Endangered status and threatened population ecological factors in Salvia daiguii, an endemic species from Hunan, China.

Many species of Salvia have excellent ornamental, culinary, and medicinal values. Salvia daiguii, is an ornamental and highly medicinal perennial herb endemic to the prefecture-level city of Zhangjiajie in Hunan Province, China, with a narrow geographical distribution. However, currently, it has only been assessed as a Critically Endangered species according to the IUCN classification criteria, but its conservation has not yet been studied. This study investigated the distribution and niche characteristics of S. daiguii, and compared the differences in growth, flowering characteristics, and soil nutrients between the wild and ex situ populations. We also analyzed the effects of soil nutrients on plant growth and flowering characteristics. During the survey, we found 274 individuals on a rock approximately 200 m from ZEFR1. Nevertheless, S. daiguii were still restricted in three populations, TNFP, TGM, and ZEFR in Zhangjiajie City, with a total of about 500 plants and less than 250 mature individuals. Our results show that aspects such as adverse environmental conditions, low seedling renewal rate, a lack of soil nutrients, and competition for the characteristic niche of this and other dominant plants in the natural community are the main ecological factors affecting the growth, flowering, and geographic distribution of S. daiguii. Based on the results of field surveys, we recommend that (1) S. daiguii be classified as Critically Endangered C2b and China's List of Plant Species with Extremely Small Populations. (2) Comprehensive conservation strategies were developed, such as the establishment of nature reserves, reintroduction, public education, and institutional development to provide management recommendations related to the conservation of S. daiguii and other endangered plants.

Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation.

To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in computer vision and pattern recognition communities. In UDA scenario, the training and test data belong to different domains while the task model is learned to be invariant. Recently, empirical connections between transferability and discriminability have received increasing attention, which is the key to understand the invariant representations. However, theoretical study of these abilities and in-depth analysis of the learned feature structures are unexplored yet. In this work, we systematically analyze the essentials of transferability and discriminability from the geometric perspective. Our theoretical results provide insights into understanding the co-regularization relation and prove the possibility of learning these abilities. From methodology aspect, the abilities are formulated as geometric properties between domain/cluster subspaces (i.e., orthogonality and equivalence) and characterized as the relation between the norms/ranks of multiple matrices. Two optimization-friendly learning principles are derived, which also ensure some intuitive explanations. Moreover, a feasible range for the co-regularization parameters is deduced to balance the learning of geometric structures. Based on the theoretical results, a geometry-oriented model is proposed for enhancing the transferability and discriminability via nuclear norm optimization. Extensive experiment results validate the effectiveness of the proposed model in empirical applications, and verify that the geometric abilities can be sufficiently learned in the derived feasible range.

"RNSP (Rannasangpei)" Rescued MK-801-induced Schizophrenia-like Behaviors in Mice via Oxidative Stress and BDNF-TrkB/Akt Pathway.

Schizophrenia (SCZ) is a complex, severe psychotic disorder that is highly persistent. Patients often cannot control their emotions and have delusions of victimization, world-weariness, and even suicide. Therefore, safer and more effective drugs are urgently needed. Rannasangpei (RNSP) from "the four medicine tantras" was used as a neuroprotective agent. The objective of this study was to investigate the effect and mechanism of RNSP on MK-801-induced SCZ in mice. Fifty C57BL/6J mice were randomly divided into a normal group, a model group, an RNSP group, a crocin (CRO) group, and an olanzapine (OLA) group, except for the normal group. The remaining mice were used to establish the MK-801-induced SCZ model. Changes in positive symptoms and cognitive impairment in mice before and after drug intervention were assessed by using the prepulse inhibition (PPI) test, Y-maze test (YMT), and open-field test (OFT). Intragastric administration of RNSP alleviated the symptoms of SCZ in SCZ mice, as demonstrated by the PPI, YMT, and OFT results. Compared with the model group, the first-line antipsychotic olanzapine reversed the anxiety-like phenotypes, hypermotility, and PPI deficits in the SCZ model mice. Further analysis revealed that RNSP reduced oxidative stress in SCZ model mice, as evidenced by increased superoxide dismutase (SOD) levels and decreased malondialdehyde (MDA) levels in the hippocampus, cortex, and blood of SCZ model mice. In our study, RNSP treatment restored the expression of brain-derived neurotrophic factor (BDNF), dopamine D2 receptor, p-Trkb, Akt/p-Akt, and doublecortin and inhibited the expression of IBA1 and Bax in the hippocampus of SCZ model mice. The polymerase chain reaction data indicated that RNSP treatment increased the expression of Bcl-2 and TGF-ß and decreased the expression of Bax, IL-1ß, and TNF-α in the brains of the model mice. Our results are the first to show that RNSP reverses SCZ-like behaviors in rodents (both positive symptoms and cognitive deficits) by reducing oxidative stress and activating the BDNF-TrkB/Akt pathway, suggesting that RNSP is a novel approach for treating SCZ.

