Mode Of Recognition Research Articles

Phenomenology of Perception (for Preservation) of Cultural Heritage: Insights on Coastal Kenya

Contemporary social awareness of cultural heritage mostly expresses the understanding of intangible heritage according to rationalist theories of perception. These theories bind modes of recognition to empirical and sensory domains. Yet in order to value and safeguard cultural heritage, be it in the form of ritual practices, symbolic narratives, creation myths or religious and mystical experiences, it is necessary to interpret and appreciate the being and doing of diverse cultural groups. A meaningful analysis of intangible dimensions in cultural heritage needs to consider the role and ontological status of human emotion and imagination.
 Individuals are actors in a given space and interact with it as much as they can, depending on the frequency of their visits. Repeated research stays in the Kenyan coastal region offered the author opportunities for communication with a specific natural and cultural environment. This paper explores a participatory process of enhancement and interpretation of intangible cultural heritage that includes feelings and emotions in analysis of the body of knowledge that results from this research approach.

One stone two birds: electrochemical and colorimetric dual-mode biosensor based on copper peroxide/covalent organic framework nanocomposite for ultrasensentive norovirus detection

Norovirus (NoV) is regarded as one of the most common causes of foodborne diarrhea in the world. It is urgent to identify the pathogenic microorganism of the diarrhea in short time. In this work, we developed an electrochemical and colorimetric dual-mode detection for NoV based on the excellent dual catalytic properties of copper peroxide/COF-NH2 nanocomposite (CuO2@COF-NH2). For the colorimetric detection, NoV can be directly detected by the naked eye based on CuO2@COF-NH2 as a laccase-like nonazyme using “peptide-NoV-antibody” recognition mode. The colorimetric assay displayed a wide and quality linear detection range from 1 copy/mL to 5000 copies/mL of NoV with a low limit of detection (LOD) of 0.125 copy/mL. For the electrochemical detection of NoV, CuO2@COF-NH2 showed an oxidation peak of copper ion from Cu+ to Cu2+ using “peptide-NoV-antibody” recognition mode. The electrochemical assay showed a linear detection range was 15000 copies/mL with a LOD of 0.152 copy/mL. It’s worthy to note that this assay does not need other electrical signal molecule, which provide the stable and sensitive electrochemial detection for NoV. The electrochemical and colorimetric dual-mode detection was used to detect NoV in foods and faceal samples, which has the potential for improving food safety and diagnosing of NoV-infected diarrhea.

Reconstruction of Water-Filled Pipe Ultrasonic Guided Wave Signals in the Distance Domain by Orthogonal Matching Pursuit Based on Dispersion and Multi-Mode.

Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%.

A Multifunctional and Selective Ionic Flexible Sensor with High Environmental Suitability for Tactile Perception

AbstractInspired by the tactile sensory mechanism of human skin, ionic hydrogels‐derived ionic flexible sensors have attracted much attention since they can produce output signals that match the recognition mode of nerves, showing a potential application in the human‐machine interaction. Unfortunately, the practical sensing properties of ionic hydrogels are restricted by the drawbacks of hydroelastic instability and non‐selective response ability, such as poor mechanical strength, irretentive solvent retaining capacity, and low‐temperature intolerance. Herein, in this study, a novel physical‐crosslink enhanced ionic hydrogel‐PVA/PEG/TA‐MXene‐Na+/Li+ (PPM‐NL) nanocomposite is prepared and shows well comprehensive properties of mechanical strength (400% elongation at break, 0.93 MPa), electrical conductivity (8.1 S m−1), tear resistance, self‐healing and anti‐freezing/drying features (93% water retention after sixty days and frost resistance −27 °C). The PPM‐NL hydrogel‐derived flexible sensor displays selective response behavior to tensile and compressive deformation with high sensitivity (G = 1.12) and rapid response time (only 60 ms). Further, this ionic flexible device is applied to monitor the joint motions of humanoid hands and integrated into manipulators to recognize the thickness and softness of objects, showing superior environmental stability. It can be believed that this ionic flexible sensor will provide inspiration for developing next‐generation biomimetic tactile perception of robots.

