Mode Output Research Articles

Type-B response regulator RRB12 regulates nodule formation in Lotus japonicus

BackgroundThe mutualistic beneficial relationship between legume plants and rhizobia enables the growth of plants in nitrogen-limiting conditions. Rhizobia infect legumes through root hairs and trigger nodule organogenesis in the cortex. The plant hormone cytokinin plays a pivotal role in regulating both rhizobial infection and the initiation of nodule development. However, the mechanism used by the cytokinin output module to control symbiosis remains poorly documented.ResultsIn this study, we identified a cytokinin signaling output component encoded by the Type-B RESPONSE REGULATOR (RRB) gene, LjRRB12, which is expressed in Lotus japonicus nodule primordia and young nodules. Disruption of LjRRB12 leads to a reduction in nodulation and to an increase in the number of infection threads. Overexpression of LjRRB12D76E, an active form of the LjRRB12 protein, induces nodule-like structures in wild type and hit1 (hyperinfected 1/lotus histidine kinase 1) mutants but not in nin2 (nodule inception 2) mutants. Additionally, we utilized nCUT&Tag and EMSA to demonstrate that LjRRB12 can bind a CE (cytokinin response element) from the LjNIN promoter.ConclusionsOur results provide a deeper understanding of nodule organogenesis by establishing a link between the cytokinin signal and the transcriptional regulation of LjNIN.

Development of an Indoor Line Scanning System for Photovoltaic Modules Performance Characterization

Uganda’s interest in using solar energy for various applications began 20 years ago. However, there have been numerous reports by the public over the poor performance of the photovoltaic (PV) modules during their operations. This study determined the performance of selected PV modules openly sold in the Ugandan markets. The low-cost indoor line scanning system developed using locally available materials was used to assess the performance of each of the solar cells in the PV module by determining their photogenerated currents. In addition, the electrical characteristics of the PV modules were determined using the outdoor characterization method, which assumed the actual operation of the PV modules under direct sunlight. From the indoor line scans, the percentage of performing solar cells in the three PV modules of single-crystalline silicon (c-Si), multi-crystalline silicon (mc-Si), and amorphous silicon (a-Si) technologies were determined as 79%, 61%, and 95% respectively. The outdoor characterization results revealed that the c-Si, mc-Si, and a-Si PV modules had measured maximum power outputs of 18.5 W, 19.0 W, and 18.9 W, respectively and these were lower than the rated power values of 20 W. The line scan results had a direct relationship with the measured power output of the PV module, which implied that solar cells mismatch, affected the performance of the PV modules by lowering their performance. This developed testing system is affordable for developing countries with limited testing systems and would in the long run contribute to significant increase in the deployment of PV technology in Uganda since only performing PV modules would be allowed into the open market.

Short-Term Prediction of the Solar Photovoltaic Power Output Using Nonlinear Autoregressive Exogenous Inputs and Artificial Neural Network Techniques Under Different Weather Conditions

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R2) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN.

Experimental investigation of heralded Gaussification of phase-randomized coherent states of light

Probabilistic heralded Gaussification of quantum states of light is an important ingredient of protocols for distillation of continuous variable entanglement and squeezing. An elementary step of the heralded Gaussification protocol consists of interference of two copies of the state at a balanced beam splitter, followed by conditioning on the outcome of a suitable Gaussian quantum measurement on one output mode. When iterated, the protocol either converges to a Gaussian state or diverges. Here, we report on experimental investigation of the convergence properties of iterative heralded Gaussification. We experimentally implement two iterations of the protocol, which requires simultaneous processing of four copies of the input state. We utilize the phase-randomized coherent states as the input states, which greatly facilitates the experiment because these states can be generated deterministically, and their mean photon number can easily be tuned. We comprehensively characterize the input and partially Gaussified states by balanced homodyne detection and quantum state tomography. Our experimental results are in good agreement with theoretical predictions and they provide new insights into the convergence properties of heralded quantum Gaussification.

IN SILICO BASED RE-ENGINEERING OF A COMPUTATIONALLY DESIGNED BIOSENSOR WITH ALTERED SIGNALLING MODE AND IMPROVED DYNAMIC RANGE.

