Effects of G and SH Truncation on the Replication, Virulence, and Immunogenicity of Avian Metapneumovirus.

Four mutants varying the length of the G and SH genes, including a G-truncated mutant (ΔG) and three G/SH-truncated mutants (ΔSH/G-1, ΔSH/G-2, and ΔSH/G-3), were generated via serially passaging the avian metapneumovirus strain SNU21004 into the cell lines Vero E6 and DF-1 and into embryonated chicken eggs. The mutant ΔG particles resembled parental virus particles except for the variance in the density of their surface projections. G and G/SH truncation significantly affected the viral replication in chickens' tracheal ring culture and in infected chickens but not in the Vero E6 cells. In experimentally infected chickens, mutant ΔG resulted in the restriction of viral replication and the attenuation of the virulence. The mutants ΔG and ΔSH/G-1 upregulated three interleukins (IL-6, IL-12, and IL-18) and three interferons (IFNα, IFNß, and IFNγ) in infected chickens. In addition, the expression levels of innate immunity-related genes such as Mda5, Rig-I, and Lgp2, in BALB/c mice were also upregulated when compared to the parental virus. Immunologically, the mutant ΔG induced a strong, delayed humoral immune response, while the mutant ΔSH/G-1 induced no humoral immune response. Our findings indicate the potential of the mutant ΔG but not the mutant ΔSH/G-1 as a live attenuated vaccine candidate.

Organization of sensorimotor activity in anterior cruciate ligament reconstructed individuals: an fMRI conjunction analysis.

Introduction: Anterior cruciate ligament reconstruction (ACLR) is characterized by persistent involved limb functional deficits that persist for years despite rehabilitation. Previous research provides evidence of both peripheral and central nervous system adaptations following ACLR. However, no study has compared functional organization of the brain for involved limb motor control relative to the uninvolved limb and healthy controls. The purpose of this study was to examine sensorimotor cortex and cerebellar functional activity overlap and non-overlap during a knee motor control task between groups (ACLR and control), and to determine cortical organization of involved and uninvolved limb movement between groups. Methods: Eighteen participants with left knee ACLR and 18 control participants performed a knee flexion/extension motor control task during functional magnetic resonance imaging (fMRI). A conjunction analysis was conducted to determine the degree of overlap in brain activity for involved and uninvolved limb knee motor control between groups. Results: The ACLR group had a statistically higher mean percent signal change in the sensorimotor cortex for the involved > uninvolved contrast compared to the control group. Brain activity between groups statistically overlapped in sensorimotor regions of the cortex and cerebellum for both group contrasts: involved > uninvolved and uninvolved > involved. Relative to the control group, the ACLR group uniquely activated superior parietal regions (precuneus, lateral occipital cortex) for involved limb motor control. Additionally, for involved limb motor control, the ACLR group displayed a medial and superior shift in peak voxel location in frontal regions; for parietal regions, the ACLR group had a more posterior and superior peak voxel location relative to the control group. Conclusion: ACLR may result in unique activation of the sensorimotor cortex via a cortically driven sensory integration strategy to maintain involved limb motor control. The ACLR group's unique brain activity was independent of strength, self-reported knee function, and time from surgery.

Tracing the Evolutionary Pathways of Serogroup O78 Avian Pathogenic Escherichia coli.

Avian pathogenic E. coli (APEC) causes severe economic losses in the poultry industry, and O78 serogroup APEC strains are prevalent in chickens. In this study, we aimed to understand the evolutionary pathways and relationships between O78 APEC and other E. coli strains. To trace these evolutionary pathways, we classified 3101 E. coli strains into 306 subgenotypes according to the numbers and types of single nucleotide polymorphisms (RST0 to RST63-1) relative to the consensus sequence (RST0) of the RNA polymerase beta subunit gene and performed network analysis. The E. coli strains showed four apparently different evolutionary pathways (I-1, I-2, I-3, and II). The thirty-two Korean O78 APEC strains tested in this study were classified into RST4-4 (45.2%), RST3-1 (32.3%), RST21-1 (12.9%), RST4-5 (3.2%), RST5-1 (3.2%), and RST12-6 (3.2%), and all RSTs except RST21-1 (I-2) may have evolved through the same evolutionary pathway (I-1). A comparative genomic study revealed the highest relatedness between O78 strains of the same RST in terms of genome sequence coverage/identity and the spacer sequences of CRISPRs. The early-appearing RST3-1 and RST4-4 prevalence among O78 APEC strains may reflect the early settlement of O78 E. coli in chickens, after which these bacteria accumulated virulence and antibiotic resistance genes to become APEC strains. The zoonotic risk of the conventional O78 APEC strains is low at present, but the appearance of genetically distinct and multiple virulence gene-bearing RST21-1 O78 APEC strains may alert us to a need to evaluate their virulence in chickens as well as their zoonotic risk.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Receptor binding motif surrounding sites in the spike 1 protein of infectious bronchitis virus have high susceptibility to mutation related to selective pressure.

BACKGROUND: To date, various genotypes of infectious bronchitis virus (IBV) have co-circulated and in Korea, GI-15 and GI-19 lineages were prevailing. The spike protein, particularly S1 subunit, is responsible for receptor binding, contains hypervariable regions and is also responsible for the emerging of novel variants. OBJECTIVE: This study aims to investigate the putative major amino acid substitutions for the variants in GI-19. METHODS: The S1 sequence data of IBV isolated from 1986 to 2021 in Korea (n = 188) were analyzed. Sequence alignments were carried out using Multiple alignment using Fast Fourier Transform of Geneious prime. The phylogenetic tree was generated using MEGA-11 (ver. 11.0.10) and Bayesian analysis was performed by BEAST v1.10.4. Selective pressure was analyzed via online server Datamonkey. Highlights and visualization of putative critical amino acid were conducted by using PyMol software (version 2.3). RESULTS: Most (93.5%) belonged to the GI-19 lineage in Korea, and the GI-19 lineage was further divided into seven subgroups: KM91-like (Clade A and B), K40/09-like, QX-like (I-IV). Positive selection was identified at nine and six residues in S1 for KM91-like and QX-like IBVs, respectively. In addition, several positive selection sites of S1-NTD were indicated to have mutations at common locations even when new clades were generated. They were all located on the lateral surface of the quaternary structure of the S1 subunits in close proximity to the receptor-binding motif (RBM), putative RBM motif and neutralizing antigenic sites in S1. CONCLUSIONS: Our results suggest RBM surrounding sites in the S1 subunit of IBV are highly susceptible to mutation by selective pressure during evolution.

Development and Reliability of a Visual-Cognitive Reactive Triple Hop Test.

CONTEXT: Current lower-extremity return to sport testing primarily considers the physical status of an athlete; however, sport participation requires continuous cognitive dual-task engagement. Therefore, the purpose was to develop and evaluate the reliability of a visual-cognitive reactive (VCR) triple hop test that simulates the typical sport demand of combined online visual-cognitive processing and neuromuscular control to improve return to sport testing after lower-extremity injury. DESIGN: Test-retest reliability. METHODS: Twenty-one healthy college students (11 females, 23.5 [3.7] y, 1.73 [0.12] m, 73.0 [16.8] kg, Tegner Activity Scale 5.5 [1.1] points) participated. Participants performed a single-leg triple hop with and without a VCR dual task. The VCR task incorporated the FitLight system to challenge peripheral response inhibition and central working memory. Maximum hop distance, reaction time, cognitive errors, and physical errors were measured. Two identical testing visits were separated by 12 to 17 days (14 [1] d). RESULTS: Traditional triple hop (intraclass correlation coefficients: ICC(3,1) = .96 [.91-.99]; standard error of the measurement = 16.99 cm) and the VCR triple hop (intraclass correlation coefficients(3,1) = .92 [.82-.97]; standard error of the measurement = 24.10 cm) both demonstrated excellent reliability for the maximum hop distance, and moderate reliability for the VCR triple hop reaction time (intraclass correlation coefficients(3,1) = .62 [.09-.84]; standard error of the measurement = 0.09 s). On average, the VCR triple hop resulted in a hop distance deficit of 8.17% (36.4 [5.1] cm; P < .05, d = 0.55) relative to the traditional triple hop. CONCLUSIONS: Hop distance on the VCR triple hop had excellent test-retest reliability and induced a significant physical performance deficit when compared with the traditional triple hop assessment. The VCR triple hop reaction time also demonstrated moderate reliability.

