Search Results (37,769)

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. Full article

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load. Full article

Magnetic soft continuum robots (MSCRs) hold significant potential in fulfilling the requirements of vascular interventional robots, enabling safe access to difficult-to-reach areas with enhanced active maneuverability, shape morphing capabilities, and stiffness variability. Their primary advantage lies in their tether-less actuation mechanism that can safely adapt to complex vessel structures. Existing commercial MSCRs primarily employ a magnetic-pull strategy, which suffers from insufficient driving force and a single actuation strategy, limiting their clinical applicability. Inspired by the inchworm crawling locomotion gait, we herein present a novel MSCR that integrates a magnetic sinusoidal actuation mechanism with adjustable frequency and kirigami structures. The developed MSCRs consist of two permanent magnets connected by a micro-spring, which is coated with a silicone membrane featuring a specific notch array. This design enables bio-inspired crawling with controllable velocity and active maneuverability. An analytical model of the magnetic torque and finite element analysis (FEA) simulations of the MSCRs has been constructed. Additionally, the prototype has been validated through two-dimensional in-vitro tracking experiments with actuation frequencies ranging from 1 to 10 Hz. Its stride efficiency has also been verified in a three-dimensional (3D) coronary artery phantom. Diametrically magnetized spherical chain tip enhances active steerability. Kirigami skin is coated over the novel guidewire and catheter, not only providing proximal anchorage for improved stride efficiency but also serving similar function as a cutting balloon. Under the actuation of an external magnetic field, the proposed MSCRs demonstrate the ability to traverse bifurcations and tortuous paths, indicating their potential for dexterous flexibility in pathological vessels. Full article

T-cell acute lymphoblastic leukemia (T-ALL) is a challenging childhood cancer to treat, with limited therapeutic options and high relapse rates. This study explores deamidated triosephosphate isomerase (dTPI) as a novel therapeutic target. We hypothesized that selectively inhibiting dTPI could reduce T-ALL cell viability without affecting normal T lymphocytes. Computational modeling and recombinant enzyme assays revealed that disulfiram (DS) and curcumin (CU) selectively bind and inhibit dTPI activity without affecting the non-deamidated enzyme. At the cellular level, treatment with DS and CU significantly reduced Jurkat T-ALL cell viability and endogenous TPI enzymatic activity, with no effect on normal T lymphocytes, whereas the combination of sodium dichloroacetate (DCA) with DS or CU showed synergistic effects. Furthermore, we demonstrated that dTPI was present and accumulated only in Jurkat cells, confirming our hypothesis. Finally, flow cytometry confirmed apoptosis in Jurkat cells after treatment with DS and CU or their combination with DCA. These findings strongly suggest that targeting dTPI represents a promising and selective target for T-ALL therapy. Full article

With the widespread adoption of 3D scanners, reverse engineering methods for obtaining CAD drawings from physical objects have become increasingly utilized. When converting point data obtained from a 3D scanner into a mesh structure, a smoothing process is typically applied because the point data often contains a lot of noise. However, this can result in the loss of important high-frequency details, or, in cases where significant high-frequency information remains, there are limitations in representing the object with basic geometric shapes. In this paper, a method to address this issue by using environment mapping and supervised learning techniques is proposed. By mapping the object to an environment and then using supervised learning to analyze the data, it can accurately identify the basic geometric shapes. This approach reduces the working time and allows for easier design of complex surfaces that were previously difficult to model. Full article

Alzheimer’s disease (AD) is characterized by complex interactions between neuropathological markers, metabolic dysregulation, and structural brain changes. In this study, we utilized a multimodal approach, combining immunohistochemistry, functional metabolic mapping, and microstructure sensitive diffusion MRI (dMRI) to progressively investigate these interactions in the 5xFAD mouse model of AD. Our analysis revealed age-dependent and region-specific accumulation of key AD markers, including amyloid-beta (Aβ), GFAP, and IBA1, with significant differences observed between the hippocampal formation and upper and lower regions of the cortex by 6 months of age. Functional metabolic mapping validated localized disruptions in energy metabolism, with glucose hypometabolism in the hippocampus and impaired astrocytic metabolism in the cortex. Notably, increased cortical glutaminolysis suggested a shift in microglial metabolism, reflecting an adaptive response to neuroinflammatory processes. While dMRI showed no significant microstructural differences between 5xFAD and wild-type controls, the study highlights the importance of metabolic alterations as critical events in AD pathology. These findings emphasize the need for targeted therapeutic strategies addressing specific metabolic disturbances and underscore the potential of integrating advanced imaging with metabolic and molecular analyses to advance our understanding of AD progression. Full article

