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Abstract Understanding stability—whether a community will eventually return to its original state after a perturbation—is a major focus in the study of various complex systems, particularly complex ecosystems. Here, we challenge this focus, showing that short-term dynamics can be a better predictor of outcomes for complex ecosystems. Using random matrix theory, we study how complex ecosystems behave immediately after small perturbations. Our analyses show that many communities are expected to be ‘reactive’, whereby some perturbations will be amplified initially and generate a response that is directly opposite to that predicted by typical stability measures. In particular, we find reactivity is prevalent for complex communities of mixed interactions and for structured communities, which are both expected to be common in nature. Finally, we show that reactivity can be a better predictor of extinction risk than stability, particularly when communities face frequent perturbations, as is increasingly common. Our results suggest that, alongside stability, reactivity is a fundamental measure for assessing ecosystem health.

Background: Low oxygen saturation (LOS) is a frequent occurrence for patients with post-tuberculosis tracheobronchial stenosis (PTTS) during bronchoscopic procedures. However, there are currently no systematic assessment tools to predict LOS risk in PTTS patients during bronchoscopy. Objectives: This study aimed to develop an effective preoperative predictive model to guide clinical practice. Design: Retrospective cohort study. Methods: Data was retrospectively collected from PTTS patients who underwent bronchoscopic interventions between January 2017 and December 2022. Among all patients included in this study, patients between January 2017 and December 2021 were used as training cohort for the logistic regression model, and patients between January 2022 and December 2022 were utilized as validation cohort for internal validation. We used consistency index (C-index), goodness-of-fit test and calibration plot to evaluate the model performance. Results: A total of 465 patients who met the inclusion criteria were enrolled in the study. The overall incidence of LOS was 26.0% (121/465). Comorbidity, degree of stenosis, bronchoscopist level, thermal ablation therapy, balloon dilation, and airway stenting, as independent risk factors for the presence of LOS, were used to construct the nomogram prediction model. The C-index of training cohort was 0.827 (95% CI, 0.786–0.869), whereas that of validation cohort was 0.836 (95% CI, 0.757–0.916), combining with the results of the calibration plot and goodness-of-fit test, demonstrating that this model had good predictive ability. Conclusion: The predictive model and derived nomogram with good predictive ability has been developed to preoperatively predict the risk of LOS in PTTS patients during bronchoscopy, allowing for individualized interventions for high-risk patients.

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM).Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve.Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid.Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

Image encryption technology plays an important role in today’s multimedia applications and Internet information security transmission.However, most image encryption algorithms still have problems, for example, cipher images occupied high bandwidth during transmission, image encryption speed is slow, image encryption algorithms are not associated with plain image, and there is no complete ciphertext feedback mechanism.These problems all affect the security and easy use of image encryption algorithms.In order to solve the above problems, the compressive sensing technology and chaotic system were studied, and a new image encryption algorithm based on double chaotic system and compressive sensing with plaintext association was proposed.The plaintext image association key was used to associate the plain image hash value with the Logistic chaotic system parameters.Discrete wavelet transform, was used to sparse the plain image.Then the random measurement matrix was generated by the Logistic chaos system, and the image was encrypted once by combining the compressive sensing technique and the random measurement matrix to obtain the intermediate image.The intermediate image was hashed again, and the Rucklidge chaos system initial value was associated with the plain image hash value together.The Rucklidge chaotic system and encryption algorithm were used to control the intermediate image for secondary encryption and accordingly obtain the cipher image.The encryption algorithm was a new plaintext correlation encryption algorithm, which used the image’s own pixel value to control the scrambling of the intermediate image.It also enhanced the plaintext association and established a ciphertext feedback mechanism.Simulation results and performance analysis show that the algorithm has good encryption performance.The encrypted image can be compressed according to the compression ratio, effectively reducing the size of the cipher image.Moreover, it is resistant to common attacks such as known plaintext attack, selective plaintext attack and differential attack, which is better than other common image encryption algorithms.