The effects of sports-related concussion history on female adolescent brain activity and connectivity for bilateral lower extremity knee motor control.

Sports-related concussions (SRCs) are associated with neuromuscular control deficits in athletes following return to play. However, the connection between SRC and potentially disrupted neural regulation of lower extremity motor control has not been investigated. The purpose of this study was to investigate brain activity and connectivity during a functional magnetic resonance imaging (fMRI) lower extremity motor control task (bilateral leg press) in female adolescent athletes with a history of SRC. Nineteen female adolescent athletes with a history of SRC and nineteen uninjured (without a history of SRC) age- and sport-matched control athletes participated in this study. Athletes with a history of SRC exhibited less neural activity in the left inferior parietal lobule/supramarginal gyrus (IPL) during the bilateral leg press compared to matched controls. Based upon signal change detected in the brain activity analysis, a 6 mm region of interest (seed) was defined to perform secondary connectivity analyses using psychophysiological interaction (PPI) analyses. During the motor control task, the left IPL (seed) was significantly connected to the right posterior cingulate gyrus/precuneus cortex and right IPL for athletes with a history of SRC. The left IPL was significantly connected to the left primary motor cortex (M1) and primary somatosensory cortex (S1), right inferior temporal gyrus, and right S1 for matched controls. Altered neural activity in brain regions important for sensorimotor integration and motor attention, combined with unique connectivity to regions responsible for attentional, cognitive, and proprioceptive processing, indicate compensatory neural mechanisms may underlie the lingering neuromuscular control deficits associated with SRC.

DEMIX: Domain-Enforced Memory Isolation for Embedded System.

Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor's accessible memory into distinct units known as domains. Despite the security offered by this approach, the Memory Protection Unit (MPU), the most common memory isolation method provided in most lightweight systems, incurs overheads during domain switching due to the privilege level intervention. However, as IoT environments become increasingly interconnected and more resources become required for protection, the significant overhead associated with domain switching under this constraint is expected to be crucial, making it harder to operate with more granular domains. To mitigate these issues, we propose DEMIX, which supports efficient memory isolation for multiple domains. DEMIX comprises two mainelements-Domain-Enforced Memory Isolation and instruction-level domain isolation-with the primary idea of enabling granular access control for memory by validating the domain state of the processor and the executed instructions. By achieving fine-grained validation of memory regions, our technique safely extends the supported domain capabilities of existing technologies while eliminating the overhead associated with switching between domains. Our implementation of eight user domains shows that our approach yields a hardware overhead of a slight 8% in Ibex Core, a very lightweight RISC-V processor.

The relationship between drop vertical jump action-observation brain activity and kinesiophobia after anterior cruciate ligament reconstruction: A cross-sectional fMRI study.

BACKGROUND: Injury and reconstruction of anterior cruciate ligament (ACL) result in central nervous system alteration to control the muscles around the knee joint. Most individuals with ACL reconstruction (ACLR) experience kinesiophobia which can prevent them from returning to activity and is associated with negative outcomes after ACLR. However, it is unknown if kinesiophobia alters brain activity after ACL injury. OBJECTIVES: To compare brain activity between an ACLR group and matched uninjured controls during an action-observation drop vertical jump (AO-DVJ) paradigm and to explore the association between kinesiophobia and brain activity in the ACLR group. METHODS: This cross-sectional study enrolled 26 individuals, 13 with ACLR (5 males and 8 females, 20.62 ± 1.93 years, 1.71 ± 0.1 m, 68.42 ± 14.75 kg) and 13 matched uninjured controls (5 males and 8 females, 22.92 ± 3.17 years, 1.74 ± 0.10 m, 70.48 ± 15.38 kg). Individuals were matched on sex and activity level. Participants completed the Tampa Scale of Kinesiophobia-11 (TSK-11) to evaluate the level of movement-related fear. To assay the brain activity associated with a functional movement, the current study employed an action-observation/motor imagery paradigm during functional magnetic resonance imaging (fMRI). RESULTS: The ACLR group had lower brain activity in the right ventrolateral prefrontal cortex relative to the uninjured control group. Brain activity of the left cerebellum Crus I and Crus II, the right cerebellum lobule IX, amygdala, middle temporal gyrus, and temporal pole were positively correlated with TSK-11 scores in the ACLR group. CONCLUSION: Brain activity for the AO-DVJ paradigm was different between the ACLR group and uninjured controls. Secondly, in participants with ACLR, there was a positive relationship between TSK-11 scores and activity in brain areas engaged in fear and cognitive processes during the AO-DVJ paradigm.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

Development and reliability of a visual-cognitive medial side hop for return to sport testing.

