General Features Research Articles

Holographic tensor networks with bulk gauge symmetries

Tensor networks are useful toy models for understanding the structure of entanglement in holographic states and reconstruction of bulk operators within the entanglement wedge. They are, however, constrained to only prepare so-called “fixed-area states” with flat entanglement spectra, limiting their utility in understanding general features of holographic entanglement. Here, we overcome this limitation by constructing a variant of random tensor networks that enjoys bulk gauge symmetries. Our model includes a gauge theory on a general graph, whose gauge-invariant states are fed into a random tensor network. We show that the model satisfies the quantum-corrected Ryu-Takayanagi formula with a nontrivial area operator living in the center of a gauge-invariant algebra. We also demonstrate nontrivial, n-dependent contributions to the Rényi entropy and Rényi mutual information from this area operator, a feature shared by general holographic states.

Are There General Features of How Immune Responses Are Regulated That Can Provide Clues to How Remitting/Relapsing Multiple Sclerosis May Be Treated?

Most basic studies directed at how immune responses are regulated employ chemically "simple antigens", usually purified proteins. The target antigens in many clinical situations, such as in autoimmunity, infectious diseases and cancer, are chemically "complex", consisting of several distinct molecules, and they often are part of a replicating entity. We examine here the relationships between how immune responses to complex and simple antigens are regulated. This examination provides a context for considering how immune responses are regulated in those clinical situations involving complex antigens. I have proposed and discuss here a mechanism by which immune responses to the envisaged complex target antigen in remitting/relapsing multiple sclerosis go back and forth between inflammatory and non-inflammatory modes, potentially accounting for the course of this disease. This proposal makes predictions that can be tested by non-invasive means. It also leads to a suggestion for simple, non-invasive treatment.

Visual representation learning using graph-based higher-order heuristic distillation for cell detection in blood smear images

Background and objectiveIn many real-world scenarios, including the blood smear domain, it is difficult for detection networks to achieve good performance because image annotation is usually time consuming and expensive. To address this issue, similarity-based distillation (SD) methods, considered the soft version of contrastive learning, are applied to learn a better visual representation without requiring any supervision of the downstream task. Motivated by our theoretical analysis, we treat standard SD methods as the maximization of common 1-hop neighboring key points between two queries in an attributed graph, where nodes represent query and key data points. However, such first-order graph heuristic methods that calculate the likelihood of an unseen link between target nodes by using up to 1-hop neighborhoods are normally limited by insufficient representation power and even lack of generalization ability. MethodsTherefore, in this paper, we propose a novel higher-order heuristic distillation (H2D) method that distills knowledge about more general and powerful higher-order heuristic features based on a more than 1-hop relationship in the attributed graph. To do this, we utilize a graph neural network model to learn the higher-order heuristic features on the attributed graph constructed by query and key data representations and transfer the knowledge from the teacher to the student encoder. ResultsOur method outperforms the previous state-of-the-art SD methods in the cell detection task on the blood smear dataset as well on open databases (Pascal VOC and MS COCO). Conclusions: Our proposed model allow teacher encoder to transfer the knowledge about more general and powerful higher-order heuristic embeddings to the student and enables better learning for visual representation on cell detection task using blood smear images.

Phenomenology of superheavy decaying dark matter from string theory

We study the phenomenology of superheavy decaying dark matter with mass around 1010 GeV which can arise in the low-energy limit of string compactifications. Generic features of string theory setups (such as high scale supersymmetry breaking and epochs of early matter domination driven by string moduli) can accommodate superheavy dark matter with the correct relic abundance. In addition, stringy instantons induce tiny R-parity violating couplings which make dark matter unstable with a lifetime well above the age of the Universe. Adopting a model-independent approach, we compute the flux and spectrum of high-energy gamma rays and neutrinos from three-body decays of superheavy dark matter and constrain its mass-lifetime plane with current observations and future experiments. We show that these bounds have only a mild dependence on the exact nature of neutralino dark matter and its decay channels. Applying these constraints to an explicit string model sets an upper bound of O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(0.1) on the string coupling, ensuring that the effective field theory is in the perturbative regime.