An upconversion biosensor based on inner filter effect for dual-role recognition of sulfadimethoxine in aquatic samples

Sulfadimethoxine (SDM) as an extensively employed veterinary drug causes potential threats to human health. Herein, a dual recognition mode novel upconversion fluorescence biosensor was designed based on inner filter effect (IFE) to sensitively and rapidly detect SDM in aquatic samples. Aldehyde-functionalized magnetic nanoparticles (MNPs) were applied to recognize and capture SDM, followed by specifically bond with biotin-labeled aptamers. The upconversion nanoparticles and the colored products resulting from the enzyme-catalyzed oxidation of 3,3,5,5-tetramethylbenzidine exhibited an IFE quenching process. Under the optimal condition, the results displayed the fluorescence intensity was correlated with the concentration of SDM within the range of 0.5–1000 ng⋅mL−1 achieving a low limit of detection of 0.13 ng⋅mL−1. The SDM detection system was further employed in the spiked aquatic samples with good recoveries (88.41–96.78 %). Consequently, the constructed fluorescence biosensor provided broad prospects for accuracy and rapid detection of SDM.

Culture’s photodermic enjoyment

ABSTRACT The aesthetic depiction of the Black corpse raises questions about scopic pleasure, empathy, and the futility of evidence. This essay engages aesthetic speculation about intended justice through Paul Stopforth’s Elegy (1981) and the Biko Series (1980), drawings of Steve Biko’s corpse that are all oriented toward a counter-evidentiary logic whose aim is to disprove the evidence provided by the apartheid police. I posit that this investment in evidence (alternative, or otherwise), capitulates to the terms of the dominant regime by participating in the struggle for evidence (alternative or otherwise) in the first place. I also engage the entanglement between scopophilia and negrophobia/negrophilia in the image of the Black dead, not merely as features of Stopforths’ individual unconscious, but as civil society’s/culture’s most consistent dreamwork. Questioning the political promise of aesthetic mobilization of the corpse, I ask: Why is it necessary for the world to see the image of the corpse (again) in aesthetic practice, in order to reflect on violence, and what modes of recognition and identification are produced? My curiosity lies in what is enacted by recruiting the viewer to adopt such forensic seeing.

Structure of the transcription open complex of distinct σI factors

Bacterial σI factors of the σ70-family are widespread in Bacilli and Clostridia and are involved in the heat shock response, iron metabolism, virulence, and carbohydrate sensing. A multiplicity of σI paralogues in some cellulolytic bacteria have been shown to be responsible for the regulation of the cellulosome, a multienzyme complex that mediates efficient cellulose degradation. Here, we report two structures at 3.0 Å and 3.3 Å of two transcription open complexes formed by two σI factors, SigI1 and SigI6, respectively, from the thermophilic, cellulolytic bacterium, Clostridium thermocellum. These structures reveal a unique, hitherto-unknown recognition mode of bacterial transcriptional promoters, both with respect to domain organization and binding to promoter DNA. The key characteristics that determine the specificities of the σI paralogues were further revealed by comparison of the two structures. Consequently, the σI factors represent a distinct set of the σ70-family σ factors, thus highlighting the diversity of bacterial transcription.

Graph based embedding learning of trajectory data for transportation mode recognition by fusing sequence and dependency relations

As an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.

The cryo-EM structure of the BoNT/Wo-NTNH complex reveals two immunoglobulin-like domains.

