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19 pages, 3136 KiB  
Article
An Optimised Live Attenuated Influenza Vaccine Ferret Efficacy Model Successfully Translates H1N1 Clinical Data
by Katarzyna E. Schewe, Shaun Cooper, Jonathan Crowe, Steffan Llewellyn, Lydia Ritter, Kathryn A. Ryan and Oliver Dibben
Vaccines 2024, 12(11), 1275; https://doi.org/10.3390/vaccines12111275 - 13 Nov 2024
Viewed by 491
Abstract
Between 2013 and 2016, the A/H1N1pdm09 component of the live attenuated influenza vaccine (LAIV) produced instances of lower-than-expected vaccine effectiveness. Standard pre-clinical ferret models, using a human-like vaccine dose and focusing on antigenic match to circulating wildtype (wt) strains, were unable [...] Read more.
Between 2013 and 2016, the A/H1N1pdm09 component of the live attenuated influenza vaccine (LAIV) produced instances of lower-than-expected vaccine effectiveness. Standard pre-clinical ferret models, using a human-like vaccine dose and focusing on antigenic match to circulating wildtype (wt) strains, were unable to predict these fluctuations. By optimising the vaccine dose and utilising clinically relevant endpoints, we aimed to develop a ferret efficacy model able to reproduce clinical observations. Ferrets were intranasally vaccinated with 4 Log10 FFU/animal (1000-fold reduction compared to clinical dose) of seven historical LAIV formulations with known (19–90%) H1N1 vaccine efficacy or effectiveness (VE). Following homologous H1N1 wt virus challenge, protection was assessed based on primary endpoints of wt virus shedding in the upper respiratory tract and the development of fever. LAIV formulations with high (82–90%) H1N1 VE provided significant protection from wt challenge, while formulations with reduced (19–32%) VE tended not to provide significant protection. The strongest correlation observed was between reduction in wt shedding and VE (R2 = 0.75). Conversely, serum immunogenicity following vaccination was not a reliable indicator of protection (R2 = 0.37). This demonstrated that, by optimisation of the vaccine dose and the use of non-serological, clinically relevant protection endpoints, the ferret model could successfully translate clinical H1N1 LAIV VE data. Full article
(This article belongs to the Section Influenza Virus Vaccines)
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Figure 1

Figure 1
<p>LAIV formulations with known H1N1 efficacy/effectiveness were assessed in an optimised ferret efficacy model. (<b>A</b>) Table detailing seven LAIV formulations used clinically between 2004 and 2018 with known H1N1 efficacy/effectiveness, assessed for their ability to protect ferrets from homologous <span class="html-italic">wt</span> H1N1 challenge. The H1N1 component is highlighted. H3N2 and B virus strains are also shown (strain abbreviations are detailed in <a href="#app1-vaccines-12-01275" class="html-app">Table S1</a>). H1N1 efficacy/effectiveness data were taken from [<a href="#B11-vaccines-12-01275" class="html-bibr">11</a>] <sup>a</sup>, [<a href="#B19-vaccines-12-01275" class="html-bibr">19</a>] <sup>b</sup>, [<a href="#B15-vaccines-12-01275" class="html-bibr">15</a>] <sup>c</sup>, [<a href="#B18-vaccines-12-01275" class="html-bibr">18</a>] <sup>d</sup>. (<b>B</b>) Study schedule for the vaccination and challenge of ferrets. The timeline shows study days, with vaccination occurring at d0 and <span class="html-italic">wt</span> challenge at d28. Coloured boxes and black arrows indicate interventions/sampling as shown in the key or in text labels.</p>
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<p>H1N1 serum immune responses provide limited differentiation of LAIV formulations with varying VE. (<b>A</b>) Serum HAI titres for unvaccinated and vaccinated animals. Points represent the Log<sub>2</sub> HAI titre for individual animals. Columns and error bars show the group geometric mean titre (GMT) and geometric standard deviation. The dashed horizontal line represents the limit of detection (LoD) of the assay. Values below the LoD are arbitrarily shown as ½ LoD. (<b>B</b>) Linear regression comparing group GMT (coloured points) with VE. The linear regression line (heavy dashed line) and 95% confidence intervals (light dotted lines) are shown. Linear regression statistics (R<sup>2</sup>, <span class="html-italic">p</span> value) are shown.</p>
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<p>LAIV formulations with higher VE protect ferrets from <span class="html-italic">wt</span> virus shedding post-challenge. At d28 post-challenge, all ferrets were challenged with 5 Log<sub>10</sub> FFU of <span class="html-italic">wt</span> virus homologous to the vaccinating H1N1 strain for that group. Shedding of <span class="html-italic">wt</span> virus was measured daily for 3 days by TCID<sub>50</sub> assay. (<b>A</b>) Daily shedding of <span class="html-italic">wt</span> challenge viruses in unvaccinated animals. (<b>B</b>) Daily <span class="html-italic">wt</span> virus shedding in vaccinated groups. (<b>C</b>) Comparison of geometric mean <span class="html-italic">wt</span> virus shedding per day in vaccinated (+) and unvaccinated control (-) animals. Points represent individual animals, while columns and error bars show group median and interquartile range. Statistical significance of comparisons in C are indicated by horizontal lines and labelled as: ns <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001. (<b>D</b>) Correlation of geometric mean <span class="html-italic">wt</span> virus shedding per day with H1N1 VE by linear regression. Points show the group median for each formulation. The heavy dashed line shows the linear regression and the light dashed lines the 95% confidence intervals. Linear regression statistics (R<sup>2</sup>, <span class="html-italic">p</span> value) are shown. Limit of detection of the TCID<sub>50</sub> assay is indicated by a dashed line (LoD) in all cases.</p>
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<p>LAIV formulations with higher VE give greater reductions in <span class="html-italic">wt</span> virus load in nasal turbinate tissues. Following culling at d31 post-vaccination, NT tissues were removed from all study animals and the <span class="html-italic">wt</span> virus load was measured by TCID<sub>50</sub> assay. (<b>A</b>) Comparison of <span class="html-italic">wt</span> challenge virus titres in vaccinated (+) and unvaccinated control (-) animals. Points represent individual animals, while columns and error bars show the group median and interquartile range. Statistical comparisons are indicated by horizontal lines and labelled as: ns <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.(<b>B</b>) Correlation of NT <span class="html-italic">wt</span> virus load and H1N1 VE (%) by linear regression. Points indicate group median for each formulation. The heavy dashed line shows the linear regression and the light dashed lines 95% the confidence intervals. Linear regression statistics (R<sup>2</sup>, <span class="html-italic">p</span> value) are shown. Limit of detection of the TCID<sub>50</sub> assay is indicated by a dashed line (LoD).</p>