Individuals with chronic diseases are more vulnerable to environmental inhalation exposures. Although metabolic syndrome (MetS) is increasingly common and is associated with susceptibility to inhalation exposures such as particulate air pollution, the underlying mechanisms remain unclear. In previous studies, we determined that, compared to a healthy mouse model, a mouse model of MetS exhibited increased pulmonary inflammation 24 h after exposure to AgNPs. This exacerbated response was associated with decreases in pulmonary levels of specific specialized pro-resolving mediators (SPMs). Supplementation with specific SPMs that are known to be dysregulated in MetS may alter particulate-induced inflammatory responses and be useful in treatment strategies. Our current study hypothesized that administration of resolvin E1 (RvE1), protectin D1 (PD1), or maresin (MaR1) following AgNP exposure will differentially regulate inflammatory responses. To examine this hypothesis, healthy and MetS mouse models were exposed to either a vehicle (control) or 50 μg of 20 nm AgNPs via oropharyngeal aspiration. They were then treated 24 h post-exposure with either a vehicle (control) or 400 ng of RvE1, PD1, or MaR1 via oropharyngeal aspiration. Endpoints of pulmonary inflammation and toxicity were evaluated three days following AgNP exposure. MetS mice that were exposed to AgNPs and received PBS treatment exhibited significantly exacerbated pulmonary inflammatory responses compared to healthy mice. In mice exposed to AgNPs and treated with RvE1, neutrophil infiltration was reduced in healthy mice and the exacerbated neutrophil levels were decreased in the MetS model. This decreased neutrophilia was associated with decreases in proinflammatory cytokines’ gene and protein expression. Healthy mice treated with PD1 did not demonstrate alterations in AgNP-induced neutrophil levels compared to mice not receiving treat; however, exacerbated neutrophilia was reduced in the MetS model. These PD1 alterations were associated with decreases in proinflammatory cytokines, as well as elevated interleukin-10 (IL-10). Both mouse models receiving MaR1 treatment demonstrated reductions in AgNP-induced neutrophil influx. MaR1 treatment was associated with decreases in proinflammatory cytokines in both models and increases in the resolution inflammatory cytokine IL-10 in both models, which were enhanced in MetS mice. Inflammatory responses to particulate exposure may be treated using specific SPMs, some of which may benefit susceptible subpopulations. Full article

Lysosomal acid lipase (LAL) is the only known enzyme that degrades cholesteryl esters and triglycerides at an acidic pH. In LAL deficiency (LAL-D), dysregulated expression of matrix metalloproteinase 12 (MMP-12) has been described. The overexpression of MMP-12 in myeloid lineage cells causes an immune cell dysfunction resembling that of Lal knockout (Lal KO) mice. Both models develop progressive lymphocyte dysfunction and expansion of myeloid-derived suppressor (CD11b+ Gr-1+) cells. To study whether MMP-12 might be a detrimental contributor to the pathology of LAL-D, we have generated Lal/Mmp12 double knockout (DKO) mice. The phenotype of Lal/Mmp12 DKO mice closely resembled that of Lal KO mice, while the weight and morphology of the thymus were improved in Lal/Mmp12 DKO mice. Cytological examination of blood smears showed a mildly reversed lymphoid-to-myeloid shift in DKO mice. Despite significant decreases in CD11b+ Ly6G+ cells in the peripheral blood, bone marrow, and spleen of Lal/Mmp12 DKO mice, the hematopoietic bone marrow progenitor compartment and markers for neutrophil chemotaxis were unchanged. Since the overall severity of LAL-D remains unaffected by the deletion of Mmp12, we conclude that MMP-12 does not represent a viable target for treating the inflammatory pathology in LAL-D. Full article

