Architecture Of Recognition Research Articles

Epistasis facilitates functional evolution in an ancient transcription factor.

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Epistasis facilitates functional evolution in an ancient transcription factor

A protein’s genetic architecture – the set of causal rules by which its sequence produces its functions – also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest – excluding the vast majority of possible genotypes and evolutionary trajectories – and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor’s specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor’s capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

The application and challenges of artificial intelligence in speech recognition

This paper provides an overview of artificial intelligence (AI) and speech recognition technology, including its history, applications, challenges, and future prospects. AI-powered speech recognition technology has significantly improved over the years, and it is used in various applications, such as virtual assistants, voice-activated devices, and dictation software. The technology leverages machine learning algorithms that are trained on vast amounts of speech data to recognize and interpret human speech with accuracy levels that are comparable to those of humans. However, the technology still faces many challenges, such as speech variability and background noise, which make it challenging to develop speech recognition algorithms that can accurately recognize all types of speech. The article provides a comprehensive review of the technical aspects of automatic speech recognition, including the process involved, the algorithms used, and the challenges and opportunities for future research in this area. The paper also discusses the architecture of automatic speech recognition (ASR) systems and the main components that make up the system. The authors explain that ASR systems consist of three main components: the acoustic model, the language model, and the decoder. They also discuss the challenges that ASR systems face, such as speaker variability, noise, and limited vocabulary. Overall, this paper provides a detailed introduction to AI and speech recognition technology and its potential for various industries.

Acoustic Wake-Up Technology for Microsystems: A Review.

Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

Extrinsic effect of remittance houses on desire to migrate and usage of irregular migration routes

Characteristics of remittance houses display the new financial and social status of migrants; hence, serve as a motivation for others to migrate abroad. However, this effect is highly general without specificity of how individual characteristics of remittance houses influence migration. This study hypotheses that different characteristics of remittance houses have different levels of influence on prospective migrants and their likelihood to use irregular migration routes. With the help of a structured questionnaire, the study interviewed 371 respondents in the Berekum Municipality (an out-migration hotspot in Ghana especially for persons using Sahara regions and the Strait of Gibraltar to Europe) on the external motivation of remittance houses, their desire to travel and the use of irregular routes. Locals perceived characteristics of remittance houses of migrants abroad (Mean [M] = 2.86, Standard deviation [SD] = 0.33) to be higher than non-migrants’ houses (M = 1.9, SD = 0.59) while they assigned similar scores for remittance houses of migrants in Africa (M = 2.58, SD = 0.47) and migrants in Ghana (M = 2.58, SD = 0.44). Furthermore, the exterior architecture of remittance houses (β = 0.33, p < 0.01) and social recognition accorded migrants outside Africa (β = 0.30, p < 0.01) motivated locals to migrate to Europe. Again, there was no extrinsic motivation from the characteristics of remittance houses on locals’ likelihood to use irregular migration routes. However, the social recognition accorded to migrants in Africa who own houses motivated locals to use irregular routes (β = 0.44, p < 0.01). The study recommends that the locals of Berekum should be educated by the traditional authorities, government agencies and NGOs that better housing characteristics and social recognition are not worthy of risking one’s life by using irregular routes.

Information Collection, Analysis, and Monitoring System of Children's Physical Training Based on Multisensor.

In order to obtain more children's physical training information and improve the accuracy of children's physical training monitoring, a multisensor-based children's physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people's physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children's physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children's physical training information acquisition. Support vector machine and decision tree are used to classify children's different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children's physical training monitoring system in this paper has a certain market value.

Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance

The automated documentation of work steps is a requirement of many modern manufacturing processes. Especially when it comes to important procedures such as safety critical screw connections or weld seams, the correct and complete execution of certain manufacturing steps needs to be properly supervised, e.g., by capturing video snippets of the worker to be checked in hindsight. Without proper technical and organizational safeguards, such documentation data carries the potential for covert performance monitoring to the disadvantage of employees. Naïve documentation architectures interfere with data protection requirements, and thus cannot expect acceptance of employees. In this paper we outline use cases for automated documentation and describe an exemplary system architecture of a workflow recognition and documentation system. We derive privacy protection goals that we address with a suitable security architecture based on hybrid encryption, secret-sharing among multiple parties and remote attestation of the system to prevent manipulation. We finally contribute an outlook towards problems and possible solutions with regards to information that can leak through accessible metadata and with regard to more modular system architectures, where more sophisticated remote attestation approaches are needed to ensure the integrity of distributed components.

Evidence For Selective Adaptation and Recalibration in the Perception of Lexical Stress.

