Generating dynamic higher-order Markov models in web usage mining

Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter

Protein Kinase Cδ Suppresses Autophagy to Induce Kidney Cell Apoptosis in Cisplatin Nephrotoxicity

Nephrotoxicity is a major adverse effect in cisplatin chemotherapy, and renoprotective approaches are unavailable. Recent work unveiled a critical role of protein kinase Cδ (PKCδ) in cisplatin nephrotoxicity and further demonstrated that inhibition of PKCδ not only protects kidneys but enhances the chemotherapeutic effect of cisplatin in tumors; however, the underlying mechanisms remain elusive. Here, we show that cisplatin induced rapid activation of autophagy in cultured kidney tubular cells and in the kidneys of injected mice. Cisplatin also induced the phosphorylation of mammalian target of rapamycin (mTOR), p70S6 kinase downstream of mTOR, and serine/threonine-protein kinase ULK1, a component of the autophagy initiating complex. In vitro, pharmacologic inhibition of mTOR, directly or through inhibition of AKT, enhanced autophagy after cisplatin treatment. Notably, in both cells and kidneys, blockade of PKCδ suppressed the cisplatin-induced phosphorylation of AKT, mTOR, p70S6 kinase, and ULK1 resulting in upregulation of autophagy. Furthermore, constitutively active and inactive forms of PKCδ respectively enhanced and suppressed cisplatin-induced apoptosis in cultured cells. In mechanistic studies, we showed coimmunoprecipitation of PKCδ and AKT from lysates of cisplatin-treated cells and direct phosphorylation of AKT at serine-473 by PKCδin vitro Finally, administration of the PKCδ inhibitor rottlerin with cisplatin protected against cisplatin nephrotoxicity in wild-type mice, but not in renal autophagy-deficient mice. Together, these results reveal a pathway consisting of PKCδ, AKT, mTOR, and ULK1 that inhibits autophagy in cisplatin nephrotoxicity. PKCδ mediates cisplatin nephrotoxicity at least in part by suppressing autophagy, and accordingly, PKCδ inhibition protects kidneys by upregulating autophagy

Identification of an immune-related gene prognostic index for predicting survival and immunotherapy efficacy in papillary renal cell carcinoma

Despite considerable progress has been made in the understanding of the genetics and molecular biology of renal cell carcinoma (RCC), therapeutic options of patients with papillary renal cell carcinoma (PRCC) are limited. Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a hot point in researching new drug for tumor and been tested in a number of human clinical trials. In this study, an immune-related gene prognostic index (IRGPI) was developed and provided a comprehensive and systematic analysis of distinct phenotypic and molecular portraits in the recognition, surveillance, and prognosis of PRCC. The reliability of the IRGPI was evaluated using independent datasets from GEO database and the expression levels of the genes in the IRGPI detected by real-time PCR. Collectively, the currently established IRGPI could be used as a potential biomarker to evaluate the response and efficacy of immunotherapy in PRCC

Immunodeficient Rabbit Models: History, Current Status and Future Perspectives

Production of immunodeficient (ID) models in non-murine animal species had been extremely challenging until the advent of gene-editing tools: first zinc finger nuclease (ZFN), then transcription activator-like effector nuclease (TALEN), and most recently clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR)/Cas9. We and others used those gene-editing tools to develop ID rabbits carrying a loss of function mutation in essential immune genes, such as forkhead box protein N1 (FOXN1), recombination activating gene 1/2 (RAG1/2), and interleukin 2 receptor subunit gamma (IL2RG). Like their mouse counterparts, ID rabbits have profound defects in their immune system and are prone to bacterial and pneumocystis infections without prophylactic antibiotics. In addition to their use as preclinical models for primary immunodeficient diseases, ID rabbits are expected to contribute significantly to regenerative medicine and cancer research, where they serve as recipients for allo- and xeno-grafts, with notable advantages over mouse models, including a longer lifespan and a much larger body size. Here we provide a concise review of the history and current status of the development of ID rabbits, as well as future perspectives of this new member in the animal model family

OR-051 Exploration of Potential Integrated Biomarkers for Sports Monitoring Based on Metabolic Profiling

