Waterlogging faced by bulbil expansion improved the growth of Pinellia ternata and its effect reinforced by brassinolide.

The bulbil expansion of P. ternata is a key period for its yield formation, and the process of bulbil expansion is often subjected to short-term heavy precipitation. It is not clear whether the short-term waterlogging can affect bulbil expansion. Brassinolide (BR) is widely believed to enhance plant tolerance to abiotic stress. The study investigated the effects of normal water (C), waterlogging (W), waterlogging + BR (W + B), waterlogging + propiconazole (W + P) on P. ternata at the bulbil expansion period in order to assess P. ternata's ability to cope with waterlogging during the bulbil expansion stage and the regulation effects of BR on the process. The biomass of P. ternata was significantly increased after waterlogging. W treatment significantly reduced the H2O2 and MDA contents, the rate of O2⋅- production and the activities of antioxidant enzymes compared with the C group. AsA and GSH contents were significantly reduced by W treatment. However, the ratios of AsA/DHA and GSH/GSSG were slightly affected by W treatment. The rate of O2∙- production and H2O2 content in W + B group were significantly lower than those in W group. The POD, APX, and GR activities, and GSH content in W + B group were evidently increased compared with the W group. Soluble sugar and active ingredients contents were significantly increased after waterlogging, and the enhancement was reinforced by BR. In conclusion, waterlogging reduced oxidative stress in P. ternata under the experimental conditions. BR treatment under waterlogging had a positive effect on P. ternata by enhancing antioxidant capacity and promoting the accumulation of soluble sugars and active ingredients.

Effect of mechanical ventilation under intubation on respiratory tract change of bacterial count and alteration of bacterial flora.

Background: The most common 'second strike' in mechanically ventilated patients is a pulmonary infection caused by the ease with which bacteria can invade and colonize the lungs due to mechanical ventilation. At the same time, metastasis of lower airway microbiota may have significant implications in developing intubation mechanical ventilation lung inflammation. Thus, we establish a rat model of tracheal intubation with mechanical ventilation and explore the effects of mechanical ventilation on lung injury and microbiological changes in rats. To provide a reference for preventing and treating bacterial flora imbalance and pulmonary infection injury caused by mechanical ventilation of tracheal intubation. Methods: Sprague-Dawley rats were randomly divided into Control, Mechanical ventilation under intubation (1, 3, 6 h) groups, and Spontaneously breathing under intubation (1, 3, 6 h). Lung histopathological injury scores were evaluated. 16SrDNA sequencing was performed to explore respiratory microbiota changes, especially, changes of bacterial count and alteration of bacterial flora. Results: Compared to groups C and SV, critical pathological changes in pulmonary lesions occurred in the MV group after 6 h (p < 0.05). The Alpha diversity and Beta diversity of lower respiratory tract microbiota in MV6, SV6, and C groups were statistically significant (p < 0.05). The main dominant bacterial phyla in the respiratory tract of rats were Proteobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria. Acinetobacter radioresistens in group C was significant, Megaonas in group MV6 was significantly increased, and Parvibacter in group SV6 was significantly increased. Anaerobic, biofilm formation, and Gram-negative bacteria-related functional genes were altered during mechanical ventilation with endotracheal intubation. Conclusion: Mechanical ventilation under intubation may cause dysregulation of lower respiratory microbiota in rats.

WOA-Based Robust Congestion Control Scheme with Two Kinds of Propagation Latencies and External Disturbance in Software-Defined Wireless Networks.

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device-controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov-Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme.

Postoperative awake prone position in geriatric patients with hip fractures: a protocol for a randomized controlled trial on the efficacy of postoperative prone position in reducing pulmonary complications and improving oxygenation.

