Development and preclinical assessment of a bioartificial pancreas.

Transplantation of insulin secreting cells is regarded as a possible treatment for type 1 diabetes. One major difficulty in this approach is, however, that the transplanted cells are exposed to the patient's inflammatory and autoimmune environment, which originally destroyed their own beta-cells. Therefore, even if a good source of insulin-secreting cells can be identified for transplantation therapy, these cells need to be protected against these destructive influences. The aim of this project was to evaluate, using a clonal mouse beta-cell line, whether genetic engineering of protective genes could be a viable option to allow these cells to survive when transplanted into autoimmune diabetic mice. We demonstrated that transfer of the Bcl-2 anti-apoptotic gene and of several genes specifically interfering with cytokines intracellular signalling pathways, greatly improved resistance of the cells to inflammatory stresses in vitro. We further showed that these modifications did not interfere with the capacity of these cells to correct hyperglycaemia for several months in syngeneic or allogeneic streptozocin-diabetic mice. However, these cells were not protected against autoimmune destruction when transplanted into type 1 diabetic NOD mice. This suggests that in addition to inflammatory attacks by cytokines, autoimmunity very efficiently kills the transplanted cells, indicating that multiple protective mechanisms are required for efficient transplantation of insulin-secreting cells to treat type 1 diabetes.

A New Large-DNA-Fragment Delivery System Based on Integrase Activity from an Integrative and Conjugative Element.

During the past few decades, numerous plasmid vectors have been developed for cloning, gene expression analysis, and genetic engineering. Cloning procedures typically rely on PCR amplification, DNA fragment restriction digestion, recovery, and ligation, but increasingly, procedures are being developed to assemble large synthetic DNAs. In this study, we developed a new gene delivery system using the integrase activity of an integrative and conjugative element (ICE). The advantage of the integrase-based delivery is that it can stably introduce a large DNA fragment (at least 75 kb) into one or more specific sites (the gene for glycine-accepting tRNA) on a target chromosome. Integrase recombination activity in Escherichia coli is kept low by using a synthetic hybrid promoter, which, however, is unleashed in the final target host, forcing the integration of the construct. Upon integration, the system is again silenced. Two variants with different genetic features were produced, one in the form of a cloning vector in E. coli and the other as a mini-transposable element by which large DNA constructs assembled in E. coli can be tagged with the integrase gene. We confirmed that the system could successfully introduce cosmid and bacterial artificial chromosome (BAC) DNAs from E. coli into the chromosome of Pseudomonas putida in a site-specific manner. The integrase delivery system works in concert with existing vector systems and could thus be a powerful tool for synthetic constructions of new metabolic pathways in a variety of host bacteria.

Protein recovery, separation and purification: selection of optimal techniques using an expert system

The paper discusses the utilization of new techniques ot select processes for protein recovery, separation and purification. It describesa rational approach that uses fundamental databases of proteins molecules to simplify the complex problem of choosing high resolution separation methods for multi component mixtures. It examines the role of modern computer techniques to help solving these questions.

Intel·ligència artificial aplicada a un joc beat'em up en 2D

Creació d'un joc del tipus Arcade beat'em up en 2D utilitzant escenaris amb una certa profunditat de moviment i dotant als personatges no jugadors i altres objectes d'Intel·ligència Artificial de manera que el seu comportament no sigui sempre lineal i aprofitant-ho per afegir nivells de dificultat.

Collective intelligence

Estudio e implantación de algoritmos de recomendación, búsqueda, ranking y aprendizaje.

Mejora diagnóstica de hepatopatías de afectación difusa mediante técnicas de inteligencia artificial

The automatic diagnostic discrimination is an application of artificial intelligence techniques that can solve clinical cases based on imaging. Diffuse liver diseases are diseases of wide prominence in the population and insidious course, yet early in its progression. Early and effective diagnosis is necessary because many of these diseases progress to cirrhosis and liver cancer. The usual technique of choice for accurate diagnosis is liver biopsy, an invasive and not without incompatibilities one. It is proposed in this project an alternative non-invasive and free of contraindications method based on liver ultrasonography. The images are digitized and then analyzed using statistical techniques and analysis of texture. The results are validated from the pathology report. Finally, we apply artificial intelligence techniques as Fuzzy k-Means or Support Vector Machines and compare its significance to the analysis Statistics and the report of the clinician. The results show that this technique is significantly valid and a promising alternative as a noninvasive diagnostic chronic liver disease from diffuse involvement. Artificial Intelligence classifying techniques significantly improve the diagnosing discrimination compared to other statistics.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Ambulatory monitoring of physical activities in patients with Parkinson's disease.

A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.

Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature

Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

Spacial debris collector robot : An AI-based and autonomous debris collector satellite simulation

In this paper the core functions of an artificial intelligence (AI) for controlling a debris collector robot are designed and implemented. Using the robot operating system (ROS) as the base of this work a multi-agent system is built with abilities for task planning.

Impedance field and transition from thermal to shot noise in Cd1-xZnxTe semi-insulating Ohmic detectors

Within a drift-diffusion model we investigate the role of the self-consistent electric field in determining the impedance field of a macroscopic Ohmic (linear) resistor made by a compensated semi-insulating semiconductor at arbitrary values of the applied voltage. The presence of long-range Coulomb correlations is found to be responsible for a reshaping of the spatial profile of the impedance field. This reshaping gives a null contribution to the macroscopic impedance but modifies essentially the transition from thermal to shot noise of a macroscopic linear resistor. Theoretical calculations explain a set of noise experiments carried out in semi-insulating CdZnTe detectors.

