Asymptotic proximal point algorithm for wavefront sensorless adaptive optics.

Solving the distorted wavefront in wavefront sensorless adaptive optics (WFSL-AO) relies on excellent optimizers. Many local or global optimization algorithms have been applied to WFSL-AO; however, there is still a challenge to balance the effect and speed of correcting aberrations. To overcome this, a novel global optimization algorithm named asymptotic proximal point (APP) method is introduced into WFSL-AO in this Letter. We compare this algorithm with the various existing optimization algorithms in convergence speed and correction capability by performing numerical simulations. The results show that the APP method beats all competitors with a better correction effect and faster speed.

Estimation of minimum foot clearance using a single foot-mounted inertial sensor and personalized foot geometry scan.

The real-world measurement of minimum foot clearance (mFC) during the swing phase of gait is critical in efforts to understand and reduce the risk of trip-and-fall incidents in populations with gait impairments. Past research has focused on measuring clearance of a single point on a person's foot, typically the toe-however, this may overestimate mFC and may even be the wrong region of the foot in cases of gait impairments or interventions. In this work, we present a novel method to reconstruct the swing-phase trajectory of an arbitrary number of points on a person's shoe and estimate the instantaneous height and location of whole-foot mFC. This is achieved using a single foot-mounted inertial sensor and personalized shoe geometry scan, assuming a rigid-body IMU-shoe system. This combination allows collection and analysis using out-of-lab tests, potentially including clinical environments. Validation of single marker location using the proposed method vs. motion capture showed height errors with bias less than 0.05 mm, and 95% confidence interval of - 8.18 to + 8.09 mm. The method is demonstrated in an example data set comparing different interventions for foot drop, and it shows clear differences among no intervention, functional electrical stimulation, and ankle-foot orthosis conditions. This method offers researchers and clinicians a rich understanding of a person's gait by providing objective 3D foot kinematics and allowing a unique opportunity to view the regions of the foot where minimum clearance occurs. This information can contribute to a more informed recommendation of specific interventions or assistive technology than is currently possible in standard clinical practice.

Associated impact mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze river basin based on ocean-atmosphere anomaly patterns.

Heavy rainfall and flooding disasters are increasing due to global warming. A clear understanding of the mechanism of heavy rain and floods is the basic premise of disaster risk management. However, most previous studies emphasized more on the single anomalous signal from the average state in the whole season, which may neglect the combined influence of multiple signals in the ocean-atmosphere and differential characteristics of anomalous signals at different periods. Here, our study aimed to reveal the possible influence mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze River Basin (MLRYRB) by systematically analyzing the monthly-scale and daily-scale ocean-atmosphere anomaly patterns in the preceding periods of heavy rainfall and flooding events. The results showed that heavy rainfall and flooding events were highly likely to occur in the region one month after El Niño decayed, with the flooding intensity in June having the negative correlation with the sea ice concentration anomaly in the Arctic with a lag of about 5 months (150 days). Besides, North Atlantic Oscillation, Western Pacific subtropical high, blocking, East Asian subtropical westerly jet, and the water vapor fluxes from the Arabian Sea and western Pacific Ocean could be used as the anomalous signals inducing heavy rain and floods. The daily-scale conceptual model inducing heavy rainfall and flooding events was built based on the patterns of all anomalous signals, which detailed the possible impact mechanism of heavy rain and floods in the region. By making targeted forecasts of anomalous signals and using this information in water resources planning and management based on climate mechanisms, it will have a significant impact on water management in the country.

Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit.

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03±0.08 cm on level ground, 0.95±0.37 cm on ramps, and 1.27±1.22 cm on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs.

Cerium-Luteolin Nanocomplexes in Managing Inflammation-Related Diseases by Antioxidant and Immunoregulation.

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) production and the antioxidant defense system, plays a pivotal role in inflammation-related diseases. Excessive ROS levels can induce cellular damage and impair normal physiological functions, triggering the release of inflammatory mediators and exacerbating the inflammatory response, ultimately leading to irreversible tissue damage. In this study, we synthesized cerium ion-luteolin nanocomplexes (CeLutNCs) by coordinating Ce ions with the natural product luteolin, aiming to develop a therapeutic agent with excellent antioxidant and immunoregulation properties for ROS-related inflammation treatment. In vitro experiments demonstrated that the prepared CeLutNCs effectively scavenged excess ROS, prevented cell apoptosis, down-regulated levels of important inflammatory cytokines, regulated the response of inflammatory macrophages, and suppressed the activation of the nuclear factor-κ-gene binding (NF-κB) pathway. In an acute kidney injury (AKI) animal model, CeLutNCs exhibited significant efficacy in improving kidney function, repairing damaged renal tissue, and reducing oxidative stress, inflammatory response, and cellular apoptosis. Moreover, the therapeutic potential of CeLutNCs in an acute lung injury (ALI) model was confirmed through the assessment of inflammatory responses and histopathological studies. This study emphasizes the effectiveness of these metal-natural product coordination nanocomplexes as a promising therapeutic approach for preventing AKI and other diseases associated with oxidative stress.