Revealing the Nature of Binary-Phase on Structural Stability of Sodium Layered Oxide Cathodes.

The emergence of layered sodium transition metal oxides featuring a multiphase structure presents a promising approach for cathode materials in sodium-ion batteries, showcasing notably improved energy storage capacity. However, the advancement of cathodes with multiphase structures faces obstacles due to the limited understanding of the integrated structural effects. Herein, the integrated structural effects by an in-depth structure-chemistry analysis in the developed layered cathode system NaxCu0.1Co0.1Ni0.25Mn0.4Ti0.15O2 with purposely designed P2/O3 phase integration, are comprehended. The results affirm that integrated phase ratio plays a pivotal role in electrochemical/structural stability, particularly at high voltage and with the incorporation of anionic redox. In contrast to previous reports advocating solely for the enhanced electrochemical performance in biphasic structures, it is demonstrated that an inappropriate composite structure is more destructive than a single-phase design. The in situ X-ray diffraction results, coupled with density functional theory computations further confirm that the biphasic structure with P2:O3 = 4:6 shows suppressed irreversible phase transition at high desodiated states and thus exhibits optimized electrochemical performance. These fundamental discoveries provide clues to the design of high-performance layered oxide cathodes for next-generation SIBs.

Predefined-time distributed optimization and anti-disturbance control for nonlinear multi-agent system with neural network estimator: A hierarchical framework.

This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering unknown nonlinear functions and external disturbances, we propose a two-layer hierarchical control framework. At the first layer, a predefined-time distributed estimator is employed to produce optimal consensus trajectories. At the second layer, a neural-network-based predefined-time disturbance observer is introduced to estimate the disturbance, with neural networks used to approximate the unknown nonlinear functions. A neural-network-based anti-disturbance sliding mode control mechanism is presented to ensure that the system trajectories can track the optimal trajectories within a predefined time. The feasibility of this hierarchical control framework is verified by utilizing the Lyapunov method. Numerical simulations are conducted separately using models of robotic arms and mobile robots to validate the effectiveness of the proposed method.

Cytisine-N-methylene-(5,7,4'-trihydroxy)- isoflavone ameliorates ischemic stroke-induced brain injury in mouse by regulating the oxidative stress and BDNF-Trkb/Akt pathway.