Spinel ferrites demonstrate extensive applications in different areas, like electrodes for electrochemical devices, gas sensors, catalysts, and magnetic adsorbents for environmentally important processes. However, defects in the real spinel structure can change the many physical and chemical properties of spinel ferrites. Although the number of defects in a crystal spinel lattice is small, their influence on the vast majority of physical properties could be really decisive. This review provides an overview of the structural characteristics of spinel compounds (e.g., CoFe₂O₄, NiFe₂O₄, ZnFe₂O₄, Fe₃O₄, γ–Fe₂O₃, Co₃O₄, Mn₃O₄, NiCo₂O₄, ZnCo₂O₄, Co₂MnO₄, etc.) and examines the influence of defects on their properties. Attention was paid to the classification (0D, 1D, 2D, and 3D defects), nomenclature, and the formation of point and surface defects in ferrites. An in-depth description of the defects responsible for the physicochemical properties and the methodologies employed for their determination are presented. DFT as the most common simulation approach is described in relation to modeling the point defects in spinel compounds. The significant influence of defect distribution on the magnetic interactions between cations, enhancing magnetic properties, is highlighted. The main defect-engineering strategies (direct synthesis and post-treatment) are described. An antistructural notation of active centers in spinel cobalt ferrite is presented. It is shown that the introduction of cations with different charges (e.g., Cu(I), Mn(II), Ce(III), or Ce(IV)) into the cobalt ferrite spinel matrix results in the formation of various point defects. The ability to predict the type of defects and their impact on material properties is the basis of defect engineering, which is currently an extremely promising direction in modern materials science. Full article

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture. Full article

The South China Sea is rich in sea anemone resources, and the protein and peptide components from sea anemone toxins comprise an important treasure trove for researchers to search for leading compounds. This study conducted a comprehensive transcriptomic analysis of the tentacles and column of Macrodactyla doreensis and explored the distribution and diversity of proteins and peptides in depth using bioinformatics, initially constructing a putative protein and peptide database. In this database, typical peptide families are identified through amino acid sequence analysis, and their 3D structures and potential biological activities are revealed through AlphaFold2 modeling and molecular docking. A total of 4,239 transcripts were identified, of which the putative protein accounted for 81.53%. The highest content comprised immunoglobulin and a variety of proteases, mainly distributed in the column and related to biological functions. Importantly, the putative peptide accounted for 18.47%, containing ShK domain and Kunitz-type peptides, mainly distributed in the tentacles and related to offensive predatory behavior. Interestingly, 40 putative peptides belonging to eight typical peptide families were identified, and their structures and targets were predicted. This study reveals the diversity and complexity of Macrodactyla doreensis toxins and predicts their structure and targets based on amino acid sequences, providing a feasible approach for research regarding the discovery of peptides with potentially high activity. Full article

Exogenous DNA damage represents one of the most harmful outcomes produced by environmental, physical, or chemical agents. Here, a comparative analysis of DNA fragmentation was carried out on Paracentrotus lividus sea urchin embryos exposed to four common pollutants of the marine environment: vanadium, cadmium, gadolinium and selenium. Using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, fragmented DNA was quantified and localized in apoptotic cells mapping whole-mount embryos. This is the first study reporting how different chemicals are able to activate distinctive apoptotic features in sea urchin embryos, categorized as follows: (i) cell-selective apoptosis, showing DNA fragmentation restricted to a subset of extremely damaged cells, acting as an embryo survival mechanism; or (ii) total apoptosis, with fragmented DNA widespread throughout the cells of the entire embryo, leading to its death. Also, this is the first report of the effects of Se exposure on P. lividus sea urchin embryos. These data confirm the TUNEL assay as the most suitable test to study DNA fragmentation in the sea urchin embryo model system. Taken together, this research highlights embryos’ ability to find alternative pathways and set physiological limits for development under stress conditions. Full article

