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17 pages, 5171 KiB  
Article
Molecular Dynamics Reveal Key Steps in BAR-Related Membrane Remodeling
by Shenghan Song, Tongtong Li, Amy O. Stevens, Temair Shorty and Yi He
Pathogens 2024, 13(10), 902; https://doi.org/10.3390/pathogens13100902 (registering DOI) - 15 Oct 2024
Viewed by 166
Abstract
Endocytosis plays a complex role in pathogen-host interactions. It serves as a pathway for pathogens to enter the host cell and acts as a part of the immune defense mechanism. Endocytosis involves the formation of lipid membrane vesicles and the reshaping of the [...] Read more.
Endocytosis plays a complex role in pathogen-host interactions. It serves as a pathway for pathogens to enter the host cell and acts as a part of the immune defense mechanism. Endocytosis involves the formation of lipid membrane vesicles and the reshaping of the cell membrane, a task predominantly managed by proteins containing BAR (Bin1/Amphiphysin/yeast RVS167) domains. Insights into how BAR domains can remodel and reshape cell membranes provide crucial information on infections and can aid the development of treatment. Aiming at deciphering the roles of the BAR dimers in lipid membrane bending and remodeling, we conducted extensive all-atom molecular dynamics simulations and discovered that the presence of helix kinks divides the BAR monomer into two segments—the “arm segment” and the “core segment”—which exhibit distinct movement patterns. Contrary to the prior hypothesis of BAR domains working as a rigid scaffold, we found that it functions in an “Arms-Hands” mode. These findings enhance the understanding of endocytosis, potentially advancing research on pathogen-host interactions and aiding in the identification of new treatment strategies targeting BAR domains. Full article
(This article belongs to the Special Issue Current Research on Host–Pathogen Interaction in 2024)
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Figure 1
<p>(<b>a</b>) Root Mean Square Deviation (RMSD) averaged over 10 trajectories. (<b>b</b>) Root Mean Square Fluctuation (RMSF). (<b>c</b>) Secondary structure analysis of Helix 2 in the 4ATM BAR monomer. (<b>d</b>) Secondary structure analysis of Helix 3 in the 4ATM BAR monomer. (<b>e</b>) Secondary structure segmentation of the 4ATM BAR monomer.</p>
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<p>Hydrogen bonds. (<b>a</b>) Number of hydrogen bonds with error bands. Blue lines represent hydrogen bonds between the 4ATM BAR domain and the membrane; pink lines represent hydrogen bonds between positively charged amino acids and the membrane. Solid lines indicate the mean number of hydrogen bonds, while faded lines depict error bands. (<b>b</b>) Hydrogen bonds between lipid molecules and the membrane. (<b>c</b>) Hydrogen bonds between amino acid residue groups and the membrane. (<b>d</b>) Positions of amino acid residue groups and bound lipid molecules. Residues colored red have hydrogen bond interactions with lipids; residues colored black have minimal hydrogen bonding with lipids.</p>
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<p>Curvature of the membrane and 4ATM BAR dimer. (<b>a</b>). Red line and dots with error band: Curvature of the membrane with the 4ATM BAR over time. The results are the average of 10 trajectories. Black line: Curvature of the membrane without the 4ATM BAR. The curvature is the average of 300 ns simulations. Blue line: Curvature of the 4ATM BAR crystal for RCSB. (<b>b</b>–<b>d</b>): Curved membrane with the 4ATM BAR showing the fitting radiuses. Lipid molecules are shown in cyan, in line style, while phosphorus atoms are shown in yellow.</p>
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<p>Pearson correlation. (<b>a</b>). Segmentation of the 4ATM BAR. Red: “arm segments”. Blue: “core segments”. Black: Helix 2. Green: Span of the 4ATM BAR dimer. Distance between SER157 Chain A and SER157 Chain B. (<b>b</b>). Pearson correlation between the (<b><span class="html-italic">d</span></b>) distance and angles (<b><span class="html-italic">α</span>, <span class="html-italic">β</span>, and <span class="html-italic">γ</span></b>) and Pearson correlation between the “arm segment” (<b><span class="html-italic">α</span></b>) and “core segment” (<b><span class="html-italic">β</span></b>).</p>
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<p>Centripetal extrusion and upward bulge of the lipid membrane. (<b>a</b>). Definition of the horizontal distance from the lipid molecules bound at both ends to the central residue. The central residues are GLY69A and GLY69B, and the reference atom is Ca. The reference atom of lipid molecules is the phosphorus atom. (<b>b</b>). The 4ATM BAR with the membrane after 300ns of simulation. The orange atom marks the position of the membrane at 0 ns. (<b>c</b>). The average horizontal distance between lipid molecules and the center of the concave surface of the 4ATM BAR dimer. The calculated result is the average of ten trajectories. When calculating, the reference atom on the lipid molecule is a phosphorus atom. The average coordinates of GLY69A and GLY69B determine the center of the concave surface of the 4ATM BAR dimer.</p>
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22 pages, 2514 KiB  
Article
Specialized Pro-Resolving Lipid Mediators Distinctly Modulate Silver Nanoparticle-Induced Pulmonary Inflammation in Healthy and Metabolic Syndrome Mouse Models
by Arjun Pitchai, Akshada Shinde, Jenna N. Swihart, Kiley Robison and Jonathan H. Shannahan
Nanomaterials 2024, 14(20), 1642; https://doi.org/10.3390/nano14201642 - 13 Oct 2024
Viewed by 416
Abstract
Individuals with chronic diseases are more vulnerable to environmental inhalation exposures. Although metabolic syndrome (MetS) is increasingly common and is associated with susceptibility to inhalation exposures such as particulate air pollution, the underlying mechanisms remain unclear. In previous studies, we determined that, compared [...] Read more.
Individuals with chronic diseases are more vulnerable to environmental inhalation exposures. Although metabolic syndrome (MetS) is increasingly common and is associated with susceptibility to inhalation exposures such as particulate air pollution, the underlying mechanisms remain unclear. In previous studies, we determined that, compared to a healthy mouse model, a mouse model of MetS exhibited increased pulmonary inflammation 24 h after exposure to AgNPs. This exacerbated response was associated with decreases in pulmonary levels of specific specialized pro-resolving mediators (SPMs). Supplementation with specific SPMs that are known to be dysregulated in MetS may alter particulate-induced inflammatory responses and be useful in treatment strategies. Our current study hypothesized that administration of resolvin E1 (RvE1), protectin D1 (PD1), or maresin (MaR1) following AgNP exposure will differentially regulate inflammatory responses. To examine this hypothesis, healthy and MetS mouse models were exposed to either a vehicle (control) or 50 μg of 20 nm AgNPs via oropharyngeal aspiration. They were then treated 24 h post-exposure with either a vehicle (control) or 400 ng of RvE1, PD1, or MaR1 via oropharyngeal aspiration. Endpoints of pulmonary inflammation and toxicity were evaluated three days following AgNP exposure. MetS mice that were exposed to AgNPs and received PBS treatment exhibited significantly exacerbated pulmonary inflammatory responses compared to healthy mice. In mice exposed to AgNPs and treated with RvE1, neutrophil infiltration was reduced in healthy mice and the exacerbated neutrophil levels were decreased in the MetS model. This decreased neutrophilia was associated with decreases in proinflammatory cytokines’ gene and protein expression. Healthy mice treated with PD1 did not demonstrate alterations in AgNP-induced neutrophil levels compared to mice not receiving treat; however, exacerbated neutrophilia was reduced in the MetS model. These PD1 alterations were associated with decreases in proinflammatory cytokines, as well as elevated interleukin-10 (IL-10). Both mouse models receiving MaR1 treatment demonstrated reductions in AgNP-induced neutrophil influx. MaR1 treatment was associated with decreases in proinflammatory cytokines in both models and increases in the resolution inflammatory cytokine IL-10 in both models, which were enhanced in MetS mice. Inflammatory responses to particulate exposure may be treated using specific SPMs, some of which may benefit susceptible subpopulations. Full article
(This article belongs to the Special Issue Advances in Nanotoxicology: Health and Safety)
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<p>Experiment design timeline. Mice were fed a healthy or high-fat western diet for 14 weeks and exposed to either water (control) or AgNPs (50 µg) via oropharyngeal aspiration; 24 h post-exposure, mice were treated with saline (control) or 400 ng of a lipid resolution mediator (RvE1, PD1, or MaR1) via oropharyngeal aspiration. Endpoints associated with inflammation and lipid metabolism were examined at 2 days following treatment.</p>
