Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation.

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences.

DNA computing is a novel computing method that does not rely on traditional computers. The design of DNA sequences is a crucial step in DNA computing, and the quality of the sequence design directly affects the results of DNA computing. In this paper, a new constraint called the consecutive base pairing constraint is proposed to limit specific base pairings in DNA sequence design. Additionally, to improve the efficiency and capability of DNA sequence design, the Hierarchy-ant colony (H-ACO) algorithm is introduced, which combines the features of multiple algorithms and optimizes discrete numerical calculations. Experimental results show that the H-ACO algorithm performs well in DNA sequence design. Finally, this paper compares a series of constraint values and NUPACK simulation data with previous design results, and the DNA sequence set designed in this paper has more advantages.

A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems.

In today's fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm Optimization (RPSO) based on cosine similarity. RPSO is evaluated using both the IEEE Congress on Evolutionary Computation (CEC) 2022 test dataset and Convolutional Neural Network (CNN) classification experiments. The RPSO algorithm builds upon the traditional PSO algorithm by incorporating several key enhancements. Firstly, the parameter selection is adapted and a mechanism called Random Contrastive Interaction (RCI) is introduced. This mechanism fosters information exchange among particles, thereby improving the ability of the algorithm to explore the search space more effectively. Secondly, quadratic interpolation (QI) is incorporated to boost the local search efficiency of the algorithm. RPSO utilizes cosine similarity for the selection of both QI and RCI, dynamically updating population information to steer the algorithm towards optimal solutions. In the evaluation using the CEC 2022 test dataset, RPSO is compared with recent variations of Particle Swarm Optimization (PSO) and top algorithms in the CEC community. The results highlight the strong competitiveness and advantages of RPSO, validating its effectiveness in tackling global optimization tasks. Additionally, in the classification experiments with optimizing CNNs for medical images, RPSO demonstrated stability and accuracy comparable to other algorithms and variants. This further confirms the value and utility of RPSO in improving the performance of CNN classification tasks.

Integrated improved Harris hawks optimization for global and engineering optimization.

The original Harris hawks optimization (HHO) algorithm has the problems of unstable optimization effect and easy to fall into stagnation. However, most of the improved HHO algorithms can not effectively improve the ability of the algorithm to jump out of the local optimum. In this regard, an integrated improved HHO (IIHHO) algorithm is proposed. Firstly, the linear transformation escape energy used by the original HHO algorithm is relatively simple and lacks the escape law of the prey in the actual nature. Therefore, intermittent energy regulator is introduced to adjust the energy of Harris hawks, which is conducive to improving the local search ability of the algorithm while restoring the prey's rest mechanism; Secondly, to adjust the uncertainty of random vector, a more regular vector change mechanism is used instead, and the attenuation vector is obtained by modifying the composite function. Finally, the search scope of Levy flight is further clarified, which is conducive to the algorithm jumping out of the local optimum. Finally, in order to modify the calculation limitations caused by the fixed step size, Cardano formula function is introduced to adjust the step size setting and improve the accuracy of the algorithm. First, the performance of IIHHO algorithm is analyzed on the Computational Experimental Competition 2013 (CEC 2013) function test set and compared with seven improved evolutionary algorithms, and the convergence value of the iterative curve obtained is better than most of the improved algorithms, verifying the effectiveness of the proposed IIHHO algorithm. Second, the IIHHO is compared with another three state of the art (SOTA) algorithms with the Computational Experimental Competition 2022 (CEC 2022) function test set, the experiments show that the proposed IIHHO algorithm still has a strong ability to search for the optimal value. Third, IIHHO algorithm is applied in two different engineering experiments. The calculation results of minimum cost prove that IIHHO algorithm has certain advantages in dealing with the problem of search space. All these demonstrate that the proposed IIHHO is promising for numeric optimization and engineering applications.

Diffusion Model for DAS-VSP Data Denoising.

Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models, a type of generative model, for DAS vertical seismic profile (VSP) data denoising. The diffusion network is trained on a new generated synthetic dataset that accommodates variations in the acquisition parameters. The trained model is applied to suppress noise in synthetic and field DAS-VSP data. The results demonstrate the model's effectiveness in removing various noise types with minimal signal leakage, outperforming conventional methods. This research signifies diffusion models' potential for DAS processing.