OBJECTIVES: To develop and evaluate the reliability of a new visual-cognitive medial side hop (VCMH) test that challenges physical and cognitive performance to potentially improve return to sport testing. DESIGN: Test-retest experimental design. SETTING: Laboratory. PARTICIPANTS: Twenty-two healthy college students participated (11 females; 23.5 ± 3.64 years; 172.9 ± 11.58 cm; 74.1 ± 17.25 kg; Tegner Score 5.6 ± 1.1). MAIN OUTCOME MEASURES: Subjects performed a medial side hop for distance with and without a visual-cognitive task (VCMH). Maximum hop distance and cognitive errors were measured. RESULTS: There was strong reliability for the traditional medial side hop (ICC3,1 = 0.88[0.72, 0.95]; SEM = 7.16 cm) and VCMH distances (ICC3,1 = 0.86[0.66, 0.94]; SEM = 6.82 cm). Maximum hop distance was significantly lower during the VCMH (86.9 ± 18.2 cm) compared to the traditional medial side hop (96.3 ± 20.7 cm; p < 0.05; d = 0.74), with a performance deficit of 9.69%. CONCLUSION: The VCMH has high test-retest reliability and resulted in a significant dual-task cost with a reduction in physical performance when compared to the traditional medial side hop.

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

CoMeT: Configurable Tagged Memory Extension.

Commodity processor architectures are releasing various instruction set extensions to support security solutions for the efficient mitigation of memory vulnerabilities. Among them, tagged memory extension (TME), such as ARM MTE and SPARC ADI, can prevent unauthorized memory access by utilizing tagged memory. However, our analysis found that TME has performance and security issues in practical use. To alleviate these, in this paper, we propose CoMeT, a new instruction set extension for tagged memory. The key idea behind CoMeT is not only to check whether the tag values in the address tag and memory tag are matched, but also to check the access permissions for each tag value. We implemented the prototype of CoMeT on the RISC-V platform. Our evaluation results confirm that CoMeT can be utilized to efficiently implement well-known security solutions, i.e., shadow stack and in-process isolation, without compromising security.

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches.

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.

High-Speed and Unified ECC Processor for Generic Weierstrass Curves over GF(p) on FPGA.

In this paper, we present a high-speed, unified elliptic curve cryptography (ECC) processor for arbitrary Weierstrass curves over GF(p), which to the best of our knowledge, outperforms other similar works in terms of execution time. Our approach employs the combination of the schoolbook long and Karatsuba multiplication algorithm for the elliptic curve point multiplication (ECPM) to achieve better parallelization while retaining low complexity. In the hardware implementation, the substantial gain in speed is also contributed by our n-bit pipelined Montgomery Modular Multiplier (pMMM), which is constructed from our n-bit pipelined multiplier-accumulators that utilizes digital signal processor (DSP) primitives as digit multipliers. Additionally, we also introduce our unified, pipelined modular adder/subtractor (pMAS) for the underlying field arithmetic, and leverage a more efficient yet compact scheduling of the Montgomery ladder algorithm. The implementation for 256-bit modulus size on the 7-series FPGA: Virtex-7, Kintex-7, and XC7Z020 yields 0.139, 0.138, and 0.206 ms of execution time, respectively. Furthermore, since our pMMM module is generic for any curve in Weierstrass form, we support multi-curve parameters, resulting in a unified ECC architecture. Lastly, our method also works in constant time, making it suitable for applications requiring high speed and SCA-resistant characteristics.

A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization.