Background: Postoperative new atrial fibrillation (POAF) is a commonly observed complication after off-pump coronary artery bypass surgery (OPCABG), and models based on radiomics features of epicardial adipose tissue (EAT) on non-enhanced computer tomography (CT) to predict the occurrence of POAF after OPCABG remains unclear. This study aims to establish and validate models based on radiomics signature to predict POAF after OPCABG. Methods: Clinical characteristics, radiomics signature and features of non-enhanced CT images of 96 patients who underwent OPCABG were collected. The participants were divided into a training and a validation cohort randomly, with a ratio of 7:3. Clinical characteristics and EAT CT features with statistical significance in the multivariate logistic regression analysis were utilized to build the clinical model. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify significant radiomics features to establish the radiomics model. The combined model was constructed by integrating the clinical and radiomics models. Results: The area under the curve (AUC) of the clinical model in the training and validation cohorts were 0.761 (95% CI: 0.634–0.888) and 0.797 (95% CI: 0.587–1.000), respectively. The radiomics model showed better discrimination ability than the clinical model, with AUC of 0.884 (95% CI: 0.806–0.961) and 0.891 (95% CI: 0.772–1.000) respectively for the training and the validation cohort. The combined model performed best and exhibited the best predictive ability among the three models, with AUC of 0.922 (95% CI: 0.853–0.990) in the training cohort and 0.913 (95% CI: 0.798–1.000) in the validation cohort. The calibration curve demonstrated strong concordance between the predicted and actual observations in both cohorts. Furthermore, the Hosmer-Lemeshow test yielded p value of 0.241 and 0.277 for the training and validation cohorts, respectively, indicating satisfactory calibration. Conclusions: The superior performance of the combined model suggests that integrating of clinical characteristics, radiomics signature and features on non-enhanced CT images of EAT may enhance the accuracy of predicting POAF after OPCABG.

Chronic skin inflammatory diseases including atopic dermatitis (AD) and psoriasis have been considered uncontrolled inflammatory responses, which have usually troubled patients around the world. Moreover, the recent method to treat AD and psoriasis has been based on the inhibition, not regulation, of the abnormal inflammatory response, which can induce a number of side effects and drug resistance in long-term treatment. Mesenchymal stem/stromal cells (MSCs) and their derivatives have been widely used in immune diseases based on their regeneration, differentiation, and immunomodulation with few adverse effects, which makes MSCs a promising treatment for chronic skin inflammatory diseases. As a result, in this review, we aim to systematically discuss the therapeutic effects of various resources of MSCs, the application of preconditioning MSCs and engineering extracellular vesicles (EVs) in AD and psoriasis, and the clinical evaluation of the administration of MSCs and their derivatives, which can provide a comprehensive vision for the application of MSCs and their derivatives in future research and clinical treatment.

Blood-brain barrier (BBB) is the most important component of central nervous system (CNS) to keep toxins and pathogens from CNS. Although our studies demonstrated that using interleukin-6 antibodies (IL-6-AB) reversed the increased permeability of BBB, IL-6-AB is limited in their application that only could be used a few hours before surgery and seemed delayed the surgical wounds healing process, which urges us to find another more effective method. In this study, we employed the C57BL/6J female mice to investigate the potential effects of umbilical cord-derived mesenchymal stem cells (UC-MSCs) transplantation on BBB dysfunction induced by surgical wound. Compared to IL-6-AB, the transplantation of UC-MSCs more effectively decreased the BBB permeability after surgical wound evaluated by dextran tracer (immunofluorescence imaging and luorescence quantification). In addition, UC-MSCs can largely decrease the ratio of proinflammatory cytokine IL-6 to the anti-inflammatory cytokine IL-10 in both serum and brain tissue after surgical wound. Moreover, UC-MSCs successfully increased the levels of tight junction proteins (TJs) in BBB such as ZO-1, Occludin, and Claudin-5 and extremely decreased the level of matrix metalloproteinase-9 (MMP-9). Interestingly, UC-MSCs treatment also had positive effects on wound healing while protecting the BBB dysfunction induced by surgical wound compared to IL-6-AB treatment. These findings suggest that UC-MSCs transplantation is a highly efficient and promising approach on protecting the integrity of BBB which caused by peripheral traumatic injuries.

