Filamentous bacteriophages for size, in vivo biodistribution and easiness of engineering, are considered as natural nanoparticles. The developed technology allows the construction of bio-nanoparticles based on filamentous bacteriophages delivering proteic antigens and immunomodulating lipids. Thanks to the high content of hydrophobic residues, phage capsid proteins have high binding affinity to lipids, allowing the conjugation of immunostimulating lipids.
Technologies
In this section it is possible to view, also through targeted research, the technologies inserted in the PROMO-TT Database. For further information on the technologies and to contact the CNR Research Teams who developed them, it is necessary to contact the Project Manager (see the references at the bottom of each record card).
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Quartz tuning forks are employed in scanning atomic force microscopy (AFM), as well as in some derived techniques, as high sensitivity detectors of interactions, of both conservative and dissipative kind, between the AFM nanometric probe and the investigated surface. However, the contributions of the two kinds of interaction result as convoluted in the sensor response, preventing fully quantitative measurements of the quantities of interest.
The technology refers to an innovative plasma (ionized gas) source operating at atmospheric pressure and low electric power levels. A cold plasma is produced, characterized by an ion temperature significantly lower than the electron temperature. Partial ionization of a Helium flux is induced by a time-varying electric field in between two parallel grids, both perpendicular to the flux itself.
The platform allows acquisition of data from commercial and custom sensors. By now, the system has been embedded in a wearable wristband where elastomeric based strain gauge have been integrated to detect fine hand/wrist/arm movements. The platform integrates inertial sensors (accelerometers, gyroscopes) to acquire more details about the subject movements. A sensor-fusion algorithm enables advanced movement recognition (gesture, 3D orientation). A machine-learning algorithm is in development to increase the performance of the platform.
The environment as well as the food production provide a number of both natural and synthetic compounds whose effects on human being as an organism have not yet been determined nor investigated.
The prototype uses soil moisture sensors which, through a measurement of dielectric permittivity, estimate the soil moisture based on which irrigation is started through relay-controlled solenoid valve. The system was developed using Open Source technologies. Specifically, for the hardware components, a small sized board computer Raspberry PI 3B + was used together with a 4G LTE Wi-Fi router and a Modbus rs485 / USB converter.
To the enterprises working in the field of nutrition/nutraceutics and drug development/repositioning, we offer the know-how and state-of-the-art instrumentation of our labs to monitor multiple relevant biological parameters at the cellular level: metabolic activity, vitality, health, but also stress and toxicity. The use of advanced imaging techniques based on fluorescent/bioluminescent probes together with the availability of time-lapse acquisitions, guarantee the cutting-edge analysis of different biological parameters over time.
The study of proteins is typically limited to notions, sometimes with the aid of virtual 3D models, obtained from visualization programs. A knowledge of this type, although useful, limits the ability to acquire a more direct knowledge, almost never leads to awareness of dimensions, and is particularly difficult for those who do not have a strong capacity for three-dimensional imagination.
This form describes a programmable, autonomous and stand-alone imaging system for the acquisition and processing of images containing subjects whose size is larger than 1cm (e.g. gelatinous zooplankton, fishes, litter, manufacts), form the seafloor or along the water column, in shallow or deep waters. It is capable to recognize and classify the image content through pattern recognition algorithms that combine computer vision and artificial intelligence methodologies.
Environmental monitoring is a rapidly growing field, both in academia and industry. The use of wearables for environmental monitoring is a promising technique, as it allows data to be collected continuously and comprehensively. The main problem with using wearables for environmental monitoring is the size and weight of the system, as well as the high degree of specialization required to develop a fully functional device.