Resolving Protein Conformational Plasticity and Substrate Binding Through the Lens of Machine-Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. In particular, we consider multi-microsecond long Molecular dynamics simulation trajectories of ligand recognition process in solvent-inaccessible cavity of two archtypal systems: L99A mutant of T4 Lysozyme and Cytochrome P450. We first show that if the substrate-recognition occurs via long-lived intermediate, the protein conformations can be automatically classified into substrate-bound and unbound state through an unsupervised dimensionality reduction technique. On the contrary, if the recognition process is mediated by selection of transient protein conformation by the ligand, a clear correspondence between protein conformation and binding-competent macrostates can only be established via a combination of supervised machine learning (ML) and unsupervised dimension reduction approach. In such scenario, we demonstrate that a priori random forest based supervised classification of the simulated trajectories recognition process would help characterize key amino-acid residue-pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction via time-lagged independent component analysis of the selected residue-pairs would delineate a conformational landscape of protein which is able to demarcate ligand-bound pose from the unbound ones. As a key breakthrough, the ML-based protocol would identify distal protein locations which would be allosterically important for ligand binding and characterise their roles in recognition pathways.

Comparing Plan Recognition Algorithms Through Standard Plan Libraries

Plan recognition deals with reasoning about the goals and execution process of an actor, given observations of its actions. It is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber-security. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared using a common testbed. This paper provides a first step towards bridging this gap by providing a standard plan library representation that can be used by hierarchical, discrete-space plan recognition and evaluation criteria to consider when comparing plan recognition algorithms. This representation is comprehensive enough to describe a variety of known plan recognition problems and can be easily used by existing algorithms in this class. We use this common representation to thoroughly compare two known approaches, represented by two algorithms, SBR and Probabilistic Hostile Agent Task Tracker (PHATT). We provide meaningful insights about the differences and abilities of these algorithms, and evaluate these insights both theoretically and empirically. We show a tradeoff between expressiveness and efficiency: SBR is usually superior to PHATT in terms of computation time and space, but at the expense of functionality and representational compactness. We also show how different properties of the plan library affect the complexity of the recognition process, regardless of the concrete algorithm used. Lastly, we show how these insights can be used to form a new algorithm that outperforms existing approaches both in terms of expressiveness and efficiency.

Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent speech signals and three speech disturbances—blocks before words starting with plosives, syllable repetitions, and sound-initial prolongations—was transformed using principal component analysis. The result was a model containing four principal components describing analysed utterances. Distances between standardised original variables and elements of the observation matrix in a new system of coordinates were calculated and then applied in the recognition process. As a classifying algorithm, the multilayer perceptron network was used. Achieved results were compared with outcomes from previous experiments where speech samples were parameterised with the Kohonen network application. The classifying network achieved overall accuracy at 76% (from 50% to 91%, depending on the dysfluency type).

The identity parade – through the eyes of the defence counsel

A few Hungarian cases of justice miscarriage demonstrate that the identity parade (line up) method in the criminal procedure could be a “dangerous act”, because the witnesses sometimes give false testimonies, make wrong choices, the authority sometimes fails the recognition process, and lastly the “result” could be a “justizmord”. Based on scientific research, the present study reveals the most frequent criminal procedural and criminalistic wrongdoings. It also focuses on preventing legal and criminal tactical possibilities and suggestions. It can be read mostly from the defence counsel’s point of view. The author declares the lawyers’ legal and factual tasks in this field, especially for preventing wrongful sentences. This is the duty of all legal representatives (detectives, prosecutors, judges) as well.

Morphing Task: The Emotion Recognition Process in Children with Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder

Recognizing a person’s identity is a fundamental social ability; facial expressions, in particular, are extremely important in social cognition. Individuals affected by autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) display impairment in the recognition of emotions and, consequently, in recognizing expressions related to emotions, and even their identity. The aim of our study was to compare the performance of participants with ADHD, ASD, and typical development (TD) with regard to both accuracy and speed in the morphing task and to determine whether the use of pictures of digitized cartoon faces could significantly facilitate the process of emotion recognition in ASD patients (particularly for disgust). This study investigated the emotion recognition process through the use of dynamic pictures (human faces vs. cartoon faces) created with the morphing technique in three pediatric populations (7–12 years old): ADHD patients, ASD patients, and an age-matched control sample (TD). The Chi-square test was used to compare response latency and accuracy between the three groups in order to determine if there were statistically significant differences (p < 0.05) in the recognition of basic emotions. The results demonstrated a faster response time in neurotypical children compared to ASD and ADHD children, with ADHD participants performing better than ASD participants on the same task. The overall accuracy parameter between the ADHD and ASD groups did not significantly differ.

Design and Realization of Garage Access Control System Based on STM32 microcontroller minimum system technology

This paper designs and implements a community access control system based on STM32 microcontroller minimum system technology. A power unit composed of steering gear and simple lifting rod device is designed. According to the actual demand, a display device composed of buzzer alarm device and display screen is designed. Considering the need of camera recognition, the program of camera recognition process is designed, the database is written, and the license plate of this area is stored in advance. The system can make the license plate can be recognized by the camera automatically, and through the comparison with the database, the lifting rod or the buzzer alarm is realized. The access control system is of great significance to the control of vehicle entering and leaving and the safety of the community.

Script Identification for Devanagari and Gurumukhi using OCR

The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

Bioinspired Auditory Model for Vowel Recognition

In this work, a bioinspired or neuromorphic model to replicate the vowel recognition process for an auditory system is presented. A bioinspired peripheral and central auditory system model is implemented and a neuromorphic higher auditory system model based on artificial neuronal nets for vowel recognition is proposed. For their verification, ten Hispanic Spanish language-speaking adults (five males and five females) were used. With the proposed bioinspired model based on artificial neuronal nets it is possible to recognize with high levels of accuracy and sensibility the vowels phonemes of speech signals and the assessment of cochlear implant stimulation strategies in terms of vowel recognition.

Reading Process of Arab Children: An Eye-Tracking Study on Saudi Elementary Students

Many previous eye-tracking studies were conducted to examine how adult readers process different written languages. Relatively, only few eye-tracking studies have been conducted to observe the reading process of Arab children. This study investigated the influence of orthographic regularity on Saudi elementary grades’ English and Arabic words recognition. The eye movements of 15 grade-four students and 15 grade-six students were recorded while they read words that differ in frequency and regularity. Analysis of the visual information from the word-recognition process shows differences in the students’ eye movements for the two languages. There were statistically significant differences in the total fixation duration and fixation count between the two languages and between both groups. All the students showed longer processing time for English sentences than Arabic ones. However, Arabic-speaking students were influenced by English orthography with more processing difficulty for English irregular words. The visual information shows that more cross-linguistic differences are found in grade-four students’ results. Grade-four students transferred their first language (L1) reading strategies to read English words; however, Arabic reading methods cannot be effectively applied to reading irregular orthographies like English. This explains the increased eye-movement measurements of grade-four students compared to grade-six students, who fixated more on unfamiliar English words. Although orthographic regularity had a major effect on the word-recognition process in this study, the development of the students’ Arabic and English orthographic knowledge affected the progress of their visual word recognition across the two levels.