BackgroundTrichilemmal carcinoma (TLC) is a rare malignant cutaneous adnexal neoplasm, with no relatively comprehensive research.ObjectiveThe aim of this study is to perform an updated statistical analysis so as to better understand TLC’s epidemiology, clinical features, diagnosis, and treatment.MethodsThe diagnosis and treatment of three TLC cases in our department were summarized. Then, all TLC cases published in the literature were retrieved for a comprehensive analysis, followed by the analysis of global trends and regional distribution, demographic characteristics, clinical features, pathogenesis, histopathological features, and treatment and prognosis of TLC.ResultsOf the 231 cases, the incidence of TLC has shown an upward trend recently, especially in China, in Asia. The susceptible population is men aged 60–80 and women over 80, and the most prone location is head and neck. The phenotype of TLC is not always typical and may be misdiagnosed because of the coexistence of other diseases. There is a linear relationship between the diameter and its duration or thickness. UV, locally present skin lesions, trauma, scarring, organ transplantation, and genetic disorders may trigger the occurrence of TLC. Periodic acid–Schiff staining and CD34, but not Epithelial Membrane Antigen (EMA), were helpful in the diagnosis of TLC. Although effective, surgical excision and Mohs micrographic surgery need further improvement to reduce recurrence of TLC. Carcinoma history is an independent risk factor for TLC recurrence.LimitationsThe limitation of this study is the lack of randomized controlled trial on TLC treatment and recurrence.ConclusionTLC has the possibility of invasive growth and recurrence, especially in patients with longer duration and carcinoma history.

BackgroundCommunication between fibroblasts and endothelial cells is essential for skin wound repair and regeneration. Extracellular vesicles (EVs) are crucial for intracellular communication by transporting active molecules. However, whether EVs derived from diabetic fibroblasts can perform the nomal communication function is unclear. Here, we compared the effects of EVs from human skin fibroblasts (HSFs) induced with or without HG on the angiogenic function of endothelial cells and wound healing.MethodsWe first collected EVs from HSFs cultured with normal glucose concentration (NG-EVs) or with HG concentration (HG-EVs) and applied them to treat human umbilical vein endothelial cells (HUVECs). The cells were divided into three groups: control group, NG-EVs group, and HG-EVs group. We then examined the proliferation, migration, apoptosis, and tube formation of HUVECs. To illustrate the mechanism, the expression of β-catenin, GSK-3β, and p-GSK-3β was detected by western-blot. Finally, NG-EVs or HG-EVs were used to treat the wounds of mice to determine their role in wound closure.ResultsBy DNA content detection, Annexin V/PI staining, and EdU staining, we found that NG-EVs promoted HUVEC proliferation, while HG-EVs exhibited an opposite effect (p

Designing intelligent microrobots that can autonomously navigate and perform instructed routines in blood vessels, a crowded environment with complexities including Brownian disturbance, concentrated cells, confinement, different flow patterns, and diverse vascular geometries, can offer enormous opportunities and challenges in biomedical applications. Herein, a biological‐agent mimicking a hierarchical control scheme that enables a microrobot to efficiently navigate and execute customizable routines in simplified blood vessel environments is reported. The control scheme consists of two decoupled components: a high‐level controller decomposing complex navigation tasks into short‐ranged, simpler subtasks and a low‐level deep reinforcement learning (DRL) controller responsible for maneuvering microrobots to accomplish subtasks. The proposed DRL controller utilizes 3D convolutional neural networks and is capable of learning control policies directly from raw 3D sensory data. It is shown that such a control scheme achieves effective and robust decision‐making within unseen, diverse complicated environments and offers flexibility for customizable task routines. This study provides a proof of principle for designing intelligent control systems for autonomous navigation in vascular networks for microrobots.

