Fast Adaptation Research Articles

Factors Predicting the Adaptation of Studens Attending National Defense and Security Education at VNU National Defense and Security Traning Cennter

Adaptation is important for all students attending universities. Students with a good and fast adaptation will help promote their own strengths and achieve high learning results. The study aimed to analyze the adaptation of students attending National Defense and Security Education at VNU National Defense and SecurityTraining Center and analyze the factors predicting the adaptation of students attending National Defense and Security Education at the Center. Through the quantitative research method with a research sample of 300 students taking the course National Defense and Security Education at the Center, the research results show that the factors belonging to students, the factors belonging to VNU National Defense and Security Training Center and family and social factors have different influences on the adaptability of students taking the course National Defense and Security Education at the Center. In particular, family and social factors have a great influence on the level of adaptation in students taking the course National Defense and Security Education at the Center. From there, the study proposes solutions to improve the adaptability of students taking the course National Defense and Security Education at the Center in the coming time.

Maternal effects of human-mediated disturbances on the growth and reproductive traits of different ecotypes of an annual invasive plant Erigeron annuus

A multifactorial combination of human-mediated disturbances is now recognized as a major driver of the global rise and spread of invasive alien plants. Among the numerous invasive alien plants, annuals have fast adaptability and evolution via maternal effect in human-mediated disturbance environments, such as chemical and physical controls. The characteristic enables them to quickly disperse and establish new habitats. Thus, understanding the adaptability and evolution of invasive plants in response to multifactorial human-mediated disturbances is crucial for developing effective management strategies to control their spread and minimize their impacts on ecosystems and human activities. In order to test the growth and reproductive responses in both the parent and offspring generations, two ecotypes (clump and scatter) of an annual invasive plant, Erigeron annuus, were subjected to simulated trampling, defoliation (simulated herbivory), and herbicide disturbances. We found that disturbances negatively affected the growth and reproduction of E. annuus in parent and offspring generations. In comparison to the scatter ecotype plants, the clump ecotype plants had greater growth and reproductive production, as well as a reduced maternal interference effect to cope with human-mediated disturbances. It enhanced the adaptability of their offspring to disturbances. The findings imply that the evolutionary advantage of clump ecotype plants may explain why they can thrive in disturbed habitats and become invasive in some regions.

Adaptive micro-projection system based on liquid lens

Projectors play an important role in the fields of education, entertainment, etc. The conventional projectors are generally bulky, lack of portability and adaptability. In this paper, an adaptive micro-projection system based on liquid lens is proposed. The main part of the system consists of a sequential three-color light source module, a light-homogenized module, a piece of liquid–crystal-on-silicon (LCoS) with high refresh rate, a polarization beam splitter (PBS), a zoom projection lens based on liquid lenses, a liquid lens controller and a display driver board. By adjusting the driving voltages on the liquid lenses, the adaptive micro-projection system can work with the distance at least from 150 mm to 310 mm continuously. Moreover, by synchronously controlling the refresh of the LCoS and the sequential lighting of the three-color light source through the display driver board, a 60 Hz color video display can be realized with only one piece of LCoS. We introduce the design method of the liquid lens based zoom projection lens in detail and provide an optical structure of the liquid lens based zoom projection lens with excellent performance. The control software and hardware of the system are carefully developed. Simulations and experiments demonstrate the feasibility of the system. The proposed system has the characteristics of fast response, portability, integration and high adaptability and is expected to be used in virtual reality (VR), augmented reality (AR), on-board display, head-mounted display and so on.

Peculiarities of Respiration and Photosynthesis Processes of Plants Belonging to the Genera Catalpa L. and Paulownia L. Used in Landscaping of the City of Vanadzor