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<p>LAIV formulations with higher VE protect ferrets from fever. At d28 post-challenge, mock vaccinated and vaccinated ferrets were challenged intranasally with 5 Log<sub>10</sub> FFU of <span class="html-italic">wt</span> virus homologous to the vaccinating H1N1 strain for that group. Generation of fever as a measure of influenza-like illness was monitored via implanted data loggers. Data points were taken at least hourly for the duration of the study, versus a pre-challenge average (baseline). ‘Fever’ for each animal was then calculated as the average temperature difference vs. baseline during a post-challenge window in which the unvaccinated control animal temperature was &gt;1.5SD above baseline. (<b>A</b>) Change in body temperature vs. baseline in unvaccinated control animals, from 24 h pre-challenge to 72 h post-challenge (cull). <span class="html-italic">wt</span> challenge viruses are labelled (top of panel). (<b>B</b>) Maximum recorded temperature vs. baseline for <span class="html-italic">wt</span> viruses in unvaccinated animals. <span class="html-italic">wt</span> strains are labelled on the x-axis and the y-axis shows the single highest temperature vs. baseline recorded for each ferret. (<b>C</b>) Change in body temperature vs. baseline in vaccinated groups. Vaccine groups are labelled at the top of the panels. Individual lines are shown for each animal in a group, with spline curves fitted using 6 knots of smoothing. (<b>D</b>) Comparison of fever temperatures for vaccinated (+) and unvaccinated control (-) groups. In all panels, points represent individual animals while columns and error bars show the group median and interquartile range. The statistical significance of comparisons is indicated by horizontal lines and labelled as: ns <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>) Correlation of fever with H1N1 VE by linear regression. Points show the group median for each formulation. The heavy dashed line shows the linear regression and the light dashed lines the 95% confidence intervals. Linear regression statistics (R<sup>2</sup>, <span class="html-italic">p</span> value) are shown.</p>
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<p>LAIV formulations with higher VE protect ferrets from fever. At d28 post-challenge, mock vaccinated and vaccinated ferrets were challenged intranasally with 5 Log<sub>10</sub> FFU of <span class="html-italic">wt</span> virus homologous to the vaccinating H1N1 strain for that group. Generation of fever as a measure of influenza-like illness was monitored via implanted data loggers. Data points were taken at least hourly for the duration of the study, versus a pre-challenge average (baseline). ‘Fever’ for each animal was then calculated as the average temperature difference vs. baseline during a post-challenge window in which the unvaccinated control animal temperature was &gt;1.5SD above baseline. (<b>A</b>) Change in body temperature vs. baseline in unvaccinated control animals, from 24 h pre-challenge to 72 h post-challenge (cull). <span class="html-italic">wt</span> challenge viruses are labelled (top of panel). (<b>B</b>) Maximum recorded temperature vs. baseline for <span class="html-italic">wt</span> viruses in unvaccinated animals. <span class="html-italic">wt</span> strains are labelled on the x-axis and the y-axis shows the single highest temperature vs. baseline recorded for each ferret. (<b>C</b>) Change in body temperature vs. baseline in vaccinated groups. Vaccine groups are labelled at the top of the panels. Individual lines are shown for each animal in a group, with spline curves fitted using 6 knots of smoothing. (<b>D</b>) Comparison of fever temperatures for vaccinated (+) and unvaccinated control (-) groups. In all panels, points represent individual animals while columns and error bars show the group median and interquartile range. The statistical significance of comparisons is indicated by horizontal lines and labelled as: ns <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>) Correlation of fever with H1N1 VE by linear regression. Points show the group median for each formulation. The heavy dashed line shows the linear regression and the light dashed lines the 95% confidence intervals. Linear regression statistics (R<sup>2</sup>, <span class="html-italic">p</span> value) are shown.</p>
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12 pages, 3526 KiB  
Article
A Numerical Study of Dynamic Behaviors of Graphene-Platelet-Reinforced ETFE Tensile Membrane Structures Subjected to Harmonic Excitation
by Yu Wang, Jiajun Gu, Xin Zhang, Jian Fan, Wenbin Ji and Chuang Feng
Buildings 2024, 14(11), 3597; https://doi.org/10.3390/buildings14113597 - 12 Nov 2024
Viewed by 368
Abstract
This study presents a numerical investigation of the dynamic behavior of graphene platelet (GPL)-reinforced ethylene tetrafluoroethylene (ETFE) tensile membrane structures subjected to harmonic excitation. Modal and harmonic response analyses were performed to assess both the natural frequencies and the dynamic responses of the [...] Read more.
This study presents a numerical investigation of the dynamic behavior of graphene platelet (GPL)-reinforced ethylene tetrafluoroethylene (ETFE) tensile membrane structures subjected to harmonic excitation. Modal and harmonic response analyses were performed to assess both the natural frequencies and the dynamic responses of the ETFE membrane. GPLs were employed as the reinforcements to enhance the mechanical properties of the membrane materials, whose Young’s modulus was predicted through the effective medium theory (EMT). Parametric studies were conducted to examine the impact of pre-strain and the attributes of the GPL reinforcements, including weight fraction and aspect ratio, on the natural frequencies and amplitude–frequency response curves of the membrane structure. The first natural frequency substantially increased from 5.46 Hz without initial strain to 31.0 Hz with the application of 0.1% initial strain, resulting in a frequency shift that moved the natural frequency out of the range of typical wind-induced pulsations. Embedding GPL fillers into ETFE membrane was another potential solution to enhance the dynamic stability of the membrane structure, with a 1% addition of GPLs resulting in a 48.6% increase in the natural frequency and a 45.1% reduction in resonance amplitude. GPLs with larger aspect ratios provided better reinforcement, offering a means to fine-tune the membrane’s dynamic response. These results underscore that by strategically adjusting both pre-strain levels and GPL characteristics, the membrane’s dynamic behavior can be optimized, offering a promising approach for improving the stability of structures subjected to wind-induced loads. Full article
(This article belongs to the Special Issue Research on Structural Analysis and Design of Civil Structures)
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Figure 1
<p>(<b>a</b>) ETFE composite membrane unit. (<b>b</b>) Multiview orthographic projection with dimensions.</p>
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<p>Boundary conditions of the numerical wind tunnel simulation.</p>
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<p>The first six natural modes of ETFE tensile membrane with 0.0% pre-strain.</p>
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<p>Frequency–amplitude curve of ETFE tensile membrane structure under 1000 Pa harmonic excitation.</p>
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<p>Effect of pre-strain level on natural frequencies of GPL/ETFE tensile membrane.</p>
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<p>Frequency–amplitude curves of GPL/ETFE tensile membrane in different pre-strain levels under 1000 Pa harmonic excitation.</p>
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<p>Effect of GPL weight fraction on the first six natural frequencies of GPL/ETFE tensile membrane.</p>
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<p>Frequency–amplitude curves of ETFE tensile membrane structure with different GPL weight fractions under 1000 Pa harmonic excitation.</p>
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<p>Effect of GPL aspect ratio on the first six natural frequencies of GPL-reinforced ETFE tensile membrane.</p>
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<p>The frequency–amplitude curves of ETFE tensile membrane structures reinforced by 1% GPLs with different aspect ratios under 1000 Pa harmonic excitation.</p>
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12 pages, 3022 KiB  
Article
MYH6 Variants Are Associated with Atrial Dysfunction in Neonates with Hypoplastic Left Heart Syndrome
by Melissa Quintanilla Anfinson, Sara Creighton, Pippa M. Simpson, Jeanne M. James, Phoebe Lim, Peter C. Frommelt, Aoy Tomita-Mitchell and Michael E. Mitchell
Genes 2024, 15(11), 1449; https://doi.org/10.3390/genes15111449 - 10 Nov 2024
Viewed by 461
Abstract
Background: MYH6 variants are the most well-known genetic risk factor (10%) for hypoplastic left heart syndrome (HLHS) and are associated with decreased cardiac transplant-free survival. MYH6 encodes for α-myosin heavy chain (α-MHC), a contractile protein expressed in the neonatal atria. We therefore [...] Read more.