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<p>Characterization of (<b>A</b>) body weight and serum levels of (<b>B</b>) high-density lipoprotein, (<b>C</b>) low-density lipoprotein, and (<b>D</b>) total cholesterol in healthy and MetS mouse models following 14 weeks of either a healthy or high-fat western diet (HFW diet) and 3 days after oropharyngeal aspiration exposure to pharmaceutical grade sterile water (vehicle) or 50 μg of AgNPs. Subsets of mice were treated with sterile saline (vehicle) or 400 ng of individual SPMs (RvE1, PD1, or MaR1) 24 h post-exposure. Values are expressed as mean ± S.E.M. # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>AgNP exposure and modulation by distinct SPM treatment on BALF: (<b>A</b>) total protein, (<b>B</b>) total cell counts, (<b>C</b>) macrophage counts, and (<b>D</b>) neutrophil counts from healthy and MetS mice; 24 h following oropharyngeal aspiration of pharmaceutical grade sterile water (control) or 50 μg of AgNPs in sterile water, mice were treated via oropharyngeal aspiration with 400 ng of individual SPMs (RvE1, PD1, or MaR1) or sterile saline (vehicle). Endpoints were evaluated at 3 days post-AgNP exposure. Values are expressed as mean ± S.E.M. * AgNP exposure, # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>AgNP exposure and modulation by distinct SPM treatment on the pulmonary gene expression of inflammatory factors including (<b>A</b>) (C-C motif) ligand 2 (CCL2), (<b>B</b>) interleukin-6 (IL-6), (<b>C</b>) chemokine (C-X-C motif) ligand 1 (CXCL1), (<b>D</b>) chemokine (C-X-C motif) ligand 2 (CXCL2), (<b>E</b>) tumor necrosis factor-α (TNF-α), and (<b>F</b>) interleukin-10 (IL-10) from healthy and MetS mice; 24 h following oropharyngeal aspiration of pharmaceutical grade sterile water (control) or 50 μg of AgNPs in sterile water, mice were treated via oropharyngeal aspiration with 400 ng of individual SPMs (RvE1, PD1, or MaR1) or sterile saline (vehicle). Endpoints were evaluated at 3 days post-AgNP exposure. Values are expressed as mean ± S.E.M. * AgNP exposure, # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>AgNP exposure and modulation by distinct SPM treatment on pulmonary lipid metabolism gene expression, including (<b>A</b>) <span class="html-italic">phospholipase A2</span> (<span class="html-italic">iPLA2</span>), (<b>B</b>) <span class="html-italic">arachidonate 5-lipoxygenase</span> (<span class="html-italic">ALOX-5</span>), (<b>C</b>) <span class="html-italic">arachidonate 15-lipoxygenase</span> (<span class="html-italic">ALOX-15</span>), (<b>D</b>) <span class="html-italic">cyclooxygenase 2</span> (<span class="html-italic">COX 2</span>), and (<b>E</b>) <span class="html-italic">epoxide hydrolase 2</span> (<span class="html-italic">Ephx2</span>) from healthy and MetS mice; 24 h following oropharyngeal aspiration of pharmaceutical grade sterile water (control) or 50 μg of AgNPs in sterile water, mice were treated via oropharyngeal aspiration with 400 ng of individual SPMs or sterile saline (vehicle). Endpoints were evaluated at 3 days post-AgNP exposure. Values are expressed as mean ± S.E.M. * AgNP exposure, # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>AgNP exposure and modulation by distinct SPM treatment on pulmonary lipid receptor gene expression including (<b>A</b>) the RvE1 receptor, <span class="html-italic">chemerin receptor 23</span> (<span class="html-italic">ChemR23</span>), (<b>B</b>) the PD1 receptor, <span class="html-italic">G protein-coupled receptor 37</span> (<span class="html-italic">GPR37</span>), and (<b>C</b>) the MaR1 receptor, <span class="html-italic">leucine-rich repeat containing G protein-coupled receptor 6</span> (<span class="html-italic">LGR6</span>) from healthy and MetS mice; 24 h following oropharyngeal aspiration of pharmaceutical grade sterile water (control) or 50 μg of AgNPs in sterile water, mice were treated via oropharyngeal aspiration with 400 ng of individual SPMs or sterile saline (vehicle). Endpoints were evaluated at 3 days post-AgNP exposures. Values are expressed as mean ± S.E.M. * AgNP exposure, # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>AgNP exposure and modulation by distinct SPM treatment on BALF inflammatory cytokine and chemokine levels including (<b>A</b>) chemokine (C-X-C motif) ligand 2 (CXCL2), (<b>B</b>) interleukin-6 (IL-6), and (<b>C</b>) interleukin-10 (IL-10) from healthy and MetS mice; 24 h following oropharyngeal aspiration of pharmaceutical grade sterile water (control) or 50 μg of AgNPs in sterile water, mice were treated via oropharyngeal aspiration with 400 ng of individual SPMs or sterile saline (vehicle). Endpoints were evaluated at 3 days post-AgNP exposure. Values are expressed as mean ± S.E.M. * AgNP exposure, # disease model, and <span>$</span> treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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12 pages, 592 KiB  
Article
Cardiac Function and Structure before and after Mild SARS-CoV-2 Infection in Elite Athletes Using Biventricular and Left Atrial Strain
by Jana Schellenberg, Lynn Matits, Daniel A. Bizjak, Freya S. Jenkins and Johannes Kersten
Biomedicines 2024, 12(10), 2310; https://doi.org/10.3390/biomedicines12102310 - 11 Oct 2024
Viewed by 1223
Abstract
Background/Objectives: Myocardial involvement has been observed in athletes following SARS-CoV-2 infection. It is unclear if these changes are due to myocardial damage per se or to an interruption in training. The aim of this study was to assess cardiac function and structure in [...] Read more.
Background/Objectives: Myocardial involvement has been observed in athletes following SARS-CoV-2 infection. It is unclear if these changes are due to myocardial damage per se or to an interruption in training. The aim of this study was to assess cardiac function and structure in elite athletes before and after infection (INFAt) and compare them to a group of healthy controls (CON). Methods: Transthoracic echocardiography was performed in 32 elite athletes, including 16 INFAt (median 21.0 (19.3–21.5) years, 10 male) before (t0) and 52 days after (t1) mild SARS-CoV-2 infection and 16 sex-, age- and sports type-matched CON. Left and right ventricular global longitudinal strain (LV/RV GLS), RV free wall longitudinal strain (RV FWS) and left atrial strain (LAS) were assessed by an investigator blinded to patient history. Results: INFAt showed no significant changes in echocardiographic parameters between t0 and t1, including LV GLS (−21.8% vs. −21.7%, p = 0.649) and RV GLS (−29.1% vs. −28.7%, p = 0.626). A significant increase was observed in LA reservoir strain (LASr) (35.7% vs. 47.8%, p = 0.012). Compared to CON, INFAt at t1 had significantly higher RV FWS (−33.0% vs. −28.2%, p = 0.011), LASr (47.8% vs. 30.5%, p < 0.001) and LA contraction strain (−12.8% vs. −4.9%, p = 0.050) values. Conclusions: In elite athletes, mild SARS-CoV-2 infection does not significantly impact LV function when compared to their pre-SARS-CoV-2 status and to healthy controls. However, subtle changes in RV and LA strain may indicate temporary or training-related adaptions. Further research is needed, particularly focusing on athletes with more severe infections or prolonged symptoms. Full article
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<p>Differences in left and right ventricular strain, circumferential strain and left atrial strain between pre- and post-SARS-CoV-2 infection in athletes (INFAt) and in healthy controls (CON). (<b>A</b>): Left ventricular global longitudinal strain (LV GLS) in %; (<b>B</b>): Global circumferential strain basal (GCS basal) in %; (<b>C</b>): Global circumferential strain midventricular (GCS mid) in %; (<b>D</b>): Right ventricular free wall longitudinal strain (RV FWS) in %; (<b>E</b>): Right ventricular global longitudinal strain (RV GLS) in %; (<b>F</b>): Left atrial reservoir strain (LASr) in %; (<b>G</b>): Left atrial conduit strain (LAScd) in %; (<b>H</b>): Left atrial contraction strain (LASct) in %. Significant results are presented as follows: * &lt; 0.05; *** &lt; 0.001.</p>
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13 pages, 2032 KiB  
Article
Investigation of the Effect of the COVID-19 Pandemic Period on Respiratory Tract Viruses at Istanbul Medical Faculty Hospital, Turkey
by Sevim Mese, Aytaj Allahverdiyeva, Mustafa Onel, Hayriye Kırkoyun Uysal and Ali Agacfidan
Infect. Dis. Rep. 2024, 16(5), 992-1004; https://doi.org/10.3390/idr16050079 - 10 Oct 2024
Viewed by 350
Abstract
Aim: Respiratory viruses significantly impact public health, contributing to high morbidity and mortality rates in both children and adults. This study evaluates the distribution and incidence of respiratory tract viruses in our hospital from 2019 to 2022, focusing on changes post-COVID-19 pandemic. Material [...] Read more.
