[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (35,252)

Search Parameters:
Keywords = NO

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
19 pages, 2026 KiB  
Article
T-Smade: A Two-Stage Smart Detector for Evasive Spectre Attacks under Various Workloads
by Jiajia Jiao, Ran Wen and Yulian Li
Electronics 2024, 13(20), 4090; https://doi.org/10.3390/electronics13204090 (registering DOI) - 17 Oct 2024
Abstract
Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only [...] Read more.
Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only 59.8% for realistic applications, while it is much lower 27.52% for memory stress test. Therefore, this paper proposes a two-stage smart detector T-Smade designed for evasive Spectre attacks (e.g., evasive Spectre nop and evasive Spectre memory) under various workloads. T-Smade uses the first-stage detector to identify the type of workloads and then selects the appropriate second-stage detector, which uses four hardware performance counter events to characterize the high cache miss rate and low branch miss rate of Spectre attacks. More importantly, the second stage detector adds one dimension of reusing cache miss rate and branch miss rate to exploit the characteristics of various workloads to detect evasive Spectre attacks effectively. Furthermore, to achieve the good generalization for more unseen evasive Spectre attacks, the proposed classification detector T-Smade is trained by the raw data of Spectre attacks and non-attacks in different workloads using simple Multi-Layer Perception models. The comprehensive results demonstrate that T-Smade makes the average attack detection successful rate of evasive Spectre nop under different workload return from 27.52% to 95.42%, and that of evasive Spectre memory from 59.8% up to 100%. Full article
Show Figures

Figure 1

Figure 1
<p>Evasive Spectre nop and evasive Spectre memory.</p>
Full article ">Figure 2
<p>Spectre and evasive Spectre attack under different configurations.</p>
Full article ">Figure 3
<p>The framework of evasive Spectre attack detector.</p>
Full article ">Figure 4
<p>The process of selecting the appropriate second-stage detector based on the first-stage result.</p>
Full article ">Figure 5
<p>The effectiveness of T-Smade against evasive Spectre under realistic application.</p>
Full article ">Figure 6
<p>The effectiveness of T-Smade against evasive Spectre under CPU stress test.</p>
Full article ">Figure 7
<p>The effectiveness of T-Smade against evasive Spectre under memory stress test.</p>
Full article ">Figure 8
<p>3D separation plot by proposed detector T-Smade.</p>
Full article ">Figure 9
<p>3D separation plot comparison under varying realistic applications.</p>
Full article ">Figure 10
<p>The accuracy loss between ideal detector and two-stage detector.</p>
Full article ">
31 pages, 6207 KiB  
Article
A Distributed VMD-BiLSTM Model for Taxi Demand Forecasting with GPS Sensor Data
by Hasan A. H. Naji, Qingji Xue and Tianfeng Li
Sensors 2024, 24(20), 6683; https://doi.org/10.3390/s24206683 (registering DOI) - 17 Oct 2024
Abstract
With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance [...] Read more.
With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Figure 1

Figure 1
<p>The structure of the distributed VMD-BiLSTM prediction model.</p>
Full article ">Figure 2
<p>Road map network of Wuhan City.</p>
Full article ">Figure 3
<p>Typical trajectory of taxi trips.</p>
Full article ">Figure 4
<p>Study area of Wuchang district.</p>
Full article ">Figure 5
<p>The distribution of taxi demands on the weekdays and weekends.</p>
Full article ">Figure 6
<p>Taxi demand distribution in the target area during holidays.</p>
Full article ">Figure 7
<p>The distribution of taxi demands in the target area over 24 h.</p>
Full article ">Figure 8
<p>Schematic diagram of the VMD-BiLSTM model.</p>
Full article ">Figure 9
<p>Flowchart of VMD algorithm.</p>
Full article ">Figure 10
<p>The transformation of the taxi demands time series into a two-dimensional array.</p>
Full article ">Figure 11
<p>Architecture of bidirectional LSTM network.</p>
Full article ">Figure 12
<p>Distributed implementation of VMD-BiLSTM model on Spark.</p>
Full article ">Figure 13
<p>Results of our prediction model on Wuhan’s dataset. (<b>a</b>) 1 day; (<b>b</b>) 1 week; (<b>c</b>) 2 weeks; (<b>d</b>) 1 month; (<b>e</b>) 2 months; and (<b>f</b>) whole dataset.</p>
Full article ">Figure 13 Cont.
<p>Results of our prediction model on Wuhan’s dataset. (<b>a</b>) 1 day; (<b>b</b>) 1 week; (<b>c</b>) 2 weeks; (<b>d</b>) 1 month; (<b>e</b>) 2 months; and (<b>f</b>) whole dataset.</p>
Full article ">Figure 14
<p>VMD renderings.</p>
Full article ">Figure 14 Cont.
<p>VMD renderings.</p>
Full article ">Figure 15
<p>Wavelet threshold denoising method for VMD renderings (IMF1, IMF2, and IMF3).</p>
Full article ">Figure 15 Cont.
<p>Wavelet threshold denoising method for VMD renderings (IMF1, IMF2, and IMF3).</p>
Full article ">Figure 16
<p>Box-plot of MOEs for Wuhan dataset.</p>
Full article ">Figure 17
<p>Comparison of loss function of distributed VMD-BiLSM.</p>
Full article ">Figure 18
<p>Running time (seconds) of VMD-BiLSTM based on Spark platform.</p>
Full article ">Figure 19
<p>Scaleup comparative analysis of distributed VMD-BiLSTM for different computing nodes.</p>
Full article ">Figure 20
<p>Speedup comparative analysis of the proposed model for different computing nodes.</p>
Full article ">
10 pages, 1926 KiB  
Communication
Construction of a Miniaturized Detector for Flow Injection Spectrophotometric Analysis
by T. Alexandra Ferreira, Mario Ordaz, Jose A. Rodriguez, M. Elena Paez-Hernandez and Evelin Gutierrez
Chemosensors 2024, 12(10), 216; https://doi.org/10.3390/chemosensors12100216 (registering DOI) - 17 Oct 2024
Abstract
Analytical instrumentation is essential for chemical analysis in many fields, including biology and chemistry, but it can be costly and inaccessible to many educational institutions because it often requires expensive and sophisticated equipment. To address this issue, there has been growing interest in [...] Read more.
Analytical instrumentation is essential for chemical analysis in many fields, including biology and chemistry, but it can be costly and inaccessible to many educational institutions because it often requires expensive and sophisticated equipment. To address this issue, there has been growing interest in developing new and accessible alternatives. In this study, we developed a low-cost and user-friendly spectrophotometric detector based on an Arduino UNO platform. This detector was coupled with a flow injection analysis system (FIA) and used to quantify the concentration of tartrazine in commercial beverages and candy samples. The proposed miniaturized detector offers an affordable and portable alternative to conventional spectrophotometers. We evaluated the performance of our detector by comparing its results with those obtained using high-performance liquid chromatography (HPLC-DAD), and the accuracy and precision were comparable. The results demonstrate the potential of the Arduino-based spectrophotometric detector as a cost-effective and accessible tool, with potential applications in food science, environmental monitoring, and other fields. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The 3D-printed piece designed for the construction of the detector.</p>
Full article ">Figure 2
<p>Connection diagram of the detection system.</p>
Full article ">Figure 3
<p>Representation of the FIA manifold where S is the sample, CS is the carrier solution, PP is the peristaltic pump, V is the 4-way valve, RC is the reacting coil, W is the waste outlet and D is the detector.</p>
Full article ">Figure 4
<p>Chromatic circle for LED identification.</p>
Full article ">Figure 5
<p>Calibration line of tartrazine using the proposed detector.</p>
Full article ">Figure 6
<p>Box plot for reproducibility analysis.</p>
Full article ">
23 pages, 12281 KiB  
Article
Research on the Hydrodynamic Characteristics of a Rectangular Otter Board in Different Work Postures Based on a Dynamic Model
by Wenhua Chu, Minghao Zhai, Senqi Cui, Yu Cao, Xinyang Zhang and Qiaoli Zhou
J. Mar. Sci. Eng. 2024, 12(10), 1856; https://doi.org/10.3390/jmse12101856 (registering DOI) - 17 Oct 2024
Abstract
This paper investigates the hydrodynamic characteristics of a rectangular otter board in different working postures by using a dynamic model. Dynamic models are mainly based on dynamic mesh techniques. The results of the dynamic model are, compared to the model test, carried out [...] Read more.
This paper investigates the hydrodynamic characteristics of a rectangular otter board in different working postures by using a dynamic model. Dynamic models are mainly based on dynamic mesh techniques. The results of the dynamic model are, compared to the model test, carried out in a flume tank. Furthermore, different rotation speeds of dynamic model were analyzed. The research results are as follows: compared to flume tank results, the maximum error of the dynamic model is 23.77%. Moreover, the influence of rotation speed on the hydrodynamic board is not obvious, and 2 deg./s was chosen as the rotation speed. When the board is tilted slightly (including four working postures), its lift-to-drag ratio first increases slightly and then gradually decreases. Compared with the other three working postures, the pressure center coefficient of the board does not change significantly when it is tilted inward. When studying different working angles (including AOA and tilt angle) of the otter board, the numerical dynamic model significantly reduces repetitive setup work, making simulations more efficient. Its ability to provide continuous curves and a large volume of results offers researchers a more detailed and comprehensive understanding of the board’s hydrodynamics. Additionally, the dynamic model supports innovative fishery equipment development by allowing more accurate and continuous numerical simulations. Full article
Show Figures

