Spatial ecology of crested porcupine in a metropolitan landscape.

Human settlements, including cities, may provide wildlife with new ecological niches, in terms of habitat types and food availability, thus requiring plasticity for adaptation. The crested porcupine Hystrix cristata is a habitat-generalist, large-sized rodent, also recorded in some suburban areas, but no information is available on its habitat use in metropolitan landscapes. Here, we assessed the land-use factors influencing the presence of crested porcupines in a metropolitan area of Central Italy. We collected data on the occurrence of crested porcupines from the metropolitan area of Rome, following an observer-oriented approach to record occurrences and retreive pseudo-absences. We then related the presence/absence of H. cristata to landscape composition. Occupancy models showed that cultivations and scrubland were positively related to porcupine presence, most likely as they provide food resources and shelter sites, respectively. Although the crested porcupine has been confirmed as a "generalist" species in terms of habitat selection, a strong preference for areas limiting the risk of being killed and providing enough food and shelter was observed. We therefore suggest that the crested porcupine may adapt to deeply modified landscapes such as large cities by selecting specific favourable land-use types.

Shadows of quantum machine learning.

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a 'shadow model' from which the classical deployment becomes possible. We prove that: (i) this class of models is universal for classically-deployed quantum machine learning; (ii) it does have restricted learning capacities compared to 'fully quantum' models, but nonetheless (iii) it achieves a provable learning advantage over fully classical learners, contingent on widely believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.

A novel chemically defined medium for the biotechnological and biomedical exploitation of the cell factory Leishmania tarentolae.

The development of media for cell culture is a major issue in the biopharmaceutical industry, for the production of therapeutics, immune-modulating molecules and protein antigens. Chemically defined media offer several advantages, as they are free of animal-derived components and guarantee high purity and a consistency in their composition. Microorganisms of the genus Leishmania represent a promising cellular platform for production of recombinant proteins, but their maintenance requires supplements of animal origin, such as hemin and fetal bovine serum. In the present study, three chemically defined media were assayed for culturing Leishmania tarentolae, using both a wild-type strain and a strain engineered to produce a viral antigen. Among the three media, Schneider's Drosophila Medium supplemented with Horseradish Peroxidase proved to be effective for the maintenance of L. tarentolae promastigotes, also allowing the heterologous protein production by the engineered strain. Finally, the engineered strain was maintained in culture up to the 12th week without antibiotic, revealing its capability to produce the recombinant protein in the absence of selective pressure.