The FUS::DDIT3 fusion oncoprotein inhibits BAF complex targeting and activity in myxoid liposarcoma.

Mammalian SWI/SNF (mSWI/SNF or BAF) ATP-dependent chromatin remodeling complexes play critical roles in governing genomic architecture and gene expression and are frequently perturbed in human cancers. Transcription factors (TFs), including fusion oncoproteins, can bind to BAF complex surfaces to direct chromatin targeting and accessibility, often activating oncogenic gene loci. Here, we demonstrate that the FUS::DDIT3 fusion oncoprotein hallmark to myxoid liposarcoma (MLPS) inhibits BAF complex-mediated remodeling of adipogenic enhancer sites via sequestration of the adipogenic TF, CEBPB, from the genome. In mesenchymal stem cells, small-molecule inhibition of BAF complex ATPase activity attenuates adipogenesis via failure of BAF-mediated DNA accessibility and gene activation at CEBPB target sites. BAF chromatin occupancy and gene expression profiles of FUS::DDIT3-expressing cell lines and primary tumors exhibit similarity to SMARCB1-deficient tumor types. These data present a mechanism by which a fusion oncoprotein generates a BAF complex loss-of-function phenotype, independent of deleterious subunit mutations.

BRCA-1 depletion impairs pro-inflammatory polarization and activation of RAW 264.7 macrophages in a NF-κB-dependent mechanism.

BRCA-1 is a nuclear protein involved in DNA repair, transcriptional regulation, and cell cycle control. Its involvement in other cellular processes has been described. Here, we aimed to investigate the role of BRCA-1 in macrophages M(LPS), M(IL-4), and tumor cell-induced differentiation. We used siRNAs to knockdown BRCA-1 in RAW 264.7 macrophages exposed to LPS, IL-4, and C6 glioma cells conditioned medium (CMC6), and evaluated macrophage differentiation markers and functional phagocytic activity as well as DNA damage and cell survival in the presence and absence of BRCA-1. LPS and CMC6, but not by IL-4, increased DNA damage in macrophages, and this effect was more pronounced in BRCA-1-depleted cells, including M(IL-4). BRCA-1 depletion impaired expression of pro-inflammatory cytokines, TNF-α and IL-6, and reduced the phagocytic activity of macrophages in response to LPS. In CMC6-induced differentiation, BRCA-1 knockdown inhibited TNF-α and IL-6 expression which was accompanied by upregulation of the anti-inflammatory markers IL-10 and TGF-ß and reduced phagocytosis. In contrast, M(IL-4) phenotype was not affected by BRCA-1 status. Molecular docking predicted that the conserved BRCA-1 domain BRCT can interact with the p65 subunit of NF-κB. Immunofluorescence assays showed that BRCA-1 and p65 co-localize in the nucleus of LPS-treated macrophages and reporter gene assay showed that depletion of BRCA-1 decreased LPS and CMC6-induced NF-κB transactivation. IL-4 had no effect upon NF-κB. Taken together, our findings suggest a role of BRCA-1 in macrophage differentiation and phagocytosis induced by LPS and tumor cells secretoma, but not IL-4, in a mechanism associated with inhibition of NF-κB.

A Hybrid Method Applied to Improve the Efficiency of Full-Waveform Inversion for Pavement Characterization.

Ground penetrating radar (GPR), as a nondestructive testing tool, is suitable for estimating the thickness and permittivity of layers within the pavement. However, it would become problematic when the layer is thin with respect to the probing pulse width, in which case overlapping between the reflected pulses occurs. In order to deal with this problem, a hybrid method based on multilayer perceptrons (MLPs) and a local optimization algorithm is proposed. This method can be divided into two stages. In the first stage, the MLPs roughly estimate the thickness and the permittivity of the GPR signal. In the second stage, these roughly estimated values are used as the initial solution of the full-waveform inversion algorithm. The hybrid method and the conventional global optimization algorithm are respectively used to perform the full-waveform inversion of the simulated GPR data. Under the same inversion precision, the objective function needs to be calculated for 450 times and 30 times for the conventional method and the hybrid method, respectively. The hybrid method is also applied to a measured data, and the thickness estimation error is 1.2 mm. The results show the high efficiency and accuracy of such hybrid method to resolve the problem of estimating the thickness and permittivity of a "thin layer".