Aim: Respiratory viruses significantly impact public health, contributing to high morbidity and mortality rates in both children and adults. This study evaluates the distribution and incidence of respiratory tract viruses in our hospital from 2019 to 2022, focusing on changes post-COVID-19 pandemic. Material and Methods: Utilizing molecular methods, we analyzed nasopharyngeal swabs with the FTD Respiratory Pathogens 21 kit and the QIAStat Dx Respiratory Panel kit at Istanbul Faculty of Medicine. A total of 1186 viruses were detected in 2488 samples (47.6% of the total) examined with the FTD Respiratory Pathogens 21 kit between 2019 and 2022. Results: It was determined that the detection rates were 52.8% in 2019, 44.3% in 2020, 50.0% in 2021, and 40.0% in 2022. Notable changes in prevalence were observed for pandemic influenza A (IAV-H1N1pdm2009), parainfluenza virus (PIV)-3, rhinovirus (RV), and respiratory syncytial virus (RSV)-A/B (p < 0.05). RV consistently showed the highest detection rates across all years (17.6% to 7.9%). Additionally, 1276 viruses were detected in 1496 samples using the QIAStat DX kit, with 91.3% positivity in 2021 and 78.6% in 2022, highlighting the kit’s effectiveness in rapid diagnosis. Conclusions: This study enhances understanding of respiratory virus epidemiology during and after the pandemic, emphasizing the need for ongoing surveillance and strategic public health measures to address the evolving landscape of respiratory infections. Full article
(This article belongs to the Section Viral Infections)
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<p>The percentage distribution of mixed viruses detected via VSP among total positive samples (<span class="html-italic">n</span> = 986).</p>
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<p>A visual representation of the distribution of respiratory viruses detected via the VSP test according to year. * The data of 2019 stars from May. ** The data of 2022 ends on May.</p>
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<p>The distribution of the positivity rates of the VSP test in the years 2019–2022. hAV: human adenovirus; hBoV: human bocavirus; hCoV: human coronavirus; EV: enterovirus; IAV: influenza A virus; IVB: influenza B virus; hMPV: human metapneumovirus; <span class="html-italic">M. pneumonia</span>: <span class="html-italic">Mycoplasma pneumonia</span>; PIV: parainfluenza virus; PeV: paraechovirus; RV: rhinovirus; RSV: respiratory syncytial virus.</p>
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<p>The positivity of the VSP test by months within the period May 2019–May 2022.</p>
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<p>Illustrates the percentage distribution of mixed viruses detected via HSSP in positive samples (<span class="html-italic">n</span> = 991).</p>
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<p>A graphical representation of the distribution of respiratory viruses detected via the HSSP test in the examined years.</p>
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<p>The distribution of the positivity rates of the viruses included in the HSSP test in 2021 and 2022. hAV: human adenovirus; hBoV: human bocavirus; hCoV: human coronavirus; RV/EV: entero-virus/rhinovirus; IAV: influenza A virus; IVB; influenza B virus; hMPV: human metapneumovirus; PIV: parainfluenza virus; RSV: respiratory syncytial virus, SARS-CoV-2: severe acute respiratory syndrome coronavirus-2.</p>
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<p>The positivity of the HSSP test each month from January 2021 to August 2022.</p>
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8 pages, 221 KiB  
Article
Left Atrial Structural and Functional Changes in Adults with Congenital Septal Defects and Paroxysmal Atrial Fibrillation
by Anton V. Minaev, Marina Yu. Mironenko, Vera I. Dontsova, Yulia D. Pirushkina, Bektur Sh. Berdibekov, Alexander S. Voynov, Julia A. Sarkisyan and Elena Z. Golukhova
J. Clin. Med. 2024, 13(19), 6023; https://doi.org/10.3390/jcm13196023 - 9 Oct 2024
Viewed by 418
Abstract
Aims. To identify the difference between adult patients with septal defects and paroxysmal atrial fibrillation (AF) and patients without a history of arrhythmia using the left atrial (LA) volume and function parameters, to reveal the parameters associated with AF development. Methods and [...] Read more.
Aims. To identify the difference between adult patients with septal defects and paroxysmal atrial fibrillation (AF) and patients without a history of arrhythmia using the left atrial (LA) volume and function parameters, to reveal the parameters associated with AF development. Methods and results. In this prospective study, 81 patients with septal defects and left-to-right shunts were enrolled between 2021 and 2023 and divided into two groups: with paroxysmal AF and without AF. Left atrial function was analyzed based on the indexed left atrial volumes (LAVI and preA-LAVI), ejection fraction (LAEF), expansion index (LAEI), reservoir (LAS-r), conduit (LAS-cd) and contractile (LAS-ct) strain, and stiffness index (LASI) using a Philips CVx3D ultrasound system (Philips, Amsterdam, The Netherlands) and corresponding software. In total, 26 patients with paroxysmal atrial fibrillation (mean age: 59.6 ± 11.7 years, female: 80.8%) and 55 patients with septal defects without any history of arrhythmias (mean age: 44.8 ± 11.6 years, female: 81.8%) were included. All patients were in the NYHA class I or II at baseline. Our findings demonstrated a significant difference between all LA function parameters in the two groups. Upon univariable analysis, the LAVI, preA-LAVI, LASI, LAEF, LAEI, LAS-r, LAS-c, LAS-ct, age, cardiac index, E/A, and RV pressure were found to be associated with AF. The multivariate analysis identified LAVI (OR 1.236, 95% CI 1.022–1.494, p = 0.03), LAS-r (OR 0.723, 95% CI 0.556–0.940, p = 0.02), and LAS-ct (OR 1.518, 95% CI 1.225–1.880, p < 0.001) as independent predictors of AF development. The proposed model demonstrated high sensitivity and specificity with an adjusted classification threshold of 0.38 (AUC: 0.97, 95% CI 0.93–1.00, sensitivity 92% and specificity 92%, p < 0.001). Conclusions. The assessment of LA function using speckle-tracking echocardiography demonstrated significantly different values in the AF group among patients with congenital septal defects. This technique can therefore be implemented in routine clinical management. The key message. Atrial fibrillation development in adult patients with congenital septal defects and a left-to-right shunt is associated with the changes in left atrial function under conditions of an increased preload. Full article
16 pages, 3634 KiB  
Article
Alleviation of NaCl Stress on Growth and Biochemical Traits of Cenchrus ciliaris L. via Arbuscular Mycorrhizal Fungi Symbiosis
by Jahangir A. Malik, Abdulaziz A. Alqarawi, Fahad Alotaibi, Muhammad M. Habib, Salah N. Sorrori, Majed B. R. Almutairi and Basharat A. Dar
Life 2024, 14(10), 1276; https://doi.org/10.3390/life14101276 - 8 Oct 2024
Viewed by 520
Abstract
Soil salinization, especially in arid and semi-arid regions, is one of the major abiotic stresses that affect plant growth. To mediate and boost plant tolerance against this abiotic stress, arbuscular mycorrhizal fungi (AMF) symbiosis is commonly thought to be an effective tool. So, [...] Read more.