Smoking is an essential risk factor for peri-implant diseases. It also hampers the clinical outcomes of peri-implant therapies. Nonetheless, the effect of smoking can go undetected until the emergence of clinical signs. Bacterial-induced inflammation is responsible for the initiation and progression of peri-implant diseases. We hypothesize that smoking impacts the peri-implant microbiome even in status of clinical health, putting it into a sub-healthy condition that responds poorly to peri-implant treatments. To validate this, peri-implant plaque samples from 18 participants including 10 smokers (S) and 8 non-smokers (NS), who had received implant prostheses were analyzed using metagenomic shotgun sequencing. The results showed that in addition to taxonomical and functional differences, the local stability in the S group was also shown to be much higher than that in the NS group, indicating greater stubbornness of the peri-implant microbiome associated with smoking. Besides, the topological structures were also distinct between the two groups. The highly connected species interacted more preferentially with each other in the S group (eigenvector centralization, 0.0273 in S and 0.0183 in NS), resulting in a greater tendency of forming small-world modules (modularity, 0.714 in S and 0.582 in NS). While in the NS group, inter-species correlations were more evenly distributed (clustering coefficient, 0.532 in S and 0.666 in NS). These alterations overall explained the greater stubbornness of the peri-implant microbiome associated with smoking, which may cause poor responsiveness to peri-implant therapies. From a microbial perspective, this may be a potential reason why smoking impacts negatively on the outcome of peri-implant treatments.

Neurological degeneration after neuroinflammation, such as that resulting from Alzheimer’s disease (AD), stroke, multiple sclerosis (MS), and post-traumatic brain injury (TBI), is typically associated with high mortality and morbidity and with permanent cognitive dysfunction, which places a heavy economic burden on families and society. Diagnosing and curing these diseases in their early stages remains a challenge for clinical investigation and treatment. Recent insight into the onset and progression of these diseases highlights the permeability of the blood–brain barrier (BBB). The primary factor that influences BBB structure and function is inflammation, especially the main cytokines including IL-1β, TNFα, and IL-6, the mechanism on the disruption of which are critical component of the aforementioned diseases. Surprisingly, the main cytokines from systematic inflammation can also induce as much worse as from neurological diseases or injuries do. In this review, we will therefore discuss the physiological structure of BBB, the main cytokines including IL-1β, TNFα, IL-6, and their mechanism on the disruption of BBB and recent research about the main cytokines from systematic inflammation inducing the disruption of BBB and cognitive impairment, and we will eventually discuss the need to prevent the disruption of BBB.

BackgroundIntraplaque hemorrhage (IPH) is an important feature of unstable plaques and an independent risk factor for cardiovascular events. However, the molecular mechanisms contributing to IPH are incompletely characterized. We aimed to identify novel biomarkers and interventional targets for IPH and to characterize the role of immune cells in IPH pathogenesis.MethodsThe microarray dataset GSE163154 which contain IPH and non-IPH plaque samples was obtained from the Gene Expression Omnibus (GEO). R software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation. The hub genes were carried by protein-protein interaction (PPI) network and were validated by the GSE120521 dataset. CIBERSORT deconvolution was used to determine differential immune cell infiltration and the relationship of immune cells and hub genes. We confirmed expression of proteins encoded by the hub genes by immunohistochemistry and western blotting in 8 human carotid endarterectomy samples with IPH and 8 samples without IPH (non-IPH).ResultsWe detected a total of 438 differentially expressed genes (DEGs), of which 248 were upregulated and 190 were downregulated. DEGs were mainly involved in inflammatory related pathways, including neutrophil activation, neutrophil degranulation, neutrophil-mediated immunity, leukocyte chemotaxis, and lysosomes. The hub genes found through the method of degree in the PPI network showed that ITGB2 and ITGAM might play an important role in IPH. Receiver operating characteristic (ROC) results also showed a good performance of these two genes in the test and validation dataset. We found that the proportions of infiltrating immune cells in IPH and non-IPH samples differed, especially in terms of M0 and M2 macrophages. Immunohistochemistry and western blotting analysis showed that expression levels of ITGB2 and ITGAM increased significantly in carotid atherosclerotic plaques with IPH.ConclusionITGB2 and ITGAM are key hub genes of IPH and may play an important role in the biological process of IPH. Our findings advance our understanding of the underlying mechanisms of IPH pathogenesis and provide valuable information and directions for future research into novel targets for IPH diagnosis and immunotherapy.