Individuals vary in how they produce speech. This variability affects both the segments (vowels and consonants) and the suprasegmental properties of their speech (prosody). Previous literature has demonstrated that listeners can adapt to variability in how different talkers pronounce the segments of speech. This study shows that listeners can also adapt to variability in how talkers produce lexical stress. Experiment 1 demonstrates a selective adaptation effect in lexical stress perception: repeatedly hearing Dutch trochaic words biased perception of a subsequent lexical stress continuum towards more iamb responses. Experiment 2 demonstrates a recalibration effect in lexical stress perception: when ambiguous suprasegmental cues to lexical stress were disambiguated by lexical orthographic context as signaling a trochaic word in an exposure phase, Dutch participants categorized a subsequent test continuum as more trochee-like. Moreover, the selective adaptation and recalibration effects generalized to novel words, not encountered during exposure. Together, the experiments demonstrate that listeners also flexibly adapt to variability in the suprasegmental properties of speech, thus expanding our understanding of the utility of listener adaptation in speech perception. Moreover, the combined outcomes speak for an architecture of spoken word recognition involving abstract prosodic representations at a prelexical level of analysis.

Research on a software architecture of speech recognition and detection based on interactive reconstruction model

The natural, fast, stable and reliable interaction between human and machine is the ideal interaction mode pursued by human beings. Speech recognition is a process of pattern matching recognition. Effective speech detection technology can not only reduce the processing time of the system, improve the real-time and accuracy of the system processing, but also eliminate the noise interference of the silent segment, so as to improve the subsequent recognition performance. Through speech recognition, the machine can understand human language, complete the corresponding calculation tasks according to these instructions, and meet the needs of people. The traditional speech recognition method uses acoustic features to describe the model. No matter using model compensation method or normalization method, it can not solve the influence of speaker difference on the performance of recognition system. In this paper, we study a speech recognition detection system based on almost interactive reconstruction model, and analyze the continuous speech signal. The experimental results show that the proposed method has a high recognition rate and a shorter time. Compared with the state-of-the-art methodologies, the proposed model can achieve the better performance.

A New Architecture based AR for Detection and Recognition of Objects and Text to Enhance Navigation of Visually Impaired People

Abstract Navigation in unfamiliar places is a big challenge for partially sighted and visually impaired people. Improving visual information on the location and content of objects such as drugs can help navigation in unfamiliar environments. There are several existing navigation solutions capable of helping these people. However, navigation solutions are rarely adopted and implemented in reality. In order to optimize the perception of digital information of objects from sensors and to naturally interact with the pervasive computing landscape, Augmented Reality (AR) equipment has to be seamlessly integrated into the user’s environment. For this purpose, we develop an architecture of text and objects recognition based on AR in order to assist partially sighted and visually impaired people. Such architecture makes navigation easier in the environment by helping users to find drugs and to verify the number of pills using speech. The proposed architecture uses context-aware mobile computing and shows great potential to integrate AR for object recognition through AR engines.

The Refugees We Are: Solidarity in the European Constitutional Imagination

The Refugees We Are: Solidarity in the European Constitutional Imagination

Fine-grained image classification with network architecture of focus and recognition

Fine-grained image classification with network architecture of focus and recognition

A Survey on Human Behavior Recognition Using Channel State Information

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Singer Recognition Based on Convolutional Deep Belief Networks

Singer recognition is an important branch of music retrieval and classification. This paper focuses on the application of convolutional deep belief networks (CDBN) for singer recognition. First, the system architecture of singer recognition based on CDBN is given, and then the pre-processing of the song signal is described in detail, including sampling, framing, pre-emphasis and windowing. The feature extraction of song signal based on MFCC is described in detail, and the composition and principle of CDBN and its application in singer recognition are introduced. Experiment based on three different feature extraction techniques of LPCC, MFCC and CDBN is carried out and the result is compared and analysed, the experimental results show that CDBN is effective for singer recognition.

FEEDFORWARD COMPUTATIONAL MODEL FOR PATTERN RECOGNITION WITH SPIKING NEURONS

Humans and primates are remarkably good at pattern recognition and outperform the best machine vision systems with respect to almost any measure. Building a computational model that emulates the architecture and information processing in biological neural systems has always been an attractive target. To build a computational model that closely follows the information processing and architecture of the visual cortex, in this paper, we have improved the latency-phase encoding to express the external stimuli in a more abstract manner. Moreover, inspired by recent findings in the biological neural system, including architecture, encoding, and learning theories, we have proposed a feedforward computational model of spiking neurons that emulates object recognition of the visual cortex for pattern recognition. Simulation results showed that the proposed computational model can perform pattern recognition task well. In addition, the success of this computational model suggests a plausible proof for feedforward architecture of pattern recognition in the visual cortex.