Soil salinization, especially in arid and semi-arid regions, is one of the major abiotic stresses that affect plant growth. To mediate and boost plant tolerance against this abiotic stress, arbuscular mycorrhizal fungi (AMF) symbiosis is commonly thought to be an effective tool. So, the main purpose of this study was to estimate the role of AMF (applied as a consortium of Claroideoglomus etunicatum, Funneliformis mosseae, Rhizophagus fasciculatum, and R. intraradices species) symbiosis in mitigating deleterious salt stress effects on the growth parameters (shoot length (SL), root length (RL), shoot dry weight (SDW), root dry weight (RDW), root surface area (RSA), total root length (TRL), root volume (RV), root diameter (RD), number of nodes and leaves) of Cenchrus ciliaris L. plants through improved accumulations of photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll), proline and phenolic compounds. The results of this experiment revealed that the roots of C. ciliaris plants were colonized by AMF under all the applied salinity levels (0, 75, 150, 225, and 300 mM NaCl). However, the rate of colonization was negatively affected by increasing salinity as depicted by the varied colonization structures (mycelium, vesicles, arbuscules and spores) which were highest under non-saline conditions. This association of AMF induced an increase in the growth parameters of the plant which were reduced by salinity stress. The improved shoot/root indices are likely due to enhanced photosynthetic activities as the AMF-treated plants showed increased accumulation of pigments (chlorophyll a, chlorophyll b and total chlorophyll), under saline as well as non-saline conditions, compared to non-AMF (N-AMF) plants. Furthermore, the AMF-treated plants also exhibited enhanced accumulation of proline and phenolic compounds. These accumulated metabolites act as protective measures under salinity stress, hence explaining the improved photosynthetic and growth parameters of the plants. These results suggest that AMF could be a good tool for the restoration of salt-affected habitats. However, more research is needed to check the true efficacy of different AMF inoculants under field conditions. Full article
(This article belongs to the Special Issue Plant Biotic and Abiotic Stresses 2024)
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<p>(<b>A</b>–<b>D</b>): The photomicrographs of colonized <span class="html-italic">C. ciliaris</span> roots taken under a microscope (10 × 40 magnification) show the perfect abundance of AMF colonization under different levels of salinity treatments. Perfect colonization is evident by the presence of: (v) vesicles; (a) arbuscules; (ch) coiled hyphae; (ih) interradical hyphae; (at) arbuscular trunk; (es) extra-radical spore; (eh) extraradical hyphae; (gt) germinating tube; and (hn) hyphal network.</p>
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<p>(<b>A</b>,<b>B</b>): AMF colonization rate vis-à-vis (<b>A</b>) mycelium, (<b>B</b>) vesicles and (<b>C</b>) arbuscules in the roots of <span class="html-italic">C. ciliaris</span> and (<b>D</b>) total spore count in the rhizosphere soil. The results are presented as a mean ± standard deviation of five replicates. Different letters on top of the bars indicate the significant difference in treatments <span class="html-italic">p</span> = 0.05 (Tukey’s HSD test).</p>
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<p>(<b>A</b>–<b>F</b>): indicates the role of AMF application as consortium (<span class="html-italic">C. etunicatum</span>, <span class="html-italic">F. mosseae</span>, <span class="html-italic">R. fasciculatum</span>, and <span class="html-italic">R. intraradices</span>) on vegetative growth factors such as (<b>A</b>) shoot length (SL), (<b>B</b>) root length (RL), (<b>C</b>) shoot dry weight (SDW), (<b>D</b>) root dry weight (RDW), (<b>E</b>) nodes (no/plant) and (<b>F</b>) leaves (no/plant) of <span class="html-italic">C. ciliaris</span> plants grown under salinity stress. The results are presented as a mean ± standard error of five replicates. Different letters on top of the bars indicate the significant difference in treatments <span class="html-italic">p</span> = 0.05 (Tukey’s HSD test).</p>
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<p>(<b>A</b>–<b>D</b>): indicates the role of AMF application as consortium (<span class="html-italic">C. etunicatum</span>, <span class="html-italic">F. mosseae</span>, <span class="html-italic">R. fasciculatum</span>, and <span class="html-italic">R. intraradices</span>) on root indices such as (<b>A</b>) root surface area (RSA, cm<sup>2</sup>), (<b>B</b>) root volume (RV, cm<sup>3</sup>), (<b>C</b>) root diameter (RD, cm), and (<b>D</b>) number of roots (RT, no/plant) of <span class="html-italic">C. ciliaris</span> plants grown under salinity stress. The results are presented as a mean ± standard error of five replicates. Different letters on top of the bars indicate the significant difference in treatments <span class="html-italic">p</span> = 0.05 (Tukey’s HSD test).</p>
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<p>(<b>A</b>–<b>C</b>): indicates the role of AMF application as a consortium (<span class="html-italic">C. etunicatum</span>, <span class="html-italic">F. mosseae</span>, <span class="html-italic">R. fasciculatum</span>, and <span class="html-italic">R. intraradices</span>) on the photosynthetic pigment concentration, such as (<b>A</b>) chlorophyll <span class="html-italic"><sub>a</sub></span> (µg/gFW), (<b>B</b>) chlorophyll <span class="html-italic"><sub>b</sub></span> (µg/gFW) and (<b>C</b>) total chlorophyll (µg/gFW) of <span class="html-italic">C. ciliaris</span> plants grown under salinity stress. The results are presented as a mean ± standard error of five replicates. Different letters on top of the bars indicate the significant difference in treatments <span class="html-italic">p</span> = 0.05 (Tukey’s HSD test).</p>
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<p>(<b>A</b>–<b>D</b>) indicates the role of AMF application as a consortium (<span class="html-italic">C. etunicatum</span>, <span class="html-italic">F. mosseae</span>, <span class="html-italic">R. fasciculatum</span>, and <span class="html-italic">R. intraradices</span>) on the proline and phenolic content (<b>A</b>) proline shoot (µg/gFW), (<b>B</b>) proline root (µg/gFW), (<b>C</b>) phenolic content shoot (µg/gFW) and (<b>D</b>) phenolic content root (µg/gFW) in <span class="html-italic">C. ciliaris</span> plants grown under salinity stress. The results are presented as a mean ± standard error of five replicates. Different letters on top of the bars indicate the significant difference in treatments <span class="html-italic">p</span> = 0.05 (Tukey’s HSD test).</p>
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15 pages, 2623 KiB  
Article
NT-proBNP Reflects Left Ventricular Hypertrophy Rather than Left Ventricular Dilatation or Systolic Dysfunction in Patients with Fabry Disease
by Constantin Gatterer, Dietrich Beitzke, Gere Sunder-Plassmann, Maximilian Friedl, Philipp Hohensinner, Christopher Mann, Markus Ponleitner, Senta Graf and Max Lenz
J. Clin. Med. 2024, 13(19), 5953; https://doi.org/10.3390/jcm13195953 - 7 Oct 2024
Viewed by 456
Abstract
Background: The diagnosis and follow-up of cardiac involvement in Fabry disease constitutes an important challenge for clinicians caring for affected patients. Combining cardiac imaging with laboratory biomarkers appears most appropriate for longitudinal monitoring. Therefore, we examined the use of NT-proBNP and its [...] Read more.
