Locally advanced cervical cancer (LACC) is frequently treated with neoadjuvant chemotherapy (NACT), which includes paclitaxel and platinum. However, the development of severe chemotherapy toxicity is ...a barrier to successful NACT. Phosphatidylinositol 3-kinase (PI3K)/serine/threonine kinase (AKT) pathway is related to the occurrence of chemotherapeutic toxicity. In this research work, we employ a random forest (RF) machine learning model to forecast NACT toxicity (neurological, gastrointestinal, and hematological reactions).
Twenty-four single nucleotide polymorphisms (SNPs) in the PI3K/AKT pathway from 259 LACC patients were used to construct a dataset. Following the data preprocessing, the RF model was trained. The Mean Decrease in Impurity approach was adopted to evaluate the relevance of 70 selected genotypes' importance by comparing chemotherapy toxicity grades 1–2 vs. 3.
In the Mean Decrease in Impurity analysis, neurological toxicity was much more likely to occur in LACC patients with homozygous AA in Akt2 rs7259541 than in those with AG or GG genotypes. The CT genotype of PTEN rs532678 and the CT genotype of Akt1 rs2494739 increased the risk of neurological toxicity. The top three loci were rs4558508, rs17431184, and rs1130233, which were attributed to an elevated risk of gastrointestinal toxicity. LACC patients who had heterozygous AG in Akt2 rs7259541 exhibited an obviously greater risk of hematological toxicity than those who had AA or GG genotypes. And the CT genotype for Akt1 rs2494739 and the CC genotype in PTEN rs926091 showed a tendency to increase the risk of suffering from hematological toxicity.
Akt2 rs7259541 and rs4558508, Akt1 rs2494739 and rs1130233, PTEN rs532678, rs17431184, and rs926091 polymorphisms are associated with different toxic effects during the chemotherapy treatment of LACC.
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•Chemotherapy toxicity is predicted by SNPs & genotypes from MDI-based feature importance analysis.•The RF prediction model improves clinical treatment plans by reducing chemotherapeutic side effects.•This methodology is applicable to a variety of features with additional genotype datasets.
Basic magnesium sulfate cement has the advantages of fast setting, high strength, high toughness, water resistance, corrosion resistance, etc. But the cost has become a reason for limiting its ...widespread application. With the widespread application of circulating fluidized bed combustion (CFBC) technology in China, the accumulation of CFBC ash is increasing. Reasonable use of CFBC ash can not only reduce cost of basic magnesium sulfate cement but also protect environment. In this paper, the effect of a high volume of CFBC ash on fluidity, flexural strength and compressive strength of basic magnesium sulfate cement is studied. Hydration products and micromorphology analyses are measured by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results reveal that the strength of basic magnesium sulfate cement with 20% CFBC ash is the highest, and its microstructure is the model that CFBC ash and MgO fill a three-dimensional network structure established by needle-shaped 5·1·7 phase. When the amount of CFBC ash is more than 40%, the formation of 5·1·7 phase is affected severely, which greatly reduces the strength.
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•The strength of basic magnesium sulfate cement with 20% CFBC ash is the highest.•The best microstructure is that powders fill in a 3D network of 5·1·7 phase.•The amount of CFBC ash can be by up to 40%.
The PI3K/Akt pathway involves in regulating resistance to platinum-based neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC) patients. Single nucleotide polymorphisms (SNPs) ...reflect the basic genetic variation between individuals. Random forest (RF) is one of the machine-learning models that can predict drug sensitivity with high accuracy. We applied the RF model for genomic prediction of NACT sensitivity in LACC patients.
A total of 259 LACC patients were separated to two groups (i) effective and (ii) ineffective NACT group, depending on the NACT response. The 24 SNPs in four genes (PTEN, PIK3CA, Akt1, and Akt2) were genotyped by the Sequenom MassArray system in these patients. We implemented the SNPs as the feature to train the RF model, calculated the feature importance using mean decreases in impurity based on the model, and further analyzed the importance of each SNP.
