Biocompatibility and biofunctionalization of mesoporous silicon particles

Several of the newly developed drug molecules experience poor biopharmaceutical behavior, which hinders their effective delivery at the proper site of action. Among the several strategies employed in order to overcome this obstacle, mesoporous silicon-based materials have emerged as promising drug carriers due to their ability to improve the dissolution behavior of several poorly water-soluble drugs compounds confined within their pores. In addition to improve the dissolution behavior of the drugs, we report that porous silicon (PSi) nanoparticles have a higher degree of biocompatibility than PSi microparticles in several cell lines studied. In addition, the degradation of the nanoparticles showed its potential to fast clearance in the body. After oral delivery, the PSi particles were also found to transit the intestines without being absorbed. These results constituted the first quantitative analysis of the behavior of orally administered PSi nanoparticles compared with other delivery routes in rats. The self-assemble of a hydrophobin class II (HFBII) protein at the surface of hydrophobic PSi particles endowed the particles with greater biocompatibility in different cell lines, was found to reverse their hydrophobicity and also protected a drug loaded within its pores against premature release at low pH while enabling subsequent drug release as the pH increased. These results highlight the potential of HFBII-coating for PSi-based drug carriers in improving their hydrophilicity, biocompatibility and pH responsiveness in drug delivery applications. In conclusion, mesoporous silicon particles have been shown to be a versatile platform for improving the dissolution behavior of poorly water-soluble drugs with high biocompatibility and easy surface modification. The results of this study also provide information regarding the biofunctionalization of the THCPSi particles with a fungal protein, leading to an improvement in their biocompatibility and endowing them with pH responsive and mucoadhesive properties.Heikot biofarmaseuttiset ominaisuudet vaikeuttavat uusien lääkemolekyylien keksintää ja kehittämistä, mikä estää molekyylien tehokasta annostelua vaikutuspaikkaansa elimistössä. Tämän ongelman ratkaisemiseksi on tutkittu useita menetelmiä ja materiaaleja, joista yksi lupaavimmista perustuu mesohuokoisten silikonipohjaisten (PSi) materiaalien käyttöön lääkeannostelussa. PSi-pohjaiset lääkekantajat parantavat niukkaliukoisten lääkeaineiden liukenemisnopeutta, mikä perustuu mesohuokosten pieneen kokoon ja suureen pinta-alaan. Useissa solulinjoissa tehdyissä kokeissa havaittiin, että huokoisesta piistä valmistetut nanopartikkelit ovat biologiselta yhteensopivuudeltaan parempia kuin vastaavat PSi-mikropartikkelit. PSi-nanopartikkelien etuna on lisäksi nopea hajoaminen ja sitä kautta nopea poistuminen elimistöstä. Väitöskirjatyössä annosteltiin radio-leimattuja PSi-nanopartikkeleita rotan laskimoverenkiertoon, jolloin ne kohdentuivat nopeasti koe-eläimen maksaan ja pernaan ilman näkyviä toksisia vaikutuksia. Suun kautta annosteltuina PSi-nanopartikkelit kulkeutuivat suoliston läpi. PSi-partikkelien biologista yhteensopivuutta tutkituissa solulinjoissa parannettiin päällystämällä ne itsejärjestäytyvillä hydrofobiini-proteiineilla (hydrofobiini-luokka II, HFBII), mikä muutti hydrofobiset PSi-partikkelit hydrofiilisemmiksi. Päällystäminen myös suojasi PSi-partikkeleita ennenaikaiselta lääkeaineen vapautumiselta matalassa pH:ssa; kun ympäristön pH nousi, myös lääkevapautuminen nopeutui. Tulosten perusteella HFBII-päällystetyt PSi-pohjaiset lääkekantajat paransivat materiaalin hydrofiilisyyttä, biologista yhteensopivuutta ja pH-herkkyyttä, mitä voidaan käyttää hyväksi erilaisissa lääkeannosteluun liittyvissä sovellutuksissa. Yhteenvetona väitöskirjan tuloksista voidaan todeta, että biologisesti hyvin yhteensopivat mesohuokoiset silikonipohjaiset mikro- ja nanopartikkelit soveltuvat erinomaisesti niukkaliukoisten lääkemolekyylien liukoisuusnopeuden parantamiseen. Lääkeannostelua ja biologista yhteensopivuutta voidaan edelleen helposti parantaa ja säädellä partikkelien pintaa muokkaamalla. Hydrofobiini-proteiinien kanssa suoritetut PSi-partikkelien biofunktionalisoinnit mahdollistavat lääkekantajan pH-herkkyyteen ja mukoadhesiivisuuteen perustuvan säädellyn lääkeannostelun

Context-Aware Trajectory Prediction

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles. In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions. The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings. To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.Comment: Submitted to BMVC 201

A Data-Driven Approach for Tag Refinement and Localization in Web Videos

Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g. using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select on the fly from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU

Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

View Registration Using Interesting Segments of Planar Trajectories

We introduce a method for recovering the spatial and temporal alignment between two or more views of objects moving over a ground plane. Existing approaches either assume that the streams are globally synchronized, so that only solving the spatial alignment is needed, or that the temporal misalignment is small enough so that exhaustive search can be performed. In contrast, our approach can recover both the spatial and temporal alignment. We compute for each trajectory a number of interesting segments, and we use their description to form putative matches between trajectories. Each pair of corresponding interesting segments induces a temporal alignment, and defines an interval of common support across two views of an object that is used to recover the spatial alignment. Interesting segments and their descriptors are defined using algebraic projective invariants measured along the trajectories. Similarity between interesting segments is computed taking into account the statistics of such invariants. Candidate alignment parameters are verified checking the consistency, in terms of the symmetric transfer error, of all the putative pairs of corresponding interesting segments. Experiments are conducted with two different sets of data, one with two views of an outdoor scene featuring moving people and cars, and one with four views of a laboratory sequence featuring moving radio-controlled cars