Coculture of bladder urothelial and smooth muscle cells on small intestinal submucosa: potential applications for tissue engineering technology.

PURPOSE: Small intestinal submucosa is a xenogenic, acellular, collagen rich membrane with inherent growth factors that has previously been shown to promote in vivo bladder regeneration. We evaluate in vitro use of small intestinal submucosa to support the individual and combined growth of bladder urothelial cells and smooth muscle cells for potential use in tissue engineering techniques, and in vitro study of the cellular mechanisms involved in bladder regeneration. MATERIALS AND METHODS: Primary cultures of human bladder urothelial cells and smooth muscle cells were established using standard enzymatic digestion or explant techniques. Cultured cells were then seeded on small intestinal submucosa at a density of 1 x 105 cells per cm.2, incubated and harvested at 3, 7, 14 and 28 days. The 5 separate culture methods evaluated were urothelial cells seeded alone on the mucosal surface of small intestinal submucosa, smooth muscle cells seeded alone on the mucosal surface, layered coculture of smooth muscle cells seeded on the mucosal surface followed by urothelial cells 1 hour later, sandwich coculture of smooth muscle cells seeded on the serosal surface followed by seeding of urothelial cells on the mucosal surface 24 hours later, and mixed coculture of urothelial cells and smooth muscle cells mixed and seeded together on the mucosal surface. Following harvesting at the designated time points small intestinal submucosa cell constructs were formalin fixed and processed for routine histology including Masson trichrome staining. Specific cell growth characteristics were studied with particular attention to cell morphology, cell proliferation and layering, cell sorting, presence of a pseudostratified urothelium and matrix penetrance. To aid in the identification of smooth muscle cells and urothelial cells in the coculture groups, immunohistochemical analysis was performed with antibodies to alpha-smooth muscle actin and cytokeratins AE1/AE3. RESULTS: Progressive 3-dimensional growth of urothelial cells and smooth muscle cells occurred in vitro on small intestinal submucosa. When seeded alone urothelial cells and smooth muscle cells grew in several layers with minimal to no matrix penetration. In contrast, layered, mixed and sandwich coculture methods demonstrated significant enhancement of smooth muscle cell penetration of the membrane. The layered and sandwich coculture techniques resulted in organized cell sorting, formation of a well-defined pseudostratified urothelium and multilayered smooth muscle cells with enhanced matrix penetration. With the mixed coculture technique there was no evidence of cell sorting although matrix penetrance by the smooth muscle cells was evident. Immunohistochemical studies demonstrated that urothelial cells and smooth muscle cells maintain the expression of the phenotypic markers of differentiation alpha-smooth muscle actin and cytokeratins AE1/AE3. CONCLUSIONS: Small intestinal submucosa supports the 3-dimensional growth of human bladder cells in vitro. Successful combined growth of bladder cells on small intestinal submucosa with different seeding techniques has important future clinical implications with respect to tissue engineering technology. The results of our study demonstrate that there are important smooth muscle cell-epithelial cell interactions involved in determining the type of in vitro cell growth that occurs on small intestinal submucosa. Small intestinal submucosa is a valuable tool for in vitro study of the cell-cell and cell-matrix interactions that are involved in regeneration and various disease processes of the bladder.

Magnetoresistance at artificial interfaces in the itinerant SrRuO3 ferromagnet

The magnetoresistance across interfaces in the itinerant ferromagnetic oxide SrRuO3 have been studied. To define appropriately the interfaces, epitaxial thin films have been grown on bicrystalline and laser-patterned SrTiO3 substrates. Comparison is made with results obtained on similar experiments using the double-exchange ferromagnetic oxide La2/3Sr1/3MnO3. It is found that in SrRuO3, interfaces induce a substantial negative magnetoresistance, although no traces of the low-field spin tunneling magnetoresistance are found. We discuss these results on the basis of the distinct degree of spin polarization in ruthenates and manganites and the different nature of the surface magnetic layer formed at interfaces.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Detection and Characterization of Preferential Flow Paths in the Downstream Area of a Hazardous Landfill

We have used surface-based electrical resistivity tomography to detect and characterize preferential hydraulic pathways in the immediate downstream area of an abandoned, hazardous landfill. The landfill occupies the void left by a former gravel pit and its base is close to the groundwater table and lacking an engineered barrier. As such, this site is remarkably typical of many small- to medium-sized waste deposits throughout the densely populated and heavily industrialized foreland on both sides of the Alpine arc. Outflows of pollutants lastingly contaminated local drinking water supplies and necessitated a partial remediation in the form of a synthetic cover barrier, which is meant to prevent meteoric water from percolating through the waste before reaching the groundwater table. Any future additional isolation of the landfill in the form of lateral barriers thus requires adequate knowledge of potential preferential hydraulic pathways for outflowing contaminants. Our results, inferred from a suite of tomographically inverted surfaced-based electrical resistivity profiles oriented roughly perpendicular to the local hydraulic gradient, indicate that potential contaminant outflows would predominantly occur along an unexploited lateral extension of the original gravel deposit. This finds its expression as a distinct and laterally continuous high-resistivity anomaly in the resistivity tomograms. This interpretation is ground-truthed through a litholog from a nearby well. Since the probed glacio-fluvial deposits are largely devoid of mineralogical clay, the geometry of hydraulic and electrical pathways across the pore space of a given lithological unit can be assumed to be identical, which allows for an order-of-magnitude estimation of the overall permeability structure. These estimates indicate that the permeability of the imaged extension of the gravel body is at least two to three orders-of-magnitude higher than that of its finer-grained embedding matrix. This corroborates the preeminent role of the high-resistivity anomaly as a potential preferential flow path.