Background: The diagnosis and follow-up of cardiac involvement in Fabry disease constitutes an important challenge for clinicians caring for affected patients. Combining cardiac imaging with laboratory biomarkers appears most appropriate for longitudinal monitoring. Therefore, we examined the use of NT-proBNP and its association with imaging findings in patients with Fabry disease. Methods: We analysed cardiac MRI and echocardiography data, as well as laboratory results, from a single-centre prospective registry. Results: Repetitive follow-ups of 38 patients with Fabry disease, of whom 18 presented with left ventricular hypertrophy (LVH), revealed a correlation of NT-proBNP with left ventricular (LV) interventricular septal thickness, LV maximum wall thickness, LV and right ventricular (RV) mass index and trabecular mass in patients with LVH. Patients without LVH did not exhibit any tangible association between NT-proBNP and the mentioned parameters. Conversely, we could not detect an association of NT-proBNP with impairment of LV or RV ejection fraction or diastolic volume. Conclusions: NT-proBNP plays a pivotal role as a biomarker for cardiac involvement in patients with Fabry disease. Interestingly, in this specific population with mostly preserved ejection fraction, it seems to reflect ventricular hypertrophy rather than ventricular dysfunction or dilatation. While strong associations were found in hypertrophic patients, NT-proBNP’s prognostic value appears limited in non- or pre-hypertrophic stages. Full article
(This article belongs to the Section Cardiovascular Medicine)
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Graphical abstract

Graphical abstract
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<p>Longitudinal development of NT-proBNP values in patients with (red boxplots) or without (green boxplots) specific therapy ((<b>A</b>), <span class="html-italic">n</span> = indicated). There is a trend towards differences after 60 months (<span class="html-italic">p</span> = 0.083) and significant differences after 90 months (<span class="html-italic">p</span> = 0.020). NT-proBNP values of all available time points are stratified by therapy and the presence of left ventricular hypertrophy ((<b>B</b>), <span class="html-italic">n</span> = 87, the red bar indicates both factors, the yellow bar one, and the green bar none). Patients on specific therapy with left ventricular hypertrophy (red bar) display increased NT-proBNP levels in contrast to those without these two factors (green bar, <span class="html-italic">p</span> = 0.026). The Y-axis is shown as a percentage function (<b>A</b>) to highlight low NT-proBNP values. A <span class="html-italic">p</span>-value of &lt;0.05 was considered statistically significant. “*” indicates outliers within the cohort.</p>
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<p>Changes in average (+standard deviation) NT-proBNP values from baseline to 30 ± 15, 60 ± 15 and 90 ± 15 months follow-ups. The red line represents hypertrophic individuals (defined as an interventricular septum &gt;12 mm (TTE) or LVMI of &gt;75 g g/m<sup>2</sup> ♂ or &gt;59 g g/m<sup>2</sup> ♀), whereas the green line depicts non-hypertrophic patients.</p>
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<p>Correlations of NT-proBNP values and imaging markers of hypertrophy in patients with ((<b>A</b>) <span class="html-italic">n</span> = 47, (<b>B</b>) <span class="html-italic">n</span> = 27, (<b>C</b>) <span class="html-italic">n</span> = 46) or without ((<b>D</b>) <span class="html-italic">n</span> = 21, (<b>E</b>) <span class="html-italic">n</span> = 11, (<b>F</b>) <span class="html-italic">n</span> = 33) left ventricular hypertrophy. The Y-axis is shown as a percentage function to highlight low NT-proBNP values. A <span class="html-italic">p</span>-value of &lt;0.05 was considered statistically significant.</p>
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<p>Patients with elevated LysoGb3 values exhibit increased NT-proBNP levels ((<b>A</b>), <span class="html-italic">p</span> = 0.022, <span class="html-italic">n</span> = indicated). Correlations of NT-proBNP and LysoGb3 values in patients with ((<b>B</b>), <span class="html-italic">n</span> = 24) or without ((<b>C</b>), <span class="html-italic">n</span> = 11) left ventricular hypertrophy. The Y-axis is shown as a percentage function to highlight low NT-proBNP values. A <span class="html-italic">p</span>-value of &lt; 0.05 was considered statistically significant.</p>
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<p>Patients with low T1 values exhibit increased NT-proBNP levels ((<b>A</b>), <span class="html-italic">p</span> = 0.004, <span class="html-italic">n</span> = indicated). Furthermore, individuals with left ventricular diastolic dysfunction show elevated NT-proBNP levels compared to those with normal function ((<b>B</b>), <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = indicated). The Y-axis is shown as a percentage function to highlight low NT-proBNP values. A <span class="html-italic">p</span>-value of &lt;0.05 was considered statistically significant. “*” indicates outliers within the cohort.</p>
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<p>Patients with left ventricular hypertrophy show no correlations between NT-proBNP and right ventricular end-diastolic diameter ((<b>A</b>), <span class="html-italic">n</span> = 44) and right ventricular function ((<b>B</b>), <span class="html-italic">n</span> = 38) but with right ventricular mass index ((<b>C</b>), <span class="html-italic">n</span> = 38) and right ventricular trabecular mass ((<b>D</b>), <span class="html-italic">n</span> = 38). Individuals without hypertrophy exhibit no significant correlations with the mentioned parameters ((<b>E</b>) <span class="html-italic">n</span> = 22, (<b>F</b>) <span class="html-italic">n</span> = 31, (<b>G</b>) <span class="html-italic">n</span> = 31, (<b>H</b>) <span class="html-italic">n</span> = 29). The Y-axis is shown as a percentage function to highlight low NT-proBNP values. A <span class="html-italic">p</span>-value of &lt;0.05 was considered statistically significant.</p>
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16 pages, 1349 KiB  
Article
Electrocardiogram Features of Left Ventricular Excessive Trabeculation with Preserved Cardiac Function in Light of Cardiac Magnetic Resonance and Genetics
by Kristóf Attila Farkas-Sütő, Kinga Grebur, Balázs Mester, Flóra Klára Gyulánczi, Csaba Bödör, Hajnalka Vágó, Béla Merkely and Andrea Szűcs
J. Clin. Med. 2024, 13(19), 5906; https://doi.org/10.3390/jcm13195906 - 3 Oct 2024
Viewed by 413
Abstract
Background and Objectives: Although left ventricular excessive trabeculation (LVET) can cause heart failure, arrhythmia and thromboembolism, limited literature is available on the ECG characteristics of primary LVET with preserved left ventricular function (EF). We aimed to compare the ECG characteristics and cardiac [...] Read more.
Background and Objectives: Although left ventricular excessive trabeculation (LVET) can cause heart failure, arrhythmia and thromboembolism, limited literature is available on the ECG characteristics of primary LVET with preserved left ventricular function (EF). We aimed to compare the ECG characteristics and cardiac MR (CMR) parameters of LVET individuals with preserved left ventricular EF to a control (C) group, to identify sex-specific differences, and to compare the genetic subgroups of LVET with each other and with a C population. Methods: In our study, we selected 69 LVET individuals (EF > 50%) without any comorbidities and compared them to 69 sex- and age-matched control subjects (42% females in both groups, p = 1.000; mean age LVET-vs-C: 38 ± 14 vs. 38 ± 14 years p = 0.814). We analyzed the pattern and notable parameters of the 12-lead ECG recordings. We determined the volumetric and functional parameters, as well as the muscle mass values of the left and right ventricles (LV, RV) based on the CMR recordings. Based on the genotype, three subgroups were established: pathogenic, variant of uncertain significance and benign. Results: In the LVET group, we found normal but elevated volumetric and muscle mass values and a decreased LV_EF, wider QRS, prolonged QTc, higher RV Sokolow index values and lower T wave amplitude compared to the C. When comparing MR and ECG parameters between genetic subgroups, only the QTc showed a significant difference. Over one-third of the LVET population had arrhythmic episodes and a positive family history. Conclusions: The subclinical morphological and ECG changes and the clinical background of the LVET group indicate the need for follow-up of this population, even with preserved EF. Full article
(This article belongs to the Special Issue Advances in the Diagnosis and Treatment of Cardiomyopathy)
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<p>Threshold-based postprocessing method. The short-axis CMR image of an LVET individual with the threshold-based postprocessing method. The green highlight shows the LV, and the blue highlight shows the RV myocardial mass. The MassK algorithm analyzes each voxel and classifies them as either myocardial tissue or blood tissue; therefore, the highlighted myocardial tissue within the endocardial (red in the LV and yellow in the RV) border represents the TPM. Abbreviations: CMR, cardiac magnetic resonance imaging; LV, left ventricle; LVET, left ventricular excessive trabeculation; RV, right ventricle, TPM, trabeculated and papillary muscle mass.</p>
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<p>ECG characteristics in the aspect of morphology and sex. QRS_duration: LVET male characteristic; LV_SI: male characteristic, LVET independent; RV_SI: LVET male characteristic; QTc: female characteristic, prolonged in LVET; T_amplitude: characteristic to healthy controls, sex independent. Abbreviations: LVET, left ventricular excessive trabeculation; C, control population; LV, left ventricle; RV, right ventricle; SI, Sokolow–Lyon index.</p>
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<p>Comparisons of the (<b>A</b>) CMR and (<b>B</b>) ECG parameters between the genetic subgroups. Abbreviations: * significant at the <span class="html-italic">p</span> &lt; 0.05 level; EDV, end-diastolic volume; EF, ejection fraction; ESV, end-systolic volume; i, indexed to body surface area; LV, left ventricle; LVET, left ventricular excessive trabeculation; QTc, corrected QT interval; RV, right ventricle; SI, Sokolow–Lyon index; SV, stroke volume; TM, total muscle mass; TPM, trabeculated and papillary muscle mass; VUS, variant of unknown significance.</p>
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29 pages, 8143 KiB  
Article
Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
by Haider Ali, Muhammad Aftab, Faheem Aslam and Paulo Ferreira
Fractal Fract. 2024, 8(10), 571; https://doi.org/10.3390/fractalfract8100571 - 29 Sep 2024
Viewed by 736
Abstract
Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major [...] Read more.
Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability. Full article
(This article belongs to the Special Issue Complex Dynamics and Multifractal Analysis of Financial Markets)
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<p>5 min high-frequency returns of cryptocurrency markets.</p>
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<p>5 min high-frequency returns of forex markets.</p>
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<p>Daily jump estimates of cryptocurrency markets derived from 5 min high-frequency data.</p>
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<p>Daily jump estimates of forex markets derived from 5 min high frequency data.</p>
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<p>This figure presents the MFDFA outcomes pertaining to the jumps observed in cryptocurrency markets. In the (<b>top-left</b>) section, fluctuation functions for <span class="html-italic">q</span> = 10, <span class="html-italic">q</span> = 0, and <span class="html-italic">q</span> = −10 are displayed. The (<b>top-right</b>) segment illustrates the GHE corresponding to each <span class="html-italic">q</span> value. Additionally, the (<b>bottom-left</b>) section showcases the Mass exponent, <span class="html-italic">τ</span>(<span class="html-italic">q</span>), while the (<b>bottom-right</b>) portion presents the Multifractal Spectrum.</p>
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<p>This figure presents the MFDFA outcomes pertaining to the jumps observed in forex markets. In the (<b>top-left</b>) section, fluctuation functions for <span class="html-italic">q</span> = 10, <span class="html-italic">q</span> = 0, and <span class="html-italic">q</span> = −10 are displayed. The (<b>top-right</b>) segment illustrates the GHE corresponding to each <span class="html-italic">q</span> value. Additionally, the (<b>bottom-left</b>) section showcases the Mass exponent, <span class="html-italic">τ</span>(<span class="html-italic">q</span>), while the (<b>bottom-right</b>) portion presents the Multifractal Spectrum.</p>
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<p>Dynamic Hurst exponent evolution of the jumps of cryptocurrencies (<math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>).</p>
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<p>Dynamic Hurst exponent evolution of the jumps of forex markets (<math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>).</p>
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18 pages, 3454 KiB  
Article
Prescribed Time Interception of Moving Objects’ Trajectories Using Robot Manipulators
by Juan Alejandro Flores-Campos, Christopher René Torres-San-Miguel, Juan Carlos Paredes-Rojas and Adolfo Perrusquía
Robotics 2024, 13(10), 145; https://doi.org/10.3390/robotics13100145 - 27 Sep 2024
Viewed by 368
Abstract
Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task [...] Read more.
Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task is exasperated by (i) the proper gain tuning of the controller, (ii) the dynamic response of the robotic platform, (iii) the velocity constraints in the actuators, and (iv) the trajectory profile exhibited by the moving object. This means that the interception time is not controlled, which is critical for energy optimization, resources, and production. This paper proposes a prescribed time trajectory interception algorithm for robot manipulators. The approach uses the finite-time convergence properties of sliding mode control combined with a terminal attractor based on a time base generator. The combined approach guarantees trajectory interception in a prescribed time with robust properties. Simulation studies are conducted using the first three degrees of freedom (DOFs) of a RV-M1 robot under single- and multi-object interception tasks. The results verify the effectiveness of the proposed methodology under different hyperparameter configurations. Full article
(This article belongs to the Special Issue Adaptive and Nonlinear Control of Robotics)
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<p>Proposed methodology.</p>
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<p>Time Base Generator under different <math display="inline"><semantics> <mi>β</mi> </semantics></math> values.</p>
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<p>Time Base Generator under different <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> values.</p>
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<p>Time-evolution of <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> under different hyperparameters.</p>
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<p>Mitsubishi RV-M1 robot.</p>
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<p>Trajectory interception in each axis. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>.</p>
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<p>3D trajectory interception. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>.</p>
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<p>Trajectory interception in each axis. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
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<p>3D trajectory interception. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
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<p>Trajectory interception in each axis. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>3D trajectory interception. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.5 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>Trajectory interception in each axis. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.2 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>3D trajectory interception. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.2 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>Trajectory interception in each axis. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.9 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>3D trajectory interception. Results for <math display="inline"><semantics> <msub> <mi>t</mi> <mi>f</mi> </msub> </semantics></math> = 1.9 s and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p>
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<p>Cartesian velocity error in the assembly task.</p>
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<p>Torques applied to the robot.</p>
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<p>Robot trajectories for the assembly task.</p>
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<p>Multiple interception trajectories.</p>
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21 pages, 1807 KiB  
Review
Right Ventricular Longitudinal Strain-Related Indices in Acute Pulmonary Embolism
by Ioannis Tzourtzos, Lampros Lakkas and Christos S. Katsouras
Medicina 2024, 60(10), 1586; https://doi.org/10.3390/medicina60101586 - 27 Sep 2024
Viewed by 377
Abstract
Pulmonary embolism (PE) is correlated with serious morbidity and mortality. Efforts have been made to establish and validate mortality predictive scores based mainly on clinical parameters. Patients with PE and traditional indices of echocardiographic right ventricular (RV) dysfunction or pressure overload have a [...] Read more.
Pulmonary embolism (PE) is correlated with serious morbidity and mortality. Efforts have been made to establish and validate mortality predictive scores based mainly on clinical parameters. Patients with PE and traditional indices of echocardiographic right ventricular (RV) dysfunction or pressure overload have a higher probability of a worse outcome. During the last two decades, studies regarding the use of two-dimensional speckle-tracking echocardiography (2DSTE) and its derived indices in the setting of acute PE have been conducted. In this comprehensive review of the literature, we aimed to summarize these studies. Safe conclusions and comparisons among the reviewed studies are prone to statistical errors, mainly because the studies published were heterogenous in design, different 2DSTE-derived parameters were tested, and different clinical outcomes were used as endpoints. Nonetheless, RV strain indices and, more commonly, regional longitudinal strain of the RV free wall have shown a promising correlation with mortality, assisting in the differential diagnosis between PE and other acute or chronic disorders. Full article
(This article belongs to the Section Cardiology)
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<p>Strain-related indices used in the reviewed clinical studies. Abbreviations: LS = longitudinal strain, LV = left ventricle, PSS = peak systolic strain, RV = right ventricle, and SR = strain rate.</p>
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<p>Example of time–strain curves in the RV longitudinal direction in the three free-wall myocardial segments.</p>
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<p>Examples of global (right panel, dotted line) and free-wall RV (right panel, dashed line) longitudinal strain curves. A six-segment model was created (three segments for the free wall and three for the ventricular septum) using a tracking algorithm software after delineation of the endocardial borders in a RV-focused, four-chamber view. Each myocardial segment has a unique color code. White right arrows represent the time to PSS.</p>
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17 pages, 2194 KiB  
Article
Long Terminal Repeats of Gammaretroviruses Retain Stable Expression after Integration Retargeting
by Dalibor Miklík, Martina Slavková, Dana Kučerová, Chahrazed Mekadim, Jakub Mrázek and Jiří Hejnar
Viruses 2024, 16(10), 1518; https://doi.org/10.3390/v16101518 - 25 Sep 2024
Viewed by 509
Abstract
Retroviruses integrate into the genomes of infected host cells to form proviruses, a genetic platform for stable viral gene expression. Epigenetic silencing can, however, hamper proviral transcriptional activity. As gammaretroviruses (γRVs) preferentially integrate into active promoter and enhancer sites, the high transcriptional activity [...] Read more.
