Gating Function Research Articles

Structure, Function and Engineering of the Nonribosomal Peptide Synthetase Condensation Domain

The nonribosomal peptide synthetase (NRPS) is a highly precise molecular assembly machinery for synthesizing structurally diverse peptides, which have broad medicinal applications. Withinthe NRPS, the condensation (C) domain is a core catalytic domain responsible for the formation of amide bonds between individual monomer residues during peptide elongation. This review summarizes various aspects of the C domain, including its structural characteristics, catalytic mechanisms, substrate specificity, substrate gating function, and auxiliary functions. Moreover, through case analyses of the NRPS engineering targeting the C domains, the vast potential of the C domain in the combinatorial biosynthesis of peptide natural product derivatives is demonstrated.

Realization of a hybrid multi-qubit quantum phase gate in circuit QED

We propose to implement a hybrid multi-qubit quantum phase gate based on a setup comprising multiple microwave cavities coupled to a common superconducting transmon qutrit in the circuit quantum electrodynamics. The function of this hybrid quantum phase gate is that a phase related to the total number of cavities in non-vacuum state will be introduced when the transmon qubit is in an excited state. Furthermore, we propose an application, quantum voting machine, with this hybrid quantum gate. This scheme is scalable and simple to operate, requiring just a single step and necessitating only the reading of quantum states of one target qubit. While for quantum voting machine, it ensures the verifiability of the voting results through the measurement of the phase information of the target qubit. Additionally, the anonymity of the voters is ensured as the voting outcome is solely tied to the total number of affirmative votes. Numerical simulations indicate the feasibility of this hybrid quantum gate and quantum voting machine within the current quantum technology.

Active logic modulator of terahertz metamaterial for future 6G communication system

A tunable G-shaped metamaterial (GSM) for the upcoming 6G communication system is presented. GSM device shows polarization-dependent characteristics with three or five resonances. The GSM unit cell is composed of two identical G-shaped structures on a silicon (Si) substrate. By utilizing micro-electro-mechanical system (MEMS) technology to change the vertical height (h) between the two G-shaped structures and the horizontal distance (g) between them or change the polarization angle (PA) of the incident light, the electromagnetic responses of GSM device can be switched three to five resonances. The tuning range of resonant frequency of the GSM device is up to 15 GHz by changing the g value, and the modulation depth can reach 93 % by changing PA and h values. This MEMS-based GSM device exhibits multi-resonance and polarization-dependent characteristics, offering an efficient and economical way to modulate terahertz (THz) signals. Moreover, the GSM device also exhibits good optical switching performance at PA = 0°, 30°, and 75° under the condition of h = 2 μm. These characteristics can be expressed as logic signals 'ON' and 'OFF' states, enabling their application in the logic operation function of the NAND gate and programmable two-bit binary digital coding metamaterials.

Universal all-optical zero-bias logic gates in silicon photonics

Universal all-optical (optical input – optical output) logic gates, realized in a commercial foundry silicon photonics process, are reported. Microring resonator modulator and zero-biased stacked photodiodes are used to create the required optical input-output nonlinear transfer function for optical gate realizations. Several logic gate functions, specifically NOR which is a universal logic gate that can be used to implement any logic, are demonstrated. Logic operation on 10 Mbps data streams is demonstrated using universal optical logic gates without requiring any electrical supply voltage. The total optical power required for one such universal logic gate is 160 µW.

3D Variability Analysis Reveals a Hidden Conformational Change Controlling Ammonia Transport in Human Asparagine Synthetase.

How motions in enzymes might be linked to catalytic function is of considerable general interest. Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we use 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) to identify a functional role for the Arg-142 side chain as a gate that mediates ammonia access to a catalytically relevant intramolecular tunnel. Our 3DVA-derived hypothesis is assessed experimentally, using the R142I variant in which Arg-142 is replaced by isoleucine, and by molecular dynamics (MD) simulations on independent, computational models of the WT human ASNS monomer and its catalytically relevant, ternary complex with β-aspartyl-AMP and MgPPi. Residue fluctuations in the MD trajectories for the human ASNS monomer are consistent with those determined for 3DVA-derived structures. These MD simulations also indicate that the gating function of Arg-142 is separate from the molecular events that form a continuous tunnel linking the two active sites. Experimental support for Arg-142 playing a role in intramolecular ammonia translocation is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS. MD simulations of computational models for the R142I variant and the R142I/β-aspartyl-AMP/MgPPi ternary complex provide a possible molecular basis for this observation. Overall, the combination of 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

Gaussian Process-Gated Hierarchical Mixtures of Experts.

