OsKANADI1 and OsYABBY5 regulate rice plant height by targeting GIBERELLIN 2-OXIDASE6.

Plant height is an important agronomic characteristic of rice (Oryza sativa L.). Map-based cloning analyses of a natural semi-dwarf rice mutant with inwardly curled leaves found in the field revealed that the defects were due to a mutation of a SHAQKYF-class MYB family transcription factor, OsKANADI1 (OsKAN1). OsKAN1 directly bound to the OsYABBY5 (OsYAB5) promoter to repress its expression and interacted with OsYAB5 to form a functional OsKAN1-OsYAB5 complex. GIBERELLIN 2-OXIDASE6 (OsGA2ox6), encoding an enzyme in the gibberellin (GA) catabolic pathway, was activated by OsYAB5. Furthermore, the OsKAN1-OsYAB5 complex suppressed the inhibitory effect of OsKAN1 toward OsYAB5 and inhibited OsYAB5-induced OsGA2ox6 expression. The proOsKAN1:OsYAB5 transgenic plants were taller than wild-type plants, whereas oskan1 proOsKAN1:OsYAB5 plants exhibited a severe dwarf phenotype due to the absence of the OsKAN1-OsYAB5 complex. The OsKAN1-OsYAB5 complex modulated OsGA2ox6 expression, thereby regulating the levels of bioactive gibberellins and, consequently, plant height. This study elucidated the mechanism underlying the effect of the OsKAN1-OsYAB5-OsGA2ox6 regulatory pathway on plant height at different positions in rice stems and provided insights on stem development and candidate genes for the aerial architecture improvement of crop plants.

T cell infiltration mediates neurodegeneration and cognitive decline in Alzheimer's disease.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with pathological features of ß-amyloid (Aß) and hyperphosphorylated tau protein accumulation in the brain, often accompanied by cognitive decline. So far, our understanding of the extent and role of adaptive immune responses in AD has been quite limited. T cells, as essential members of the adaptive immune system, exhibit quantitative and functional abnormalities in the brains of AD patients. Dysfunction of the blood-brain barrier (BBB) in AD is considered one of the factors leading to T cell infiltration. Moreover, the degree of neuronal loss in AD is correlated with the quantity of T cells. We first describe the differentiation and subset functions of peripheral T cells in AD patients and provide an overview of the key findings related to BBB dysfunction and how T cells infiltrate the brain parenchyma through the BBB. Furthermore, we emphasize the risk factors associated with AD, including Aß, Tau protein, microglial cells, apolipoprotein E (ApoE), and neuroinflammation. We discuss their regulation of T cell activation and proliferation, as well as the connection between T cells, neurodegeneration, and cognitive decline. Understanding the innate immune response is crucial for providing comprehensive personalized therapeutic strategies for AD.

Total Synthesis of Quebrachamine and Kopsiyunnanine D.

The total syntheses of (±)-quebrachamine and (±)-kopsiyunnanine D are reported. Key transformations include an intermolecular Horner-Wadsworth-Emmons olefination to merge the two fragments convergently and an intramolecular Mitsunobu reaction to introduce the synthetically challenging nine-membered azonane ring efficiently.

Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features.

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis.

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University's (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.

Design and Synthesis of Novel Indole Ethylamine Derivatives as a Lipid Metabolism Regulator Targeting PPARα/CPT1 in AML12 Cells.

Peroxisome proliferator-activated receptor alpha (PPARα) and carnitine palmitoyltransferase 1 (CPT1) are important targets of lipid metabolism regulation for nonalcoholic fatty liver disease (NAFLD) therapy. In the present study, a set of novel indole ethylamine derivatives (4, 5, 8, 9) were designed and synthesized. The target product (compound 9) can effectively activate PPARα and CPT1a. Consistently, in vitro assays demonstrated its impact on the lipid accumulation of oleic acid (OA)-induced AML12 cells. Compared with AML12 cells treated only with OA, supplementation with 5, 10, and 20 µM of compound 9 reduced the levels of intracellular triglyceride (by 28.07%, 37.55%, and 51.33%) with greater inhibitory activity relative to the commercial PPARα agonist fenofibrate. Moreover, the compound 9 supplementations upregulated the expression of hormone-sensitive triglyceride lipase (HSL) and adipose triglyceride lipase (ATGL) and upregulated the phosphorylation of acetyl-CoA carboxylase (ACC) related to fatty acid oxidation and lipogenesis. This dual-target compound with lipid metabolism regulatory efficacy may represent a promising type of drug lead for NAFLD therapy.

