Design and Characteristic Analysis of Multicarrier Chaotic Phase Coded Radar Pulse Train Signal

By introducing phase code into multicarrier orthogonal frequency division multiplex signal, the multicarrier phase coded (MCPC) radar signal possesses a good spectrum utilization rate and can achieve a good combination of narrowband and wideband processing. Radar pulse train signal not only reserves the high range resolution of monopulse signal, but also has the same velocity resolution performance as continuous wave signal does. In this study, we use the chaotic biphase code generated by Chebyshev mapping to conduct a phase modulation on MCPC pulse train so as to design two different types of multicarrier chaotic phase coded pulse train signal. The ambiguity functions of the two pulse train signals are compared with that of P4 code MCPC pulse train. In addition, we analyze the influences of subcarrier number, phase-modulated bit number, and period number on the pulse train’s autocorrelation performance. The low probability of intercept (LPI) performance of the two signals is also discussed. Simulation results show that the designed pulse train signals have a thumbtack ambiguity function, a periodic autocorrelation side lobe lower than P4 code MCPC pulse train, and excellent LPI performance, as well as the feature of waveform diversity

Research Progress on Mechanism of Action of DHODH in Progression of Malignant Tumors

Dihydroorotate dehydrogenase (DHODH) is a flavin-dependent metabolic enzyme that oxidizes dihydroorotate acid to orotic acid in the de novo synthesis pathway of pyrimidine metabolism. DHODH is located in mitochondria, closely related to cellular oxidative phosphorylation, and an important suppressor of the ferroptosis pathway. This study investigates the influence of DHODH on the progression of malignant tumors, including its important role in the de novo synthesis of pyrimidine, oxidative phosphorylation, and ferroptosis. The objective is to present evidence that DHODH is a potential target for the clinical treatment of tumors

Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks

Effects of different stocking densities on the CO2 fluxes at water-air interface and the respiration metabolism in sea cucumber Apostichopus japonicus (Selenka)

Recently, abundant research has been devoted to investigating the variations of CO2 concentration in the atmosphere. However, the information of CO2 fluxes at the water-air interface remains limited, especially those from the respiratory metabolism of aquatic organisms. In the present study, a comprehensive analysis was carried out to evaluate the effects of different stocking densities of sea cucumber (Apostichopus japonicus) on the CO2 fluxes at water-air interface, and to explore the relationships between CO2 fluxes and respiratory metabolism. A total of 60 sea cucumbers were randomly classified into 4 groups with different stocking densities, including 2, 5 and 8 ind./tank (namely D2, D5 and D8 groups). After 34-day feeding trial, individuals in D5 had superior growth performance rather than D2 and D8. The analysis of modified floating static chambers clearly showed that the mean CO2 flux at the water-air interface in D5 was significantly higher than D2 and D8. Meanwhile, energy budget analysis revealed that D5 had higher carbon and nitrogen utilization, excretion energy and metabolizable energy, suggesting relatively active respiration metabolism in moderate stocking density. The activities of pyruvate dehydrogenase (PDH) and α-ketoglutarate dehydrogenase (OGDH) in respiratory tree and body wall tissues provided additional evidence for the higher respiration metabolism rate of individuals at D5, which may be responsible for the higher CO2 fluxes at the water-air interface. Transcriptome analysis was performed to uncover the molecular mechanism of respiratory metabolism affected by different stocking densities. The differentially expressed genes in respiration trees and body walls were significantly enriched in peroxisome, fatty acid degradation, and oxidative phosphorylation pathways. It may explain the differences of respiration metabolism rates at different stocking densities. The present study preliminarily revealed the CO2 fluxes variation at the water-air interface from aquatic invertebrates, and provided the scientific basis for the efficient and low-carbon agricultural technologies of sea cucumber

Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder

Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease

Highly controllable and reliable ultra-thin Parylene deposition

Thanks to the excellent barrier property and fabrication accessibility, Parylene has been actively used in the microelectromechanical system. An ultra-thin Parylene film with thickness smaller than 100 nm is usually required to precisely tune the surface property of substrate or protect the functional unit. The commercially available regular Parylene deposition is a dimer mass determined chemical vapor deposition process with a high output (i.e. a low deposition precision in term of thickness control), around 1.6 μm/g (the ratio of film thickness to the loaded dimer mass) for the machine in the author’s lab. Therefore, it is hard to controllably and reliably prepare a Parylene film with thickness smaller than 100 nm, which requires a dimer mass less than 62.5 mg. This paper reported a method to prepare ultra-thin Parylene films with the nominal thickness down to 1 nm. A home-made deposition chamber was put inside and connected with the regular machine chamber through a microfabricated orifice with feature size smaller than 1 mm. According to the free molecular flow theory, the pressure inside the deposition chamber can be predictably and controllably reduced, thereby an ultra-low output of Parylene deposition, as low as 0.08 nm/g, was successfully obtained. The deposition precision was increased by 4 orders of magnitude compared to that of a direct Parylene deposition. This highly controllable and reliable ultra-thin Parylene deposition technique will find promising applications in flexible electronics and biomedical microdevices

Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development