Bracts are the metamorphic non-flower organ in angiosperm plants. The variation of the color and shape of bracts was found to be neo-functionalized (i.e., similar to petals), garnering research ...interest as a pollinator attractor.
Bougainvillea
is known for its specialized, large, and colorful bracts, which contrast with its tiny colorless flowers. As a plant whose bracts vary greatly in terms of coloration, the molecular mechanisms for
Bougainvillea
bract coloration and polychroism are largely unknown. The lack of genomic information for
Bougainvillea
largely hinders studies into the evolution and genetic basis of bract color variation. In this study, a pan-transcriptome of bracts obtained from 18
Bougainvillea glabra
accessions was employed to investigate the global population-level germplasm kinship and the gene regulation network for bract color variation. Our results showed that the bracts of
B. glabra
accessions have largely differentiated International Commission on Illumination (CIE) L-a-b values. Moreover, germplasm kinship detected using principal component analysis, phylogeny, and admixture analysis showed three optimal subgroups, two of them distinctly clustered, which were not directly correlated with bract color variation at the population level. Differentially expressed genes (DEGs) between accessions of high vs. low L-a-b values revealed several considerable upregulated genes related to bract color L-a-b variation. A weighted gene co-expression network was constructed, and eight co-expressed regulation modules were identified that were highly correlated with variation in bract CIE L-a-b color values. Several candidate DEGs and co-expressed hub genes (e.g.,
GERD
,
SGR
,
ABCA3
,
GST
,
CYP76AD1
,
CYP76C
, and
JAZ
) that were tightly associated with bract color variation were eventually determined responsible for L-a-b colorations, which might be the core regulation factors contributing to the
B. glabra
bract color variation. This study provides valuable insights into the research on germplasm kinship, population-level pan-transcriptome expression profiles, and the molecular basis of color variation of key innovative bracts in horticultural
Bougainvillea
.
Motivation and Problem Statement. Thiopental is an anesthetic drug related to the condition of controlling the area of neurological contexts. This study is related to the analysis of effectiveness ...for the condition of thiopental application on spontaneously hypertensive rats. Methodology. We have evaluated the thiopental induction as the anesthetic agent. The hypertensive rats were selected to administer thiopental in the form of anesthesia. The selection and application of hypertensive strokes are related to the derivation of an inducible model to assess the efficacy for analyzing the ischemic stroke parameters which relate to the human body. We used middle cerebral artery occlusion (MCAO) models related to spontaneous hypertension with the area of examining the complications in ischemic stroke. Results and Conclusion. The study focused on the experimental analysis based on the selection of spontaneously hypertensive rats associated with the incidence of ischemic stroke. Application of thiopental has reported the weak functionality and mechanism on the relaxation of neuronal activity in the case of rat brain. The considered population of the spontaneously hypertensive rats is evaluated based on the condition of effectiveness as well as the duration of the medication effects within the rat brain. Involvement of thiopental in the case of ischemic stroke has provided the area of risk development for high rate of death incidences after occurrence of acute ischemic stroke. A complication in the area of defining neuroprotective actions provides difficulty in drawing an appropriate conclusion of the study.
Accurate load forecasting is challenging due to the significant uncertainty of load demand. Deep reinforcement learning, which integrates the nonlinear fitting ability of deep learning with the ...decision-making ability of reinforcement learning, has obtained effective solutions to various optimization problems. However, no study has been reported, which used deep reinforcement learning for short-term load forecasting because of the difficulties in handling the high temporal correlation and high convergence instability. In this study, a novel asynchronous deep reinforcement learning model is proposed for short-term load forecasting by addressing the above difficulties. First, a new asynchronous deep deterministic policy gradient method is proposed to disrupt the temporal correlation of different samples to reduce the overestimation of the expected total discount reward of the agent. Further, a new adaptive early forecasting method is proposed to reduce the time cost of model training by adaptively judging the training situation of the agent. Moreover, a new reward incentive mechanism is proposed to stabilize the convergence of model training by taking into account the trend of agent actions at different time steps. The experimental results show that the proposed model achieves higher forecasting accuracy, less time cost, and more stable convergence compared with eleven baseline models.
•A novel deep reinforcement learning model is proposed to predict short-term load.•A new asynchronous deep deterministic policy gradient method is proposed.•A new adaptive early forecasting method reduces the time cost of model training.•A new reward incentive mechanism stabilizes the convergence of model training.•The performance of the proposed model is superior to other comparative models.
