Expression of PSA-RP2, an alternatively spliced variant from the PSA gene, is increased in prostate cancer tissues but the protein is not secreted from prostate cancer cells

PSA-RP2 is a variant transcript expressed from the PSA gene that is conserved in gorillas, chimpanzees and humans suggesting a particular relevance for this transcript in these primates. We demonstrated by qRT-PCR that PSA-RP2 is upregulated in prostate cancer compared with benign prostatic hyperplasia tissues. The PSA-RP2 protein was not detected in seminal fluid and was cytoplasmically localised but not secreted from LNCaP or transfected PC3 prostate cells, despite secretion from transfected Cos-7 and HEK293 kidney cell lines. PSA-RP2-transfected PC3 cells showed slightly decreased proliferation and increased migration towards PC3-conditioned medium that could suggest a functional role in prostate cancer.

A collagen network phase improves cell seeding of open-pore structure scaffolds under perfusion

Scaffolds with open-pore morphologies offer several advantages in cell-based tissue engineering, but their use is limited by a low cell seeding efficiency. We hypothesized that inclusion of a collagen network as filling material within the open-pore architecture of polycaprolactone-tricalcium phosphate (PCL-TCP) scaffolds increases human bone marrow stromal cells (hBMSC) seeding efficiency under perfusion and in vivo osteogenic capacity of the resulting constructs. PCL-TCP scaffolds, rapid prototyped with a honeycomb-like architecture, were filled with a collagen gel and subsequently lyophilized, with or without final crosslinking. Collagen-free scaffolds were used as controls. The seeding efficiency was assessed after overnight perfusion of expanded hBMSC directly through the scaffold pores using a bioreactor system. By seeding and culturing freshly harvested hBMSC under perfusion for 3 weeks, the osteogenic capacity of generated constructs was tested by ectopic implantation in nude mice. The presence of the collagen network, independently of the crosslinking process, significantly increased the cell seeding efficiency (2.5-fold), and reduced the loss of clonogenic cells in the supernatant. Although no implant generated frank bone tissue, possibly due to the mineral distribution within the scaffold polymer phase, the presence of a non crosslinked collagen phase led to in vivo formation of scattered structures of dense osteoids. Our findings verify that the inclusion of a collagen network within open morphology porous scaffolds improves cell retention under perfusion seeding. In the context of cell-based therapies, collagen-filled porous scaffolds are expected to yield superior cell utilization, and could be combined with perfusion-based bioreactor devices to streamline graft manufacture.

Optimal control of distributed generators and capacitors by hybrid DPSO

In this paper, a comprehensive planning methodology is proposed that can minimize the line loss, maximize the reliability and improve the voltage profile in a distribution network. The injected active and reactive power of Distributed Generators (DG) and the installed capacitor sizes at different buses and for different load levels are optimally controlled. The tap setting of HV/MV transformer along with the line and transformer upgrading is also included in the objective function. A hybrid optimization method, called Hybrid Discrete Particle Swarm Optimization (HDPSO), is introduced to solve this nonlinear and discrete optimization problem. The proposed HDPSO approach is a developed version of DPSO in which the diversity of the optimizing variables is increased using the genetic algorithm operators to avoid trapping in local minima. The objective function is composed of the investment cost of DGs, capacitors, distribution lines and HV/MV transformer, the line loss, and the reliability. All of these elements are converted into genuine dollars. Given this, a single-objective optimization method is sufficient. The bus voltage and the line current as constraints are satisfied during the optimization procedure. The IEEE 18-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate the unavoidable need for optimal control on the DG active and reactive power and capacitors in distribution networks.

Selection of solar energy for green building using superiority and inferiority multi-criteria ranking (SIR) method

Green energy is one of the key factors, driving down electricity bill and zero carbon emission generating electricity to green building. However, the climate change and environmental policies are accelerating people to use renewable energy instead of coal-fired (convention type) energy for green building that energy is not environmental friendly. Therefore, solar energy is one of the clean energy solving environmental impact and paying less in electricity fee. The method of solar energy is collecting sun from solar array and saves in battery from which provides necessary electricity to whole house with zero carbon emission. However, in the market a lot of solar arrays suppliers, the aims of this paper attempted to use superiority and inferiority multi-criteria ranking (SIR) method with 13 constraints establishing I-flows and S-flows matrices to evaluate four alternatives solar energies and determining which alternative is the best, providing power to sustainable building. Furthermore, SIR is well-known structured approach of multi-criteria decision support tools and gradually used in construction and building. The outcome of this paper significantly gives an indication to user selecting solar energy.

Active Notch1 confers a transformed phenotype to primary human melanocytes

The importance of mitogen-activated protein kinase signaling in melanoma is underscored by the prevalence of activating mutations in N-Ras and B-Raf, yet clinical development of inhibitors of this pathway has been largely ineffective, suggesting that alternative oncogenes may also promote melanoma. Notch is an interesting candidate that has only been correlated with melanoma development and progression; a thorough assessment of tumor-initiating effects of activated Notch on human melanocytes would clarify the mounting correlative evidence and perhaps identify a novel target for an otherwise untreatable disease. Analysis of a substantial panel of cell lines and patient lesions showed that Notch activity is significantly higher in melanomas than their nontransformed counterparts. The use of a constitutively active, truncated Notch transgene construct (N(IC)) was exploited to determine if Notch activation is a "driving" event in melanocytic transformation or instead a "passenger" event associated with melanoma progression. N(IC)-infected melanocytes displayed increased proliferative capacity and biological features more reminiscent of melanoma, such as dysregulated cell adhesion and migration. Gene expression analyses supported these observations and aided in the identification of MCAM, an adhesion molecule associated with acquisition of the malignant phenotype, as a direct target of Notch transactivation. N(IC)-positive melanocytes grew at clonal density, proliferated in limiting media conditions, and also exhibited anchorage-independent growth, suggesting that Notch alone is a transforming oncogene in human melanocytes, a phenomenon not previously described for any melanoma oncogene. This new information yields valuable insight into the basic epidemiology of melanoma and launches a realm of possibilities for drug intervention in this deadly disease.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.