The importance analysis indicated that the top three SNPs (rs4558508, rs1130233, and rs7259541) and the last six loci (rs892120, rs62107593, rs34716810, rs10416620, rs41275748, and rs41275746) were all located in Akt. The patients carrying heterozygous GA in Akt2 rs4558508 had a considerably higher risk of chemoresistance than those carrying GG or AA genotype.
The RF model could accurately predict the response to platinum-based NACT of LACC patients. The variables of Akt2 rs4558508 and rs7259541, and Akt1 rs1130233 were major polymorphic loci for NACT inefficiency. The LACC patients carrying heterozygous GA of Akt2 rs4558508 had a significantly increased risk of chemoresistance. Akt was an important gene in PI3K/Akt pathway that could predict the response of platinum-based NACT. The study applied the basis for an individualized approach to LACC patient therapy.
Purpose As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of ...pressure on the classification step afterward. Image recognition based on deep learning algorithms can help to identify and classify the date fruit species, even in natural light. Method In this paper, a deep fusion model based on whale optimization and an artificial neural network for Arabian date classification is proposed. The dataset used in this study includes five classes of date fruit images (Barhi, Khalas, Meneifi, Naboot Saif, Sullaj). The process of designing each model can be divided into three phases. The first phase is feature extraction. The second phase is feature selection. The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50). Results The experimental results show that, after trying different combinations of optimization algorithms and classifiers, the highest test accuracy achieved by DeepDate was 95.9%. It takes less time to achieve a balance between classification accuracy and time consumption. In addition, the performance of DeepDate is better than that of many deep transfer learning models such as Alexnet, Squeezenet, Googlenet, VGG-19, NasNet, and Inception-V3. Conclusion The proposed DeepDate improves the accuracy and efficiency of classifying date fruits and achieves better results in classification metrics such as accuracy and F1. DeepDate provides a promising classification solution for date fruit classification with higher accuracy. To further advance the industry, it is recommended that stakeholders invest in technology transfer programs to bring advanced image recognition and AI tools to smaller producers, enhancing sustainability and productivity across the sector. Collaborations between agricultural technologists and growers could also foster more tailored solutions that address specific regional challenges in date fruit production.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The crystal structure of modulated martensite in Mn-rich off-stoichiometric Ni2Mn1.44In0.56 alloy was determined with high-resolution powder neutron diffraction and synchrotron X-ray diffraction in ...the frame of (3+1)-dimensional superspace theory. The average crystal structure and the modulation wave vector were firstly derived by analyzing the reflection separations induced by the martensitic transformation on the basis of the transformation orientation inheritance. This treatment could be applied to predetermine the modulated structures of materials with displacive structural transformation. The crystal structure of modulated martensite was finally refined by the Rietveld method. Results show that the martensite possesses an incommensurate 6M modulated structure of superspace group I2/m(α0γ)00, with lattice parameters a=4.3919(4)Å, b=5.6202(1)Å, c=4.3315(7)Å, and β=93.044(1)°, and the modulation wave vector q=0.343(7) c*. The detailed site occupations for extra-Mn atoms with respect to the stoichiometric case were investigated by ab initio calculations. The extra-Mn atoms have a preference to be uniformly dispersed. A threefold layered superstructure in the 3-dimensional space was proposed to approximately describe the incommensurate modulated structure. This 6M superstructure model is considered to be representative for off-stoichiometric Ni–(Co)–Mn–In modulated martensite with martensitic transformation around room temperature. The present study is expected to offer an important basis for reliable crystallographic and microstructural characterizations on Ni–Mn–In alloys, so as to understand the underlying mechanisms of their multifunctional magneto-responsive properties.
