Role Of Different Domains Research Articles

Towards Robust Colour Texture Analysis with Limited Training Data

Texture analysis plays an important role in different domains of healthcare, agriculture, and industry, where multi-channel sensors are gaining more attention. This contribution presents an interpretable and efficient framework for texture classification and segmentation that exploits colour or channel information and does not require much data to produce accurate results. This makes such a framework well-suited for medical applications and resource-limited hardware. Our approach builds upon a distance-based generalized matrix learning vector quantization (GMLVQ) algorithm. We extend it with parametrized angle-based dissimilarity and introduce a special matrix format for multi-channel images. Classification accuracy evaluation of various model designs was performed on VisTex and ALOT data, and the segmentation application was demonstrated on an agricultural data set. Our extension of parametrized angle dissimilarity measure leads to better model generalization and robustness against varying lighting conditions than its Euclidean counterpart. The proposed matrix format for multichannel images enhances classification accuracy while reducing the number of parameters. Regarding segmentation, our method shows promising results, provided with a small class-imbalanced training data set. Proposed methodology achieves higher accuracy than prior work benchmarks and a small-scale CNN while maintaining a significantly lower parameter count. Notably, it is interpretable and accurate in scenarios where limited and unbalanced training data are available.

Emotion detection using Word2Vec and convolution neural networks

<div>Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems.</div>

Solving the H_{0} tension in f(T) gravity through Bayesian machine learning

Bayesian Machine Learning (BML) and strong lensing time delay (SLTD) techniques are used in order to tackle the H_{0} tension in f(T) gravity. The power of BML relies on employing a model-based generative process which already plays an important role in different domains of cosmology and astrophysics, being the present work a further proof of this. Three viable f(T) models are considered: a power law, an exponential, and a squared exponential model. The learned constraints and respective results indicate that the exponential model, f(T)=alpha T_{0}left( 1-e^{-p T / T_{0}}right) , has the capability to solve the H_{0} tension quite efficiently. The forecasting power and robustness of the method are shown by considering different redshift ranges and parameters for the lenses and sources involved. The lesson learned is that these values can strongly affect our understanding of the H_{0} tension, as it does happen in the case of the model considered. The resulting constraints of the learning method are eventually validated by using the observational Hubble data (OHD).

Extracellular domain of semaphorin 5A serves a tumor‑suppressing role by activating interferon signaling pathways in lung adenocarcinoma cells.

Semaphorin 5A (SEMA5A), which was originally identified as an axon guidance molecule in the nervous system, has been subsequently identified as a prognostic biomarker for lung cancer in nonsmoking women. SEMA5A acts as a tumor suppressor by inhibiting the proliferation and migration of lung cancer cells. However, the regulatory mechanism of SEMA5A is not clear. Therefore, the purpose of the present study was to explore the roles of different domains of SEMA5A in its tumor‑suppressive effects in lung adenocarcinoma cell lines. First, it was revealed that overexpression of full length SEMA5A or its extracellular domain significantly inhibited the proliferation and migration of both A549 and H1299 cells using MTT, colony formation and gap closure assays. Next, microarray analyses were performed to identify genes regulated by different domains of SEMA5A. Among the differentially expressed genes, the most significant function of these genes that were enriched was the 'Interferon Signaling' pathway according to Ingenuity Pathway Analysis. The activation of the 'Interferon Signaling' pathway was validated by reverse transcription‑quantitative PCR and western blotting. In summary, the present study demonstrated that the extracellular domain of SEMA5A could upregulate genes in interferon signaling pathways, resulting in suppressive effects in lung adenocarcinoma cells.

Association between Patient Satisfaction and the Willingness to Return for Rehabilitation- A Pilot Study

Background: Patient satisfaction has played an important role in the development of health systems. However, the information on patient satisfaction and the returning of the patients for rehabilitation is limited. This study aims to explore the role of different domains in patient satisfaction with the willingness to return for further rehabilitation. Methods: The cross-sectional study was conducted among all 163 patients who were treated at different departments but needed a combination of treatment with rehabilitation at Viettiep hospital, Haiphong city. The Patient Satisfaction Index (PSI) Questionnaire was applied to record the average score of satisfaction. Multivariate logistic regression was used to determine the association between each domain of the PSI and the return for rehabilitation treatment. Results: Among the PSI scale, the strongest correlation was observed between the attitude of medical staff and the equipment and infrastructure of the hospital (r=0.305). The linear regression model indicated that patient satisfaction was associated significantly with the willingness to return for further rehabilitation (OR= 58.442). There were significant associations between the accessibility, equipment and infrastructure, quality of care and treatment, and cost of treatment with the returning for treatment of the patients. Conclusion: The patient satisfaction regarding individual domains should be considered in more detail in order to enhance the rehabilitation treatment of patients after discharging them from the hospital.