Regulation of cancer progression by circRNA and functional proteins.

Circular RNAs (circRNAs) are closed back-splicing products of precursor mRNA in eukaryotes. Compared with linear mRNAs, circRNAs have a special structure and stable expression. A large number of studies have provided different regulatory mechanisms of circRNAs in tumors. Challenges exist in understanding the control of circRNAs because of their sequence overlap with linear mRNA. Here, we survey the most recent progress regarding the regulation of circRNA biogenesis by RNA-binding proteins, one of the vital functional proteins. Furthermore, substantial circRNAs exert compelling biological roles by acting as protein sponges, by being translated themselves or regulating posttranslational modifications of proteins. This review will help further explore more types of functional proteins that interact with circRNA in cancer and reveal other unknown mechanisms of circRNA regulation.

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network.

To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault.

Detection of Unresolved Targets for Wideband Monopulse Radar.

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms.

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network.

Accurately detecting the depolarization QRS complex in the ventricles is a fundamental requirement for cardiovascular disease detection using electrocardiography (ECG). In contrast to traditional signal enhancement algorithms, emerging neural network approaches have shown promise for QRS detection because of their generalizability on complex data. However, the inevitable noise present during ECG recording leads to a decrease in the performance of neural networks. To enhance the robustness and performance of neural network-based QRS detectors, we propose a simulated degeneration unit (SDU)-assisted convolutional neural network (CNN). An SDU simulates the physical degeneration process of interfering optical pulses, which can effectively suppress in-band noise. Through comprehensive performance evaluations on three open-source databases, the SDU-enhanced CNN-based approach demonstrated better performance in detecting QRS complexes than other recently reported QRS detectors. Furthermore, real-world noise injection tests indicate that the optimal noise robustness boundary for the CNN equipped with SDU is 167-300% higher than that for the CNN without SDU.

Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems.

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance.

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing.

Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu3Fe5O12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance.

Injectable Decorin/Gellan Gum Hydrogel Encapsulating Adipose-Derived Stem Cells Enhances Anti-Inflammatory Effect in Cartilage Injury via Autophagy Signaling.

Adipose-derived stem cells (ADSCs) are employed as a promising alternative in treating cartilage injury. Regulating the inflammatory "fingerprint" of ADSCs to improve their anti-inflammatory properties could enhance therapy efficiency. Herein, a novel injectable decorin/gellan gum hydrogel combined with ADSCs encapsulation for arthritis cartilage treatment is proposed. Decorin/gellan gum hydrogel was prepared according to the previous manufacturing protocol. The liquid-solid form transition of gellan gum hydrogel is perfectly suitable for intra-articular injection. Decorin-enriched matrix showing an immunomodulatory ability to enhance ADSCs anti-inflammatory phenotype under inflammation microenvironment by regulating autophagy signaling. This decorin/gellan gum/ADSCs hydrogel efficiently reverses interleukin-1ß-induced cellular injury in chondrocytes. Through a mono-iodoacetate-induced arthritis mice model, the synergistic therapeutic effect of this ADSCs-loaded hydrogel, including inflammation attenuation and cartilage protection, is demonstrated. These results make the decorin/gellan gum hydrogel laden with ADSCs an ideal candidate for treating inflammatory joint disorders.

Reconfigurable magnon interference by on-chip dynamic wavelength conversion.

Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields. Breaking the taboo, this study explores a novel technique to systematically control magnon interference using asymmetric wavevectors from two different SW modes (magnetostatic surface SWs and backward volume magnetostatic SWs) in a microstructured yttrium iron garnet crossbar. Using this system, we demonstrate phase reconfigurability in the interference pattern by modulating the thermal landscape, modifying the dispersion of the interfering SW modes. Thus, we manifest that such a tunable interference can be used to implement reconfigurable logic gates operating between the XNOR and XOR modes by using symmetric and asymmetric interference, respectively.