The botulinum neurotoxin-like toxin from Weissella oryzae (BoNT/Wo) is one of the BoNT-like toxins recently identified outside of the Clostridium genus. We show that, like the canonical BoNTs, BoNT/Wo forms a complex with its non-toxic non-hemagglutinin (NTNH) partner, which in traditional BoNT serotypes protects the toxin from proteases and the acidic environment of the hosts' guts. We here report the cryo-EM structure of the 300 kDa BoNT/Wo-NTNH/Wo complex together with pH stability studies of the complex. The structure reveals molecular details of the toxin's interactions with its protective partner. The overall structural arrangement is similar to other reported BoNT-NTNH complexes, but NTNH/Wo uniquely contains two extra bacterial immunoglobulin-like (Big) domains on the C-terminus. Although the function of these Big domains is unknown, they are structurally most similar to bacterial proteins involved in adhesion to host cells. In addition, the BoNT/Wo protease domain contains an internal disulfide bond not seen in other BoNTs. Mass photometry analysis revealed that the BoNT/Wo-NTNH/Wo complex is stable under acidic conditions and may dissociate at neutral to basic pH. These findings established that BoNT/Wo-NTNH/Wo shares the general fold of canonical BoNT-NTNH complexes. The presence of unique structural features suggests that it may have an alternative mode of activation, translocation and recognition of host cells, raising interesting questions about the activity and the mechanism of action of BoNT/Wo as well as about its target environment, receptors and substrates.

Fault diagnosis of RV reducer based on denoising time–frequency attention neural network

RV reducer operating conditions are complex, often with low speed, reciprocating, non-integral cycle and other characteristics, coupled with the RV reducer itself complex structure and fault signal instantaneous, so the fault signal sample is small, which brings challenges to the bearing fault diagnosis of RV reducer. Therefore, an improved DNCNN denoising algorithm is proposed to realize the failure mode recognition of RV reducer. The algorithm improves the basic unit module (Conv(3*3) + BN + Relu) in DNCNN network, which can effectively solve the problem that the stacking of convolutional blocks leads to a large number of network parameters, makes the training time long, and most of the training parameters are concentrated in a certain layer of the network. The SE attention module is introduced to avoid the problem that the CNN model cannot effectively highlight the key points when extracting features. At the same time, the Swish activation function is used to replace the Relu activation function, which improves the phenomenon that the latter cannot learn the value of the function input less than 0, and the parameters cannot be updated. Based on improving the DNCNN denoising algorithm, the neural network fault diagnosis method of the main bearing state of the three RV reducers after denoising is studied, and the CBAM attention mechanism module is added to the network to obtain the InceptionV4-CBAM network model. Multiple sets of experimental analysis show that the failure mode recognition rate of the main bearing of the RV reducer under noise interference by the Improved DNCNN combined with InceptionV4-CBAM is 97.3 %.

Cryo-EM structures of human GPR34 enable the identification of selective antagonists

GPR34 is a functional G-protein-coupled receptor of Lysophosphatidylserine (LysoPS), and has pathogenic roles in numerous diseases, yet remains poorly targeted. We herein report a cryo-electron microscopy (cryo-EM) structure of GPR34 bound with LysoPS (18:1) and Gi protein, revealing a unique ligand recognition mode with the negatively charged head group of LysoPS occupying a polar cavity formed by TM3, 6 and 7, and the hydrophobic tail of LysoPS residing in a lateral open hydrophobic groove formed by TM3-5. Virtual screening and subsequent structural optimization led to the identification of a highly potent and selective antagonist (YL-365). Design of fusion proteins allowed successful determination of the challenging cryo-EM structure of the inactive GPR34 complexed with YL-365, which revealed the competitive binding of YL-365 in a portion of the orthosteric binding pocket of GPR34 and the antagonist-binding-induced allostery in the receptor, implicating the inhibition mechanism of YL-365. Moreover, YL-365 displayed excellent activity in a neuropathic pain model without obvious toxicity. Collectively, this study offers mechanistic insights into the endogenous agonist recognition and antagonist inhibition of GPR34, and provides proof of concept that targeting GPR34 represents a promising strategy for disease treatment.