A current challenge in the rational design of biomolecular sensors is the ability to custom design binding affinities and detection mode in silico. To this end, we re-engineered a previously reported computationally-designed fluorescent maltooligosaccharide (MOS)-detecting biosensor to both alter its ligand-binding affinity and to analyse the underlying sensing mechanism. The dynamic range of the biosensor was expanded through the computer aided introduction of a series of amino acid substitutions in the starting protein scaffold (MalX from Streptococcus pneumoniae), which generated a biosensor set with binding affinities spanning over five orders of magnitude. The impact of the introduced substitutions on the underlying mode of signal generation was assessed in silico using our previously reported Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline. CINC utilizes molecular dynamics simulations and an in-house developed algorithm to examine and exploit the structural dynamics of a protein at amino acid-level resolution. Using CINC, we demonstrate that re-engineering of the MOS-detecting biosensor set resulted in sensors with two distinct output modes which differed based on local conformational changes at the fluorescently modified reporter position. These output modes were classified as "ligand-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the ligand-bound state), and "apo-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the apo state). Together, these results demonstrate that structural dynamics at the individual amino acid residue level can be used as an engineer-able feature to rationally alter the fluorescence reporting properties of a biosensing device. Moving forward, the CINC workflow can also be adapted for the rational design of protein dynamic properties maximizing its utility as an in silico design platform for custom biomolecular tools.

Theoretical studies of two phase-independent frequency spectrum models in atomic selective photoionization processes

Abstract The laser frequency spectrum, whose full width at half-maximum in the frequency domain is laser bandwidth, plays a critical role in atomic multi-step photoionization processes, and it has a significant influence on isotopic selective photoionization results. In this study, two phase-independent frequency spectrum models, that is, mode jitter model based on the “mode jitter” phenomenon and the multi-longitudinal mode model based on multi-longitudinal mode output, are proposed for describing the spectral characteristics in the frequency domain. In the mode jitter model, it is assumed that there is a varying longitudinal mode in every laser pulse, with different central frequencies between adjacent pulses in the pulse train; in the multi-longitudinal model, it is assumed that multiple longitudinal modes are output simultaneously with fixed central frequencies. Selective photoionization properties of these two frequency spectrum models and the chaotic field model are simulated and compared with each other under the Lorentzian frequency spectrum condition. Influences of the excitation intensity, laser frequency spectral line-shape, and cut-off frequency on selective photoionization processes are calculated and compared among the above-mentioned three frequency spectrum models. Finally, the measurement of the laser frequency spectrum and the introduction of individual longitudinal mode’s bandwidth are discussed.

Advances in visual detection of pathogen nucleic acids by CRISPR-Cas

Visual detection is a technique for evaluating the results through visual judgment without relying on complex optical detection systems. It obtains results quickly based on signals, such as visible light, changes in air pressure, and migration distance, that can be directly observed by naked eyes, being widely used in the in vitro diagnostics industry. The CRISPR-Cas system has the potential to be used in the development of point of care testing (POCT) technologies due to the advantages of mild reaction conditions, no need for thermal cycling or other control measures, and a robust signal amplification capability. In recent years, the combination of visual detection and CRISPR-Cas has significantly reduced the need for laboratory infrastructures, precision instruments, and specialized personnel for nucleic acid detection. This has promoted the development of POCT technology and methods for nucleic acids. This article summarizes the signal output modes and characteristics of the visual detection of nucleic acid by CRISPR-Cas and discusses the issues in the application. Finally, its future clinical translation is envisioned with a view to informing the development of CRISPR-Cas visualization assays.

Coupled theoretical modelling for the photoelectric performance by the concave-bent flexible PV module

Flexible PV module can fit with complex building skins and provide electricity for the building users. However, for the concave bent PV module, the special structure will cast shadow on itself, resulting in power loss during the working conditions, which is rarely investigated in previous studies. Combined optical-electrical-thermal model is decoupled to describe the realistic physical field and predict the concave module's electrical power output and working temperature. The beam shading ratio (BSR) and the diffuse shading ratio (DSR) are proposed based on the microfacet subdivision, which describes the real-time solar irradiance distribution and quantify the influence of self-shadow on the photoelectric performance by the concave-bent module under dynamic real-time working conditions. The concave PV module with the longitudinal/lateral layout is constructed and outdoor experiments are conducted to collect the data for the theoretical model verification. The parametric analysis is carried out to investigate the influence of inclination, curvature, and azimuth, demonstrating that increasing inclination and curvature angles raise power loss due to shading. Weather data of four cities are imported into the model to conduct annual performance simulation with longitudinal/lateral installation respectively. The model established in this paper has significant value for performance prediction in complex surface photovoltaic systems.