Background: MYH6 variants are the most well-known genetic risk factor (10%) for hypoplastic left heart syndrome (HLHS) and are associated with decreased cardiac transplant-free survival. MYH6 encodes for α-myosin heavy chain (α-MHC), a contractile protein expressed in the neonatal atria. We therefore assessed atrial function in HLHS patients with MYH6 variants. Methods: We performed a retrospective, blinded assessment of pre-stage I atrial function using 2D speckle-tracking echocardiography (2D-STE). Variant carriers were control-matched based on AV valve anatomy, sex, and birth year. Studies were obtained postnatally from awake patients prior to surgical intervention. Right atrial (RA) and right ventricular (RV) strain and strain rate (SR) were measured from the apical four-chamber view. Results: A total of 19 HLHS patients with MYH6 variants had echocardiograms available; 18 were matched to two controls each, and one had a single control. RA active strain (ASct) was decreased in variant carriers (−1.41%, IQR −2.13, −0.25) vs. controls (−3.53%, IQR −5.53, −1.28; p = 0.008). No significant differences were identified in RV strain between the groups. RA reservoir strain (ASr) and conduit strain (AScd) positively correlated with heart rate (HR) in MYH6 variant carriers only (ASr R = 0.499, p = 0.029; AScd R = 0.469, p = 0.043). RV global longitudinal strain (GLS) as well as RV systolic strain (VSs) and strain rate (VSRs) correlated with HR in controls only (GLS R = 0.325, p = 0.050; VSs R = 0.419, p = 0.010; VSRs R = 0.410, p = 0.012). Conclusions: We identified functional consequences associated with MYH6 variants, a known risk factor for poor outcomes in HLHS. MYH6 variant carriers exhibit impaired RA contractility despite there being no differences in RV function between variant carriers and controls. MYH6 variants are also associated with an ineffective RA reservoir and conduit function at high heart rates, despite preserved RV diastolic function. RA dysfunction and reduced atrial “kick” may therefore be a significant contributor to RV failure and worse clinical outcomes in HLHS patients with MYH6 variants. Full article
(This article belongs to the Special Issue Genetics, Genomics and Precision Medicine in Heart Diseases)
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Figure 1
<p>Representative tracings and time-rate plots of atrial and ventricular myocardium in postnatal HLHS. AScd, conduit atrial strain; ASr, reservoir atrial strain; ASct, active/contractile atrial strain; ASRr, reservoir atrial strain rate; ASRct, active/contractile atrial strain rate; VSs, systolic ventricular strain; VSRs, systolic ventricular strain rate; and VSRed, early diastolic ventricular strain rate.</p>
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<p>Kaplan-Meier curves comparing 15-year event-free survival in <span class="html-italic">MYH6</span> variant carriers vs. controls. Shaded areas represent 95% confidence intervals. ‘+’ signs indicate censored patients.</p>
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17 pages, 14481 KiB  
Article
Compression Law and Settlement Calculation Method of Over-Wet Soil Based on Large Samples
by Bo Peng, Lijian Wu, Ruiling Feng, Changjun Song, Jiangxin Liu and Xiaolin Bian
Appl. Sci. 2024, 14(22), 10247; https://doi.org/10.3390/app142210247 - 7 Nov 2024
Viewed by 384
Abstract
Previous studies have shown that over-wet soil is challenging to compact and exhibits large creep deformation. The consolidation test of small specimens cannot accurately reflect the compression law, and creep is underestimated owing to size effects, which affects the engineering quality. In order [...] Read more.
Previous studies have shown that over-wet soil is challenging to compact and exhibits large creep deformation. The consolidation test of small specimens cannot accurately reflect the compression law, and creep is underestimated owing to size effects, which affects the engineering quality. In order to accurately analyze the compression process of over-wet soil and establish its settlement calculation method, this study focuses on over-wet soil in Anhui Province, China, and uses a large-sized tester to load and analyze its compression law. The thermogravimetric analysis method was employed to investigate the changes in water with different binding forces during the compression process, and the settlement calculation method for over-wet soil was explored. The results show that the creep of over-wet soil is large and long-lasting, and the three-stage consolidation division method based on the dt curve is more effective in analyzing its regularity. The creep of over-wet soil is directly proportional to its water content. When the load exceeds the pre-consolidation pressure, the creep deformation becomes more significant, accounting for about 60% of the deformation under a single level load. It is recommended to use the creep coefficient (λ) for calculation. The results of the thermogravimetric analysis indicate that during the primary consolidation stage, free water is discharged, and weakly bound water is mainly discharged during the third consolidation stage, which is the main cause of creep. Finally, based on the relationship between the creep strain and water content of large samples, a calculation method for the settlement of over-wet soil foundations based on the layered summation method was established, which had a higher prediction accuracy than the conventional layered summation method. The results of this study will help clarify the deformation process and principle of over-wet soil and improve the quality of engineering. Full article
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<p>Sampling location.</p>
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<p>Sampling site for over-wet soil.</p>
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<p>Compaction curve.</p>
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<p>Schematic of loading.</p>
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<p>Large compression instrument.</p>
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<p>Thermogravimetric analysis system.</p>
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<p><math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">g</mi> <mi>P</mi> </mrow> </semantics></math> curves of the samples.</p>
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<p><math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">g</mi> <mi>t</mi> </mrow> </semantics></math> curves of standard samples (Number 3).</p>
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<p><math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">g</mi> <mi>t</mi> </mrow> </semantics></math> curves of five samples [<a href="#B17-applsci-14-10247" class="html-bibr">17</a>,<a href="#B18-applsci-14-10247" class="html-bibr">18</a>,<a href="#B19-applsci-14-10247" class="html-bibr">19</a>].</p>
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<p>Division method of three stages of consolidation [<a href="#B19-applsci-14-10247" class="html-bibr">19</a>].</p>
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<p><math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi>t</mi> </mrow> </semantics></math> curve of over-wet soil at 300 kPa.</p>
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<p>Analysis of the compression stage of over-wet soil. (<b>a</b>) Deformation during the primary consolidation stage. (<b>b</b>) Deformation during the secondary consolidation stage. (<b>c</b>) Deformation during the tertiary consolidation stage. (<b>d</b>) Proportion of creep.</p>
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<p>Analysis of the compression stage of over-wet soil. (<b>a</b>) Deformation during the primary consolidation stage. (<b>b</b>) Deformation during the secondary consolidation stage. (<b>c</b>) Deformation during the tertiary consolidation stage. (<b>d</b>) Proportion of creep.</p>
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<p>Creep strain under different loads.</p>
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<p>Thermogravimetric curve of the sample before loading at 200 kPa.</p>
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<p>Changes in water with different binding forces.</p>
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<p>Relationship between water and load with different binding forces.</p>
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<p>Compression data of five samples.</p>
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<p>Variation law of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>.</p>
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<p>Comparison between measured and calculated values.</p>
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16 pages, 2856 KiB  
Article
Composition and Morphological Characteristics of Extracellular Polymeric Substances of Different Tolerant Bacteria Under Perfluorobutanesulfonic Acid (PFBS) Stress
by Rui Tang, Lina Sun, Guo Yu, Jiayao Xu, Qing Luo, Xiaoxu Wang and Luge Rong
Toxics 2024, 12(11), 797; https://doi.org/10.3390/toxics12110797 - 31 Oct 2024
Viewed by 517
Abstract
This investigation studies the properties and composition of extracellular polymeric substances (EPS) of the four tolerant bacterial strains [NH (Cellulosimicrobium cellulans), TH, YH, and HE (Pseudomonas aeruginosa)] under perfluorobutanesulfonic acid (PFBS) stress. The strains were acquired from athickened sludge [...] Read more.
This investigation studies the properties and composition of extracellular polymeric substances (EPS) of the four tolerant bacterial strains [NH (Cellulosimicrobium cellulans), TH, YH, and HE (Pseudomonas aeruginosa)] under perfluorobutanesulfonic acid (PFBS) stress. The strains were acquired from athickened sludge in a fluorine chemical park. Each strain’s EPS were isolated by heating and centrifugation, and their growth, metabolic activity, and EPS alteration research pre- and post-stress were assessed and compared. The strain type was identified by morphological observation and 16S rDNA gene sequence analysis. Under PFBS (100 μg·L−1) stress, the four tolerant strains NH, TH, YH, and HE showed 38.10%, 29.26%, 35.92%, and 30.48% removal of PFBS on day 4, respectively, and the strain’s EPS had a substantial impact on main component protein (PR) and polysaccharide (PS) contents. The NH microorganism’s ability to metabolize organic matter was markedly stronger; it had a higher quantity, and its impact on main EPS content was greater than the other three tolerant strains. The three-dimensional excitation–emission matrix results showed marked alterations in tryptophan and aromatic protein peaks in the tolerant strain’s EPS. Furthermore, the FTIR analysis showed that the intensity of the functional groups in the proteins (-OH, C=O, -NH, and -CN) and the polysaccharides (-OH, C-O-C, and C-O) changed significantly. This investigation indicated that the proteins and polysaccharides of the tolerant strain’s EPS could provide more binding sites for PFBS adsorption, where the NH strain had the best biosorption capacity. This research provides a theoretical basis for elucidating efficient biosorbents. Full article
(This article belongs to the Section Toxicity Reduction and Environmental Remediation)
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Figure 1
<p>Phylogenetic tree depicting NH (<b>A</b>), TH (<b>B</b>), YH (<b>C</b>), HE (<b>D</b>) strain affiliations and related bacterial taxonomy. The tree reveals NJ bootstrap analysis data based on the sequences of 16S rRNA gene. Internal node numbers are bootstrap support values (%). Numbers between brackets are GenBank accession. Halopseudomonas oceani was arbitrarily selected as the outgroup for the phylogenetic tree.</p>
Full article ">Figure 1 Cont.
