Multilevel Signals Research Articles

Holistic inside and outside: The impact of food taste on ARCAGRP neuron activity in feeding regulation

The activities of appetite-regulated neurons-ARCAGRP neurons-are modulated by multi-level feedback signals during feeding. In this issue of Neuron, Aitken etal.1 expand our understanding of the feedback control within feeding circuits, revealing that food taste signals can causally and precisely regulate meal patterns through ARCAGRP neurons.

MosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

MosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

Advancing LWIR FSO communication through high-speed multilevel signals and directly modulated quantum cascade lasers

This study investigates the potential of long-wave infrared (LWIR) free-space optical (FSO) transmission using multilevel signals to achieve high spectral efficiency. The FSO transmission system includes a directly modulated-quantum cascade laser (DM-QCL) operating at 9.1 µm and a mercury cadmium telluride (MCT) detector. The laser operated at the temperature settings of 15°C and 20°C. The experiment was conducted over a distance of 1 m and in a lab as a controlled environment. We conduct small-signal characterization of the system, including the DM-QCL chip and MCT detector, evaluating the end-to-end response of both components and all associated electrical elements. For large-signal characterization, we employ a range of modulation formats, including non-return-to-zero on-off keying (NRZ-OOK), 4-level pulse amplitude modulation (PAM4), and 6-level PAM (PAM6), with the objective of optimizing both the bit rate and spectral efficiency of the FSO transmission by applying pre- and post-processing equalization. At 15°C, the studied LWIR FSO system achieves net bitrates of 15 Gbps with an NRZ-OOK signal and 16.9 Gbps with PAM4, both below the 6.25% overhead hard decision-forward error correction (6.25%-OH HD-FEC) limit, and 10 Gbps NRZ-OOK below the 2.7% overhead Reed-Solomon RS(528,514) pre-FEC (KR-FEC limit). At 20°C, we obtained net bitrates of 14.1 Gbps with NRZ-OOK, 16.9 Gbps with PAM4, and 16.4 Gbps with PAM6. Furthermore, we evaluate the BER performance as a function of the decision feedback equalization (DFE) tap number to explore the role of equalization in enhancing signal fidelity and reducing errors in FSO transmission. Our findings accentuate the competitive potential of DM-QCL and MCT detector-based FSO transceivers with digital equalization for the next generation of FSO communication systems.

A foreground calibration technique with multi-level dither for a 14-bit 1-MS/s SAR ADC

A foreground calibration is proposed to obtain the real weights in the split capacitor digital-to-analog- converter (CDAC) of a 14-bit 1-MS/s successive-approximation-register (SAR) ADC. Since the non-linearity of high-resolution SAR ADC is mainly caused by the mismatch of capacitors, calibration of weights of more significant bits is necessary when the resolution of SAR ADC comes to more than 12 bits. By injecting a multi-level dither signal in both the calibration and conversion phases of the calibration scheme, the precision and non-linearity of SAR ADC can be significantly improved. Simulation results indicate that the peak signal-to-noise-and-distortion ratio (SNDR) and the spurious-free dynamic range (SFDR) achieve 79.93 dB and 91.21 dB by employing the proposed calibration technique in a 14-bit split-CDAC SAR ADC. Besides, integral non-linearity (INL) achieves 0.22 least-significant bit (LSB).

Cultural transmission and religious belief: An extended replication of Gervais and Najle (2015) using data from the International Social Survey Programme.

Beliefs in supernatural agents or religious beliefs are pervasive, yet there are individual differences in such beliefs. Although various factors have been proposed as relevant, recent research has increasingly emphasized the importance of cultural learning, showing that enthusiastic religious behavior (credibility enhancing displays; CREDs) from parents predicts increased religious beliefs among their children. In addition to this kin-biased learning, Gervais and Najle (2015) analyzed data from the World Values Survey to demonstrate that the number of adults who show religious CREDs is also an important predictor of people's beliefs, indicating that individuals develop their religious beliefs through conformist learning. This pre-registration study aimed to replicate and extend these findings by analyzing data from the International Social Survey Programme (ISSP), which is another large social survey. We examined the generalizability of the results by analyzing multigenerational samples. Multilevel regression and signal detection analyses revealed that the presence of both kin-biased and conformist learning cues was significantly associated with respondents' religious beliefs. Moreover, they suggested tension between the two cultural learning cues, thereby suggesting that the effect of kin-biased learning on religious beliefs becomes stronger (weaker) when the cue for conformist learning is unclear (clear). These results support the idea that these two types of cultural learning are crucial to the development of religious beliefs.

