Complex Modulation Research Articles

Main targets of ibogaine and noribogaine associated with its putative anti-addictive effects: A mechanistic overview.

There is a growing interest in studying ibogaine (IBO) as a potential treatment for substance use disorders (SUDs). However, its clinical use has been hindered for mainly two reasons: First, the lack of randomized, controlled studies informing about its safety and efficacy. And second, IBO's mechanisms of action remain obscure. It has been challenging to elucidate a predominant mechanism of action responsible for its anti-addictive effects. To describe the main targets of IBO and its main metabolite, noribogaine (NOR), in relation to their putative anti-addictive effects, reviewing the updated literature available. A comprehensive search involving MEDLINE and Google Scholar was undertaken, selecting papers published until July 2022. The inclusion criteria were both theoretical and experimental studies about the pharmacology of IBO. Additional publications were identified in the references of the initial papers. IBO and its main metabolite, NOR, can modulate several targets associated with SUDs. Instead of identifying key targets, the action of IBO should be understood as a complex modulation of multiple receptor systems, leading to potential synergies. The elucidation of IBO's pharmacology could be enhanced through the application of methodologies rooted in the polypharmacology paradigm. Such approaches possess the capability to describe multifaceted patterns within multi-target drugs. IBO displays complex effects through multiple targets. The information detailed here should guide future research on both mechanistic and therapeutic studies.

Genetic diversity and natural selection of apical membrane antigen-1 (ama-1) in Cameroonian Plasmodium falciparum isolates

Antigenic variation associated with genetic diversity in global Plasmodium falciparum apical membrane antigen-1 (PfAMA-1) is a major impediment to designing an effective malaria vaccine. Here, we report the first study on genetic diversity and natural selection of the Pfama-1 gene in P. falciparum isolates from Cameroon. A total of 328 P. falciparum positive samples collected during 2016 and 2019 from five localities of Cameroon were analysed. The ectodomain coding fragment of Pfama-1 gene was amplified for polymorphism profiling and natural selection analysis. A total of 108 distinct haplotypes were found in 203 P. falciparum isolates with considerable nucleotide diversity (π = 0.016) and haplotype diversity (Hd = 0.976). Most amino acid substitutions detected were scattered in ectodomain-I and few specific mutations viz P145L, K148Q, K462I, L463F, N471K, S482L, E537G, K546R and I547F were seen only in Cameroonian isolates. A tendency of natural selection towards positive diversifying selection was observed (Taj-D = 2.058). Five positively selected codon sites (P145L, S283L, Q308E/K, P330S and I547F) were identified, which overlapped with predicted B-cell epitopes and red blood cell (RBC) binding sites, suggesting their potential implication in host immune pressure and parasite-RBC binding complex modulation. The Cameroonian P. falciparum populations indicated a moderate level of genetic differentiation when compared with global sequences, with few exceptions from Vietnam and Venezuela. Our findings provide baseline data on existing Pfama-1 gene polymorphisms in Cameroonian field isolates, which will be useful information for malaria vaccine design.

ZmELF3.1 integrates the RA2-TSH4 module to repress maize tassel branching.

Tassel branch number (TBN) is a key agronomic trait for adapting to high-density planting and grain yield in maize. However, the molecular regulatory mechanisms underlying tassel branching are still largely unknown. Here, we used molecular and genetic studies together to show that ZmELF3.1 plays a critical role in regulating TBN in maize. Previous studies showed that ZmELF3.1 forms the evening complex through interacting with ZmELF4 and ZmLUX to regulate flowering in maize and that RA2 and TSH4 (ZmSBP2) suppresses and promotes TBN in maize, respectively. In this study, we show that loss-of-function mutants of ZmELF3.1 exhibit a significant increase of TBN. We also show that RA2 directly binds to the promoter of TSH4 and represses its expression, thus leading to reduced TBN. We further demonstrate that ZmELF3.1 directly interacts with both RA2 and ZmELF4.2 to form tri-protein complexes that further enhance the binding of RA2 to the promoter of TSH4, leading to suppressed TSH4 expression and consequently decreased TBN. Our combined results establish a novel functional link between the ELF3-ELF4-RA2 complex and miR156-SPL regulatory module in regulating tassel branching and provide a valuable target for genetic improvement of tassel branching in maize.

