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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor‐made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text‐based ED systems. The article further discusses some recent state‐of‐the‐art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion‐labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text‐based ED. The article surveys the concept of emotion detection from texts and highlights the major contributions, approaches, datasets, and weaknesses of recent text‐based emotion detection schemes. Also, emotion‐labeled data sources are presented to provide neophytes with text databases that are eligible for emotion detection. The paper further explores possible opportunities for improving the detection of emotions from texts.

Xanthatin (XT) is a sesquiterpene lactone isolated from the Chinese herb Xanthium, which belongs to the Asteraceae family. In this study, we developed an inflammation model via stimulating macrophage cell line (RAW 264.7 cells) with lipopolysaccharide (LPS), which was applied to assess the anti-inflammatory effect and probable mechanisms of xanthatin. When compared with the only LPS-induced group, cells that were pretreated with xanthatin were found to decrease the amount of nitric oxide (NO), reactive oxygen species (ROS) and associated pro-inflammatory factors (TNF-α, IL-1β and IL-6), and downregulate the mRNA expression of iNOS, COX-2, TNF-α, IL-1β, and IL-6. Interestingly, phosphorylated levels of related proteins (STAT3, ERK1/2, SAPK/JNK, IκBα, p65) were notably increased only with the LPS-activated cells, while the expression of these could be reverted by pre-treatment with xanthatin in a dose-dependent way. Meanwhile, xanthatin was also found to block NF-κB p65 from translocating into the nucleus and activating inflammatory gene transcription. Collectively, these results demonstrated that xanthatin suppresses the inflammatory effects through downregulating the nuclear factor kappa-B (NF-κB), mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription (STATs) signaling pathways. Taken together, xanthatin possesses the potential to act as a good anti-inflammatory medication candidate.

Driven by the wave of Industry 4.0, intelligent manufacturing is gradually becoming the mainstream trend in manufacturing industry. As a new generation of quality management concept, Quality 4.0 provides a new set of thinking and methods for quality management in various industries. This paper first systematically reviews the research progress of Quality 4.0 in China and abroad, then introduces the definition of Quality 4.0, the relationship between Quality 4.0 and Industry 4.0, traditional quality management and intelligent manufacturing, and finally sorts out the theoretical framework of Quality 4.0 and gives a summary.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation.

Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject’s chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years ( p  < 0.0001, Cohen’s d  = 0.31, 95% CI: 2.23–3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years ( p  < 0.05, Cohen’s d  = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

Background Immune escape is a defining feature of malignant tumor initiation and progression. CDC25A is an oncogenic gene, highly expressed in various cancers, yet the molecular mechanisms behind its upregulation in lung adenocarcinoma (LUAD) and its role in immune evasion remain incompletely understood. Methods We assessed CDC25A and lysine acetyltransferase 2 A (KAT2A) expression in LUAD patients, leveraging the TCGA-LUAD database, and verified their mRNA and protein levels in LUAD cell lines and tissues with qRT-PCR and WB. Kaplan-Meier analysis was used to explore the prognostic relevance of CDC25A expression in LUAD, while Pearson correlation was performed to analyze the relationship between CDC25A and KAT2A. Cell viability and proliferation were determined using CCK-8 and colony formation assays. A cell metabolic analyzer was used to assess metabolic rates (ECAR and OCR). Glucose and lactate levels in the supernatant were measured using respective assay kits. CD8 + T cells were co-cultured with LUAD cells, with their activation monitored by flow cytometry. The killing capacity of CD8 + T cells towards LUAD cells was evaluated with lactate dehydrogenase (LDH) and ELISA kits. The interaction between CDC25A and hexokinase 2 (HK2) and their cytoplasmic co-localization were confirmed by CO-immunoprecipitation (CO-IP) and immunofluorescence. Chromatin Immunoprecipitation (ChIP) verified the binding relationship between KAT2A and CDC25A. In vivo validation was conducted in a mouse allograft tumor model. Results CDC25A was upregulated in LUAD tissues and cells, with an identified link to poorer patient survival. Knocking down CDC25A expression hindered LUAD cell activation and proliferation, lowered aerobic glycolysis, elevated the ratio of CD8 + IFN-γ + T cells, and boosted the cytotoxicity of CD8 + T cells against tumor cells. Moreover, CDC25A could interact with HK2 to impact its protein expression, thereby modulating aerobic glycolysis in LUAD cells. KAT2A, highly expressed in LUAD tissues and cells, was positively correlated with CDC25A levels. KAT2A promoted the acetylation of CDC25A, promoting immune evasion in LUAD cells. The in vivo and in vitro studies had similar results. Conclusion This research reveals that KAT2A promotes the expression of CDC25A via acetylation. Subsequently, CDC25A interacts with HK2 to control aerobic glycolysis, thereby driving the immune evasion process in LUAD. Graphical abstract

As a Gram‐negative anaerobic bacterium, Akkermansia muciniphila (AKK) participates in the immune response in many cancers. Our study focused on the factors and molecular mechanisms of AKK affecting immune escape in lung adenocarcinoma (LUAD). We cultured AKK bacteria, prepared AKK outer membrane protein Amuc_1100 and constructed a subcutaneous graft tumour mouse model. A549, NCI‐H1395 cells and mice were respectively treated with inactivated AKK, Amuc_1100, Ruxolitinib (JAK inhibitor) and RO8191 (JAK activator). CD8+ T cells that penetrated the membrane were counted in the Transwell assay. The toxicity of CD8+ T cells was evaluated by lactate dehydrogenase assay. Western blot was applied to determine JAK/STAT‐related protein and PD‐L1 expression, whilst CCL5, granzyme B and INF‐γ expression were assessed through enzyme‐linked immunosorbent assay (ELISA). The proportion of tumour‐infiltrating CD8+ T cells and the levels of granzyme B and INF‐γ were determined by flow cytometry. AKK markedly accelerated A549 and NCI‐H1395 recruiting CD8+ T cells and enhanced CD8+ T cell toxicity. Amuc_1100 purified from AKK exerted the same promoting effects. Besides, Amuc_1100 dramatically suppressed PD‐L1, p‐STAT and p‐JAK expression and enhanced CCL5, granzyme B and INF‐γ expression. Treatment with Ruxolitinib accelerated A549 and NCI‐H1395 cells recruiting CD8+ T cells, enhanced CD8+ T cell toxicity, CCL5, granzyme B and INF‐γ expression, and inhibited PD‐L1 expression. In contrast, the RO8191 treatment slowed down the changes induced by Amuc_1100. Animal experiments showed that Amuc_1100 was found to increase the number of tumour‐infiltrating CD8+ T cells, increase the levels of granzyme B and INF‐γ and significantly inhibit the expression of PD‐L1, p‐STAT and p‐JAK, which exerted an antitumour effect in vivo. In conclusion, through inhibiting the JAK/STAT signalling pathway, AKK outer membrane protein facilitated the recruitment of CD8+ T cells in LUAD and suppressed the immune escape of cells. AKK promoted the recruitment of CD8+ T cells by lung adenocarcinoma (LUAD) cells and the toxicity of CD8+ T cells; AKK outer membrane protein was the main substance involved in the immune escape of LUAD. Through the JAK/STAT signalling pathway, AKK outer membrane protein promoted the recruitment of CD8+ T cells by LUAD cells and toxicity of CD8+ T cells, and inhibited immune escape.