Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurovisual rehabilitation: recent developments and future directions

Central visual1 and oculomotor2 disorders are present in some 20%-40% of patients in neurological rehabilitation centres.3 Gianutsos reported that 50% of the patients in a head trauma rehabilitation centre show visual system disorders not assessed before although most of the patients were chronic and had been treated in other hospitals previously.4 In a large sample of 314 patients with postchiasmatic visual field disorders from a rehabilitation department 70% had a parafoveal visual field sparing of 5° or less. Of these patients, 50%-70% subjectively reported and objectively showed chronic reading and visual exploration deficits.5 Furthermore, visual field disorders are associated with an adverse prognosis in outcome studies according to life table analysis6 and impair the success of vocational rehabilitation.4 7 Spatial-perceptual deficits may delay the rehabilitation progress in physiotherapy,8 9 lead to complications due to repeated accidents,10 and correlate highly with deficits in activities of daily living (dressing, transfers, or manoeuvring a wheelchair.11-16 In the presence of limited financial resources in health policy it might be argued that central visual disorders do not require treatment as they do not affect outcome adversely. Although nothing supports this opinion there is some evidence to the contrary. Cutting inpatient stroke rehabilitation to a maximum of 60 days (in Italy) led to a deterioration in neuropsychological and activities of daily living in patients with stroke, especially those with right hemispheric lesions.17 Hence, withdrawing neuropsychological treatment deteriorates functional performance and creates new costs. Whereas the systematic treatment of language, speech, and motor disorders is traditionally viewed as unequivocally necessary the possible influence of visual-sensory and oculomotor disorders on the patient's outcome is still neglected in neurorehabilitation. This review summarises therapeutic approaches for acquired central visual disorders. It is split into five parts. After an introduction …
Configurable analog-digital conversion using the neural engineering framework

Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.
The Growth Rate Dependence of the Internal Friction of Synthetic Quartz

The internal friction of 5 Mc/s shear waves in AT-cut synthetic quartz resonators has been measured over a temperature range of 4°-400 °K as a function of the rate of growth of the parent quartz crystal. It is found that the peak in the region of 25 °K decreases somewhat with increasing growth rate, whilst the 50 °K peak increases by two orders of magnitude for a fourfold increase in growth rate. Furthermore, the internal friction in the range 250°-400 °K rises exponentially in the faster grown material. Tentative explanations of these effects are given in terms of the current theories of the interaction of phonons and imperfections in dielectric crystals.
Efectos en la Remodelación Ósea Debido a la Aplicación de Tornillos en Fémur Proximal

The current work describes the alterations that a trabecular bone structure of the proximal femur suffers when long screws are used for fixation, this based in the strain energy density theory. The bone remodeling process initiate with the stimuli generated through the mechanicals loads applied to the bone, this fact determines the change in the bone bulk density and therefore the modulus of elasticity change; this supposes a strength magnitude increment or decrement giving as a result osseointegration or reabsorption respectively in the trabecular bone. A computing tool has been developed using Phyton language and it has been integrated to Abaqus 6.9 ® Software. The numerical procedure results let estimate the strength variation and its influence in the bone density final state before and after the screw fixation in the thigh bone, the density patterns obtained let identify quantitatively the zones where the bone-screw interface gets diminished, predicting the density patterns reported in clinical practice, which are only qualitative, furthermore, it is determined that the loss of density during a hundred weeks period is about 45% around the screw, this causes micro-movements generation which
Optimization of program invariants

Program invariants can be detected using both static and dynamic approaches. Dynamic methods that complement static methods will be more useful and effective in analyzing software artifacts. Existing dynamic invariant detection techniques detect both relevant and irrelevant invariants due to the presence of relevant and irrelevant/unused variables in the program. Therefore, the speed and efficiency of the techniques are affected. Also, displaying properties about irrelevant variables distracts the user from concentrating on properties of relevant variables. Moreover, these irrelevant invariants do not contribute to the correctness of the program. Therefore, there is a need to overcome the above deficiencies. This is done in three phases in this paper. In the first phase, only the relevant variables are considered by ignoring irrelevant variables. In the second phase, relevant variables are classified into two different types as design variables and non-design variables by using proposed metrics. These metrics are validated analytically against Weyuker's principles. Similarly, relevant invariants are categorized into three types as design invariants, non-design invariants and hybrid invariants in the third phase. For this purpose a set of rules are proposed. This process enormously improves the speed and efficiency of dynamic invariant detection techniques. Also, it drastically reduces the effort that is required to validate program correctness.
Behavioral profiles of mouse models for autism spectrum disorders