CDSNet: An automated method for assessing growth stages from various anatomical regions in lateral cephalograms based on deep learning

The assessment of growth stages, typically determined by Cervical Vertebrae Maturation (CVM), plays a crucial role in orthodontics. However, there is a potential deviation from actual growth stages when using CVM. This study aimed to introduce CDSNet, an interpretable deep learning model for assessing growth stages based on cervical vertebrae, dentition, and frontal sinus in lateral cephalograms. A dataset of 1,732 pairs of lateral cephalograms and hand-wrist radiographs from patients who underwent orthodontic treatment was annotated by four dentists. Benchmarks were conducted using CVM and logistic regression. Experiments were designed to evaluate CDSNet's performance in assessing growth stages using various methods and anatomical regions. CDSNet achieved remarkable Accuracy (90.99%), Precision (89.98%), Recall (92.50%), and F-1 Score (91.22%) in assessing growth spurt, representing significant improvements of 26.56%, 27.96%, 30.26%, and 29.30% compared to the CVM-based method. Additionally, when compared to a deep learning method based on cervical vertebrae, improvements of 12.25%, 11.40%, 14.14%, and 12.56% were observed. The interpretable module's side output revealed the involvement of cervical vertebrae, dentition, and frontal sinus in assessing growth spurt. In the clinical domain, CDSNet is able to assist clinicians in determining patients' growth stages, particularly those near the boundary between two stages with less distinct features. This study demonstrated the role of interpretable deep learning in investigating the external manifestations of craniofacial growth. Integrating algorithmic or clinical research to analyze multiple features on lateral cephalograms proved a feasible approach to assist orthodontists and improve diagnostic efficacy.

The Impact of Life-History Traits on Vulnerability to Extinction of the Oviparous Species in Reptiles.

A species' vulnerability to extinction is influenced by both extrinsic threats (e.g., habitat loss and invasive species) and intrinsic biological traits (such as life-history traits, reproductive mode, and reproductive output). In this study, we investigated the roles of intrinsic biological traits in determining the risk of extinction across 960 oviparous species of non-avian reptiles. Our findings revealed that vulnerability to extinction is negatively correlated with clutch size, but positively correlated with egg size when controlling for body size. Surprisingly, we found that body size alone is not a predictor of extinction risk. Additionally, we observed a nonsignificant relationship between the activity phase and vulnerability to extinction across oviparous species. These results suggest that the increased risk of endangerment in oviparous reptiles may stem from declining population density due to decreasing clutch size and increasing egg mass.

An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN

For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot’s motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives.

Memristive Neural Network Circuit of Negative Emotion Inhibition With Self‐Repair and Memory

ABSTRACTThe generated mechanism of negative emotion involves the prefrontal cortex, hippocampus, and amygdala in the brain. Based on the biological mechanism, this paper proposes a memristive neural network circuit of negative emotion inhibition with self‐repair and memory. The proposed memristive neural network circuit consists of five modules: memory (hippocampus) module, inhibition (prefrontal cortex) module, damage detection module, repair module, and output (amygdala) module. The memory module does not respond to a small negative signal, but a large negative signal will cause the memory module to form memories. If the large negative signal repeats, the memory module will output a memory signal to the inhibition module. The inhibition module receives the negative signal and the memory signal and then generates an inhibition signal. When the negative signal is small, the inhibition module outputs normally. When the large negative signal is applied for the first time, the inhibition module becomes damaged and the output is abnormal. As the memristor in the inhibition module has exceeded its damaged threshold, the damage detection module will generate a damaged signal to the repair module. The repair module will restore the memristance of the memristor in the inhibition module after receiving the damaged signal. If the large negative signal is applied again, the memory signal from the memory module will help the inhibition module to output normally. The output module receives the negative signal and the inhibition signal and then generates the negative emotion signal appropriately. The PSPICE simulation results show that the hippocampus generates memories after learning and transmits them to the prefrontal cortex, and then the prefrontal cortex inhibits the amygdala to generate the negative emotion appropriately. The proposed memristive neural network circuit can be applied to the robots, which can flexibly alter the intensity of the negative emotion expressed by the robots.