BACKGROUND: A novel compound Cytisine-N-methylene-(5,7,4'-trihydroxy)- isoflavone (LY01) found in the Sophora alopecuroides L is a neuroprotective agent. However, the effect and potential mechanism of LY01 treatment for ischemic stroke (IS) have not been fully elucidated. AIM OF THE STUDY: The aim of this study is to demonstrate whether LY01 can rescue ischemic stroke-induced brain injury and oxygen-glucose deprivation/reperfusion (OGD/R). RESULTS: Our results show that intragastric administration of LY01 improves ischemic stroke behaviors in mice, as demonstrated by neurological score, infarct volume, cerebral water content, rotarod test for activity. Compared with the model group, the ginkgo biloba extract (EGb) and LY01 reversed the neurological score, infarct volume, cerebral water content, rotarod test in model mice. Further analysis showed that the LY01 rescued oxidative stress in the model mice, which was reflected in the increased levels of catalase, superoxide dismutase, total antioxidant capacity and decreased levels of malondialdehyde in the serum of the model mice. Moreover, the expression of the brain-derived neurotrophic factor brain-derived neurotrophic factor (BDNF), phosphorylated protein kinase B (p-Akt), Bax, Bcl-2, (p)-tropomysin related kinase B (p-Trkb) was restored and the expression of Bax, glial fibrillary acidic protein (GFAP) in the brains of the model mice was inhibited through LY01 treatment. In the polymerase chain reaction (PCR) data, after giving LY01, the expression in the brains of model mice was that, IL-10 increased and IL-1ß, Bax, Bcl-2 decreased. Furthermore, the results indicated that LY01 improved cell viability, reactive oxygen species content, and mitochondrial membrane potential dissipation induced by OGD/R in primary culture of rat cortical neurons. Bax and caspase-3 activity was upregulated compared to the before after treatment with LY01. CONCLUSIONS: Our study suggests that LY01 reversed ischemic stroke by reducing oxidative stress and activating the BDNF-TrkB/Akt pathway and exerted a neuroprotective action against OGD/R injury via attenuation, a novel approach was suggested to treat ischemic stroke. Our observations justify the traditional use of LY01 for a treatment of IS in nervous system.

Study on chitosan/gelatin hydrogels containing ceria nanoparticles for promoting the healing of diabetic wound.

Chronic inflammation at diabetic wound sites results in the uncontrolled accumulation of pro-inflammatory factors and reactive oxygen species (ROS), which impedes cell proliferation and delays wound healing. To promote the healing of diabetic wounds, chitosan/gelatin hydrogels containing ceria nanoparticles (CNPs) of various sizes were created in the current study. CNPs' efficacy in removing O 2 • - , •OH, and H2O2 was demonstrated, and the scavenging ability of CNPs of varying sizes was compared. The in vitro experiments demonstrated that hydrogels containing CNPs could effectively protect cells from ROS-induced damage and facilitate mouse fibroblast migration. Furthermore, during the treatment of diabetic wounds in vivo, hydrogels containing CNPs exhibited anti-inflammatory activity and could reduce the expression of the pro-inflammatory factors TNF-α (above 30%), IL-6 (above 90%), and IL-1ß (above 80%), and effectively promote wound closure (above 80%) by inducing re-epithelialization, collagen deposition, and angiogenesis. In addition, the biological properties and therapeutic effects of hydrogels containing CNPs of various sizes were compared and discussed. The finding revealed that hydrogels with 4 nm CNPs exhibited more significant biological properties and had implications for diabetic wound treatment.

Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data.

Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.

One-stage posterior transpedicular debridement, hemi-interbody and unilateral-posterior bone grafting, and instrumentation for the treatment of thoracic spinal tuberculosis: a retrospective study.

PURPOSE: To investigate the clinical efficacy and feasibility of the surgical treatment of thoracic spinal tuberculosis using one-stage posterior instrumentation, transpedicular debridement, and hemi-interbody and unilateral posterior bone grafting. METHODS: Fifty-six patients with thoracic spinal tuberculosis who underwent surgery performed by a single surgeon between September 2009 and August 2020 were enrolled in this study. Based on data from the erythrocyte sedimentation rate (ESR), Visual Analog Scale (VAS), and Cobb angle before surgery, after surgery, and at the most recent follow-up, clinical effectiveness was assessed using statistical analysis. The variables investigated included operating time, blood loss, complications, neurological function, and hemi-interbody fusion. RESULTS: None of the patients experienced significant surgery-associated complications. At the last follow-up, 23 of the 25 patients (92%) with neurological impairment showed improvement. The thoracic kyphotic angle was significantly decreased from 24.1 ± 9.9° to 13.4 ± 8.6° after operation (P < 0.05), and the angle was 14.44 ± 8.8° at final follow-up (P < 0.05). The Visual Analog Scale significantly decreased from 6.7 ± 1.4 preoperatively to 2.3 ± 0.8 postoperatively (P < 0.05) and finally to 1.2 ± 0.7 at the last follow-up (P < 0.05). Bone fusion was confirmed in 56 patients at 3-6 months postoperatively. CONCLUSIONS: One-stage posterior transpedicular debridement, hemi-interbody and unilateral posterior bone grafting, and instrumentation are effective and feasible treatment methods for thoracic spinal tuberculosis.