Personalized content recommendations on smart TV: Challenges, opportunities, and future research directions

Web recommender systems play a significant role in different domains, such as movies, books, music, etc., and contributes to not only user satisfaction but also to e-business and e-commerce. It utilizes the user’s profile information, preferences, and activities for recommendations of different objects. However, the distinct nature of watching smart TV greatly affects the accuracy and efficiency of recommender systems due to the reasons that it is a lean-back and multi-user device. Hence, the predictions and calculations of a user’s profile, preferences, and activities cannot be accurately utilized by the recommender systems for recommendations to the exact viewer(s) watching smart TV. This paper presents a critical review of existing recommender systems in the perspectives of smart TV watching scenarios. It highlights the issues and challenges and presents some research opportunities to deal with it. It further presents a subjective study for validating the highlighted factors that affect the recommendation results specifically on a smart TV. Results show that watching activities on a smart TV is significantly different from other devices, such as smartphones and computers. It further shows that smart TV is a non-personalized device and normally enjoyed in groups. Hence, personalized recommendations on smart TV need further investigation. The paper concludes that the existing recommender systems need further improvement to cope with issues of recommendations on a non-personalized device i.e., smart TV. Improving the recommender system for smart TVs may contribute not only to the viewer’s satisfaction but also to the conversion rate.

The Indirect Role of Executive Functions on the Relationship between Cardiorespiratory Fitness and School Grades.

The aim of this study was to investigate the indirect effects of cardiorespiratory fitness on scholastic performance through executive functions. More precisely, we examined the contribution of the different domains of executive functions, and whether this relationship was specific to certain school topics. Children 8-12 yr old completed nine cognitive tests and the multistage fitness test. Structural equation modeling techniques were used to analyze the role of different domains of executive functions (inhibition, working memory, cognitive flexibility, and a common factor to all tasks) in the relationship between cardiorespiratory fitness and school grades in three domains: (i) mathematics; (ii) grammar, spelling, and vocabulary; and (iii) text comprehension and expression. Covariate analyses included age and socioeconomic status. The results of this study showed that an indirect effect of the various domains of executive functions explained, in part, the relationship between cardiorespiratory fitness and (i) mathematics (β = 0.12, SE = 0.03, P < 0.001), and between cardiorespiratory fitness and (ii) grammar, spelling, and vocabulary (β = 0.12, SE = 0.03, P < 0.001). No relationship between cardiorespiratory fitness and (iii) text comprehension and expression was observed. Although executive functions correlated with school grades, cognitive flexibility drove the indirect effect when all executive function domains were simultaneously taken into account. These results show the role that executive functions play in understanding the relationship between cardiorespiratory fitness and scholastic performance. Importantly, not all executive function domains contributed equally because cognitive flexibility played a leading role in this wide age range. Furthermore, the relationship between cardiorespiratory fitness and scholastic performance was strongest for mathematics and for low-level language topics but nonsignificant for higher-level language topics, providing a more modulated view of the effect of cardiorespiratory fitness on language.

Political trust on COVID-19 handling as predictor towards optimism on the new normal situation: Integrity and benevolence rather than competence

Covid-19 has turned into a global health issue since April 2020 where WHO finally decided the spread of the virus as a global pandemic that affected more than 200 countries. Previous works have found that trust toward the government is important in adapting to the pandemic situation. However, what type of trust is more important? This study aims to investigate the role of different domains of trust towards Covid-19 treatment conducted by the Indonesian government as a predictor for the perception of the new normal situation. The result shows that higher trust towards government will lead to optimism in facing the new normal situation, in which perceived integrity and benevolence become the significant predictors toward optimism. However, we did not find the same pattern for the competence domain of trust.