Interplay between the renin angiotensin system and oxidative stress contributes to alcohol addiction by stimulating dopamine accumulation in the mesolimbic pathway.

The brain renin-angiotensin system (RAS) has recently been implicated in the development of substance abuse and addiction. However, the integrative roles of the two counter-regulating RAS arms, including the ACE1/Ang II/AT1R axis and the ACE2/Ang(1-7)/MasR axis, in alcohol addiction remain unclear. Using the 20% ethanol intermittent-access two-bottle-choice (IA2BC) paradigm, we observed significant alcohol preference and addictive behaviors in rats. Additionally, we observed significant disruption in the RAS and redox homeostasis in the ventral tegmental area (VTA), as indicated by upregulation of ACE1 activities, Ang II levels, AT1R expression, and glutathione disulfide contents, as well as downregulation of ACE2 activities, Ang(1-7) levels, MasR expression and glutathione content. Moreover, dopamine accumulated in the VTA and nucleus accumbens of IA2BC rats. Intra-VTA infusion of the antioxidant tempol substantially attenuated RAS imbalance and addictive behaviors. Intra-VTA infusion of the ACE1 inhibitor captopril significantly reduced oxidative stress, alcohol preference, addictive behaviors, and dopamine accumulation, whereas intra-VTA infusion of the ACE2 inhibitor MLN4760 had the opposite effects. The anti-addictive effects of the ACE2/Ang(1-7)/MasR axis were further observed using intra-VTA infusion of Ang(1-7) and a MasR-specific antagonist A779. Therefore, our findings suggest that excessive alcohol intake causes RAS imbalance via oxidative stress, and that a dysregulated RAS in the VTA contributes to alcohol addiction by stimulating oxidative stress and dopaminergic neurotransmission. Breaking the vicious cycle of RAS imbalance and oxidative stress using brain-permeable antioxidants, ACE1 inhibitors, ACE2 activators, or Ang(1-7) mimetics thus represents a promising strategy for combating alcohol addiction.

Effects of ECM proteins (laminin, fibronectin, and type IV collagen) on the biological behavior of Schwann cells and their roles in the process of remyelination after peripheral nerve injury.

Introduction: It is important to note that complete myelination and formation of myelinated fibers are essential for functional nerve regeneration after peripheral nerve injury (PNI). However, suboptimal myelin regeneration is common and can hinder ideal nerve regeneration. Therefore, it is important to closely monitor and support myelin regeneration in patients with PNI to achieve optimal outcomes. Methods: This study analyzed the effects of three extracellular matrix (ECM) proteins on Schwann cells (SCs) in the nerve regeneration environment, including their adhesion, proliferation, and migration. The study also explored the use of composite sodium alginate hydrogel neural scaffolds with ECM components and investigated the effects of ECM proteins on remyelination following peripheral nerve injury. Results: The results showed that laminin (LN), fibronectin (FN), and collagen Ⅳ (type IV Col) promoted the early adhesion of SCs in 2-dimensional culture but the ratios of early cell adhesion were quite different and the maintenance of cells' morphology by different ECM proteins were significantly different. In transwell experiment, the ability of LN and FN to induce the migration of SCs was obviously higher than that of type IV Col. An vitro co-culture model of SCs and dorsal root ganglia (DRG) neurons showed that LN promoted the transition of SCs to a myelinated state and the maturation of the myelin sheath, and increased the thickness of neurofilaments. Animal experiments showed that LN had superior effects in promoting myelin sheath formation, axon repair, and reaching an ideal G-ratio after injury compared to FN and Col IV. The situation of gastrocnemius atrophy was significantly better in the LN group. Notably, the thickness of the regenerated myelin sheaths in the type IV Col group was the thickest. Conclusion: In this experiment, we analyzed and compared the effects of LN, FN, and type IV Col on the biological behavior of SCs and their effects on remyelination after PNI and further clarified their unique roles in the process of remyelination. Further research is necessary to explore the underlying mechanisms.

Electrochemical nitrate reduction to high-value ammonia on two-dimensional molybdenum carbide nanosheets for nitrate-containing wastewater upcycling.