<p>Phylogenetic tree depicting NH (<b>A</b>), TH (<b>B</b>), YH (<b>C</b>), HE (<b>D</b>) strain affiliations and related bacterial taxonomy. The tree reveals NJ bootstrap analysis data based on the sequences of 16S rRNA gene. Internal node numbers are bootstrap support values (%). Numbers between brackets are GenBank accession. Halopseudomonas oceani was arbitrarily selected as the outgroup for the phylogenetic tree.</p>
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<p>Effects of PFBS stress on the growth curves and ETSA of different tolerant bacteria: (<b>A</b>) <span class="html-italic">C. cellulans</span> = NH; (<b>B</b>) <span class="html-italic">P. aeruginosa</span> = YH; (<b>C</b>) <span class="html-italic">P. aeruginosa</span> Strain TH; (<b>D</b>) <span class="html-italic">P. aeruginosa</span> = HE.</p>
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<p>Effects on the content of EPS components and PFBS removal rates of different strains under the stress of PFBS. (PR-CK; protein with the pollution-free PFBS as the control, PR-PFBS; protein with the polluted PFBS, PS-CK; polysaccharide with the pollution-free PFBS as the control, PS-PFBS; polysaccharide with the polluted PFBS).</p>
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<p>Three-dimensional fluorescence of different strains of EPS under the stress of PFBS.</p>
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<p>Three-dimensional fluorescence of different strains of EPS under the stress of PFBS.</p>
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<p>FTIR spectrum of different strains of EPS under the stress of PFBS.</p>
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13 pages, 1294 KiB  
Article
Numerical Analysis of the Cell Droplet Loading Process in Cell Printing
by Yankun Wang, Fagui Pang, Shushan Lai, Renye Cai, Chenxiang Lai, Zexin Yu, Yiwei Zhu, Min Wu, Heng Zhang and Chunyu Kong
Micromachines 2024, 15(11), 1335; https://doi.org/10.3390/mi15111335 - 31 Oct 2024
Viewed by 402
Abstract
Cell printing is a promising technology in tissue engineering, with which the complex three-dimensional tissue constructs can be formed by sequentially printing the cells layer by layer. Though some cell printing experiments with commercial inkjet printers show the possibility of this idea, there [...] Read more.
Cell printing is a promising technology in tissue engineering, with which the complex three-dimensional tissue constructs can be formed by sequentially printing the cells layer by layer. Though some cell printing experiments with commercial inkjet printers show the possibility of this idea, there are some problems, such as cell damage due the mechanical impact during cell direct writing, which include two processes of cell ejection and cell landing. Cell damage observed during the bioprinting process is often simply attributed to interactions between cells and substrate. However, in reality, cell damage can also arise from complex mechanical effects caused by collisions between cell droplets during continuous printing processes. The objective of this research is to numerically simulate the collision effects between continuously printed cell droplets within the bioprinting process, with a particular focus on analyzing the consequent cell droplet deformation and stress distribution. The influence of gravity force was ignored, cell droplet landing was divided into four phases, the first phase is cell droplet free falling at a certain velocity; the second phase is the collision between the descending cell droplet and the pre-existing cell droplets that have been previously printed onto the substrate. This collision results in significant deformation of the cell membranes of both cell droplets in contact; the third phase is the cell droplet hitting a rigid body substrate; the fourth phase is the cell droplet being bounced. We conducted a qualitative analysis of the stress and strain of cell droplets during the cell printing process to evaluate the influence of different parameters on the printing effect. The results indicate that an increase in jet velocity leads to an increase in stress on cell droplets, thereby increasing the probability of cell damage. Adding cell droplet layers on the substrate can effectively reduce the impact force caused by collisions. Smaller droplets are more susceptible to rupture at higher velocities. These findings provide a scientific basis for optimizing cell printing parameters. Full article
(This article belongs to the Section B:Biology and Biomedicine)
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<p>Cell droplet diagram.</p>
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<p>The landing process at (<b>a</b>) 0 us, (<b>b</b>) 0.26 μs, (<b>c</b>) 2.89 μs and (<b>d</b>) 3.67 μs.</p>
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<p>The phenomenon of intermediate and diagonal cells.</p>
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<p>The maximum stress (Mpa) generated at different impact velocities.</p>
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<p>Schematic diagram of the simulation model.</p>
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<p>Maximum stress (Mpa) generated by double-layer cell droplets.</p>
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<p>A line plot of the changes in the maximum stress and maximum strain values in parts 5 and 10 at different diameters and speeds.</p>
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15 pages, 1191 KiB  
Article
Protective Impact of Influenza Vaccination on Healthcare Workers
by Yimei Tian, Yue Ma, Jianchao Ran, Lifang Yuan, Xianhu Zeng, Lu Tan, Li Chen, Yifan Xu, Shaxi Li, Ting Huang and Hongzhou Lu
Vaccines 2024, 12(11), 1237; https://doi.org/10.3390/vaccines12111237 - 30 Oct 2024
Viewed by 546
Abstract
Background: Influenza vaccine uptake among healthcare workers is crucial for preventing influenza infections, yet its effectiveness needs further investigation. Objectives: This prospective observational study aimed to assess the protective effect of influenza vaccination among healthcare workers in Shenzhen. Methods: We enrolled 100 participants, [...] Read more.
Background: Influenza vaccine uptake among healthcare workers is crucial for preventing influenza infections, yet its effectiveness needs further investigation. Objectives: This prospective observational study aimed to assess the protective effect of influenza vaccination among healthcare workers in Shenzhen. Methods: We enrolled 100 participants, with 50 receiving the 2023–2024 quadrivalent influenza vaccine (QIV) and 50 serving as unvaccinated controls. Epidemiological data were collected when the participants presented influenza-like illness. Serum samples were collected at three time points (pre-vaccination and 28 and 180 days after vaccination). Hemagglutination inhibition (HI) assay was performed against the strains included in the 2023–2024 QIV (H1N1, H3N2, BV and BY strains) to assess antibody protection levels. Demographics comparisons revealed no significant differences between the vaccinated and control groups (p > 0.05), ensuring group comparability. Results: The incidence of influenza-like illness was significantly lower in the vaccinated (18%) compared to the control group (36%; p = 0.046; OR = 0.39; 95% CI: 0.15 to 0.98). The vaccinated group also exhibited a higher rate of consecutive two-year vaccinations (48% vs. 24% in the control group, p < 0.05). Additionally, the vaccinated healthcare workers were more inclined to recommend vaccination to their families (80% vs. 48%, p < 0.05). HI titers against H1N1 (p < 0.01), H3N2 (p < 0.01), BV (p < 0.001) and BY (p < 0.01) significantly increased in the vaccinated group at 28 days post-vaccination. Moreover, a marked and sustained increase in HI titers against the H3N2 strain (p < 0.001) was observed at 180 days post-vaccination, highlighting the vaccine’s enduring impact on the immune response. The fold change in the HI titers, indicative of the magnitude of the immune response, was significantly higher for H1N1 (p < 0.01), H3N2 (p < 0.001), BV (p < 0.01) and BY (p < 0.05) among the vaccinated individuals compared to the control group, underscoring the vaccine’s efficacy in eliciting a robust and sustained antibody response. Conclusion: Influenza vaccination significantly reduces the incidence of influenza-like illness among healthcare workers and promotes a sustained immune response. The study supports the importance of annual vaccination for this group to enhance personal and public health. Full article
(This article belongs to the Special Issue Human Immune Responses to Infection and Vaccination)
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<p>Comparison of the hemagglutination inhibition (HI) titers against the 2023–2024 quadrivalent influenza vaccine (QIV) strains on different time points between the vaccinated and control groups. (<b>A</b>) The HI titers in response to H1N1, H3N2, BV, and BY strains were detected on the day before vaccination (d0) in the vaccinated and control groups. (<b>B</b>) The HI titers in response to H1N1, H3N2, BV, and BY strains were detected on day 28 (d28) post-vaccination in the vaccinated and control groups. (<b>C</b>) The HI titers in response to H1N1, H3N2, BV, and BY strains were detected on day 180 (d180) post-vaccination in the vaccinated and control groups. Data are presented as the mean ± deviation (SD) and analyzed using an unpaired-sample <span class="html-italic">t</span>-test. <span class="html-italic">p</span> &lt; 0.05 was considered significantly different. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; ns, no significance.</p>
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<p>Progression of the hemagglutination inhibition (HI) titers over time in the vaccinated group following the 2023–2024 quadrivalent influenza vaccine (QIV) vaccination. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; ns, no significance.</p>
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<p>The fold change in the hemagglutination inhibition (HI) titers response to the 2023–2024 quadrivalent influenza vaccine (QIV) strains on day 28 post-vaccination in the vaccinated and control groups. (<b>A</b>) Individual fold changes in HI titers in response to H1N1, H3N2, BV, and BY strains on day 28 post-vaccination in the vaccinated group. (<b>B</b>) Individual fold changes in HI titers in response to H1N1, H3N2, BV, and BY strains on day 28 post-vaccination in the control group. (<b>C</b>) Comparison of the fold change in HI titers response to H1N1, H3N2, BV, and BY strains on day 28 between the vaccinated and control groups. Data are presented as the mean ± deviation (SD) and analyzed using a paired-sample <span class="html-italic">t</span>-test. P &lt; 0.05 was considered significantly different. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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13 pages, 1157 KiB  
Article
Impact of Natural Phytosanitary Product Residuals on Yeast Fermentation Performance and Wine Composition
by Natascia Bartolozzi, Francesco Maioli, Monica Picchi, Valentina Civa, Valentina Canuti and Paola Domizio
Foods 2024, 13(21), 3484; https://doi.org/10.3390/foods13213484 - 30 Oct 2024
Viewed by 621
Abstract
Although phytosanitary treatments are necessary to protect grapes from parasitic diseases, consumers are increasingly concerned about the use of synthetic phytosanitary products and their possible residues in wine. Pre-harvest phytosanitary treatments are often inevitable, and consequently downtime is required to avoid possible residues [...] Read more.