Statistical applications of optimization methods and mathematical programming

Abstract Optimization techniques perform an important role in different domains of statistic. Examples of parameter estimation of different distributions, correlation analysis (parametric and nonparametric), regression analysis, optimal allocation of resources in partial research, exploration of response surfaces, design of experiments, efficiency tests, reliability theory, survival analysis are the most known methods of statistical analysis in which we find optimization techniques. The paper contains a synthetic presentation of the main statistical methods using classical optimization techniques, numerical optimization methods, linear and nonlinear programming, variational calculus techniques. Also, an example of applying the “simplex” algorithm in making a decision to invest an amount on the stock exchange, using a prediction model..

Bridge Helix of Cas9 Modulates Target DNA Cleavage and Mismatch Tolerance.

CRISPR-Cas systems are RNA-guided nucleases that provide adaptive immune protection for bacteria and archaea against intruding genomic materials. The programmable nature of CRISPR-targeting mechanisms has enabled their adaptation as powerful genome engineering tools. Cas9, a type II CRISPR effector protein, has been widely used for gene-editing applications owing to the fact that a single-guide RNA can direct Cas9 to cleave desired genomic targets. An understanding of the role of different domains of the protein and guide RNA-induced conformational changes of Cas9 in selecting target DNA has been and continues to enable development of Cas9 variants with reduced off-targeting effects. It has been previously established that an arginine-rich bridge helix (BH) present in Cas9 is critical for its activity. In the present study, we show that two proline substitutions within a loop region of the BH of Streptococcus pyogenes Cas9 impair the DNA cleavage activity by accumulating nicked products and reducing target DNA linearization. This in turn imparts a higher selectivity in DNA targeting. We discuss the probable mechanisms by which the BH-loop contributes to target DNA recognition.

Variations in the secondary structures of PAM proteins influence their binding affinities to human plasminogen

M-proteins (M-Prts) are major virulence determinants of Group A Streptococcus pyogenes (GAS) that are covalently anchored to the cell wall at their conserved COOH-termini while the NH2-terminal regions extend through the capsule into extracellular space. Functional M-Prts are also secreted and/or released from GAS cells where they exist as helical coiled-coil dimers in solution. Certain GAS strains (Pattern D) uniquely express an M-protein (plasminogen-binding group A streptococcal M-protein; PAM) that directly interacts with human plasminogen (hPg), a process strongly implicated in the virulence of these strains. M-Prt expressed by the emm gene is employed to serotype over 250 known strains of GAS, ∼20 of which are hitherto found to express PAMs. We have developed a modular structural model of the PAM dimer that describes the roles of different domains of this protein in various functions. While the helical COOH-terminal domains of PAM are essential for dimerization in solution, regions of its NH2-terminal domains also exhibit a weak potential to dimerize. We find that temperature controls the open (unwound) or closed (wound) states of the functional NH2-terminal domains of PAM. As temperature increases, α-helices are dramatically reduced, which concomitantly destabilizes the helical coiled-coil PAM dimers. PAMs with two a-repeats within the variable NH2-terminal A-domain (class I/III) bind to hPg tightly, but natural PAM isolates with a single a-repeat in this domain (class II) display dramatic changes in hPg binding with temperature. We conclude that coexistence of two a-repeats in PAM is critical to achieve optimal binding to hPg, especially in its monomeric form, at the biologically relevant temperature.

Abstract 3427: Targeting MUC13 to overcome the survival mechanisms for improved response to chemotherapy