Figure 1

Figure 1
<p>Diagram of rectangular otter board model (<b>a</b>) Front view (<b>b</b>) Rear view (<b>c</b>) Three-dimensional view.</p>
Full article ">Figure 2
<p>Different working postures of the otter board.</p>
Full article ">Figure 3
<p>Numerical simulation calculation area.</p>
Full article ">Figure 4
<p>Mesh division (AOA = 0°).</p>
Full article ">Figure 5
<p>Rectangular otter board model test design schematic diagram.</p>
Full article ">Figure 6
<p>The process of rectangular otter board model test.</p>
Full article ">Figure 7
<p>Moment and force of board.</p>
Full article ">Figure 8
<p>Comparison of (<b>a</b>) lift coefficient and (<b>b</b>) moment coefficient between numerical dynamic model and model test. (<b>c</b>) Comparison of drag or lift coefficient of numerical dynamic model, numerical static model and model test.</p>
Full article ">Figure 8 Cont.
<p>Comparison of (<b>a</b>) lift coefficient and (<b>b</b>) moment coefficient between numerical dynamic model and model test. (<b>c</b>) Comparison of drag or lift coefficient of numerical dynamic model, numerical static model and model test.</p>
Full article ">Figure 9
<p>The lift coefficient of the board at different speeds.</p>
Full article ">Figure 10
<p>The continuous change in lift coefficient with tilt angle (forward and backward).</p>
Full article ">Figure 11
<p>The continuous change in drag coefficient with tilt angle (forward and backward).</p>
Full article ">Figure 12
<p>The continuous change in lift-to-drag ratio with tilt angles (forward and backward).</p>
Full article ">Figure 13
<p>The continuous change of C<sub>pb</sub> with tilt attitude (forward and backward).</p>
Full article ">Figure 14
<p>The continuous change of C<sub>pc</sub> with tilt attitude (forward and backward).</p>
Full article ">Figure 15
<p>The continuous change in M<sub>y</sub> with tilt attitude (forward and backward).</p>
Full article ">Figure 16
<p>Pressure distribution on the surface of the board (forward).</p>
Full article ">Figure 17
<p>Pressure distribution on the surface of the board (backward).</p>
Full article ">Figure 18
<p>Distribution of flow field around the board in different tilt forward angles.</p>
Full article ">Figure 19
<p>Distribution of flow field around the board in different tilt backward angles.</p>
Full article ">Figure 20
<p>The continuous change in lift coefficient with tilt angles (inward and outward).</p>
Full article ">Figure 21
<p>The continuous change in drag coefficient with tilt angles (inward and outward).</p>
Full article ">Figure 22
<p>The continuous change in the lift-to-drag ratio with tilt angles (inward and outward).</p>
Full article ">Figure 23
<p>The continuous change of C<sub>pb</sub> with tilt angle (inward and outward).</p>
Full article ">Figure 24
<p>The continuous change of C<sub>pc</sub> with tilt angle (inward and outward).</p>
Full article ">Figure 25
<p>Pressure distribution on the surface of the board (inward).</p>
Full article ">Figure 26
<p>Pressure distribution on the surface of the board (outward).</p>
Full article ">Figure 27
<p>Distribution of flow field around the board in different tilt inward angle.</p>
Full article ">Figure 28
<p>Distribution of flow field around the board in different tilt outward angle.</p>
Full article ">
21 pages, 6102 KiB  
Article
Mechanisms of Action of Sea Cucumber Triterpene Glycosides Cucumarioside A0-1 and Djakonovioside A Against Human Triple-Negative Breast Cancer
by Ekaterina S. Menchinskaya, Ekaterina A. Chingizova, Evgeny A. Pislyagin, Ekaterina A. Yurchenko, Anna A. Klimovich, Elena. A. Zelepuga, Dmitry L. Aminin, Sergey A. Avilov and Alexandra S. Silchenko
Mar. Drugs 2024, 22(10), 474; https://doi.org/10.3390/md22100474 (registering DOI) - 17 Oct 2024
Abstract
Breast cancer is the most prevalent form of cancer in women worldwide. Triple-negative breast cancer is the most unfavorable for patients, but it is also the most sensitive to chemotherapy. Triterpene glycosides from sea cucumbers possess a high therapeutic potential as anticancer agents. [...] Read more.
Breast cancer is the most prevalent form of cancer in women worldwide. Triple-negative breast cancer is the most unfavorable for patients, but it is also the most sensitive to chemotherapy. Triterpene glycosides from sea cucumbers possess a high therapeutic potential as anticancer agents. This study aimed to identify the pathways triggered and regulated in MDA-MB-231 cells (triple-negative breast cancer cell line) by the glycosides cucumarioside A0-1 (Cuc A0-1) and djakonovioside A (Dj A), isolated from the sea cucumber Cucumaria djakonovi. Using flow cytometry, fluorescence microscopy, immunoblotting, and ELISA, the effects of micromolar concentrations of the compounds on cell cycle arrest, induction of apoptosis, the level of reactive oxygen species (ROS), mitochondrial membrane potential (Δψm), and expression of anti- and pro-apoptotic proteins were investigated. The glycosides caused cell cycle arrest, stimulated an increase in ROS production, and decreased Δψm in MDA-MB-231 cells. The depolarization of the mitochondrial membrane caused by cucumarioside A0-1 and djakonovioside A led to an increase in the levels of APAF-1 and cytochrome C. This, in turn, resulted in the activation of caspase-9 and caspase-3 and an increase in the level of their cleaved forms. Glycosides also affected the expression of Bax and Bcl-2 proteins, which are associated with mitochondria-mediated apoptosis in MDA-MB-231 cells. These results indicate that cucumarioside A0-1 and djakonovioside A activate the intrinsic apoptotic pathway in triple-negative breast cancer cells. Additionally, it was found that treatment with Cuc A0-1 resulted in in vivo inhibition of tumor growth and metastasis of murine solid Ehrlich adenocarcinoma. Full article
(This article belongs to the Collection Marine Compounds and Cancer)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Chemical structures of triterpene glycosides: cucumarioside A<sub>0</sub>-1 (<b>a</b>) and djakonovioside A (<b>b</b>) isolated from the sea cucumber <span class="html-italic">Cucumaria djakonovi</span>.</p>
Full article ">Figure 2
<p>Distribution of MDA-MB-231 cells according to the phases of the cell cycle after treatment with various concentrations of Cuc A<sub>0</sub>-1 and Dj A for 24 h.</p>
Full article ">Figure 3
<p>Visualization of cyclin B and A contents and cyclin-dependent kinases in MDA-MB-231 cells treated with triterpene glycosides Cuc A<sub>0</sub>-1 and Dj A at different concentrations. Representative Western blot membranes showing the effect of glycosides on cyclin and CDK protein expression levels (<b>a</b>). Processed data on cyclin B content in MDA-MB-231 cells treated with Cuc A<sub>0</sub>-1 (<b>b</b>). Processed data on cyclin A content in MDA-MB-231 cells treated with Dj A (<b>c</b>). Processed data on CDK-1 content in MDA-MB-231 cells treated with Cuc A<sub>0</sub>-1 (<b>d</b>). Processed data on CDK-2 content in MDA-MB-231 cells treated with Dj A (<b>e</b>). All data were normalized to the β-actin levels. Data are presented as means ± SEM. * <span class="html-italic">p</span> value &lt; 0.05 was considered significant.</p>
Full article ">Figure 4