Retroviruses integrate into the genomes of infected host cells to form proviruses, a genetic platform for stable viral gene expression. Epigenetic silencing can, however, hamper proviral transcriptional activity. As gammaretroviruses (γRVs) preferentially integrate into active promoter and enhancer sites, the high transcriptional activity of γRVs can be attributed to this integration preference. In addition, long terminal repeats (LTRs) of some γRVs were shown to act as potent promoters by themselves. Here, we investigate the capacity of different γRV LTRs to drive stable expression within a non-preferred epigenomic environment in the context of diverse retroviral vectors. We demonstrate that different γRV LTRs are either rapidly silenced or remain active for long periods of time with a predominantly active proviral population under normal and retargeted integration. As an alternative to the established γRV systems, the feline leukemia virus and koala retrovirus LTRs are able to drive stable, albeit intensity-diverse, transgene expression. Overall, we show that despite the occurrence of rapid silencing events, most γRV LTRs can drive stable expression outside of their preferred chromatin landscape after retrovirus integrations. Full article
(This article belongs to the Section General Virology)
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<p>MLV is stably expressed after integration retargeting. Comparison of expression stability of MLV-derived vectors after integration retargeting. (<b>A</b>) A schematic depiction of the LTR-dGFP-LTR vectors and integrase (IN) variants used in the experiment. (<b>B</b>) A dot plot representing the flow cytometry measurement of K562 cells transduced by MLV-derived vectors 3 dpi. Numbers correspond to the percentage of GFP+ cells. For each vector variant, 2400 live cells were selected to construct the dot plot. (<b>C</b>) Fold change in the fraction of GFP+ cells in the transduced population during 30 days of culture. The <span class="html-italic">y</span>-axis is on a log<sub>2</sub> scale. Timepoint 3 dpi represents the data in panel (<b>B</b>). (<b>D</b>) Fraction of GFP+ cells after the cultivation of bulk populations sorted for GFP expression at 3 dpi. Panels (<b>E</b>,<b>F</b>) demonstrate characteristics of clonal populations expanded from single cells sorted for GFP expression at 3 dpi. The fraction of cells expressing GFP (<b>E</b>) and the mean fluorescence intensity (MFI) (<b>F</b>) were measured at 30 dpi. In each category, 201 clonal populations were characterized. Schemes in panels (<b>C</b>–<b>E</b>) show a time course of experiments, with flow cytometry (F) or FACS sorting (S) performed at a given dpi.</p>
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<p>Integration site (IS) profile of active MLV proviruses after retargeting. IS distribution analysis of MLV IN<sup>wt</sup> and Bin vectors in K562 cells expressing GFP. Panels (<b>A</b>,<b>B</b>) represent an analysis of IS distances to defined chromatin segments. (<b>A</b>) Dot plot representing the results of the “Impact” effect-size analysis comparing IS of Bin<sup>CBX</sup> and IN<sup>wt</sup> vector usage on the distribution of ISs. Points represent individual segments grouped into categories differentiated by colors. CTDiff marks the change in the central tendency of the distribution. Shown are the names of the segments with Impact absolute value ≥ 0.5. (<b>B</b>) Plot showing the distribution of IS distances to the active transcription start site (Tss) and strong enhancer (Enh) chromatin segments. Each dot represents an individual IS, box plots represent medians and quartile range of the distance distributions. (<b>C</b>–<b>E</b>) Panels represent the targeting of chromatin A/B subcompartments and lamina-associated domains (LAD). (<b>C</b>) A bar plot representing a fraction of proviruses integrated into the subcompartments and LADs. Fold frequency changes in subcompartment targeting of W390A and CBX IN variants to wt IN. The <span class="html-italic">x</span>-axis is depicted in the log<sub>2</sub> scale. (<b>E</b>) Frequency of shuffled sites in the chromatin subcompartments representing a random targeting control. Each dot represents a shuffled site set prepared for each of the IN variant samples. The height of the bar represents the mean targeting frequency.</p>
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<p>Expression stability of non-MLV gammaretroviral vectors after retargeting. (<b>A</b>) Gammaretroviral vectors carrying LTRs from five different retroviruses were constructed. Viral stocks were produced with Gag-Pol variants, and expression of the proviruses in transduced cell population was observed for two weeks. (<b>B</b>) Expression of d2GFP by gammaretroviral vector-transduced cells at 3 dpi. For each sample, 10,000 cells are shown. GFP-positive cells are displayed in green. Box plots show the median and quartile range of GFP intensity for the GFP-positive population. Numbers specify the percentage of GFP-positive cells in the transduced population. Shown data represent experiment E4. Log<sub>10</sub> transformed GFP and FSC.A signal is used in a graph. (<b>C</b>) Bar plot showing the change in % of GFP-positive cells in time. The values are relative to the level of expression observed at 3 dpi of a particular experiment. (<b>D</b>) Ratio of % GFP-positive cells to a copy number (CN) of detected d2GFP-encoding genomes per 100 cells. The flow cytometry and DNA extraction were performed two weeks after transduction.</p>
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<p>Intensity and stability of αRV vector expression with γRV LTR as an internal promoter. (<b>A</b>) A schematic depiction of an αRV AS vector. The internal promoter was derived from the U3 promoter/enhancer part of the LTR of the studied γRVs. The bottom of the panel contains the scheme of the experiment, where GFP expression was followed every 3–4 days at 3–31 dpi. (<b>B</b>) Intensity of d2GFP expression in transduced K562 cells at 3 dpi. Cells inside the GFP+ gate are colored green. Box plot describes the intensity distribution of GFP+ cells. The numbers show the percentage of GFP+ cells of all alive cells. For each sample, 10,000 cells are shown. Samples are ordered by the median intensity of GFP+ cells. (<b>C</b>) Representation of the time-course experiment where the percentage of GFP+ cells in transduced populations was followed. Values on the <span class="html-italic">y</span>-axis show the percentage of GFP+ cells relative to the percentage of GFP+ cells observed at 3 dpi. Light lines and points show individual transduction experiments with divergent multiplicities of infection. Black-outlined points connected by black lines show the average of all experiments. (<b>D</b>) Copy number of proviruses (GFP copies) as per 100 genomic equivalents (200 copies of RPP30 reference target) measured by the droplet digital PCR (ddPCR). Proviral copy number was established from genomic DNA collected at 14 dpi in samples shown in panel (<b>B</b>). (<b>E</b>) Ratio of the percentage of GFP+ cells per proviral copy number per 100 genome equivalents. The value of 1 marks the point where all proviruses are expected to be active in expression. Points in (<b>D</b>,<b>E</b>) show values of technical duplicates.</p>
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8 pages, 1130 KiB  
Article
Cardiac Magnetic Resonance Speckle Tracking Analysis of Right Ventricle Function in Myocarditis with Preserved Right Ventricular Ejection Fraction
by Özge Özden, Serkan Ünlü, Ahmet Anıl Şahin, Ahmet Barutçu, Elif Ayduk Gövdeli, Sara Abou Sherif, Konstantinos Papadopoulos, Gülsüm Bingöl, Ismail Doğu Kılıç, Emre Özmen, Özden Seçkin Göbüt, Federico Landra, Matteo Cameli and Ömer Göktekin
Medicina 2024, 60(10), 1569; https://doi.org/10.3390/medicina60101569 - 25 Sep 2024
Viewed by 398
Abstract
Background and Objectives: Diagnosis of myocarditis remains a challenge in clinical practice; however, magnetic resonance imaging (CMRI) can ease the diagnostic approach by providing various parameters. The prevalence of right ventricular involvement in acute myocarditis is suggested to be more frequent than [...] Read more.
