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To address the issue of low detection accuracy caused by low illumination and occlusion in underground coal mines, this study proposes an innovative miner detection method. A large dataset encompassing complex environments, such as low-light conditions, partial strong light interference, and occlusion, was constructed. The Efficient Channel Attention (ECA) mechanism was integrated into the YOLOv11 model to enhance the model’s ability to focus on key features, thereby significantly improving detection accuracy. Additionally, a new weighted Complete Intersection over Union (CIoU) and adaptive confidence loss function were proposed to enhance the model’s robustness in low-light and occlusion scenarios. Experimental results demonstrate that the proposed method outperforms various improved algorithms and state-of-the-art detection models in both detection performance and robustness, providing important technical support and reference for coal miner safety assurance and intelligent mine management. Full article

(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)

20 pages, 1700 KiB

Open AccessReview

Potential Interactions Between Soil-Transmitted Helminths and Herpes Simplex Virus Type II: Implications for Sexual and Reproductive Health in Sub-Saharan African

by Roxanne Pillay, Pragalathan Naidoo, Zamathombeni Duma, Khethiwe N. Bhengu, Miranda N. Mpaka-Mbatha, Nomzamo Nembe-Mafa and Zilungile L. Mkhize-Kwitshana

Biology 2024, 13(12), 1050; https://doi.org/10.3390/biology13121050 (registering DOI) - 15 Dec 2024

Abstract

Sub-Saharan Africa (SSA) bears a disproportionate and overlapping burden of soil-transmitted helminths (STHs) and sexually transmitted viral infections. An estimated 232 million pre-school and school-aged children in SSA are vulnerable to STH infections. Together with this, SSA has a high prevalence of herpes simplex virus type II (HSV-2), the primary cause of genital herpes. Studies have examined the immunological interactions between STHs and human immunodeficiency virus and human papillomavirus during co-infections. However, epidemiological and immunological studies on STH-HSV-2 co-infections are lacking, therefore their impact on sexual and reproductive health is not fully understood. STH-driven Th2 immune responses are known to downregulate Th1/Th17 immune responses. Therefore, during STH-HSV-2 co-infections, STH-driven immune responses may alter host immunity to HSV-2 and HSV-2 pathology. Herein, we provide an overview of the burden of STH and HSV-2 infections in SSA, and host immune responses to STH and HSV-2 infections. Further, we emphasize the relevance and urgent need for (i) focused research into the interactions between these important pathogens, and (ii) integrated approaches to improve the clinical detection and management of STH-HSV-2 co-infections in SSA. Full article

(This article belongs to the Special Issue Host–Pathogen Interactions and Pathogenesis)

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Illustration of the immune response to STHs. Footnote: AAMs: alternatively-activated macrophages; IgE: immunoglobulin E; IgG: immunoglobulin G; IL: interleukin; STH: soil-transmitted helminth; TGF-β: transforming growth factor beta; Th2: T-helper type 2 cells; Treg: regulatory T cells; TSLP: thymic stromal lymphopoietin. Full article ">Figure 2
Illustration of the immune response to HSV-2 infection. Footnote: APCs: antigen-presenting cells; CXCL: chemokine (C-X-C motif) ligand; HSV-2: herpes simplex virus type II; IFN: interferon; IFN-α: interferon alpha; IFN-β: interferon beta; IFN-γ: interferon gamma; IL: interleukin; NK: natural killer; Th1: T-helper type 1 cells; TNF-α: tumor necrosis factor alpha. Full article ">Figure 3
Illustration of potential immune response during STH-HSV-2 co-infection. Footnote: HSV-2: herpes simplex virus type II; STHs: soil-transmitted helminths; Th1: T-helper type 1 cells; Th2: T-helper type 2 cells; Treg: regulatory T cells. Full article ">

13 pages, 708 KiB

Open AccessArticle

Genomic and Gut Microbiome Evaluations of Growth and Feed Efficiency Traits in Broilers

by Xia Xiong, Chunlin Yu, Mohan Qiu, Zengrong Zhang, Chenming Hu, Shiliang Zhu, Li Yang, Han Peng, Xiaoyan Song, Jialei Chen, Bo Xia, Jiangxian Wang, Yi Qing and Chaowu Yang

Animals 2024, 14(24), 3615; https://doi.org/10.3390/ani14243615 (registering DOI) - 15 Dec 2024