In this article, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs). Unlike other mixtures of experts with gating models linear in the input, our model employs gating functions built with Gaussian processes (GPs). These processes are based on random features that are non-linear functions of the inputs. Furthermore, the experts in our model are also constructed with GPs. The optimization of the GPHMEs is performed by variational inference. The proposed GPHMEs have several advantages. They outperform tree-based HME benchmarks that partition the data in the input space, and they achieve good performance with reduced complexity. Another advantage is the interpretability they provide for deep GPs, and more generally, for deep Bayesian neural networks. Our GPHMEs demonstrate excellent performance for large-scale data sets, even with quite modest sizes.

Improving image-based weld defects detection under interference information via a novel semantic supervision strategy

ABSTRACT Weld defects of skin-skeleton structures are invisible and image-based visual inspections using deep learning neural networks are in demand. The main limitation of previous detection algorithms is interference information in images. The reflective light and uneven brightness distribution hinder algorithms to achieve higher detection reliability. In this work, a novel supervision strategy for defects detection algorithms was proposed to break the limitation. A new semantic gate convolution block was developed to help the neural networks to distinguish between targets and interference. The block utilised semantic segmentation labels and a gate function to control the information transmitted to the detection output. Additionally, a new category-pixel-accuracy loss function was adopted to improve the effectiveness of semantic features. The new function reduced the effects of negative pixels to avoid over emphasis on semantic completeness, thus the ineffective features of interference could be eliminated. The results indicated that the back-propagated gradients from the supervision part taught the backbone networks to focus on correct objects instead of interference. To testify the validity of the current method, the algorithms was verified in burns and collapse defects. The detection accuracy under the new semantic supervision reached 99.1%, which was superior to common CNN.

Influence of gate cutoff effect on flow mode conversion and energy dissipation during power-off of prototype tubular pump system

Understanding the cutoff effect of gates is essential for enhancing the overall quality of the pump system's power-off process, minimizing energy losses, and reducing potential risks associated with hydraulic transients. In this study, both numerical simulations and experimental investigations were conducted on the power-off process of a tubular pump system, considering scenarios with and without gate functionality. The simulations utilized a dynamic mesh method to model gate movement, incorporated the torque balance equation to determine the real-time impeller speed, and applied the 3D-VOF method for free surface modeling in reservoirs. Power-off experiments were performed on a model pump system and a prototype pump system to validate the numerical simulation results. To elucidate the mechanism of the gate's cutoff effect, flow modes during the power-off process were categorized based on the four-quadrant static test results of the pump, revealing the deviations between transient and static characteristics. By comparing the transient flow structures of the pump system under different gate operating states, specific energy dissipation behaviors during gate cutoff were analyzed. The research findings enhance the understanding of hydraulic transients, which is essential for developing more sustainable and resilient energy systems.

All-optical temporal logic gates in localized exciton polaritons

Exciton polaritons—quasi-particle excitations consisting of strongly coupled photons and excitons—present fascinating possibilities for photonic circuits, owing to their strong nonlinearity, ultrafast reaction times and their ability to form macroscopic quantum states at room temperature via non-equilibrium condensation. Past implementations of transistors and logic gates with exciton polaritons have been mostly realized using the spatial propagation of polariton fluids, which place high demands on the fabrication of the microcavities and typically require complex manipulations. In this work we have implemented the full set of logical gate functionalities (that is, temporal AND, OR and NOT gates) in localized exciton polaritons at room temperature, on the basis of precisely controlling the interplay between polariton condensate and exciton reservoir dynamics, using a two-pulse excitation scheme. The dynamics intrinsically covers the cascadability required by the logical operations, enabling efficient information processing without the need for spatial flow. The temporal polariton logic gates demonstrate advantages in ultrafast switching, universality and simplified compatibility with other dimensional controls, showing great potential for building polariton logic networks in strongly coupled light–matter systems.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.

High-Speed 2x1 Multiplexer with Carrier-Reservoir Semiconductor Optical Amplifiers

Leveraging the rapid carrier recovery times and minimal polarization sensitivity of carrier-reservoir semiconductor optical amplifiers (CR-SOAs), this study embeds them in a Mach–Zehnder interferometer (MZI) setup to emulate a 2x1 multiplexer (MUX) operating at 120 Gb/s. The focus is on incorporating AND logic gate functionalities into the CR-SOAs-based MZI structure to facilitate high-quality multiplexing. The proposed methodology utilizes the intrinsic gain and phase modulation capabilities of CR-SOAs-based MZI to effectively manipulate data streams. This innovative approach capitalizes on the unique properties of CR-SOAs, such as fast response times and low polarization sensitivity, to achieve optimal signal transmission quality and efficient multiplexing. To assess MUX performance, a quality factor metric is introduced as a comprehensive measure of signal integrity. Through exhaustive simulations and meticulous analysis, the study demonstrates the feasibility of achieving the desired data rate while maintaining superior signal transmission quality. The results underscore the efficacy of CR-SOAs-based MZI as versatile modules for high-speed multiplexing applications, offering unparalleled performance and efficiency. This research represents a significant advancement in understanding optical communication systems and provides valuable insights for optimizing signal quality and mitigating interference in practical real-world scenarios.