A reliable jumping-based classification methodology for environment sector.

Decision-makers have consistently developed a range of classification models, each possessing unique features within the domain of intelligent models. These endeavors are all directed toward achieving the highest levels of accuracy. In recent developments, two notable methodologies-reliable modeling and jumping modeling approaches-offer specific advantages in formulating cost functions and have been recognized for their role in enhancing classifier accuracy. Specifically, the jumping methodology is based on aligning the learning process with the discrete nature of the classification goal, while the reliable methodology integrates the reliability factor into the learning paradigm. However, their innovative combination, leveraging both accuracy and reliability factors in guiding learning processes, leads to the creation of a high-performing classifier. This addresses a research gap in tackling classification challenges, which remains the core focus of the present study. To evaluate the performance of the proposed reliable jumping-based intelligent classifier in environmental decision-making, we considered ten benchmark datasets spanning various application domains. The numerical results demonstrate that the proposed Reliable Jumping-based intelligent classifier consistently outperforms traditional intelligent classifiers across all studied cases. As a result, the proposed approach proves to be a viable and effective alternative to other intelligent methods in environmental applications.

CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?

BACKGROUND: Convolutional Neural Networks (CNNs) and the hybrid models of CNNs and Vision Transformers (VITs) are the recent mainstream methods for COVID-19 medical image diagnosis. However, pure CNNs lack global modeling ability, and the hybrid models of CNNs and VITs have problems such as large parameters and computational complexity. These models are difficult to be used effectively for medical diagnosis in just-in-time applications. METHODS: Therefore, a lightweight medical diagnosis network CTMLP based on convolutions and multi-layer perceptrons (MLPs) is proposed for the diagnosis of COVID-19. The previous self-supervised algorithms are based on CNNs and VITs, and the effectiveness of such algorithms for MLPs is not yet known. At the same time, due to the lack of ImageNet-scale datasets in the medical image domain for model pre-training. So, a pre-training scheme TL-DeCo based on transfer learning and self-supervised learning was constructed. In addition, TL-DeCo is too tedious and resource-consuming to build a new model each time. Therefore, a guided self-supervised pre-training scheme was constructed for the new lightweight model pre-training. RESULTS: The proposed CTMLP achieves an accuracy of 97.51%, an f1-score of 97.43%, and a recall of 98.91% without pre-training, even with only 48% of the number of ResNet50 parameters. Furthermore, the proposed guided self-supervised learning scheme can improve the baseline of simple self-supervised learning by 1%-1.27%. CONCLUSION: The final results show that the proposed CTMLP can replace CNNs or Transformers for a more efficient diagnosis of COVID-19. In addition, the additional pre-training framework was developed to make it more promising in clinical practice.

A novel discrete learning-based intelligent methodology for breast cancer classification purposes.

Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required.

Optimization of Processing Conditions and Mechanical Properties for PEEK/PEI Multilayered Blends.

The goal of producing polyetheretherketone/polyetherimide (PEEK/PEI) blends is to combine the outstanding properties that both polymers present separately. Despite being miscible polymers, it is possible to achieve PEEK/PEI multilayered blends in which PEEK crystallinity is not significantly inhibited, as opposed to conventional extruding processes that lead to homogeneous mixtures with total polymer chain interpenetration. This study investigated a 50/50 (volume fraction) PEEK/PEI multilayered polymer blend in which manufacturing parameters were tailored to simultaneously achieve PEEK-PEI adhesion while keeping PEEK crystallinity in order to optimize the mechanical properties of this heterogeneous polymer blend. The interface adhesion was characterized with the use of three-point bending tests, which proved that a processing temperature below the melting point of PEEK produced weak PEEK-PEI interfaces. Results from differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and X-ray diffraction analysis (XRD) showed that under a 350 °C consolidation temperature, there is a partial mixing of PEEK and PEI layers in the interface that provides good adhesion. The thickness of the mixed homogeneous region at this temperature exhibits reduced sensitivity to processing time, which ensures that both polymers essentially remain separate phases. This also entails that multilayered blends with good mechanical properties can be reliably produced with short manufacturing cycles. The combination of mechanical performance and potential joining capability supports their use in a wide range of applications in the automotive, marine, and aerospace industries.

