Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information

Examinar a ampliação do uso de TICs por organizações sociais e governamentais na gestão da cidade é o objetivo do presente estudo. Nossa intenção é entender de que forma as tecnologias da informação e comunicação podem ser uma via alternativa que redefine as relações entre Estado e sociedade, substituindo políticas urbanas tradicionais por formas colaborativas de interação dos atores sociais. Entre os resultados alcançados pela pesquisa, é possível destacar a elaboração de uma metodologia capaz de mapear os princípios de organização, articulação, conexão e interação que constituem a existência de redes tecnossociais. A aplicação da metodologia nas cidades do Rio de Janeiro e de São Paulo demonstrou indicadores, gráficos e práticas políticas. A análise desses dados revela como as redes se constituem por uma arquitetura móvel, fluída, flexível, organizadas em torno de políticas comuns de ação e formadas por uma identidade coletiva que aproxima os atores das redes tecnossociais. Os princípios que mediam esta coesão são de compartilhamento, confiança e solidariedade, que redefinem as formas da organização do poder em direção a alternativas de organização política e desenvolvimento social

A dynamic neighborhood learning-based gravitational search algorithm

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the Kbest model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA

Deep segmentation networks predict survival of non-small cell lung cancer

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new premise for cancer image analysis, with significantly enhanced predictive power compared to other hand-crafted radiomics features. Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET/CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-net algorithm has not seen any other clinical information (e.g. survival, age, smoking history) than the images and the corresponding tumor contours provided by physicians. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of progression appear to match with the regions where the U-Net features identified patterns that predicted higher likelihood of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination

Fibroblast activation protein-α promotes the growth and migration of lung cancer cells via the PI3K and sonic hedgehog pathways

A characteristic of the epithelial-to-mesenchymal transition in cancer cells is the upregulation of mesenchymal markers. Fibroblast activation protein α (FAPα) is predominantly expressed by stromal fibroblasts. Previous studies have demonstrated that FAPα is also expressed by certain epithelium-derived cancer cells and is involved in the regulation of certain signaling pathways. One of our previous studies showed that FAPα promoted the proliferation of breast cancer cells via the phosphatidylinositol-3-kinase (PI3K) signaling pathway. In the present study, the A549 adenocarcinoma (AC) and SK-MES-1 squamous cell carcinoma (SCC) lung cancer cell lines were transfected with FAPα. The FAPα-expressing SK-MES-1 cells exhibited an increased growth rate, whereas the FAPα-expressing A549 cells exhibited a similar growth rate, compared with respective empty vector‑transfected control cells. Electric cell-substrate impedance sensing (ECIS)-based attachment and wound-healing assays showed that the overexpression of FAPα markedly increased the adhesive and migratory properties of the SK-MES-1 cells but not those of the A549 cells. Additionally, inhibitors of focal adhesion kinase, agonist-induced phospholipase C, neural Wiskott‑Aldrich syndrome protein, extracellular signal‑regulated kinase, Rho-associated protein kinase, PI3K, and sonic hedgehog (SHH) were used to evaluate the interaction between FAPα and signaling pathways. Only the inhibitors of SHH and PI3K inhibited the increased motility of the FAPα‑expressing SK-MES-1 cells. Western blot analysis confirmed the activation of PI3K/AKT and SHH/GLI family zinc finger 1 signaling in the FAPα-expressing SK-MES-1 cells. These results revealed that FAPα promoted the growth, adhesion and migration of lung SCC cells. In addition, FAPα regulated lung cancer cell function, potentially via the PI3K and SHH pathways. Further investigations are required to examine the role of FAPα in lung AC cells