Autism spectrum disorders (ASD) are characterized by impairments in reciprocal social communication, and stereotyped verbal and nonverbal behaviors. In approximately 10–25% of the affected individuals, a genetic mutation associated with the condition can be identified. Recently, mutations altering synapse formation, cellular/synaptic growth rate and regulation of excitatory and inhibitory currents were identified in patients with intellectual disability, typical autism, Asperger syndrome or neurological syndromes associated with autistic traits. Following these genetic findings, mouse models carrying mutations similar to those identified in patients have been generated. These models offer the opportunity to investigate in vivo the physiological and behavioral consequences of the mutations. Here, we review the existing data on the phenotypes of mice carrying mutations in genes associated with ASD including neuroligin, neurexin and Shank mutant mice as well as the Fmr1, Mecp2, Ube3a, Nf1, Pten and Tsc1/Tsc2 mutant mice. The diversity and complexity of the phenotype of these mouse models reflect the broad range of phenotypes observed in patients with ASD. Remarkably, results from therapeutic approaches (e.g., modulation of gene expression, administration of pharmacological and nonpharmacological substances, enriched environment) are encouraging since some behavioral alterations could be reversed even when treatment was performed on adult mice. These ongoing studies should therefore increase our understanding of the biological alterations associated with ASD as well as the development of knowledge‐based treatments.
Computerized Assessment of Communication for Cognitive Stimulation for People with Cognitive Decline Using Spectral-Distortion Measures and Phylogenetic Inference

Therapeutic communication and interpersonal relationships in care homes can help people to improve their mental wellbeing. Assessment of the efficacy of these dynamic and complex processes are necessary for psychosocial planning and management. This paper presents a pilot application of photoplethysmography in synchronized physiological measurements of communications between the care-giver and people with dementia. Signal-based evaluations of the therapy can be carried out using the measures of spectral distortion and the inference of phylogenetic trees. The proposed computational models can be of assistance and cost-effectiveness in caring for and monitoring people with cognitive decline.
Nucleus Classification in Colon Cancer H&E Images using Deep Learning

Classification of nuclei, cells, and tissues is one of the most challenging tasks in biomedical image analysis. A large variety of computational approaches in the fields of image processing and machine learning including deep learning has been reported in the literature [1],[2],[3]. Performance of pattern recognition and classification tasks greatly depends on the features they use. Hand-crafted features may not always capture underlying structure of the data or generalize to other tasks. Deep learning uses multi-layer networks to reconstruct hierarchy of representations and enables automatic learning of complex features required for visual pattern recognition. We have recently developed an automated deep learning system for tissue classification in histopathology images [2]. Nuclei classification in the images of colon cancer H&E slides is also a challenging task due to large variation of cell morphologies. Hematoxylin & Eosin (H&E) is one of the most commonly employed stains in pathology. The digital images obtained from the H&E stained specimens can introduce additional challenges due to batch variations and illumination changes. To implement the nucleus classification task, we designed a Convolution Neural Network (CNN) [4]. The ability of CNN in learning high-dimensional and complex classes of objects makes it a very useful tool for a variety of classification tasks in biomedical imaging. Our network was trained and evaluated by using a large colon cancer nuclei images dataset reported in [5] with 22,444 nuclei divided into four classes (Epithelial, Inflammatory, Fibroblast, and Miscellaneous) with the following number of samples (7,722, 6,971, 5,712, and 2,039), respectively. Our model consists of six layers (2 convolution layers, 2 subsampling layers, 1 fully connected layer, and output layer). The details of our deep learning network are shown in Figure 1. The original size of H&E images is 500x500x3 and the total number
Molecular Sciences Characterization of Behaviour and Remote Degeneration following Thalamic Stroke in the Rat

Subcortical ischemic strokes are among the leading causes of cognitive impairment. Selective atrophy of remote brain regions connected to the infarct is thought to contribute to deterioration of cognitive functions. The mechanisms underlying this secondary degenerative process are incompletely understood, but are thought to include inflammation. We induce ischemia by unilateral injection of endothelin-I into the rat dorsomedial thalamic nucleus, which has defined reciprocal connections to the frontal cortex. We use a comprehensive test battery to probe for changes in behaviour, including executive functions. After a four-week recovery period, brain sections are stained with markers for degeneration, microglia, astrocytes and myelin. Degenerative processes are localized within the stroke core and along the full thalamocortical projection, which does not translate into measurable behavioural deficits. Significant microglia recruitment, astrogliosis or myelin loss along the axonal projection or within the frontal cortex cannot be detected. These findings indicate that critical effects of stroke-induced axonal degeneration may only be measurable beyond a threshold of stroke severity and/or follow a different time course. Further investigations are needed to clarify the impact of inflammation accompanying axonal degeneration on delayed remote atrophy after stroke.
The effect of xylitol on dental caries and oral flora