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

The Designed Phase Mask for Suppressing the Inter-Pixel Crosstalk Noise in Intensity-Modulated Multilevel Holographic Data Storage Systems

Intensity-modulated signals have the advantage of being directly detectable by the image sensor but have the drawback that the signal quality is easily deteriorated by crosstalk noise, in contrast to phase-modulated signals. In order to suppress the crosstalk noise, we propose a new signal arrangement for multilevel intensity-modulated signals. The concept of our method is to reduce the number of adjacent pixels that are a source of inter-pixel crosstalk noise and to minimize intensity modulation owing to interference with crosstalk noise. We have numerically and experimentally demonstrated that our method can reduce the error rate and improve the recording density compared to the conventional signal arrangement. Our proposed method offers a promising solution for achieving higher recording densities in intensity-modulated holographic data storage systems.

Compensation schemes for uneven illumination and LED light-emitting instability in optical camera communication system

In order to increase the data rate of the optical camera communication (OCC) system, the 8-composite-amplitude-shift-keying modulation (8CASK) OCC system is used in this work. However, if the static decision thresholds are employed to demodulate multi-level ASK signal, uneven illumination of LED lamps and LED light-emitting instability lead to the fluctuation of the gray range in the picture and degrade the bit-error-rate (BER) performance. In this work, we propose and demonstrate a demodulation scheme, using the uneven illumination compensation algorithm, the pixel matrix threshold overall update algorithm and the secondary decision algorithm, to mitigate the impact of illumination unevenness and LED light-emitting instability. The BER performance is evaluated and compared with other demodulation schemes. The experimental results demonstrate that the communication rate of our proposed scheme can reach 9kbit/s at a distance of 250 cm where the illumination is 135lux, and the BER is 8.01 × 10−5.

Effect of non-uniform carrier injection on two-state lasing in quantum dot microdisks with split electrical contact

In this papaer, the emission characteristics of InAs/InGaAs quantum dot (QD) microdisk lasers, of different cavity diameters, with a top split electrical contact formed using the focused ion beam technique are investigated. The dependences of the threshold currents of two-state lasing (i.e. currents corresponding to the start of the ground- and excited-state lasing) for microdisks of 24 and 28 μm diameters on the electrical contact area are presented. The contact area was found to influence the threshold currents of two-state lasing in microdisks. It is shown that a decrease in the area of the injected electrical contact leads to a decrease in the current corresponding to the start of the excited-state lasing, while the ground-state (GS) lasing threshold remains virtually unchanged. The temperature evolution of the threshold currents for two-state lasing was also studied in microdisks with different electrical contact areas. We demonstrate that the use of contacts of different areas is a method of controlling the threshold currents of two-state lasing and can be used in engineering of QD lasers intended, for example, for multi-level signal transmission with wavelength multiplexing by switching from the GS to excited-state lasing.

Integrated segmented IQ-modulator for orthogonal sampling and multi-level high-bandwidth signal generation.

Photonic-assisted signal processing of high-bandwidth signals emerges as a solution for challenges encountered in electronic-based processing. Here we present a concept for a compact, photonic-assisted digital-to-analog converter (DAC) and optical IQ-modulator in one single integrated device based on two innovative concepts: a segmented Mach-Zehnder modulator and orthogonal sampling. For electrically driving the modulator, only a single radio frequency oscillator and no pulse source or electrical DAC are required. The presented and simulated proof-of-concept device with six segments can generate a multi-level and high-bandwidth signal from low-bandwidth electronic drivers; e.g., we show the generation of a 120 Gbps data rate, 16-quadrature amplitude modulation (16-QAM, 30 Gbaud) signal solely based on low-bandwidth (5 GHz) non-return-to-zero (NRZ) signals. Integrated on a silicon photonic platform, the device provides fixable speed and bandwidth operations, positioning it as a viable solution for diverse communication systems.