Key words: global climate change, biodiversity, greening, introduced species The species composition of biodiversity is negatively affected by global climate change. As a result of global warming, changes in the physiological processes of many plant species occur. Due to global warming, it has been recorded that many introduced plant species are well adaptable in the wild and suitable for cultivation. Presumably plants, belonging to the genera Catalpa L. and Paulownia L. are such species of plants. Given the projections of global warming, the selection of plant species for urban greening should prioritize not only their fast growth rate and adaptability but also an examination of their fundamental physiological processes, specifically focusing on photosynthesis and respiration, within the specific urban environment. Based on the research conducted in the city of Vanadzor in June and August 2023, it has been determined that plants from the genera Catalpa L. and Paulownia L. are among the introduced species utilized for the city's landscaping. Previously, plant species belonging to these genera were not used in landscaping. In all likelihood, the climatic conditions of Vanadzor were unfavorable for growing these plants. Due to the increase in average annual temperature, plants belonging to these two genera were discovered in the green spaces of the city of Vanadzor. In the trees belonging to the genus Catalpa L. in the city of Vanadzor, there was a recorded increase in photosynthetic activity by 1.025 times at the end of the growing season. This is significant because the utilization of this plant type for city greening can help reduce CO2 concentration in the atmosphere. On the other hand, the examination of the basic physiological processes in trees belonging to the genus Paulownia L. indicates a high rate of respiration intensity, resulting in the release of CO2 gas into the atmosphere in these plant species. These findings emphasize the need for a scientific approach to landscaping, taking into account the projected climate changes in the city of Vanadzor.

Two-Dimensional Angular Super-Resolution for Airborne Real Aperture Radar by Fast Conjugate Gradient Iterative Adaptive Approach

Two-Dimensional Angular Super-Resolution for Airborne Real Aperture Radar by Fast Conjugate Gradient Iterative Adaptive Approach

Dönüşümcü Liderliğin Havayolu İşletmelerinin Başarısındaki Rolünü İncelemeye Yönelik Karşılaştırmalı Bir Araştırma: Bulutların Üstüne Tırmanırken ve From Worst To First Kitaplarının İncelenmesi

The aviation sector is highly affected by technological, political and economic developments. It is an undeniable fact that businesses must keep up with these rapid changes and developments, especially as they are affected by technological developments. The leadership styles and leadership characteristics of the managers are not only effective in the formation of a certain culture, but also in the life course of the enterprises and the processes of adapting to the changes. This adaptation process can be reduced and the faster adaptation can be achieved, the business success will follow in parallel. Based on this view, in our study, the effect of leadership characteristics of managers in airline companies on business success has been examined. The aim of the study is to explain the transformational leadership characteristics of managers in airline companies and the roles of these characteristics in the positive or negative processes of airline businesses. In the study, "Bulutların Üstüne Tırmanırken" and "From Worst To First" books' content analysis was made by considering the subjects of airline management, leadership and transformational leadership. At the end of research; It has been concluded that leaders do not take risks and display an innovative approach in times of crisis.

Task-based Meta Focal Loss for Multilingual Low-resource Speech Recognition

Low-resource automatic speech recognition is a challenging task due to a lack of labeled training data. To resolve this issue, multilingual meta-learning learns a better model initialization from many source language tasks for fast adaptation to unseen target languages. However, for diverse source languages, the quantity and difficulty vary greatly because of their different data scales and phonological systems. These differences lead to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR. In this work, we propose a task-based meta focal loss (TMFL) approach to address this tough challenge. Specifically, we introduce a hard-task moderator and update the meta-parameters using gradients from both the support set and query set. Our proposed approach focuses more on hard tasks and makes full use of the data from hard tasks. Moreover, we analyze the significance of the hard task moderator and interpret its significance at the sample level. Experiment results show that the proposed method, TMFL, significantly outperforms the state-of-the-art multilingual meta-learning on all target languages for the IARPA BABEL and OpenSLR datasets, especially under very-low-resource conditions. In particular, it can reduce character error rate from 72% to 60% by fine-tuning the pre-trained model with about 22 hours of Vietnamese data.

Mapping evidence-based interventions to the care of unaccompanied minor refugees using a group formulation approach.