<p>Analysis of apoptosis induced by triterpene glycosides in MDA-MB-231 cells after 24 h of incubation. Flow cytometry assay for Annexin V-FITC/PI staining (<b>a</b>). Quantitative calculation of the data obtained via flow cytometry: Cuc A<sub>0</sub>-1 (0.5 and 1 μM)—(<b>b</b>) and Dj A (1 and 2 μM)—(<b>c</b>). Data are presented as means ± SEM. * <span class="html-italic">p</span> value &lt; 0.05 was considered significant. Apoptosis assay using Hoechst 33342 in a fluorescent microscopy analysis (<b>d</b>). Hoechst 33342 staining showed an increase in chromatin condensation and DNA fragmentation in apoptotic cells treated with Cuc A<sub>0</sub>-1 (1 μM) and Dj A (2 μM) compared with untreated control cells. Arrows indicate nuclei with condensed chromatin.</p>
Full article ">Figure 5
<p>Quantitative evaluation of ROS levels in MDA-MB-231 cells after incubation with Cuc A<sub>0</sub>-1 (<b>a</b>) and Dj A (<b>b</b>) for different times (6, 12, and 24 h) using the fluorescent dye H<sub>2</sub>DCF-DA. Glycosides Cuc A<sub>0</sub>-1 (<b>c</b>) and Dj A (<b>d</b>), at various concentrations, reduced the mitochondrial membrane potential (Δψm), as measured using the fluorescent dye TMRE. Data are presented as means ± SEM. * <span class="html-italic">p</span> value &lt; 0.05 was considered significant. Staining of MDA-MB-231 cells with the fluorescent dye JC-1 showed a change in the mitochondrial membrane potential (<b>e</b>).</p>
Full article ">Figure 6
<p>Western blot analysis of cytoplasmic proteins: apoptosis promoter Bax (<b>a</b>,<b>b</b>) and apoptosis inhibitor Bcl-2 (<b>a</b>,<b>c</b>) with β-actin as a protein loading control under the treatment of MDA-MB-231 cells with different concentrations of Cuc A<sub>0</sub>-1 and Dj A. Cytoplasmic protein levels were normalized to the control group (untreated cells). * <span class="html-italic">p</span> &lt; 0.05 compared with untreated MDA-MB-231 cells.</p>
Full article ">Figure 7
<p>Quantitative assessment of the contents of cytochrome C (<b>a</b>,<b>b</b>) and APAF-1 (<b>c</b>,<b>d</b>) in MDA-MB-231 cells after treatment with different concentrations of glycosides Cuc A<sub>0</sub>-1 (<b>a</b>,<b>c</b>) and Dj A (<b>b</b>,<b>d</b>) at different times (6, 12, and 24 h) using ELISA kits. * <span class="html-italic">p</span> &lt; 0.05 compared with untreated MDA-MB-231 cells.</p>
Full article ">Figure 8
<p>Caspase-3/7 activity in the control cells and cells treated with triterpene glycosides for 12 and 24 h was measured using the Muse™ Caspase-3/7 Kit and flow cytometry in MDA-MB-231 cells.</p>
Full article ">Figure 9
<p>Western blot analysis of apoptotic markers (<b>a</b>) and quantitative analysis of the levels of cleaved caspase-9 (<b>b</b>), cleaved caspase-3 (<b>c</b>), and cleaved PARP-1 (<b>d</b>) in MDA-MB-231 cells treated with different concentrations of Cuc A<sub>0</sub>-1 and Dj A. β-actin was used as a protein loading control (<b>a</b>). The levels of apoptotic markers were normalized to those of the control group (untreated cells). * <span class="html-italic">p</span> &lt; 0.05 compared to untreated MDA-MB-231 cells.</p>
Full article ">Figure 10
<p>Influence of Cuc A<sub>0</sub>-1 on the area (<b>a</b>) and integrated density (<b>b</b>) of the fluorescence zone detected by in vivo fluorescence imager, Fluor I. Effect of Cuc A<sub>0</sub>-1 (0.4 µg/mL) on tumor volume (<b>c</b>) and tumor growth index (<b>d</b>). The data are presented as a mean ± SEM (n = 7). Asterisks indicate the significance of the differences at <span class="html-italic">p</span> ≤ 0.05 * and <span class="html-italic">p</span> ≤ 0.01 ** according to one-factor analysis of variance (ANOVA) with Tukey’s correction.</p>
Full article ">Figure 11
<p>The visualization of tumor cells labeled with PKH800 NIR fluorescent dye using the fluorescence imager system, “Fluor I IN VIVO”, in untreated mice (<b>a</b>), mice treated with Cuc A<sub>0</sub>-1 in group II (<b>b</b>), mice treated with Cuc A<sub>0</sub>-1 in group III (<b>c</b>), and mice treated with doxorubicin in group IV (<b>d</b>). On day 12, the tumor area was visualized in live mice; afterward, the mice were euthanized, the skin was opened, and tumor cells were visualized again. Arrows indicate tumor metastasis in the abdominal cavity.</p>
Full article ">Figure 12
<p>Three-dimensional plot of cytotoxic activity (pIC<sub>50</sub>) dependence on the principal component values (PCA1–PCA3) calculated for 25 conformational forms of 20 glycosides tested against MDA-MB-231 cells. The glycosides demonstrating cytotoxic activity with IC<sub>50</sub> ≤ 10 μM were outlined as active and are marked in red, while inactive glycosides are marked in violet.</p>
Full article ">Figure 13
<p>The PLS QSAR model correlation plot reflecting the relationship of predicted and experimental cytotoxicity of the glycosides against MDA-MB-231 cells. The cytotoxic action was expressed as pIC<sub>50</sub>.</p>
Full article ">
26 pages, 8947 KiB  
Article
Angle of Attack Characteristics of Full-Active and Semi-Active Flapping Foil Propulsors
by Lei Mei, Wenhui Yan, Junwei Zhou, Yongqi Tang and Weichao Shi
Water 2024, 16(20), 2957; https://doi.org/10.3390/w16202957 (registering DOI) - 17 Oct 2024
Abstract
As a propulsor with a good application prospect, the flapping foil has been a hot research topic in the past decade. Although the research results of flapping foils have been very abundant, the performance-influencing mechanism of flapping foils is still not perfect, and [...] Read more.
As a propulsor with a good application prospect, the flapping foil has been a hot research topic in the past decade. Although the research results of flapping foils have been very abundant, the performance-influencing mechanism of flapping foils is still not perfect, and the research considering three-dimensional (3D) effects for engineering applications is still very limited. Based on the above considerations, a systematic and parametric analysis of a small aspect ratio flapping foil is conducted to correlate the influencing factors including angle of attack (AoA) characteristics and wake vortex on the propulsive efficiency. Three-dimensional numerical analyses of full-active and semi-active flapping foils are carried out in this paper, in which the former focuses on different heave amplitudes and pitch amplitudes, and the latter concentrates on different spring stiffnesses. The analysis covers the full range of advance coefficient, which starts around 0 and ends at a thrust drop of 0. Firstly, the influence of the maximum AoA (αmax) on the efficiency and thrust coefficient of these two kinds of flapping foils is analyzed. The results show that for the small aspect ratio flapping foil in this paper, regardless of the full-active or semi-active form, the peak efficiency as high as 75% for both generally appears around αmax = 0.2 rad, while the peak thrust coefficient of 0.5 occurs near αmax = 0.3 rad. Then, by analyzing the wake flow field, it is found that the lower efficiency of larger αmax working points is mainly due to the larger vortex dissipation loss, while the lower efficiency of smaller αmax working points is mainly due to the larger friction loss of the foil surface. Furthermore, the plumpness of different AoA curves is compared and analyzed. It was found that, unlike the results of full-active flapping foils, the shape of the AoA curve of semi-active flapping foils with different spring stiffnesses is similar, and the relationship with efficiency is not strictly corresponding. This study is expected to provide guidance on both academics and industries in relevant fields. Full article
(This article belongs to the Special Issue CFD in Fluid Machinery Design and Optimization)
Show Figures