Real-time detection of acetone gas molecules at ppt levels in an air atmosphere using a partially suspended graphene surface acoustic wave skin gas sensor.

To improve the quality of modern life in the current society, low-power, highly sensitive, and reliable healthcare technology is necessary to monitor human health in real-time. In this study, we fabricated partially suspended monolayer graphene surface acoustic wave gas sensors (G-SAWs) with a love-mode wave to effectively detect ppt-level acetone gas molecules at room temperature. The sputtered SiO2 thin film on the surface of a black 36°YX-LiTaO3 (B-LT) substrate acted as a guiding layer, effectively reducing the noise and insertion loss. The G-SAWs exhibited enhanced gas response towards acetone gas molecules (800 ppt) in a real-time atmosphere. The high sensitivity of the G-SAW sensor can be attributed to the elasticity and surface roughness of the SiO2 film. In addition, the G-SAW sensor exhibited rapid response and recovery at room temperature. This study provides a potential strategy for diagnosing different stages of diabetes in the human body.

Broadband Dielectric Spectroscopic Analysis toward Characterization of the Hydration State and Bioprotective Superiority of Trehalose.

Alteration of the hydrogen-bond (H-bond) network by trehalose is acknowledged as a bioprotective agent. However, most studies exploring the hydration superiority of the trehalose structure are limited structure are limited by the computational cost or a narrow-range spectrum. In the present study, the structural and dynamical behaviors of the H-bond network of trehalose and maltose solutions were observed and compared with a broadband dielectric spectrum (100 MHz-18 THz) to investigate the influence of the trehalose structure on the bioprotective function. From the relaxation time, the reorientation cooperativity, resonant frequency, and damping constant of water-water vibration, the symmetric structure of trehalose allowed a more significant H-bond strengthening effect and homogeneous aqueous environment. In contrast, the difference in the hydration number between trehalose and maltose was negligible. Thus, the enhanced H-bond strengthening effect and homogeneous aqueous environment owing to the symmetric structure are the essential factors that contribute to the remarkable bioprotective effect of trehalose.

Sensitivity enhancement in magnetic sensor using CoFeB/Y3Fe5O12 resonator.

Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y3Fe5O12 bilayer structure. We demonstrated that the CoFeB/Y3Fe5O12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10-2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10-6 T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y3Fe5O12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y3Fe5O12 and Y3Fe5O12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of [Formula: see text] T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe.

Dynamic Changes in Community Deprivation of Access to Urban Green Spaces by Multiple Transport Modes.

Urban green spaces (UGSs) improve the quality of life of urban inhabitants. With the acceleration of urbanization and changes in traffic networks, it remains unclear whether changes in the distribution of UGSs can satisfy the needs of all inhabitants and offer equal services to inhabitants from different socioeconomic backgrounds. This study addresses this issue by analyzing dynamic changes in UGS accessibility in 2012, 2016, and 2020 for inhabitants of the central urban area of Fuzhou in China at the community level. The study introduces multiple transportation modes for an accessibility estimation based on a framework using the two-step floating catchment area method and examines the dynamic changes in community deprivation of UGS accessibility using Kernel regularized least squares, a machine learning algorithm. The results demonstrate that spatial disparities of UGS accessibility exist among the multi-transport modes and vary with time. Communities with high accessibility to UGSs by walking are scattered around the urban area; for accessibility by cycling, the high accessibility regions expand and surround the regions with low accessibility in the core urban areas, forming a semi-enclosed spatial pattern. However, the core urban spatial orientation of UGS accessibility by public transit demonstrates a reverse trend to the above two modes. The spatial pattern of UGS accessibility also varies over time, and the growth rate of accessibility slightly declined during the study period. Furthermore, the increase in UGS accessibility tended to slow from 2016-2020 compared with 2012-2016, and the trend toward equality was also erratic. The degree of deprivation for communities first weakened and was then aggravated, corresponding to the slowdown in the growth rate of accessibility, leading to the persistence existence of social inequality. Moreover, significant deprivation mainly exists among less educated people or those using the cycling and integrated travel modes. Although public transport is developing, deprived communities, such as communities with large proportion of older people, have experienced a decline in access to UGSs by public transport. Based on these findings, the study proposes a policy framework for the balanced distribution of UGSs as part of urbanization.