Landscape structure shapes carnivore-mediated seed dispersal kernels

Context Seed dispersal is recognized as having profound effects on the distribution, dynamics and structure of plant populations and communities. However, knowledge of how landscape structure shapes carnivore-mediated seed dispersal patterns is still scarce, thereby limiting our understanding of large-scale plant population processes. Objectives We aim to determine how the amount and spatial configuration of forest cover impacted the relative abundance of carnivorous mammals, and how these effects cascaded through the seed dispersal kernels they generated. Methods Camera traps activated by animal movement were used for carnivore sampling. Colour-coded seed mimics embedded in common figs were used to know the exact origin of the dispersed seed mimics later found in carnivore scats. We applied this procedure in two sites differing in landscape structure. Results We did not find between-site differences in the relative abundance of the principal carnivore species contributing to seed dispersal patterns, Martes foina. Mean dispersal distance and the probability of long dispersal events were higher in the site with spatially continuous and abundant forest cover, compared to the site with spatially aggregated and scarcer forest cover. Seed deposition closely matched the spatial patterning of forest cover in both study sites, suggesting behaviour-based mechanisms underpinning seed dispersal patterns generated by individual frugivore species. Conclusions Our results provide the first empirical evidence of the impact of landscape structure on carnivore-mediated seed dispersal kernels. They also indicate that seed dispersal kernels generated strongly depend on the effect that landscape structure exerts on carnivore populations, particularly on habitat-use preferences.

Spatially defined peptidomimetics for pain relief and for targeted cancer therapy and diagnostics

Cancer is a disease that has plagued scientists for decades, and how to treat cancer and its complications are inevitable topics in current scientific research. Cancer pain is a major factor that reduces the quality of life of patients. Therefore, the development of analgesic agents with minimal adverse side effects, especially with low addiction, has attracted more and more attention. Among them, opioid analgesics are widely used to alleviate cancer pain and improve the quality of life of patients with advanced cancer, such as in the palliative therapy. Although peptide drugs are efficient, selective and safe, they have several unignorable disadvantages such as poor biological stability, rapid excretion, difficulty in penetrate blood brain barrier. In order to solve these problems, peptidomimetics were developed by introducing unnatural/modified amino acids, decorated peptide backbone, conformational restrictions and secondary structure mimics in peptide sequence. Compared with peptides, peptidomimetics have improved biological stability, increased bioavailability, high affinity and selectivity for receptor binding, and decreased adverse side effects. As the second part of this thesis, I explored the opportunity to design peptide-functionalized responsive biomaterials for the detection of cancer cell and the selective delivery of cytotoxic drugs. The conjugation of peptides with biomaterials enhanced the stability of the loaded drugs, improved targeted delivery, decreased side effects, and increased bioavailability. The precise and controllable drug delivery platform has profound application prospects in cancer treatment. Grafting specific peptides sequence on the surface of biomaterials can satisfy different drug delivery demands according to the characteristics of both peptides and biomaterials. For example, the introduction of tumor-targeting peptides can guide biomaterials into tumor lesions, and blood-brain barrier (BBB) shuttle peptides can lead biomaterials to penetrate the BBB, etc.

Development of numerical methodologies to predict the liquid fuel sprays - wall interaction to optimize the mixing process of direct injection spark ignition engines

Nowadays the development of new Internal Combustion Engines is mainly driven by the need to reduce tailpipe emissions of pollutants, Green-House Gases and avoid the fossil fuels wasting. The design of dimension and shape of the combustion chamber together with the implementation of different injection strategies e.g., injection timing, spray targeting, higher injection pressure, play a key role in the accomplishment of the aforementioned targets. As far as the match between the fuel injection and evaporation and the combustion chamber shape is concerned, the assessment of the interaction between the liquid fuel spray and the engine walls in gasoline direct injection engines is crucial. The use of numerical simulations is an acknowledged technique to support the study of new technological solutions such as the design of new gasoline blends and of tailored injection strategies to pursue the target mixture formation. The current simulation framework lacks a well-defined best practice for the liquid fuel spray interaction simulation, which is a complex multi-physics problem. This thesis deals with the development of robust methodologies to approach the numerical simulation of the liquid fuel spray interaction with walls and lubricants. The accomplishment of this task was divided into three tasks: i) setup and validation of spray-wall impingement three-dimensional CFD spray simulations; ii) development of a one-dimensional model describing the liquid fuel – lubricant oil interaction; iii) development of a machine learning based algorithm aimed to define which mixture of known pure components mimics the physical behaviour of the real gasoline for the simulation of the liquid fuel spray interaction.