Electrochemical conversion of nitrate wastewater into high-value ammonia fertilizer has attracted extensive attention in wastewater treatment and resource recovery, but presents great challenges due to complicated reaction pathways and competing side reactions. Herein, we report a feasible method for the successful fabrication of Mo2C nanosheets (Mo2C NSs) as electrocatalyst for the electroreduction of nitrate to ammonia. Compared to Mo2C nanoparticles, the Mo2C NSs exhibited superior activity and selectivity in NH3 electrosynthesis with an NH3 yield rate of 25.2 mg·h-1·mg-1cat. at -0.4 V and a Faradaic efficiency of 81.4 % at -0.3 V versus reversible hydrogen electrode. The X-ray diffraction and transmission electron microscopy characterization verifted the controllable conversion of 2D MoO2 NSs into 2D Mo2C NSs. In situ spectroscopic studies and on-line differential electrochemical mass spectrometry revealed the proposed reaction pathway of NO3- to NH3 conversion, *NO3- → *NO2- â†’ *NO→*NOH â†’ *NH2OH â†’ *NH3. Density functional theory calculations further verified the effective N-end NOH pathway with the conversion of *NH2OH to *NH2 as the rate-determining step requiring a low energy barrier of 0.58 eV. Importantly, the key hydrogenation of *NO to form *NOH species underwent a lower energy barrier of 0.39 eV compared with the formation of *ONH species (1.06 eV).

DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy.

DNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change the position of the population to enhance the global search scope. The use of a stochastic lens opposites learning mechanism can increase the capacity of the algorithm to get rid of locally optimal. The harmonic search algorithm is introduced to clarify some unqualified secondary structures and improve the quality of the solution. 12 benchmark functions and six other algorithms are used for comparison and ablation experiments to ensure the effectiveness of the algorithms. Finally, the DNA sequences we designed are of higher quality compared to other advanced algorithms.

Study on the Mechanism and Experiment of Styrene Butadiene Rubber Reinforcement by Spent Fluid Catalytic Cracking Catalyst.

Spent Fluid Catalytic Cracking (FCC) Catalyst is a major waste in the field of the petroleum processing field, with a large output and serious pollution. The treatment cost of these waste catalysts is high, and how to achieve their efficient reuse has become a key topic of research at home and abroad. To this end, this paper conducted a mechanistic and experimental study on the replacement of some carbon blacks by spent FCC catalysts for the preparation of rubber products and explored the synergistic reinforcing effect of spent catalysts and carbon blacks, in order to extend the reuse methods of spent catalysts and reduce the pollution caused by them to the environment. The experimental results demonstrated that the filler dispersion and distribution in the compound are more uniform after replacing the carbon black with modified spent FCC catalysts. The crosslinking density of rubber increases, the Payne effect is decreased, and the dynamic mechanical properties and aging resistance are improved. When the number of replacement parts reached 15, the comprehensive performance of the rubber composites remained the same as that of the control group. In this paper, the spent FCC catalysts modified by the physical method instead of the carbon-black-filled SBR can not only improve the performance of rubber products, but also can provide basic technical and theoretical support to realize the recycling of spent FCC catalysts and reduce the environmental pressure. The feasibility of preparing rubber composites by spent catalysts is also verified.

Umbilical Cord Wharton's Jelly-Derived Mesenchymal Stem Cells Inhibit the TGF-ß1 Pathway in Hepatic Fibrosis Rats Through a Paracrine Regulation Process.