Objective Metabolomic analysis is extensively applied to identify sensitive and specific biomarkers capable of reflecting pathological processes and physical responses or adaptations. Exercise training leads to profound metabolic changes, manifested as detectable alterations of metabolite levels and significant perturbations of metabolic pathways in sera, urine, and rarely, in saliva. Several metabolites have been exploited as biomarkers for generally evaluating physical states in almost all sports. However, alterations of metabolic profile caused by specific sports would be heterogeneous. Thus, developments of new techniques are eagerly required to identify characteristic metabolites as unique biomarkers for specifically accessing training stimulus and sports performances. In the present work, we conducted both metabolic profiling and a binary logistic regression model (BRM) of biological fluids derived from rowing ergometer test with the following aims: 1) to examine changes of metabolite profiles and identify characteristic metabolites in the samples of sera, urine, and saliva; 2) to screen out potential integrated biomarkers for sports-specific monitoring. Methods A total of 11 rowers (6 male, 5 female; aged 15±1 years; 4±2 years rowing training) underwent an indoor 6000m rowing ergometer test. Samples of sera, urine and saliva were collected before and immediately after the test. 1D 1H NMR spectra were recorded with a Bruker Avance III 650 MHz NMR spectrometer. NMR spectra were processed and aligned, resonances of metabolites were assigned and confirmed, and metabolite levels were calculated based on NMR integrals. Multivariate statistical analysis was carried out using partial least-squares discrimination analysis (PLS-DA) to distinguish metabolic profiles between the groups. The validated PLS-DA model gave the variable importance in the projection (VIP) for a given metabolite. Moreover, inter-group comparisons of metabolite levels were quantitatively conducted using the paired-sample t-test. Then, we identified characteristic metabolites with VIP>1 in PLS-DA and p<0.05 in t-test. Furthermore, we screened out potential biomarkers based on the characteristic metabolites identified from the three types of biological fluids using the BRM (stepwise). Results The rowing training induced profound changes of metabolic profiles in serum and saliva samples rather than in urine samples. Totally, 44 metabolites were assigned in which 19, 20, and 19 metabolites were identified from serum, urine and saliva samples, respectively. Seven metabolites were shared by the three types of samples. Moreover, five characteristic metabolites (pyruvate, lactate, succinate, N-acetyl-L-cysteine, and acetone) were identified from the serum samples. The elevated levels of pyruvate, lactate and succinate suggested that, the rowing training evidently promoted both oxidative phosphorylation and glycolysis pathways. Furthermore, three characteristic metabolites (tyrosine, formate, and methanol) were identified from the saliva samples. Given that tyrosine is the precursor of dopamine, the increased level of salivary tyrosine in all rowers experiencing the test, suggesting that salivary tyrosine could be explored as a potential indicator closely related to nervous fatigue in the test. On the other hand, PLS-DA did not show observable distinction of metabolic profiles between the urine samples before and immediately after the test. Moreover, 20 urinary metabolites did not display detectable altered levels. We then established the BRM with the identified characteristic metabolites, from which we selected one optimal regression model based on serum pyruvate and salivary tyrosine (adjusted R square was 0.935, P<0.001), indicating that the two selected metabolites would efficiently reflect the metabolic alterations in the test. Conclusions As far as the 6000m rowing ergometer test is concerned, serum samples could be a preferred resource for assessing the changes of energy metabolism in the test, while urine samples might have a relatively lower sensitivity to exercise-induced metabolic responses. Even though metabolite levels in saliva samples are generally lower than those in serum and urine samples, some salivary metabolites potentially have higher sensitivities to exercise-induced metabolic responses. Thus, the integration of multiple biomarkers identified from different type of species could potentially provide more sensitive and specific manners to monitor physical states in sports and exercise. This work may be of benefit to the exploration of integrated biomarkers for sports-specific monitoring

A taxonomy of web prediction algorithms

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. © 2012 Elsevier Ltd. All rights reserved.This work has been partially supported by Spanish Ministry of Science and Innovation under Grant TIN2009-08201, Generalitat Valenciana under Grant GV/2011/002 and Universitat Politecnica de Valencia under Grant PAID-06-10/2424.Domenech, J.; De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Gil Salinas, JA.; Pont Sanjuan, A. (2012). A taxonomy of web prediction algorithms. Expert Systems with Applications. 39(9):8496-8502. https://doi.org/10.1016/j.eswa.2012.01.140S8496850239

Decreased plasma levels of PDGF-BB, VEGF-A, and HIF-2α in preterm infants after ibuprofen treatment

IntroductionIbuprofen is one of the most common non-steroidal anti-inflammatory drugs used to close patent ductus arteriosus (PDA) in preterm infants. PDA is associated with bronchopulmonary dysplasia (BPD), while PDA closure by ibuprofen did not reduce the incidence of BPD or death. Previous studies have indicated an anti-angiogenesis effect of ibuprofen. This study investigated the change of angiogenic factors after ibuprofen treatment in preterm infants.MethodsPreterm infants with hemodynamically significant PDA (hsPDA) were included. After confirmed hsPDA by color doppler ultrasonography within 1 week after birth, infants received oral ibuprofen for three continuous days. Paired plasma before and after the ibuprofen treatment was collected and measured by ELISA to determine the concentrations of platelet-derived growth factor-BB (PDGF-BB) and vascular endothelial growth factor A (VEGF-A), and hypoxia-inducible factor-2α (HIF-2α).Results17 paired plasma from infants with hsPDA were collected. The concentration of PDGF-BB and VEGF-A significantly decreased after ibuprofen treatment (1,908 vs. 442 pg/mL for PDGF-BB, 379 vs. 174 pg/mL for VEGF-A). HIF-2α level showed a tendency to decrease after ibuprofen treatment, although the reduction was not statistically significant (p = 0.077).ConclusionThis study demonstrated decreased vascular growth factors after ibuprofen exposure in hsPDA infants

Tacky Elastomers to Enable Tear-Resistant and Autonomous Self-Healing Semiconductor Composites

Mechanical failure of π-conjugated polymer thin films is unavoidable under cyclic loading conditions, due to intrinsic defects and poor resistance to crack propagation. Here, the first tear-resistant and room-temperature self-healable semiconducting composite is presented, consisting of conjugated polymers and butyl rubber elastomers. This new composite displays both a record-low elastic modulus

Tacky Elastomers to Enable Tear-Resistant and Autonomous Self-Healing Semiconductor Composites

Mechanical failure of π-conjugated polymer thin films is unavoidable under cyclic loading conditions, due to intrinsic defects and poor resistance to crack propagation. Here, the first tear-resistant and room-temperature self-healable semiconducting composite is presented, consisting of conjugated polymers and butyl rubber elastomers. This new composite displays both a record-low elastic modulus