Figure 1

Figure 1
<p>Three-dimensional geometric shape of the flapping foil.</p>
Full article ">Figure 2
<p>Sketch of the flapping foil propulsion motion.</p>
Full article ">Figure 3
<p>Schematic illustration of a semi-active flapping foil with forced heave motion and attached torsion spring (The blue part is the torsion spring, and the red part is the rigid connection with the actuator).</p>
Full article ">Figure 4
<p>Schematic diagram of the computational domain and gradual mesh refinement.</p>
Full article ">Figure 5
<p>Comparisons of the propulsive efficiency <span class="html-italic">η</span> and the thrust coefficient <span class="html-italic">c<sub>T</sub></span> with previous experimental results for <span class="html-italic">α<sub>max</sub></span> = 20°.</p>
Full article ">Figure 6
<p>Comparison of vorticity patterns visualized in the foil wake (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> <mi>t</mi> </mrow> <mrow> <mi>D</mi> </mrow> </msub> <mo>=</mo> <mn>0.08</mn> <mo>,</mo> <mtext> </mtext> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>D</mi> </mrow> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>). (Experimental results are from Figure 3c in Schnipper [<a href="#B29-water-16-02957" class="html-bibr">29</a>]).</p>
Full article ">Figure 7
<p>Experimental site and related equipment.</p>
Full article ">Figure 8
<p>Comparisons of the propulsive efficiency <span class="html-italic">η</span> with experimental results for <span class="html-italic">α<sub>max</sub></span> = 20°.</p>
Full article ">Figure 9
<p>Comparison of hydrodynamic force between simulation and experimental results. (<b>a</b>) <span class="html-italic">J</span> = 2.45, (<b>b</b>) <span class="html-italic">J</span> = 5.24.</p>
Full article ">Figure 10
<p>Propulsive efficiency <span class="html-italic">η</span> and thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for different pitching angles. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Propulsive efficiency <span class="html-italic">η</span> and thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of advance coefficient <span class="html-italic">J</span>, for different pitching angles. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Propulsive efficiency <span class="html-italic">η</span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for a series of heaving amplitudes. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
Full article ">Figure 13
<p>Thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for a series of heaving amplitudes. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
Full article ">Figure 14
<p>Propulsive efficiency <span class="html-italic">η</span> of a semi-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mtext> </mtext> </mrow> </semantics></math>and advance coefficient <span class="html-italic">J</span> for a series of spring stiffness ratios. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
Full article ">Figure 15
<p>Thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a semi-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math><span class="html-italic">,</span> for a series of heaving amplitudes. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 16
<p>Vortex structure and distribution of an active flapping foil under six working conditions (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>/</mo> <mi>c</mi> <mo>=</mo> <mn>2.5</mn> <mo>,</mo> <mtext> </mtext> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>).</p>
Full article ">Figure 17
<p>Velocity distributions and tip vortex structure of flow field at different pitching angles.</p>
Full article ">Figure 18
<p>Sketch of forces on a flapping foil.</p>
Full article ">Figure 19
<p>Velocity cloud diagrams and tip vortex structures of flow fields at different heave amplitudes.</p>
Full article ">Figure 20
<p>Flow field vortex structure of a semi-active flapping foil with different spring stiffnesses.</p>
Full article ">Figure 21
<p>Comparison of AoA time-history curves at different pitch amplitudes.</p>
Full article ">Figure 22
<p>AoA duration curves of a semi-active flapping foil with different spring stiffnesses. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.18</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 23
<p>Pitch motion and AoA time-history curves of a semi-active flapping foil (<math display="inline"><semantics> <mrow> <msup> <mi>K</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.18</mn> <mo>,</mo> <mtext> </mtext> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>).</p>
Full article ">
10 pages, 255 KiB  
Article
Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei
by Tran Thi Mai Huong, Nguyen Huu Hung, Vu Dinh Ty, Dinh Cong Tri and Nguyen Hong Nguyen
J. Mar. Sci. Eng. 2024, 12(10), 1855; https://doi.org/10.3390/jmse12101855 (registering DOI) - 17 Oct 2024
Abstract
The genotype-by-environment interaction (G × E) might have crucial impacts on the performance and fitness of agricultural species, such as Pacific whiteleg shrimp (Litopenaeus vannamei). This study explores how enhancements in management practices can counteract G × E effects on growth [...] Read more.
The genotype-by-environment interaction (G × E) might have crucial impacts on the performance and fitness of agricultural species, such as Pacific whiteleg shrimp (Litopenaeus vannamei). This study explores how enhancements in management practices can counteract G × E effects on growth traits. We analyzed a selectively bred population of whiteleg shrimp spanning the latest two generations, encompassing 259 full-sib and half-sib families with 40,862 individual shrimp, measured for body weight and total length. Our analysis revealed moderate genetic correlations (0.60–0.65) between trait expressions in pond and tank environments, a significant improvement compared to earlier generations. Employing the average information-restricted maximum likelihood (REML) approach in mixed model analysis showed significant differences in heritability (h2) estimates between the two environments; however, the extent of these differences varied by trait (h2 = 0.68 in pond vs. 0.37 in tank for weight, and 0.41 vs. 0.67 for length). Our results indicate that G × E effects on growth traits in this population of L. vannamei were moderate but biologically significant. Consistent with our previous estimates in this population, genetic correlations between body weight and total length remained high (close to one) in pond and tank environments. The present findings collectively demonstrate that management improvements targeting stocking density, aeration, water quality, feeds, and feeding regimes mitigated the G × E effects on two economically significant traits in this population of whiteleg shrimp. Full article
(This article belongs to the Section Marine Biology)
7 pages, 500 KiB  
Brief Report
Efficacy of Sustained-Release Formulation of Moxidectin (Guardian SR) in Preventing Heartworm Infection over 18 Months in Dogs Living in a Hyperendemic Area
by Agustina Isabel Quintana-Mayor, Elena Carretón and José Alberto Montoya-Alonso
Animals 2024, 14(20), 3001; https://doi.org/10.3390/ani14203001 (registering DOI) - 17 Oct 2024
Abstract
This study investigates the efficacy of a sustained-release (SR) moxidectin microsphere formulation in preventing canine heartworm infection over 18 months in Canary Hound dogs, a hunting breed common in the Canary Islands, which is a hyperendemic region. These dogs typically do not receive [...] Read more.
This study investigates the efficacy of a sustained-release (SR) moxidectin microsphere formulation in preventing canine heartworm infection over 18 months in Canary Hound dogs, a hunting breed common in the Canary Islands, which is a hyperendemic region. These dogs typically do not receive preventive treatments and act as reservoirs for the disease. This field study was conducted across 11 hunting kennels with 109 dogs living outdoors, none of whom were receiving heartworm prophylaxis, with Dirofilaria immitis prevalence ranging from 11.1% to 57.1% (average 36.7%). Among these, 20 clinically healthy, heartworm-negative dogs were randomly selected to receive a single subcutaneous injection of moxidectin SR (0.17 mg/kg body weight). Antigen and Knott’s tests were performed at 6, 12, 18, and 24 months. All dogs completed the study without adverse reactions and remained heartworm-negative throughout. By the end of the study, kennel heartworm prevalence ranged from 14.3% to 46.7% (average 35.4%). A single subcutaneous dose of moxidectin SR at the recommended dosage may prevent patent heartworm infection in dogs for up to 18 months in hyperendemic regions. Further studies are required to confirm these findings. Extending the efficacy period of moxidectin could improve owner compliance, particularly among those with lower animal health awareness. Full article
(This article belongs to the Special Issue Vector-Borne and Zoonotic Diseases in Dogs and Cats)
Show Figures

Figure 1

Figure 1
<p>Map of Gran Canaria with the geographical distribution of the sampled kennels. Kennels are marked as blue triangles. Legend: BWh (hot desert climate), BSh (hot steppe), BSk (cold steppe), Csa (temperate with hot and dry summers), Csb (temperate with dry and warm summers). Map of Gran Canaria with the Köppen–Geiger climate classification extracted and modified from the Climate Atlas of the Archipelagos of the Canary Islands, Madeira and the Azores, with permission [<a href="#B18-animals-14-03001" class="html-bibr">18</a>].</p>
Full article ">
23 pages, 37649 KiB  
Article
Research on Forage–Livestock Balance in the Three-River-Source Region Based on Improved CASA Model
by Chenlu Hu, Yichen Tian, Kai Yin, Huiping Huang, Liping Li and Qiang Chen
Remote Sens. 2024, 16(20), 3857; https://doi.org/10.3390/rs16203857 - 17 Oct 2024
Abstract
As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past [...] Read more.
As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance in the Three-River-Source Region (TRSR) directly impacts both the degradation and recovery of grassland. This study examines the forage–livestock balance in the TRSR over the past 13 years (2010–2022) by calculating both the theoretical and actual livestock carrying capacity, thereby providing a scientific basis for regional animal husbandry policies. Firstly, the Carnegie–Ames–Stanford Approach (CASA) model was improved to fit the specific characteristics of alpine grassland ecosystem in the TRSR. This enhanced model was subsequently used to calculate the net primary productivity (NPP) of the grassland, from which the regional grassland yield and theoretical livestock carrying capacity were derived. Secondly, the actual livestock carrying capacity was calculated and spatialized based on the number of regional year-end livestock. Finally, the livestock carrying pressure index was determined using both the theoretical and actual livestock carrying capacity. The results revealed several key findings: (1) The average grassland NPP in the TRSR was 145.44 gC/m2, the average grassland yield was 922.7 kg/hm2, and the average theoretical livestock carrying capacity was 0.55 SU/hm2 from 2010 to 2022. Notably, all three metrics showed an increasing trend over the past 13 years, which indicates the rise in grassland vegetation activities. (2) The average actual livestock carrying capacity over the 13-year period was 0.46 SU/hm2, showing a decreasing trend on the whole. The spatial distribution displayed a pattern of higher capacity in the east and lower in the west. (3) Throughout the 13 years, the TRSR generally maintained a forage–livestock balance, with an average livestock carrying pressure index of 0.96 (insufficient). However, the trend of livestock carrying pressure is on the rise, with serious overloading observed in the western part of Qumalai County and the northern part of Tongde County. Slight overloading was also noted in Zhiduo, Maduo, and Zeku Counties. Notably, Tanggulashan Town, Zhiduo, Qumalai, and Maduo Counties showed significant increases in livestock carrying pressure, while Zaduo County and the eastern regions experienced significant decreases. In conclusion, this study not only provides feasible technical methods for assessing and managing the forage–livestock balance in the TRSR but also contributes significantly to the sustainable development of the region’s grassland ecosystem and animal husbandry industry. Full article
(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)
Show Figures

