounting for uncertainty in penetrance estimates.

Panel germline testing allows for the efficient detection of deleterious variants for multiple conditions, but the benefits and harms of identifying these variants are not always well understood. We present a multi-gene, multi-disease aggregate utility formula that allows the user to consider adding or removing each gene in a panel based on variant frequency, estimated penetrances, and subjective disutilities for testing positive but not developing the disease and testing negative but developing the disease. We provide credible intervals for utility that reflect uncertainty in penetrance estimates. Rare, highly penetrant deleterious variants tend to contribute positive net utilities for a wide variety of user-specified disutilities, even when accounting for parameter estimation uncertainty. However, the clinical utility of deleterious variants with moderate, uncertain penetrance depends more on assumed disutilities. The decision to include a gene on a panel depends on variant frequency, penetrance, and subjective utilities and should account for uncertainties around these factors.

The effects of intra-detector Compton scatter on low-frequency DQE for photon-counting CT using edge-on-irradiated silicon detectors.

Edge-on-irradiated silicon detectors are currently being investigated for use in full-body photon-counting computed tomography (CT) applications. The low atomic number of silicon leads to a significant number of incident photons being Compton scattered in the detector, depositing a part of their energy and potentially being counted multiple times. Even though the physics of Compton scatter is well established, the effects of Compton interactions in the detector on image quality for an edge-on-irradiated silicon detector have still not been thoroughlyinvestigated. To investigate and explain effects of Compton scatter on low-frequency detective quantum efficiency (DQE) for photon-counting CT using edge-on-irradiated silicondetectors. We extend an existing Monte Carlo model of an edge-on-irradiated silicon detector with 60mm active absorption depth, previously used to evaluate spatial-frequency-based performance, to develop projection and image domain performance metrics for pure density and pure spectral imaging tasks with 30and 40cm water backgrounds. We show that the lowest energy threshold of the detector can be used as an effective discriminator of primary counts and cross-talk caused by Compton scatter. We study the developed metrics as functions of the lowest threshold energy for root-mean-square electronic noise levels of 0.8, 1.6, and 3.2keV, where the intermediate level 1.6keV corresponds to the noise level previously measured on a single sensor element in isolation. We also compare the performance of a modeled detector with 8, 4, and 2 optimized energy bins to a detector with 1-keV-widebins. In terms of low-frequency DQE for density imaging, there is a tradeoff between using a threshold low enough to capture Compton interactions and avoiding electronic noise counts. For 30cm water phantom, 4 energy bins, and a root-mean-square electronic noise of 0.8, 1.6, and 3.2keV, it is optimal to put the lowest energy threshold at 3, 6, and 1keV, which gives optimal projection-domain DQEs of 0.64, 0.59, and 0.52, respectively. Low-frequency DQE for spectral imaging also benefits from measuring Compton interactions with respective optimal thresholds of 12, 12, and 13keV. No large dependence on background thickness was observed. For the intermediate noise level (1.6keV), increasing the lowest threshold from 5 to 35keV increases the variance in a iodine basis image by 60%-62% (30cm phantom) and 67%-69% (40cm phantom), with 8 bins. Both spectral and density DQE are adversely affected by increasing the electronic noise level. Image-domain DQE exhibits similar qualitative behavior as projection-domainDQE. Compton interactions contribute significantly to the density imaging performance of edge-on-irradiated silicon detectors. With the studied detector topology, the benefit of counting primary Compton interactions outweighs the penalty of multiple counting at all lowest threshold energies. Compton interactions also contribute significantly to the spectral imaging performance for measured energies above10keV.

Copper-doped titanium dioxide nanoparticles decorated on 2D-hexagonal boron nitride nanosheets for susceptible electrochemical detection of an anti-cancer drug in environmental and biological samples.