Background. Osteosarcoma is one of the most prominent bone cancers which has a predominant occurrence in children and adolescents. This study is focused on determining the effects of treatment of gas ...anesthetics on invasion, metastasis, and chemosensitivity in the progression of osteosarcoma cells. Material and Methods. The biological effects of the common gas anesthetics—desflurane, isoflurane, and sevoflurane—on osteosarcoma cells were studied and compared. The biological assays were performed for analysis of cell migration and proliferation. Results. Isoflurane and sevoflurane have shown significant inhibition in the osteosarcoma cells at clinically relevant concentrations. Desflurane has shown less potent action on cell migration and inhibition. All three gas anesthetics have shown inhibition in cell proliferation. The effective antiproliferative action was at a clinically significant dose. At low millimolar concentrations, cell apoptosis was moderately affected. Drug combination analysis with chemotherapeutic drugs showed relevant inhibition in cell migration. All three agents showed significant augmentation of chemotherapeutic drugs in suppression and inhibition of inducing apoptosis. The antimigration action is likely to affect the PI3K/AKT pathway and IGF-1. Conclusion. The study demonstrates the proposed mechanisms of gas anesthetics and their differential effects on osteosarcoma cells and their survival, migration, growth, and chemosensitivity.
Background. Propofol acts as an intravenous anesthetic cure which is widely used as a therapy for the craniocerebral injury that comprised surgical anesthesia as well as the sedation done in the ...intensive care units. Propofol is one of the most commonly used and efficient anesthetics where the painful effects are followed by an injection of propofol. In many cases, patients experience pain followed by anxiety, boredom, fear, and even myocardial ischemia. Objective. This study was performed to investigate the underlying mechanism of propofol and its effect on regulating spinal glun2b-p38mapkepac1 pathways in chronic contractile injury. Material and Methods. Contractile injury was performed by ligation around the nerve of the thigh region postanesthesia. Rats were divided into three groups to analyze the changes like mechanical allodynia by the paw withdrawal threshold and histopathological analysis for assessing cellular degradation. L4-L6 from the spinal dorsal horns were isolated and harvested for studying protein expression, by the method of western blotting and immunofluorescence analysis. Results. The pain caused due to mechanical allodynia in the paw region was highest at 1 hour postinduction and lasted for three days postinjury. Pain was significantly less in the group receiving propofol when compared with the isoflurane group for the first two hours of injury. In the propofol group, EPAC1, GluN2B, and p38 MAP K were significantly lower. Conclusion. In the rat model of induced chronic contractile injury, postsurgery there was a suppression of the GluN2B-p38MAPK/EPAC1 signaling pathway in the propofol group. As the p38MAPK/EPAC pathway has a significant role in the postoperative hyperalgesia, thus our experiment suggests that propofol has analgesic effects.
This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in ...the POPN online fault diagnoser in current use, an improved online fault diagnosis algorithm that integrates generalized mutual exclusion constraints (GMECs) and integer linear programming (ILP) is proposed. Assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. GMEC is used for elementary diagnosis of the system behavior, then the ILP problem of POPN is solved for further diagnosis. Finally, an example of a real DES to test the new fault diagnoser is analyzed. The proposed algorithm increases the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP is verified.
This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram ...(ECG) arrhythmias classification. The LSTM-based AE network (LSTM-AE) is used to learn the features from ECG arrhythmias signals, and the SVM is used to classify those signals from the learned features. The LSTM-AE consists of an encoder model, which extracts high-level feature information from ECG arrhythmias signals through LSTM network, and a decoder model which outputs reconstruct ECG arrhythmias signals from high-level features through LSTM network. Experiments show that the proposed method can learn better features than the traditional method without any prior knowledge, presenting a good potential for the ECG arrhythmias classification. In the classification of five heartbeats types, including normal, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complexes (APC), premature ventricular contractions (PVC), the proposed method achieved average accuracy, sensitivity, and specificity of 99.74%, 99.35%, and 99.84%, respectively, in the beat-based cross-validation approach, and 85.20%, 62.99%, and 90.75%, respectively, in the record-based cross-validation approach, in public MIT-BIH Arrhythmia Database. While based on the Advancement of Medical Instrumentation (AAMI) standards, the proposed method achieved average accuracy, sensitivity, and specificity of 99.45%, 98.63%, and 99.66%, respectively, in the beat-based cross-validation approach.
Structured and unstructured uncertainties are the main obstacles in the development of advanced controllers for high-accuracy tracking control of hydraulic servo systems. For the structured ...uncertainties, nonlinear adaptive control can be employed to achieve asymptotic tracking performance. But modeling errors, such as nonlinear frictions, always exist in physical hydraulic systems and degrade the tracking accuracy. In this paper, a robust integral of the sign of the error controller and an adaptive controller are synthesized via backstepping method for motion control of a hydraulic rotary actuator. In addition, an experimental internal leakage model of the actuator is built for precise model compensation. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties), but also the unstructured uncertainties (i.e., nonlinear frictions). Furthermore, the controller theoretically guarantees asymptotic tracking performance in the presence of various uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive comparative experimental results are obtained to verify the high-accuracy tracking performance of the proposed control strategy.
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