Figure 1

Figure 1
<p>The Three–River–Source Region: (<b>a</b>) digital elevation model and location; (<b>b</b>) grassland vegetation types.</p>
Full article ">Figure 2
<p>The spatiotemporal patterns of grassland NPP in the TRSR from 2010 to 2022: (<b>a</b>) mean annual NPP: the inset chart shows the interannual dynamics of NPP from 2010 to 2022, where the red dashed line shows the overall trend of NPP; (<b>b</b>) change trend of NPP: the inset chart shows the area proportion of each; (<b>c</b>) radial accumulation bar chart of NPP in different grassland vegetation types from 2010 to 2022.</p>
Full article ">Figure 2 Cont.
<p>The spatiotemporal patterns of grassland NPP in the TRSR from 2010 to 2022: (<b>a</b>) mean annual NPP: the inset chart shows the interannual dynamics of NPP from 2010 to 2022, where the red dashed line shows the overall trend of NPP; (<b>b</b>) change trend of NPP: the inset chart shows the area proportion of each; (<b>c</b>) radial accumulation bar chart of NPP in different grassland vegetation types from 2010 to 2022.</p>
Full article ">Figure 3
<p>The spatiotemporal patterns of grassland yield and theoretical livestock carrying capacity in the TRSR from 2010 to 2022: (<b>a</b>) mean annual grassland yield: the inset chart shows the interannual dynamics of grassland yield from 2010 to 2022, where the red dashed line shows the overall trend of grassland yield; (<b>b</b>) change trend of grassland yield: the inset chart shows the area proportion of each; (<b>c</b>) mean annual theoretical livestock carrying capacity: the inset chart shows the interannual dynamics of theoretical livestock carrying capacity from 2010 to 2022, where the red dashed line shows the overall trend of theoretical livestock carrying capacity; (<b>d</b>) change trend of theoretical livestock carrying capacity: the inset chart shows the area proportion of each.</p>
Full article ">Figure 4
<p>Validations of simulated NPP and grassland yield by improved CASA model: (<b>a</b>) the correlation of simulated NPP by improved CASA model and MOD17A3 NPP, where the solid black line represents the fitting curve of simulated NPP and MOD17A3 NPP; (<b>b</b>) the correlation of simulated grassland yield by improved CASA model and observed grassland yield, where the solid black line represents the fitting curve of simulated grassland yield and observed grassland yield.</p>
Full article ">Figure 5
<p>The spatiotemporal patterns of actual livestock carrying capacity in the TRSR from 2010 to 2022: (<b>a</b>) mean annual actual livestock carrying capacity: the inset chart shows the interannual dynamics of actual livestock carrying capacity from 2010 to 2022, where the red dashed line shows the overall trend of actual livestock carrying capacity; (<b>b</b>) change trend of actual livestock carrying capacity: the inset chart shows the area proportion of each.</p>
Full article ">Figure 6
<p>The spatiotemporal patterns of the livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) mean annual livestock carrying pressure: the inset chart shows the interannual dynamics of livestock carrying pressure from 2010 to 2022, where the red dashed line shows the overall trend of livestock carrying pressure; (<b>b</b>) change trend of livestock carrying pressure: the inset chart shows the area proportion of each.</p>
Full article ">Figure 6 Cont.
<p>The spatiotemporal patterns of the livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) mean annual livestock carrying pressure: the inset chart shows the interannual dynamics of livestock carrying pressure from 2010 to 2022, where the red dashed line shows the overall trend of livestock carrying pressure; (<b>b</b>) change trend of livestock carrying pressure: the inset chart shows the area proportion of each.</p>
Full article ">Figure 7
<p>Simulated NPP by improved CASA model and unimproved CASA model compared to MOD17A3 NPP product: (<b>a</b>) scatter plot with MOD17A3 NPP; (<b>b</b>) change curve of NPP from 2010 to 2022; (<b>c</b>) histogram of NPP in different grassland vegetation types; (<b>d</b>) radar map of NPP with different elevation grades.</p>
Full article ">Figure 8
<p>Livestock carrying pressure in the TRSR from 2010 to 2022: (<b>a</b>) heat map of livestock carrying pressure in each county from 2010 to 2022; (<b>b</b>) changes and average values of livestock carrying pressure in each county for 13 years.</p>
Full article ">Figure 9
<p>The spatial distributions of grazing condition and Three–River–Source Nature Reserve (<a href="https://sthjt.qinghai.gov.cn" target="_blank">https://sthjt.qinghai.gov.cn</a>, accessed on 14 July 2024) from 2010 to 2022: (<b>a</b>) mean annual actual livestock carrying capacity; (<b>b</b>) mean annual livestock carrying pressure; (<b>c</b>) mean annual NDVI.</p>
Full article ">
25 pages, 34340 KiB  
Article
Establishment and Verification of a Novel Gene Signature Connecting Hypoxia and Lactylation for Predicting Prognosis and Immunotherapy of Pancreatic Ductal Adenocarcinoma Patients by Integrating Multi-Machine Learning and Single-Cell Analysis
by Ying Zheng, Yang Yang, Qunli Xiong, Yifei Ma and Qing Zhu
Int. J. Mol. Sci. 2024, 25(20), 11143; https://doi.org/10.3390/ijms252011143 - 17 Oct 2024
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has earned a notorious reputation as one of the most formidable and deadliest malignant tumors. Within the tumor microenvironment, cancer cells have acquired the capability to maintain incessant expansion and increased proliferation in response to hypoxia via metabolic reconfiguration, [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC) has earned a notorious reputation as one of the most formidable and deadliest malignant tumors. Within the tumor microenvironment, cancer cells have acquired the capability to maintain incessant expansion and increased proliferation in response to hypoxia via metabolic reconfiguration, leading to elevated levels of lactate within the tumor surroundings. However, there have been limited studies specifically investigating the association between hypoxia and lactic acid metabolism-related lactylation in PDAC. In this study, multiple machine learning approaches, including LASSO regression analysis, XGBoost, and Random Forest, were employed to identify hub genes and construct a prognostic risk signature. The implementation of the CERES score and single-cell analysis was used to discern a prospective therapeutic target for the management of PDAC. CCK8 assay, colony formation assays, transwell, and wound-healing assays were used to explore both the proliferation and migration of PDAC cells affected by CENPA. In conclusion, we discovered two distinct subtypes characterized by their unique hypoxia and lactylation profiles and developed a risk score to evaluate prognosis, as well as response to immunotherapy and chemotherapy, in PDAC patients. Furthermore, we indicated that CENPA may serve as a promising therapeutic target for PDAC. Full article
(This article belongs to the Section Molecular Immunology)
Show Figures