ISRM: introspective self-supervised reconstruction model for rail surface defect detection and segmentation

The problems of intrinsic imbalance of the sample and interference from complex backgrounds limit the performance of existing deep learning methods when applied to the detection and segmentation of rail surface defects. To address these issues, an introspective self-supervised reconstruction model (ISRM) is proposed, which only requires normal samples in the training phase and incorporates the concept of self-supervised learning into an introspective autoencoder. The training framework of ISRM first extracts general features using a pretrained Feature Extractor. Subsequently, a Feature Transformer transfers the features to the target domain. Next, a synthetic defect embedder embeds Bessel-Gaussian random defects into the feature space. Finally, the asymmetric autoencoder reconstructs the rail surface features back into image space. The transformation of pretrained features into target-oriented features helps mitigate domain bias. Since defects exhibit higher commonality in the feature space relative to the image space, embedding synthetic defects into the feature space effectively improves training efficiency. Moreover, the adversarial training architecture enhances the clarity of reconstructed images. The impact of core parameters on the model performance is analyzed through ablation experiments. The results from comparative experiments demonstrate that ISRM achieves 98.5% and 97.2% accuracy on defect detection and segmentation tasks, respectively, reducing the error rate by 11.8% and 3.4% compared to the current state-of-the-art model.

Measuring Perceived Walkability at the City Scale Using Open Data

The walkability of the built environment has been shown to be critical to the health of residents, and open data have been widely used to assess walkability. However, previous research has focused on the relationship between the built environment and walking behavior rather than perceived walkability, and there is a lack of systematic research on walkability at the urban scale using open data. This paper presents a methodological framework for systematically measuring and assessing perceived walkability at the urban scale, considering general and specific features. The walkability indices are obtained using variables from open data or calculated automatically through machine learning and algorithms to ensure they can be evaluated at a larger urban scale. The proposed method is applied to Harbin, China, to assess the perceived walkability of streets using hundreds of thousands of street view images and points of interest obtained from open data. The results are compared with a subjective evaluation of walkability to validate the proposed method. The results demonstrate that measures of the urban built environment can describe perceived walkability. Thus, the proposed framework shows promise for assessing the walkability of urban spaces, supporting policy proposals, and establishing design guidelines for optimising urban spaces.

State bodies’ formation for ensuring railway safety in Russia (October 1917 – July 1918)

Importance. Scientific interest in studying the turning points of history contributes to the development of the most effective mechanisms for organizing the activities of state law enforcement agencies in modern conditions. The conduct of a special military operation and the current geopolitical situation require the state to take special measures to ensure law and order at key elements of government, including transport facilities. After the February and then the October Revolutions of 1917, the continuity of law enforcement at railway transport facilities was lost. Armed formations of various kinds were spontaneously created on the railways to commit crimes, seize and retain power. The experience of developing measures to ensure law and order in the context of armed interference in the railways’ activities, measures taken to stabilize the situation, is of scientific and practical importance. The purpose of the study is to determine the formation sequence of state agencies for ensuring the safety of railways during the formation of Soviet power, their competence and competence.Research Methods. The historicism principle along with the application of the historical and genetic method, make it possible to analyze the formation sequence of state agencies to ensure the safety of railways in the period from October 1917 to July 1918 and, based on individual facts, identify the special. The comparative method shows the general and special features of the phenomena considered within the framework of the formation of the entire law enforcement system of the Soviet state. Based on the analytical method, it is possible to compare the activities of the agencies created by the Soviet state to ensure law and order at railway transport facilities with the gendarmerie police units of the railways that operated before the February Revolution of 1917.Results and Discussion. Based on the conducted research, the reasons why the Soviet authority created centralized law enforcement agencies at railway transport facilities are clarified. The description of the events against which these organizations and services were created is given. Steps have been taken to explain the contradictions between the interests of local and central authorities in the period under review, as well as the prerequisites for which the Soviet authority went from completely denying the experience of previous agencies to the need to use it.Conclusions. It is concluded that the creation by the Soviet authoruty of law enforcement units and services and ensuring the safety of transport infrastructure facilities took place situationally, initially without any plan, through the denial of the legacy of tsarist Russia, centralization and resumption of the linear principle of the work of agencies, taking into account the military-political, economic situation in the country and the specifics of the object of law enforcement. The workers’ militsiya could not cope with the tasks of ensuring human and civil rights and freedoms at railway transport facilities at the first stage of its formation.

