Classification of Infant Cry Based on Hybrid Audio Features and ResLSTM.

Crying is one of the primary means by which infants communicate with their environment in the early stages of life. These cries can be triggered by physiological factors such as hunger or sleepiness, or by pathological factors such as illness or discomfort. Therefore, analyzing infant cries can assist inexperienced parents in better caring for their babies. Most studies have predominantly utilized a single-speech feature, such as Mel Frequency Cepstral Coefficients (MFCC), for classifying infant cries, while other speech features, such as Mel Spectrogram and Tonnetz, are often overlooked. In this study, we manually designed a hybrid feature set, MMT (including MFCC, Mel Spectrogram, and Tonnetz), and explored its application in infant cry classification. Additionally, we proposed a convolutional neural network based on residual connections and long short-term memory (LSTM) networks, termed ResLSTM. We compared the performance of different deep learning models using the hybrid feature set MMT and the single MFCC feature. This study utilized the Baby Crying, Dunstan Baby Language, and Donate a Cry datasets. The results indicate that the hybrid feature set MMT outperforms the single MFCC feature. The MMT combined with the ResLSTM method achieved the best performance, obtaining accuracy rates of 94.15%, 92.92%, and 95.98% on the three datasets, respectively.

Rapid complete blood count and C-reactive protein determination with the Horiba Microsemi analyzer: the experience in neonatal intensive care unit of Careggi University Hospital.

Microsystems represent an alternative but proficient approach of analysis outside the laboratory, and their use could help in reducing the impact of pre-analytical errors, in particular in challenging newborn samples. The study purpose is to compare the Horiba Microsemi CRP LC-767G system for rapid 3-part complete blood count (CBC) and C-reactive protein (CRP) determination with the laboratory reference systems (respectively Sysmex XN-9100™ and Roche Cobas® c702) in samples of adult patients and newborns hospitalized in the neonatal intensive care unit (NICU) samples. The comparison between the analyzers was performed through Passing-Bablok regression analysis and Bland-Altman plot. One hundred eighty-three blood samples were analyzed. The regression analysis results, performed in the newborn (n = 70) and in adult (n = 113) populations, showed a good agreement between the instruments. The evaluation of the Bland-Altman plots showed comparable values of bias < 10% for most of the parameters, but not for MPV, lymphocyte, and monocyte count. CONCLUSION: The comparison between the Microsemi CRP LC-767G system and the laboratory instrumentations demonstrated comparable results. The Microsemi CRP LC-767G system provides reliable analytical data and faster turnaround time, particularly useful in NICU. WHAT IS KNOWN: • Microsystems for point-of-care testing (POCT) represent an alternative but proficient approach of analysis outside the laboratory, in order to perform a rapid, safe, and exhaustive evaluation for critical patients' management, acting as a valid support for treatment in acute care. WHAT IS NEW: • The Microsemi CRP LC-767G system can represent an alternative but effective testing approach outside the laboratory, particularly in NICU, to reduce the impact of pre-analytical errors on newborn samples.

A Survey of Visible-Light-Communication-Based Indoor Positioning Systems.

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.

The educational resource management based on image data visualization and deep learning.

In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.

Performance and CO2 emission of a single cylinder compression ignition engine powered by Khaya senegalensis non-edible seeds fuel blends.

This work aimed at investigating blends of Khaya senegalensis biodiesel in a compression ignition engine, attempting to improve engine performance and reduce CO2 emission compared with conventional diesel. Analysis of System (ANSYS) was used to predict in-cylinder behavior of the fuel. ANSYS SpaceClaim generated the geometric model on which 5° sector and mesh refinement was on ANSYS Internal Combustion Engine Modeler (ICEM). Computational domain of interest lies within the compression and expansion strokes. Experimental validation followed: 5% biodiesel, 95% diesel (B5); 15% biodiesel, 85% diesel (B15); 25% biodiesel, 75% diesel (B25); pure diesel (D100); pure biodiesel (B100) in volume proportions. B15 has the highest brake mean effective pressure (BMEP) of 4 bar as load increases. An experimental and numerical comparison reveals pressure declination against speed increment. Ignition temperature fluctuated between 799.76 and 806.256 K for D100 and 760.73-790.62 K for B100 within 1800-2800 rpm speed limit prediction. Power and brake thermal efficiency (BTE) had parallel load increment with all blends. CO2 emission on increasing load conditions were 47.01%, 8.07%, 21.72% and 6.06% for B5, B15, B25, and B100 respectively lower than D100. Pressure and temperature contours gave proper combustion predicted behaviors. All blends possess replaceable performance potential for D100 however, B5 offers better reliable potentials.

Analysis of the potential application of a residential composite energy storage system based on a double-layer optimization model.

Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.

Structure, Synthesis, and Catalytic Performance of Emerging MXene-Based Catalysts.

As traditional fossil fuel energy development faces significant challenges, two-dimensional layered materials have become increasingly popular in various fields and have generated widespread research interest. MXene is an exceptional catalytic material that is typically integrated into functional composite materials with other substances to enhance its catalytic-reaction performance. Improving the thermal stability, electrical conductivity, and electrochemical activity, as well as enhancing the specific surface structure, can make the material an excellent catalyst for photoelectrocatalysis and energy-regeneration reactions. The article mainly outlines the structural characteristics, preparation methods, and applications of MXene in the field of catalysis. This text highlights the latest progress and performance comparison of MXene-based catalytic functional materials in various fields such as electrochemical conversion, photocatalysis, renewable energy, energy storage, and carbon capture and conversion. It also proposes future prospects and discusses the current bottlenecks and challenges in the development of MXene-based catalytic materials.

Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise.

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.

Comparative study of high-performance mesophilic and thermophilic anaerobic membrane bioreactors in the co-digestion of sewage sludge and food waste: Methanogenic performance and energy recovery potential.

To support smart cities in terms of waste management and bioenergy recovery, the co-digestion of sewage sludge (SeS) and food waste (FW) was conducted by the anaerobic membrane bioreactor (AnMBR) under mesophilic and thermophilic conditions in this study. The biogas production rate of the thermophilic AnMBR (ThAnMBR) at the SeS to FW ratio of 0:100, 75:25, 50:50 and 100:0 was 2.84 ± 0.21, 2.51 ± 0.26, 1.54 ± 0.26 and 1.31 ± 0.08 L-biogas/L/d, inconspicuous compared with that of the mesophilic AnMBR (MeAnMBR) at 3.00 ± 0.25, 2.46 ± 0.30, 1.63 ± 0.23 and 1.30 ± 0.17 L-biogas/L/d, respectively. The higher hydrolysis ratio and the poorer rejection efficiencies of the membrane under thermophilic conditions, resulting that the permeate COD, carbohydrate and protein of the ThAnMBR was higher than that of the MeAnMBR. The lost COD that might be converted into biogas was discharged with the permeate in the ThAnMBR, which was partly responsible for the inconspicuous methanogenic performance. Furthermore, the results of energy recovery potential assessment showed that the energy return on investment (EROI) of the MeAnMBR was 4.54, 3.81, 2.69 and 2.22 at the four SeS ratios, which was higher than that of the ThAnMBR at 3.29, 2.97, 2.02 and 1.80, respectively, indicating the advantage of the MeAnMBR over the ThAnMBR in energy recovery potential. The outcomes of this study will help to choose a more favorable temperature to co-digest SeS and FW to support the construction of smart cities.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications.

This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and low-performance loss. To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons. The findings of the study provide valuable insights into the benefits and challenges of SNNs and their models, emphasizing the significance of comparing multiple models to identify the most effective one. Moreover, the study quantifies the number of spiking operations required by each model to process the same inputs and produce equivalent outputs, enabling a thorough assessment of computational efficiency. The findings provide valuable insights into the benefits and limitations of SNNs and their models. The research underscores the significance of comparing different models to make informed decisions in practical applications. Additionally, the results reveal essential variations in biological plausibility and computational efficiency among the models, further emphasizing the importance of selecting the most suitable model for a given task. Overall, this study contributes to a deeper understanding of SNNs and offers practical guidelines for using their potential in real-world scenarios.

Recent advances in the synthesis and applications of single-atom nanozymes in food safety monitoring.

Nanozymes are synthetic compounds with enzyme-like tunable catalytic properties. The success of nanozymes for catalytic applications can be attributed to their small dimensions, cost-effective synthesis, appreciable stability, and scalability to molecular dimensions. The emergence of single atom nanozymes (SANzymes) has opened up new possibilities in bioanalytical applications. In this regard, this review outlines enzyme-mimicking features of SANzymes for food safety applications in relation to the key variables controlling their catalytic performance. The discussion is extended further to cover the applications of SANzymes for the monitoring of various compounds/biomaterials of significance with respect to food safety (e.g., pesticides, veterinary drug residues, foodborne pathogenic bacteria, mycotoxins/bacterial endotoxin, antioxidant residues, hydrogen peroxide residues, and heavy metal ions). Furthermore, the performance of SANzymes is evaluated in terms of various performance metrics such as limit of detection (LOD), linear dynamic range, and figure of merit (FoM). The challenges and future road map for the applications of SANzymes are also addressed along with their upscaling in the area of food safety.

The DNA-Buster: The evaluation of an alternative DNA recovery approach.

