How word semantics and phonology affect handwriting of Alzheimer's patients: A machine learning based analysis.

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.

Protein Binding of Benzofuran Derivatives: A CD Spectroscopic and In Silico Comparative Study of the Effects of 4-Nitrophenyl Functionalized Benzofurans and Benzodifurans on BSA Protein Structure.

Benzofuran derivatives are synthetic compounds that are finding an increasing interest in the scientific community not only as building blocks for the realization of new materials, but also as potential drugs thanks to their ability to interact with nucleic acids, interfere with the amyloid peptide aggregation and cancer cell cycle. However, their ability to interact with proteins is a theme still in need of investigation for the therapeutic importance that benzofurans could have in the modulation of protein-driven processes and for the possibility of making use of serum albumins as benzofurans delivery systems. To this scope, we investigated the protein binding ability of two 4-nitrophenyl-functionalized benzofurans previously synthesized in our laboratory and herein indicated as BF1 and BDF1, which differed for the number of furan rings (a single moiety in BF1, two in BDF1), using bovine serum albumin (BSA) as a model protein. By circular dichroism (CD) spectroscopy we demonstrated the ability of the two heteroaromatic compounds to alter the secondary structure of the serum albumin leading to different consequences in terms of BSA thermal stability with respect to the unbound protein (ΔTm > 3 °C for BF1, -0.8 °C for BDF1 with respect to unbound BSA, in PBS buffer, pH 7.5) as revealed in our CD melting studies. Moreover, a molecular docking study allowed us to compare the possible ligand binding modes of the mono and difuranic derivatives showing that while BF1 is preferentially housed in the interior of protein structure, BDF1 is predicted to bind the albumin surface with a lower affinity than BF1. Interestingly, the different affinity for the protein target predicted computationally was confirmed also experimentally by fluorescence spectroscopy (kD = 142.4 ± 64.6 nM for BDF1 vs. 28.4 ± 10.1 nM for BF1). Overall, the above findings suggest the ability of benzofurans to bind serum albumins that could act as their carriers in drug delivery applications.

From Online Handwriting to Synthetic Images for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach.

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of AD. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. To verify the role played by dynamic information, we also generated simple binary images, containing only shape information. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. The experimental results have confirmed that dynamic information allows a performance improvement with respect to the binary images.

An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents.

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers' suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a "classical" machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the "Avila Bible". This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems.