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• The biological effect of natural compounds goes beyond the 'target specific' mode of action. • Network pharmacology paradigm privileges weak a-specific binders over selective strong ones. • The activity of herbal mixtures far exceeds the summation of single components action added of interaction effects. • Purely physical modes of action are crucial in many applications of natural remedies. • Time is ripe to put the activity of traditional remedies into a biophysically motivated frame. Most drugs have a natural compound 'ancestor' acting as the lead molecule. Classic pharmacology does not explicitly take into consideration the peculiarities of natural origin compounds, the mechanism of action of which is interpreted by the same target-specific mode of action used for synthetic molecules. Over the past few decades, this approach has entered a crisis of efficacy, requiring general reconsideration of the nature of chemobiological interactions. Taking both the unique properties of natural compounds and their original presence in complex mixtures into account pushes researchers to enlarge the range of mechanisms of action well beyond the drug–receptor interaction and has the potential to overcome the current drug discovery crisis. [ABSTRACT FROM AUTHOR]

• Reversibility of differentiation trajectories fueled interest in tumor reversion. • Cancer re-differentiation involves intracellular and microenvironment factors converging in modifying cytoskeleton and nucleoskeleton. • Pushing cancer to normal tissue attractor asks for interventions at still largely unknown critical points of differentiation process. • New paradigms like 'network pharmacology' and 3D stromal/cancer cell co-cultures are needed to devise differentiation modulators able to induce a stable normal phenotype. The widely recognized problems of pharmacological strategies based on killing cancer cells demand a rethink of therapeutic approaches. Tumor reversion strategies that aim to shift cancer cells to a healthy differentiated state are a promising alternative. Although many studies have firmly demonstrated the possibility of reverting cancer to a normal differentiated state, we are still unable (with the exception of retinoic acid in a form of leukemia) to revert cancer cells to a stable differentiated healthy state. Here, we review the main biological bases of redifferentiation strategies and provide a description of the most promising research avenues. [ABSTRACT FROM AUTHOR]

The literature on psychotherapy research makes use of the so-called "Dodo Bird Verdict" to show that therapeutic change owes more to common factors than to specific techniques. According to the bulk of the empirical literature, common factors explain 30–70% of therapy outcome variance, while specific factors account for between 5% and 15%. This formulation is based on the assumption that common and specific factors are independent of each other. The present study uses a systematic review of the literature to empirically demonstrate that common and specific factors of change are actually correlated. In other words, the prevalent practice in the literature of using correlated common and specific factors as independent predictors in classical ANOVA models is both statistically unsound and conceptually distorted. We offer several alternative proposals for a sensible re-evaluation of the Dodo Bird verdict. [ABSTRACT FROM AUTHOR]

There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems. Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a ‘hypothesis generating’ tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches. [ABSTRACT FROM AUTHOR]

The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure–function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p < 0.0001) with a varying degree of ‘peculiarity’ across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid–amino acid interaction (AAI) networks. We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of ‘not-physiological’ (in this case cancer metabolic networks acting as test set) wiring patterns. We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations. [ABSTRACT FROM AUTHOR]

Protein molecules work as a whole, so that any local perturbation may resonate on the entire structure: allostery deals with this general property of protein molecules. It is worth noting a perturbation does not necessarily involve a conformational change but, more generally, it travels across the structure as an ‘energy signal’. The atomic interactions within the network provide the structural support for this ‘signaling highway’. Network descriptors, capturing network signaling efficiency, explain allostery in terms of signal transmission. In this review we will survey the key applications of graph theory to protein allostery. The complex network approach introduces a new perspective in biochemistry; as for applications, the development of new drugs relying on allosteric effects (allo-network drugs) represents a promising avenue of contact network formalism. [ABSTRACT FROM AUTHOR]

Nowadays, video-sharing portals' popularity has entailed massive growth in data uploads over the Internet. For several applications (e.g., browsing, retrieval, or recommendation of videos), dealing with vast data volumes has become a critical issue. In a video-sharing scenario, the devising of tools and infrastructures able to completely satisfy users' interests and requests is becoming increasingly crucial to influence their online experiences. On the one hand, annotating a video with meaningful human-friendly words (i.e., tags) plays an essential role in matching users' interests. On the other hand, providing a condensed and straightforward preview of the video content (i.e., thumbnails) is crucial to capture the user's attention immediately. In this context, we propose VSTAR (Visual Semantic Thumbnails and tAgs Revitalization), a novel approach in video optimization aimed at generating both suitable tags and thumbnails from a different perspective than classical approaches. The novelty lies in: (i) exploiting image captioning to extract visual and semantic information for generating both tags and thumbnails; (ii) identifying semantically related popular search queries (i.e., trends) to be suggested as new tags; (iii) giving the final user the control on a trade-off between quality and quantity of the generated items (tags and thumbnails); (iv) creating a proper dataset and making it publicly available. Experiments demonstrate the viability of our proposal. • Exploiting image captioning to simultaneously suggest tags and thumbnails. • Retrieval of semantically relevant trends to be suggested as video tags. • Providing of user-driven trade-off between tags/thumbnails quality and quantity. • A proper dataset of YouTube videos has been built and released. • Results confirmed that the system suggests strongly relevant tags and thumbnail. [ABSTRACT FROM AUTHOR]