Abstract Background: Pancreatic cancer (PanCa) is a third leading cause of cancer-related deaths in the US due to late diagnosis and development of chemoresistance. Therefore, understanding molecular mechanisms that confer survival benefit to PanCa cells may offer new therapeutic strategies for PanCa treatment.Mucin, MUC13 aberrantly expressed in PanCa,promoting cancer growth and progression and these effects are abrogated by microRNA-145 (miR-145) restoration. Unlike other cancer types, PanCa is highly resistant to Tumor necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) that emerges as one of the most promising experimental cancer therapeutic drugs.Herein, we demonstrate the integration of novel approach to overcome chemoresistance and offer TRAIL-based therapeutic strategies. Methods: MUC13 expressing and null stable PanCa cells were generated to investigate the role of MUC13 in cell survival.miR-145 mimics were used to investigate the effect of MUC13 silencing in promoting survival and inhibiting apoptosis in presence of TRAIL using Western blotting, cell proliferation (MTT), Acridine orange staining and flow cytometry apoptosis assays(cell cycle, Annexin V/7AAD staining). Various distinct domain specific constructs of MUC13 were constructed such as the mucin (α), Beta (β) sea urchin sperm protein enterokinase arginine (SEA) domain and cytoplasmic (CD) domains and transfected into MUC13 null Panc-1 cells to identify the role of different domains in eliciting survival benefit to PanCa cells. Results: Results demonstrate that MUC13 expression blocks activation of caspase-8 and death receptor-mediated apoptosis in PanCa cells in response toTRAIL treatment as observed through Western blotting and flow cytometer. Inhibition of MUC13 using shRNA knockdown or miR-145 restoration resulted in the TRAIL-mediated increase in apoptotic cell death as evidenced by AnnexinV/ 7AAD and sub G0 population, as well as rendered PanCa cells sensitive to treatment with drugs, such as paclitaxel. Additionally, cells treated with TRAIL in combination with paclitaxel or Abraxane showed enhanced apoptosis on inhibition of MUC13 expression using miR-145 restoration. Further investigation showed that cytoplasmic domain of MUC13 (MUC13-CD) is indispensable for blockingcaspase-8 activation and PARP cleavage, indicating that the MUC13-CD blocks TRAIL-induced signaling upstream to Bid by inhibiting caspase-8 activation. Conclusion: These observations suggest that MUC13 contributes to the survival advantage in PanCa cells in response to treatment with drugs or death inducing ligands such as, Tumor necrosis factor related apoptosis-inducing ligand (TRAIL) which can be strategically overcome by miR-145 replenishment. These findings indicate that MUC13 silencing sensitizes PanCa cells towards TRAIL therapy and counteracts chemoresistance mechanisms in PanCa that may lead to novel combination therapies for PanCa treatment. Citation Format: Saini Setua, Sheema Khan, Murali M. Yallapu, Stephen W. Behrman, Nadeem Zafar, Meena Jaggi, Subhash C. Chauhan. Targeting MUC13 to overcome the survival mechanisms for improved response to chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3427. doi:10.1158/1538-7445.AM2017-3427

The Emergence of Episodic Foresight and Its Consequences

AbstractImagining future events and adjusting current behavior accordingly is a hallmark of human cognition. The development of such episodic foresight is attracting increasing research attention. In this article, I review a selection of recent work on the emergence of episodic foresight and its role in different domains, including learning, deliberate practice, affective forecasting, intertemporal choices, and anxiety. Studies suggest that during the preschool years, children begin to consider future scenarios, enabling them to plan, prepare, and shape their future, but many other consequences, such as the role of developing foresight in anticipating hazards, remain unexamined. The potential predictive effects of early individual differences in episodic foresight on later cognitive capacities and developmental outcomes deserve closer scrutiny.

Truncations of xyloglucan xylosyltransferase 2 provide insights into the roles of the N- and C-terminus

Xyloglucan is the most abundant hemicellulose in the primary cell wall of dicotyledonous plants. In Arabidopsis, three xyloglucan xylosyltransferases, XXT1, XXT2, and XXT5, participate in xylosylation of the xyloglucan backbone. Despite the importance of these enzymes, there is a lack of information on their structure and the critical residues required for substrate binding and transferase activity. In this study, the roles of different domains of XX2 in protein expression and catalytic activity were investigated by constructing a series of N- and C-terminal truncations. XXT2 with an N-terminal truncation of 31 amino acids after the predicted transmembrane domain showed the highest protein expression, but truncations of more than 31 residues decreased protein expression and catalytic activity. XXT2 constructs with C-terminal truncations showed increased protein expression but decreased activity, particularly for truncations of 44 or more amino acids. Site-directed mutagenesis was also used to investigate six positively charged residues near the C-terminus and found that four of the mutants showed decreased enzymatic activity. We conclude that the N- and C-termini of XXT2 have important roles in protein folding and enzymatic activity: the stem region (particularly the N-terminus of the catalytic domain) is critical for protein folding and the C-terminus is essential for enzymatic activity but not for protein folding.