Although phytosanitary treatments are necessary to protect grapes from parasitic diseases, consumers are increasingly concerned about the use of synthetic phytosanitary products and their possible residues in wine. Pre-harvest phytosanitary treatments are often inevitable, and consequently downtime is required to avoid possible residues on the grapes. Instead, natural phytosanitary products, such as essential oil (EO)-based products, can be applied close to the harvest without specific restrictions, with results that are not only technically convenient but also more attractive for the consumers. Because of the high antimicrobial activity of EO products, in the present study we evaluated the effect of different residual amounts of two new EO-based phytosanitary products on the alcoholic fermentation and the chemical composition of the final fermented products. In particular, two EO-based new formulations, exploitable in organic viticulture management, were evaluated. Increasing concentrations of each formulation were tested during laboratory scale fermentations and in comparison with synthetic and natural commercial phytosanitary products. Growth and fermentation kinetics of a commercial yeast strain of Saccharomyces cerevisiae and the chemical and sensory profiles of the final products were evaluated. Both new formulations showed no significant impact on the growth and fermentation kinetic of S. cerevisiae at any of the concentrations tested. In all trials, alcoholic fermentation was completed in 15 days. Instead, a different chemical composition of the final products was observed. Therefore, these new products might represent an interesting alternative tool to the conventional phytosanitary treatments, being applicable close to the harvest without negative impacts on the kinetics of alcoholic fermentation and also being more acceptable to wine consumers. Full article
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<p>Growth kinetic of <span class="html-italic">S. cerevisiae</span> in the synthetic grape juice with the phytosanitary products added: P1 and P2 at three increasing doses (A, B, C); P3 and P4 at a single dose. Synthetic grape juice, without product addition, was used as control (CTR). Error bars represent standard deviation of three independent experiments.</p>
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<p>Fermentation kinetics of <span class="html-italic">S. cerevisiae</span> in the synthetic grape juice, with phytosanitary products added: P1 and P2, at three increasing doses (A, B, C); P3 and P4, at a single dose. Synthetic grape juice, without product addition, was used as control (CTR). Error bars represent standard deviations of three independent experiments.</p>
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<p>Discriminant difference test results of treated samples (P1, P2, P3, P4) versus untreated samples (CTR): A—lowest dose; B—intermediate dose; C—highest dose. Black bars: sample is different than the control (CTR); striped bars: sample is the same as the control (CTR).</p>
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13 pages, 870 KiB  
Article
Isolation of Candida Species Is Associated with Comorbidities, Prolonged Mechanical Ventilation, and Treatment Outcomes in Surgical ICU Patients, a Cross-Sectional Study
by Josipa Glavaš Tahtler, Ana Cicvarić, Despoina Koulenti, Marios Karvouniaris, Maja Bogdan, Kristina Kralik, Irena Krajina Kmoniček, Marina Grbić Mlinarević and Slavica Kvolik
J. Fungi 2024, 10(11), 743; https://doi.org/10.3390/jof10110743 - 28 Oct 2024
Viewed by 529
Abstract
The isolation of Candida may be related to comorbidity, prolonged mechanical ventilation, and survival during intensive care unit (ICU) stay, especially with non-albicans Candida (NAC). To examine the frequency of Candida isolation, associated comorbidities and outcomes in the surgical ICU in Osijek University [...] Read more.
The isolation of Candida may be related to comorbidity, prolonged mechanical ventilation, and survival during intensive care unit (ICU) stay, especially with non-albicans Candida (NAC). To examine the frequency of Candida isolation, associated comorbidities and outcomes in the surgical ICU in Osijek University Hospital, Croatia, the data from the electronic database from May 2016 to 30 June 2023 were analyzed. In a cross-sectional study examining 15,790 microbiological samples, different strains of Candida were observed in 581 samples from 236 patients. The control group (N = 261) was 130 consecutive patients from March to May 2019 and 131 in the same months in 2020 (pre- and post-COVID-19). Comorbidities, duration of mechanical ventilation, and survival were compared. Patients with isolated Candida were more often non-elective and had significantly more heart, kidney, and liver diseases and sepsis than the control group (p < 0.001). The duration of mechanical ventilation was 9.2 [2.2–9.24], 96 [24–146], 160 [19.5–343], and 224 [73.5–510] hours in the controls, in patients with Candida albicans, in patients with NAC, and in patients with ≥2 Candida species isolated, respectively. The mortality was significantly higher (42%) in patients with isolated Candida than in the control group (19%, p < 0.001). In a multivariate analysis adjusted for patients’ age, the Simplified Acute Physiology Score II, days of ICU, and type of admission, only sepsis on admission was an independent predictor of mortality (odds ratio = 2.27). Full article
(This article belongs to the Section Fungal Pathogenesis and Disease Control)
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<p>Patient flow in the period between 22 May 2016 and 30 June 2023. ICU—intensive care unit, MB—microbiology.</p>
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<p>Mechanical ventilation in the surgical intensive care unit (ICU) patients according to the species of <span class="html-italic">Candida</span> isolated. Boxplots present medians and interquartile ranges of mechanical ventilation in a control group of ICU patients and in patients with <span class="html-italic">Candida</span> isolation during their ICU stay. Dots indicate outliers.</p>
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22 pages, 8505 KiB  
Article
Analytical and Finite Element Analysis of the Rolling Force for the Three-Roller Cylindrical Bending Process
by Doina Boazu, Ionel Gavrilescu and Felicia Stan
Materials 2024, 17(21), 5230; https://doi.org/10.3390/ma17215230 - 27 Oct 2024
Viewed by 613
Abstract
In the roll bending process, the rolling force acting on the roller shafts is one of the most important parameters since, on the one hand, it determines the process settings including the pre-loading, and, on the other hand, its distribution and size may [...] Read more.