Periodontitis and peri-implantitis are common biofilm-mediated infectious diseases affecting teeth and dental implants and have been considered to be initiated with microbial dysbiosis. To further understand the essence of oral microbiome dysbiosis in terms of bacterial interactions, community structure, and microbial stability, we analyzed 64 plaque samples from 34 participants with teeth or implants under different health conditions using metagenomic sequencing. After taxonomical annotation, we computed the inter-species correlations, analyzed the bacterial community structure, and calculated the microbial stability in supra- and subgingival plaques from hosts with different health conditions. The results showed that when inflammation arose, the subgingival communities became less connective and competitive with fewer hub species. In contrast, the supragingival communities tended to be more connective and competitive with an increased number of hub species. Besides, periodontitis and peri-implantitis were associated with significantly increased microbial stability in subgingival microbiome. These findings indicated that the periodontal and peri-implant dysbiosis is associated with aberrant alterations in the bacterial correlations, community structures, and local stability. The highly connected hub species, as well as the major contributing species of negative correlations, should also be given more concern in future studies.

Motivated by the quantum Fourier transform (QFT), a sequential quantum multiparty signature (QMS) scheme is proposed. Several signatories jointly accomplish the task in a chaotic encryption system. Alice generates and sends the encrypted message with the quantum Fourier transform. Signatories provide individual signatures in a certain order, while Bob verifies the authenticity of the combined multiple signatures after the eavesdropping check phase with help of the arbitrator. Analysis shows the correctness and security of the proposed scheme. This QMS protocol increases the efficiency of verification compared to arbitrated quantum signatures and broadcasting multiparty signature. This QMS protocol will be widely used for online e-government and e-business systems.

Intelligent microrobot systems at the microscopic scale provide enormous opportunities for emerging biomedical and environmental applications. Herein, a multiagent stochastic feedback control framework to control colloidal microrobot swarms for capturing Brownian cargo particles in complex environments such as mazes is proposed. The decision‐making module in the control framework consists of the adaptive generation of target sites surrounding the cargo, optimal target assignment, and approximate motion planning. The stochastic trajectories of robot swarms are efficiently navigated toward their exclusively assigned target around the cargo particle and enable the cargo to be captured. The capture strategy realized by the control framework is robust, adaptive, and flexible in that it accommodates diverse local geometries in the vicinity of a cargo, swarm, and maze sizes and is able to spontaneously split the workforce to catch multiple Brownian cargo particles via multitasking. The present intelligent robot swarm enabled by the multiagent control offers a path to realize complex functions at the microscopic scale in a resilient and flexible manner.

