An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation.

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.

Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control.

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a novel approach aimed at efficiently optimizing robot controller parameters to enhance its motion performance. While spiking neural P systems have shown great potential in addressing optimization problems, there has been limited research and validation concerning their application in continuous numerical, multi-objective, and multi-dimensional multi-parameter contexts. To address this research gap, our paper proposes the Entropy-Weighted Numerical Gradient Optimization Spiking Neural P System, which combines the strengths of entropy weighting and spiking neural P systems. First, the introduction of entropy weighting eliminates the subjectivity of weight selection, enhancing the objectivity and reproducibility of the optimization process. Second, our approach employs parallel gradient descent to achieve efficient multi-dimensional multi-parameter optimization searches. In conclusion, validation results on a biped robot simulation model show that our method markedly enhances walking performance compared to traditional approaches and other optimization algorithms. We achieved a velocity mean absolute error at least 35% lower than other methods, with a displacement error two orders of magnitude smaller. This research provides an effective new avenue for performance optimization in the field of robotics.

Mycotoxins in grains (products), Gansu province, China and risk assessment.

This study aimed to estimate the dietary exposure towards mycotoxins of residents in Gansu province, China, from 2014-2020 through surveillance data on mycotoxins in grains and grain products. Fumonisin B1 (FB1), Deoxynivalenol (DON), 3- and 15-Acetyl-deoxynivalenol (3-ADON and 15-ADON), Tentoxin (TEN), Tenuazonic acid (TeA) and Zearalenone (ZEN) in 863 grains and grain products were detected by HPLC-MS and UPLC-MS. DON was the most detected mycotoxin of all samples. For women, the average dietary exposure to DON was 1.49 µg/kg bw/day, with 55.8% of the individuals eating dried noodles exceeding tolerable daily intake. The hazard quotient values were 1.24-12.60, so greater than 1 for DON at the average, 90th percentile, 95th percentile, and maximum levels: 44.6% of the HQ values for men and 45.7% for women were greater than 1.

A New Assistance Navigation Method for Substation Inspection Robots to Safely Cross Grass Areas.

With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency.

ResSKNet-SSDP: Effective and Light End-To-End Architecture for Speaker Recognition.

In speaker recognition tasks, convolutional neural network (CNN)-based approaches have shown significant success. Modeling the long-term contexts and efficiently aggregating the information are two challenges in speaker recognition, and they have a critical impact on system performance. Previous research has addressed these issues by introducing deeper, wider, and more complex network architectures and aggregation methods. However, it is difficult to significantly improve the performance with these approaches because they also have trouble fully utilizing global information, channel information, and time-frequency information. To address the above issues, we propose a lighter and more efficient CNN-based end-to-end speaker recognition architecture, ResSKNet-SSDP. ResSKNet-SSDP consists of a residual selective kernel network (ResSKNet) and self-attentive standard deviation pooling (SSDP). ResSKNet can capture long-term contexts, neighboring information, and global information, thus extracting a more informative frame-level. SSDP can capture short- and long-term changes in frame-level features, aggregating the variable-length frame-level features into fixed-length, more distinctive utterance-level features. Extensive comparison experiments were performed on two popular public speaker recognition datasets, Voxceleb and CN-Celeb, with current state-of-the-art speaker recognition systems and achieved the lowest EER/DCF of 2.33%/0.2298, 2.44%/0.2559, 4.10%/0.3502, and 12.28%/0.5051. Compared with the lightest x-vector, our designed ResSKNet-SSDP has 3.1 M fewer parameters and 31.6 ms less inference time, but 35.1% better performance. The results show that ResSKNet-SSDP significantly outperforms the current state-of-the-art speaker recognition architectures on all test sets and is an end-to-end architecture with fewer parameters and higher efficiency for applications in realistic situations. The ablation experiments further show that our proposed approaches also provide significant improvements over previous methods.

A preliminary analysis of global neonatal disorders burden attributable to PM2.5 from 1990 to 2019.

BACKGROUND: Prenatal fine particulate matter (PM2.5) exposure is related to various neonatal diseases (ND). However, data and studies assessing the neonatal disease burden caused by PM2.5 at the global level are limited, especially comparing countries with various socioeconomic development levels. We, therefore, assessed three-decades spatiotemporal changes in neonatal disease burden from 1990 at a national level, combined with the socio-demographic index (SDI). METHODS: We extracted statistics from the Global Burden of Disease Study database for this retrospective study, and analyzed differences in the age-standardized mortality rate (ASMR) of ND and five sub-causes related to PM2.5 by gender, nationality, and SDI. To describe the trend of ASMR, the Joinpoint model was adopted to predict the annual percentage change (APC) and the average annual percentage changes (AAPCs). We executed the Gaussian process regression model to predict the relevance between SDI and ASMR. RESULTS: The ND burden associated with PM2.5 kept rising since 1990, especially in low-middle SDI regions, South Asia, and Sub-Saharan Africa, and the sex ratio of ASMR was >1 at the global level and all five SDI regions. The leading cause of death was neonatal preterm birth. The global ASMR level of ND was 2.09 per 100,000 population in 2019 and AAPCs was 0.91 (98 % CI: 0.28, 1.55) meanwhile AAPCs decreased with rising SDI levels. The decreasing trend of ASMR in ND was detected in regions with higher SDI, such as North America, Europe, and Australasia. CONCLUSIONS: In the past three decades, the global burden of ND related to PM2.5 has ascended considerably in lower SDI regions hence PM2.5 is still considered a notable environmental hazard factor for newborn diseases.