Application of Eu(III) activated tungstates in solid state lighting

Tungstates play a major role in the, now for more than 60 years, ongoing scientific research of Eu(III) activated phosphors. Similarly, to other compounds featuring a closed shell transition metal anion like molybdates, vanadates, niobdates, tantalates, and tungstates are a family of materials that offer a wide range of potential host matrices for rare earth ions. Furthermore, tungstates provide several favourable characteristics like facile synthetic conditions via solid state synthesis, the possibility of high activator concentrations (low concentrations quenching), as well as intrinsic sensitization of lanthanides ions through a ligand-to-metal charge transfer (W6+/O2−). This review sets in by discussing the fundamentals of Eu(III) luminescence followed by a closer look at some general features of tungstates and their respective intrinsic luminescence. Various Eu(III) activated tungstates are explored in detail regarding their synthesis conditions and optical properties. Their compositional makeups and structures are correlated to the resulting optical properties. Potential strategies to improve the luminescent efficiency of new rare earth doped tungstates are derived from these correlations. In the closing section some general remarks regarding thermal quenching, concentration quenching, and charge transfer energies are discussed. Finally, comparisons to the related molybdates are drawn and the prospects of commercial application towards different fields of optical technologies are explored.

Systematic Status of Copepods Infecting the Rosy Goatfish (Parupeneus rubescens)

Fish represent one of the most important sources of protein. All fish species are vulnerable to great hazards due to infection with various parasitic taxa and other disease agents. The rosy goatfish (Parupeneus rubescens) is one of the most widely distributed and edible species. Little information about the copepod parasites infecting this fish remains. This review included all related published scientific articles in the electronic databases NCBI, ScienceDirect, Saudi digital library and GenBank database. Relevant studies were reviewed through the generic features and molecular analysis of copepods. Parasitologists use many scientific tools to classify copepods and the morphological and morphometric levels are a basic key to identifying copepods. The presence of many copepod species makes it difficult to confirm their taxonomic status. Therefore, scientists focused on the molecular tools to identify each copepod species based on nuclear ribosomal genes as markers to discriminate copepodid species. Scientists can confirm the parasite species’ systematic status by combining fundamental and innovative identification tools.

Impact of climate change on persistent cold-air pools in an alpine valley during the 21st century

Abstract. When anticyclonic conditions persist over mountainous regions in winter, cold-air pools (i.e. thermal inversions) develop in valleys and persist from a few days to a few weeks. During these persistent cold-air pool (PCAP) episodes the atmosphere inside the valley is stable and vertical mixing is prevented, promoting the accumulation of pollutants close to the valley bottom and worsening air quality. The purpose of this paper is to address the impact of climate change on PCAPs until the end of this century for the alpine Grenoble valleys. The long-term projections produced with the general circulation model MPI (from the Max Planck Institute) downscaled over the Alps with the regional climate model MAR (Modèle Atmosphérique Régional) are used to perform a statistical study of PCAPs over the period 1981–2100. The trends of the main characteristics of PCAPs, namely their intensity, duration, and frequency, are investigated for two future scenarios, SSP2–4.5 and SSP5–8.5. We find that the intensity of PCAPs displays a statistically significant decreasing trend for the SSP5–8.5 scenario only. This decay is explained by the fact that air temperature over the century increases more at 2 m above the valley bottom than in the free air at mid-altitudes in the valley; this might be due to the increase of specific humidity near the ground. The vertical structure of two PCAPs, one in the past and one around 2050, is next investigated in detail. For this purpose, the WRF (Weather Research and Forecasting) model, forced by MAR for the worst-case scenario (SSP5–8.5), is used at a high resolution (111 m). The PCAP episodes are carefully selected from the MAR data so that a meaningful comparison can be performed. The future episode is warmer at all altitudes than the past episode (by at least 4 ∘C) and displays a similar inversion height, which are very likely generic features of future PCAPs. The selected episodes also have similar along-valley wind but different stability, with the future episode being more stable than the past episode. Overall, this study shows that the atmosphere in the Grenoble valleys during PCAP episodes tends to be slightly less stable in the future under the SSP5–8.5 scenario, and statistically unchanged under the SSP2–4.5 scenario, but that very stable PCAPs can still form.

Cell contacts and pericellular matrix in the Xenopus gastrula chordamesoderm.