Touch DNA recovery techniques can have limitations, as their effectiveness depends on the substrate on which the DNA of a person of interest can be found. In this study, an in-house dry-vacuuming device, the DNA-Buster, was compared to traditional methods for its DNA recovery performance from items typically examined in forensic casework. The aim was to evaluate whether this dry-vacuuming approach can recover DNA efficiently, potentially complementing the well-established recovery strategies. For this, the performances of swabbing, taping, wet- (M-Vac®) and dry-vacuuming (DNA-Buster) were investigated quantitatively and qualitatively for touch DNA deposited on carpet, cotton sweater, stone, tile and wood. For the sweater, both vacuuming methods outperformed the other collection tools quantitatively. While the highest DNA amounts for the carpet were yielded by swabbing and taping, dry-vacuuming was equally good in reaching full DNA profiles, whereas less complete profiles were observed for the M-Vac®. For stone and tile, swabbing was optimal, whereas dry-vacuuming clearly underperformed for these substrates. Taping was the best recovery method for wood. Despite applying single donor DNA after thoroughly cleaning the items, undesired DNA mixtures were detected for all recovery techniques and all substrates. The overall research findings show first that the novel dry-vacuuming method is suited for DNA recovery from textiles. Secondly, they indicate that more attention should be paid to the substrate-collection dependency to ensure best practices in recovering genetic material in a precise, confident and targeted manner from the variety of forensic casework material.

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization.

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification.

Periodical vision-based inspection is a principal form of structural health monitoring (SHM) technique. Over the last decades, vision-based artificial intelligence (AI) has successfully facilitated an effortless inspection system owing to its exceptional ability of accuracy of defects' pattern recognition. However, most deep learning (DL)-based methods detect one specific type of defect, whereas DL has a high proficiency in multiple object detection. This study developed a dataset of two types of defects, i.e., concrete crack and spalling, and applied various pre-built convolutional neural network (CNN) models, i.e., VGG-19, ResNet-50, InceptionV3, Xception, and MobileNetV2 to classify these concrete defects. The dataset developed for this study has one of the largest collections of original images of concrete crack and spalling and avoided the augmentation process to replicate a more real-world condition, which makes the dataset one of a kind. Moreover, a detailed sensitivity analysis of hyper-parameters (i.e., optimizers, learning rate) was conducted to compare the classification models' performance and identify the optimal image classification condition for the best-performed CNN model. After analyzing all the models, InceptionV3 outperformed all the other models with an accuracy of 91%, precision of 83%, and recall of 100%. The InceptionV3 model performed best with optimizer stochastic gradient descent (SGD) and a learning rate of 0.001.

Extraction of blue roofs using BRSAM and the newly created spectral index derived from WorldView-2/3 imagery.

To extract blue roofs (BRs) from remote sensing images quickly, first, this study used WorldView-2 and WorldView-3 as the data sources and created a new BR spectral area model (BRSAM) using the polygon area difference between the spectral curves of BRs and other image categories in bands 2-8. Then the extraction effect of BRSAM with those of the blue ground object spectral index (BGOSI), blue object spectrum index (BOSI) and maximum likelihood classification was compared; the results showed that BRSAM overcomes the shortcomings of BGOSI and BOSI, i.e. erroneously extracted shadow and white and yellow ground objects as BRs. However, BRSAM has the disadvantage of erroneously extracting some vegetation and green plastic playground as BRs. Considering that the disadvantage of one of BRSAM, BGOSI, and BOSI in extracting BRs can be compensated by the two other spectral models/indices, we combined the three spectral models/indices and used the synthetic spectral model to extract BRs. Notably, the synthetic spectral model overcomes the shortcomings of the three spectral models in BR extraction, and its effect is better than any one of them separately used. Meanwhile, the spectral model/index method used in BR extraction is better than the classification method. The spectral model/index method is a convenient and effective method for BR extraction, which could be used as a reference in the classification of other data.

A Fair Performance Comparison between Complex-Valued and Real-Valued Neural Networks for Disease Detection.

Our aim is to contribute to the classification of anomalous patterns in biosignals using this novel approach. We specifically focus on melanoma and heart murmurs. We use a comparative study of two convolution networks in the Complex and Real numerical domains. The idea is to obtain a powerful approach for building portable systems for early disease detection. Two similar algorithmic structures were chosen so that there is no bias determined by the number of parameters to train. Three clinical data sets, ISIC2017, PH2, and Pascal, were used to carry out the experiments. Mean comparison hypothesis tests were performed to ensure statistical objectivity in the conclusions. In all cases, complex-valued networks presented a superior performance for the Precision, Recall, F1 Score, Accuracy, and Specificity metrics in the detection of associated anomalies. The best complex number-based classifier obtained in the Receiving Operating Characteristic (ROC) space presents a Euclidean distance of 0.26127 with respect to the ideal classifier, as opposed to the best real number-based classifier, whose Euclidean distance to the ideal is 0.36022 for the same task of melanoma detection. The 27.46% superiority in this metric, as in the others reported in this work, suggests that complex-valued networks have a greater ability to extract features for more efficient discrimination in the dataset.