Exposure-lag response of fine particulate matter on intrauterine fetal death: an analysis using a distributed lag non-linear model in Linxia Hui Autonomous Prefecture, China.

The results of studies on intrauterine fetal death (IUFD) caused by exposure to fine particulate matter (PM2.5) during pregnancy are inconsistent. Further exploration of the dose-response relationship and exposure window is required. We aimed to provide a reference for policy formulation by estimating the exposure-lag relationship of PM2.5 on IUFD and looking for sensitive exposure windows. IUFD data was obtained from China Children Under 5 Death Surveillance Network in Linxia Hui Autonomous Prefecture from 2016 to 2020. Air pollution data and temperature data were obtained from ambient air monitoring stations and China Meteorological Data Network, respectively. The moving average is used to describe the trend and seasonality of PM2.5 exposure; the distributed lag non-linear model (DLNM) is used to estimate the exposure-lag effect; the sandwich estimators are used to correct the variance-covariance matrix; and the model selected by Akaike's Information Criterion (AIC) finally adjusts gender, temperature, and district. About 180,622 infants were enrolled in the study, including 952 IUFDs (5.27‰). The median of PM2.5 exposure is 34.08 µg/m3. There is an exposure-lag effect of PM2.5 on IUFD approximate to a wavy shape; the concentration with effect is 40-90 µg/m3; and the sensitive lag time is 1, 2, 3, 8, 9, and 10 months. The maximum RR value of the exposure-lag effect of PM2.5 on IUFD is 1.61 [95% CI 1.19, 2.19], in which the concentration of PM2.5 is 62 µg/m3, and the lag month is 9 months. In the case of less than 6 months lag, the maximum RR value of the exposure-lag effect of PM2.5 on IUFD is 1.43 [95% CI 1.24, 1.67], in which the concentration of PM2.5 is 73 µg/m3, and the lag month is 3 months. Exposure to PM2.5 concentrations above 40 µg/m3 may increase the risk of IUFD, especially in the first and third trimesters.

Complete Mitogenomes of Ticks Ixodes acutitarsus and Ixodes ovatus Parasitizing Giant Panda: Deep Insights into the Comparative Mitogenomic and Phylogenetic Relationship of Ixodidae Species.

Ticks rank second in the world as vectors of disease. Tick infestation is one of the factors threatening the health and survival of giant pandas. Here, we describe the mitogenomes of Ixodes acutitarsus and Ixodes ovatus parasitizing giant pandas, and perform comparative and phylogenetic genomic analyses on the newly sequenced and other available mitogenomes of hard ticks. All six newly determined mitogenomes contain a typical gene component and share an ancient Arthropoda gene arrangement pattern. Our study suggests that I. ovatus is a species complex with high genetic divergence, indicating that different clades of I. ovatus represent distinct species. Comparative mitogenomic analyses show that the average A + T content of Ixodidae mitogenomes is 78.08%, their GC-skews are strongly negative, while AT-skews fluctuate around 0. A large number of microsatellites are detected in Ixodidae mitogenomes, and the main microsatellite motifs are mononucleotide A and trinucleotide AAT. We summarize five gene arrangement types, and identify the trnY-COX1-trnS1-COX2-trnK-ATP8-ATP6-COX3-trnG fragment is the most conserved region, whereas the region near the control region is the rearrangement hotspot in Ixodidae mitogenomes. The phylogenetic trees based on 15 genes provide a very convincing relationship (Ixodes + (Robertsicus + ((Bothriocroton + Haemaphysalis) + (Amblyomma + (Dermacentor + (Rhipicentor + (Hyalomma + Rhipicephalus))))))) with very strong supports. Remarkably, Archaeocroton sphenodonti is embedded in the Haemaphysalis clade with strong supports, resulting in paraphyly of the Haemaphysalis genus, so in-depth morphological and molecular studies are essential to determine the taxonomic status of A. sphenodonti and its closely related species. Our results provide new insights into the molecular phylogeny and evolution of hard ticks, as well as basic data for population genetics assessment and efficient surveillance and control for the giant panda-infesting ticks.

Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane Controllers.