Abstract

In this study, we combined genomic and gut microbiome data to evaluate 13 economically important growth and feed efficiency traits in 407 Dahen broilers, including body weight (BW) at four, six, nine, and ten weeks of age (BW4, BW6, BW9, and BW10), as well as the average daily gain (ADG6, ADG9, and ADG10), feed conversion ratio (FCR6, FCR9, and FCR10), and residual feed intake (RFI6, RFI9, and RFI10) for the three growing ages. The highest ADG and lowest FCR were observed at nine and six weeks of age, respectively. We obtained 47,872 high-quality genomic single-nucleotide polymorphisms (SNPs) by sequencing the genomes and 702 amplicon sequence variants (ASVs) of the gut microbiome by sequencing the 16S rRNA gene, both of which were used for analyses of linear mixed models. The heritability estimates (± standard error, SE) ranged from 0.103 ± 0.072 to 0.156 ± 0.079 for BW, 0.154 ± 0.074 to 0.276 ± 0.079 for the ADG, 0.311 ± 0.076 to 0.454 ± 0.076 for the FCR, and 0.413 ± 0.077 to 0.609 ± 0.076 for the RFI traits. We consistently observed moderate and low negative genetic correlations between the BW traits and the FCR and RFI traits (r = −0.562 to −0.038), whereas strong positive correlations were observed between the FCR and RFI traits (r = 0.564 to 0.979). For the FCR and RFI traits, strong positive correlations were found between the measures at the three ages. In contrast to the genomic contribution, we did not detect a gut microbial contribution to all of these traits, as the estimated microbiabilities did not confidently deviate from zero. We systematically evaluated the contributions of host genetics and gut microbes to several growth and feed efficiency traits in Dahen broilers, and the results show that only the host genetics had significant effects on the phenotypic variations in a flock. The parameters obtained in this study, based on the combined use of genomic and gut microbiota data, may facilitate the implementation of efficient breeding schemes in Dahen broilers. Full article

(This article belongs to the Section Poultry)

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Linkage disequilibrium decay (A) and sample clustering (B) of SNPs, and taxonomical composition (C) of gut microbiome. r2 is square of correlation coefficient between allelic values at two loci. PC1, PC2, and PC3 are three top components. Full article ">

21 pages, 6673 KiB

Open AccessArticle

arterioscope.sim: Enabling Simulations of Blood Flow and Its Impact on Bioimpedance Signals

by Thomas Krispel, Vahid Badeli, Alireza Jafarinia, Alice Reinbacher-Köstinger, Christian Tronstad, Sascha Ranftl, Ørjan Grottem Martinsen, Håvard Kalvoy, Jonny Hisdal, Manfred Kaltenbacher and Thomas Hochrainer

Bioengineering 2024, 11(12), 1273; https://doi.org/10.3390/bioengineering11121273 (registering DOI) - 15 Dec 2024

Abstract

Objectives: Early detection of cardiovascular diseases and their pre-existing conditions, arteriosclerosis and atherosclerosis, is crucial to increasing a patient’s chance of survival. While imaging technologies and invasive procedures provide a reliable diagnosis, they carry high costs and risks for patients. This study aims to explore impedance plethysmography (IPG) as a non-invasive and affordable alternative for diagnosis. Methods: To address the current lack of large-scale, high-quality impedance data, we introduce arterioscope.sim, a simulation platform that models arterial blood flow and computes the electrical conductivity of blood. The platform simulates bioimpedance measurements on specific body segments using patient-specific parameters. The study investigates how introducing arterial diseases into the simulation affects the bioimpedance signals. Results: The simulation results demonstrate that introducing atherosclerosis and arteriosclerosis leads to significant changes in the computed signals compared to simulations of healthy arteries. Furthermore, simulation of a patient-specific healthy artery strongly correlates with measured signals from a healthy volunteer. Conclusions and significance: arterioscope.sim effectively simulates bioimpedance signals in healthy and diseased arteries and highlights the potential of using these signals for early diagnosis of arterial diseases, offering a non-invasive and cost-effective alternative to traditional diagnostic methods. Full article

(This article belongs to the Special Issue Computational Models in Cardiovascular System)

