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PurposeWith the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.Design/methodology/approachCombining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.FindingsExperimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.Originality/valueWith the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Humans constantly receive massive amounts of information, both perceived from the external environment and imagined from the internal world. To function properly, the brain needs to correctly identify the origin of information being processed. Recent work has suggested common neural substrates for perception and imagery. However, it has remained unclear how the brain differentiates between external and internal experiences with shared neural codes. Here we tested this question by systematically investigating the neural processes underlying both the generation and maintenance of information from voluntary imagery, veridical perception, and illusion. The inclusion of illusion allowed us to differentiate between objective and subjective internality: while illusion has an objectively internal origin and can be viewed as involuntary imagery, it is also subjectively perceived as having an external origin like perception. Combining fMRI, eye-tracking, multivariate decoding and encoding approaches, we observed superior orientation representations in parietal cortex during imagery compared to perception, and conversely in early visual cortex. This imagery dominance gradually developed along a posterior-to-anterior cortical hierarchy from early visual to parietal cortex, emerged in the early epoch of imagery and sustained into the delay epoch, and persisted across varied imagined contents. Moreover, representational strength of illusion was more comparable to imagery in early visual cortex, but more comparable to perception in parietal cortex, suggesting content-specific representations in parietal cortex differentiate between subjectively internal and external experiences, as opposed to early visual cortex. These findings together support a domain-general engagement of parietal cortex in the generation and maintenance of internally-generated experience.Significance StatementHow does the brain differentiate between imagined and perceived experiences? Combining fMRI, eye-tracking, multivariate decoding and encoding approaches, the current study revealed enhanced stimulus-specific representations in visual imagery originating from IPS, supporting the subjective experience of imagery. This neural principle was further validated by evidence from visual illusion, wherein illusion resembled perception and imagery at different levels of cortical hierarchy. Our findings provide direct evidence for the critical role of parietal cortex as a domain-general source region for the generation and maintenance of content-specific imagery, and offer new insights into the neural mechanisms underlying the differentiation between subjectively internal and external experiences.

Web page classification is critical for information retrieval. Most web page classification methods have the following two faults: (1) need to analyze based on the overall web page and (2) do not pay enough attention to the existence of noise information inside the web page, which will thus decrease the efficiency and classification performance, especially when classifying the contaminated web page. To solve these problems, this paper proposes a denoising disposal algorithm. We choose the top-down method for hierarchical classification to improve the prediction efficiency. The experimental results demonstrate that our method is about 7 times faster than the full-page method and achieves good classification results in most categories. The precision of 7 parent categories is all above 88% and is 24% higher than the other meta tag-based method on average.

Calreticulin (CRT) is a multifunctional protein found within the endoplasmic reticulum (ER). In addition, CRT participates in the formation and development of tumors and promotes the proliferation and migration of tumor cells. When a malignant tumor occurs in the human body, cancer cells that die from immunogenic cell death (ICD) expose CRT on their surface, and CRT that is transferred to the cell surface represents an "eat me" signal, which promotes dendritic cells to phagocytose the tumor cells, thereby increasing the sensitivity of tumors to anticancer immunotherapy. Expression of CRT in tumor tissues is higher than in normal tissues and is associated with disease progression in many malignant tumors. Thus, the dysfunctional production of CRT can promote tumorigenesis because it disturbs not only the balance of healthy cells but also the body's immune surveillance. CRT may be a diagnostic marker and a therapeutic target for cancer, which is discussed extensively in this review.