Sampling complex topology structures for spiking neural networks.

Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.

Recent Advances in Photothermal Therapy at Near-Infrared-II Based on 2D MXenes.

The use of photothermal therapy (PTT) with the near-infrared II region (NIR-II: 1000-1700 nm) is expected to be a powerful cancer treatment strategy. It retains the noninvasive nature and excellent temporal and spatial controllability of the traditional PTT, and offers significant advantages in terms of tissue penetration depth, background noise, and the maximum permissible exposure standards for skin. MXenes, transition-metal carbides, nitrides, and carbonitrides are emerging inorganic nanomaterials with natural biocompatibility, wide spectral absorption, and a high photothermal conversion efficiency. The PTT of MXenes in the NIR-II region not only provides a valuable reference for exploring photothermal agents that respond to NIR-II in 2D inorganic nanomaterials, but also be considered as a promising biomedical therapy. First, the synthesis methods of 2D MXenes are briefly summarized, and the laser light source, mechanism of photothermal conversion, and evaluation criteria of photothermal performance are introduced. Second, the latest progress of PTT based on 2D MXenes in NIR-II are reviewed, including titanium carbide (Ti3 C2 ), niobium carbide (Nb2 C), and molybdenum carbide (Mo2 C). Finally, the main problems in the PTT application of 2D MXenes to NIR-II and future research directions are discussed.

Unlocking the Antioxidant Potential of White Tea and Osmanthus Flower: A Novel Polyphenol Liquid Preparation and Its Impact on KM Mice and Their Offspring.

White tea, known for its high polyphenol content, boasts impressive antioxidant properties, but its practical applications remain promising. In this study, we successfully developed a liquid polyphenolic preparation (wtofLPP) using white tea and osmanthus flowers, characterized by its rich antioxidant content and favorable rheological properties. This formulation offers a strong foundation for the creation and utilization of innovative antioxidant-rich food products. Notably, wtofLPP significantly enhanced the activity of certain antioxidant enzymes in both KM mice and their offspring, leading to a reduction in malondialdehyde (MDA) levels, prolonged swimming endurance, and a marked increase in levels of active antioxidant compounds. Furthermore, our study highlights that fatigue stress can impact offspring mice, suggesting that oxidative damage in parents may influence their offspring, potentially affecting their genetic function.

Preoperative contrast-enhanced CT imaging and clinicopathological characteristics analysis of mismatch repair-deficient colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) can develop through various pathogenetic pathways, and one of the primary pathways is high microsatellite instability (MSI-H)/deficient mismatch repair (dMMR). This study investigated the correlation between preoperative contrast-enhanced CT (CECT) and clinicopathologic characteristics of colorectal cancer (CRC) according to different mismatch repair (MMR) statuses. METHODS: From April 2021 to July 2022, a total of 281 CRC patients with preoperative CECT and available MMR status were enrolled from a single centre for this retrospective study. Preoperative CECT features and clinicopathologic characteristics were analysed. Univariate and multivariate logistic regression analyses were used for statistical analysis. A nomogram was established based on the multivariate logistic regression results. Preoperative and postoperative dynamic nomogram prediction models were established. The C-index, a calibration plot, and clinical applicability of the two models were evaluated, and internal validation was performed using three methods. RESULTS: In total, 249 patients were enrolled in the proficient mismatch repair (pMMR) group and 32 patients in the deficient mismatch repair (dMMR) group. In multivariate analysis, tumour location (right-hemi colon vs. left-hemi colon, odds ratio (OR) = 2.90, p = .036), the hypoattenuation-within-tumour ratio (HR) (HR > 2/3 vs. HR < 1/3, OR = 36.7, p < .001; HR 1/3-2/3 vs. HR < 1/3, OR = 6.05, p = .031), the number of lymph nodes with long diameter ≥ 8 mm on CECT (OR = 1.32, p = .01), CEA status (CEA positive vs. CEA negative, OR = 0.07, p = .002) and lymph node metastasis (OR = 0.45, p = .008) were independent risk factors for dMMR. Pre- and postoperative C-statistic were 0.861 and 0.908, respectively. CONCLUSION: The combination of pre-operative CECT and clinicopathological characteristics of CRC correlates with MMR status, providing possible non-invasive MMR prediction. Particularly for dMMR CRC, tumour-draining lymph node status should be prudently evaluated by CECT.