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13 pages, 3641 KiB

Open AccessReview

Current Role of CT Pulmonary Angiography in Pulmonary Embolism: A State-of-the-Art Review

by Ignacio Diaz-Lorenzo, Alberto Alonso-Burgos, Alfonsa Friera Reyes, Ruben Eduardo Pacios Blanco, Maria del Carmen de Benavides Bernaldo de Quiros and Guillermo Gallardo Madueño

J. Imaging 2024, 10(12), 323; https://doi.org/10.3390/jimaging10120323 (registering DOI) - 15 Dec 2024

Abstract

The purpose of this study is to conduct a literature review on the current role of computed tomography pulmonary angiography (CTPA) in the diagnosis and prognosis of pulmonary embolism (PE). It addresses key topics such as the quantification of the thrombotic burden, its role as a predictor of mortality, new diagnostic techniques that are available, the possibility of analyzing the thrombus composition to differentiate its evolutionary stage, and the applicability of artificial intelligence (AI) in PE through CTPA. The only finding from CTPA that has been validated as a prognostic factor so far is the right ventricle/left ventricle (RV/LV) diameter ratio being >1, which is associated with a 2.5-fold higher risk of all-cause mortality or adverse events, and a 5-fold higher risk of PE-related mortality. The increasing use of techniques such as dual-energy computed tomography allows for the more accurate diagnosis of perfusion defects, which may go undetected in conventional computed tomography, identifying up to 92% of these defects compared to 78% being detected by CTPA. Additionally, it is essential to explore the latest advances in the application of AI to CTPA, which are currently expanding and have demonstrated a 23% improvement in the detection of subsegmental emboli compared to manual interpretation. With deep image analysis, up to a 95% accuracy has been achieved in predicting PE severity based on the thrombus volume and perfusion deficits. These advancements over the past 10 years significantly contribute to early intervention strategies and, therefore, to the improvement of morbidity and mortality outcomes for these patients. Full article

(This article belongs to the Special Issue Tools and Techniques for Improving Radiological Imaging Applications)

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Fifty-six-year-old woman diagnosed with acute pulmonary thromboembolism, by axial CT angiography. (A). Axial RV/LV diameter ratio > 1 measured at the base of both ventricles (black arrows). (B). Filling defects in both main pulmonary arteries (*), with a saddle thrombus. Full article ">Figure 2
Eighty-nine-year-old woman diagnosed with chronic pulmonary thromboembolism. (A) Axial CT angiography (maximum intensity projection—MIP—reconstruction) showing severe narrowing in the superior segmental artery of the left lower lobe (white arrow) as sequela of PE. (B) Fusion image of CT angiography and color-coded iodine density showing wedge-shaped perfusion defects (*) in the middle lobe, lingula, and left lower lobe, with the latter corresponding to the findings in image (A). (C) SPECT-CT fusion image showing wedge-shaped perfusion defects (*) similar to those obtained with dual-energy CT (B). Full article ">

10 pages, 1943 KiB

Open AccessArticle

Towards Reliable Methodology: Microbiome Analysis of Fresh Frozen vs. Formalin-Fixed Paraffin-Embedded Bladder Tissue Samples: A Feasibility Study

by Dominik Enderlin, Uwe Bieri, Jana Gadient, Yasser Morsy, Michael Scharl, Jan Hendrik Rüschoff, Lukas John Hefermehl, Anna Nikitin, Janine Langenauer, Daniel Stephan Engeler, Beat Förster, Fabian Obrecht, Jonathan Surber, Thomas Paul Scherer, Daniel Eberli and Cédric Poyet

Microorganisms 2024, 12(12), 2594; https://doi.org/10.3390/microorganisms12122594 (registering DOI) - 15 Dec 2024

Abstract

Studies have shown that the human microbiome influences the response to systemic immunotherapy. However, only scarce data exist on the impact of the urinary microbiome on the response rates of bladder cancer (BC) to local Bacillus Calmette-Guérin instillation therapy. We launched the prospective SILENT-EMPIRE study in 2022 to address this question. We report the results of the pilot study of SILENT-EMPIRE, which aimed to compare the microbiome between fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples in the cancerous tissue and adjacent healthy tissue of BC patients. Our results show that alpha diversity is increased in FF samples compared to FFPE (coverage index p = 0.041, core abundance index p = 0.008). No significant differences concerning alpha diversity could be detected between cancerous and non-cancerous tissue in the same BC patients. This study demonstrates that microbiome analysis from both FF and FFPE samples is feasible. Implementing this finding could aid in the translation of research findings into clinical practice. Full article