The Role of Cognitive Factors in Predicting Balance and Fall Risk in a Neuro-Rehabilitation Setting.

IntroductionThere is a consistent body of evidence supporting the role of cognitive functions, particularly executive function, in the elderly and in neurological conditions which become more frequent with ageing. The aim of our study was to assess the role of different domains of cognitive functions to predict balance and fall risk in a sample of adults with various neurological conditions in a rehabilitation setting.MethodsThis was a prospective, cohort study conducted in a single centre in the UK. 114 participants consecutively admitted to a Neuro-Rehabilitation Unit were prospectively assessed for fall accidents. Baseline assessment included a measure of balance (Berg Balance Scale) and a battery of standard cognitive tests measuring executive function, speed of information processing, verbal and visual memory, visual perception and intellectual function. The outcomes of interest were the risk of becoming a faller, balance and fall rate.ResultsTwo tests of executive function were significantly associated with fall risk, the Stroop Colour Word Test (IRR 1.01, 95% CI 1.00–1.03) and the number of errors on part B of the Trail Making Test (IRR 1.23, 95% CI 1.03–1.49). Composite scores of executive function, speed of information processing and visual memory domains resulted in 2 to 3 times increased likelihood of having better balance (OR 2.74 95% CI 1.08 to 6.94, OR 2.72 95% CI 1.16 to 6.36 and OR 2.44 95% CI 1.11 to 5.35 respectively).ConclusionsOur results show that specific subcomponents of executive functions are able to predict fall risk, while a more global cognitive dysfunction is associated with poorer balance.

Design and implementation of type‐II and type‐III controller for DC–DC switched‐mode boost converter by usingK‐factor approach and optimisation techniques

DC - DC power supplies are playing significant role in different domains of engineering applications. Some converters such as boost, buck-boost, and fly-back have a right-half-plane zero (non-minimum phase system), hence it is difficult for the PID controller to exhibit good performance with load, line variations and parametric uncertainty. In this proposed work, design and implementation of type controllers have been performed by using k -factor approach and two different optimisation techniques (gravitational search algorithm and particle swarm optimisation) for obtaining better stability and performance for a closed loop DC-DC Switched mode boost converter. The closed loop control system has been implemented in real time dSPACE platform. The comparative closed-loop performances of a boost converter with classical, optimised PID and optimised type II/type III controllers have been produced. Simulations and experimental results are provided to demonstrate the effectiveness of optimised controllers for the proposed converter. Design and implementation of optimised type controller for switch mode converters has not been reported earlier in any literature.

Nucleoporin's Like Charge Regions Are Major Regulators of FG Coverage and Dynamics Inside the Nuclear Pore Complex.

Nucleocytoplasmic transport has been the subject of a large body of research in the past few decades. Recently, the focus of investigations in this field has shifted from studies of the overall function of the nuclear pore complex (NPC) to the examination of the role of different domains of phenylalanine-glycine nucleoporin (FG Nup) sequences on the NPC function. In our recent bioinformatics study, we showed that FG Nups have some evolutionarily conserved sequence-based features that might govern their physical behavior inside the NPC. We proposed the ‘like charge regions’ (LCRs), sequences of charged residues with only one type of charge, as one of the features that play a significant role in the formation of FG network inside the central channel. In this study, we further explore the role of LCRs in the distribution of FG Nups, using a recently developed coarse-grained molecular dynamics model. Our results demonstrate how LCRs affect the formation of two transport pathways. While some FG Nups locate their FG network at the center of the NPC forming a homogeneous meshwork of FG repeats, other FG Nups cover the space adjacent to the NPC wall. LCRs in the former group, i.e. FG Nups that form an FG domain at the center, tend to regulate the size of the highly dense, doughnut-shaped FG meshwork and leave a small low FG density area at the center of the pore for passive diffusion. On the other hand, LCRs in the latter group of FG Nups enable them to maximize their interactions and cover a larger space inside the NPC to increase its capability to transport numerous cargos at the same time. Finally, a new viewpoint is proposed that reconciles different models for the nuclear pore selective barrier function.