INTRODUCTION: Postoperative pulmonary complications (PPCs) are prevalent in geriatric patients with hip fractures. Low oxygen level is one of the most important risk factors for PPCs. Prone position has been proven efficacy in improving oxygenation and delaying the progress of pulmonary diseases, especially in patients with acute respiratory distress syndrome induced by multiple etiologies. The application of awake prone position (APP) has also attracted widespread attention in recent years. A randomized controlled trial (RCT) will be carried out to measure the effect of postoperative APP in a population of geriatric patients undergoing hip fracture surgery. METHODS: This is an RCT. Patients older than 65 years old admitted through the emergency department and diagnosed with an intertrochanteric or femoral neck fracture will be eligible for enrollment and assigned randomly to the control group with routine postoperative management of orthopedics or APP group with an additional prone position for the first three consecutive postoperative days (PODs). Patients receiving conservative treatment will not be eligible for enrollment. We will record the difference in the patient's room-air-breathing arterial partial pressure of oxygen (PaO2) values between the 4th POD (POD 4) and emergency visits, the morbidity of PPCs and other postoperative complications, and length of stay. The incidence of PPCs, readmission rates, and mortality rates will be followed up for 90 PODs. DISCUSSION: We describe the protocol for a single-center RCT that will evaluate the efficacy of postoperative APP treatment in reducing pulmonary complications and improving oxygenation in geriatric patients with hip fractures. ETHICS AND DISSEMINATION: This protocol was approved by the independent ethics committee (IEC) for Clinical Research of Zhongda Hospital, Affiliated to Southeast University, and is registered on the Chinese Clinical Trial Registry. The findings of the trial will be disseminated through peer-reviewed journals. ETHICS APPROVAL NUMBER: 2021ZDSYLL203-P01 TRIAL REGISTRATION: ChiCTR ChiCTR2100049311 . Registered on 29 July 2021. TRIAL STATUS: Recruiting. Recruitment is expected to be completed in December 2024.

Modulating the Catalytic Properties of Bimetallic Atomic Catalysts: Role of Dangling Bonds and Charging.

Bimetallic atomic catalysts (BACs) exhibit great potential in CO2 electroreduction. However, modulation and improvement of their catalytic performance are still challenging. To address these issues, an intrinsic descriptor ψ based on the valence properties of active centers was used. The role of the dangling bonds and charging in modulating the catalytic properties of BACs called M1 M2 -N6 -G (M1 =Ru and Fe) was studied. It was shown that linear relationships between the adsorption energy of the C-species are broken under the effect of the dangling bonds and that they are restored with charging. However, charging has minor effects on the adsorption of the O-species. These findings enable screening promising BACs for CH3 OH production. This research provides effective schemes for modulating the properties of catalysts, which is beneficial to enriching high-performance catalysts for various reactions.

General Anesthesia Versus Regional Anesthesia in the Elderly Patients Undergoing Hip Fracture Surgeries: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

BACKGROUND: Surgery is the preferred treatment option for the elderly patients with hip fractures. However, the choice of general anesthesia (GA) or regional anesthesia (RA) remains controversial. The quality of evidence has further improved with the advent of several high-quality randomized clinical trials (RCTs) in the last two years. The purpose of this study was to compare the clinical outcomes of two anesthetic techniques in elderly patients undergoing hip fracture surgeries. METHODS: Eligible studies were identified from PubMed/MEDLINE, Web of Science, Scopus, EMBASE and reference lists from January 2000 to June 2022 in this current systematic review and meta-analysis. The outcomes included the surgery-related outcomes (duration of surgery, duration of anesthesia, intraoperative blood loss and number of transfusions) and postoperative outcomes (30-day mortality, postoperative delirium,cardiovascular events and other complications). RESULTS: A total of 10 RCTs were included, and a total of 3594 patients were analyzed. RA was associated with shorter duration of surgery, shorter length of hospital stays and less intraoperative blood loss compared to GA. There were no significant differences between the two groups in the number of blood transfusions, duration of anesthesia, 30-day mortality or postoperative delirium. CONCLUSIONS: Our pooled analysis identified no significant differences in terms of the safety between RA and GA, while RA reduces intraoperative blood loss, length of hospital stays and duration of surgery. These results suggest that RA appears to be preferable for the elderly patients with hip fractures.

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints.

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

OL-SLAM: A Robust and Versatile System of Object Localization and SLAM.

This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

What are Risk Factors of Postoperative Pneumonia in Geriatric Individuals after Hip Fracture Surgery: A Systematic Review and Meta-Analysis.

