Global characteristics and trends in research on Candida auris

IntroductionCandida auris, a fungal pathogen first reported in 2009, has shown strong resistance to azole antifungal drugs and has caused severe nosocomial outbreaks. It can also form biofilms, which can colonize patients’ skin and transmit to others. Despite numerous reports of C. auris isolation in various countries, many studies have reported contradictory results.MethodA bibliometric analysis was conducted using VOSviewer to summarize research trends and provide guidance for future research on controlling C. auris infection. The analysis revealed that the United States and the US CDC were the most influential countries and research institutions, respectively. For the researchers, Jacques F. Meis published the highest amount of related articles, and Anastasia P. Litvintseva’s articles with the highest average citation rate. The most cited publications focused on clade classification, accurate identification technologies, nosocomial outbreaks, drug resistance, and biofilm formation. Keyword co-occurrence analysis revealed that the top five highest frequencies were for ‘drug resistance,’ ‘antifungal susceptibility test,’ ‘infection,’ ‘Candida auris,’ and ‘identification.’ The high-frequency keywords clustered into four groups: rapid and precise identification, drug resistance research, pathogenicity, and nosocomial transmission epidemiology studies. These clusters represent different study fields and current research hotspots of C. auris.ConclusionThe bibliometric analysis identified the most influential country, research institution, and researcher, indicating current research trends and hotspots for controlling C. auris

Digital Twin Brain: a simulation and assimilation platform for whole human brain

In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.Comment: 12 pages, 11 figure

Transcriptome and association mapping revealed functional genes respond to drought stress in Populus

Drought frequency and severity are exacerbated by global climate change, which could compromise forest ecosystems. However, there have been minimal efforts to systematically investigate the genetic basis of the response to drought stress in perennial trees. Here, we implemented a systems genetics approach that combines co-expression analysis, association genetics, and expression quantitative trait nucleotide (eQTN) mapping to construct an allelic genetic regulatory network comprising four key regulators (PtoeIF-2B, PtoABF3, PtoPSB33, and PtoLHCA4) under drought stress conditions. Furthermore, Hap_01PtoeIF-2B, a superior haplotype associated with the net photosynthesis, was revealed through allelic frequency and haplotype analysis. In total, 75 candidate genes related to drought stress were identified through transcriptome analyses of five Populus cultivars (P. tremula × P. alba, P. nigra, P. simonii, P. trichocarpa, and P. tomentosa). Through association mapping, we detected 92 unique SNPs from 38 genes and 104 epistatic gene pairs that were associated with six drought-related traits by association mapping. eQTN mapping unravels drought stress-related gene loci that were significantly associated with the expression levels of candidate genes for drought stress. In summary, we have developed an integrated strategy for dissecting a complex genetic network, which facilitates an integrated population genomics approach that can assess the effects of environmental threats

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments

Enhanced Antifungal Activities of Eugenol-Entrapped Casein Nanoparticles against Anthracnose in Postharvest Fruits

This study aims to improve the antifungal effects of eugenol through low-energy self-assembly fabrication and optimization of eugenol-casein nanoparticles (EC-NPs). Optimized EC-NPs (eugenol/casein ratio of 1:5) were obtained with a mean size of 307.4 ± 2.5 nm and entrapment efficiency of 86.3% ± 0.2%, and showed high stability under incubated at 20 and 37 °C for 48 h. EC-NPs exhibited satisfactory sustained-release effect at 20 °C or 37 °C, with remaining eugenols amounts of 79.51% and 53.41% after 72 h incubation, respectively, which were significantly higher than that of native eugenol (only 26.40% and 19.82% after the first 12 h). EC-NPs exhibited a greater antifungal activity (>95.7%) against spore germination of fungus that was greater than that of native eugenol, showed 100% inhibition of the anthracnose incidence in postharvest pear after 7 d. EC-NPs is potential as an environmental-friendly preservatives in the food industry

The Actor-Dueling-Critic Method for Reinforcement Learning

Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments.Peer reviewe

A Thermal Performance Analysis and Comparison of Fiber Coils with the D-CYL Winding and QAD Winding Methods

The thermal performance under variable temperature conditions of fiber coils with double-cylinder (D-CYL) and quadrupolar (QAD) winding methods is comparatively analyzed. Simulation by the finite element method (FEM) is done to calculate the temperature distribution and the thermal-induced phase shift errors in the fiber coils. Simulation results reveal that D-CYL fiber coil itself has fragile performance when it experiences an axially asymmetrical temperature gradient. However, the axial fragility performance could be improved when the D-CYL coil meshes with a heat-off spool. Through further simulations we find that once the D-CYL coil is provided with an axially symmetrical temperature environment, the thermal performance of fiber coils with the D-CYL winding method is better than that with the QAD winding method under the same variable temperature conditions. This valuable discovery is verified by two experiments. The D-CYL winding method is thus promising to overcome the temperature fragility of interferometric fiber optic gyroscopes (IFOGs)

Multi-Level Fusion Indoor Positioning Technology Considering Credible Evaluation Analysis

Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity

Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration

Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences

Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.Peer reviewe