Objective: Gastric cancer is one of the most common malignant tumors in the world. IGHG1 is a differentially expressed protein screened out in gastric cancer in the early stage of the subject group. This topic explores the expression of IGHG1 in gastric cancer and the effect of IGHG1 on the proliferation, migration, invasion and EMT of gastric cancer SGC7901 cells and its mechanism of action. Methods: Twenty cases of gastric cancer were purified by laser Capture Microdissection. The isotopic tags for relative and absolute quantification was used to label the proteins, and then analyzed and identified them by quantitative proteomics. Immunohistochemical staining method was used to detect the expression of IGHG1 protein in gastric cancer tissues. Western blot was used to detect the expression of IGHG1 in gastric cancer cells. The MTT and Petri dish clone formation experiment analyzed the effect of low expression of IGHG1 on the proliferation of SGC7901 cells. Scratch test and Transwell migration and invasion test to observe the effect of low expression of IGHG1 on the migration and invasion of SGC7901 cells. Western blot was used to detect the effect of low expression of IGHG1 on the expression of EMT-related proteins. Results: 243 proteins related to gastric mucosal lesions were preliminarily identified. We found that IGHG1 is highly expressed in gastric cancer tissues compared with normal control tissues. IGHG1 promotes the proliferation, migration and invasion of gastric cancer cells. Compared with the control group, the expression of EMT-related proteins Vimentin, N-cadherin, TGF-β, P-SMAD3 was decreased and the expression of E-cadherin was increased after IGHG1 low expression. Conclusions: IGHG1 induces EMT in SGC7901 cells by regulating the TGF-β/SMAD3 signaling pathway.

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

Photodynamic therapy (PDT) is a promising strategy for tumor treatment. Still, its therapeutic efficacy is compromised by the unsatisfactory cytotoxicity to specific subcellular organelles and insidious tumor microenvironment properties like hypoxia and high glutathione levels. Here, we fabricated a novel nanoenzyme that derived from metal-organic framework (MOF) with intrinsic catalase-like activities to decompose H2O2 to O2 and simultaneous glutathione consumption for enhancing PDT efficacy. The obtained Mn3O4 nanoparticle shows a larger pore size and surface area compared to native MOF particles, which can be used to load high dose photosensitizer. When decorated with AS1411 aptamer and polyethylene glycol (PEG), the obtained Mn3O4-PEG@C&A particle exhibits excellent stability and cell nucleus targeting ability. Remarkably, Mn3O4-PEG@C&A particle inhibited the tumor growth in the mouse model with high efficacy without any biotoxicity. This is the first report that applied MOF-derived nanoparticle to nucleus-targeted PDT. It may provide a new approach for designing functional nanoenzyme to subcellular organelles-targeted tumor modulation.

The success of a detection model depends heavily on feature engineering. Deep learning has been successfully applied in numerous research fields as a universal representation learning method. However, the heterogeneity of flow-level communication data obstructs the application of deep learning to communication representation learning, and research on this problem is still lacking. To cope with this problem, we propose a heterogeneous communication data-encoding approach to extract fixed-size encoding data to apply deep learning to heterogeneous communication data by preserving the spatiotemporal characteristics of the data. Then, we propose a feature extractor based on deep learning to automatically learn hierarchical and robust communication representations without expert knowledge. We show that the proposed approach can replicate and optimize the key steps of feature engineering well and learn hierarchical representations directly from heterogeneous communication data. Moreover, compared with features extracted with principal component analysis (PCA), manifold learning and manually crafted methods, the features extracted by deep learning are more robust and are characterized by their better adaptability to various classifiers and datasets. To the best of our knowledge, the initial work here is the first to apply deep learning techniques to heterogeneous flow-level data; consequently, the heterogeneous communication data processing method can provide technical means for the application of deep learning in other communication-related research fields.

The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work. Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed. However, the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges: the difficulty of extracting effective features in advance and the complexity of the actual sample types. To address these problems, we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network. This method supports continuous learning and optimization feature representation while labeling sample, and can handle uncertain samples that are outside the concerned sample types. According to the experimental results, our proposed deep neural network can automatically learn effective feature representation, and the validity of features is close to or even higher than that of features which extracted based on expert knowledge. Furthermore, our proposed method can achieve the labeling accuracy of 97.64%∼98.50%, which is more accurate than the train-then-detect, kNN and LPA methods in any labeled-sample proportion condition. The problem of insufficient labeled samples in many network attack detecting scenarios, and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.