Chlorambucil (CML) cures chronic lymphatic leukemia (white blood cell cancer). A high dose of CML can cause several side effects like bone marrow suppression, anemia, peripheral neuropathy, and infertility in the human body. In this research, we have synthesized a nanocomposite based on copper-doped titanium dioxide (CuTiO2) adorned with 2D hexagonal boron nitride (CuTiO2@BN) for the efficient electrochemical detection of CML. A series of characterization techniques FT-IR, XRD, Raman spectroscopy, SEM, TEM, EDAX XPS, and electrochemical characterization were used to analyze the CuTiO2@BN nanocomposite structural and morphological compositions. The sensing performance of the CuTiO2@BN modified GCE for CML detection has been assessed using voltammetry methods. The chronoamperometry technique analyzed the kinetics of the electrochemical oxidation of CML at CuTiO2@BN/GCE. The CuTiO2@BN-based glassy carbon electrode (GCE) has a synergetic electro-catalytic effect on CML oxidation due to its many active sites, enhanced surface area, fast charge transfer, and numerous defects. For the detection of CML, the suggested electrochemical sensor exhibits excellent selectivity, low limit of detection (LOD) as found 5.0nM, wide linear ranges (0.02-8000µM), and quick reaction times.

Open-Nanogap-Induced Strong Electromagnetic Enhancement in Au/AgAu Monolayer as a Stable and Uniform SERS Substrate for Ultrasensitive Detection.

Nanogap-based plasmonic metal nanocrystals have been applied in surface-enhanced Raman scattering detection, while the closed and insufficient electromagnetic fields as well as the nonreproducible Raman signal of the substrate greatly restrict the actual application. Herein, a highly uniform Au/AgAu monolayer with abundant nanogaps and huge electromagnetic enhancement is prepared, which shows ultrasensitive and reproducible SERS detection. Au/AgAu with an inner nanogap is first prepared based on Au nanotriangles, and the nanogap is opened from the three tips via a subsequent etching process. The open-gap Au/AgAu displays much higher SERS efficiency than Au and Au/AgAu with an inner nanogap on detecting crystal violet due to the open-gap induced electromagnetic enhancement and improved molecular absorption. Furthermore, the open-gap Au/AgAu monolayer is prepared via interfacial self-assembly, which shows further improved SERS due to the dense and strong hotspots in the nanocavities induced by the electromagnetic coupling between adjacent open gaps. The monolayer possesses excellent signal stability, uniformity, and reproducibility. The analytic enhancement factor and relative standard deviation reach to 2.12 × 108 and 4.65% on detecting crystal violet, respectively. Moreover, the monolayer achieves efficient detection of thiram in apple juice, biphenyl-4-thiol, 4-mercaptobenzoic, melamine, and a mixed solution of four different molecules, showing great promise in practical detection.

MSGC-YOLO: An Improved Lightweight Traffic Sign Detection Model under Snow Conditions

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions.

Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.

Fabrication of an Antifouling Surface Plasmon Resonance Sensor with Stratified Zwitterionic Peptides for Highly Efficient Detection of Peanut Allergens in Biscuits.

Peanut allergen monitoring is currently an effective strategy to avoid allergic diseases, while food matrix interference is a critical challenge during detection. Here, we developed an antifouling surface plasmon resonance sensor (SPR) with stratified zwitterionic peptides, which provides both excellent antifouling and sensing properties. The antifouling performance was measured by the SPR, which showed that stratified peptide coatings showed much better protein resistance, reaching ultralow adsorption levels (<5 ng/cm2). Atomic force microscopy was used to further analyze the antifouling mechanism from a mechanical perspective, which demonstrated lower adsorption forces on hybrid peptide coatings, confirming the better antifouling performance of stratified surfaces. Moreover, the recognition of peanut allergens in biscuits was performed using an SPR with high efficiency and appropriate recovery results (98.2-112%), which verified the feasibility of this assay. Therefore, the fabrication of antifouling sensors with stratified zwitterionic peptides provides an efficient strategy for food safety inspection.

Genome-wide association study and genomic selection of spike-related traits in bread wheat.