Figure 1

Figure 1
<p>Identification of prognostic hypoxia- and lactylation-related genes (HALRGs) and mutation landscape. (<b>A</b>) Intersection of differentially expressed genes (DEGs) in PDAC samples with hypoxia- and lactylation-related genes. (<b>B</b>) Univariate Cox analysis of these genes. (<b>C</b>) Biological network integration of these prognostic genes analyzed by GeneMANIA. (<b>D</b>) Kaplan–Meier survival curve of certain prognostic genes.</p>
Full article ">Figure 2
<p>Pan-cancer analysis of the prognostic hypoxia- and lactylation-related genes. (<b>A</b>) Survival differences between high and low GSVA score groups across various cancers. (<b>B</b>) Association between GSVA scores and cancer-related pathway activity (*: <span class="html-italic">p</span>-value ≤ 0.05; #: FDR ≤ 0.05). (<b>C</b>,<b>D</b>) Summary of the relationship between gene expression and responsiveness of top 30 GDSC and CTRP drugs in the pan-cancer analysis.</p>
Full article ">Figure 3
<p>Unsupervised clustering analysis identified two PDAC subtypes with distinctive biological functional characteristics in the TCGA and GSE183795 cohorts. (<b>A</b>) Consensus matrix heatmap defining two subtypes (k = 2). (<b>B</b>) PCA indicating the significant differences in transcriptomes between the subtypes. (<b>C</b>) Survival analysis indicates cluster A has a poor prognosis compared to cluster B. (<b>D</b>) Using PROGENy (Pathway RespOnsive GENes for activity inference) to assess the pathway activation in the above two subtypes (ns <span class="html-italic">p</span> &gt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001). (<b>E</b>) KEGG enrichment analysis of the two subtypes. (<b>F</b>) GO enrichment analysis of the two subtypes.</p>
Full article ">Figure 4
<p>Identification of hub genes using various machine learning algorithms, and construction of a hypoxia- and lactylation-related prognostic signature for PDAC. (<b>A</b>,<b>B</b>) LASSO Cox regression was used to identify signature genes and develop a prognostic module for PDAC patients. (<b>C</b>) Bar graph of the coefficient index of the hub genes. (<b>D</b>) Heatmap of hub gene expression in the low- and high-risk groups. (<b>E</b>,<b>F</b>) Risk score distribution and survival status in the two risk groups. (<b>G</b>) Kaplan–Meier survival curve showing overall survival (OS) in the two risk groups. (<b>H</b>) ROC curves predicting the sensitivity and specificity of the risk score model for the 1-, 3-, and 5-year survival rates. (<b>I</b>) Time-dependent ROC analysis indicating the predictive power of the risk signature and other clinical characteristics. (<b>J</b>,<b>K</b>) Mutation landscape of the low- and high-risk groups.</p>
Full article ">Figure 5
<p>Validation of the prognostic module in independent external datasets (GSE62452, GSE78299, and GSE85916) and nomogram construction. (<b>A</b>–<b>C</b>) Kaplan–Meier analysis validating the predictive power of the prognostic model in the GSE62452, GSE78299, and GSE85916 datasets. (<b>D</b>–<b>F</b>) ROC curves demonstrating the sensitivity and specificity of the risk score model for the 1-, 3-, and 5-year survival rates in these test cohorts. (<b>G</b>,<b>H</b>) Nomogram construction integrating the risk score and clinical characteristics (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01). (<b>I</b>,<b>J</b>) Forest plots of the univariate and multivariate Cox regression analyses show that the risk score is an independent prognostic factor for PDAC in the training cohort.</p>
Full article ">Figure 6
<p>The immunogenomic landscape of signature genes and their predictive values for immunotherapy and chemotherapy. (<b>A</b>) Correlation between risk scores and immune cell abundance analyzed using various immune cell profiling methods. (<b>B</b>) Evaluation of the potential efficacy of immunotherapy in low- and high-risk groups, showing a less favorable response in the high-risk group.(*** <span class="html-italic">p</span> &lt; 0.001) (<b>C</b>) Correlation analysis between signature genes and genes associated with immune evasion. (<b>D</b>) Analysis of chemotherapeutic sensitivity between the low- and high-risk groups (*** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 7
<p>CERES score of signature genes and HAL score analysis at the single-cell level. (<b>A</b>) CERES score of signature genes. (<b>B</b>) UMAP-1 plot showing cell subtypes identified from scRNA-seq data. (<b>C</b>) Distribution of <span class="html-italic">CENPA</span> in metastasis, normal, and primary PADC scRNA samples. (<b>D</b>) Heatmap displaying variations in interaction numbers. (<b>E</b>) Bar graph showing key signaling pathways differing between the high- and low-scoring groups. (<b>F</b>) Circular plot visualizing differences in cell–cell communication networks between the high- and low-scoring groups.</p>
Full article ">Figure 8
<p>The expression profile of <span class="html-italic">CENPA</span> in PDAC; the knockdown of <span class="html-italic">CENPA</span> hampers the proliferation and migratory potential of PDAC cells. (<b>A</b>) Validation of <span class="html-italic">CENPA</span> expression in the HPA database. (<b>B</b>) The expression level of <span class="html-italic">CENPA</span> in the PDAC expression data cohort from the TCGA and GETx database. (<b>C</b>) Associations between <span class="html-italic">CENPA</span> expression and overall survival of PDAC patients. (<b>D</b>) Relative mRNA expression of <span class="html-italic">CENPA</span> in PDAC cell lines (BXPC-3, CAPAN-1, CAPAN-2, CFPAC-1, MIA PaCa-2, PANC-1, and SW1990) and HPDE normal pancreatic ductal epithelial cells. (<b>E</b>) <span class="html-italic">CENPA</span> knockdown in PANC-1 and MIA PaCa-2 cells verified by qRT-PCR and Western blot. The cck8 assay (<b>F</b>) and colony formation assay (<b>G</b>) show reduced cell viability in <span class="html-italic">CENPA</span> knockdown PANC-1 and MIA PaCa-2 cells. (<b>H</b>,<b>I</b>) Wound-healing and transwell assays indicate significantly reduced migration ability in <span class="html-italic">CENPA</span> knockdown PANC-1 and MIA PaCa-2 cells. <span class="html-italic">n</span> = 3, 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. Error bars represent mean ± SD.</p>
Full article ">Figure 9
<p>Correlation between <span class="html-italic">CENPA</span> expression and drug sensitivity, and molecular docking of drugs correlated with the high expression of <span class="html-italic">CENPA</span>. (<b>A</b>) Correlation analysis between <span class="html-italic">CENPA</span> expression and drug sensitivity, conducted using BEST. (<b>B</b>) Molecular docking diagrams of <span class="html-italic">CENPA</span> with the two drugs showing the strongest binding affinity: betulinic acid (−8.1 kcal/mol) and GSK2126458 (−8.6 kcal/mol).</p>
Full article ">
24 pages, 1342 KiB  
Review
Overview of Recombinant Tick Vaccines and Perspectives on the Use of Plant-Made Vaccines to Control Ticks of Veterinary Importance
by Edgar Trujillo, Abel Ramos-Vega, Elizabeth Monreal-Escalante, Consuelo Almazán and Carlos Angulo
Vaccines 2024, 12(10), 1178; https://doi.org/10.3390/vaccines12101178 - 17 Oct 2024
Abstract
Ticks are obligate hematophagous ectoparasites that affect animals, and some of them transmit a wide range of pathogens including viruses, bacteria, and protozoa to both animals and humans. Several vaccines have shown immunogenicity and protective efficacy against ticks in animal models and definitive [...] Read more.
Ticks are obligate hematophagous ectoparasites that affect animals, and some of them transmit a wide range of pathogens including viruses, bacteria, and protozoa to both animals and humans. Several vaccines have shown immunogenicity and protective efficacy against ticks in animal models and definitive hosts. After several decades on anti-tick vaccine research, only a commercial vaccine based on a recombinant antigen is currently available. In this context, plants offer three decades of research and development on recombinant vaccine production to immunize hosts and as a delivery vehicle platform. Despite the experimental advances in plant-made vaccines to control several parasitosis and infectious diseases, no vaccine prototype has been developed against ticks. This review examines a panorama of ticks of veterinary importance, recombinant vaccine experimental developments, plant-made vaccine platforms, and perspectives on using this technology as well as the opportunities and limitations in the field of tick vaccine research. Full article
(This article belongs to the Special Issue Vaccines against Arthropods and Arthropod-Borne Pathogens)
Show Figures

Figure 1

Figure 1
<p>Schematic representation of step-by-step approach for plant-made vaccine development against ticks of veterinary importance.</p>
Full article ">Figure 2
<p>Tick-borne pathogens affect animals and humans.</p>
Full article ">
12 pages, 1792 KiB  
Article
Circulating miR-18a and miR-532 Levels in Extrahepatic Cholangiocarcinoma
by Rares Ilie Orzan, Adrian Bogdan Țigu, Vlad-Ionuț Nechita, Madalina Nistor, Renata Agoston, Diana Gonciar, Cristina Pojoga and Andrada Seicean
J. Clin. Med. 2024, 13(20), 6177; https://doi.org/10.3390/jcm13206177 - 17 Oct 2024
Abstract
Background: Cholangiocarcinoma (CCA) is a highly aggressive cancer of the bile ducts with a poor prognosis and limited diagnostic markers. This study aims to investigate the potential of miR-18a and miR-532 as biomarkers for CCA by exploring their correlations with clinical parameters [...] Read more.
Background: Cholangiocarcinoma (CCA) is a highly aggressive cancer of the bile ducts with a poor prognosis and limited diagnostic markers. This study aims to investigate the potential of miR-18a and miR-532 as biomarkers for CCA by exploring their correlations with clinical parameters and traditional tumor markers such as CA19.9, CEA, and AFP. Methods: This study involved a cohort of patients diagnosed with CCA. Serum levels of miR-18a and miR-532 were measured and analyzed in relation to various clinical parameters, including age, tumor markers, and histological features. Results: Serum levels of miR-18a and miR-532 were upregulated in patients with extrahepatic cholangiocarcinoma (eCCA) compared to healthy controls (p < 0.05). MiR-18a and miR-532 levels were correlated with each other (p = 0.011, Spearman’s rho = 0.482) but showed no significant correlation with age or traditional tumor markers (CA19.9, CEA, AFP). No significant differences in miR-18a and miR-532 levels were observed concerning tumor localization or histological grading. For predicting tumor resectability, miR-532 at a cut-off point of 2.12 showed a sensitivity of 72.73%, specificity of 81.25%, and an AUC of 71.3%, while miR-18a, at a cut-off of 1.83, had a sensitivity of 63.64%, specificity of 75%, and an AUC of 59.7%. ROC curve analysis suggested moderate diagnostic potential for miR-18a and miR-532, with AUC values of 0.64 and 0.689, respectively. Conclusions: Although miR-18a and miR-532 showed significant upregulation in eCCA patients compared to healthy controls, they did not demonstrate significant associations with key clinical parameters, limiting their effectiveness as standalone diagnostic biomarkers. Further research involving larger, multi-center cohorts and additional molecular markers is necessary to validate these findings and explore the broader diagnostic potential of miRNAs in CCA. Full article
(This article belongs to the Section Gastroenterology & Hepatopancreatobiliary Medicine)
Show Figures