Glucose oxidase-like Co-MOF nanozyme-catalyzed self-powered sensor for sensitive detection of trace atrazine in complex environments

The self-powered sensor (SPS) is a sensor method that does not require the external power source and has the potential for portable detection of environmental contaminants. In this work, for the first time, a biomolecule-free SPS for detection of ultra-trace triazine endocrine disruptor atrazine (ATZ) with high sensitivity and selectivity is constructed using a glucose oxidase (GOD)-like cobalt metal-organic framework (Co-MOF) nanozyme-modified high-performance anode and a molecularly imprinted cathode. By modulating the size and morphology of the prepared materials, Co-MOF nanozyme with superior GOD-like property (Michaelis constant Km = 15.8 mM) has been obtained and modified at the anode to catalyze glucose oxidation with high efficiency and provide energy continuously and stably for the SPS. The separation mode of anodic energy supply-cathodic recognition ensures the recognition effect without affecting the catalytic characteristic of Co-MOF and the output signal of the SPS. The designed SPS has a wide linear range of 1 pM–100 nM and a detection limit as low as 0.65 pM, as well as superior selectivity and good stability. The present work provides a promising approach for the design of self-powered sensors which can be extended to detection of a wider range of environmental pollutants.

Design and implementation of IMU-based locomotion mode recognition system on Zynq SoC

Active prostheses are becoming an increasingly viable option for lower limb amputees, as they can significantly improve their quality of life and mobility. However, to ensure a robust and effective control of these prostheses, the terrain environment must be considered. In this study, we have proposed a hardware implementation of a real-time low-latency human Locomotion Mode Recognition (LMR) system on a system on chip (SoC) platform from Xilinx, which can be customized and optimized based on the user's needs without requiring external computing resources or human intervention. The system uses a single IMU sensor placed on the shank, and includes all the stages in a LMR process, i.e. signal pre-processing, feature extraction, and terrains estimation using a MLP neural network classifier to classify five terrains (level walking, stair ascent/descent, ramp ascent/descent). To achieve a flexible and efficient hardware design, the proposed system architecture was optimized using parallelism and quantization optimization techniques. This approach has resulted in a significant performance improvement, with a processing speed 15 times faster than the PL non-optimized approach, and 4.3 times faster than the PS(-O3) optimized implementation on the same Zynq FPGA device. The proposed architecture was also validated in real time via the analog discovery device.

Structural basis of CD97 activation and G-protein coupling

CD97 (ADGRE5) is an adhesion G protein-coupled receptor (aGPCR) which plays crucial roles in immune system and cancer. However, the mechanism of CD97 activation and the determinant of G13 coupling selectivity remain unknown. Here, we present the cryo-electron microscopy structures of human CD97 in complex with G13, Gq, and Gs. Our structures reveal the stalk peptide recognition mode of CD97, adding missing information of the current tethered-peptide activation model of aGPCRs. For instance, a revised "FXφφφ" motif and a framework of conserved aromatic residues in the ligand-binding pocket. Importantly, structural comparisons of G13, Gq, and Gs engagements of CD97 reveal key determinants of G13 coupling selectivity, where a deep insertion of the α helix 5 and a closer contact with the transmembrane helix 6, 5, and 3 dictate coupling preferences. Taken together, our structural study of CD97 provides a framework for understanding CD97 signaling and the G13 coupling selectivity.

Contextualising Statelessness: Contingent Citizenship and The Politics of (non)Recognition in Thailand

AbstractThe legal evidentiary approach to “solving” statelessness can sometimes lead to the issue being framed in terms of certain groups of people not meeting objective citizenship criteria or lacking required legal documents. Building on critical interdisciplinary scholarship in anthropology, history and legal studies, this article demonstrates the “constructedness” of citizenship and statelessness through the lens of the politics of recognition and documentation. Using Thailand as a case study, I highlight how global economic, political and social contexts play a significant and dynamic role in delineating the legal line of membership. By tracing how Thai nationality has been instrumentalised by the state throughout the twentieth century, this article contextualises statelessness as a legal and social by-product of statemaking. As such, it challenges the framing of nationality as a non-discriminatory mode of recognition founded in legal objectivity and reiterates the politics of statelessness. In emphasising the fragility of citizenship when granted without genuine social, political and moral recognition, I argue that the objective of statelessness advocacy should not simply be about turning stateless persons into citizens, but rather about creating a more equitable society wherein one's rights are upheld regardless of legal status.