Dental caries, the most chronic disease affecting mankind, has been in the limelight with regard to its prevention and treatment. Professional clinical management of caries has been very successful in cases of different severities of disease manifestations. However, tertiary management of this disease has been gaining attention, with numerous methods and agents emerging on a daily basis. Higher intake of nutritive sweeteners can result in higher energy intake and lower diet quality and thereby predispose an individual to conditions like obesity, cardiovascular disorders, and type 2 diabetes mellitus. Non-nutritive sweeteners have gained popularity as they are sweeter and are required in substantially lesser quantities. Xylitol, a five-carbon sugar polyol, has been found to be promising in reducing dental caries disease and also reversing the process of early caries. This paper throws light on the role and effects of various forms of xylitol on dental caries and oral hygiene status of an individual.
Masquerader Detection in Mobile Context Based on Behaviour and Environment Monitoring Based on Behaviour and Environment Monitoring Masquerader Detection in Mobile Context Masquerader Detection in Mobile Context Based on Behaviour and Environment Monitoring Jyväskylä Studies in Computing 74 Masquera

In recent years, mobile terminals such as mobile phones and PDAs have evolved into functionally powerful devices that can be used to store, process, and communicate valuable information. The access to this information should be properly secured, and hence a great demand for security systems exists. The security issue emphasised in this thesis is the necessity to ensure that the current user of the terminal is its legitimate user. The importance of this issue stems from the fact that a number of mobile terminals are lost or stolen daily: the unauthorised use of such a terminal by a masquerader impersonating the legitimate user may involve abuse of sensitive information kept locally on the terminal or accessible over a network connection. The aim of the reported research is to develop a conceptual basis for differentiating between the legitimate user of the terminal and other individuals by analysing the information about user behaviour and environment, and to address the practical issue of applying it to the problem of mobile-masquerader detection. The main contribution of this thesis is a three-part model of mobile-masquerader detection developed and verified empirically in the thesis. In this model, the problem of mobile-masquerader detection is approached as a classification problem. The first part of the model identifies behav-ioural and environmental characteristics and measures that can be used for differentiating the user from other individuals. The second part of the model defines how the values of the identified measures can be classified by a set of one-class classifiers each analysing a subset of the measures. The third part addresses the issue of combining the outputs of the classifiers so that accurate final classification can be achieved; a new combining technique is proposed and validated using numerical experiments. Finally, the feasibility of the proposed mobile-masquerader detection model is experimentally validated on a real-world dataset describing the behaviour and environment of smart-phone users.
Far-Infrared Spectroscopy of Weakly Bound Hydrated Cluster Molecules

The thermodynamic properties of condensed phases, the functionality of many materials and the molecular organization in biological organisms are all governed by the classes of non-covalent interactions that occur already on the microscopic scale between pairs of molecules. A detailed investigation of the intermolecular interactions between prototypical molecular assemblies are valuable for accurate descriptions of larger supramolecular systems such as materials, gas hydrates and biological macromolecules. The aim of this PhD dissertation is to investigate intermolecular interactions for a series of medium-sized molecular clusters with water by means of far-infrared and terahertz neon matrix isolation spectroscopy. The embedding of non-covalent cluster molecules in solid cryogenic neon matrices at 2.8 K ensures a high sensitivity for direct spectroscopic observations of the large-amplitude intermolecular vibrational bands of the cluster molecules in the challenging far-infrared and terahertz spectral regions. A key parameter in the validation of the performance of theoretical predictions for weak non-covalent intermolecular interactions is the dissociation energy D0 that depends heavily on the class of large-amplitude intermolecular vibrational modes for an accurate estimation of the zero-point vibrational energy. The spectroscopic observation of intermolecular vibrational modes for the weak van der Waals complex of carbon dioxide and water provides crucial observables for characterization of the shallow potential energy surface spanned between the two molecular subunits. The combination of thick matrices and isotopic substitution of the water subunit allows observation of three intermolecular vibrational modes corresponding to 75 % of the predicted intermolecular zero-point vibrational energy. The combination with high-level electronic structure calculations has enabled a (semi)-empirical value for the dissociation energy D0 of 738 ± 15 cm−1 for the weak van der Waals complex. The weak intermolecular interactions between unsaturated hydrocarbons and water are investigated by combined infrared and terahertz absorption spectroscopy studies. The hydrogen bond interaction of either C-H · · · O or O-H · · · π character can be revealed by the nature of the large-amplitude intermolecular vibrational modes. Two high-frequency librational modes for the water/ethylene dimer reveal an intermolecular O-H · · · π hydrogen bond, while the single intermolecular acceptor wagging mode for the acetylene/water dimer reveals a C-H · · · O hydrogen bond. The spec-
Machine Learning Methods Used in Evaluations of Secure Biometric System Components