(This article belongs to the Special Issue Feature Papers in Microbiomes)

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Alpha diversity (FF 1 vs. FFPE 2). 1 FF = fresh frozen; 2 FFPE = formalin-fixed paraffin-embedded. Alpha diversity: (a) shows a significantly higher evenness Pielou in FF compared to FFPE samples (p = 0.016); the Shannon index (b) (p = 0.095), Gini–Simpson index (c) (p = 0.095), and inverse Simpson index (d) (p = 0.095) were not significant. Full article ">Figure 2
Alpha diversity (tumour vs. non-tumour). No significant differences in alpha diversity were detected between normal (non-cancerous) and tumour (cancerous) tissue in the same bladder cancer patients analysed by evenness_Pielou (a) (p = 1); Shannon index (b) (p = 0.914); Gini–Simpson index (c) (p = 0.914); and inverse Simpson index (d) (p = 0.914). Full article ">Figure 3
Beta diversity (FF 1 vs. FFPE 2). 1 FF = fresh frozen; 2 FFPE = formalin-fixed paraffin-embedded. Beta diversity: the principal coordinate analysis (PCoA) plot shows a clear separation between the FF and FFPE groups based on weighted UniFrac distances (a). The larger confidence ellipse area indicates a higher intragroup variability in FF compared to FFPE (b). Full article ">

11 pages, 3096 KiB

Open AccessArticle

The Impact of Selective Spatial Attention on Auditory–Tactile Integration: An Event-Related Potential Study

by Weichao An, Nan Zhang, Shengnan Li, Yinghua Yu, Jinglong Wu and Jiajia Yang

Brain Sci. 2024, 14(12), 1258; https://doi.org/10.3390/brainsci14121258 (registering DOI) - 15 Dec 2024

Abstract

Background: Auditory–tactile integration is an important research area in multisensory integration. Especially in special environments (e.g., traffic noise and complex work environments), auditory–tactile integration is crucial for human response and decision making. We investigated the influence of attention on the temporal course and spatial distribution of auditory–tactile integration. Methods: Participants received auditory stimuli alone, tactile stimuli alone, and simultaneous auditory and tactile stimuli, which were randomly presented on the left or right side. For each block, participants attended to all stimuli on the designated side and detected uncommon target stimuli while ignoring all stimuli on the other side. Event-related potentials (ERPs) were recorded via 64 scalp electrodes. Integration was quantified by comparing the response to the combined stimulus to the sum of the responses to the auditory and tactile stimuli presented separately. Results: The results demonstrated that compared to the unattended condition, integration occurred earlier and involved more brain regions in the attended condition when the stimulus was presented in the left hemispace. The unattended condition involved a more extensive range of brain regions and occurred earlier than the attended condition when the stimulus was presented in the right hemispace. Conclusions: Attention can modulate auditory–tactile integration and show systematic differences between the left and right hemispaces. These findings contribute to the understanding of the mechanisms of auditory–tactile information processing in the human brain. Full article

(This article belongs to the Section Cognitive, Social and Affective Neuroscience)

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Experimental paradigm. (a) Sequence of events and their duration. (b) Stimulus conditions: There were 12 stimulus conditions in total: 8 unisensory stimuli (4 standard stimuli and 4 target stimuli) and 4 multisensory stimuli (2 standard stimuli and 2 target stimuli). The black dots in the tactile stimulus column indicate that the probe was raised. Full article ">Figure 2
Event-related potentials (ERPs) of standard stimulus. The figure shows the grand average of ERPs for unisensory stimulus summation (blue trace) and simultaneous auditory and somatosensory stimulation (red trace) in 20 participants. ERPs are shown at central electrode positions (AFz, FCz, and CPz) and lateral electrode positions (C3, CP5, C4, and CP6). (a) Stimulus presented in the left half-space; (b) stimulus presented in the right half-space. The shaded gray part indicates a significant difference in stimulus type. Full article ">Figure 3
Influence of attention on topographic voltage distribution. The topographic voltage distributions of the grand average event-related potential (ERP) components for the standard stimulus in the 4 time windows of interest for attended and unattended stimuli when the stimuli were presented in the left and right hemispaces, respectively. The maps show the mean voltage of AT − (A + T) within the corresponding time windows (70–90 ms, 90–110 ms, 110–130 ms, and 180–220 ms). A, auditory; T, tactile; AT, auditory–tactile. Black circles indicate electrodes with significant differences in stimulus type. Full article ">

