3D shape clustering with Nonnegative Least Squares coding and fusion on multilayer graphs

Multimedia Tools and Applications - The well-known method of spectral clustering for 3D shape datasets is revisited. The graph construction by NNLS (Nonnegative Least Squares) coding technique is...     

Biomarkers in AL Amyloidosis

Systemic AL amyloidosis is a rare complex hematological disorder caused by clonal plasma cells which produce amyloidogenic immunoglobulins. Outcome and prognosis is the combinatory result of the extent and pattern of organ involvement secondary to amyloid fibril deposition and the biology and burden of the underlying plasma cell clone. Prognosis, as assessed by overall survival, and early outcomes is determined by degree of cardiac dysfunction and current staging systems are based on biomarkers that reflect the degree of cardiac damage. The risk of progression to end-stage renal disease requiring dialysis is assessed by renal staging systems. Longer-term survival and response to treatment is affected by markers of the underlying plasma cell clone; the genetic background of the clonal disease as evaluated by interphase fluorescence in situ hybridization in particular has predictive value and may guide treatment selection. Free light chain assessment forms the basis of hematological response criteria and minimal residual disease as assessed by sensitive methods is gradually being incorporated into clinical practice. However, sensitive biomarkers that could aid in the early diagnosis and that could reflect all aspects of organ damage and disease biology are needed and efforts to identify them are continuous.     

On the definition of shape parts: a dominant sets approach

In the present paper, a novel graph-based approach to the shape decomposition problem is addressed. The shape is appropriately transformed into a visibility graph enriched with local neighborhood information. A two-step diffusion process is then applied to the visibility graph that efficiently enhances the information provided, thus leading to a more robust and meaningful graph construction. Inspired by the notion of a clique as a strict cluster definition, the dominant sets algorithm is invoked, slightly modified to comport with the specific problem of defining shape parts. The cluster cohesiveness and a node participation vector are two important outputs of the proposed graph partitioning method. Opposed to most of the existing techniques, the final number of the clusters is determined automatically, by estimating the cluster cohesiveness on a random network generation process. Experimental results on several shape databases show the effectiveness of our framework for graph-based shape decomposition.     

Automatic estimation of exercises' difficulty levels in a tutoring system for teaching the conversion of natural language into first‐order logic

The estimation of the difficulty level of exercises is a fundamental aspect of intelligent tutoring systems, and it is necessary in order to achieve better adaptation to the students' needs and maximize learning efficiency. In this article, we present an approach to automatically estimates the difficulty level of exercises in natural language (NL) to first‐order of logic (FOL). The estimation of an exercise's difficulty level is based on the complexity of the corresponding answer, that is the FOL formula, as well as the structure and the semantics of the exercise, that is a natural language sentence and it is carried out in two main steps. Initially, a preliminary estimation is performed based on the complexity of the FOL formula. The system takes as input parameters the number, the type and the order of quantifiers, the number of implications, and the number of different connectives. Afterwards, the final estimation is made based on both semantic aspects of the NL sentence and the structure of the FOL formula. An evaluation study was conducted to assess the system's performance, and the results are very encouraging.     

The use of molecular markers in the verification of fish and seafood authenticity and the detection of adulteration

The verification of authenticity and detection of food mislabeling are elements that have been of high importance for centuries. During the last few decades there has been an increasing consumer demand for the verification of food identity and the implementation of stricter controls around these matters. Fish and seafood are among the most easily adulterated foodstuffs mainly due to the significant alterations of the species' morphological characteristics that occur during the different types of processing, which render the visual identification of the animals impossible. Even simple processes, such as filleting remove very important morphological elements and suffice to prevent the visual identification of species in marketed products. Novel techniques have therefore been developed that allow identification of species, the differentiation between species and also the differentiation of individuals that belong to the same species but grow in different populations and regions. Molecular markers have been used during the last few decades to fulfill this purpose and several improvements have been implemented rendering their use applicable to a commercial scale. The reliability, accuracy, reproducibility, and time-and cost-effectiveness of these techniques allowed them to be established as routine methods in the industry and research institutes. This review article aims at presenting the most important molecular markers used for the authentication of fish and seafood. The most important techniques are described, and the results of numerous studies are outlined and discussed, allowing interested parties to easily access and compare information about several techniques and fish/seafood species.