Identification of heterosis and combining ability in the hybrids of male sterile and restorer sorghum [Sorghum bicolor (L.) Moench] lines.

In sorghum [Sorghum bicolor (L.) Moench], combining ability and heterosis analysis are commonly used to evaluate superior parental lines and to screen for strongly heterotic hybrids, which helps in sorghum variety selection and breeding. In this context, combining ability and heterosis analysis were assessed using 14 restorer lines and seven cytoplasmic male sterile (CMS) lines in 2019 and 2020. The analysis of variance of all cross combinations had highly significant differences for all characters studied, which indicated a wide variation across the parents, lines, testers, and crosses. Combining ability analysis showed that the general combining ability (GCA) and specific combining ability (SCA) of the different parents were differed significantly among different traits. Most combinations with high SCA also showed high GCA in their parent lines. The heritability in the narrow sense of grain weight per panicle and grain yield was relatively low, indicating that the ability of these traits to be directly inherited by offspring was weak, that they were greatly affected by the environment. The better-parent heterosis for plant height, grain weight per panicle, panicle length, and 1000-grain weight was consistent with the order of mid-parent heterosis from strong to weak. The GCA effects of two lines 10480A, 3765A and three testers 0-30R, R111, and JY15R were significant for the majority of the agronomic traits including grain yield and might be used for improving the yield of grains in sorghum as parents of excellent specific combining ability. Seven strongly heterotic F1 hybrids were screened; of these, hybrids 3765A × R111, 1102A × L2R, and 3765A × JY15R showed significant increases in seed iristectorigenin A content and will feature into the creation of new sorghum varieties rich in iristectorigenin A.

Distributed deep reinforcement learning based on bi-objective framework for multi-robot formation.

Improving generalization ability in multi-robot formation can reduce repetitive training and calculation. In this paper, we study the multi-robot formation problem with the ability to generalize the target position. Since the generalization ability of neural network is directly proportional to spatial dimension, we adopt the strategy of using different networks to solve different objectives, so that the network learning can focus on the learning of one objective to obtain better performance. In addition, this paper presents a distributed deep reinforcement learning method based on soft actor-critic algorithm for solving multi-robot formation problem. At the same time, the formation evaluation assignment function is designed to adapt to distributed training. Compared with the original algorithm, the improved algorithm can get higher reward cumulative values. The experimental results show that the proposed algorithm can better maintain the desired formation in the moving process, and the rotation design in the reward function makes the multi-robot system have better flexibility in formation. The comparison of control signal curve shows that the proposed algorithm is more stable. At the end of the experiments, the universality of the proposed algorithm in formation maintenance and formation variations is demonstrated.

Revealing the Dominance of the Dissolution-Deposition Mechanism in Aqueous Zn-MnO2 Batteries.

Zn-MnO2 batteries have attracted extensive attention for grid-scale energy storage applications, however, the energy storage chemistry of MnO2 in mild acidic aqueous electrolytes remains elusive and controversial. Using α-MnO2 as a case study, we developed a methodology by coupling conventional coin batteries with customized beaker batteries to pinpoint the operating mechanism of Zn-MnO2 batteries. This approach visually simulates the operating state of batteries in different scenarios and allows for a comprehensive study of the operating mechanism of aqueous Zn-MnO2 batteries under mild acidic conditions. It is validated that the electrochemical performance can be modulated by controlling the addition of Mn2+ to the electrolyte. The method is utilized to systematically eliminate the possibility of Zn2+ and/or H+ intercalation/de-intercalation reactions, thereby confirming the dominance of the MnO2 /Mn2+ dissolution-deposition mechanism. By combining a series of phase and spectroscopic characterizations, the compositional, morphological and structural evolution of electrodes and electrolytes during battery cycling is probed, elucidating the intrinsic battery chemistry of MnO2 in mild acid electrolytes. Such a methodology developed can be extended to other energy storage systems, providing a universal approach to accurately identify the reaction mechanism of aqueous aluminum-ion batteries as well.