Almost all of the previous quantum private query (QPQ) protocols are always based on an ideal environment without any noise. Nevertheless, due to the defects of the channel, channel noise inevitably exists in the actual transmission process, which will give rise to transmission error and reduce the probability of successful quantum transmission. An eavesdropper may disguise her actions as channel noise to avoid being detected by legitimate parties during security checks. To solve this problem, we present a QPQ protocol with perfect performance universally applicable against collective noise. Concretely, the protocol encodes bits in noiseless subspace and thus can function over a quantum channel subjected to an arbitrary degree of collective noise, as occurs, for instance, due to polarization rotation in an optical fiber. Furthermore, Bob cannot get the information about Alice's choice at all by taking fake entangled attack and by taking fake photon attack, the probability Bob can get the information about Alice's choice is only approximately 6.7%, which is far less than the probability of 25% in Yang et al.'s protocol. Moreover, the conclusiveness of the user Alice's measurement results is subject to quantum randomness rather than allowing Alice to choose by herself to obtain a conclusive result in Wei et al.'s protocol, which prevents Alice's intuitive attack and meanwhile makes it arduous for Bob to perform the joint-measurement attack. Our protocol uses only one state, which reduces the communication complexity, and is, therefore, an effective QPQ solution with the high-security level.

A quantum secure direct communication (QSDC) protocol is presented. In the proposed protocol, the omega state is introduced to detect the eavesdropping during the quantum communication, eavesdropping behaviors will change the state of the omega state, and the sender Alice and the receiver Bob detect eavesdropping through the state measurement results. The relationship between the amount of information that the eavesdropper gets and the probability of being detected is also given. Compared with the original QSDC protocol based on the Bell state, when the same amount of information is obtained, the eavesdropper must face a higher detection probability in the proposed protocol, and the eavesdropper mostly can obtain 0.676 (bit) of information. The security analysis is also given, and the result indicates that the proposed protocol is more secure. A simulation based on the law of large number and the Monte Carlo method is also given, and the mean square error is introduced to describe the similarity between the simulation data and the theoretical value. The simulation result indicates that the proposed security in an ideal environment and the security analysis are correct, and the proposed protocol is more secure by sending more quantum particles in detecting eavesdropping.

With the development of quantum wireless networks, the practical application of quantum private query (QPQ) should conform to the features of quantum wireless networks to achieve any-to-any query, and QPQ requires a new query mechanism. We first propose a scheme for a network private query in a quantum wireless network with multiple third parties, in which any user can query any database with the assistance of the third parties, by measuring each of the distant nodes that initially share entanglement with each other. The required entanglement among these nodes is established by the third-party intermediate node equipped with GHZ generator using multi-qubit GHZ states. A bilayer network topology, as well as the corresponding private query protocol, ensures the rapid and secure private query. Under the protection of the multi-qubit GHZ state's quantum correlation, the privacy of Alice and Bob is protected from the third parties' fake entangled attack and external attack, and they cannot obtain the privacy of each other by taking quantum memory attack. The performance parameters of the proposed scheme are described based on the analysis of classical wireless communication cost and communication delay.

Motivated by the dense coding and teleportation of two-mode squeezed vacuum (TMSV) state, a continuous-variable arbitrated quantum signature (CV-AQS) scheme is proposed. The signer Alice generates the signature by the dense coding and sends it to the verifier Bob. The measurement results of two quadrature components serve for the recovery of the secret message. With the help of the arbitrator Charlie, Bob verifies the validity of the signature. The security of the presented protocol is protected by the calculation of the fidelity parameter and the implementation process of two-mode squeezed vacuum (TMSV) state teleportation. Moreover, the proposed scheme can be correctly implemented in the lossy and lossless transmission with a higher capacity.

Considering the security and efficiency of quantum communication system, one step quantum key distribution protocol based on hyperentangled Bell state is proposed. Every 6 classical bits are divided into a group, which are encoded by single particle and hyperentangled Bell state, the sender Alice sends them to receiver Bob, Bob randomly selects the position of hyperentangled Bell state to measure and detects the existence of eavesdropper Eve by comparing measurement results in his hand and transmitted by Alice. Quantum key distribution protocol based on grouping is proposed to solve the storage problem of quantum information and improve the maneuverability, a security analysis is calculated under the intercept-measure-resend attack which introduce at least an error rate of 46.875%, if Eve wants to get all the information, the probability of detection eavesdropping under the entanglement-measure attack is 93.75%, so it proves that proposed protocol does not need to store the quantum state and asymptotically secure under intercept-measure-resend attack and entanglement-measure attack.