A double sideband combined tracking method for Galileo E5 AltBOC signals

The Galileo navigation satellite system (Galileo) E5 Alternative Binary Offset Carrier (AltBOC) signal brings various challenges due to its complex modulation, wide bandwidth, and multi-peaked auto-correlation function. While wideband tracking needs to solve the ambiguity problem and design dedicated baseband channels, the single-sideband cannot have the outstanding performance of the AltBOC signal. We propose a new tracking method called “Double Sideband Combined Tracking” (DSCT), which can fully exploit the AltBOC signal’s code tracking accuracy without ambiguity and ensure compatibility with Binary Phase Shift Keying (BPSK) processing channels, easily implemented in hardware. The DSCT employs one phase locked loop and one delay locked loop to track the carrier and code, respectively. The double-sideband correlation results used by the two loops are recovered by coherently combining the single-sideband correlation results of the two BPSK channels. Meanwhile, the combined model, the loop discriminator, and the ambiguity detection of the DSCT are discussed. Furthermore, the code tracking error caused by thermal noise is modeled and analyzed. The test results based on real Galileo E5 signals show that the DSCT exhibits better or comparable code tracking accuracy to the AltBOC wideband tracking method. When the loop falsely locks onto a side-peak, the DSCT can quickly detect and re-lock on the main peak.

Microbes with high–abundance attributes dominate the prokaryote communities of saline–alkali soil and construct more complex networks in the plant rhizosphere

Understanding the interactions between plants and microbes in terms of core and/or keystone taxa is of great importance for promoting salt tolerant of plants and comprehensive utilisation of saline–alkali lands. However, our knowledge about the dominant microbial categories and their response to plant growth is limited. Here, we distinguished the categories of core and keystone taxa by classifying the prokaryotic categories and conducting network analysis in saline–alkali lands. Moreover, we explored how these important taxa varied between the plant rhizosphere and bulk soils. Overall, six categories of prokaryotes were identified. Approximately one–tenth of the species with high–abundance attributes accounted for two–thirds of total prokaryotes. Moreover, network analysis showed that nearly all links among the nodes were positive; and the identified keystone taxa mainly belonged to categories with high–abundance attributes. Furthermore, in the rhizosphere, a decreasing trend in the correlations of microbial diversity indices and community compositions with the soil properties was observed compared with those in the bulk soil. However, more keystone taxa and complex modules were strongly correlated with changes in the soil properties. These results suggest that the plant rhizosphere can recruit keystone prokaryotes with high–abundance attributes by establishing a syntrophic association between the plant and keystone taxa, which subsequently construct a complex microbial network structure. Although the ecological functions of the targeted keystone taxa need to be confirmed, our findings provide a potential method for constructing a core artificial microbiome with multiple approaches, further helping to develop microbial strategies to ameliorate salt stress in plants in saline–alkali lands.

Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal decomposition method, which can decompose a complex signal into several components and extract the periodic characteristics of the signal. However, the mode generation method adopted by RFMD does not consider the physical meaning of the component signal, which makes over-decomposition when dealing with real-life gear signals with complex modulation characteristics, thus destroying the integrity of the signal sideband, increasing the difficulty of subsequent analysis, and even losing key fault information. The iterative envelope-segmentation algorithm combines the modulation characteristics of the local fault gear signal and divides the original signal into a limited number of dominant frequency bands containing the modulation region in the Fourier spectrum, thereby ensuring that the obtained frequency bands contain rich fault information. Based on the above algorithm, a new adaptive decomposition method is proposed in this paper, which is adaptive spectrum segmentation Ramanujan decomposition (ASSRD). ASSRD uses fault envelope harmonic noise ratio as the index to evaluate the fault information content of component signals and uses it to assist the iterative envelope-segmentation algorithm to complete the adaptive segmentation of the Fourier spectrum. Finally, based on the segmentation result, the inverse RFT reconstruction of each frequency band is performed. Thus, the signal is decomposed into a finite number of component signals containing rich fault information. In addition, through the experiment on the gear simulation signal and the measured crack fault gear signal, the ASSRD method is compared with the original RFMD method and the existing ensemble empirical mode decomposition (EMD), variational mode decomposition, empirical wavelet transform, and singular spectrum decomposition method, verifying the feasibility and superiority of ASSRD in gear fault diagnosis. Besides, a comparative experiment based on compound faults diagnosis is carried out, in which ensemble EMD, Fourier decomposition method, empirical wavelet transform, and sparse decomposition are involved. The results show that the proposed method can extract the local fault information in the gear signal more effectively, and the performance is better than the comparison method.

Decoupled Modeling and Wide-Range Power Distribution Strategy for the Multisource Inverter in Microgrids

Multi-source inverter (MSI) provides a low-cost and high-power-density solution for microgrids (MGs) applications due to the removal of the dc/dc converter, which offers direct power flow between dc-side and ac-side. The existing modulation strategies for MSI transplanted from those of multilevel inverters experience a limited power distribution range due to the finite control freedom of degree provided by redundant vectors. Moreover, the power distribution between the dc ports is highly coupled with the vector synthesis, this is especially true when the voltages of the dc-port are unbalanced. To address the above issue, this article proposes a decoupled modeling method and a wide-range power distribution strategy for the MSI. The decoupled model is proposed to simplify the modulation implementation process and power distribution analysis. In the proposed model, the reference voltage vector of MSI is proportionally decomposed into two decoupled parts which can be generated independently. As a result, it can not only avoid complex modulation calculations but also ensure decoupled power distribution. Moreover, detailed theoretical analysis indicates that the proposed solution can offer an expanded power distribution range by increasing the switching actions in one switching cycle. Finally, the effectiveness of the proposed multiple-carrier modulation strategy is verified by islanded MGs experimental tests.

Generation of structured light beams by dual phase modulation with a single spatial light modulator

Precise control of amplitude and wavefront of optical fields are prerequisites for many applications, especially in singular optics. This has led to the increasing efforts for developing efficient techniques to control the shape of the light in different dimensions. A spatial light modulator (SLM) can be efficiently used for phase-only or amplitude-only modulation; but offers limitation in complex light field modulation. Hence, shaping the complex amplitude of optical beams is challenging mainly because there are no complex modulators. While there is ongoing research to develop complex amplitude modulating SLMs, a solution is still non-existent. In this study, to achieve complex light modulations, a simple experimental set-up employing single phase-only SLM has been proposed. The SLM has been used as operating in a split-screen-mode. The non-iterative approach of dual-pass modulation has been applied where two cascaded phase value distributions (PVD) are encoded side-by-side onto the SLM. The first PVD is designed to enable amplitude modulation in the second PVD plane which finally helps achieve wavefront shaping. Hence, both amplitude and phase modulation of light beam are possible in this configuration. Commonly known singular beams such as Laguerre–Gaussian and Bessel-Gaussian modes have been generated theoretically as well as experimentally to verify the feasibility of the proposed technique. The method used helps to achieve arbitrary shaped beams as well.

Micro-Doppler Effect and Sparse Representation Analysis of Underwater Targets.

At present, the micro-Doppler effects of underwater targets is a challenging new research problem. This paper studies the micro-Doppler effect of underwater targets, analyzes the moving characteristics of underwater micro-motion components, establishes echo models of harmonic vibration points and plane and rotating propellers, and reveals the complex modulation laws of the micro-Doppler effect. In addition, since an echo is a multi-component signal superposed by multiple modulated signals, this paper provides a sparse reconstruction method combined with time-frequency distributions and realizes signal separation and time-frequency analysis. A MicroDopplerlet time-frequency atomic dictionary, matching the complex modulated form of echoes, is designed, which effectively realizes the concise representation of echoes and a micro-Doppler effect analysis. Meanwhile, the needed micro-motion parameter information for underwater signal detection and recognition is extracted.