A flexible thermal-coupled InGaZnO adaptive synapse

The development of neuromorphic sensory systems necessitates synaptic devices with adaptivity to a wide range of stimuli. Furthermore, the introduction of multimodal adaptivity is highly favorable, which holds immense potential for improving the processing capability of the neuromorphic system under complex environments. In this work, we report a thermal-coupled adaptive synapse (TCAS) by integrating an IGZO-based synaptic transistor with a laser-induced graphene micro-heater. This synapse enables active modulation of nonlinear short-term plasticity gains through temperature and voltage co-mediated ion/electron coupling, which facilitates the high adaptivity for image denoising. The images with multilevel signals can be effectively denoised with an average reduction of ∼84.0% in the Euclidean distance in comparison with the noisy images. This outcome indicates the effectiveness of TCASs in complex sensory processing and offers a promising solution for the development of neuromorphic systems with multimodal adaptability.

Robust multimodulus blind equalization algorithm for multilevel QAM signals in impulsive noise

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed.

MosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining critical biomarkers. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. Nevertheless, it is nontrivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of The Cancer Genome Atlas (TCGA) and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

Method for the Simultaneous Generation of Two Nonlinear Pseudo Random Sequences: 5-ary and Binary

Multi-level signals and sequences have become a significant aspect of modern high-speed communication systems. Hence, to ensure the confidentiality and integrity of the transmitted information, advanced methods and devices are necessary to produce strong cryptographic properties for not only binary but also for nonbinary keystreams, which can be used in resource-constrained microcontrollers. The proposed method and apparatus generate both a balanced nonlinear 5-ary pseudo-random sequence and a binary keystream sequence. The nonlinearity is determined by applying shrinking and multiplexing techniques to the generated linear 5-ary pseudo-random sequence and the running zero in its conversion to a binary balanced sequence.

DNA Polymerase-Steered Self-Propelled and Self-Enhanced DNA Walker for Rapid and Distinctly Amplified Electrochemical Sensing.

The development of a simple, rapid, easy-to-operate, and ultrasensitive DNA walker-based sensing system is challenging but would be very intriguing for the enormous applications in biological analysis and disease monitoring. Herein, a new self-propelled and self-enhanced DNA walking strategy was developed on the basis of a simple DNA polymerase-steered conversion from a typical alternate DNA assembly process. The sensing platform was fabricated easily by immobilizing only one hairpin probe (H1) and the sensing process was based on a simple one-step mixing with another hairpin-like DNA probe (H2) and DNA polymerase. The DNA polymerization could achieve target recycling and successive DNA walking steps. Interestingly, along with each DNA walking step, the new DNA walker sequence could be autonomously accumulated for a self-enhanced DNA walking effect. This provided a multilevel signal amplification ability for the ultrasensitive detection of the target with a low detection limit of 0.18 fM. Moreover, it could greatly reduce the reaction time with the sensing process finished within 1 h. The detection selectivity and the applicative potential in a complicated biological matrix were also demonstrated. Furthermore, the flexible control of sensing modes (self-enhanced DNA walking or the alternate DNA assembly) by using DNA polymerase or not offered a powerful means for sensing performance modulation. It thus opens a new avenue toward the development of a DNA walker-based sensing platform with both rapid and ultrasensitive features and might hold a huge potential for point-of-care diagnostic applications.

A 12bit 1.6 GS/s pipelined ADC with multi-level dither injection achieving 68 dB SFDR over PVT

This paper presents a 12-bit 1.6 GS/s pipelined analog-to-digital converter (ADC) in a 40-nm CMOS process. A novel dual-capacitor dither injection technique was proposed, based on a randomize multi-level uniform logic, aimed at leveraging multi-level signal jitter to disperse higher-order harmonics and spurious signals within the spectrum. The technique proves highly effective in preserving the linearity of high-speed pipeline ADCs under Process, Voltage, and Temperature (PVT) variations. Additionally, a high-speed amplifier with SC net was designed, and its matching bias circuit design can achieve configurable performance adjustment according to circumstances. Measurement results show that the ADC attains an impressive Signal-to-Noise and Distortion Ratio (SNDR) of 55.9 dB and Spurious-Free Dynamic Range (SFDR) of 71.7 dB at the Nyquist frequency. Furthermore, the SFDR variation remains below 4.2 dB over the temperature range of −40 °C–85 °C.