Currently, pJP4 is one of the best-known plasmids for the biodegradation of xenobiotics that mediate the degradation of 2,4-dichlorophenoxyacetic acid (2,4-D), which is associated with serious health and environmental risks. Although the sequencing and proposed theory of pJP4 formation occurred almost 20 years ago (2004), pJP4 is still the model object of many studies focused on the biodegradation of 2,4-D. The uniqueness of this plasmid is due to the presence of two evolutionarily distinct gene clusters, tfd_I and tfd_II, controlling the degradation of 2,4-D. Recent advances in plasmid biology, especially those concerning the characterization of new IncP-1 plasmids and the systematization of tfd gene cluster findings, serve as a basis for proposing new insights into the formation of the clusters’ architecture of the canonical plasmid, pJP4, and their distribution among other plasmids. In the present work, a comparative genomic and phylogenetic in silico study of plasmids with tfd_I and tfd_II clusters was carried out. The possible initial distribution patterns of tfd_I clusters among plasmids of different incompatibility groups (non-IncP-1) and tfd_II clusters among IncP-1 plasmids using the IS1071-based composite transposon were revealed. A new theory on the formation of the architecture of the tfd_I and tfd_II clusters of pJP4 through sequential internal rearrangements, recombination, and ISJP4 insertion, is proposed. In addition, small gene clusters resulting from internal rearrangements of pJP4 (tfd_IISA and ORF31/32) served as fingerprints for exploring the distribution of tfd_I and tfd_II clusters. The revealed patterns and formulated theory extend the frontiers of plasmid biology and will be beneficial for understanding the role of plasmids in bacterial adaptation to xenobiotic-contaminated environments. Full article

The Weixinan Sag in the Beibuwan Basin is rich in shale oil resources. However, the reservoirs exhibit rapid phase changes, strong compartmentalization, thin individual layers, and high-frequency vertical variations in the thin interbedded sandstone and mudstone. These factors can restrict the height of hydraulic fracture propagation. Additionally, the low-porosity and low-permeability shale oil reservoirs face challenges such as low production rates and rapid decline. To address these issues, the Plannar3D full 3D fracturing model was used to simulate hydraulic fracture propagation and to study the main controlling factors for fracture propagation in the second member of the Liushagang Formation. Based on the concept of geological–engineering integration, a sweet spot evaluation was conducted to identify reservoirs with relatively better brittleness, reservoir properties, and oil content as the fracturing targets for horizontal wells. The UFM model was then applied to optimize fracturing parameters. This study indicates that the matrix-type oil shale has a high clay mineral content, resulting in a low Young’s modulus and poor brittleness. This makes hydraulic fracture propagation difficult and leads to less effective reservoir stimulation. In contrast, hydraulic fractures propagate more easily in high-brittleness interlayer-type oil shale. Therefore, it is recommended to prioritize the extraction of shale oil from interlayer-type oil shale reservoirs. The difference in interlayer stress is identified as the primary controlling factor for cross-layer fracture propagation in the study area. Based on the concept of geological–engineering integration, a sweet spot evaluation standard was established for the second member of the Liushagang Formation, considering both reservoir quality and engineering quality. Four sweet spot zones of interlayer-type oil shale reservoirs were identified according to this evaluation standard. To achieve uniform fracture initiation, a differentiated segment and cluster design was implemented for certain high-angle sections of well WZ11-6-5d. Interlayer-type oil shale was selected as the fracturing target, and the UFM was used for hydraulic fracture propagation simulation. Fracturing parameters were optimized with a focus on hydraulic fracture characteristics and the estimated ultimate recovery (EUR). The optimization results were as follows: a single-stage length of 50 m, cluster spacing of 15 m, pump injection rate of 10 m³/min, fluid intensity of 25 m³/m, and proppant intensity of 3.5 t/m. The application of these optimized fracturing parameters in field operations resulted in successful fracturing and the achievement of industrial oil flow. Full article

Depth map super-resolution (DSR) is a technique aimed at restoring high-resolution (HR) depth maps from low-resolution (LR) depth maps. In this process, color images are commonly used as guidance to enhance the restoration procedure. However, the intricate degradation of LR depth poses a challenge, and previous image-guided DSR approaches, which implicitly model the degradation in the spatial domain, often fall short of producing satisfactory results. To address this challenge, we propose a novel approach called the Degradation-Guided Multi-modal Fusion Network (DMFNet). DMFNet explicitly characterizes the degradation and incorporates multi-modal fusion in both spatial and frequency domains to improve the depth quality. Specifically, we first introduce the deep degradation regularization loss function, which enables the model to learn the explicit degradation from the LR depth maps. Simultaneously, DMFNet converts the color images and depth maps into spectrum representations to provide comprehensive multi-domain guidance. Consequently, we present the multi-modal fusion block to restore the depth maps by leveraging both the RGB-D spectrum representations and the depth degradation. Extensive experiments demonstrate that DMFNet achieves state-of-the-art (SoTA) performance on four benchmarks, namely the NYU-v2, Middlebury, Lu, and RGB-D-D datasets. Full article