Convergent extension of the chordamesoderm is the best-examined gastrulation movement in Xenopus. Here we study general features of cell-cell contacts in this tissue by combining depletion of adhesion factors C-cadherin, Syndecan-4, fibronectin, and hyaluronic acid, the analysis of respective contact width spectra and contact angles, and La3+ staining of the pericellular matrix. We provide evidence that like in other gastrula tissues, cell-cell adhesion in the chordamesoderm is largely mediated by different types of pericellular matrix. Specific glycocalyx structures previously identified in Xenopus gastrula tissues are absent in chordamesoderm but other contact types like 10-20 nm wide La3+ stained structures are present instead. Knockdown of any of the adhesion factors reduces the abundance of cell contacts but not the average relative adhesiveness of the remaining ones: a decrease of adhesiveness at low contact widths is compensated by an increase of contact widths and an increase of adhesiveness proportional to width. From the adhesiveness-width relationship, we derive a model of chordamesoderm cell adhesion that involves the interdigitation of distinct pericellular matrix units. Quantitative description of pericellular matrix deployment suggests that reduced contact abundance upon adhesion factor depletion is correlated with excessive accumulation of matrix material in non-adhesive gaps and the loss of some contact types.

The State of Artificial Intelligence in Skin Cancer Publications.

Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI. A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.

Sleep deprivation detected by voice analysis.

Sleep deprivation has an ever-increasing impact on individuals and societies. Yet, to date, there is no quick and objective test for sleep deprivation. Here, we used automated acoustic analyses of the voice to detect sleep deprivation. Building on current machine-learning approaches, we focused on interpretability by introducing two novel ideas: the use of a fully generic auditory representation as input feature space, combined with an interpretation technique based on reverse correlation. The auditory representation consisted of a spectro-temporal modulation analysis derived from neurophysiology. The interpretation method aimed to reveal the regions of the auditory representation that supported the classifiers' decisions. Results showed that generic auditory features could be used to detect sleep deprivation successfully, with an accuracy comparable to state-of-the-art speech features. Furthermore, the interpretation revealed two distinct effects of sleep deprivation on the voice: changes in slow temporal modulations related to prosody and changes in spectral features related to voice quality. Importantly, the relative balance of the two effects varied widely across individuals, even though the amount of sleep deprivation was controlled, thus confirming the need to characterize sleep deprivation at the individual level. Moreover, while the prosody factor correlated with subjective sleepiness reports, the voice quality factor did not, consistent with the presence of both explicit and implicit consequences of sleep deprivation. Overall, the findings show that individual effects of sleep deprivation may be observed in vocal biomarkers. Future investigations correlating such markers with objective physiological measures of sleep deprivation could enable "sleep stethoscopes" for the cost-effective diagnosis of the individual effects of sleep deprivation.

Constructional modeling in the formalism of cognitive-quantitative construction grammar

This paper discusses the findings of constructional modeling in the formalism of cognitive quantitative construction grammar, a newly developed research framework within a cognitive-quantitative grammar approach. Triangulating theoretical tenets, methodological principles and research tools of cognitivesemiotic frameworks with quantitative corpus studies, cognitive quantitative construction grammar provides a comprehensive qualitative-quantitative approach to examining cognitive foundations, general and idiosyncratic linguistic features, usage patterns and distribution of linguistic constructions. In this context, constructional modeling entails applying a computerized linguoquantitative procedure for a construction profile parametrization. This procedure yields operationalized and statistically verified data on the essential parameters that determine a construction’s linguistic behavior. The modeling uses the boxbracket notation, which integrates the box notation to represent holistic construction-level information and construction’s constituent-level information, and the bracket notation to detail specific linguistic properties and constructional constraints.The constructional modeling in the integrated box-bracket notational system applied to English ꞌdetached nonfinite/nonverbal with explicit subjectꞌ-constructions allows for a comprehensive representation of their external and internal linguistic properties and captures inheritance links betweenthe constructions at macro-, meso- and micro-levels of the constructional network. The research findings demonstrate the feasibility of applying the cognitive quantitative construction grammar formalism to model the linguistic properties and constraints of complex clause-level constructions and how these constructions are likely to be represented in the mental grammar of speakers.

The Precautionary Approach Design Pattern

The precautionary approach is a way to manage problems related to the uncertainty of scientific knowledge regarding potential serious harms and to enable decision-making under uncertainty with due diligence. I have developed a model of the essential structural elements of the application of the precautionary approach, particularly the precautionary principle in EU law, as a legal design pattern—a formalized presentation of the core generic features and processes of how the law successfully addresses recurring problems. The precautionary approach appears as a decision tree with weighting and balancing. It serves as an intervening rule to resolve inter-system conflicts between different action systems such as the economy, civil society, and ecological systems. It is related to other design patterns like the proportionality test, risk and impact assessment, and judicial review. Presenting the application of the precautionary approach as a legal design pattern facilitates interdisciplinary communication regarding the essential features of legally required problem-solving. Such a presentation enhances the problem-solving capacity of the application of the law beyond the legal domain, for instance, in improving digitalization efforts and enabling the integration of anticipatory quality controls of justice, legality and good governance into decision-making processes.