Postoperative pneumonia (POP) is a common postoperative complication. Negative consequences associated with POP included prolonged hospital length of stay, more frequent intensive care unit (ICU) stays, and a higher rate of sepsis, readmission, and mortality. This meta-analysis aimed to assess the incidence and risk factors associated with POP after hip fracture surgery in elderly patients. PubMed, Web of Science, and Cochrane Library were searched (up to March 31, 2022). All studies on the risk factors for POP after hip fracture surgery in elderly patients, published in English, were reviewed. The qualities of the included studies were assessed using the Newcastle-Ottawa Scale. Data were pooled, and a meta-analysis was performed. Ten studies, including 12,084 geriatric patients undergoing hip fracture surgery, were included. Of these 12,084 patients, POP occurred in 809 patients. The results indicated that age (mean difference [MD] = 4.95, 95% confidence interval [CI]: 3.22-6.69), male (odds ratio [OR] = 1.41, 95% CI: 1.02-1.93), the American Society of Anaesthesiologists classification ≥3 (OR = 3.48, 95% CI: 1.87-6.47), dependent functional status (OR = 5.23, 95% CI: 2.18-12.54, P = 0.0002), smoking (OR = 1.33, 95% CI: 1.07-1.65), chronic obstructive pulmonary disease (OR = 3.76, 95% CI: 2.07-6.81), diabetes mellitus (OR = 1.19, 95% CI: 1.01-1.40), coronary heart disease (OR = 1.74, 95% CI: 1.23-2.46), arrhythmia (OR = 1.47, 95% CI: 1.01-2.14), cerebrovascular disease (OR = 1.88, 95% CI: 1.56-2.27), dementia (OR = 2.36, 95% CI: 1.04-5.36), chronic renal failure (OR = 1.85, 95% CI: 1.29-2.67), hip arthroplasty (OR = 1.30, 95% CI: 1.08-1.56), delayed surgery (OR = 6.40, 95% CI: 3.00-13.68), preoperative creatinine (MD = 5.32, 95% CI: 0.55-10.08), and preoperative serum albumin (MD = -3.01, 95% CI: -4.21 - -1.80) were risk factors for POP. Related prophylactic measures should be provided in geriatric patients with the above-mentioned risk factors to prevent POP after hip fracture surgery.

Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System.

In this research, we present an intelligent forklift cargo precision transfer system to address the issue of poor pallet docking accuracy and low recognition rate when using current techniques. The technology is primarily used to automatically check if there is any pallet that need to be transported. The intelligent forklift is then sent to the area of the target pallet after being recognized. Images of the pallets are then collected using the forklift's camera, and a deep learning-based recognition algorithm is used to calculate the precise position of the pallets. Finally, the forklift is controlled by a high-precision control algorithm to insert the pallet in the exact location. This system creatively introduces the small target detection into the pallet target recognition system, which greatly improves the recognition rate of the system. The application of Yolov5 into the pallet positional calculation makes the coverage and recognition accuracy of the algorithm improved. In comparison with the prior approach, this system's identification rate and accuracy are substantially higher, and it requires fewer sensors and indications to help with deployment. We have collected a significant amount of real data in order to confirm the system's viability and stability. Among them, the accuracy of pallet docking is evaluated 1000 times, and the inaccuracy is kept to a maximum of 6 mm. The recognition rate of pallet recognition is above 99.5% in 7 days of continuous trials.

Understanding the oxygen-evolution-reaction catalytic activity of metal oxides based on the intrinsic descriptors.

Metal oxides show great potential in catalyzing the oxygen evolution reaction (OER), which is taken as the bottleneck of many energy-conversion and -storage processes. However, it is still a major challenge to deeply understand the catalytic mechanism and to rapidly screen out novel metal-oxide catalysts. Herein, we find that the trend of adsorption energies of O-intermediates and the theoretical overpotentials on metal monoxides (MO), metal dioxides (MO2), and perovskite oxides (ABO3) can be determined using the descriptor ψ, which is related to the valence-electron numbers and the electronegativities of the active center. The underlying mechanism is that ψ reflects the p-band properties of superficial and adsorbed O atoms of metal oxides and thus can reveal the commonality and difference of adsorption and catalysis of the three types of metal oxides. Moreover, the ψ-determined relationships indicate the possibility of breaking the previously proposed thermodynamic limitation on the post-transition metal oxides and rationalizing the trend of experimental OER catalytic activity of metal oxide catalysts. With this easily accessible intrinsic descriptor ψ, we provide a convenient and feasible scheme for designing new candidates for OER catalysts.

Short- and Long-Term Effects of Different Antibiotics on the Gut Microbiota and Cytokines Level in Mice.

Background: Antibiotics are the first line of treatment for infectious diseases. However, their overuse can increase the spread of drug-resistant bacteria. The present study analyzed the impact of different types of antibiotics on the gut microbiome and cytokines level of mice. Methods: A total of five groups of 8-week-old male BALB/c mice (n = 35) were treated with piperacillin-tazobactam (TZP), ceftriaxone (CRO), tigecycline (TGC), levofloxacin (LEV) or normal saline (Ctrl), respectively, for up to 4 weeks. Fecal samples were analyzed by bacterial 16S rRNA gene sequencing for bacterial identification. Blood samples were used for the determination of 23 serum cytokines using multiplex immunoassay. Results: Exposure to antibiotics was shown to affect the normal weight gain of mice. Significant changes in gut composition caused by TZP, CRO and TGC treatment included the decreased abundance of Bacteroidetes (p < 0.01), Muribaculaceae (p < 0.01) and Lachnospiraceae (p < 0.01), and the increased abundance of Proteobacteria (p < 0.05), Enterobacteriaceae (including Klebsiella and Enterobacter) (p < 0.01) and Enterococcaceae (including Enterococcus) (p < 0.01). After 4-week treatment, the TZP, CRO and LEV groups had significantly lower concentrations of several serum cytokines. Correlation analysis of the top 30 bacterial genera and cytokines showed that Enterococcus and Klebsiella were strongly positively correlated with tumor necrosis factor-α (TNF-α), interleukins (IL) IL-12p70 and IL-1ß. Desulfovibrio, Candidatus Saccharimonas, norank_f__norank_o__Clostridia_UCG-014, Lactobacillus, and Roseburia were strongly negatively correlated with these cytokines. Conclusion: This study demonstrates the effects of various antibiotics on the intestinal microflora and immune status of mice. Compared with TZP, CRO and TGC, LEV had minimal impact on the gut microbiota. In addition to TGC, long-term TZP, CRO and LEV intervention can lead to a decrease in serum cytokine levels, which may depend on the intestinal microflora, antibiotic used and the duration of treatment.