Исследование коэффициентных обратных задач с учетом реологии для функционально-градиентных стержней

The problems of determining the variable rheological properties of functionally gradient beams from some additional information about displacements are considered. Vibrations of a cantilevered inhomogeneous viscoelastic beam are studied in the framework of two models - Euler-Bernoulli and Timoshenko. The oscillations are caused by a moment concentrated at the end of the beam, oscillating in time. Within the framework of the concept of complex modules, operator relations linking the given and desired functions are obtained. Functions reflecting the laws of change of long-term and instantaneous modules have been restored. The results of computational experiments on the restoration of functions of various types are presented.

Cascaded optically injection-locked semiconductor laser, rate equations analysis, frequency response, and its application for complex optical signal generation.

In this paper, we introduce the concept of the cascaded optically injection-locked (OIL) semiconductor laser and present its novel rate equations. Then, the new locking range for this configuration has been obtained by mathematical demonstration. Subsequently, we modified a new adjustment for the detuning frequencies (Δ f i n j ) of the cascaded OIL system as well as the linewidth enhancement factors (α) values. Utilizing these tunings, improvements in the steady-state photon number and the phase modulation (PM) range become possible. Afterward, we define the generation of the complex optical signal area and extract the transfer function for investigating the frequency response of the cascaded system. The simulations have been performed once with identical α values and once with the various α values in the slave laser (SL) stages. We conclude that these novel proposed adjustments, combined with a strong injection ratio (Rinj) of 15 dB and a high bias current, can significantly broaden the bandwidth near 700 GHz while maintaining the fair gain available up to 180 GHz. Further, the generation of complex optical signal areas has been boosted for high-quality complex modulation applications. Eventually, we exhibit a novel approach for generating different α values in the SL stages by applying managed temperature variations in the experimental setup of the cascaded system, regardless of employing similar SLs.

A Reduced Switch Count, Self-Balanced, 13-Level Inverter Based on a Dual T-Type Configuration

A new Dual T-Type 13-Level inverter topology is presented in this paper, featuring less power switches, gate drivers, and diodes, than inverters based on traditional multilevel topologies, such as Neutral Point Clamped, Flying Capacitor, T-Type and Cascaded H-Bridge, or previously proposed reduced switch count 13-level inverter topologies. Moreover, no additional circuits are required to equalize the voltage of capacitors present in each pole, nor complex modulation strategies, leading to a reduction of cost and power losses. Exploiting a Nearest Level Modulation technique, the proposed topology performs well in terms of efficiency and total harmonic distortion, as confirmed by experimental tests.

GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements

Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized complex modules to improve their performance for skin disease image segmentation. However, limited computational resources have hindered their practical application in clinical environments. To address this challenge, this paper proposes a lightweight model, named GSCEU-Net, which is able to achieve superior skin lesion segmentation performance at a lower cost. GSCEU-Net is based on the U-Net architecture with additional enhancements. Firstly, the partial convolution (PConv) module, proposed by the FasterNet model, is modified to an SConv module, which enables channel segmentation paths of different scales. Secondly, a newly designed Ghost SConv (GSC) module is proposed for incorporation into the model’s backbone, where the Separate Convolution (SConv) module is aided by a Multi-Layer Perceptron (MLP) and the output path residuals from the Ghost module. Finally, the Efficient Channel Attention (ECA) mechanism is incorporated at different levels into the decoding part of the model. The segmentation performance of the proposed model is evaluated on two public datasets (ISIC2018 and PH2) and a private dataset. Compared to U-Net, the proposed model achieves an IoU improvement of 0.0261 points and a DSC improvement of 0.0164 points, while reducing the parameter count by 190 times and the computational complexity by 170 times. Compared to other existing segmentation models, the proposed GSCEU-Net model also demonstrates superiority, along with an advanced balance between the number of parameters, complexity, and segmentation performance.