Structural convergence endows nuclear transport receptor Kap114p with a transcriptional repressor function toward TATA-binding protein.

The transcription factor TATA-box binding protein (TBP) modulates gene expression in nuclei. This process requires the involvement of nuclear transport receptors, collectively termed karyopherin-ß (Kap-ß) in yeast, and various regulatory factors. In previous studies we showed that Kap114p, a Kap-ß that mediates nuclear import of yeast TBP (yTBP), modulates yTBP-dependent transcription. However, how Kap114p associates with yTBP to exert its multifaceted functions has remained elusive. Here, we employ single-particle cryo-electron microscopy to determine the structure of Kap114p in complex with the core domain of yTBP (yTBPC). Remarkably, Kap114p wraps around the yTBPC N-terminal lobe, revealing a structure resembling transcriptional regulators in complex with TBP, suggesting convergent evolution of the two protein groups for a common function. We further demonstrate that Kap114p sequesters yTBP away from promoters, preventing a collapse of yTBP dynamics required for yeast responses to environmental stress. Hence, we demonstrate that nuclear transport receptors represent critical elements of the transcriptional regulatory network.

Wearable sensing for understanding and influencing human movement in ecological contexts.

Wearable sensors offer a unique opportunity to study movement in ecological contexts - that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.

Functional Nucleic Acid Probes Based on Two-Photon for Biosensing.

Functional nucleic acid (FNA) probes have been widely used in environmental monitoring, food analysis, clinical diagnosis, and biological imaging because of their easy synthesis, functional modification, flexible design, and stable properties. However, most FNA probes are designed based on one-photon (OP) in the ultraviolet or visible regions, and the effectiveness of these OP-based FNA probes may be hindered by certain factors, such as their potential for photodamage and limited light tissue penetration. Two-photon (TP) is characterized by the nonlinear absorption of two relatively low-energy photons of near-infrared (NIR) light with the resulting emission of high-energy ultraviolet or visible light. TP-based FNA probes have excellent properties, including lower tissue self-absorption and autofluorescence, reduced photodamage and photobleaching, and higher spatial resolution, making them more advantageous than the conventional OP-based FNA probes in biomedical sensing. In this review, we summarize the recent advances of TP-excited and -activated FNA probes and detail their applications in biomolecular detection. In addition, we also share our views on the highlights and limitations of TP-based FNA probes. The ultimate goal is to provide design approaches for the development of high-performance TP-based FNA probes, thereby promoting their biological applications.

High-power, sub-100-fs, 1600-1700-nm all-fiber laser for deep multiphoton microscopy.

The 1600-1700-nm ultrafast fiber lasers attract great interests in the deep multiphoton microscopy, due to the reduced levels of the tissue scattering and absorption. Here, we report on the 86.7-MHz, 717-mW, 91.2-fs, all-fiber laser located in the spectral range from 1600 nm to 1700nm. The soliton self-frequency shift (SSFS) was introduced into the Er:Yb co-doped fiber amplifier (EYDFA) to generate the high-power, 1600-1700-nm Raman soliton. Detailed investigations of the nonlinear fiber amplification process were implemented in optimizing the generated Raman soliton pulses. The miniature multiphoton microscopy was further realized with this home-built laser source. The clearly imaging results can be achieved by collecting the generated harmonic signals from the mouse tail skin tissue with a penetration depth of ∼500 µm. The experimental results indicate the great potential in utilizing this 1600-1700-nm fiber laser in the deep multiphoton microscopy.

Equilibrium Image Denoising with Implicit Differentiation.

Recent efforts on learning-based image denoising approaches use unrolled architectures with a fixed number of repeatedly stacked blocks. However, due to difficulties in training networks corresponding to deeper layers, simply stacking blocks may cause performance degradation, and the number of unrolled blocks needs to be manually tuned to find an appropriate value. To circumvent these problems, this paper describes an alternative approach with implicit models. To our best knowledge, our approach is the first attempt to model iterative image denoising through an implicit scheme. The model employs implicit differentiation to calculate gradients in the backward pass, thus avoiding the training difficulties of explicit models and elaborate selection of the iteration number. Our model is parameter-efficient and has only one implicit layer, which is a fixed-point equation that casts the desired noise feature as its solution. By simulating infinite iterations of the model, the final denoising result is given by the equilibrium that is achieved through accelerated black-box solvers. The implicit layer not only captures the non-local self-similarity prior for image denoising, but also facilitates training stability and thereby boosts the denoising performance. Extensive experiments show that our model leads to better performances than state-of-the-art explicit denoisers with enhanced qualitative and quantitative results.