Spike-timing-dependent plasticity induction reveals dissociable supplementary– and premotor–motor pathways to automatic imitation

Humans tend to spontaneously imitate others' behavior, even when detrimental to the task at hand. The action observation network (AON) is consistently recruited during imitative tasks. However, whether automatic imitation is mediated by cortico-cortical projections from AON regions to the primary motor cortex (M1) remains speculative. Similarly, the potentially dissociable role of AON-to-M1 pathways involving the ventral premotor cortex (PMv) or supplementary motor area (SMA) in automatic imitation is unclear. Here, we used cortico-cortical paired associative stimulation (ccPAS) to enhance or hinder effective connectivity in PMv-to-M1 and SMA-to-M1 pathways via Hebbian spike-timing-dependent plasticity (STDP) to test their functional relevance to automatic and voluntary motor imitation. ccPAS affected behavior under competition between task rules and prepotent visuomotor associations underpinning automatic imitation. Critically, we found dissociable effects of manipulating the strength of the two pathways. While strengthening PMv-to-M1 projections enhanced automatic imitation, weakening them hindered it. On the other hand, strengthening SMA-to-M1 projections reduced automatic imitation but also reduced interference from task-irrelevant cues during voluntary imitation. Our study demonstrates that driving Hebbian STDP in AON-to-M1 projections induces opposite effects on automatic imitation that depend on the targeted pathway. Our results provide direct causal evidence of the functional role of PMv-to-M1 projections for automatic imitation, seemingly involved in spontaneously mirroring observed actions and facilitating the tendency to imitate them. Moreover, our findings support the notion that SMA exerts an opposite gating function, controlling M1 to prevent overt motor behavior when inadequate to the context.

Study on trap sensitivity for single material gate and double material gate nano-ribbon FETs

In this work, the trap sensitivity of single material gate (SMG) and dual material gate (DMG) nano ribbon FETs (NRFETs) are reported using TCAD Sentaurus Device simulator. The trap sensitivity is extracted for Gaussian trap distribution of both acceptor and donor type traps. We have reported the trap sensitivity for the variation in trap concentration, energy peak position, work function of metal gate, and temperature for both the NRFETs. It is realized that trap sensitivity is greater and lesser than 100% for acceptor type trap in SMG and DMG NRFETs, respectively, whereas, such sensitivity is 147% and 123% for donor type trap concentration, respectively. Temperature also shows a significant variation in trap sensitivity for both the NRFETs. The increase in work function leads to the reduction in trap sensitivity for both NRFETs in existence of acceptor and donor trap charges. The maximum sensitivity in trap are 400% and 275% for SMG and DMG NRFETs, respectively, in presence of donor trap concentration. Moreover, the trap sensitivity is very insignificant at high gate bias for donor type trap concentration with wide variation in parameters.

The Opto‐Ionic Voltage Effect Soft Triode: An Optical‐To‐Ionic Signal Converter

AbstractA novel opto‐ionic device based on the recently reported Ionic Voltage Effect Soft Triode (IVEST) is introduced. This device can convert optical to ionic signals as it is shown here. The characterization of the opto‐IVEST points to an optically induced charging mechanism and to various potential applications. The opto‐IVEST shows the ability to detect and memorize optical signals. Furthermore, the device exhibits learning behavior and a hybrid logic gate function.

Auditory Sensory Gating: Effects of Noise.

Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals' neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processing in noise, the impact of noise on the neural response during the pre-attentive phase remains unclear. Therefore, this preliminary study aimed to assess the auditory gating response, reflective of the sensory inhibitory stage, to repeated vowel pairs presented in background noise. CAEPs were recorded via high-density EEG in fifteen normal-hearing adults in quiet and noise conditions with low and high informational masking. The difference between the average CAEP peak amplitude evoked by each vowel in the pair was compared across conditions. Scalp maps were generated to observe general cortical inhibitory networks in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception.

Switch-2 determines Mg2+ADP-release kinetics and fine-tunes the duty ratio of Dictyostelium class-1 myosins.

