A non standard finite difference model for a class of renewal equations in epidemiology
Mathematical models based on non-linear integral and integro-differential equations are gaining
increasing attention in mathematical epidemiology due to their ability to incorporate the past
infection dynamic into its current development. This property is particularly suitable to represent
the evolution of diseases where the dependence of infectivity on the time since becoming
infected plays a crucial role.
Enhanced pClustering and its applications to gene expression data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes and experiments. This "biclustering "paradigm aims at discovering clusters that consist of a subset of the genes showing a coherent expression pattern over a subset of conditions. The pClustering approach is a technique that belongs to this paradigm. Despite many theoretical advantages, this technique has been rarely applied to actual gene expression data analysis.
PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy
In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyper-spectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400-2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400-750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021.
Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams
The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence.