Nano/micromotors (NMMs) are tiny objects capable of converting energy into mechanical motion. Recently, a wealth of active matter including synthetic colloids, cytoskeletons, bacteria, and cells have been used to construct NMMs. The self-sustained motion of active matter drives NMMs out of equilibrium, giving rise to rich dynamics and patterns. Alongside the spontaneous dynamics, external stimuli such as geometric confinements, light, magnetic field, and chemical potential are also harnessed to control the movements of NMMs, yielding new application paradigms of active matter. Here, we review the recent advances, both experimental and theoretical, in exploring biological NMMs. The unique dynamical features of collective NMMs are focused on, along with some possible applications of these intriguing systems.

Equipping micro‐/nanoscale colloidal robots with artificial intelligence (AI) such that they can efficiently navigate in unknown complex environments can dramatically impact their use in emerging applications such as precision surgery and targeted nanodrug delivery. Herein, a model‐free deep reinforcement learning algorithm is developed that trains colloidal robots to efficiently navigate in unknown environments with random obstacles. A deep neural network architecture is used that enables the colloidal robots to mimic animal navigation decision‐making by directly processing raw sensor input and decomposing long‐range navigations to short‐range ones. The trained robot agents learn to make navigation decisions regarding both obstacle avoidance and travel time minimization, based solely on local sensory inputs without prior knowledge of the global environment. Such agents with biologically inspired mechanisms can acquire competitive navigation capabilities in large‐scale, complex environments containing obstacles of diverse shapes, sizes, and configurations. Herein, the potential of AI to enable colloidal robots in extensive applications is illustrated.

An ultrafast high-sensitivity refractive index (RI) and temperature-sensing system based on an optoelectronic oscillator (OEO) is proposed and demonstrated in this paper. A Fabry-Perot fiber Bragg grating (FP-FBG), which combines a gap with two FBGs in a silica V-shaped slot and characterizes a narrow notch in the reflection spectrum, is incorporated in the OEO to implement a microwave photonic filter and perform oscillating frequency selection. A microwave signal is generated by the OEO, whose oscillating frequency is determined by the center frequency of the FP-FBG notch, which varies with the surrounding environments. The RI or the temperature change can be accordingly measured by monitoring the frequency shift of the microwave signal using an electrical spectrum analyzer or a digital signal processor. An experiment is performed to verify the proposal. An RI sensitivity of 413.8 MHz/0.001RIU and a temperature sensitivity of 2516 MHz/°C are successively demonstrated.

Existing bridges with large uncertainties can be assessed with a proof load test. In a proof load test, a load representative of the factored live load is applied to the bridge at the critical position. If the bridge can carry this load without distress, the proof load test shows experimentally that the bridge fulfills the requirements of the code. Because large loads are applied during proof load tests, the structure or element that is tested needs to be carefully monitored during the test. The monitored structural responses are interpreted in terms of stop criteria. Existing stop criteria for flexure in reinforced concrete can be extended with theoretical considerations. These proposed stop criteria are then verified with experimental results: reinforced concrete beams failing in flexure and tested in the laboratory, a collapse test on an existing reinforced concrete slab bridge that reached flexural distress, and the pilot proof load tests that were carried out in the Netherlands and in which no distress was observed. The tests in which failure was obtained are used to evaluate the margin of safety provided by the proposed stop criteria. The available pilot proof load tests are analyzed to see if the proposed stop criteria are not overly conservative. The result of this comparison is that the stop criteria are never exceeded. Therefore, the proposed stop criteria can be used for proof load tests for the failure mode of bending moment in reinforced concrete structures.