Abstract: The effects of radiofrequency electromagnetic field (RF-EMF) exposure on neuronal phenotype maturation have been studied in two different in vitro models: murine SN56 cholinergic cell line and rat primary cortical neurons. The samples were exposed at a dose of 1W/kg at 900MHz GSM modulated. The phenotype analysis was carried out at 48 and 72h (24 and 48h of SN56 cell line differentiation) or at 24, 72, 120h (2, 4 and 6 days in vitro for cortical neurons) of exposure, on live and immunolabeled neurons, and included the morphological study of neurite emission, outgrowth and branching. Moreover, cortical neurons were studied to detect alterations in the expression pattern of cytoskeleton regulating factors, e.g. β-thymosin, and of early genes, e.g. c-Fos and c-Jun through real-time PCR on mRNA extracted after 24h exposure to EMF. We found that RF-EMF exposure reduced the number of neurites generated by both cell systems, and this alteration correlates to increased expression of β-thymosin mRNA. [Copyright &y& Elsevier]

Motoneuron phenotype in the spinal cord is regulated by an intrinsic genetic program, extrinsic environmental signals and target-derived molecules. Axonal lesions trigger a phenotype switch to foster repair phenomena and axonal re-growth. We have investigated the influence of the long-term treatment with thyroid hormone and all trans retinol palmitate (RA) on motoneuron phenotype and spinal cord reaction to axotomy in adult male rats. Neurochemical markers, investigated by in situ hybridization and immunocytochemistry, included choline acetyltransferase (ChAT), calcitonin gene-related peptide (CGRP) and neurotrophin low affinity receptor p75. Treatment was administered for 56 days and then mid-thigh sciatic axotomy was performed on a number of animals from each experimental groups; the rats were examined 9 days after surgery. The results indicate that: (1) Number and size of ChAT-immunoreactive neurons in the lumbar tract of the spinal cord was reduced in hypothyroid compared to control rats, whereas steady-state level of ChAT mRNA in labelled motoneurons failed to be modified by hypo and hyperthyroidism, but was increased by RA administration; (2) none of the administered treatments did alter CGRP mRNA level, whereas all of them influenced the axotomy-induced changes of motoneuron phenotype; (3) in hyperthyroid rats ChAT mRNA level of lumbar motoneurons not reduced homolateral to lesion while the number of ChAT-IR profiles was pronouncedly reduced; (4) up-regulation of p75 induced by peripheral nerve lesion was reduced in RA-treated rats. These data indicate that the motoneuron phenotype is regulated by transcription factors, which also play a role in phenotype switch regulation after axotomy. [Copyright &y& Elsevier]

• Novel information granulation and knowledge discovery technique for structured data. • Information granules as relevant substructures (high sensitivity and specificity). • Information granules are further filtered at classification stage (suboptimal subset). • Tests on real metabolic pathways data classification show remarkable performances. • Knowledge discovery over filtered granules paves the way for further studies. Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges. The proposed classification system has been tested on the entire set of organisms (5299) for which metabolic networks are known, allowing for both a perfect mirroring of the underlying taxonomy and the identification of most discriminant metabolic reactions and pathways. The widespread diffusion of graph (network) structures in biology makes the proposed pattern recognition approach potentially very useful in many different fields of application. More specifically, the possibility to have a reliable metric to compare different metabolic systems is instrumental in emerging fields like microbiome analysis and, more in general, for proposing metabolic networks as a universal phenotype spanning the entire tree of life and in direct contact with environmental cues. [ABSTRACT FROM AUTHOR]

Abstract: Re-myelination in the adult CNS has been demonstrated in different experimental models of demyelinating diseases. However, there is no clear evidence that re-myelination is effective in multiple sclerosis (MS), the most diffuse demyelinating disease. Moreover, chronic disabilities in MS are believed to be due to remyelination failure and consequent neuron damage and degeneration. Due to the presence of numerous oligodendrocyte precursors inside demyelination plaques, reasons for remyelination failure are unknown. In this paper, we reviewed data from embryonic development and in vitro studies supporting the primary role of thyroid hormone in oligodendrocyte maturation. We also reviewed personal data on the possibility of promoting myelination in chronic experimental allergic encephalomyelitis (EAE), a widely used experimental model of MS, by recruiting progenitors and channeling them into oligodendroglial lineage through the administration of thyroid hormone. [Copyright &y& Elsevier]