Mitochondrion-located peptides and their pleiotropic physiological functions.

With the development of advanced technologies, many small open reading frames (sORFs) have been found to be translated into micropeptides. Interestingly, a considerable proportion of micropeptides are located in mitochondria, which are designated here as mitochondrion-located peptides (MLPs). These MLPs often contain a transmembrane domain and show a high degree of conservation across species. They usually act as co-factors of large proteins and play regulatory roles in mitochondria such as electron transport in the respiratory chain, reactive oxygen species (ROS) production, metabolic homeostasis, and so on. Deficiency of MLPs disturbs diverse physiological processes including immunity, differentiation, and metabolism both in vivo and in vitro. These findings reveal crucial functions for MLPs and provide fresh insights into diverse mitochondrion-associated biological processes and diseases.

An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs.

A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed. Nevertheless, it is still challenging to predict AD by classifying them into AD, Mild Cognitive Impairment (MCI), along with Normal Control (NC) regarding larger features. By utilizing the Momentum Golden Eagle Optimizer-centric Transient Multi-Layer Perceptron network (Momentum GEO-Transient MLP), an effectual AD prediction technique has been proposed to trounce the aforementioned issues. Firstly, the input images are supplied for post-processing. In post-processing, by employing Patch Wise L1 Norm (PWL1N), the image resizing along with noise removal is engendered. Then, by utilizing Truncate Intensity Based Operation (TIBO) from the post-processed images, the unwanted brain parts are taken away. Next, the skull-stripped images are pre-processed. In this, by deploying Carnot Cycle Entropy-centric Global and Local technique (c2EBGAL), the images are normalized along with ameliorated. Afterward, by implementing Modified Emperor Penguins Colony-centered Sparse Subspace Clustering (MEPC-SSC), the pre-processed images are segmented. Then, for extracting the features, the segmented images are utilized; subsequently, the features being extracted are fed to the Momentum GEO-Transient MLPs.For transferring images fromMRI into more compact higher-level features, this system is wielded for fusing features from diverse layers. The parameters, which minimize the computation complexity, are decreased. For AD classification, the proposed technique is analogized to the prevailing methodologies regardingaccuracy, sensitivity, specificity et cetera along with acquired enhanced outcomes. Thus, the proposed system is apt for the AD diagnosis.

Myxoid liposarcoma with cartilaginous differentiation showing DDIT3 rearrangement.

Myxoid liposarcoma (MLPS) is the second most common histologic subtype of liposarcoma. However, cartilaginous differentiation within MLPS is an extremely rare phenomenon, with only 7 cases of MLPS with cartilaginous differentiation reported to date. The majority of MLPS cases show the t(12;16)(q13;p11) translocation, resulting in the fused in sarcoma-DNA damage-inducible transcript 3 (FUS-DDIT3) fusion gene. This fusion gene as a hallmark of MLPS is very useful for differential diagnosis from other soft tissue sarcomas, and the associated protein, FUS-DDIT3, performs an important role in the phenotypic selection of targeted multipotent mesenchymal cells during oncogenesis. In this report, a case of MLPS with cartilaginous differentiation that occurred in the thigh of a 44-year-old woman is described. Histopathologically, the tumor was composed of a typical myxoid liposarcoma area and a mature hyaline cartilaginous area. Using fluorescence in situ hybridization analysis, rearrangement of the DDIT3 gene was detected in not only the liposarcomatous area but also in the chondrocytes of the cartilaginous area. Based on these findings, the cartilaginous differentiation area appears to be partially associated with oncogenesis through the specific fusion gene FUS-DDIT3.