Maize white spot (MWS), caused by the bacterium Pantoea ananatis, is a serious disease that significantly impacts maize production and productivity. In recent years, outbreaks of white spot disease ...have resulted in substantial maize yield losses in southwest China. Researchers from various countries worldwide have conducted extensive research on this pathogen, including its isolation and identification, the localization of resistance genes, transmission pathways, as well as potential control measures. However, the information related to this disease remains fragmented, and standardized preventive and control strategies have not yet been established. In light of this, this review aims to comprehensively summarize the research findings on MWS, providing valuable insights into understanding its occurrence, prevention, and control measures in the southwestern and southern regions of China while also mitigating the detrimental impact and losses caused by MWS on maize production in China and across the world.
Orientation related grain growth stimuli during primary and secondary recrystallization of the metal matrix in laminated metal/non-metal composites demonstrate unique effect on orientation evolution ...but has rarely been investigated. In this work, the recrystallization and grain growth of Cu in a graphene nanosheets (GNSs) reinforced laminated Cu matrix composite during sintering was thoroughly investigated. The microstructure, texture and lattice strain evolution of the Cu/GNS composite was examined referenced to the Cu stack without GNSs by ex-situ and in-situ orientation characterization techniques (SEM-EBSD, neutron diffraction and synchrotron radiation) from mesoscale to macroscopic scale. The results evidenced that a strong Cube orientation was produced in the Cu/GNS composite instead of the individual non-Cube orientations in the pure Cu stack without GNSs. Detailed strain-state analysis of the Cu foils in the Cu/GNS composite revealed that the anisotropic expansion behavior of the GNS that is incompatible with that of the Cu foils imposed multiple elastic constraints to the foils during the sintering process, resulting in a biaxial isostrain state in the surface layers and a uniaxial compressive strain state in the central layer of each Cu foil. The elastic anisotropy of Cu favors the growth of the Cube oriented grains to minimize the total strain energy. This work clarified the thermal strain induced abnormal grain growth of selected orientations. The mechanism revealed can be useful for analysing abnormal grain growth in elastically strained materials and can also be applied to fabrication process for texturization or even monocrystallization.
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The morphology of dendritic spines is highly correlated with the neuron function. Therefore, it is of positive influence for the research of the dendritic spines. However, it is tried to manually ...label the spine types for statistical analysis. In this work, we proposed an approach based on the combination of wavelet contour analysis for the backbone detection, wavelet packet entropy, and fuzzy support vector machine for the spine classification. The experiments show that this approach is promising. The average detection accuracy of "MushRoom" achieves 97.3%, "Stubby" achieves 94.6%, and "Thin" achieves 97.2%.
Human melanomas frequently harbor amplifications of EZH2. However, the contribution of EZH2 to melanoma formation has remained elusive. Taking advantage of murine melanoma models, we show that EZH2 ...drives tumorigenesis from benign BrafV600E- or NrasQ61K-expressing melanocytes by silencing of genes relevant for the integrity of the primary cilium, a signaling organelle projecting from the surface of vertebrate cells. Consequently, gain of EZH2 promotes loss of primary cilia in benign melanocytic lesions. In contrast, blockade of EZH2 activity evokes ciliogenesis and cilia-dependent growth inhibition in malignant melanoma. Finally, we demonstrate that loss of cilia enhances pro-tumorigenic WNT/β-catenin signaling, and is itself sufficient to drive metastatic melanoma in benign cells. Thus, primary cilia deconstruction is a key process in EZH2-driven melanomagenesis.
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•EZH2 is an oncogene that drives metastatic BRAF and NRAS melanoma•EZH2 promotes primary cilium disassembly by suppressing ciliary genes•The primary cilium inhibits pro-tumorigenic WNT/β-catenin signaling in melanoma•Loss of cilia initiates metastatic melanoma
Zingg et al. show that EZH2 promotes melanomagenesis by silencing genes critical for primary cilium integrity, leading to loss of primary cilia and enhanced WNT signaling. Inhibition of EZH2 evokes cilia-dependent growth inhibition of melanoma, while loss of cilia is sufficient to drive melanoma formation.
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