A deep kernel regression-based forecasting framework for temperature-induced strain in large-span bridges

The strain data from health monitoring systems of large-span bridges is influenced by various load effects, with the extraction and forecasting of temperature-induced strain being particularly significant for precise analysis and early warning of monitoring data. This paper presents a forecasting framework for temperature-induced strain in large-span bridges, employing a deep kernel regression (DKR) approach that integrates deep learning with Bayesian regression to enhance accuracy and certainty. Initially, this paper addresses the influence of additional response increment induced by vehicle strain effects and employs a robust data smoothing algorithm to extract temperature-induced effect components from measured strain data offline. Subsequently, a DKR model is proposed, integrating a long short-term memory (LSTM) layer with a fully connected layer. The output of the deep learning module serves as the kernel function parameter for the Gaussian process regression (GPR) module, and the GPR module with updated hyperparameters is used for time series forecasting. This method effectively extracts and utilizes time series features from historical data alongside key environmental factors, enabling real-time forecasting of strain effects and significantly improving the performance and utility of health monitoring systems in large-span bridges. Compared to commonly used time series forecasting algorithms, the algorithm proposed in this paper exhibits significantly improved accuracy, stability, and certainty. Through comparing the inference time, it was verified that the algorithm can meet the performance requirements of real-time inference, which underscores the model's potential as a robust tool in bridge structural health monitoring.

A lightweight Future Skeleton Generation Network(FSGN) based on spatio-temporal encoding and decoding

Since early warning in industrial applications is far more valuable than post-event analysis, human activity prediction based on partially observed skeleton sequences has become a popular research area. Recent studies focus on building complex deep learning networks to generate accurate future skeleton data, but overlook the requirement for timeliness. Different from such frame-by-frame generation methods, we propose a Future Skeleton Generation Network (FSGN) based on spatio-temporal encoding and decoding framework. Firstly, we design a dynamically regulated input module to ensure equal-length input of partially observed data, and set modules like discrete cosine transform(DCT) and low-pass filtering(LPF) to filter important information. Then, we employ an improved multi-layer perceptron(MLP) structure as the basic computational unit for the encoding and decoding framework to extract spatio-temporal information, and propose using multi-dimensional motion error of human skeleton to form the loss function. Finally, we use an output module symmetrical to the input module to achieve the generation of future activity data. Results show that the proposed FSGN achieves fewer parameters(0.12 M) and higher generation accuracy, which can effectively provide future information for human activity prediction tasks.

GPe Projections to the Retrorubral Field Give Rise to Diverging GABAergic and DAergic Circuits.

The external segment of the globus pallidus (GPe) is involved in the modulation of motor output and limbic components of behavior. The circuitry by which the GPe contributes to limbic components of behavior remains unknown. While many investigations have focused on the local circuitry within the basal ganglia, numerous GPe projections to target regions outside of the basal ganglia including the midbrain, thalamus, and cortex exist and remain largely unexplored. Here, by anatomical and electrophysiological means we investigate the projections of the GPe to the Retrorubral Field (RRF), a dopaminergic nucleus in the caudal midbrain, in both male and female mice. We find that the GPe targets both GABAergic and DAergic neurons in the RRF (RRFDA and RRFGABA). Viral-assisted circuit mapping to trace the cell-type specific projection patterns of RRFDA and RRFGABA populations revealed diverging pathways. The connectivity of GPe-recipient RRFDA neurons followed the traditional medial forebrain bundle path, innervating the ventral striatum and to a lesser extent the dorsal striatum. GPe-recipient RRFGABA neurons, on the other hand, displayed broad projections targeting numerous brain regions involved in limbic function including the parabrachial nucleus, amygdala, and hypothalamus. These contrasting projection profiles reveal multiple nodes by which the GPe is disynaptically connected to the limbic system, representing novel access points for the GPe to modulate limbic components of behavior.Significance Statement Progress in our understanding of GPe composition, circuits, and functions has allowed for a greater appreciation of the role of the GPe in shaping behavior. Here, we elaborate on the connectivity of the GPe with the RRF, the third-largest dopaminergic nucleus of the ventral midbrain. We reveal the targeting of both RRFDA and RRFGABA neurons by the GPe and demonstrate the vast non-overlapping projection profiles of RRFDA and RRFGABA Via the multi-level limbic connectivity of the RRF, these results add a novel branch of circuitry to the GPe, which may represent the foundation for some of its non-motor functions.