Effect of anti-inflammatory drugs on the storm of inflammatory factors in respiratory tract infection caused by SARS-CoV-2: an updated meta-analysis.

Background: New reports suggest that anti-inflammatory drugs are widely used to treat respiratory tract infections caused by SARS-CoV-2. Anti-inflammatory drugs were the most frequently used treatment for the COVID-19-related cytokine storm in China. However, the efficacy of anti-inflammatory drugs has yet to be systematically analyzed, and clinicians are often uncertain which class of anti-inflammatory drug is the most effective in treating patients with respiratory tract infections caused by SARS-CoV-2, especially those with severe disease. Methods: From 1 October 2022, relevant studies were searched in the PubMed, Embase, Medline, Cochrane Library, and Web of Science databases. A total of 16,268 publications were retrieved and collated according to inclusion and exclusion criteria, and sensitivity analyses were performed using STATA 14 software. Publication bias was assessed using funnel plots and Egger's test. Study quality was assessed using the PEDro scale, and the combined advantage ratio was expressed as a 95% confidence interval (CI). In total, 19 randomized controlled trials were included in the study. STATA 14 software was used for all random effects model analyses, and the results are expressed as relative risk ratios (RR) with 95% CI. Results: Quantitative analyses were performed on 14,514 patients from 19 relevant randomized controlled clinical trials. Pooled estimates (RR = 0.59, 95% CI 0.44-0.80) revealed that the use of anti-inflammatory drugs resulted in a significant reduction in mortality in patients with respiratory tract infection caused by SARS-CoV-2 compared with controls, and methylprednisolone (RR = 0.14, 95% CI 0.03-0.56) was more effective than other anti-inflammatory drugs. Anti-inflammatory drugs were effective in reducing mortality in critically ill patients (RR = 0.67, 95% CI 0.45-0.98) compared with non-critically ill patients (RR = 0.50, 95% CI 0.34-0.76); however, more clinical evidence is needed to confirm these findings. Conclusion: The use of anti-inflammatory drugs in patients with respiratory infections caused by SARS-CoV-2 reduces patient mortality, especially in severe cases. In individual studies, methylprednisolone was more effective than other drugs.

Effect of High-Hydrostatic-Pressure Treatment on the Physicochemical Properties of Kafirin.

The kafirin derived from Jin Nuo 3 sorghum underwent a high-hydrostatic-pressure (HHP) treatment of 100, 300, and 600 MPa for 10 min to investigate alterations in its physicochemical attributes. The findings exhibited a reduction in protein solubility, declining from 83% to 62%, consequent to the application of the HHP treatment. However, this treatment did not lead to subunit-specific aggregation. The absorption intensity of UV light diminished, and the peak fluorescence absorption wavelength exhibited a shift from 342 nm to 344 nm, indicating an increased polarity within the amino acid microenvironment. In an aqueous solution, the specific surface area expanded from 294.2 m2/kg to 304.5 m2/kg, while the average particle-size value in a 70% ethanol solution rose to 26.3 nm. Conversely, the zeta-potential value decreased from 3.4 mV to 1.3 mV, suggesting a propensity for aggregation in ethanol solutions. A notable rise in the intermolecular ß-sheet content to 21.06% was observed, along with a shift in the peak denaturation temperature from 76.33 °C to 86.33 °C. Additionally, the content of disulfide bonds increased to 14.5 µmol/g. Collectively, the application of the HHP treatment not only enhanced the thermal stability but also induced a more ordered secondary structure within the kafirin.