This paper provides a comprehensive overview of theories, methodologies, techniques, standards and frameworks of biometric systems. The studies conducted between 2007-2017 are examined in order to ensure the security of the equipment used in a biometric system, to secure the characteristic feature extraction, to provide secure data storage in the biometric database, to maintain transmission channels used in biometric applications from vulnerabilities, and to ensure the correctness of the results obtained from intelligent decision mechanism. Machine learning techniques used to detect and protect existing attacks are analyzed, obtained results are shared and recommendations are made in the last part of the study.
Knowledge-based Word Sense Disambiguation with Feature Words Based on Dependency Relation and Syntax Tree

Context feature words are important for word sense disambiguation (WSD). There are two kinds of methods to extract feature words: window-based and dependency relation method. Both of them have some defects. In order to solve the problems of the existing methods, this paper proposes a knowledgebased WSD method which obtains context feature words by dependency relation and syntax tree. Firstly, according to dependency relation between context words and the ambiguous word, the layer relation and path distance in the phrase structure syntax tree, direct distance in the sentence, feature words are selected from the context and are assigned different WSD weights. Secondly, Based on WordNet (WN), semantic relatedness between each sense of the ambiguous word and feature words are computed and the sense with most semantic relatedness is selected as the right sense. Evaluation is performed over a publicly available lexical sample dataset. The results show that our WSD method is better than the methods that obtain feature words with window or dependency relation. The method is a preferred strategy to select feature words to disambiguate the target words.
Differential effects of chlorpromazine on the in vitro generation and effector function of cytotoxic lymphocytes

Allograft rejection represents a cytotoxic response mediated to a large degree by thymus-derived T lymphocytes (1). The study of such cell-mediated cytotoxic phenomena has been greatly facilitated by the discovery first noted by Hayry and Defendi (2) and Wunderlich and Cany (3), that a natural consequence of allogeneic stimulation in an unidirectional mixed lymphocyte culture (MLC) was the appearance of cytotoxic lymphocytes specific for antigens present on the stimulator cells. Subsequent studies have shown that such in vitro generation of cytotoxic lymphocytes was dependent on the proliferative response in an MLC (4), was genetically determined (5), and possibly required the interaction of several subpopulations of T cells (6). We now report that the surface active agent chlorpromazine: (a) inhibits allogeneic stimulation of the proliferative response in an MLC; (b) inhibits the MLC generation of cytotoxic lymphocytes, (c) has no effect on the recognition, binding, or lysis of target cells by already sensitized lymphocytes; and (d) blocks a postproliferative membrane-mediated event, independent of proliferation, and necessary for the MLC generation of cytotoxic lymphocytes.
Interaction Kinetics between Sn-Pb Solder Droplet and Au/Ni/Cu Pad

The interfacial phenomena of the Sn-Pb solder droplet on Au/Ni/Cu pad are investigated. A continuous AuSn2 and needle-like AuSn4 are formed at the interface after the liquid state reaction (soldering). The interfacial reaction between the solder and Au layer continues during solid state aging with AuSn4 breaking off from the interface and felling into the solder. The kinetics of Au layer dissolution and diffusion into the solder during soldering and aging is analyzed to elucidate intermetallic formation mechanism at the solder/Au pad interface. The concentration of Au near the solder/pad interface is identified to increase and reach the solubility limit during the period of liquid state reaction. During solid state reaction, the thickening of Au-Sn compound is mainly controlled by element diffusion.
muDog: Smart Monitoring Mechanism for Wireless Sensor Networks Based on IEEE 802.15.4 MAC

In this paper we propose an analytical model to detect and remove malicious nodes while taking into account MAC IEEE 802.15. 4 beacon-enabled technology. The proposed solution called muDog enables to monitor nodes activities with a minimal energy consumption in order to detect the suspicious behavior particularly the non-cooperative nodes in the routing process. Moreover, we analyze the cost of the monitoring mechanism in terms of energy consumption and the quality of detection by the evaluation of the monitor's observation. The impact of nodes density, packets' size, network traffic load: saturated/unsaturated cases and distance between monitor and monitored nodes are taken into account in our evaluation. The obtained results illustrate that muDog is more efficient than Watchdog whatever the parameter is.