Universal cover-time distribution of heterogeneous random walks.

The cover-time problem, i.e., the time to visit every site in a system, is one of the key issues of random walks with wide applications in natural, social, and engineered systems. Addressing the full distribution of cover times for random walk on complex structures has been a long-standing challenge and has attracted persistent efforts. Usually it is assumed that the random walk is noncompact, to facilitate theoretical treatments by neglecting the correlations between visits. The known results are essentially limited to noncompact and homogeneous systems, where different sites are on an equal footing and have identical or close mean first-passage times, such as random walks on a torus. In contrast, realistic random walks are prevailingly heterogeneous with diversified mean first-passage times. Does a universal distribution still exist? Here, by considering the most general situations of noncompact random walks, we uncover a generalized rescaling relation for the cover time, exploiting the diversified mean first-passage times that have not been accounted for before. This allows us to concretely establish a universal distribution of the rescaled cover times for heterogeneous noncompact random walks, which turns out to be the Gumbel universality class that is ubiquitous for a large family of extreme value statistics. Our analysis is based on the transfer matrix framework, which is generic in that, besides heterogeneity, it is also robust against biased protocols, directed links, and self-connecting loops. The finding is corroborated with extensive numerical simulations of diverse heterogeneous noncompact random walks on both model and realistic topological structures. Our technical ingredient may be exploited for other extreme value or ergodicity problems with nonidentical distributions.

SPIDE: A purely spike-based method for training feedback spiking neural networks.

Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural networks or direct training with surrogate gradients, require complex computation rather than spike-based operations of spiking neurons during training. In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs. Specifically, we introduce ternary spiking neuron couples and prove that implicit differentiation can be solved by spikes based on this design, so the whole training procedure, including both forward and backward passes, is made as event-driven spike computation, and weights are updated locally with two-stage average firing rates. Then we propose to modify the reset membrane potential to reduce the approximation error of spikes. With these key components, we can train SNNs with flexible structures in a small number of time steps and with firing sparsity during training, and the theoretical estimation of energy costs demonstrates the potential for high efficiency. Meanwhile, experiments show that even with these constraints, our trained models can still achieve competitive results on MNIST, CIFAR-10, CIFAR-100, and CIFAR10-DVS.

Integrating Ecosystem Health and Services for Assessing Ecological Risk and its Response to Typical Land-Use Patterns in the Eco-fragile Region, North China.

Changes in land-use patterns may increase the ecological risks faced by Eco-Fragile regions. It is vital for regional ecological restoration and management of Eco-Fragile regions to reasonably assess ecological risk and study its response to typical land-use patterns. Existing study on regional ecological risk largely ignored the internal representation of ecosystem health and ecosystem services to ecological risk, and also ignored the internal relationship between ecological risk and land use patterns. This study developed a regional ecological assessment model by describing the relationship between ecosystem health, ecosystem services and ecological risks. Among them, the ecosystem health assessment used the Net Primary Productivity, landscape index and ecosystem elasticity coefficient based on different land use patterns to build Vigor-Organization-Resilience (VOR) model, and the improved equivalent factor method was used to calculate the ecosystem service value. Taking the Fen River Basin (FRB), a typical Eco-Fragile region of the Loess Plateau, as a study region, spatial auto-correlation analysis was used to reveal the temporal and spatial changes and spatial clustering characteristics of regional ecological risk, and regression analysis was used to study the relationship between typical land use patterns and ecological risks, which was included in the consideration of ecological and environmental risk management strategies. The results show that the regions with high ecological risk are mainly distributed in the middle and southwest of the FRB; the regions with low ecological risk are mainly distributed in the north, east and west of the FRB. Both high-risk and low-risk areas show significant spatial clustering effects. The change of ecological risk in FRB is related to the land use patterns. The ecological risk is negatively related to the expansion of construction land and cultivated land at the county and patch scales. On this basis, the environmental management strategies at different scales are discussed. This study can helpful deepen the understanding of the impact of land use patterns on ecological risk, and can also provide important reference for regional ecological risk management and land use policies.