Awake rabbit model of ischemic spinal cord injury with delayed paraplegia: The role of ambient temperature.

BACKGROUND: Paraplegia after spinal cord ischemia is a devastating condition in the clinic. Here, we develop an awake rabbit model of spinal cord ischemia with delayed paraplegia and explore the influence of ambient temperature on the outcomes after injury. METHODS: A total of 47 male rabbits were involved in the present study. Transient spinal cord ischemia was induced by occluding the infrarenal abdominal aorta of awake rabbits at different ambient temperatures. To find the optimal conditions for developing delayed paraplegia, hindlimb motor function after ischemia was evaluated between experiments. RESULTS: The onset and magnitude of ischemic injury varied with the ambient temperature maintained during the peri-ischemia period. More serious spinal cord injury occurred when ischemia was induced at higher temperatures. At 18°C, 25-minute ischemia resulted in 74% of rabbits developing delayed paraplegia. At a temperature of 28°C or higher, most of the animals developed acute paraplegia immediately. While at 13°C, rabbits usually regained normal motor function without paraplegia. CONCLUSION: This awake rabbit model is highly reproducible and will be helpful in future studies of delayed paraplegia after spinal cord ischemia. The ambient temperature must be considered while using this model during investigation of therapeutic interventions.

The efficacy and safety of post-stroke cognitive impairment therapies: an umbrella review.

Background: Stroke survivors are at significantly increased risk of cognitive impairment, which affects patients' independence of activities of daily living (ADLs), social engagement, and neurological function deficit. Many studies have been done to evaluate the efficacy and safety of post-stroke cognitive impairment (PSCI) treatment, and due to the largely inconsistent clinical data, there is a need to summarize and analyze the published clinical research data in this area. Objective: An umbrella review was performed to evaluate the efficacy and safety of PSCI therapies. Methods: Three independent authors searched for meta-analyses and systematic reviews on PubMed, the Cochrane Library, and the Web of Science to address this issue. We examined ADL and Barthel index (BI), Montreal Cognitive Assessment (MoCA), neurological function deficit as efficacy endpoints, and the incidence of adverse events as safety profiles. Results: In all, 312 studies from 19 eligible publications were included in the umbrella review. The results showed that angiotensin-converting enzyme inhibitors (ACEI) and N-methyl-D-aspartate (NMDA) antagonists, cell therapies, acupuncture, and EGB76 can improve the MoCA and ADL, and the adverse effects were mild for the treatment of PSCI. Moreover, Vinpocetine, Oxiracetam, Citicoline, thrombolytic therapy, Actovegin, DL-3-n-Butylphthalide, and Nimodipine showed adverse events or low article quality in patients with PSCI. However, the research evidence is not exact and further research is needed. Conclusion: Our study demonstrated that ACEI inhibitors (Donepezil) and NMDA antagonists (Memantine), EGB761, and acupuncture are the ADL and BI, MoCA, and neurological function deficit medication/therapy, respectively, for patients with PSCI. Clinical Trial Registration: https://inplasy.com/inplasy-2022-11-0139/; Identifier: INPLASY2022110139.

Acute effects of foam rolling and dynamic stretching on angle-specific change of direction ability, flexibility and reactive strength in male basketball players.

