Outputs In Parallel Research Articles

Quality Control of Mass-Encoded Nanodevices by Compartmented DNA Origami Frames for Precision Information Coding and Logic Mapping.

Encoded nanostructures afford an ideal platform carrying multi-channel signal components for multiplexed assay and information security. However, with the demand on exclusivity and reproducibility of coding signals, precise control on the structure and composition of nanomaterials featuring fully distinguishable signals remains challenging. By using the multiplexing capability of mass spectrometry (MS) and spatial addressability of DNA origami nanostructures, we herein propose a quality control methodology for constructing mass-encoded nanodevices (namely MNTs-TDOFs) in the scaffold of compartmented tetrahedral DNA origami frames (TDOFs), in which the arrangement and stoichiometry of four types of mass nanotags (MNTs) can be finely regulated and customized to generate characteristic MS patterns. The programmability of combinatorial MNTs and orthogonality of individual compartments allows further evolution of MNTs-TDOFs to static tagging agents and dynamic nanoprobes for labeling and sensing of multiple targets. More importantly, structure control at single TDOF level ensures the constancy of prescribed MS outputs, by which a high-capacity coding system was established for secure information encryption and decryption. In addition to the multiplexed outputs in parallel, the nanodevices could also map logic circuits with interconnected complexity and logic events of c-Met recognition and dimerization on cell surface for signaling regulation by MS interrogation.

Quality Control of Mass‐Encoded Nanodevices by Compartmented DNA Origami Frames for Precision Information Coding and Logic Mapping

AbstractEncoded nanostructures afford an ideal platform carrying multi‐channel signal components for multiplexed assay and information security. However, with the demand on exclusivity and reproducibility of coding signals, precise control on the structure and composition of nanomaterials featuring fully distinguishable signals remains challenging. By using the multiplexing capability of mass spectrometry (MS) and spatial addressability of DNA origami nanostructures, we herein propose a quality control methodology for constructing mass‐encoded nanodevices (namely MNTs‐TDOFs) in the scaffold of compartmented tetrahedral DNA origami frames (TDOFs), in which the arrangement and stoichiometry of four types of mass nanotags (MNTs) can be finely regulated and customized to generate characteristic MS patterns. The programmability of combinatorial MNTs and orthogonality of individual compartments allows further evolution of MNTs‐TDOFs to static tagging agents and dynamic nanoprobes for labeling and sensing of multiple targets. More importantly, structure control at single TDOF level ensures the constancy of prescribed MS outputs, by which a high‐capacity coding system was established for secure information encryption and decryption. In addition to the multiplexed outputs in parallel, the nanodevices could also map logic circuits with interconnected complexity and logic events of c‐Met recognition and dimerization on cell surface for signaling regulation by MS interrogation.

Mechanisms of type I interferon production by chicken TLR21

The innate immune response relies on the ability of host cells to rapidly detect and respond to microbial nucleic acids. Toll-like receptors (TLRs), a class of pattern recognition receptors (PRRs), play a fundamental role in distinguishing self from non-self at the molecular level. In this study, we focused on TLR21, an avian TLR that recognizes DNA motifs commonly found in bacterial genomic DNA, specifically unmethylated CpG motifs. TLR21 is believed to act as a functional homologue to mammalian TLR9. By analysing TLR21 signalling in chickens, we sought to elucidate avian TLR21 activation outputs in parallel to that of other nucleic acid species. Our analyses revealed that chicken TLR21 (chTLR21) triggers the activation of NF-κB and induces a potent type-I interferon response in chicken macrophages, similar to the signalling cascades observed in mammalian TLR9 activation. Notably, the transcription of interferon beta (IFNB) by chTLR21 was found to be dependent on both NF-κB and IRF7 signalling, but independent of the TBK1 kinase, a distinctive feature of mammalian TLR9 signalling. These findings highlight the conservation of critical signalling components and downstream responses between avian TLR21 and mammalian TLR9, despite their divergent evolutionary origins. These insights into the evolutionarily conserved mechanisms of nucleic acid sensing contribute to the broader understanding of host-pathogen interactions across species.

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network.

Handling Differential Mode Conducted EMC in Modular Converters

This article presents the analysis and management of differential mode conducted EMI in modular converters made from associations of standard conversion cells. Two main configurations, inputs in series—outputs in parallel and inputs in parallel—outputs in series are studied with the objective to define a generic EMI management technique, independent from the number of conversion standard cells implemented. First, analysis is carried out from a theoretical point of view based on simplified models. Second, filtering solutions are introduced and compared. Especially, centralized versus distributed filtering techniques are compared with the objective to find generic solutions. The results are compared and validated with practical characterizations.

Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks

In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3D) space. The proposed network takes a sequence of consecutive spectrogram time-frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time-frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.

Low‐power consumption ternary full adder based on CNTFET

This paper presents low-power circuits to implement ternary full adder (TFA) using carbon nanotube field-effect transistors (CNTFETs). Besides the unique characteristics of the CNTs, the threshold voltage simple control is the best property to implement ternary logic circuits. Low-complexity, low-power consumption and low-power delay product (PDP) are the benefits of the proposed circuits in comparison with all previous presented designs of TFA. The final proposed TFA is robust and has proper noise margins. The structure of the final proposed TFA is more appropriate to use in ripple adders, since the first ternary half sum generators (THSGs) in all cells produce their outputs in parallel (in the final proposed TFA, the output of the first THSG of the sum-generation unit is also used in the carry-generation unit). The proposed circuits are simulated using HSPICE with 32 nm-CNTFET technology. According to simulation results, the final proposed TFA has reduced the power consumption significantly and results in 86.92 and 97% reductions in terms of the PDP in comparison with two recent proposed designs.

A Spatiotemporal-Chaos-Based Cryptosystem Taking Advantage ofBoth Synchronous and Self-Synchronizing Schemes

Two-dimensional one-way coupled map lattices are used for cryptography wheremultiple space units produce chaotic outputs in parallel. One of the outputsplays the role of driving for synchronization of the decryption system whilethe others perform the function of information encoding. With thisseparation of functions the receiver can establish a self-checking andself-correction mechanism, and enjoys the advantages of both synchronous andself-synchronizing schemes. A comparison between the present system with thesystem of advanced encryption standard (AES) is presented in the aspect ofchannel noise influence. Numerical investigations show that our system ismuch stronger than AES against channel noise perturbations, and thus can bebetter used for secure communications with large channel noise.

Waveform synthesis using multiplexed parallel synthesizers

A novel signal synthesizer provides high frequency synthesized waveforms for the user. By converting phase information into digital outputs in parallel and by selectively coupling these digital outputs, an ordered digital output is formed to provide the high frequency waveforms. This ordered digital output, which represents points on a sine function, is converted to an analog signal for the synthesizer output. Furthermore, frequency and phase modulations of the synthesized waveforms are easily implemented with this novel signal synthesizer; the modulation information is simply added to the digital outputs prior to selectively coupling. Thus when the ordered digital output is converted to an analog signal, the analog signal contains the modulation information.