How we adapt treatment algorithms to complex, clinically untested, difficult-to-engage patient groups without losing evidence base in everyday practice is a clinical challenge. Here we describe process and reasoning for fast, pragmatic, context-relevant and service-based adaptations of a group intervention for unaccompanied minor asylum seekers (UASC) arriving in Europe. We employed a distillation-matching model and deployment-focused process in a mixed-method, top-down (theory-driven) and bottom-up (participant-informed) approach. Prevalence of mental disorders amongst UASC is extremely high. They also represent a marginalised and hard-to-engage group with limited evidence for effective treatments. Content and process adaptations followed four steps: (1) descriptive local group characterisation and theoretical formulation of problems; (2) initial adaptation of evidenced treatment, based on problem-to-component grid; (3) iterative adaptation using triangulated feedback; and (4) small-scale pilot evaluation. Based on evidence and participant feedback, adaptations included minimising verbal demands, facilitating in-session inductive learning, fostering social connectedness via games, enhancing problem-solving skills, accounting for multi-traumatisation, uncertainty and deportation. Quantitative evaluation suggested improved feasibility, with increased attendance, low drop-out and symptom improvement on depression and trauma scores. By describing the principles under-pinning development of a group intervention for severely traumatised UASC, we contribute to the literature supporting dynamic adaptations of psychological interventions, without losing reference to evidence base. Complex and difficult-to-reach clinical groups are often those in most need of care, yet least researched and most affected by inequality of care. Pragmatic adaptations of proven programs are often necessary to increase feasibility.

An open-loop hybrid photovoltaic solar thermal evacuated tube energy system: A new configuration to enhance techno economic of conventional photovoltaic solar thermal system

One inherent problem of the conventional hybrid photovoltaic solar thermal system is the gradual loss of module power efficiency with low energy harvesting due to steady increase in module temperature with repeated circulation of the cooling water as a result of its closed loop configuration. This poor energy harvesting couple with high initial investment has made investment in this technology unattractive. This study proposes an enhanced hybridisation of the Photovoltaic Solar Thermal System collector with an evacuated tube based on an open-loop cooling configuration and unique flow control strategy. The aim is to improve cooling of solar panel leading to higher energy harvesting and utilisation that improves economic viability that will promote faster adoption. A numerical model is formulated using TRNSYS 18 software to assess the techno economics of the hybrid system. The model is validated using the verification and experimental methods. Overall, the findings show that the enhanced hybridisation and flow control strategy has resulted in higher annual renewable power generation and utilisation by 10 % than the basic hybrid Photovoltaic Solar Thermal system. This has resulted in significant economic savings, cutting operating expenses by 48 %. The ability to tailor the intended use of the system to meet with hot water and electrical power demand of different residential building is another attribute of proposed system that improve its energy harvesting reducing it payback period and doubling it economic benefit hence promoting it fast adaptability.

Meta-IRLSOT++: A meta-inverse reinforcement learning method for fast adaptation of trajectory prediction networks

Recent research on pedestrian trajectory prediction based on deep learning has made significant progress. However, the previous methods do not deeply explore the relationship between scene information and trajectory. Moreover, the training model requires massive data and only targets specific scenes, so the prediction performance is poor when the new scene samples are limited. To solve the above problems, a meta-inverse reinforcement learning-based framework, dubbed Meta-IRLSOT++, is proposed in this work. IRLSOT++ improves the solid baseline IRLSOT, and the main contributions are as follows: (1) An inverse reinforcement learning framework is introduced to explore the trajectory–scene association to achieve task-level scene understanding, enhancing the correlation between trajectory and scene. The trajectory heat maps describing pedestrians dynamic characteristics are leveraged to align better the trajectory and scene semantic segmentation predicted by TopFormer. (2) A Transformer-based encoder–decoder network is proposed to fuse the trajectory and plan cues for better generating multi-modal trajectories with multi-head attention. The social graph attention and pseudo-oracle predictor are introduced to capture pedestrians’ social interactions and intent states, ameliorating trajectory prediction performance. (3) Meta-learning is leveraged to achieve collaborative training based on IRLSOT++ to improve the model generalization in new scenes, enabling fast adaptation of trajectory prediction. Experimental results on the Stanford Drone Dataset (SDD) indicate that IRLSOT++ can precisely forecast future trajectories by improving IRLSOT, decreasing Average Displacement Error/ Final Displacement Error (ADE/FDE) values from 9.66/13.05 to 8.36/12.28. Moreover, the meta-learning strategy quickly adapts IRLSOT++ to the new scene, achieving ADE/FDE values of 7.31/11.02 and 15.42/26.55 when Tpred is set to 12 and 24, respectively. Both quantitative and qualitative experimental results show that Meta-IRLSOT++ has accuracy and fast adaptability, which is beneficial for real-world trajectory prediction tasks.

Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques

Landslide susceptibility assessment (LSA) evaluates the likelihood of landslide occurrences and can help mitigate and prevent landslide risks. Recently, there have been vast applications of data-driven LSA methods owing to the increased availability of high-quality satellite data and landslide inventories. However, two issues remain to be addressed, as follows: (a) Items in a landslide inventory are mainly historical landslides from the interpretation of optical images and site investigation, resulting in predictive models trained with these items being insensitive to undetectable slope movements, such as slow-moving landslides that have not yet occurred; (b) Most study areas contain a variety of landslide-prone geographical settings that a single model can not accommodate well. Considering the complex landslide causes in Hong Kong with a land area of approximately 1108 km2, we proposed the utilization of multi-temporal InSAR techniques to generate weak landslide samples from slopes with ground surface movements for landslide inventory augmentation; and meta-learn intermediate representations for the fast adaptation of LSA models corresponding to different landslide-prone geographical settings. Besides, we performed feature permutation to identify dominant landslide-predisposing factors. The LSA results in Hong Kong revealed that slope deformation in several mountainous areas is closely associated with the occurrence of recorded landslides. By augmenting the landslide inventory using InSAR techniques, the proposed method enhanced the LSA models' capacity to identify slow-moving landslides and achieved better statistical performance. The discussion highlights that slope and stream power index (SPI) are the key landslide-predisposing factors in Hong Kong, but the dominant landslide-predisposing factors will vary under different geographical conditions. By comparison with the methods that treat LSA as a binary classification problem, such as support vector machine, multilayer perceptron, deep belief network, and random forest based LSA methods, the proposed method entails a fast-learning strategy and outperforms these methods in data-driven model evaluation indicators, e.g., by 3–6% in accuracy, 2–6% in precision, 1–2% in recall, 3–5% in F1-score, and approximately 10% in Cohen Kappa. The information about the relative importance of landslide predisposing factors, derived through feature permutation, can foster guidance for targeted landslide prevention schemes, such as constructing and maintaining slope consolidation facilities in areas where slope is the dominant landslide-predisposing factor.

Parental exposure to heat waves improves offspring reproductive investment in Tetranychus urticae (Acari: Tetranychidae), but not in its predator, Phytoseiulus persimilis (Acari: Phytoseiidae).

The more frequent and intense occurrence of heat waves is a challenge for arthropods because their unpredictable incidence requires fast adaptations by the exposed individuals. Phenotypic plasticity within and across generations might be a solution to cope with the detrimental effects of heat waves, especially for fast-developing, small arthropods with limited dispersal abilities. Therefore, we studied whether severe heat may affect the reproduction of a pest species, the spider mite Tetranychus urticae, and its counterpart, the predatory mite Phytoseiulus persimilis. Single offspring females with different parental thermal origins (reared under mild or extreme heat waves) of both species were exposed to mild or extreme heat waves on bean leaves over 10 days, and the oviposition, egg sizes, survival, and escape behavior of the females were evaluated daily. The total losses of predators mainly via escapers were very high compared to prey, which makes a separation between selective and plastic effects on shifted reproductive traits impossible. Predator females laid smaller eggs, while their consumption and oviposition rates were unaffected during extreme heat waves. In comparison, larger prey females fed more and produced more, but smaller, eggs due to within- and trans-generational effects. These advantages for the prey in comparison to its predator when exposed to extreme heat waves during the reproductive phase support the trophic sensitivity hypothesis: higher trophic levels (i.e., the predator) are more sensitive to thermal stress than lower trophic levels (i.e., the prey). Furthermore, the species-specific responses may reflect their lifestyles. The proactive and mobile predator should be selected for behavioral thermoregulation under heat waves via spatiotemporal avoidance of heat-exposed locations rather than relying on physiological adaptations in contrast to the more sessile prey. Whether these findings also influence predator-prey interactions and their population dynamics under heat waves remains an open question.