High-performance omnidirectional-sliding hybrid nanogenerator for self-powered wireless nodes

Energy-autonomous smart electronics have been the focus of the next generation of the Internet of Things. Recently, advanced power technology with sustainable energy supply and pollution-free characteristics has become a research hotspot. Herein, to realize a reliable and unattended power unit, we propose a high-performance omnidirectional-sliding triboelectric-electromagnetic hybrid nanogenerator (OS-TEHNG) to convert the mechanical energy into electricity. The free-standing triboelectric nanogenerator (TENG) achieves a charge transfer of 24.4 μC in one cycle and a short-circuit current of 0.36 mA after optimizing the electrode structure. The TENG with 130 kΩ impedance allows efficient charging of energy storage units without complex power management modules (PMMs). The electromagnetic part achieves an open-circuit voltage of 300 V after optimization. OS-TEHNG achieves a peak power density of 1568.4 W/m3 and an average power density of 352.3 W/m3 at 2 Hz. As a result, OS-TEHNG charges a 1 mF capacitor to 46 V within 8 s. The strong power supply capability allows OS-TEHNG to charge smartwatches and phones. Furthermore, a completely self-powered wireless keyboard with a PMM and an energy storage unit based on OS-TEHNG is developed. The wireless self-powered keyboard sends data 213 times after only sliding the OS-TEHNG once. Finally, OS-TEHNG is successfully demonstrated to drive a 40 mW wireless sensor node with a 1.3-inch OLED screen in continuous operation. This work represents a significant advance toward self-powered microsystems and practical power sources.

Driver gene combinations dictate cutaneous squamous cell carcinoma disease continuum progression

The molecular basis of disease progression from UV-induced precancerous actinic keratosis (AK) to malignant invasive cutaneous squamous cell carcinoma (cSCC) and potentially lethal metastatic disease remains unclear. DNA sequencing studies have revealed a massive mutational burden but have yet to illuminate mechanisms of disease progression. Here we perform RNAseq transcriptomic profiling of 110 patient samples representing normal sun-exposed skin, AK, primary and metastatic cSCC and reveal a disease continuum from a differentiated to a progenitor-like state. This is accompanied by the orchestrated suppression of master regulators of epidermal differentiation, dynamic modulation of the epidermal differentiation complex, remodelling of the immune landscape and an increase in the preponderance of tumour specific keratinocytes. Comparative systems analysis of human cSCC coupled with the generation of genetically engineered murine models reveal that combinatorial sequential inactivation of the tumour suppressor genes Tgfbr2, Trp53, and Notch1 coupled with activation of Ras signalling progressively drives cSCC progression along a differentiated to progenitor axis. Taken together we provide a comprehensive map of the cSCC disease continuum and reveal potentially actionable events that promote and accompany disease progression.

Theoretical and experimental study of CO2 removal from biogas employing a hollow fiber polyimide membrane

In the present work, the efficient removal of CO2 from CH4 for biogas upgrade is investigated, employing a commercial polyimide (PI) membrane (UBE Industries Ltd., Japan). The goal is to meet the requirements of high purity biomethane (>95%) for effective energy use (e.g., by connecting the CH4-rich gaseous stream into the natural gas grid) with simultaneous CO2 capture, for potential reuse. The separation process is studied experimentally employing a commercial hollow fiber polyimide membrane module at laboratory scale conditions, while mathematical process models of various complexity in terms of the flow conditions are employed and compared with the experiments. The results demonstrate that, among the different flow models, the crossflow model is in much better agreement with the experimental results and can be further employed to design more complex membrane module configurations, such as two- and three-stage processes, recycle, etc.

Inhibition of the Exocyst Complex Attenuates the LRRK2 Pathological Effects.