In the roll bending process, the rolling force acting on the roller shafts is one of the most important parameters since, on the one hand, it determines the process settings including the pre-loading, and, on the other hand, its distribution and size may affect the integrity of both the bending system and the final product. In this study, the three-roller bending process was modeled using a two-dimensional plane–strain finite element method, and the rolling force was determined as a function of plate thickness, upper roller diameter, and yield strength for various API steel grades. Based on the numerical simulation results, a critical bending angle of 41° was identified and the rolling systems were divided into two categories, of less than or equal to, and greater than 41°, and an analytical model for predicting the maximum rolling force was developed for each category. To determine the optimal pre-tensioning force, two optimization formulations were proposed by minimizing the maximum equivalent stress and the absolute maximum displacement. The rolling forces predicted by the analytical models were found to be in good agreement with the numerical simulation results, with relative errors generally less than 10%. The predictive analytical models developed in this study capture well the complex deformation behavior that occurs during the roll bending process of steel plates, providing guidelines and predictions for industrial applications of this process. Full article
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<p>Research methodology for the determination of the rolling force.</p>
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<p>The basic principle of three-roller bending process and the main working phases.</p>
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<p>Schematic representation of the three-roller bending process with the vertical displacement of the lower rollers in geometric compatibility.</p>
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<p>Elements of the three-roller bending system and the 2D FE model with applied boundary conditions.</p>
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<p>Finite element model of the three-roller bending system—contact zone between the steel plate and the upper and lower rollers.</p>
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<p>Schematic representation of the rolling system with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>≤</mo> <mn>41</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Rolling system with a bending angle <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>&gt;</mo> <mn>41</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Flow chart for the verification of the upper roller shaft.</p>
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<p>Schematic representation of the three individual loading stages: (<b>a</b>) load from pre-tensioning forces; (<b>b</b>) load from roll bending; (<b>c</b>) load from the weight of the upper roller shaft (the red line indicates the deformed position of the upper roller shaft).</p>
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<p>Von Mises stress (in Pa) at the end of the rolling stages for rolling an X70 plate with 23.6 mm thickness: (<b>a</b>) stage 1; (<b>b</b>) stage 2; (<b>c</b>) stage 3.</p>
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<p>Von Mises stress (in Pa) at the end of the rolling stages for rolling an X70 plate with 20.6 mm thickness: (<b>a</b>) stage 1; (<b>b</b>) stage 2; (<b>c</b>) stage 3.</p>
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<p>Analytical and numerically predicted rolling forces versus plate thickness for rolling X70 steel (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>40.1</mn> <mo>°</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>).</p>
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<p>Rolling force versus plate thickness for bending angle <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>36.54</mn> <mo>°</mo> </mrow> </semantics></math>: (<b>a</b>) Grade B; (<b>b</b>) Grade X42; (<b>c</b>) Grade X52; (<b>d</b>) Grade X60; (<b>e</b>) Grade X65; (<b>f</b>) Grade X70.</p>
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<p>Rolling force versus plate thickness for bending angle <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>36.54</mn> <mo>°</mo> </mrow> </semantics></math>: (<b>a</b>) Grade B; (<b>b</b>) Grade X42; (<b>c</b>) Grade X52; (<b>d</b>) Grade X60; (<b>e</b>) Grade X65; (<b>f</b>) Grade X70.</p>
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<p>Rolling force versus yield strength for rolling a steel plate with 22.2 mm thickness (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>36.54</mn> <mo>°</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math>).</p>
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<p>Comparison of analytical and numerical simulation results of the rolling force for a bending angle of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>53.8</mn> <mo>°</mo> </mrow> </semantics></math>: (<b>a</b>) Grade B; (<b>b</b>) Grade X42; (<b>c</b>) Grade X52; (<b>d</b>) Grade X60; (<b>e</b>) Grade X65; (<b>f</b>) Grade X70.</p>
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<p>Effect of upper roller diameter on the rolling forces for the Grade X70: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>v</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>47.2</mn> <mo>°</mo> </mrow> </semantics></math> and upper roller diameter of 500 mm; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>v</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>44.59</mn> <mo>°</mo> </mrow> </semantics></math> and upper roller diameter of 530 mm; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>v</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>42.2</mn> <mo>°</mo> </mrow> </semantics></math> and upper roller diameter of 560 mm.</p>
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<p>Boundary conditions for evaluating the state of stresses and deformations for the upper roller shaft.</p>
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<p>Distribution of vertical displacement (in m) with a maximum value in the central area of the upper roller shaft.</p>
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<p>Distribution of the equivalent von Mises stress (in Pa) with a maximum value in the central area of the upper roller shaft.</p>
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<p>Variations in the maximum stress and vertical displacement in the central area of the shaft as a function of the pre-tensioning force based on the analytical approach.</p>
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21 pages, 5695 KiB  
Article
Screening and Immune Efficacy Evaluation of Antigens with Protection Against Feline Calicivirus
by Yupeng Yang, Ruibin Qi, Mengru Chen, Kexin Feng, Zhe Liu, Hongtao Kang, Qian Jiang, Liandong Qu and Jiasen Liu
Vaccines 2024, 12(11), 1205; https://doi.org/10.3390/vaccines12111205 - 24 Oct 2024
Viewed by 667
Abstract
Background: Feline calicivirus (FCV), a pathogen that causes upper respiratory tract diseases in felids, primarily leads to oral ulcers and various respiratory symptoms, which can be fatal in severe cases. Currently, FCV prevention and control rely primarily on vaccination; however, the existing vaccine [...] Read more.
Background: Feline calicivirus (FCV), a pathogen that causes upper respiratory tract diseases in felids, primarily leads to oral ulcers and various respiratory symptoms, which can be fatal in severe cases. Currently, FCV prevention and control rely primarily on vaccination; however, the existing vaccine types in China are mainly inactivated vaccines, leading to a single prevention and control method with suboptimal outcomes. Methods and Results: This study commences with a genetic evolution analysis of Chinese FCV isolates, confirming the presence of two major genotypes, GI and GII with GI emerging as the dominant form. We subsequently selected the broadly neutralizing vaccine candidate strain DL39 as the template for the truncation and expression of multiple recombinant proteins. Through serological assays, we successfully confirmed the optimal protective antigen region, which is designated CE39 (CDE). Further investigation revealed the location of the optimal protective antigen region within the CE region for both the GI and GII genotype strains. Capitalizing on this discovery, a bivalent recombinant protein, designated CE39-CEFB, was generated. Cat antisera generated against CE39 and CE39-CEFB proteins were used in cross-neutralization against various strains of different genotypes, yielding high neutralization titers ranging from 1:45 to 1:15 and from 1:48 to 1:29, respectively, which surpassed those induced by antisera from cats vaccinated with Mi-aosanduo (commercial vaccine, strain 255). Ultimately, in vivo challenge experiments were per-formed after immunizing cats with the CE39 and CE39-CEFB proteins, utilizing Miaosanduo as a control for comparison. The results demonstrated that immunization with both proteins effectively made cats less susceptible to FCV GI, GII, and VSD strains infection, resulting in superior immune efficacy compared with that in the Miaosanduo group. Conclusion: These results indicate that this study successfully identified the antigen CE39, which has broad-spectrum antigenicity, through in vivo and in vitro experiments. These findings pre-liminarily demonstrate that the optimal protective antigen region of FCV strains is the CE region, laying a theoretical foundation for the development of novel broad-spectrum vaccines against FCV disease. Full article