19 pages, 21832 KiB

Open AccessArticle

Automatic Wood Species Classification and Pith Detection in Log CT Images

by Ondrej Vacek, Tomáš Gergeľ, Tomáš Bucha, Radovan Gracovský and Miloš Gejdoš

Forests 2024, 15(12), 2207; https://doi.org/10.3390/f15122207 (registering DOI) - 15 Dec 2024

Abstract

This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries. Full article

(This article belongs to the Special Issue Advances in Technology and Solutions for Wood Processing)

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18 pages, 1882 KiB

Open AccessArticle

Genome-Wide Association Study for Resistance to Phytophthora sojae in Soybean [Glycine max (L.) Merr.]

by Hee Jin You, Ruihua Zhao, Yu-Mi Choi, In-Jeong Kang and Sungwoo Lee

Plants 2024, 13(24), 3501; https://doi.org/10.3390/plants13243501 (registering DOI) - 15 Dec 2024

Abstract

Phytophthora sojae (Kauffman and Gerdemann) is an oomycete pathogen that threatens soybean (Glycine max L.) production worldwide. The development of soybean cultivars with resistance to this pathogen is of paramount importance for the sustainable management of the disease. The objective of this study was to identify genomic regions associated with resistance to P. sojae isolate 40468 through genome-wide association analyses of 983 soybean germplasms. To elucidate the genetic basis of resistance, three statistical models were employed: the compressed mixed linear model (CMLM), Bayesian-information and linkage disequilibrium iteratively nested keyway (BLINK), and fixed and random model circulating probability unification (FarmCPU). The three models consistently identified a genomic region (3.8–5.3 Mbp) on chromosome 3, which has been previously identified as an Rps cluster. A total of 18 single nucleotide polymorphisms demonstrated high statistical significance across all three models, which were distributed in eight linkage disequilibrium (LD) blocks within the aforementioned interval. Of the eight, LD3-2 exhibited the discernible segregation of phenotypic reactions by haplotype. Specifically, over 93% of accessions with haplotypes LD3-2-F or LD3-2-G displayed resistance, whereas over 91% with LD3-2-A, LD3-2-C, or LD3-2-D exhibited susceptibility. Furthermore, the BLINK and FarmCPU models identified new genomic variations significantly associated with the resistance on several other chromosomes, indicating that the resistance observed in this panel was due to the presence of different alleles of multiple Rps genes. These findings underscore the necessity for robust statistical models to accurately detect true marker–trait associations and provide valuable insights into soybean genetics and breeding. Full article

(This article belongs to the Special Issue Crop Genetic Mechanisms and Breeding Improvement)

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Genetic diversity of a panel of 983 soybean (Glycine max L. Merr.) accessions based on principal component analysis. Full article ">Figure 2
Phenotypic reactions to Phytophthora sojae isolate 40468 in the GWAS panel (n = 983): (A) Frequency distribution of the percentages of dead seedlings. (B) Segregation ratio of resistant (R), susceptible (S), and intermediate (I) reactions. (C) Phenotypic reactions of the Daepung (S check), CheonAl (R check), and selected resistant genotypes. Full article ">Figure 3
Manhattan plots (left) and QQ plots (right) for the genome-wide association study of the 983 soybean accessions for P. sojae resistance: (A) The compressed mixed linear (CMLM) model. (B) The Bayesian-information and linkage disequilibrium iteratively nested keyway (BLINK) model. (C) The fixed and random model circulating probability unification (FarmCPU) model. Full article ">Figure 4
Linkage disequilibrium blocks, 18 consistently significant SNPs, and positions of annotated LRR and STK-coding genes within the genomic region of 3.8–5.3 Mb. Full article ">

20 pages, 8579 KiB

Open AccessArticle

Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms

by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu and Mengmeng Qiao

Foods 2024, 13(24), 4044; https://doi.org/10.3390/foods13244044 (registering DOI) - 15 Dec 2024