Biomolecular phase separation is currently emerging in both the medical and life science fields. Meanwhile, the application of liquid-liquid phase separation has been extended to many fields including drug discovery, fibrous material fabrication, 3D printing, and polymer design. Although more than 8600 proteins and other synthetic macromolecules are capable of phase separation as recently reported, there is still a lack of a high-throughput approach to quantitatively characterize its phase behaviors. To meet this requirement, here, we proposed fast and high-resolution acquisition of biomolecular phase diagrams using microfluidic chips. Using this platform, we demonstrated the phase behavior of polyU/RRASLRRASLRRASL in a quantitative manner. Up to 1750 concentration conditions can be generated in 140 min. The detection limitation of our device to capture the saturation concentration for phase separation is about 5 times lower than that of the traditional turbidity method. Thus, our results provide a basis for the rapid acquisition of phase diagrams with high-throughput and pave the way for its wide application.

Gastric cancer is one of the most common malignant tumours worldwide. Genetic and epigenetic alterations are key factors in gastric carcinogenesis and drug resistance to chemotherapy. Competing endogenous RNA (ceRNA) regulation models have defined circRNA/lncRNA as miRNA sponges that indirectly regulate miRNA downstream target genes. The ceRNA regulatory network is related to the malignant biological behaviour of gastric cancer. The circRNA/lncRNA-miRNA-mRNA axis may be a marker for the early diagnosis and prognosis of gastric cancer and a potential therapeutic target for gastric cancer. Exosomal ncRNAs play an important role in gastric cancer and are expected to be ideal biomarkers for the diagnosis, prognosis, and treatment of gastric cancer. This review summarizes the specific ceRNA regulatory network (circRNA/lncRNA-miRNA-mRNA) discovered in gastric cancer in recent years, which may provide new ideas or strategies for early clinical diagnosis, further development, and application.

Left ventricular diastolic dysfunction is a structural and functional condition that precedes the development of heart failure with preserved ejection fraction (HFpEF). The etiology of diastolic dysfunction includes alterations in fuel substrate metabolism that negatively impact cardiac bioenergetics, and may precipitate the eventual transition to heart failure. To date, the molecular mechanisms that regulate early changes in fuel metabolism leading to diastolic dysfunction remain unclear. In this report, we use a diet-induced obesity model and quantitative acetylproteomics in aged mice to show that inhibitory lysine acetylation of the pyruvate dehydrogenase (PDH) complex promotes energetic deficits and diastolic dysfunction in mouse hearts. Cardiomyocyte-specific deletion of the mitochondrial lysine acetylation regulatory protein GCN5L1 prevented hyperacetylation of the PDH complex subunit PDHA1, allowing aged obese mice to continue using pyruvate as a bioenergetic substrate in the heart. Our findings suggest that changes in mitochondrial protein lysine acetylation represent a key metabolic component of diastolic dysfunction that precedes the development of heart failure.

Among cancers, breast cancer has the highest incidence rate among women and poses a tremendous threat to women's health. Messenger RNA (mRNA), microRNA (miRNA) and circular RNA (circRNA) play vital roles in the progression of breast cancer through a variety of biological effects and mechanisms. Recently, the regulatory network formed by circRNAs, miRNAs and mRNAs has piqued attention and garnered interest. CircRNAs bind to miRNAs through a regulatory mechanism in which endogenous RNAs compete to indirectly regulate the expression of mRNA corresponding to downstream target genes of miRNAs, contributing to the progression of breast cancer. The circRNA-miRNA-mRNA axis may be a marker for the early diagnosis and prognosis of breast cancer and a potential breast cancer treatment target, providing unlimited possibilities for the development of breast cancer biomarkers and therapeutic strategies. This article reviews recent research progress on the circRNA-miRNA-mRNA axis as a regulatory network of competing endogenous RNAs in breast cancer. Herein, we focus on the mechanism and function of the circRNA-miRNA-mRNA axis in the occurrence and metastasis of breast cancer, and resistance to chemotherapy.