(This article belongs to the Section Veterinary Vaccines)
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<p>Schematic diagrams of protein structures. (<b>a</b>) Schematic diagram of VP1 structure. (<b>b</b>) Schematic diagrams of the structures of individual segmented proteins.</p>
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<p>Schematic representation of the immunological attack of the CE<sub>39</sub> and CE<sub>39</sub>-CE<sub>FB</sub> recombinant proteins. The experiment was executed in a two-phase process. In the first phase, the cats were immunized with comprehensive immunization procedures meticulously annotated in the accompanying figure. Subsequently, in the second phase, the immunized cats were subjected to viral challenge, and the detailed challenge procedures were similarly meticulously annotated in the figure.</p>
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<p>The ML phylogenetic tree estimated from the genome nucleotide sequences of FCV stains circulating in China as well as F9 and 2280. Note: Phylogenetic analysis was performed via MEGA-X software (10.1.8), and the model was JTT+G+I with 1000 replicates. Each genotype is shaded by a different color, and the DL39 strain and FB-NJ-13 strain are marked by a red dot and blue dot, respectively.</p>
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<p>Schematic diagram and identification of purified recombinant proteins. (<b>a</b>) SDS–PAGE analysis of purified recombinant proteins. (<b>b</b>) Wb specificity identification results of purified recombinant proteins using commercial His mouse antibody as the primary antibody. (<b>c</b>) Wb specificity identification results of purified recombinant proteins using mouse anti-2280 serum as the primary antibody.</p>
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<p>Results of competitive ELISA assays. (<b>a</b>) Competitive ELISA results of various recombinant proteins from DL39 against the parent strain. (<b>b</b>) Competitive ELISA results of various recombinant proteins from FB-NJ-13 against the parent strain. “ns” denotes no significant difference, and “*” denotes significant difference ( “***”, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Results of competitive neutralization tests. (<b>a</b>) Results of the competitive neutralization test between the feline antiserum of DL39 and various recombinant proteins. (<b>b</b>) Results of the competitive neutralization test between the feline antiserum of FB-NJ-13 and various recombinant proteins.</p>
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<p>Results of competitive neutralization assays for the recombinant proteins CE and E. (<b>a</b>) Competitive neutralization assay result for the CE<sub>39</sub> protein. (<b>b</b>) Competitive neutralization assay result for the E<sub>39</sub> protein. (<b>c</b>) Competitive neutralization assay of the resulting CE<sub>FB</sub> protein. (<b>d</b>) Competitive neutralization assay results for the E<sub>FB</sub> protein.</p>
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<p>Identification of purified recombinant proteins. (<b>a</b>) SDS–PAGE analysis of purified CE<sub>39</sub>-CE<sub>FB</sub> proteins. (<b>b</b>) Wb specificity identification results of purified CE<sub>39</sub>-CE<sub>FB</sub> proteins using commercial His mouse antibody as the primary antibody. (<b>c</b>) Wb specificity identification results of purified CE<sub>39</sub>-CE<sub>FB</sub> proteins using mouse anti-2280 sera as the primary antibody.</p>
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<p>Results of the cross-neutralization test between feline anti-recombinant protein serum and the various strains. “*” denotes significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The results of the animal challenge test with the HRB48 strain. (<b>a</b>) Clinical symptom score of each group (“***” <span class="html-italic">p</span> &lt; 0.001). (<b>b</b>) Body temperature measurement. (<b>c</b>) Body weight measurement. (<b>d</b>–<b>f</b>) Virus load measurements in oral swabs, anal swabs, and blood.</p>
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<p>The results of the animal challenge test with FB-NJ-13 strain. (<b>a</b>) Clinical symptom score of each group (“***” <span class="html-italic">p</span> &lt; 0.001). (<b>b</b>) Body temperature measurement. (<b>c</b>) Body weight measurement. (<b>d</b>–<b>f</b>) Virus load measurements in oral swabs, anal swabs, and blood.</p>
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<p>The results of the animal challenge test with 2280 strain. (<b>a</b>) Clinical symptom score of each group (“***” <span class="html-italic">p</span> &lt; 0.001). (<b>b</b>) Body temperature measurement. (<b>c</b>) Body weight measurement. (<b>d</b>–<b>f</b>) Virus load measurements in oral swabs, anal swabs, and blood.</p>
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<p>Histopathological images of lung tissue section (scale, 200 μM). Note: The name of the virus in the challenge group is indicated in the upper left corner, and the black arrow indicates the site of the tissue lesions.</p>
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11 pages, 513 KiB  
Article
Comparison of Left Ventricular Global Longitudinal Strain and Left Ventricular Ejection Fraction in Acute Respiratory Failure Patients Requiring Invasive Mechanical Ventilation
by Zubair Bashir, Feven Ataklte, Shuyuan Wang, Edward W. Chen, Vishnu Kadiyala, Charles F. Sherrod, Phinnara Has, Christopher Song, Corey E. Ventetuolo, James Simmons and Philip Haines
J. Cardiovasc. Dev. Dis. 2024, 11(11), 339; https://doi.org/10.3390/jcdd11110339 - 24 Oct 2024
Viewed by 484
Abstract
Left ventricular (LV) dysfunction is associated with poor clinical outcomes in acute respiratory failure (ARF). This study evaluates the efficacy of LV strain in detecting LV dysfunction in ARF patients requiring invasive mechanical ventilation (IMV) compared to conventionally measured left ventricular ejection fraction [...] Read more.
Left ventricular (LV) dysfunction is associated with poor clinical outcomes in acute respiratory failure (ARF). This study evaluates the efficacy of LV strain in detecting LV dysfunction in ARF patients requiring invasive mechanical ventilation (IMV) compared to conventionally measured left ventricular ejection fraction (LVEF). ARF patients requiring IMV who had echocardiography performed during MICU admission were included. LV global longitudinal strain (LVGLS) and LVEF were measured retrospectively using speckle tracking (STE) and traditional transthoracic echocardiography (TTE), respectively, by investigators blinded to the status of IMV and clinical data. The cohort was divided into three groups: TTE during IMV (TTE-IMV), before IMV (TTE-bIMV), and after IMV (TTE-aIMV). Multivariable regression models, adjusted for illness severity score, chronic cardiac disease, acute respiratory failure etiology, body mass index, chronic obstructive pulmonary disease, and obstructive sleep apnea, evaluated associations between LV function parameters and the presence of IMV. Among 376 patients, TTE-IMV, TTE-bIMV, and TTE-aIMV groups constituted 223, 68, and 85 patients, respectively. The median age was 65 years (IQR: 56–74), with 53.2% male participants. Adjusted models showed significantly higher LVGLS in groups not on IMV at the time of TTE (TTE-bIMV: β = 4.19, 95% CI 2.31 to 6.08, p < 0.001; TTE-aIMV: β = 3.79, 95% CI 2.03 to 5.55, p < 0.001), while no significant differences in LVEF were observed across groups. In a subgroup analysis of patients with LVEF ≥55%, the significant difference in LVGLS among the groups remained (TTE-bIMV: β = 4.18, 95% CI 2.22 to 6.15, p < 0.001; TTE-aIMV: β = 3.45, 95% CI 1.50 to 5.40, p < 0.001), but was no longer present in those with LVEF < 55%. This suggests an association between IMV and lower LVGLS in ARF patients requiring IMV, indicating that LVGLS may be a more sensitive marker for detecting subclinical LV dysfunction compared to LVEF in this population. Future studies should track and assess serial echocardiography data in the same cohort of patients pre-, during, and post-IMV in order to validate these findings and prognosticate STE-detected LV dysfunction in ARF patients requiring IMV. Full article
(This article belongs to the Special Issue Heart Failure: Clinical Diagnostics and Treatment)
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<p>Flowchart of the analytic sample.</p>
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21 pages, 2485 KiB  
Systematic Review
Echocardiographic Assessment of Left Atrial Mechanics in Patients with Atrial Fibrillation Undergoing Electrical Cardioversion: A Systematic Review
by Andrea Sonaglioni, Gian Luigi Nicolosi, Antonino Bruno, Michele Lombardo and Paola Muti
J. Clin. Med. 2024, 13(21), 6296; https://doi.org/10.3390/jcm13216296 - 22 Oct 2024
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Abstract
Background: To date, only a few studies have evaluated left atrial (LA) mechanics in patients with atrial fibrillation (AF) scheduled for electrical cardioversion (ECV). The present systematic review has been primarily designed to summarize the main findings of these studies and to [...] Read more.