Adversarial attack techniques in deep learning have been studied extensively due to its stealthiness to human eyes and potentially dangerous consequences when applied to real-life applications. However, current attack methods in black-box settings mainly employ a large number of queries for crafting their adversarial examples, hence making them very likely to be detected and responded by the target system (e.g., artificial intelligence (AI) service provider) due to its high traffic volume. A recent proposal able to address the large query problem utilizes a gradient-free approach based on Particle Swarm Optimization (PSO) algorithm. Unfortunately, this original approach tends to have a low attack success rate, possibly due to the model's difficulty of escaping local optima. This obstacle can be overcome by employing a multi-group approach for PSO algorithm, by which the PSO particles can be redistributed, preventing them from being trapped in local optima. In this paper, we present a black-box adversarial attack which can significantly increase the success rate of PSO-based attack while maintaining a low number of query by launching the attack in a distributed manner. Attacks are executed from multiple nodes, disseminating queries among the nodes, hence reducing the possibility of being recognized by the target system while also increasing scalability. Furthermore, we utilize Multi-Group PSO with Random Redistribution (MGRR-PSO) for perturbation generation, performing better than the original approach against local optima, thus achieving a higher success rate. Additionally, we propose to efficiently remove excessive perturbation (i.e, perturbation pruning) by utilizing again the MGRR-PSO rather than a standard iterative method as used in the original approach. We perform five different experiments: comparing our attack's performance with existing algorithms, testing in high-dimensional space in ImageNet dataset, examining our hyperparameters (i.e., particle size, number of clients, search boundary), and testing on real digital attack to Google Cloud Vision. Our attack proves to obtain a 100% success rate on MNIST and CIFAR-10 datasets and able to successfully fool Google Cloud Vision as a proof of the real digital attack by maintaining a lower query and wide applicability.

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification.

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

Long-range focusing of magnetic bound states in superconducting lanthanum.

Quantum mechanical systems with long-range interactions between quasiparticles provide a promising platform for coherent quantum information technology. Superconductors are a natural choice for solid-state based quantum devices, while magnetic impurities inside superconductors give rise to quasiparticle excitations of broken Cooper pairs that provide characteristic information about the host superconductor. Here, we reveal that magnetic impurities embedded below a superconducting La(0001) surface interact via quasiparticles extending to very large distances, up to several tens of nanometers. Using low-temperature scanning probe techniques, we observe the corresponding anisotropic and giant oscillations in the LDOS. Theoretical calculations indicate that the quasi-two-dimensional surface states with their strongly anisotropic Fermi surface play a crucial role for the focusing and long-range extension of the magnetic bound states. The quasiparticle focusing mechanism should facilitate the design of versatile magnetic structures with tunable and directed magnetic interactions over large distances, thereby paving the way toward the design of low-dimensional magnet-superconductor hybrid systems exhibiting topologically non-trivial quantum states as possible elements of quantum computation schemes based on Majorana quasiparticles.

Atomic-scale interface engineering of Majorana edge modes in a 2D magnet-superconductor hybrid system.

Topological superconductors are predicted to harbor exotic boundary states-Majorana zero-energy modes-whose non-Abelian braiding statistics present a new paradigm for the realization of topological quantum computing. Using low-temperature scanning tunneling spectroscopy, here, we report on the direct real-space visualization of chiral Majorana edge states in a monolayer topological superconductor, a prototypical magnet-superconductor hybrid system composed of nanoscale Fe islands of monoatomic height on a Re(0001)-O(2 × 1) surface. In particular, we demonstrate that interface engineering by an atomically thin oxide layer is crucial for driving the hybrid system into a topologically nontrivial state as confirmed by theoretical calculations of the topological invariant, the Chern number.

O2, NO2 and NH3 coordination to Co-porphyrin studied with scanning tunneling microscopy on Au(111).

The coordination structure between small molecules and metalloporphyrins plays a crucial role in functional reactions such as bio-oxidation and catalytic activation. Their vertical, tilting, and dynamic structures have been actively studied with diffraction and resonance spectroscopy for the past four decades. Contrastingly, real-space visualization beyond simple protrusion and depression is relatively rare. In this paper, high-resolution scanning tunnelling microscopy (STM) images are presented of di-, tri-, and tetra-atomic small molecules (O2, NO2, and NH3, respectively) coordinated to Co-porphyrin on Au(111). A square ring structure was observed for O2, a rectangular ring structure for NO2, and a bright-center structure for NH3 at 80 K. The symmetries of experimental STM images were reproduced in density functional theory (DFT) calculations, considering the precession motion of the small molecules. Thus, this study shows that the structure of small molecules coordinated to metalloporphyrins can be visualized using high-resolution STM and DFT calculations.