Design, Performance Analysis, and Experiments of a Soft Robot for Rescue

Abstract In the narrow and irregular environment of the ruins, the existing rescue robots are struggling to achieve their performance. Inspired by the process of termite predation by giant anteaters, we propose a soft rescue robot that utilizes motion propulsion similar to gear meshing and the adaptability of a continuum manipulator. The robot, consisting of a soft continuum manipulator and driving equipment, has the characteristics of fast propulsion and adaptation to unstructured environments. The driving device can give the manipulator a maximum speed of 14.67 cm/s and a propulsive force of 19.20 N. With the flexibility of the soft robot, the soft manipulator can adapt to the environment under propulsion to pass obstacles. The experiments of self-adaptability performance tests under different conditions show that the robot can pass over obstacles with an angle of up to 80.57 deg between its axis and the contact surface. In the actual ruin experiment, the robot could penetrate 1.3 m deep in the narrow passage formed by the bricks with the mode. The experiment indicates the presented rescue robot design's feasibility. Our work could contribute to the research on the interaction of soft robots with their environment.

Ensemble learning model identifies adaptation classification and turning points of river microbial communities in response to heatwaves.

Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 to 37°C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 μm (PM2.5, an indicator of human activities) increase by twofold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events.

An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).

Dual Adaptive Representation Alignment for Cross-Domain Few-Shot Learning.

Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications. Toward this issue, we propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains. Under this realistic setting, we focus on the fast adaptation capability of meta-learners by proposing an effective dual adaptive representation alignment approach. In our approach, a prototypical feature alignment is first proposed to recalibrate support instances as prototypes and reproject these prototypes with a differentiable closed-form solution. Therefore feature spaces of learned knowledge can be adaptively transformed to query spaces by the cross-instance and cross-prototype relations. Besides the feature alignment, we further present a normalized distribution alignment module, which exploits prior statistics of query samples for solving the covariant shifts among the support and query samples. With these two modules, a progressive meta-learning framework is constructed to perform the fast adaptation with extremely few-shot samples while maintaining its generalization capabilities. Experimental evidence demonstrates our approach achieves new state-of-the-art results on 4 CDFSL benchmarks and 4 fine-grained cross-domain benchmarks.

Autonomous Cross Domain Adaptation Under Extreme Label Scarcity.

A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing environments. Notwithstanding that existing multistream classifiers assume no labeled samples in the target stream, they still incur expensive labeling costs since they require fully labeled samples of the source stream. This article aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labeled samples of the source stream are provided before process runs. Our solution, namely, Learning Streaming Process from Partial Ground Truth (LEOPARD), is built upon a flexible deep clustering network where its hidden nodes, layers, and clusters are added and removed dynamically with respect to varying data distributions. A deep clustering strategy is underpinned by a simultaneous feature learning and clustering technique leading to clustering-friendly latent spaces. A domain adaptation strategy relies on the adversarial domain adaptation technique where a feature extractor is trained to fool a domain classifier by classifying source and target streams. Our numerical study demonstrates the efficacy of LEOPARD where it delivers improved performances compared to prominent algorithms in 15 of 24 cases. Source codes of LEOPARD are shared in https://github.com/wengweng001/LEOPARD.git to enable further study.

On relaxation times of heteroclinic dynamics.

Heteroclinic dynamics provide a suitable framework for describing transient dynamics such as cognitive processes in the brain. It is appreciated for being well reproducible and at the same time highly sensitive to external input. It is supposed to capture features of switching statistics between metastable states in the brain. Beyond the high sensitivity, a further desirable feature of these dynamics is to enable a fast adaptation to new external input. In view of this, we analyze relaxation times of heteroclinic motion toward a new resting state, when oscillations in heteroclinic networks are arrested by a quench of a bifurcation parameter from a parameter regime of oscillations to a regime of equilibrium states. As it turns out, the relaxation is underdamped and depends on the nesting of the attractor space, the size of the attractor's basin of attraction, the depth of the quench, and the level of noise. In the case of coupled heteroclinic units, it depends on the coupling strength, the coupling type, and synchronization between different units. Depending on how these factors are combined, finite relaxation times may support or impede a fast switching to new external input. Our results also shed some light on the discussion of how the stability of a system changes with its complexity.