Task-oriented human-robot interaction control of a robotic glove utilizing forearm electromyography

Task-oriented myoelectric assistive control of a robotic glove based on forearm electromyography for practical grasping tasks is investigated in this work. Three grasping functions are considered: human intent detection of grasping tasks, grasping mode recognition, and grasping force control. Different combinations of forearm electromyographic signals are adopted for the three functions. Firstly, the overall electromyographic signal is used to trigger the whole grasping task. Secondly, a novel Long Short-Term Memory network is utilized to classify various grasping modes, including pinch grasping and palmar grasping, by analyzing eight-channel electromyographic signals. Thirdly, two-channel proportional control of grasping force using electromyographic flexor/extensor signals is adopted for the robotic glove such that the human hand can relax during the grasping task, while the robotic glove maintains the grasping force. To this end, finite state machine based hierarchical control architecture is proposed for the whole grasping task. Experiments are conducted to validate the proposed task-oriented assistive control method, and the results clearly demonstrates the potential of the proposed method in rehabilitation therapy.

A Method of User Travel Mode Recognition Based on Convolutional Neural Network and Cell Phone Signaling Data

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application.

Motif-dependent binding on the intervening domain regulates O-GlcNAc transferase.

The modification of intracellular proteins with O-linked β-N-acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Using proteomic peptide phage display (ProP-PD) coupled with structural, biochemical and cellular characterizations, we discovered a strongly enriched peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs, including response to nutrient deprivation and glucose metabolism. These findings illustrate a mode of OGT substrate recognition and offer key insights into the biological roles of this unique domain.

Fragment-based structural exploration and chemico-biological interaction study of HDAC3 inhibitors through non-linear pattern recognition, chemical space, and binding mode of interaction analysis

HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, n-alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition. Moreover, hydrogen bond forming capabilities, the length and substitution position of the linker moiety, the importance of phenyl ring in the linker motif, the contribution of heterocyclic cap moieties for effective inhibitor binding at the HDAC3 catalytic site that correspondingly affects the HDAC3 inhibitory potency. Again, macrocyclic ring structure and cyclohexyl cap moiety are responsible for lower HDAC3 inhibition. The MD simulation study of selected compounds explained strong binding patterns at the HDAC3 active site as evidenced by the lower RMSD and RMSF values. Nevertheless, it also explained the importance of the crucial structural fragments derived from the fragment-based analysis during ligand-enzyme interactions. Therefore, the outcomes of this current structural analysis will be a useful tool for fragment-based drug discovery of effective HDAC3 inhibitors for clinical therapeutics in the future. Communicated by Ramaswamy H. Sarma

Online few-shot learning for multi-function radars mode recognition based on backtracking contextual prototypical memory

Modern multi-function radars (MFRs) can flexibly generate multiple fine-grained working modes for different missions through programmable parameters. Precise recognition of these working modes by analyzing the parameter combinations lays foundation for situational awareness. This recognition becomes more challenging to electromagnetic reconnaissance system when prior information of radiation source is unavailable and effective labeled signal data is not sufficient in adversarial scenarios. The presence of incremental mode involved in radar data flow requires that the recognition model enables to dynamic adjust and online perceive current data. This issue incorporates online learning to few-shot learning to accomplish efficiently recognition to a novel mode with the support of small amount of data, and still retain model's ability to recognize existing modes. This paper designed a backtracking contextual prototypical memory (BCPM) network for online MFR mode recognition. The proposed method learns temporal and spatial information from data stream as a reference predicting the signal segments as existing working modes or a novel mode. The BCPM network also designs a backtracking module and an alignment regularization term to utilize the data without annotation adequately and obtain a more reliable category representation. The experimental results verified the proposed method's good performance and high robustness to non-ideal factors and distractors.