Identification of 337 stable MTAs for wheat spike-related traits improved model accuracy, and favorable alleles of MTA259 and MTA64 increased grain weight and yield per plant. Wheat (Triticum aestivum L.) is one of the three primary global, staple crops. Improving spike-related traits in wheat is crucial for optimizing spike and plant morphology, ultimately leading to increased grain yield. Here, we performed a genome-wide association study using a dataset of 24,889 high-quality unique single-nucleotide polymorphisms (SNPs) and phenotypic data from 314 wheat accessions across eight diverse environments. In total, 337 stable and significant marker-trait associations (MTAs) related to spike-related traits were identified. MTA259 and MTA64 were consistently detected in seven and six environments, respectively. The presence of favorable alleles associated with MTA259 and MTA64 significantly reduced wheat spike exsertion length and spike length, while enhancing thousand kernel weight and yield per plant. Combined gene expression and network analyses identified TraesCS6D03G0692300 and TraesCS6D03G0692700 as candidate genes for MTA259 and TraesCS2D03G0111700 and TraesCS2D03G0112500 for MTA64. The identified MTAs significantly improved the prediction accuracy of each model compared with using all the SNPs, and the random forest model was optimal for genome selection. Additionally, the eight stable and major MTAs, including MTA259, MTA64, MTA66, MTA94, MTA110, MTA165, MTA180, and MTA164, were converted into cost-effective and efficient detection markers. This study provided valuable genetic resources and reliable molecular markers for wheat breeding programs.

Single Effective Complex Loading into Zero-Mode Waveguides Optimized with Fluorescence Evaluation at Quenching and Accumulation Checkpoints.

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction. Both the quantity and activity of the complex loaded into ZMWs have a critical effect on the efficiency of detection. Herein, a fluorescence evaluation at quenching and accumulation checkpoints was established to assess and optimize single effective complex loading into ZMWs. A primer-template-enzyme ternary complex was designed, and then an evaluation for quantity statistics at the quenching checkpoint and functional activity at the accumulation checkpoint was used to validate the effectiveness of complexes loaded into ZMWs. By optimizing the parameters such as loading time, procedures, and enzyme amount, the single-molecule effective occupancy was increased to 25.48%, achieving 68.86% of the theoretical maximum value (37%) according to Poisson statistics. It is of great significance to provide effective complex-loading validation for improving the sample-loading efficiency of single-molecule assays or sequencing in the future.

Split ring hole metamaterial-enhanced pyroelectric detector for efficient multi-narrowband terahertz detection

Derived from infrared pyroelectric detection, typical terahertz (THz) pyroelectric detectors have low sensitivity at low-frequency THz bands. Based on the high-efficiency absorption of the metamaterial perfect absorber (MPA), a novel split ring hole metamaterial-enhanced pyroelectric detector is proposed to achieve efficient multi-narrowband THz detection. Using high frequency simulation software (HFSS), the dimensional parameters including ring radius, ring width, connection beam width, array period, and thickness, are optimized to enhance efficient multi-narrowband absorption. The as-optimized metamaterial-enhanced detectors are fabricated via micro-nano manufacturing technology. The voltage responsiveness and noise equivalent power of the metamaterial-enhanced detector are tested by THz focused optical path and compared with those of the typical pyroelectric detector and the simulated MPA absorptivity. The results indicate that the metamaterial-enhanced detector has a multi-narrowband detection capability at 0.245 THz, 0.295 THz, and 0.38 THz, which is close to the simulated MPA absorptivity. Compared to the typical pyroelectric detector, the split ring hole metamaterial-enhanced detector can simultaneously achieve thermal absorption, thermal conduction, and pyroelectricity in the same MPA structure, providing faster response speed above 100 Hz chopper frequency and two times higher detection sensitivity at multi-narrowband THz frequencies. This research can be used for THz sensing, absorption filtering, biological macromolecule detection, and other applications.

Fast kV-switching and dual-layer flat-panel detector enabled cone-beam CT joint spectral imaging