Molecular epidemiology and genomic dynamics of Pseudomonas aeruginosa isolates causing relapse infections.

Pseudomonas aeruginosa (P. aeruginosa) is one of the leading causes of chronic infections, including reinfection, relapse, and persistent infection, especially in cystic fibrosis patients. Relapse P. aeruginosa infections are more harmful because of repeated hospitalization and undertreatment of antimicrobials. However, relapse P. aeruginosa infection in China remains largely unknown. Herein, we performed a 3-year retrospective study from 2019 to 2022 in a tertiary hospital, which included 442 P. aeruginosa isolates from 196 patients. Relapse infection was identified by screening clinical records and whole-genome sequencing (WGS). We found that 31.6% (62/196) of patients had relapsed infections. The relapse incidence of carbapenem-resistant P. aeruginosa infection (51.4%) is significantly higher than that of carbapenem-susceptible P. aeruginosa infection (20.2%, P < 0.0001). These isolates were assigned to 50 distinct sequence types and sporadically distributed in phylogeny, indicating that relapsed infections were not caused by certain lineages. Fast adaptation and evolution of P. aeruginosa isolates were reflected by dynamic changes of antimicrobial resistance, gene loss and acquisition, and single-nucleotide polymorphisms during relapse episodes. Remarkably, a convergent non-synonymous mutation that occurs in a pyochelin-associated virulence gene fptA (T1056C, M252T) could be a considerable target for the diagnosis and treatment of relapse P. aeruginosa infection. These findings suggest that integrated utilization of WGS and medical records provides opportunities for improved diagnostics of relapsed infections. Continued surveillance of the genomic dynamics of relapse P. aeruginosa infection will generate further knowledge for optimizing treatment and prevention in the future.IMPORTANCEPseudomonas aeruginosa is a predominant pathogen that causes various chronic infections. Relapse infections promote the adaptation and evolution of antimicrobial resistance and virulence of P. aeruginosa, which obscure evolutionary trends and complicate infection management. We observed a high incidence of relapse P. aeruginosa infection in this study. Whole-genome sequencing (WGS) revealed that relapse infections were not caused by certain lineages of P. aeruginosa isolates. Genomic dynamics of relapse P. aeruginosa among early and later stages reflected a plasticity scattered through the entire genome and fast adaptation and genomic evolution in different ways. Remarkably, a convergent evolution was found in a significant virulence gene fptA, which could be a considerable target for diagnosis and treatment. Taken together, our findings highlight the importance of longitudinal surveillance of relapse P. aeruginosa infection in China since cystic fibrosis is rare in Chinese. Integrated utilization of WGS and medical records provides opportunities for improved diagnostics of relapse infections.

Dopaminergic Dependency of Cholinergic Pallidal Neurons.

We employed the whole-cell patch-clamp method and ChAT-Cre mice to study the electrophysiological attributes of cholinergic neurons in the external globus pallidus. Most neurons were inactive, although approximately 20% displayed spontaneous firing, including burst firing. The resting membrane potential, the whole neuron input resistance, the membrane time constant and the total neuron membrane capacitance were also characterized. The current-voltage relationship showed time-independent inward rectification without a "sag". Firing induced by current injections had a brief initial fast adaptation followed by tonic firing with minimal accommodation. Intensity-frequency plots exhibited maximal average firing rates of about 10Hz. These traits are similar to those of some cholinergic neurons in the basal forebrain. Also, we examined their dopamine sensitivity by acutely blocking dopamine receptors. This action demonstrated that the membrane potential, excitability, and firing pattern of pallidal cholinergic neurons rely on the constitutive activity of dopamine receptors, primarily D2-class receptors. The blockade of these receptors induced a resting membrane potential hyperpolarization, a decrease in firing for the same stimulus, the disappearance of fast adaptation, and the emergence of a depolarization block. This shift in physiological characteristics was evident even when the hyperpolarization was corrected with D.C. current. Neither the currents that generate the action potentials nor those from synaptic inputs were responsible. Instead, our findings suggest, that subthreshold slow ion currents, that require further investigation, are the target of this novel dopaminergic signaling.