Self-mixing thinly sliced ruby laser for laser Doppler velocimetry with high optical sensitivity

In self-mixing laser Doppler velocimetry (LDV), the motion of a moving target is observed by using intensity-modulated laser light detected by a simple photodetector. Here, the self-mixing laser output modulation takes place, reflecting the pronounced effective loss modulation index, which is proportional to the fluorescence-to-photon lifetime ratio. The fluorescence lifetime of a ruby laser is extremely long, so if a ruby crystal can be used as a laser light source for a self-mixing LDV system, high-sensitivity LDV measurements can be performed with it. We describe a method for velocimetry of moving targets using self-mixing LDV in which a CW oscillating ruby laser is the light source. The oscillation mechanism of the thin-slice ruby laser with a large fluorescence-to-photon lifetime ratio, which is suitable for LDV measurements, is clarified and the results of highly sensitive LDV measurements are presented, featuring nonlinear dynamics observed associated with the self-mixing velocimetry experiment. The measurement accuracy is clarified by measuring the rotating disc with various conditions using self-mixing LDV.

Power Generation Characteristics of Disk-Shaped Magnetohydrodynamic Generator Driven by Rotating Detonation

The power generation characteristics of a disk-shaped magnetohydrodynamic generator driven by rotating detonation with potassium-seeded hydrogen–air mixture are examined via quasi-two-dimensional numerical simulations. For unburned gas injection in the outward and inward directions, the rotating direction of the detonation wave is set so that the azimuthal velocity enhances the radial electromotive force. The simulation results show that the power output in the outward flow configuration exceeds that of the inward flow configuration. In the outward flow configuration, the radial gas velocity around the contact surface is the main contributor to the power generation, while in the inward flow configuration, the low gas velocity in almost the entire region and the vortices around the contact surface lead to poor power output. The characteristics in not only the power output mode but also the power input mode in which the voltage is applied externally between the electrodes are investigated. Under the positive load voltage, high enough for providing negative current, the Lorentz force occurs in the same direction as the propagation of the detonation wave; otherwise, the Lorentz force is induced in the opposite direction of the detonation wave.

Multi-wavelength Erbium-doped Fiber Laser with High-purity LP11 Mode Output Based on Mechanically Induced Long-period Fiber Gratings and Double Sagnac Fiber Filter

Multi-wavelength Erbium-doped Fiber Laser with High-purity LP11 Mode Output Based on Mechanically Induced Long-period Fiber Gratings and Double Sagnac Fiber Filter

Ultra-Wide Modulation and Reversible Reconfiguration of a Flexible Organic Crystalline Optical Waveguide Between 645 and 731 nm.

Flexible organic crystalline optical waveguides, which deliver input or self-emit light through various dynamic organic crystals, have attracted increasing attention in the past decade. However, the modulation of the waveguide output relies on chemical design and substituent modification, being time-consuming and laborious. Here we report an elastic organic crystal that displays long-distance light transduction up to 2.0 cm and an ultra-wide modulation of crystalline optical waveguides between red (645 nm) and near infrared (731 nm) in both the pristine and the elastically bent states based on a pre-designed self-absorption effect. The flexible organic crystalline optical waveguides can be precisely and reversibly reconfigured by controlling the irradiation point. In addition, deep-red amplified spontaneous emission (ASE) that is able to transduce through a 5.0 mm bent crystal with an ultra-low optical loss coefficient of 0.093 dB/mm has been attained. To the best of our knowledge, this is the first report of flexible organic ASE waveguides. The present study not only provides a simple yet effective strategy to remarkably modulate flexible organic crystalline optical waveguides but also demonstrates the superiority of lasing over normal emission as flexible optical communication elements.

Ultra‐Wide Modulation and Reversible Reconfiguration of a Flexible Organic Crystalline Optical Waveguide Between 645 and 731 nm

AbstractFlexible organic crystalline optical waveguides, which deliver input or self‐emit light through various dynamic organic crystals, have attracted increasing attention in the past decade. However, the modulation of the waveguide output relies on chemical design and substituent modification, being time‐consuming and laborious. Here we report an elastic organic crystal that displays long‐distance light transduction up to 2.0 cm and an ultra‐wide modulation of crystalline optical waveguides between red (645 nm) and near infrared (731 nm) in both the pristine and the elastically bent states based on a pre‐designed self‐absorption effect. The flexible organic crystalline optical waveguides can be precisely and reversibly reconfigured by controlling the irradiation point. In addition, deep‐red amplified spontaneous emission (ASE) that is able to transduce through a 5.0 mm bent crystal with an ultra‐low optical loss coefficient of 0.093 dB/mm has been attained. To the best of our knowledge, this is the first report of flexible organic ASE waveguides. The present study not only provides a simple yet effective strategy to remarkably modulate flexible organic crystalline optical waveguides but also demonstrates the superiority of lasing over normal emission as flexible optical communication elements.