Purpose. Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy spectral imaging solutions for FPD-based cone-beam computed tomography (CT). However, cone-beam CT (CBCT) spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance. That is because the energy separation by either FKS or DL-FPD, alone, is still limited, along with apparently unpaired signal levels in the effective low- and high-energy projections in real applications, not to mention the x-ray scatter in cone-beam scan which will make the material decomposition almost impossible if no correction is applied. To further improve CBCT spectral imaging capability, this work aims to promote a source-detector joint multi-energy spectral imaging solution which takes advantages of both FKS and DL-FPD, and to conduct a feasibility study on the first tabletop CBCT system with the joint spectral imaging capability developed. Methods. For CBCT, development of multi-energy spectral imaging can be jointly realized by using an x-ray source with a generator whose kilo-voltages can alternate in tens of Hertz (i.e. FKS), and a DL-FPD whose top- and bottom-layer projections corresponds to different effective energy levels. Thanks to the complimentary characteristics inherent in FKS and DL-FPD, the overall energy separation will be significantly better when compared with FKS or DL-FPD alone, and the x-ray photon detection efficiency will be also improved when compared with FKS alone. In this work, a noise performance analysis using the Cramér–Rao lower bound (CRLB) method is conducted. The CRLB for basis material after a projection-domain material decomposition is derived, followed by a set of numerical calculations of CRLBs, for the FKS, the DL-FPD and the joint solution, respectively. To compensate for the slightly angular mismatch between low- and high- projections in FKS, a dual-domain projection completion scheme is implemented. Afterwards material decomposition from the complete projection data is carried out by using the maximum-likelihood method, followed by reconstruction of basis material and virtual monochromatic images (VMI). In this work, the first FKS and DL-FPD jointly enabled multi-energy tabletop CBCT system, to the best of our knowledge, has been developed in our laboratory. To evaluate its spectral imaging performance, a set of physics experiments are conducted, where the multi-energy and head phantoms are scanned using the 80/105/130 kVp switching pairs and projection data are collected using a prototype DL-FPD, whose both top and bottom layer of panels are composed of 550 μm of cesium iodine (CsI) scintillators with no intermediate metal filter in-between. Results. The numerical simulations show that the joint spectral imaging solution can lead to a significant improvement in energy separation and lower noise levels in most of material decomposition cases. The physics experiments confirmed the feasibility and superiority of the joint spectral imaging, whose CNRs in the selected regions of interest of the multi-energy phantom were boosted by an average improvement of 21.9%, 20.4% for water basis images and 32.8%, 62.8% for iodine images when compared with that of the FKS and DL-FPD, respectively. For the head phantom case, the joint spectral imaging can effectively reduce the streaking artifacts as well, and the standard deviation in the selected regions of interest are reduced by an average decrement of 19.5% and 8.1% for VMI when compared with that of the FKS and DL-FPD, respectively. Conclusions. A feasibility study of the joint spectral imaging solution for CBCT by utilizing both the FKS and DL-FPD was conducted, with the first tabletop CBCT system having such a capability being developed, which exhibits improved CNR and is more effective in avoiding streaking artifacts as expected.

Efficient detection of nitric oxide a biomarker associated with COVID19 via N, P co-doped C60 fullerene: a computational study.

Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations.

Prompt gamma imaging system in particle therapy: a mini-review

Accurate in-vivo verification of beam range and dose distribution is crucial for the safety and effectiveness of particle therapy. Prompt gamma (PG) imaging, as a method for real-time verification, has gained prominence in this area. Currently, several PG imaging systems are under development, including gamma electron vertex imaging (GEVI), the Compton camera, the slit camera, and the multi-array type collimator camera. However, challenges persist in dose prediction accuracy, largely due to patient positioning uncertainty and anatomical changes. Although each system demonstrates potential in verifying PG range, further improvements in detection efficiency, spatial resolution, background reduction, and integration into clinical workflows are essential.

Coumarin-Modified Starch Fluorescent Nanoparticles as Sensor of Fe3+ and Zn2+ ions Utilizing Dynamic Quenching and Chelation Mechanisms.

Zinc and iron are two essential trace minerals that play a pivotal role in maintaining optimal health and well-being in the human body. Despite being required in relatively small quantities, their significance can be understated as they participate in a wide array of critical physiological processes such as oxygen transport, DNA synthesis, controlling nutrient availability, etc. Understanding the distribution and behavior of these ions in natural water bodies is essential for assessing water quality, studying ecological processes, and managing environmental impacts. In this study, we have developed a dual fluorescence probe using starch which was functionalized with coumarin derivatives, for efficient detection of Fe3+ and Zn2+ ions. This structure led a self-assembled starch/coumarin (SC) fluorescent nanoparticles with strong fluorescence intensity under ultraviolet light (365nm). The quenching effect of Fe3+ on the SC fluorescent probe enabled efficient specific detection of Fe3+. Furthermore, Zn2+ ions increased fluorescence intensity of coumarin compounds (λemission = 459). This phenomenon occurs when the coumarin compound forms a complex or interacts with the zinc ion, resulting in enhanced fluorescence emission. In summary, the developed fluorescent probe offered a promising approach for sensitive and specific detection of iron and zinc ions in aqueous solutions.