Background: To date, only a few studies have evaluated left atrial (LA) mechanics in patients with atrial fibrillation (AF) scheduled for electrical cardioversion (ECV). The present systematic review has been primarily designed to summarize the main findings of these studies and to examine the overall effect of AF on left atrial reservoir strain (LASr) in patients undergoing ECV. Methods: All the echocardiographic studies evaluating the effect of AF on LA mechanics in patients scheduled for ECV, selected from the PubMed and EMBASE databases, were included. There was no limitation of time period. The risk of bias was assessed by using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: The full texts of 12 studies with 880 AF patients were analyzed. The pooled ECV success rate was 91.5% (range 65.8–100%). Over a median follow-up of 5.4 months (range 0.3–12 months), 35.2% of the patients (range 5–68.8%) experienced AF recurrence. At baseline, the average LASr was 11.4% (range 6.2–17.7%). A reduced LASr before ECV was strongly correlated with reduced left atrial appendage (LAA) flow velocities and/or thrombosis. The main independent predictors of cardioversion failure were impaired LASr and previous AF history. A severe LASr deterioration was independently correlated with AF recurrence after ECV. The other independent predictors of AR relapses were LA asynchrony, reduced difference between post- and pre-ECV LASr, and reduced right atrial reservoir strain. Conclusions: LASr assessment before ECV may provide useful prognostic information about AF relapses and improve the refinement of the thromboembolic risk of AF patients scheduled for ECV. Full article
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<p>Flow diagram used for identifying the included studies. ECV, electrical cardioversion; STE, speckle tracking echocardiography.</p>
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<p>Representative examples of LASr calculation from the apical 4-chamber view in a patient with persistent AF of greater than 3 months’ duration and previous AF history planned for ECV (Panel (<b>A</b>)), and in a healthy individual without AF (Panel (<b>B</b>)). The longitudinal strain curves of the seven atrial segments are depicted with different colors. The dotted line indicates the average atrial longitudinal strain. The yellow line indicates the LASr magnitude. In the present case, the peak systolic LASr obtained in the patient with AF duration &gt; 6 months and previous AF history was severely impaired in comparison to the healthy control without AF and to the accepted reference values. Moreover, the nonhomogeneous distribution of the regional atrial strain curves indicated significant LA asynchrony in AF patients. AF, atrial fibrillation; ECV, electrical cardioversion; LA, left atrial; LASr, left atrial reservoir strain.</p>
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<p>The pathophysiological mechanisms underpinning LASr impairment in persistent AF patients. AF, atrial fibrillation; LA, left atrial; LASr, left atrial reservoir strain.</p>
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<p>Example of modified Haller index assessment in an elderly male with circular transverse thoracic shape section, affected by asymptomatic persistent atrial fibrillation. (Panel (<b>A</b>)): L-L thoracic diameter, measured with the patient in the standing position and with open arms by using a rigid ruler in centimeters coupled to a level (the measuring device) placed at the distal third of the sternum. (Panel (<b>B</b>)): A-P thoracic diameter, obtained from the echocardiographic parasternal long-axis view, as the distance between the true apex of the sector and the posterior wall of the descending thoracic aorta, visualized behind the left atrium. Ao, aorta; A-P, antero-posterior; Asc, ascending; Desc, descending; LA; left atrium; L-L, latero-lateral; LV, left ventricle; RV, right ventricle.</p>
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17 pages, 2645 KiB  
Article
Insights into Genetic and Antigenic Characteristics of Influenza A(H1N1)pdm09 Viruses Circulating in Sicily During the Surveillance Season 2023–2024: The Potential Effect on the Seasonal Vaccine Effectiveness
by Fabio Tramuto, Carmelo Massimo Maida, Giulia Randazzo, Adriana Previti, Giuseppe Sferlazza, Giorgio Graziano, Claudio Costantino, Walter Mazzucco and Francesco Vitale
Viruses 2024, 16(10), 1644; https://doi.org/10.3390/v16101644 - 21 Oct 2024
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Abstract
After disruption in the influenza circulation due to the emergence of SARS-CoV-2, the intensity of seasonal outbreaks has returned to the pre-pandemic levels. This study aimed to evaluate the evolution and variability of whole-genome sequences of A(H1N1)pdm09, the predominant influenza virus in Sicily [...] Read more.
After disruption in the influenza circulation due to the emergence of SARS-CoV-2, the intensity of seasonal outbreaks has returned to the pre-pandemic levels. This study aimed to evaluate the evolution and variability of whole-genome sequences of A(H1N1)pdm09, the predominant influenza virus in Sicily (Italy) during the season 2023–2024. The potential vaccine efficacy was calculated using the pepitope model based on amino acid changes in the dominant epitope of hemagglutinin. The HA gene sequences showed several amino acid substitutions, some of which were within the major antigenic sites. The phylogenetic analysis showed that Sicilian strains grouped into two main genetic clades (6B.1A.5a.2a.1 and 6B.1A.5a.2a) and several subclades. Notably, about 40% of sequences partially drifted from the WHO-recommended vaccine strain A/Victoria/4897/2022 for the Northern Hemisphere. These sequences mostly belonged to the subclades C.1.8 and C.1.9 and harboured the amino acid mutations responsible for the modest predicted vaccine efficacy (E = 38.12% of 53%, pepitope = 0) against these viruses. Amino acid substitutions in other gene segments were also found. Since influenza viruses are constantly evolving, genomic surveillance is crucial in monitoring their molecular evolution and the occurrence of genetic and antigenic changes, and, thus, their potential impact on vaccine efficacy. Full article
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<p>Weekly distribution of influenza A and B laboratory-confirmed cases from October 2023 to June 2024 in Sicily (Italy).</p>
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<p>Neighbour-joining phylogenetic tree of HA nucleotide sequences of influenza A(H1N1)pdm09 strains collected in Sicily between October 2023 and April 2024. Solid red circles indicate the study sequences, while A(H1N1)pdm09-like vaccine strains are shown in blue. The strain A/California/7/2009 (GenBank: MN596845) was used as an outgroup. Clades and subclades are defined according to the Nextstrain classification.</p>
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<p>Neighbour-joining phylogenetic tree of NA nucleotide sequences of influenza A(H1N1)pdm09 strains collected in Sicily between October 2023 and April 2024. Solid red circles indicate the study sequences, while A(H1N1)pdm09-like vaccine strains are shown in blue. The strain A/California/7/2009 (GenBank: MN596847) was used as an outgroup. Clades and subclades are defined according to the Nextstrain classification.</p>
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<p>Lollipop plot of amino acid substitutions in the HA protein of Sicilian A(H1N1)pdm09 circulating during the season 2023/2024, in comparison to the seasonal vaccine strain for the Northern Hemisphere (A/Victoria/4897/2022).</p>
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<p>Lollipop plot of amino acid substitutions in the NA protein of Sicilian A(H1N1)pdm09 circulating during the season 2023/2024, in comparison to the seasonal vaccine strain for the Northern Hemisphere (A/Victoria/4897/2022).</p>
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24 pages, 4102 KiB  
Article
Plastic Constitutive Training Method for Steel Based on a Recurrent Neural Network
by Tianwei Wang, Yongping Yu, Haisong Luo and Zhigang Wang
Buildings 2024, 14(10), 3279; https://doi.org/10.3390/buildings14103279 - 16 Oct 2024
Viewed by 550
Abstract
The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive [...] Read more.
The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively. Full article
(This article belongs to the Special Issue Intelligent Design, Green Construction, and Innovation)
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<p>Traditional constitutive model construction and deep learning constitutive model construction flow.</p>
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<p>Linear hardening constitutive model: (<b>a</b>) linear isotropic hardening constitutive; (<b>b</b>) linear kinematic hardening constitutive.</p>
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<p>Original RNN structure.</p>
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<p>LSTM network structure.</p>
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<p>GRU network structure.</p>
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<p>Comparison of data pre-processing methods.</p>
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<p>The effect of the number of neurons on the model: (<b>a</b>) the effect on the model performance; (<b>b</b>) the effect on the training time.</p>
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<p>Influence of hidden layers on model performance: (<b>a</b>) influence on model performance; (<b>b</b>) influence on training time.</p>
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<p>Influence of neural network topology on model performance and training time: (<b>a</b>) influence on model performance; (<b>b</b>) influence on training time.</p>
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<p>Effects of training frequency and training batches on the model: (<b>a</b>) LSTM; (<b>b</b>) GRU.</p>
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<p>Influence of learning rate on the model: (<b>a</b>) LSTM; (<b>b</b>) GRU.</p>
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<p>Influence of learning rate on the number of iterations.</p>
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<p>Model prediction effects of different dataset sizes: (<b>a</b>–<b>g</b>) prediction curve of LSTM when the dataset size is 500–10,000; (<b>h</b>–<b>n</b>) prediction curve of GRU when the dataset size is 500–10,000.</p>
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<p>Model prediction effects of different dataset sizes: (<b>a</b>–<b>g</b>) prediction curve of LSTM when the dataset size is 500–10,000; (<b>h</b>–<b>n</b>) prediction curve of GRU when the dataset size is 500–10,000.</p>
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<p>Prediction capabilities of LSTM and GRU: (<b>a</b>) LSTM; (<b>b</b>) GRU.</p>
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<p>Prediction effect of the model: (<b>a</b>–<b>c</b>) linear isotropic constitutive hardening, and (<b>d</b>–<b>f</b>) linear kinematic constitutive hardening.</p>
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