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

A Layered Spiking Neural System for Classification Problems.

Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems.

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis.

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

An Adaptive Optimization Spiking Neural P System for Binary Problems.

Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system.

A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion.

Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples.

Reaction Systems and Synchronous Digital Circuits.

A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause-effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design.

[Establishment of dietary balance index for children aged 3-6 in Lanzhou City].

OBJECTIVE: Drawing on the ideas of the adult dietary balance index method, based on the recipes of the kindergartens in Lanzhou City, to establish a dietary balance index that can quickly, accurately and conveniently evaluate the dietary quality of the people in the park. METHODS: The stratified random sampling method was used to select and collect 329 recipes for the 40 kindergartens in different geographical locations, grades and properties(public and private) in Lanzhou City from 2012 to 2017. Used EpiData 3. 1 to enter the main food types of the recipe, the specific cooking ingredients and the supply amount of the ingredients. In combination with the 2016 dietary guidelines for the dietary requirements of the population, determined the components and ranges of values for the dietary balance index appropriate for the population. And used this index to evaluate the quality of some complete recipes. RESULTS: The pre-school children's dietary balance index system in Lanzhou City includes 8 individual indicators: cereals, vegetables and fruits, milk and dairy products, soy products and nuts, animal foods, snacks for food consumption, food types and cooking method. Preliminary application of the index system to evaluate the dietary quality of some kindergartens showed that there were significant differences in LBS and DQD between kindergartens of different grades and different years(P<0. 05), and there was no significant difference in HBS(P>0. 05). The result of the dietary evaluation method were consistent. CONCLUSION: The established dietary balance index for preschool children in Lanzhou City can meet the rapid, accurate and convenient evaluation of the dietary quality of the population during the park. In addition to focusing on establishing relevant indicators for food group classification and evaluation, the establishment of specific indicator systems should also increase indicators on children's dietary types and cooking and processing method.

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

[Effects of cold exposure on estrous cycle of female C57BL/6 mice].

OBJECTIVE: To study the effects of cold exposure on estrous cycle of female C57BL/6 mice. METHODS: Twelve healthy female C57BL/6 mice were randomly divided into two groups: control group and cold exposure group, 6 in each group. Cold exposure group was exposed to 4℃, 4 h per day, while control group stayed in normal conditions. Vaginal smears were used to observe the estrous cycle. After 2 weeks, blood and uteri were collected from each mouse after anesthetized and weighted. Serum levels of estradiol(E2), follicle-stimulating hormone(FSH), luteinizing hormone(LH), prolactin(Prl) and progesterone(P) were determined by using mouse ELISA kits. The uterus and ovary pathological slices were prepared to observe the structural changes. RESULTS: Compared with the control group, body weight gain showed no significant differences (P＞0.05). The cold group had significant lower coefficients of uterus and the diestrus phase was significantly increased after cold exposure (P＜0.01). Serum level of FSH in cold group was higher and Prl was lower significantly (P＜0.01). Pathological examination of uterus and ovary showed that uterine glands of cold group were expanded and the amount of follicles was decreased significantly. CONCLUSION: Cold exposure might increase mouse estrous cycle and affect their reproductive function.

P Systems-Based Computing Polynomials With Integer Coefficients: Design and Formal Verification.

Automatic design of mechanical procedures solving abstract problems is a relevant scientific challenge. In particular, automatic design of membranes systems performing some prefixed tasks is an important and useful research topic in the area of Natural Computing. In this context, deterministic membrane systems were designed in order to capture the values of polynomials with natural numbers coefficients. Following that work, this paper extends the previous result to polynomials with integer numbers coefficients. Specifically, a deterministic transition P system using priorities in the weak interpretation, associated with an arbitrary such kind polynomial, is presented. The configuration of the unique computation of the system will be encoded by means of two distinguished objects, the values of the polynomial for natural numbers. The descriptive computational resources required by the designed membrane system are also analyzed.

Multi-aperture stereo reconstruction for artificial compound eye with cross image belief propagation.

The artificial compound eye is a new type of camera that has miniature volume and large field of view (FOV), while the captured image is an array of sub-images, and each sub-image captures a part of the full FOV. To obtain a complete image with a full FOV, reconstruction is needed. Due to the parallax between adjacent sub-images, the reconstruction of images is depth related. In this paper, to address the image reconstruction of a specific artificial compound eye-eCley-a cross image belief propagation method is proposed to estimate the depth map. Since the small size and small FOV of the sub-image lead to little contextual information for pairwise matching, the information of neighboring sub-images is integrated into the belief propagation step to propagate the message across images. Therefore, the belief propagation step is able to gather as much information as needed from all the sub-images to obtain an accurate depth result. As a consequence, a stereo image with the full FOV and corresponding depth map can be obtained based on the estimated depth of sub-images. Experimental results on real data show the effectiveness of the proposed method.