Impact location of metal structures based on time–frequency image features and deep residual network

Impact location detection plays an important role in the structural health monitoring of metal materials. However, the methods of metal material impact location detection based on physical analysis are often limited by the extraction accuracy of some parameters such as material and structure parameters and time difference calculation. Therefore, this paper develops a deep residual network method for impact location detection, time–frequency characteristic deep residual network (TF-DRN). This method takes the four-channel short-time Fourier transform time–frequency graph as input, uses the unique residual network architecture to automatically extract the advanced features, and then uses the global average pooling layer and the full connection layer to establish the mapping between the advanced features and the impact location, so as to detect the impact location. By introducing regularization and batch normalization, the problems of gradient disappearance and gradient explosion are alleviated, and the generalization and efficiency of impact location detection are further improved. The experimental results show that on an 800 mm × 800 mm × 2.5 mm aluminum plate, the average error of the validation set and the test set are 0.85 cm and 1.33 cm respectively, and the performance of the method is significantly better than that of CNN, ResNet18 and ResNet33 networks.

Machine Learning Approaches in Atrial Fibrillation Detection

Atrial fibrillation (AF) characterized by rapid and irregular electrical activity in the atria represents a prevalent form of cardiac arrhythmia that significantly challenges healthcare systems due to its links to heightened mortality and morbidity rates. Early detection of AF is critical for accurate and effective management and treatment. In response to this pressing need, numerous researchers have used machine learning (ML) to enhance the precision and efficiency of AF detection. By analyzing available datasets, signal lengths, preprocessing techniques, and a diverse array of ML approaches, this paper aims to cover methodologies of AF detection using electrocardiogram (ECG) data and ML.

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

Computer vision has become a fundamental area of interest in recent decades. Each area has unique data which object detection methods can analyse. However, it is important to find the most suitable parameters for the model that detects different object groups. In this research has been investigated the influence of pre-trained YOLOv5 (nano (n), small (s), medium (m), large (l), extralarge (x)) models, hyperparameters (learning rate, momentum, and weight decay) and different image augmentation (hsv_h, degrees, translate, flipud, mosaic, mixup, shear, perspective) efficiency for similar construction details detection. A newly collected dataset with twenty-two labelled categories of construction details was prepared. A total of 270 models were trained and evaluated. Every model was evaluated with 3,300 test images which backgrounds were mixed, neutral, and white backgrounds. The most accurate model was YOLOv5l with learning rate – 0.001, momentum – 0.950 and weight decay – 0.0001. This model achieved – 0.5015 (50.15%) accuracy.

Metal-ligand cross-link strategy engineered iron-doped dopamine-based superstructure as peroxidase-like nanozymes for detection of glucose.

Nanozymes are nanomaterials with mimetic enzyme properties and the related research has attracted much attention. It is of great value to develop methods to construct nanozymes and to study their application in bioanalysis. Herein, the metal-ligand cross-linking strategy was developed to fabricate superstructure nanozymes. This strategy takes advantage of beingeasy to operate, adjustable, cheap, and universal. The fabricated superstructure nanozymes possess efficient peroxidase-like catalytic activity. The enzyme reaction kinetic tests demonstrated that for TMB and H2O2, the Km is 0.229 and 1.308mM, respectively. Furthermore, these superstructure nanozymes are applied to highly efficient and sensitive detection of glucose. The linear range for detecting glucose is 20-2000μM, and the limit of detection is 17.5μM. Furthermore, mechanistic research illustrated that this integrated system oxidizes glucose to produce hydrogen peroxide and further catalyzes the production of ·OH and O2·-, which results in a chromogenic reaction of oxidized TMB for the detection of glucose. This work could not only contribute to the development of efficient nanozymes but also inspire research in the highly sensitive detection of other biomarkers.

Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging.

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.