Abstract

Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). Then, the regression model of the kernel (broken and unbroken) weight prediction and the classification model of kernel defect detection were established using the mainstream machine learning algorithm. In this way, the defect rapid identification and accurate weight prediction of broken kernels achieve the purpose of broken rate quantitative detection. The results prove that LGBM (light gradient boosting machine) and RF (random forest) algorithms were suitable for constructing weight prediction models of broken and unbroken kernels, respectively. The r values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. A strong linear relationship was observed between the predicted and actual broken rates. Therefore, this method could help to be an accurate, objective, efficient broken rate online detection method for maize harvest. Full article

(This article belongs to the Special Issue Recent Advances in the Assessment of Cereal and Cereal-Based Product Quality)

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13 pages, 1115 KiB

Open AccessSystematic Review

Heart Rate Variability During Weaning from Invasive Mechanical Ventilation: A Systematic Review

by Giovanni Giordano, Francesco Alessandri, Antonella Tosi, Veronica Zullino, Leonardo Califano, Luigi Petramala, Gioacchino Galardo and Francesco Pugliese

J. Clin. Med. 2024, 13(24), 7634; https://doi.org/10.3390/jcm13247634 (registering DOI) - 15 Dec 2024

Abstract

Background: The role of Heart Rate Variability (HRV) indices in predicting the outcome of the weaning process remains a subject of debate. The aim of this study is to investigate HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation (IMV). Methods: The protocol of this systematic review was registered with PROSPERO (CRD42024485800). We searched PubMed and Scopus databases from inception till March 2023 to identify randomized controlled trials and observational studies investigating HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation. Our primary outcome was to investigate HRV changes occurring during the weaning from IMV. Results: Seven studies (n = 342 patients) were included in this review. All studies reported significant changes in at least one HRV parameter. The indices Low Frequency (LF), High Frequency (HF), and LF/HF ratio seem to be the most promising in predicting the outcome of weaning with reliability. Some HRV indices showed modification in response to different ventilator settings or modalities. Conclusions: Available data report HRV modifications during the process of weaning and suggest a promising role of some HRV indices in predicting weaning outcomes in critically ill patients. Point-of-care HRV monitoring systems might help to early detect patients at risk of weaning failure. Full article

(This article belongs to the Special Issue Ventilation in Critical Care Medicine)

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PRISMA 2020 flow diagram for new systematic reviews that included searches of databases and registers only. Full article ">Figure 2
2.1. Risk of bias for randomized controlled trials. Guntzel Chiappa AM et al., 2017 [<a href="#B22-jcm-13-07634" class="html-bibr">22</a>]. 2.2. Risk of bias for non-randomized observational studies. Huang CT et al., 2014 [<a href="#B19-jcm-13-07634" class="html-bibr">19</a>]; Frazier SK et al., 2008 [<a href="#B18-jcm-13-07634" class="html-bibr">18</a>]; Da Silva RB et al., 2023 [<a href="#B10-jcm-13-07634" class="html-bibr">10</a>]; Chen YJ et al., 2017 [<a href="#B11-jcm-13-07634" class="html-bibr">11</a>]; Guerra M. et al., 2019 [<a href="#B20-jcm-13-07634" class="html-bibr">20</a>]; Shen HN et al., 2003 [<a href="#B21-jcm-13-07634" class="html-bibr">21</a>]. Full article ">

18 pages, 5133 KiB

Open AccessArticle

Field Scale Soil Moisture Estimation with Ground Penetrating Radar and Sentinel 1 Data

by Rutkay Atun, Önder Gürsoy and Sinan Koşaroğlu

Sustainability 2024, 16(24), 10995; https://doi.org/10.3390/su162410995 (registering DOI) - 15 Dec 2024

Abstract

This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R² = 74%) than Sentinel-1 (R² = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts. Full article

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19 pages, 2917 KiB

Open AccessArticle

Identification of Plant Diseases in Jordan Using Convolutional Neural Networks

by Moy’awiah A. Al-Shannaq, Shahed AL-Khateeb, Abed Al-Raouf K. Bsoul and Ahmad A. Saifan

Electronics 2024, 13(24), 4942; https://doi.org/10.3390/electronics13244942 (registering DOI) - 15 Dec 2024

Abstract

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as “Jordan22” was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 689 KiB

Open AccessArticle

Diagnostic Accuracy of a Blood-Based Biomarker Panel for Colorectal Cancer Detection: A Pilot Study

by Elba V. Caraballo, Hilmaris Centeno-Girona, Brenda Carolina Torres-Velásquez, Madeline M. Martir-Ocasio, María González-Pons, Sheila N. López-Acevedo and Marcia Cruz-Correa