Hybrid multilevel-multiwavelength signaling scheme for optical data transmission

We suggest an idea of data encoding scheme based on the switching from the ground state (GS) to excited state (ES) lasing in quantum dot (QD) lasers with increase in injection current. The groups of two bits are assigned to lasing spectra that comprise either one or both GS and/or ES lasing lines depending on injection current. We expect that the proposed encoding scheme can potentially combine some advantages of multilevel signaling and wavelength division multiplexing.

Meta-Analysis of Antioxidant Mutants Reveals Common Alarm Signals for Shaping Abiotic Stress-Induced Transcriptome in Plants.

Aims: Reactive oxygen species (ROS) are key regulators of plant growth, development, and stress tolerance. Stress-induced changes in ROS levels trigger multilevel signaling. However, the precise mechanisms by which ROS signals are translated into changes in gene expression remain poorly defined. Focusing on six key antioxidant enzymes, we performed a meta-analysis of transcriptome data available in public databases to analyze ROS-mediated control of nuclear gene expression. Results: An information-guided pipeline was developed, which identified 19 putative transcription factors (TFs), as components in a "common alarm signal cascade" pathway following perception of changes in ROS levels. Crucially, 30%-35% of the abiotic stress transcriptome signatures had binding sites for common alarm signal-transcription factors (CAS-TFs) in their promoter regions. Furthermore, Phloem Early Dof 2 (PEAR2), DNA binding with one finger 5.8 (DOF5.8), and Obf-Binding Protein 3 (OBP3) were identified as top-ranked TFs on the basis of a cumulative DAPseq (DNA-affinity purification sequencing) score on the promoters of selected genes regulating core pathways of salt, drought, heat, and cold stress tolerance. Innovation: This study identifies a set of CAS-TFs that may play a major role in shaping the transcriptome of abiotic stress-induced ROS signaling. Ranking analysis identified PEAR2, DOF5.8, and OBP3 as the top-ranked CAS-TFs that regulated known markers of abiotic stress tolerance. Conclusion: The current findings suggest a major role of ROS in the abiotic stress signaling and also identify a set of TFs that take part in the signaling. Taken together, these findings suggested that the common alarm signal cascade underpins broad-range tolerance against multistress conditions. The identification of associated ROS-responsive CAS-TFs may provide novel targets for crop improvement.

High Spectral Efficiency Long-Wave Infrared Free-Space Optical Transmission With Multilevel Signals

This study explores the potential of long-wave infrared free-space optical (FSO) transmission that leverages multilevel signals to attain high spectral efficiency. The FSO transmission system consists of a directly modulated-quantum cascade laser (DM-QCL) operating at 9.15 μm and a mercury cadmium telluride (MCT) detector. To fully understand the system, we conduct measurements on the DM-QCL chip and MCT detector and assess the overall amplitude response of the DM-QCL, MCT detector, and all electrical components. We apply various signals, including on-off keying (OOK), 4-level pulse amplitude modulation (PAM4), 6-level PAM (PAM6), and 8-level PAM (PAM8) to maximize the bit rate and spectral efficiency of the FSO transmission. Through a two-dimensional sweeping of the laser bias current and MCT detector photovoltage, we optimize the transmission performance. At the optimal operation point, the FSO system achieved impressive results which are up to 6 Gbaud OOK, 3.5 Gbaud PAM4, 3 Gbaud PAM6, and 2.7 Gbaud PAM8 signal transmissions, with a bit error rate performance below 6.25% overhead hard decision-forward error correction limit when the DM-QCL operates at 10°C. We also evaluate the eye diagrams and stability of the system to showcase its remarkable transmission performance. Our findings suggest that the DM-QCL and MCT detector-based FSO transceivers offer a highly competitive solution for the next generation of optical wireless communication systems.