Figure 1

Figure 1
<p>Comparison of serum levels of miR-18a and miR-532 between eCCA patients (PAT) and controls (CTR). The relative expression of circulating miR-18a and miR-532 in serum is expressed as Log(2) of the fold change calculated as 2<sup>−ΔΔCT</sup> values. The median value for miR-18a in controls is −0.2170 and 0.2404 for patients, while the median value for miR-532 in controls is −0.09518 and 0.2188 for patients. (* <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>ROC curves for miR-18a (blue) and miR-532 (yellow) in distinguishing between patients with eCCA and healthy controls.</p>
Full article ">Figure 3
<p>Heat map representing the correlations between miRNA and conventional tumor markers.</p>
Full article ">Figure 4
<p>(<b>a</b>) ROC curves for AFP (blue), CA19.9 (gray), CEA (yellow), miR-18a (green), and miR-532 (red) in distinguishing between patients with and without vascular invasion. miR-532 demonstrates the highest area under the curve (AUC), indicating a slightly higher differentiation capacity compared to the other markers. (<b>b</b>) ROC curve analysis for AFP, CA19.9, CEA, miR-18a, and miR-532 showed varying degrees of sensitivity and specificity in differentiating between different N stages of cholangiocarcinoma.</p>
Full article ">Figure 5
<p>ROC curves for AFP (blue), CA19.9 (gray), CEA (yellow), miR-18a (green), and miR-532 (red) in evaluating resectability.</p>
Full article ">
16 pages, 1844 KiB  
Article
Innovative Pathogen Reduction in Exported Sea Bass Through Atmospheric Cold Plasma Technology
by Şehnaz Yasemin Tosun, Sehban Kartal, Tamer Akan, Sühendan Mol, Serap Coşansu, Didem Üçok, Şafak Ulusoy, Hande Doğruyol and Kamil Bostan
Foods 2024, 13(20), 3290; https://doi.org/10.3390/foods13203290 - 17 Oct 2024
Viewed by 136
Abstract
The safety of sea bass is critical for the global food trade. This study evaluated the effectiveness of atmospheric cold plasma in reducing food safety risks posed by Salmonella Enteritidis and Listeria monocytogenes, which can contaminate sea bass post harvest. Cold plasma [...] Read more.
The safety of sea bass is critical for the global food trade. This study evaluated the effectiveness of atmospheric cold plasma in reducing food safety risks posed by Salmonella Enteritidis and Listeria monocytogenes, which can contaminate sea bass post harvest. Cold plasma was applied to inoculated sea bass for 2 to 18 min, achieving a maximum reduction of 1.43 log CFU/g for S. Enteritidis and 0.80 log CFU/g for L. monocytogenes at 18 min. Longer treatments resulted in greater reductions; however, odor and taste quality declined to a below average quality in samples treated for 12 min or longer. Plasma treatment did not significantly alter the color, texture, or water activity (aw) of the fish. Higher levels of thiobarbituric acid reactive substances (TBARSs) were observed with increased exposure times. Cold plasma was also tested in vitro on S. Enteritidis and L. monocytogenes on agar surfaces. A 4 min treatment eliminated the initial loads of S. Enteritidis (2.71 log CFU) and L. monocytogenes (2.98 log CFU). The findings highlight the potential of cold plasma in enhancing the safety of naturally contaminated fish. Cold plasma represents a promising technology for improving food safety in the global fish trade and continues to be a significant area of research in food science. Full article
(This article belongs to the Section Food Quality and Safety)
Show Figures

Figure 1

Figure 1
<p>Original atmospheric cold plasma equipment ((A) power supply; (B) plasma generation cite; (C) glass Petri dish lid; (D) copper plate; (E) copper wire; (F) sample site; (G) cold plasma).</p>
Full article ">Figure 2
<p>In vitro reduction in <span class="html-italic">S.</span> Enteritidis and <span class="html-italic">L. monocytogenes</span> by atmospheric cold plasma (a, b: different letters show significant differences between reduction rates, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Reduction in <span class="html-italic">S.</span> Enteritidis and <span class="html-italic">L. monocytogenes</span> on sea bass by atmospheric cold plasma (a–d: different letters show significant differences in reduction rates, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Sensory analysis of sea bass treated with atmospheric cold plasma for various durations (* decreases in odor and taste after 8 min and in overall acceptability after 10 min are significant, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Color change in plasma-treated sea bass compared to untreated samples (a; no significant differences between ΔE vales, <span class="html-italic">p</span> &gt; 0.05).</p>
Full article ">Figure 6
<p>TBARS values of sea bass treated with atmospheric cold plasma (a–i: letters indicate the significant difference between treatments, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
15 pages, 2118 KiB  
Review
Tailoring the Synthesis Method of Metal Oxide Nanoparticles for Desired Properties
by Adriana-Gabriela Schiopu, Daniela Monica Iordache, Mihai Oproescu, Laura Mădălina Cursaru and Adriana-Miruna Ioța
Crystals 2024, 14(10), 899; https://doi.org/10.3390/cryst14100899 - 17 Oct 2024
Viewed by 183
Abstract
Metal oxide nanoparticles (MONs) are particles with at least one dimension in the nanoscale range (1–100 nm). Their unique properties, significantly different from their bulk counterparts, make them promising materials for a wide range of applications in fields such as medicine, electronics, catalysis, [...] Read more.
Metal oxide nanoparticles (MONs) are particles with at least one dimension in the nanoscale range (1–100 nm). Their unique properties, significantly different from their bulk counterparts, make them promising materials for a wide range of applications in fields such as medicine, electronics, catalysis, environmental remediation, and energy storage. The precise control of MONs’ properties, including size, shape, composition, crystallinity, and surface chemistry, is significant for optimizing their performance. This study aims to investigate the characteristics of synthesis methods of MONs. Correlation between synthesis parameters and properties highlights that creating nanomaterials with defined and controlled dimensions is a complex task that requires a deep understanding of various factors. Also, this study presents a model with adaptive parameters for synthesis conditions to acquire desired nanometric scale for particles size, which represents an essential task. Full article
(This article belongs to the Special Issue Synthesis and Characterization of Oxide Nanoparticles)
Show Figures