Cancers 2024, 16(24), 4176; https://doi.org/10.3390/cancers16244176 (registering DOI) - 15 Dec 2024

Abstract

Background: Colorectal cancer (CRC) is a leading cause of death worldwide. Despite its preventability through screening, compliance still needs to improve due to the invasiveness of current tools. There is a growing demand for validated molecular biomarker panels for minimally invasive blood-based CRC screening. This study assessed the diagnostic accuracy of four promising blood-based CRC biomarkers, individually and in combination. Methods: This case–control study involved plasma samples from 124 CRC cases and 124 age- and sex-matched controls. Biomarkers tested included methylated DNA encoding the Septin-9 gene (mSEPT9) using Epi proColon^® 2.0 CE, insulin-like growth factor binding protein 2 (IGFBP2), dickkopf-3 (DKK3), and pyruvate kinase M2 (PKM2) by ELISA. Diagnostic accuracy was measured using the receiver operating characteristic (ROC), area under the curve (AUC), as well as sensitivity and specificity. Results: Diagnostic accuracy for mSEPT9, IGFBP2, DKK3, and PKM2 was 62.9% (95% CI: 56.8–62.9%), 69.7% (95% CI: 63.1–69.7%), 61.6% (95% CI: 54.6–61.6%), and 50.8% (95% CI: 43.4–50.8%), respectively. The combined biomarkers yielded an AUC of 74.4% (95% CI: 68.1–80.6%), outperforming all biomarkers except IGFBP2. Conclusions: These biomarkers show potential for developing a minimally invasive CRC detection tool as an alternative to existing approaches, potentially increasing adherence, early detection, and survivorship. Full article

(This article belongs to the Section Cancer Biomarkers)

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Graphical abstract
Full article ">Figure 1
Evaluation of biomarker diagnostic accuracy for CRC. (A) ROC plots of mSEPT9, IGFBP2, DKK3, and PKM2 analyzed individually for CRC detection in the overall cohort (left panel) or stratified by tumor stage: early-stage disease (middle panel) or advanced-stage disease (right panel). Red = mSEPT9, blue = IGFBP2, green = DKK3, and gold = PKM2. (B) ROC plots of mSEPT9, IGFBP2, DKK3 and PKM2 analyzed with multivariate models for CRC detection in the overall cohort (left panel), early-stage disease (middle panel), or advanced-stage disease (right panel). Analyses included all four putative biomarkers (Model 1, red), mSEPT9 + IGFBP2 + DKK3 (Model 2, blue), and mSEPT9 + IGFBP2 (Model 3, green). Full article ">

23 pages, 3917 KiB

Open AccessArticle

MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects

by Shuxian Zhu and Yajie Zhou

Machines 2024, 12(12), 917; https://doi.org/10.3390/machines12120917 (registering DOI) - 14 Dec 2024

Abstract

The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. As the accuracy of the algorithm increases, its complexity also rises, resulting in a decrease in detection speed, which undermines its practicality. This issue is particularly evident in the context of surface defect detection in industrial parts, where low contrast, small target features, difficult feature extraction, and low real-time detection efficiency are prominent challenges. This study proposes a novel method for steel defect detection based on the YOLO v8 algorithm, which improves detection accuracy while maintaining low computational complexity. Firstly, the global background and edge information are adaptively extracted via the MSA-SPPF module in order to obtain a more comprehensive feature representation. Furthermore, the anti-interference ability of the model is enhanced through the deformability of attention and the large convolution kernel characteristics. Secondly, the design of Dynamic Conv and C2f-OREPA enables the model to efficiently reduce the demand for computational resources while maintaining high performance. It is further proposed that the RepHead detection head approximates the multi-branch structure of the original training by a single convolution operation. This approach not only enriches the feature representation but also maintains an efficient inference process. The effectiveness of the improved MRP-YOLO algorithm is verified using the NEU-DET industrial surface defect dataset. The experimental results demonstrate that the mAP of the MRP-YOLO algorithm reaches 75.6%, which is 2.2% higher than that of the YOLOv8n algorithm, while the FLOPs are only 2.3 G higher. It indicates that the detection accuracy is significantly improved with a limited increase in computational complexity. Full article

(This article belongs to the Special Issue Recent Trends in Sensor Fusion Algorithms Using Intelligent Signal Processing Methods)

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