Not all platelet-rich plasma are created equal.

This review aims to elucidate potential variations in clinical outcomes resulting from the use of different types of platelet-rich plasma (PRPs) in reproductive medicine. It seeks to explore the reasons behind the diverse results reported in various studies and assess the general features distinguishing different PRP formulations. PRPs have found applications across diverse medical fields, generating controversy due to the variability in outcomes. The field of reproductive medicine, despite its limited published studies, is encountering a similar challenge as it integrates these treatments. The multitude of PRP product brands in the market, coupled with 'home-made' PRPs, poses a significant barrier to establishing a common protocol for the preparation of standardized PRP products. This impediment hinders widespread adoption by clinicians, particularly in endometrial or ovarian treatments. Drawing from evidence in other medical disciplines, this review endeavors to compile essential characteristics that PRPs must possess, aiming to mitigate the impact of variables affecting results in forthcoming studies.

SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.

Legal Regulation of Release of Minors From Punishment and From Service of Punishment: Foreign Experience, Administrative and Criminal Aspect

The aim of this research is to unveil the legal regulations surrounding the release of minors from punishment and the execution of penalties. The primary focus is on investigating the efforts of European countries in establishing effective laws to prevent crimes committed by adults and to create new conditions for the rehabilitation of minors involved in criminal activities. The methodological foundation of this research is the dialectical method of scientific knowledge. Through the application of this method, the research considers the legal, functional, organizational, and procedural aspects of regulating the release of minors from punishment and punishment services. The research analyzed the general features and peculiarities of the legal regulation of coercive measures of an educational nature in several European countries, such as Germany, the Netherlands, Switzerland, Lithuania, the United Kingdom, and others. It was found that a large number of countries, in the provisions of their criminal laws, provide for the individualization and differentiation of criminal responsibility of minors, depending on various factors such as age, personality of the offender, and severity of the crime.

Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques

Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.

Molecular signature of stem-like glioma cells (SLGCs) from human glioblastoma and gliosarcoma.

Glioblastoma multiforme (GBM) and the GBM variant gliosarcoma (GS) are among the tumors with the highest morbidity and mortality, providing only palliation. Stem-like glioma cells (SLGCs) are involved in tumor initiation, progression, therapy resistance, and relapse. The identification of general features of SLGCs could contribute to the development of more efficient therapies. Commercially available protein arrays were used to determine the cell surface signature of eight SLGC lines from GBMs, one SLGC line obtained from a xenotransplanted GBM-derived SLGC line, and three SLGC lines from GSs. By means of non-negative matrix factorization expression metaprofiles were calculated. Using the cophenetic correlation coefficient (CCC) five metaprofiles (MPs) were identified, which are characterized by specific combinations of 7-12 factors. Furthermore, the expression of several factors, that are associated with GBM prognosis, GBM subtypes, SLGC differentiation stages, or neural identity was evaluated. The investigation encompassed 24 distinct SLGC lines, four of which were derived from xenotransplanted SLGCs, and included the SLGC lines characterized by the metaprofiles. It turned out that all SLGC lines expressed the epidermal growth factor EGFR and EGFR ligands, often in the presence of additional receptor tyrosine kinases. Moreover, all SLGC lines displayed a neural signature and the IDH1 wildtype, but differed in their p53 and PTEN status. Pearson Correlation analysis identified a positive association between the pluripotency factor Sox2 and the expression of FABP7, Musashi, CD133, GFAP, but not with MGMT or Hif1α. Spherical growth, however, was positively correlated with high levels of Hif1α, CDK4, PTEN, and PDGFRβ, whereas correlations with stemness factors or MGMT (MGMT expression and promoter methylation) were low or missing. Factors highly expressed by all SLGC lines, irrespective of their degree of stemness and growth behavior, are Cathepsin-D, CD99, EMMPRIN/CD147, Intβ1, the Galectins 3 and 3b, and N-Cadherin.