Crystal structure of APOBEC3A bound to single-stranded DNA reveals structural basis for cytidine deamination and specificity

Nucleic acid editing enzymes are essential components of the immune system that lethally mutate viral pathogens and somatically mutate immunoglobulins, and contribute to the diversification and lethality of cancers. Among these enzymes are the seven human APOBEC3 deoxycytidine deaminases, each with unique target sequence specificity and subcellular localization. While the enzymology and biological consequences have been extensively studied, the mechanism by which APOBEC3s recognize and edit DNA remains elusive. Here we present the crystal structure of a complex of a cytidine deaminase with ssDNA bound in the active site at 2.2 Å. This structure not only visualizes the active site poised for catalysis of APOBEC3A, but pinpoints the residues that confer specificity towards CC/TC motifs. The APOBEC3A–ssDNA complex defines the 5′–3′ directionality and subtle conformational changes that clench the ssDNA within the binding groove, revealing the architecture and mechanism of ssDNA recognition that is likely conserved among all polynucleotide deaminases, thereby opening the door for the design of mechanistic-based therapeutics.

Molecular architecture and dynamics of ASH1 mRNA recognition by its mRNA-transport complex.

mRNA localization is an essential mechanism of gene regulation and is required for processes such as stem-cell division, embryogenesis and neuronal plasticity. It is not known which features in the cis-acting mRNA localization elements (LEs) are specifically recognized by motor-containing transport complexes. To the best of our knowledge, no high-resolution structure is available for any LE in complex with its cognate protein complex. Using X-ray crystallography and complementary techniques, we carried out a detailed assessment of an LE of the ASH1 mRNA from yeast, its complex with its shuttling RNA-binding protein She2p, and its highly specific, cytoplasmic complex with She3p. Although the RNA alone formed a flexible stem loop, She2p binding induced marked conformational changes. However, only joining by the unstructured She3p resulted in specific RNA recognition. The notable RNA rearrangements and joint action of a globular and an unfolded RNA-binding protein offer unprecedented insights into the step-wise maturation of an mRNA-transport complex.

Structural Basis for the Function of Complement Component C4 within the Classical and Lectin Pathways of Complement.

Complement component C4 is a central protein in the classical and lectin pathways within the complement system. During activation of complement, its major fragment C4b becomes covalently attached to the surface of pathogens and altered self-tissue, where it acts as an opsonin marking the surface for removal. Moreover, C4b provides a platform for assembly of the proteolytically active convertases that mediate downstream complement activation by cleavage of C3 and C5. In this article, we present the crystal and solution structures of the 195-kDa C4b. Our results provide the molecular details of the rearrangement accompanying C4 cleavage and suggest intramolecular flexibility of C4b. The conformations of C4b and its paralogue C3b are shown to be remarkably conserved, suggesting that the convertases from the classical and alternative pathways are likely to share their overall architecture and mode of substrate recognition. We propose an overall molecular model for the classical pathway C5 convertase in complex with C5, suggesting that C3b increases the affinity for the substrate by inducing conformational changes in C4b rather than a direct interaction with C5. C4b-specific features revealed by our structural studies are probably involved in the assembly of the classical pathway C3/C5 convertases and C4b binding to regulators.

Understanding the structural basis for controlling chromosome division.

The process of chromosome division, termed mitosis, involves a complex sequence of events that is tightly controlled to ensure that the faithful segregation of duplicated chromosomes is coordinated with each cell division cycle. The large macromolecular complex responsible for regulating this process is the anaphase-promoting complex or cyclosome (APC/C). In humans, the APC/C is assembled from 20 subunits derived from 15 different proteins. The APC/C functions to ubiquitinate cell cycle regulatory proteins, thereby targeting them for destruction by the proteasome. This review describes our research aimed at understanding the structure and mechanism of the APC/C. We have determined the crystal structures of individual subunits and subcomplexes that provide atomic models to interpret density maps of the whole complex derived from single particle cryo-electron microscopy. With this information, we are generating pseudo-atomic models of functional states of the APC/C that provide insights into its overall architecture and mechanisms of substrate recognition, catalysis and regulation by inhibitory complexes.

Macroscopic architecture of charge transfer-induced molecular recognition from electron-rich polymer interpenetrated porous frameworks.

Fluorescent and electron-rich polymer threaded into porous framework provides a scaffold for sensing acceptor molecules through noncovalent interactions. Herein, poly(9-vinylcarbazole) (PVK) threaded MIL-101 with confined nanospace was synthesized by vinyl-monomer impregnation, in situ polymerization, and interpenetration. The pore size of the resulted hybrid could be controlled by varying the time of polymerization and interpenetration. The interaction of PVK-threaded MIL-101 with guest molecules showed a charge-transfer progress with an obvious red shift in the optical spectra. Depending on the degree of the interaction, the solution color changed from blue to green or to yellow. In particular, electron-rich PVK-threaded MIL-101 could effectively probe electron-poor nitro compounds, especially 1,3,5-trinitrobenzene (TNP), a highly explosive material. This sensing approach is a colorimetric methodology, which is very simple and convenient for practical analysis and operation.