Elevated expression of the DNA damage response proteins PARP1 and poly(ADP-ribose) glycohydrolase (PARG) in glioma stem cells (GSCs) suggests that glioma may be a unique target for PARG inhibitors (PARGi). While PARGi-induced cell death is achieved when combined with ionizing radiation, as a single agent PARG inhibitors appear to be mostly cytostatic. Supplementation with the NAD+ precursor dihydronicotinamide riboside (NRH) rapidly increased NAD+ levels in GSCs and glioma cells, inducing PARP1 activation and mild suppression of replication fork progression. Administration of NRH+PARGi triggers hyperaccumulation of poly(ADP-ribose) (PAR), intra S-phase arrest and apoptosis in GSCs but minimal PAR induction or cytotoxicity in normal astrocytes. PAR accumulation is regulated by select PARP1- and PAR-interacting proteins. The involvement of XRCC1 highlights the base excision repair pathway in responding to replication stress while enhanced interaction of PARP1 with PCNA, RPA and ORC2 upon PAR accumulation implicates replication associated PARP1 activation and assembly with pre-replication complex proteins upon initiation of replication arrest, the intra S-phase checkpoint and the onset of apoptosis.

Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it consistent with normal network communication, and general network traffic identification methods are not able to detect it. To accurately and effectively discover proxy users in the organization based on s, a proxy user detection method based on communication behavior portrait offers the following: (1) analysis of the communication behavior from the perspective of the portrait. Based on not abandoning the effective information of the traffic itself, the label system is established by introducing exogenous data to identify the difference between proxy communication and normal communication. (2) Construction of the portrait feature set of proxy user detection based on the traffic file and external data by studying the differences between the attribute sets of communication behavior labels for proxy users and non-proxy users. (3) Design and implementation a data-driven machine learning method to supply guidance for automatic recognition of such behavior. The experimental results show that, compared with state-of-the-art methods, the detection accuracy for the proxy user exceeds 95%, and that of real network traffic environment exceeds 85%. These results indicate that the detection method proposed in this paper can accurately distinguish proxy communication and normal communication and thus achieves precise proxy user detection.

Cell signaling is a fast, dynamic, and complex process, which controls a variety of critical physiological functions. Methods to investigate such dynamic information, however, suffer from limited throughput in the single-cell level and a lack of precise fluid manipulation. Herein, we present a new strategy, termed dynamic microfluidic cytometry (DMC), for high-throughput probing of G protein-coupled receptor (GPCR) signaling in single-cell resolution (single-cell cellomics analysis) by creatively applied cyclical cell trapping, stimulating, and releasing automatically. Dose-response curves and half-maximal effective concentration (EC50) values for HeLa cells treated with adenosine triphosphate (ATP), histamine (HA), and acetylcholine chloride (ACH) were successfully obtained in the single-cell level. High-throughput single-cell dynamic signaling was further implemented by sequential or simultaneous stimulation, which revealed that different mechanisms were working in triggering intracellular calcium release. In addition, simultaneous stimulation to two different types of cells, HeLa and NIH-3T3 cells, was also successfully realized, which was crucial for online comparison of dynamic signaling of different types of cells. We believe that the proposed DMC provides a versatile means for high-throughput probing single-cell dynamic signaling, which is potentially useful in chemical biology, cell biology, and pharmacology.

Reprogrammed glucose metabolism is vital for cancer cells, but aspartate, an intermediate metabolic product, is the limiting factor for cancer cell proliferation. However, due to the complexity of metabolic pathways, it remains unclear whether glucose is the primary source of endogenous aspartate. Here, we report the design of an innovative molecular deactivator, based on a multifunctional upconversion nanoprobe, to explore the link between glucose and aspartate. This molecular deactivator mainly works in the acidic, hypoxic tumor microenvironment and deactivates multiple types of glucose transporters on cancer cell membranes upon illumination at 980 nm. Cancer cell proliferation in vivo is strongly inhibited by blocking glucose transporters. Our experimental data confirm that the cellular synthesis of aspartate for tumor growth is glucose-dependent. This work also demonstrates the untapped potential of molecularly engineered upconversion nanoprobes for discovering hidden metabolic pathways and improving therapeutic efficacy of conventional antitumor drugs.