BACKGROUND: The aim is to verify the therapeutic effect and possible mechanism of human umbilical cord Wharton's jelly-derived transplantation of mesenchymal stem cells (UMSCs) on CCl4-induced hepatic fibrosis rats through in vivo studies and to explore the regulatory mechanism of UMSCs on fibrosis of hepatic stellate cells (HSCs) through in vitro experiments. METHODS: In vivo experiment: Rats were randomly divided into blank control group and hepatic fibrosis group. During the entire trial, the blank control group received subcutaneous injection of normal saline, while in the hepatic fibrosis group received injections of 50% CCl4-olive oil subcutaneously for 10 weeks to establish the rat model of liver fibrosis. Hepatic fibrosis rats were then randomly and evenly divided into umbilical cord mesenchymal stem cell (UMSC) group, bone marrow mesenchymal stem cell (BMSC) group, UMSC-culture medium (CM) group, and control group. Rats in each group were infused with the following substances through the caudal vein as follows: 1 mL UMSCs (2 × 106/mL) in UMSC group, 1 mL BMSCs (2 × 106/mL) in BMSC group, 1 mL UMSCs-CM in CM group, and 1 mL saline in control group. Rats of each group were closely observed (weight, hair condition, activity, appetite, diarrhea, etc.), venous blood samples were collected, the number of white blood cells and lymphocytes were measured, and liver function indicators (ALT, AST, TBIL, ALB) were determined. Three weeks later, rat liver specimens were taken, HE stained, pathological changes were examined and quantified. In vitro experiments: HSCs were seeded in 6-well plates at 1.0 × 105/mL, with a serum-free medium for 24 hours. Then, 2 mL of UMSCs-CM was added in the study group, while an equal amount of complete medium was added to the control group. RT-PCR was used to detect TGF-ß1, Collagen-I, TIMP-2 mRNA expression in HSCs, and western blot was used to detect TGF-ß1 protein expression in HSCs. RESULTS: In vivo experiment: Compared with the control group, after the transplantation, the activity status (weight, spirit, appetite, movement, hair, diarrhea, etc.) of rats in the UMSC group, BMSC group, and CM group were improved. The liver function indexes of these groups, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL) were significantly decreased (p < 0.05), while albumin (ALB) levels were mildly but not significantly increased (p > 0.05). The Knodell score (reflecting the degree of liver inflammation) and Chevallier score (reflecting the degree of liver fibrosis) of liver specimens in pathological examination were also significantly reduced, and the difference in the quantitative scores of those indexes was statistically significant (p < 0.05). There was no statistically significant difference in the number of venous white blood cells and lymphocytes, liver function indexes (ALT, AST, TBIL, ALB), Knodell score, and Chevallier score of liver samples among the UMSC group, BMSC group, and CM group. In vitro experiments: After treatment with UMSCs-CM, the expression of TGF-ß1, Collagen-I, and TIMP-2 mRNA in HSCs was significantly down-regulated compared with that of the control group (treated with complete medium), and it gradually decreased with the extension of the treatment time. Compared with the control group, the expression of TGF-ß1 protein in the HSCs of the experimental group was down-regulated, and this effect was time-dependent, specifically, the control group (2.49 ± 0.43) > the experimental group at 48 hours (1.98 ± 0.26) > the experimental group at 72 hours (1.62 ± 0.20) (F = 7.796, p < 0.05). CONCLUSIONS: In rats with liver fibrosis, transplantation of UMSCs can improve liver function and reduce the inflammatory activity and fibrosis of the liver, possibly through the paracrine mechanism. UMSCs inhibit HSCs fibrosis through a paracrine mechanism, which is time-dependent, possibly by targeting TGF-ß1 and its downstream gene products.

Improved Bare Bones Particle Swarm Optimization for DNA Sequence Design.

DNA computing has efficient computational power, but requires high requirements on the DNA sequences used for coding, and reliable DNA sequences can effectively improve the quality of DNA encoding. And designing reliable DNA sequences is an NP problem, because it requires finding DNA sequences that satisfy multiple sets of conflicting constraints from a large solution space. To better solve the DNA sequence design problem, we propose an improved bare bones particle swarm optimization algorithm (IBPSO). The algorithm uses dynamic lensing opposition-based learning to initialize the population to improve population diversity and enhance the ability of the algorithm to jump out of local optima; An evolutionary strategy based on signal-to-noise ratio(SNR) distance is designed to balance the exploration and exploitation of the algorithm; Then an invasive weed optimization algorithm with niche crowding(NCIWO) is used to eliminate low-quality solutions and improve the search efficiency of the algorithm. In addition, we introduce the triplet-bases unpaired constraint to further improve the quality of DNA sequences. Finally, the effectiveness of the improved strategy is demonstrated by ablation experiments; and the DNA sequences designed by our algorithm are of higher quality compared with those generated by the six advanced algorithms.

SR-DSFF and FENet-ReID: A Two-Stage Approach for Cross Resolution Person Re-Identification.

