The internal representation of vowel spectra investigated using behavioral response-triggered averaging

Listeners presented with noise were asked to press a key 13 whenever they heard the vowels [a] or [i:]. The noise had a random spectrum, with levels in 60 frequency bins changing every 0.5 s. Reverse correlation was used to average the spectrum of the noise prior to each key press, thus estimating the features of the vowels for which the participants were listening. The formant frequencies of these reverse-correlated vowels were similar to those of their respective whispered vowels. The success of this response-triggered technique suggests that it may prove useful for estimating other internal representations, including perceptual phenomena like tinnitus

Casting Process Improvement by the Application of Artificial Intelligence

On the way to building smart factories as the vision of Industry 4.0, the casting process stands out as a specific manufacturing process due to its diversity and complexity. One of the segments of smart foundry design is the application of artificial intelligence in the improvement of the casting process. This paper presents an overview of the conducted research studies, which deal with the application of artificial intelligence in the improvement of the casting process. In the review, 37 studies were analyzed over the last 15 years, with a clear indication of the type of casting process, the field of application of artificial intelligence techniques, and the benefits that artificial intelligence brought. The goals of this paper are to bring to attention the great possibilities of the application of artificial intelligence for the improvement of manufacturing processes in foundries, and to encourage new ideas among researchers and engineers

Application of Machine Learning in the Control of Metal Melting Production Process

Abstract This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production

EMG Map for Designing the Electrode Shape for Functional Electrical Therapy of Upper Extremities

Achieving the functional grasp by electrical stimulation using surface electrodes is a demanding task. The innervations of muscles come via ulnar, radial and median nerves. The anatomy of nerve branches connecting various muscles in the forearm differs significantly between individuals. We hypothesize that the anatomical differences between the paretic and nonparetic arms are minimal. Based on this assumption we developed a method where the differences of muscle activities (EMG) between the healthy and paretic arms recorded by the 24-contact electrode within an array define the target zones to be stimulated on the affected forearm. We used special electrode where magnetic contacts allow simple change of the stimulation pads. The examiner positions the magnetic contact on the pads where the EMG differences are maximal. The stimulator delivers asynchronous stimulation to the selected pads. We proved that the method is working in stroke patients by measuring joint angles and the grasping force. © 2019, Springer Nature Switzerland AG.Biosystems and Biorobotics, Vol. 21This is the peer-reviewed version of the following article: Popović-Maneski, L., Topalović, I., 2019. EMG Map for Designing the Electrode Shape for Functional Electrical Therapy of Upper Extremities, in: Masia, L., Micera, S., Akay, M., Pons, J.L. (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation III, Biosystems & Biorobotics. Springer International Publishing, pp. 1003–1007, http://dx.doi.org/10.1007/978-3-030-01845-0_20

Effect of microstructure on tensile properties of austenite-ferrite welded joint

Complex microstructure of austenite-ferrite welded joint has been investigated, focused on its influence on local tensile properties. Tensile properties (yield strength and hardening coefficient) have been evaluated by using finite element method to simulate the strain distributions obtained experimentally. The three-dimensional model of V-joint specimen has been used with seven different materials, simulating two base metals, the weld metal and two sub-regions of two heat-affected zones - fine grain and coarse grain. In this way local tensile properties of the whole austenite-ferrite welded joint have been evaluated

The novel Parkinson's disease linked mutation G51D attenuates in vitro aggregation and membrane binding of α-synuclein, and enhances its secretion and nuclear localization in cells

A novel mutation in the α-Synuclein (α-Syn) gene "G51D” was recently identified in two familial cases exhibiting features of Parkinson's disease (PD) and multiple system atrophy (MSA). In this study, we explored the impact of this novel mutation on the aggregation, cellular and biophysical properties of α-Syn, in an attempt to unravel how this mutant contributes to PD/MSA. Our results show that the G51D mutation significantly attenuates α-Syn aggregation in vitro. Moreover, it disrupts local helix formation in the presence of SDS, decreases binding to lipid vesicles C-terminal to the site of mutation and severely inhibits helical folding in the presence of acidic vesicles. When expressed in yeast, α-SynG51D behaves similarly to α-SynA30P, as both exhibit impaired membrane association, form few inclusions and are non-toxic. In contrast, enhanced secreted and nuclear levels of the G51D mutant were observed in mammalian cells, as well as in primary neurons, where α-SynG51D was enriched in the nuclear compartment, was hyper-phosphorylated at S129 and exacerbated α-Syn-induced mitochondrial fragmentation. Finally, post-mortem human brain tissues of α-SynG51D cases were examined, and revealed only partial colocalization with nuclear membrane markers, probably due to post-mortem tissue delay and fixation. These findings suggest that the PD-linked mutations may cause neurodegeneration via different mechanisms, some of which may be independent of α-Syn aggregatio

CRISPR-Cas9 screens in human cells and primary neurons identify modifiers of C9ORF72 dipeptide-repeat-protein toxicity.

Hexanucleotide-repeat expansions in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD). The nucleotide-repeat expansions are translated into dipeptide-repeat (DPR) proteins, which are aggregation prone and may contribute to neurodegeneration. We used the CRISPR-Cas9 system to perform genome-wide gene-knockout screens for suppressors and enhancers of C9ORF72 DPR toxicity in human cells. We validated hits by performing secondary CRISPR-Cas9 screens in primary mouse neurons. We uncovered potent modifiers of DPR toxicity whose gene products function in nucleocytoplasmic transport, the endoplasmic reticulum (ER), proteasome, RNA-processing pathways, and chromatin modification. One modifier, TMX2, modulated the ER-stress signature elicited by C9ORF72 DPRs in neurons and improved survival of human induced motor neurons from patients with C9ORF72 ALS. Together, our results demonstrate the promise of CRISPR-Cas9 screens in defining mechanisms of neurodegenerative diseases

Diffusion-based kinetic modeling of textile dye adsorption by porous copolymer

Macroporous glycidyl dimethacrylate and ethylene glycol dimethacrylate copolymer functionalized with diethylene triamine, PGME-deta, was tested as adsorbent for removal of Acid Orange 10 (AO10) and Reactive Black 5 (RB5) from aqueous solutions. Diffusion-based kinetic models (intraparticle diffusion, Bangham and Boyd model) were used for interpretation of experimental data.Physical chemistry 2012 : 11th international conference on fundamental and applied aspects of physical chemistry; Belgrade (Serbia); 24-28 September 201

Multiuser Cognitive Radio Networks: An Information Theoretic Perspective

Achievable rate regions and outer bounds are derived for three-user interference channels where the transmitters cooperate in a unidirectional manner via a noncausal message-sharing mechanism. The three-user channel facilitates different ways of message-sharing between the primary and secondary (or cognitive) transmitters. Three natural extensions of unidirectional message-sharing from two users to three users are introduced: (i) Cumulative message sharing; (ii) primary-only message sharing; and (iii) cognitive-only message sharing. To emphasize the notion of interference management, channels are classified based on different rate-splitting strategies at the transmitters. Standard techniques, superposition coding and Gel'fand-Pinsker's binning principle, are employed to derive an achievable rate region for each of the cognitive interference channels. Simulation results for the Gaussian channel case are presented; they enable visual comparison of the achievable rate regions for different message-sharing schemes along with the outer bounds. These results also provide useful insights into the effect of rate-splitting at the transmitters, which aids in better interference management at the receivers.Comment: 50 pages, 15 figures, submitted to IEEE Transactions on Information Theor