We report herein on two cases where high-risk osseous foreign bodies that incarcerated or penetrated the bronchial wall. The foreign bodies were unable to be removed via flexible bronchoscope (FB), with the foreign bodies close to the pulmonary artery and aortic artery. After preoperative evaluation and planning with the virtual bronchoscopic navigation (VBN) system, the airway foreign bodies were extracted effectively and safely using advanced therapeutic endoscopic technique by rigid bronchoscope (RB), thus avoiding the surgical thoracotomy. Keywords: Virtual bronchoscopic navigation, LungPoint, Foreign body, High risk, Rigid bronchoscope, Removal, Artery

In the Netherlands, many prestressed concrete bridge girders are found to have insufficient shear–tension capacity. We tested four girders taken from a demolished bridge and instrumented these with traditional displacement sensors and acoustic emission (AE) sensors, and used cameras for digital image correlation (DIC). The results show that AE can detect cracking before the traditional displacement sensors, and DIC can identify the cracks with detailed crack kinematics. Both AE and DIC methods provide additional information for the structural analysis, as compared to the conventional measurements: more accurate cracking load, the contribution of aggregate interlock, and the angle of the compression field. These results suggest that both AE and DIC are suitable options that warrant further research on their use in lab tests and field testing of prestressed bridges.

The bit error in quantum communication is mainly caused by eavesdropping and noise. However, most quantum communication protocols only take eavesdropping into consideration and ignore the result of noise, making the inaccuracy situations in detecting the eavesdropper. To analyze the security of the quantum E91 protocol presented by Ekert in collective-rotation noise channel, an excellent model of noise analysis is proposed. The increment of the qubits error rate (ber) is used to detect eavesdropping. In our analysis, eavesdropper (Eve) can maximally get about 50% of the key from the communication when the noise level approximates to 0.5. The results show that in the collective-rotation noise environment, E91 protocol is secure and the raw key is available just as we have knew and proved. We also presented a new idea in analyzing the protocol security in noise channel.

In countries with steep rivers, such as Japan and the United States, bridges fail on an annual basis. Bridges on spread footings are especially susceptible to failure by hydrodynamic loading, often exacerbated by debris damming. Here, such failures are investigated via small scale model laboratory experiments and full scale numerical simulations. In the laboratory, lift and drag forces and overturning moment on bridge decks, piers, and deck-pier systems, are measured and compared with threshold of failure criteria used in design guidelines. Effects of debris on lift, drag, and moment, as well as three-dimensional effects, are quantified. Via numerical simulations, flow patterns and free surface behaviour responsible for these forces are investigated, and described in a framework as a function of the water depth, flow speed, deck clearance, and girder height. Results show that current guidelines are non-conservative in some cases. Importantly, failure of both decks and piers can be prevented by strengthening pier-deck connections, or by streamlining decks.

The relationship between shrub vegetation and precipitation is one important component of desert vegetation responses to climate change, but it has not been understood completely because of its complexity and nonlinearity. In this study, we used MODIS NDVI data and precipitation data from 2004 to 2012 to evaluate the relationship between the shrub vegetation and precipitation within Gurbantunggut Desert, Central Asia. Correlation analysis was employed to explore the relationship between NDVI and precipitation within growing season, within cross growing season, and on interannual scale. The results showed that NDVI could be classified into three temporal changing patterns within growing season, and NDVI was significantly correlated with the precipitation integrated by time durations and time lags within growing season; NDVI was significantly correlated with precipitation in the early growing season, but this relationship was not so obvious in the middle or late growing season; and the NDVI variational patterns depended on mean annual precipitation and the distribution of precipitation throughout the year. Precipitation had significant influence on shrub vegetation within Gurbantunggut Desert. Our findings provide basic knowledge for the relationship between precipitation and shrub vegetation, and it is helpful to understand how the desert vegetation responds to climate change in the future.