Nontargeted metabolomics integrated with 1 H NMR and LC-Q-TOF-MS/MS methods to depict a more comprehensive metabolic profile in response to chrysosplenetin and artemisinin co-treatment against artemisinin-sensitive and -resistant Plasmodium berghei K173.

Our previous work revealed mutual and specific metabolites/pathways in artemisinin-sensitive and -resistant Plasmodium berghei K173-infected mice. In this study, we further investigated whether chrysosplenetin, a candidate chemical to prevent artemisinin resistance, can regulate these metabolites/pathways by integrating nontargeted metabolomics with 1 H NMR and LC-Q-TOF-MS/MS spectrum. The nuclear magnetic resonance method generated specifically altered metabolites in response to co-treatment with chrysosplenetin, including: the products of glycolysis such as glucose, pyruvate, lactate and alanine; taurine, closely associated with liver injury; arginine and proline as essential amino acids for parasites; TMAO, a biomarker for dysbacteriosis and renal function; and tyrosine, which is used to generate levodopa and dopamine and may improve the torpor state of mice. Importantly, we noticed that chrysosplenetin might depress the activated glycolysis induced by sensitive parasites, but oppositely promoted the inhibited glycolysis to generate more lactate, which suppresses the proliferation of resistant parasites. Moreover, chrysosplentin possibly disturbs the heme biosynthetic pathway in mitochondria. The MS method yielded changed coenzyme A, phosphatidylcholine and ceramides, closely related to mitochondria ß-oxidation, cell proliferation, differentiation and apoptosis. These two means shared no overlapped metabolites and formed a more broader metabolic map to study the potential mechanisms of chrysosplenetin as a promising artemisinin resistance inhibitor.

H2O2/pH Dual-Responsive Biomimetic Nanoenzyme Drugs Delivery System for Enhanced Tumor Photodynamic Therapy.

Phototherapy has been recognized as a photochemical process to treat tumor via induce cancer cells necrosis and death, with minimal invasiveness, higher selectivity, and few side effects. However, the therapy effects of phototherapy are often compromised by the hypoxia, high levels of hydrogen peroxide, and glutathione of tumor microenvironment (TME). Therefore, we constructed a catalase-like activity bionic metal-organic framework drugs delivery system (FA-EM@MnO2/ZIF-8/ICG) with tumor microenvironment controllable releasing. In this system, photosensitizer indocyanine green (ICG) was introduced into zeolite imidazole salt skeleton 8 (ZIF-8) by one-step methods, forming ZIF-8/ICG nano-platform, which can effectively avoid ICG-induced phototoxicity and aggregation-induced quenching during transport. MnO2 with catalase-like activity was coated on the surface of ZIF-8/ICG nano-platform, which made it have the ability of self-supplying O2 under the condition of H2O2 in TME. Exposure under near-infrared light can alleviate the anoxic TME, thus improving the phototherapy efficiency. In addition, folate-functionalized erythrocyte membrane is coated on the surface of MnO2/ZIF-8/ICG, which can endow FA-EM@MnO2/ZIF-8/ICG with the ability of targeted drug administration and immune elimination avoidance. Therefore, FA-EM@MnO2/ZIF-8/ICG nano-platform has the catalase-like activity, which can alleviate the oxidative stress state of TME and provide a beneficial environment for photodynamic therapy of tumor.

Training much deeper spiking neural networks with a small number of time-steps.

Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN conversion method, which is the most effective SNN training method, has successfully converted moderately deep ANNs to SNNs with satisfactory performance. However, this method requires a large number of time-steps, which hurts the energy efficiency of SNNs. How to effectively covert a very deep ANN (e.g., more than 100 layers) to an SNN with a small number of time-steps remains a difficult task. To tackle this challenge, this paper makes the first attempt to propose a novel error analysis framework that takes both the "quantization error" and the "deviation error" into account, which comes from the discretization of SNN dynamicsthe neuron's coding scheme and the inconstant input currents at intermediate layers, respectively. Particularly, our theories reveal that the "deviation error" depends on both the spike threshold and the input variance. Based on our theoretical analysis, we further propose the Threshold Tuning and Residual Block Restructuring (TTRBR) method that can convert very deep ANNs (>100 layers) to SNNs with negligible accuracy degradation while requiring only a small number of time-steps. With very deep networks, our TTRBR method achieves state-of-the-art (SOTA) performance on the CIFAR-10, CIFAR-100, and ImageNet classification tasks.