Comprehensive Evaluation of RNA and DNA Viromic Methods Based on Species Richness and Abundance Analyses Using Marmot Rectal Samples.

Viral metagenomics is the most powerful tool to profile viromic composition for a given sample. Different viromic methods, including amplification-free ones, have been developed, but choosing them for different purposes requires comprehensive benchmarks. Here, we assessed the performance of four routinely used methods, i.e., multiple displacement amplification (MDA), direct metagenomic sequencing (MTG), sequence-independent single-primer amplification (SIA), and metatranscriptomic sequencing (MTT), using marmot rectal samples as the templates spiked with five known viruses of different genome types. The obtained clean data were differently contaminated by host and bacterial genomes, resulting in MDA having the most, with ~72.1%, but MTT had only ~7.5% data, useful for follow-up viromic analysis. MDA showed a broader spectrum with higher efficiency to profile the DNA virome, and MTT captured almost all RNA viruses with extraordinary sensitivity; hence, they are advisable in richness-based viromic studies. MTG was weak in capturing single-stranded DNA viruses, and SIA could detect both RNA and DNA viruses but with high randomness. Due to biases to certain types of viruses, the four methods caused different alterations to species abundance compared to the initial virus composition. SIA and MDA introduced greater stochastic errors to relative abundances of species, genus, and family taxa, whereas the two amplification-free methods were more tolerant toward such errors and thus are recommendable in abundance-based analyses. In addition, genus taxon is a compromising analytic level that ensures technically supported and biologically and/or ecologically meaningful viromic conclusions. IMPORTANCE Viral metagenomics can be roughly divided into species richness-based studies and species abundance-based analyses. Viromic methods with different principles have been developed, but rational selection of these techniques according to different purposes requires comprehensive understanding of their properties. By assessing the four most widely used methods using template samples, we found that multiple displacement amplification (MDA) and metatranscriptomic sequencing (MTT) are advisable for species richness-based viromic studies, as they show excellent efficiency to detect DNA and RNA viruses. Meanwhile, metagenomic sequencing (MTG) and MTT are more compatible with stochastic errors of methods introduced into relative abundance of viromic taxa and hence are rational choices in species abundance-based analyses. This study also highlights that MTG needs to tackle host genome contamination and ameliorate the capacity to detect single-stranded DNA viruses in the future, and the MTT method requires an improvement in bacterial rRNA depletion prior to library preparation.

Performance comparison of high-speed photoacoustic microscopy: opto-ultrasound combiner versus ring-shaped ultrasound transducer.

Photoacoustic microscopy (PAM) embedded with a 532 nm pulse laser is widely used to visualize the microvascular structures in both small animals and humans in vivo. An opto-ultrasound combiner (OUC) is often utilized in high-speed PAM to confocally align the optical and acoustic beams to improve the system's sensitivity. However, acoustic impedance mismatch in the OUC results in little improvement in the sensitivity. Alternatively, a ring-shaped ultrasound transducer (RUT) can also accomplish the confocal configuration. Here, we compare the performance of OUC and RUT modules through ultrasound pulse-echo tests and PA imaging experiments. The signal-to-noise ratios (SNRs) of the RUT-based system were 15 dB, 12 dB, and 7 dB higher when compared to the OUC-based system for ultrasound pulse-echo test, PA phantom imaging test, and PA in-vivo imaging test, respectively. In addition, the RUT-based system could image the microvascular structures of small parts of a mouse body in a few seconds with minimal loss in SNR. Thus, with increased sensitivity, improved image details, and fast image acquisition, we believe the RUT-based systems could play a significant role in the design of future fast-PAM systems.

Prognostic Value of Genomic Instability of m6A-Related lncRNAs in Lung Adenocarcinoma.

Background: Genomic instability of N6-methyladenosine (m6A)-related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m6A-associated lncRNA signature and revealed its prognostic role in LUAD. Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The "Limma" R package was used to identify a network of regulatory m6A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability-derived m6A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m6A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m6A-related lncRNAs and the immune microenvironment. Prognostic m6A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m6A-related lncRNAs in LUAD. Results: A total of 42 genomic instability-derived m6A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability-derived m6A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m6A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m6A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD. Conclusion: A signature of genomic instability of m6A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.