Figure 1

Figure 1
<p>Quantitative evolution of research articles in Web of Science and Scopus databases regarding MONs.</p>
Full article ">Figure 2
<p>Percentage evolution of number of appearances, compared to the previous year, of the topic of metal oxide nanoparticles.</p>
Full article ">Figure 3
<p>SWOT analysis of top-down synthesis approaches of metal oxide nanoparticles [<a href="#B1-crystals-14-00899" class="html-bibr">1</a>,<a href="#B2-crystals-14-00899" class="html-bibr">2</a>,<a href="#B3-crystals-14-00899" class="html-bibr">3</a>,<a href="#B4-crystals-14-00899" class="html-bibr">4</a>,<a href="#B6-crystals-14-00899" class="html-bibr">6</a>,<a href="#B7-crystals-14-00899" class="html-bibr">7</a>,<a href="#B8-crystals-14-00899" class="html-bibr">8</a>,<a href="#B9-crystals-14-00899" class="html-bibr">9</a>,<a href="#B10-crystals-14-00899" class="html-bibr">10</a>,<a href="#B11-crystals-14-00899" class="html-bibr">11</a>,<a href="#B12-crystals-14-00899" class="html-bibr">12</a>,<a href="#B13-crystals-14-00899" class="html-bibr">13</a>].</p>
Full article ">Figure 4
<p>SWOT analysis of bottom-up synthesis approaches of metal oxide nanoparticles [<a href="#B2-crystals-14-00899" class="html-bibr">2</a>,<a href="#B10-crystals-14-00899" class="html-bibr">10</a>,<a href="#B11-crystals-14-00899" class="html-bibr">11</a>,<a href="#B12-crystals-14-00899" class="html-bibr">12</a>,<a href="#B13-crystals-14-00899" class="html-bibr">13</a>,<a href="#B14-crystals-14-00899" class="html-bibr">14</a>,<a href="#B16-crystals-14-00899" class="html-bibr">16</a>,<a href="#B17-crystals-14-00899" class="html-bibr">17</a>,<a href="#B18-crystals-14-00899" class="html-bibr">18</a>,<a href="#B19-crystals-14-00899" class="html-bibr">19</a>,<a href="#B21-crystals-14-00899" class="html-bibr">21</a>,<a href="#B22-crystals-14-00899" class="html-bibr">22</a>,<a href="#B23-crystals-14-00899" class="html-bibr">23</a>,<a href="#B24-crystals-14-00899" class="html-bibr">24</a>,<a href="#B27-crystals-14-00899" class="html-bibr">27</a>,<a href="#B28-crystals-14-00899" class="html-bibr">28</a>,<a href="#B29-crystals-14-00899" class="html-bibr">29</a>,<a href="#B30-crystals-14-00899" class="html-bibr">30</a>].</p>
Full article ">Figure 5
<p>DOE chart.</p>
Full article ">Figure 6
<p>Pareto chart of the effects (A—concentration, B—microwave power, AB—concentration or microwave power)).</p>
Full article ">
16 pages, 3555 KiB  
Article
Simultaneous Degradation of AFB1 and ZEN by CotA Laccase from Bacillus subtilis ZJ-2019-1 in the Mediator-Assisted or Immobilization System
by Boquan Gao, Wei An, Jianwen Wu, Xiumin Wang, Bing Han, Hui Tao, Jie Liu, Zhenlong Wang and Jinquan Wang
Toxins 2024, 16(10), 445; https://doi.org/10.3390/toxins16100445 - 16 Oct 2024
Viewed by 331
Abstract
The global prevalence of aflatoxin B1 (AFB1) and zearalenone (ZEN) contamination in food and feed poses a serious health risk to humans and animals. Recently, enzymatic detoxification has received increasing attention, yet most enzymes are limited to degrading only one type of mycotoxin, [...] Read more.
The global prevalence of aflatoxin B1 (AFB1) and zearalenone (ZEN) contamination in food and feed poses a serious health risk to humans and animals. Recently, enzymatic detoxification has received increasing attention, yet most enzymes are limited to degrading only one type of mycotoxin, and free enzymes often exhibit reduced stability and activity, limiting their practicality in real-world applications. In this study, the laccase CotA gene from ZEN/AFB1-degrading Bacillus subtilis ZJ-2019-1 was cloned and successfully expressed in Escherichia coli BL21, achieving a protein yield of 7.0 mg/g. The recombinant CotA (rCotA) completely degraded AFB1 and ZEN, with optimal activity at 70 °C and pH 7.0. After rCotA treatment, neither AFB1 nor ZEN showed significantly cytotoxicity to mouse macrophage cell lines. Additionally, the AFB1/ZEN degradation efficiency of rCotA was significantly enhanced by five natural redox mediators: acetosyringone, syringaldehyde, vanillin, matrine, and sophoridin. Among them, the acetosyringone-rCotA was the most effective mediator system, which could completely degrade 10 μg of AFB1 and ZEN within 1 h. Furthermore, the chitosan-immobilized rCotA system exhibited higher degradation activity than free rCotA. The immobilized rCotA degraded 27.95% of ZEN and 41.37% of AFB1 in contaminated maize meal within 12 h, and it still maintained more than 40% activity after 12 reuse cycles. These results suggest that media-assisted or immobilized enzyme systems not only boost degradation efficiency but also demonstrate remarkable reusability, offering promising strategies to enhance the degradation efficiency of rCotA for mycotoxin detoxification. Full article
(This article belongs to the Special Issue Occurrence, Toxicity, Metabolism, Analysis and Control of Mycotoxins)
Show Figures

Figure 1

Figure 1
<p>Expression optimization, purification, and characterization of rCotA: (<b>a</b>) Construction of the pEASY-Blunt E1-cotA plasmid. (<b>b</b>) SDS-PAGE analysis showing rCotA expression with varying IPTG concentrations. Lane 1: 0.05 mM IPTG; Lane 2: 0.1 mM IPTG; Lane 3: 0.2 mM IPTG; Lane 4: 0.3 mM IPTG. (<b>c</b>) Densitometric analysis of the SDS-PAGE gel bands for IPTG induction. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05). (<b>d</b>) SDS-PAGE analysis of rCotA expression with different Cu<sup>2+</sup> concentrations. Lane 1: 0.1 mM Cu<sup>2+</sup>; Lane 2: 0.5 mM Cu<sup>2+</sup>; Lane 3: 1.0 mM Cu<sup>2+</sup>; Lane 4: 0 mM Cu<sup>2+</sup>. (<b>e</b>) Densitometric analysis of SDS-PAGE gels for Cu<sup>2+</sup> induction. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05). (<b>f</b>) SDS-PAGE analysis of purified rCotA showing a single band at 60 kDa. (<b>g</b>) Enzymatic activity of purified rCotA at different pH levels (2.0–10.0) using ABTS as a substrate. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Degradation efficiency of AFB1 and ZEN by rCotA under varying conditions. (<b>a</b>) Time-course analysis of AFB1 degradation by rCotA at different pH. (<b>b</b>) Time-course analysis of ZEN degradation by rCotA at different pH. (<b>c</b>) Time-course analysis of AFB1 degradation by rCotA at different temperatures. (<b>d</b>) Time-course analysis of ZEN degradation by rCotA at different temperatures. (<b>e</b>) Time-course analysis of AFB1 degradation by rCotA with different metal ions. (<b>f</b>) Time-course analysis of ZEN degradation by rCotA with different metal ions. The results are presented as the mean ± SD of three independent experiments. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>The effect of various mediators on the degradation of AFB1 and ZEN by rCotA. (<b>a</b>) AFB1 degradation rates in different rCotA mediator systems. (<b>b</b>) ZEN degradation rates in different rCotA mediator systems. (<b>c</b>) Time-course analysis of AFB1 degradation rates in the acetosyringone– and syringaldehyde–rCotA mediator system. (<b>d</b>) Time-course analysis of ZEN degradation rates in the acetosyringone– and syringaldehyde–rCotA mediator system. The results are presented as the mean ± SD of three independent experiments. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Detoxification effect of rCotA on AFB1 and/or ZEN in Raw 264.7 cells. (<b>a</b>) Detoxification effect of rCotA on AFB1 compared to the PBS-treated group. (<b>b</b>) Detoxification effect of rCotA on ZEN compared to the PBS-treated group. (<b>c</b>) Detoxification effect of rCotA on “AFB1 + ZEN” compared to the PBS-treated group. The results are presented as the mean ± SD of three independent experiments. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Morphology and characteristics of chitosan microspheres: (<b>a</b>) chitosan microspheres before activation; (<b>b</b>) chitosan microspheres cross-linked with glutaraldehyde; (<b>c</b>) chitosan microspheres immobilized with rCotA; (<b>d</b>) ABTS oxidation by chitosan microspheres with varying concentrations of immobilized rCotA: 2 mg/g (<b>I</b>), 1 mg/g (<b>II</b>), 0.5 mg/g (<b>III</b>), and 0 mg/g (<b>IV</b>); (<b>e</b>) scanning electron microscopy image of untreated chitosan microspheres; (<b>f</b>) scanning electron microscopy image of chitosan microspheres activated by glutaraldehyde; (<b>g</b>) scanning electron microscopy image of glutaraldehyde-activated chitosan microspheres immobilized with rCotA.</p>
Full article ">Figure 6
<p>Degradation of AFB1 and ZEN by free and immobilized rCotA and reusability of the immobilized enzyme: (<b>a</b>) Degradation of AFB1 by free and immobilized rCotA. (<b>b</b>) Degradation of ZEN by free and immobilized rCotA. (<b>c</b>) Degradation of AFB1 and ZEN in contaminated maize meal by free and immobilized rCotA. The “ns” indicates no significant difference between the two groups, and asterisks indicate a significant difference between the two groups (*** <span class="html-italic">p</span> &lt; 0.001). (<b>d</b>) Remaining activity of immobilized rCotA for AFB1 and ZEN degradation over multiple cycles. (<b>e</b>) The 3D binding pocket model of rCotA with AFB1. (<b>f</b>) The 3D binding pocket model of rCotA with ZEN. The results are given as the mean ± SD of three independent experiments. Different lower-case letters indicate a significant difference between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
Back to TopTop