Pathological mutations in leucine-rich repeat kinase 2 (LRRK2) gene are the major genetic cause of Parkinson's disease (PD). Multiple lines of evidence link LRRK2 to the control of vesicle dynamics through phosphorylation of a subset of RAB proteins. However, the molecular mechanisms underlying these processes are not fully elucidated. We have previously demonstrated that LRRK2 increases the exocyst complex assembly by Sec8 interaction, one of the eight members of the exocyst complex, and that Sec8 over-expression mitigates the LRRK2 pathological effect in PC12 cells. Here, we extend this analysis using LRRK2 drosophila models and show that the LRRK2-dependent exocyst complex assembly increase is downstream of RAB phosphorylation. Moreover, exocyst complex inhibition rescues mutant LRRK2 pathogenic phenotype in cellular and drosophila models. Finally, prolonged exocyst inhibition leads to a significant reduction in the LRRK2 protein level, overall supporting the role of the exocyst complex in the LRRK2 pathway. Taken together, our study suggests that modulation of the exocyst complex may represent a novel therapeutic target for PD.

A method for LPI radar signals recognition based on complex convolutional neural network

AbstractFor the low probability of intercept (LPI) radar signal recognition problem, recognition algorithms based on deep learning usually use time‐frequency analysis to convert the signal into a two‐dimensional feature image for classification and recognition. However, these methods often have problems with large network size, high computational complexity, and huge memory consumption, making them difficult to apply on small devices with limited computational power and storage space. This paper proposes an LPI radar signal recognition method based on a lightweight complex convolutional neural network, named CV‐LPINet. It uses a complex convolutional module to complete data fusion of IQ sampled signals, uses a deep separable convolutional module to extract features and reduce dimensions, and introduces residual structures to improve network training. Experiments show that the average recognition accuracy of this method is 91.76% with a signal to noise ratio in the range of −6 to 10 dB, and its recognition accuracy is similar to that of typical algorithms. However, the network size is significantly reduced and the computational complexity is low, making it suitable for small intelligent devices.

Nonreciprocal topological mode conversion by encircling an exceptional point in dynamic waveguides.

The topology of exceptional points (EPs) has been revealed by taking stationary or dynamical encircling around them, which induces eigenstate exchange or chiral mode conversion. However, the conversions are usually reciprocal obeying restricted transmittances. Here we propose the concept of nonreciprocal encircling of EPs in a dynamic waveguide under complex modulation. The waveguide allows direction-dependent EPs in their quasienergy spectra due to different phase-matching conditions for opposite propagation direction. We design a closed loop that will encircle the EP in the backward direction but not in the forward direction. In this way, a nonreciprocal topological conversion is achieved as the forward transmittance from the even to odd mode significantly exceeds the backward transmittance from the odd to even mode. As a result, the forward propagation produces two modes with equal strength while the backward propagation leads to a specific mode regardless of the input. The structure is promising for making robust optical isolators.

Towards a photonic integrated all-optical phase regenerator.

All-optical phase regeneration aims at restoring the phase information of coherently encoded data signals directly in the optical domain so as to compensate for phase distortions caused by transceiver imperfections and nonlinear impairments along the transmission link. Although it was proposed two decades ago, all-optical phase regeneration has not been seen in realistic networks to date, mainly because this technique entails complex bulk modules and relies on high-precision phase sensitive nonlinear dynamics, both of which are adverse to field deployment. Here, we demonstrate a new, to the best of our knowledge, architecture to implement all-optical phase regeneration using integrated photonic devices. In particular, we realize quadrature phase quantization by exploring the phase-sensitive parametric wave mixing within on-chip silicon waveguides, while multiple coherent pump laser tones are provided by a chip-scale micro-cavity Kerr frequency comb. Multi-channel all-optical phase regeneration is experimentally demonstrated for 40 Gbps QPSK data, achieving the best SNR improvement of more than 6 dB. Our study showcases a promising avenue to enable the practical implementation of all-optical phase regeneration in realistic long-distance fiber transmission networks.