Background and Objectives: Diagnosis of myocarditis remains a challenge in clinical practice; however, magnetic resonance imaging (CMRI) can ease the diagnostic approach by providing various parameters. The prevalence of right ventricular involvement in acute myocarditis is suggested to be more frequent than previously hypothesized. In this study, we sought to investigate subclinical RV involvement in patients with acute myocarditis and preserved RV ejection fraction (EF), using CMRI RV speckle-tracking imaging. Materials and Methods: CMRI of 27 patients with acute myocarditis (nine females, age 35.1 ± 12.2 y) was retrospectively analyzed. A control group consisting of CMRI images of 27 healthy individuals was included. Results: No significant differences were found regarding left ventricle (LV) and atrium dimensions. LV ejection fraction was significantly different between groups (56.6 ± 10.6 vs. 62.1 ± 2.6, p < 0.05). No significant differences were present between parameters used for conventional assessment of RV. However, RV strain absolute values were significantly lower in the acute myocarditis group in comparison with that of the control group (18.4 ± 5.4 vs. 21.8 ± 2.8, p = 0.018). Conclusions: Subclinical RV dysfunction detected by CMR-derived strain may be present in patients with acute myocarditis even with preserved RVEF. Full article
(This article belongs to the Section Cardiology)
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<p>Example of feature tracking derived right ventricular longitudinal strain: (<b>A</b>) 4-chamber view of a patient with normal right ventricular longitudinal strain, (<b>B</b>) 4-chamber view of a patient with impaired right ventricular longitudinal strain.</p>
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17 pages, 1523 KiB  
Article
Design and Synthesis of Pyridyl and 2-Hydroxyphenyl Chalcones with Antitubercular Activity
by Kelphina Aziafor, Ketan Ruparelia, Brandon Moulds, Mire Zloh, Tanya Parish and Federico Brucoli
Molecules 2024, 29(19), 4539; https://doi.org/10.3390/molecules29194539 - 24 Sep 2024
Viewed by 556
Abstract
A focussed library of pyridyl and 2-hydroxyphenyl chalcones were synthesized and tested for growth inhibitory activity against Mycobacterium tuberculosis H37Rv, and normal and cancer breast cell lines. Pyridyl chalcones bearing lipophilic A-ring, e.g., dichloro-phenyl-(14), pyrene-1-yl (20)- and biphenyl-4-yl ( [...] Read more.
A focussed library of pyridyl and 2-hydroxyphenyl chalcones were synthesized and tested for growth inhibitory activity against Mycobacterium tuberculosis H37Rv, and normal and cancer breast cell lines. Pyridyl chalcones bearing lipophilic A-ring, e.g., dichloro-phenyl-(14), pyrene-1-yl (20)- and biphenyl-4-yl (21) moieties were found to be the most potent of the series inhibiting the growth of M. tuberculosis H37Rv with IC90 values ranging from 8.9–28 µM. Aryl chalcones containing a 3-methoxyphenyl A-ring and either p-Br-phenyl (25) or p-Cl-phenyl (26) B-rings showed an IC90 value of 28 µM. Aryl-chalcones were generally less toxic to HepG2 cells compared to pyridyl-chalcones. Dose-dependent antiproliferative activity against MDA468 cells was observed for trimethoxy-phenyl (16) and anthracene-9-yl (19) pyridyl-chalcones with IC50 values of 0.7 and 0.3 µM, respectively. Docking studies revealed that chalone 20 was predicted to bind to the M. tuberculosis protein tyrosine phosphatases B (PtpB) with higher affinity compared to a previously reported PtpB inhibitor. Full article
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<p>Interconversion of chalcone to flavonoid catalysed by chalcone isomerase. Chalcones <span class="html-italic">cis</span>(<span class="html-italic">Z</span>)/<span class="html-italic">trans</span>(<span class="html-italic">E</span>) geometric isomers and <span class="html-italic">s-cis</span>/<span class="html-italic">s-trans</span> conformers.</p>
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<p>Chemical structures of naturally occurring and synthetic chalcone derivatives possessing anti-tubercular and anti-proliferative activities.</p>
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<p>Structure of previously reported mycobactin analogues <b>30</b> and <b>31</b> synthesized in this work for comparative purposes.</p>
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<p>Key interactions between (<b>A</b>) <b>19</b>, (<b>B</b>) <b>20,</b> and (<b>C</b>) <b>13</b>, and <span class="html-italic">M. tuberculosis</span> PtpB (PDB ID: 2OZ5). The three-dimensional representations of the ligands with the carbon atoms coloured in green interacting with the binding site residues are shown on the left side, while the two-dimensional ligand plot interactions are shown on the right side for each complex. The dotted lines indicate the interactions, with green coloured lines indicating conventional hydrogen bonds, purple lines depicting π-π interactions, pink lines indicating π-hydrophobic interactions and orange lines depicting π-sulfur interactions.</p>
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<p>Synthesis of pyridyl and aryl-chalcones used in this study. Reagents and conditions: (a) LDA, dry THF, −78 °C then RT. (b) NaOH (aq), MeOH, 0 °C. (c) NH<sub>2</sub>NH<sub>2</sub>·H<sub>2</sub>O, EtOH, RT, overnight.</p>
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26 pages, 2564 KiB  
Article
Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices
by Rangan Gupta and Christian Pierdzioch
Mathematics 2024, 12(18), 2952; https://doi.org/10.3390/math12182952 - 23 Sep 2024
Viewed by 531
Abstract
Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking [...] Read more.
Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty. Full article
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<p>RVs of agricultural commodities.</p>
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<p>Full–sample correlation matrix.</p>
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<p>Full–sample estimated coefficients (baseline stacking algorithm). The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math> (starting in the upper-left panel).</p>
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<p>Full–sample estimated coefficients (baseline stacking algorithm). The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math> (starting in the upper-left panel).</p>
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<p>RMSFE ratios for the full sample (baseline stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4 Cont.
<p>RMSFE ratios for the full sample (baseline stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a recursive window (baseline stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a rolling window (baseline stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>MAFE ratios for a recursive window (baseline stacking algorithm). MAFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. A MAFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample MAFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a recursive window (modified stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a rolling window (modified stacking algorithm). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a recursive window (multivariate shrinkage estimator). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a rolling window (multivariate shrinkage estimator). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Quantile-based RMSFE ratios for a recursive window (baseline stacking algorithm). RMSFE ratios for different quantiles of the realizations of RV. RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Quantile-based RMSFE ratios for a rolling window (baseline stacking algorithm). RMSFE ratios for different quantiles of the realizations of RV. RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Rolling-window estimates of a spillover index. The total dynamic spillover index is derived from a VAR(5) model estimated using a rolling estimation window with a length of 1000 observations and a 10-step-ahead generalized forecast error variance decomposition.</p>
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<p>Subsample analysis for a recursive window (modified stacking algorithm). The panels on the left-hand side summarize the results for the first subsample. The panels on the right-hand side summarize the results for the first subsample. The first subsample comprises the first 450 out-of-sample forecasts. The second subsample is obtained upon deleting the first 450 out-of-sample forecasts. RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Subsample analysis for a rolling window (modified stacking algorithm). The panels on the left-hand side summarize the results for the first subsample. The panels on the right-hand side summarize the results for the first subsample. The first subsample comprises the first 450 out-of-sample forecasts. The second subsample is obtained upon deleting the first 450 out-of-sample forecasts. RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 16 Cont.
<p>Subsample analysis for a rolling window (modified stacking algorithm). The panels on the left-hand side summarize the results for the first subsample. The panels on the right-hand side summarize the results for the first subsample. The first subsample comprises the first 450 out-of-sample forecasts. The second subsample is obtained upon deleting the first 450 out-of-sample forecasts. RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RVs of energy commodities.</p>
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<p>RVs of precious metals.</p>
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<p>Full–sample correlation matrix (extended sample of commodities).</p>
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<p>Rolling-window estimates of a spillover index (extended sample of commodities). The total dynamic spillover index is derived from a VAR(5) model estimated using a rolling estimation window with a length of 1000 observations and a 10-step-ahead generalized forecast error variance decomposition.</p>
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<p>Full-sample RMSFE ratios (modified stacking estimator). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a recursive window (modified stacking estimator). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>RMSFE ratios for a rolling window (modified stacking estimator). RMSFE ratios for a comparison of the HAR-RV-S model (meta learner) with the HAR-RV model. An RMSFE ratio smaller than unity indicates that the meta learner produces a smaller in-sample RMSFE than the HAR-RV model. The forecast horizons are <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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