The purpose of our study was to determine the acute effects of dynamic stretching (DS), foam rolling (FR) and foam rolling combined with dynamic stretching (Combo) protocols on angle-specific change of direction (COD) ability, drop jump (DJ) performance and flexibility. Using a counterbalance crossover study design, eleven male basketball collegiate players (20.7 ± 0.6 years) were randomly assigned to one of the four protocols - control (CON), DS, FR, Combo - for each session, for a total of four sessions. A more aggressive foam cylinder with raised nodules, which is thought to be effective in stimulating the deep layer of muscle tissue, was used to observe for changes in their performance during sit and reach (SAR), DJ and COD tasks in 45 and 180 degrees. One-way repeated measures ANOVA was used to identify differences of each variable separately between interventions. The SAR after three interventions compared to the CON was significantly improved (F (3,30) = 5.903, P = 0.003, η2 = 0.371). In the 505 test, both limbs failed to show a significant improvement in COD deficit. The non-dominant limb showed a significant improvement of 6.4% after FR when performing the Y-shaped agility (F (3,30) = 4.962, P = 0.0065 < 0.05, η2 = 0.332). In the DJ, the reactive strength index and contact time changed significantly by 17.5% and -17.5% (η2 = 0.518, η2 = 0.571), respectively, immediately after FR. The current research suggested that FR may have an enhancing effect on COD speed in a 45° cutting task and neuromuscular function, while having the potential to improve non-dominant limb deficits in both COD tasks. In contrast, the Combo warm-up protocol did not produce a cumulative effect, suggesting the need for coaches to remain cautious about excessive warm-up duration.

Analysis of the 2p-manifold population distribution in a diode-pumped metastable Ar laser.

The complex excited energy levels in the diode-pumped metastable Ar laser may induce harmful effects in laser cycling. Significantly, the influence of the population distribution in 2p energy levels on the laser performance is unclear yet. In this work, the absolute populations in all the 2p states were measured online by the simultaneous applications of tunable diode laser absorption spectroscopy and optical emission spectroscopy. The results showed that most atoms were populated to the 2p8, 2p9, and 2p10 levels while lasing, and the majority of the 2p9 population was efficiently transferred to the 2p10 level with the aid of helium, which was beneficial for the laser performance.

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems.

Cooperative localization is an arising research problem for multi-robot system, especially for the scenarios that need to reduce the communication load of base stations. This article proposes a novel cooperative localization algorithm, which can achieve high accuracy localization by using the relative measurements among robots. To address uncertainty in the measuring robots' positions and avoid linearization errors in the extended Kalman filter during the measurement update phase, a particle-based approximation method is proposed. The covariance intersection method is then employed to fuse preliminary estimations from different robots, guaranteeing a minimum upper bound for the fused covariance. Moreover, in order to avoid the negative effect of abnormal measurements, this article adopts the Kullback-Leibler divergence to calculate the distances between different estimations and rejects to fuse the preliminary estimations far from the estimation obtained in the prediction stage. Two simulations are conducted to validate the proposed algorithm. Compared with the other three algorithms, the proposed algorithm can achieve higher localization accuracy and deal with the abnormal measurement.

Health warning based on 3R ECG Sample's combined features and LSTM.

Most researches use the fixed-length sample to identify ECG abnormalities based on MIT ECG dataset, which leads to information loss. To address this problem, this paper proposes a method for ECG abnormality detection and health warning based on ECG Holter of PHIA and 3R-TSH-L method. The 3R-TSH-L method is implemented by:(1) getting 3R ECG samples using Pan-Tompkins method and using volatility to obtain high-quality raw ECG data; (2) extracting combination features including time-domain features, frequency domain features and time-frequency domain features; (3) using LSTM for classification, training and testing the algorithm based on the MIT-BIH dataset, and obtaining relatively optimal features as spliced normalized fusion features including kurtosis, skewness and RR interval time domain features, STFT-based sub-band spectrum features, and harmonic ratio features. The ECG data were collected using the self-developed ECG Holter (PHIA) on 14 subjects, aged between 24 and 75 including both male and female, to build the ECG dataset (ECG-H). The algorithm was transferred to the ECG-H dataset, and a health warning assessment model based on abnormal ECG rate and heart rate variability weighting was proposed. Experiments show that 3R-TSH-L method proposed in the paper has a high accuracy of 98.28% for the detection of ECG abnormalities of MIT-BIH dataset and a good transfer learning ability of 95.66% accuracy for ECG-H. The health warning model was also testified to be reasonable. The key technique of the ECG Holter of PHIA and the method 3R-TSH-L proposed in this paper is expected to be widely used in family-oriented healthcare.