20 watt-level single transverse mode narrow linewidth and tunable random fiber laser at 1.5 µm band.

High power 1.5 µm band fiber lasers are of great importance for many practical applications. Generally, the technical targets including high average output power, narrow linewidth, temporally suppressed intensity dynamics, high spectral purity, single transverse mode lasing, and excellent robustness are the major concerns when constructing a high-performance laser source. Here, we demonstrate the highest output power of a wavelength tunable 1.5 µm band random fiber laser based on the active fiber gain mechanism to the best of our knowledge. A master oscillator power-amplifier (MOPA) configuration is employed to greatly boost the output power to 20 watt-level with a single transverse mode lasing and the same linewidth as the seed, benefiting from the spectral broadening free feature when employing the random fiber laser as the seed. This work not only enriches the progress of random fiber laser, but also provides an attractive alternative in realizing high performance lasing light source at 1.5 µm band.

The Efficacy and Safety of Intravenous Iron in Geriatric Hip Fracture Surgeries: A Systematic Review and Meta-Analysis.

BACKGROUND: With the increasing evidence provided by recent high-quality studies, the intravenous iron appears to be a reliable therapy for blood administration in geriatric patients with hip fractures. Here, this systematic review and meta-analysis were aimed to assess the effectiveness and safety of intravenous iron in geriatric patients sustaining hip fractures. METHODS: Potential pertinent literatures evaluating the effects of intravenous iron in the geriatric patients undergoing hip fractures were identified from Web of Science, PubMed, Embase, and Scopus. We performed a pairwise meta-analysis using fixed- and random-effects models, and the pooling of data was carried out by using RevMan 5.1. RESULTS: Four randomized controlled trials and four observational studies conform to inclusion criteria. The results of meta-analysis showed that intravenous iron reduced transfusion rates compared to the control group, yet the result did not reach statistical significance. The intravenous iron was related to lower transfusion volumes, shorter length of stay, and a reduced risk of nosocomial infections. And there was no significant difference in terms of the mortality and other complications between the treatment group and the control group. CONCLUSION: Current evidence suggests that intravenous iron reduces the transfusion volume, length of hospital stay, and risk of nosocomial infections. It takes about 7 days for intravenous iron to elevate hemoglobin by 1 g/dl and about 1 month for 2 g/dl. The safety profile of intravenous iron is also reassuring, and additional high-quality studies are needed.

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas.

Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

A quantitative AOP of mitochondrial toxicity based on data from three cell lines.

Adverse Outcome Pathways (AOPs) are increasingly used to support the integration of in vitro data in hazard assessment for chemicals. Quantitative AOPs (qAOPs) use mathematical models to describe the relationship between key events (KEs). In this paper, data obtained in three cell lines, LHUMES, HepG2 and RPTEC/TERT1, using similar experimental protocols, was used to calibrate a qAOP of mitochondrial toxicity for two chemicals, rotenone and deguelin. The objectives were to determine whether the same qAOP could be used for the three cell types, and to test chemical-independence by cross-validation with a dataset obtained on eight other chemicals in LHUMES cells. Repeating the calibration approach for both chemicals in three cell lines highlighted various practical difficulties. Even when the same readouts of KEs are measured, the mathematical functions used to describe the key event relationships may not be the same. Cross-validation in LHUMES cells was attempted by estimating chemical-specific potency at the molecular initiating events and using the rest of the calibrated qAOP to predict downstream KEs: toxicity of azoxystrobin, carboxine, mepronil and thifluzamide was underestimated. Selection of most relevant readouts and accurate characterization of the molecular initiating event for cross-validation are critical when designing in vitro experiments targeted at calibrating qAOPs.

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.

At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.