In real-life scenarios, the accuracy of person re-identification (Re-ID) is subject to the limitation of camera hardware conditions and the change of image resolution caused by factors such as camera focusing errors. People call this problem cross-resolution person Re-ID. In this paper, we improve the recognition accuracy of cross-resolution person Re-ID by enhancing the image enhancement network and feature extraction network. Specifically, we treat cross-resolution person Re-ID as a two-stage task: the first stage is the image enhancement stage, and we propose a Super-Resolution Dual-Stream Feature Fusion sub-network, named SR-DSFF, which contains SR module and DSFF module. The SR-DSFF utilizes the SR module recovers the resolution of the low-resolution (LR) images and then obtains the feature maps of the LR images and super-resolution (SR) images, respectively, through the dual-stream feature fusion with learned weights extracts and fuses feature maps from LR and SR images in the DSFF module. At the end of SR-DSFF, we set a transposed convolution to visualize the feature maps into images. The second stage is the feature acquisition stage. We design a global-local feature extraction network guided by human pose estimation, named FENet-ReID. The FENet-ReID obtains the final features through multistage feature extraction and multiscale feature fusion for the Re-ID task. The two stages complement each other, making the final pedestrian feature representation has the advantage of accurate identification compared with other methods. Experimental results show that our method improves significantly compared with some state-of-the-art methods.

Effect of cerebellum stimulation on cognitive recovery in patients with Alzheimer disease: A randomized clinical trial.

INTRODUCTION: Evidence indicates that the cerebellum is involved in cognitive processing. However, the specific mechanisms through which the cerebellum repetitive transcranial magnetic stimulation (rTMS) contributes to the cognitive state are unclear. METHODS: In the current randomized, double-blind, sham-controlled trial, 27 patients with Alzheimer's disease (AD) were randomly allotted to one of the two groups: rTMS-real or rTMS-sham. We investigated the efficacy of a four-week treatment of bilateral cerebellum rTMS to promote cognitive recovery and alter specific cerebello-cerebral functional connectivity. RESULTS: The cerebellum rTMS significantly improves multi-domain cognitive functions, directly associated with the observed intrinsic functional connectivity between the cerebellum nodes and the dorsolateral prefrontal cortex (DLPFC), medial frontal cortex, and the cingulate cortex in the real rTMS group. In contrast, the sham stimulation showed no significant impact on the clinical improvements and the cerebello-cerebral connectivity. CONCLUSION: Our results depict that 5 Hz rTMS of the bilateral cerebellum is a promising, non-invasive treatment of cognitive dysfunction in AD patients. This cognitive improvement is accompanied by brain connectivity modulation and is consistent with the pathophysiological brain disconnection model in AD patients.

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism.

In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields.

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems.

The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm.

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

Comparison of Serum Triiodothyronine with Biomarkers for Alzheimer's Disease Continuum in Euthyroid Subjects.

BACKGROUND: Accumulating studies have implicated thyroid dysfunction in the pathogenesis of Alzheimer's disease (AD). OBJECTIVE: This study aimed to explore the association between thyroid hormone (TH) levels and cerebrospinal fluid (CSF) biomarkers for AD continuum among euthyroid subjects. METHODS: In all, 93 clinically euthyroid subjects with a cognitive decline were included in this prospective cross-sectional study and were divided into groups with abnormal AD biomarkers (belonging to the "Alzheimer's continuum"; A+ patients) and those with "normal AD biomarkers" or "non-AD pathological changes" (A-patients), according to the ATN research framework classification for AD. A partial correlation analysis of serum thyroid-stimulating hormone (TSH) or TH levels with CSF biomarkers was conducted. The predictor for A+ patients was analyzed via binary logistic regressions. Finally, the diagnostic significance of individual biochemical predictors for A+ patients was estimated via receiver operating characteristic curve analysis. RESULTS: Serum total triiodothyronine (TT3) and free triiodothyronine (FT3) levels were found to affect the levels of CSF amyloid-ß (Aß)42 and the ratios of Aß42/40. Further, FT3 was found to be a significant predictor for A+ via binary logistic regression modeling. Moreover, FT3 showed a high diagnostic value for A+ in euthyroid subjects. CONCLUSION: Even in a clinical euthyroid state, low serum FT3 and TT3 levels appear to be differentially associated with AD-specific CSF changes. These data indicate that serum FT3 is a strong candidate for differential diagnosis between AD continuum and non-AD dementia, which benefits the early diagnosis and effective management of preclinical and clinical AD patients.

Improved Sparrow Search Algorithm Based on Iterative Local Search.

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual's opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.