Though myosins share a structurally conserved motor domain, single amino acid variations of active site elements, including the P-loop, switch-1 and switch-2, which act as nucleotide sensors, can substantially determine the kinetic signature of a myosin, i.e., to either perform fast movement or enable long-range transport and tension generation. Switch-2 essentially contributes to the ATP hydrolysis reaction and determines product release. With few exceptions, class-1 myosin harbor a tyrosine in the switch-2 consensus sequence DIYGFE, at a position where class-2 myosins and a selection of myosins from other classes have a substitution. Here, we addressed the role of the tyrosine in switch-2 of class-1 myosins as potential determinant of the duty ratio. We generated constitutively active motor domain constructs of two class-1 myosins from the social amoeba Dictyostelium discoideum, namely, Myo1E, a high duty ratio myosin and Myo1B, a low duty ratio myosin. In Myo1E we introduced mutation Y388F and in Myo1B mutation F387Y. The detailed functional characterization by steady-state and transient kinetic experiments, combined with in vitro motility and landing assays revealed an almost reciprocal relationship of a number of critical kinetic parameters and equilibrium constants between wild-type and mutants that dictate the lifetime of the strongly actin-attached states of myosin. The Y-to-F mutation increased the duty ratio of Moy1B by almost one order of magnitude, while the introduction of the phenylalanine in switch-2 of Myo1E transformed the myosin into a low duty ratio motor. These data together with structural considerations propose a role of switch-2 in fine-tuning ADP release through a mechanism, where the class-specific tyrosine together with surrounding residues contributes to the coordination of Mg2+ and ADP. Our results highlight the importance of conserved switch-2 residues in class-1 myosins for efficient chemo-mechanical coupling, revealing that switch-2 is important to adjust the duty ratio of the amoeboid class-1 myosins for performing movement, transport or gating functions.

Age-related changes in behavior profile in male offspring of rats treated with poly I:C-induced maternal immune activation in early gestation.

Autism and schizophrenia are environmental risk factors associated with prenatal viral infection during pregnancy. It is still unclear whether behavior phenotypes change at different developmental stages in offspring following the activation of the maternal immune system. Sprague-Dawley rats received a single caudal vein injection of 10 mg/kg polyinosinic:polycytidylic acid (poly I:C) on gestational day 9 and the offspring were comprehensively tested for behaviors in adolescence and adulthood. Maternal serum levels of interleukin (IL)-6, IL-1β and tumor necrosis factor-α were elevated in poly I:C-treated dams. The offspring of maternal poly I:C-induced rats showed increased anxiety, impaired social approach, and progressive impaired cognitive and sensorimotor gating function. Maternal immune activation led to developmental specificity behavioral impairment in offspring.

Programmable Racetrack for Magnetic Domain Wall Motion via Local Tuning of Exchange Biased Field

AbstractTo overcome memory bottleneck issues in memory‐centric chip technologies, in‐memory computing has been considered an alternative route involving simultaneous data storage and computing in a memory network. In the context of spintronics, a memory‐in‐logic device based on spin‐transfer‐torque or spin‐orbit‐torque magnetoresistive random‐access memory, and a magnetic domain wall (DW) racetrack has been studied. To expand the functionalities of a conventional magnetic DW racetrack, the study devises a reprogrammable exchange‐biased DW racetrack with local engineering of the exchange bias field (HE) in continuous magnetic films without requiring a lithography process for specific patterning of the films. Furthermore, current‐driven and field‐driven DW motion along the exchange‐biased racetrack is demonstrated. Additionally, within the route of the locally different exchange‐biased racetrack, a gate function can be performed to guide or stop DW motion by locally tuning HE. The complex maze racetrack is rewritable, and multiple input channels can be controlled.

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost (e.g., running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at http://github.com/pingyang1117/SMILEtrack_official.

Efficient Spiking Neural Networks with Sparse Selective Activation for Continual Learning

The next generation of machine intelligence requires the capability of continual learning to acquire new knowledge without forgetting the old one while conserving limited computing resources. Spiking neural networks (SNNs), compared to artificial neural networks (ANNs), have more characteristics that align with biological neurons, which may be helpful as a potential gating function for knowledge maintenance in neural networks. Inspired by the selective sparse activation principle of context gating in biological systems, we present a novel SNN model with selective activation to achieve continual learning. The trace-based K-Winner-Take-All (K-WTA) and variable threshold components are designed to form the sparsity in selective activation in spatial and temporal dimensions of spiking neurons, which promotes the subpopulation of neuron activation to perform specific tasks. As a result, continual learning can be maintained by routing different tasks via different populations of neurons in the network. The experiments are conducted on MNIST and CIFAR10 datasets under the class incremental setting. The results show that the proposed SNN model achieves competitive performance similar to and even surpasses the other regularization-based methods deployed under traditional ANNs.