Open issues in model transformations for multimodal applications

Multimodal human–computer interaction refers to the interaction with the virtual and physical environment through natural modes of communication. Multimodal input user interfaces have significant role in different domains, for example industrial plants or hospitals, also they have implications for accessibility. To develop multimodal applications in formally rigorous settings, software developer teams may use tools or a software development kit to increase the efficiency and the quality of the resulted software artifacts. Such a technique is performing the design with software modeling and applying model transformations to generate well-defined components of the software. Furthermore, representation-bridging communication is a discipline of cognitive infocommunications, where the sensory information transferred to the receiver entity is filtered and/or converted. Whenever such approaches are used, the challenges associated with the modeling of information requirements, user capabilities and cross-model interactions are compounded and further increase the need for formal design and verification tools. Applying model transformations is a way to support this activity. Communication-intensive solutions often require complex methods, i.e. significant model transformation efforts between the different representations. Important semantic information should be preserved and not misinterpreted in a complex model transformations. Therefore, methods are required to verify that the semantics used during the application generation and analysis are indeed preserved across the transformation. As a case in point, such a model transformation could yield embedded code for a given type of electronic driver assistant system based on a high-level characterizations of the information to be transferred and the driver’s cognitive capabilities. Later, a multimodal interactions expert could easily modify those characterizations on demand, and regenerate a modified version of the software without having to know about the low-level details of the embedded platform. This paper provides a strong motivation regarding the necessity of methods to support verification and validation of model transformations supporting multimodal application development and cognitive infocommunications. As the main result of the paper, we compile a list of open issues in the field of verification/validation of model transformations, and link those issues to the development of multimodal interfaces. Through its discussions, the paper makes the point that the design practices behind multimodal interfaces could strongly benefit from the use of formal modeling techniques in general, and model transformation approaches in particular.

The Ectodomain of TLR3 Receptor Is Required for Its Plasma Membrane Translocation

Toll-like receptor 3 (TLR3) is a dsRNA sensing receptor that is localized in the cellular compartments but also at the plasma membrane. Overexpression of UNC93B1 promoted localization of TLR3, but not other nucleic acid sensing TLRs, to the plasma membrane. Here we show that UNC93B1 itself is localized at the plasma membrane. We investigated the role of different domains of TLR3 on cell signaling by preparing chimeric receptors between TLR3 and TLR9 where each of the transmembrane segments or cytosolic domains has been exchanged. While the ectodomain completely governs ligand specificity and the cytosolic TIR domain determines the engagement of the signaling adapters as well as the potentiation of receptor activation by UNC93B1, the ectodomain but not transmembrane segment or cytosolic domain determines plasma membrane localization of TLR3. Nevertheless, TLR3 receptor and ligand endocytosis as well as endosomal acidification are important for the robust signaling of TLR3.

Guest editorial: social networks and social Web mining

Nowadays the emergence of web-based communities and hosted services such as social networking sites – Facebook, LinkedIn, wikis – Wikipedia, microblogging Twitter and folksonomies – Delicious, Flickr and so on, brings in tremendous freedom of Web autonomy and facilitate collaboration and sharing between users. And along with the interactions between users and computers, social Web is rapidly becoming an important part of our digital experience, ranging from digital textual information to rich multimedia formats. Social networks have played an important role in different domains for about one decade, particularly involved in a broad range of social activities like user interaction, establishing friendship relationships, sharing and recommending resources, suggesting friends, creating groups and communities, commenting friend activities and opinions and so on. Recent years, has witnessed the rapid progress in the study of social networks for diverse applications, such as user profiling in Facebook and group recommendation via Flickr. These aspects and characteristics form the most active and challenging parts of Web 2.0. A large amount of challenges and opportunities have arisen with the propagation and popularity of new applications and technologies. A prominent challenge lies in modeling and mining this vast volume of data to extract, represent and exploit meaningful knowledge, and to leverage structures and dynamics of emerging social networks residing in the social Web, especially social media. Social networks and social Web mining combines data mining with social computing as a promising direction and offers unique opportunities for developing novel algorithms and tools ranging from text and content mining to link mining and community detection and so on. This special issue has gained overwhelming attention and received 52 submissions from researchers and practitioners working on social network analysis and social media mining. After initial examining of all submissions, 42 papers are selected into the regular rigorous review process and each submission has been reviewed by at least three reviewers. After 2–3 World Wide Web (2013) 16:541–544 DOI 10.1007/s11280-013-0254-0