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This study conducted a dietary exposure assessment of chloropropanols and aluminium, after quantifying their content in food. The mean dietary exposure to Al from total grain products was found to be 6.4E-04 mg/kg bw/week, not exceeding the provisional tolerable weekly intake (PTWI). The results showed that the mean dietary exposure of 3-monochloro-1,2-propanediol (3-MCPD) from grain products was higher than the tolerable daily intake (TDI). Deterministic evaluations showed that the mean HQ of 1.5 to Al from fried dough twist was higher than 1. However, the HI values to Al were lower than 1 and the Monte Carlo simulation (MCS) showed that the HQ was greater than 1 at the 99% exposure level. Deterministic and probabilistic methods indicated that HQ values of 3-MCPD from grain products were above 1, whereas the mean HI value was 0.62.
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In the present study, we analyzed mercury concentrations in 742 samples across five main food categories from 2013 to 2021 using direct mercury analysis (DMA) to understand mercury pollution in major market-sold foods in the Pingliang region of Gansu Province and assess the health risks of mercury dietary exposure in adults. Health risks of adult dietary exposure were assessed through deterministic evaluation. Total mercury content ranged from non-detectable (ND) to 0.13 mg/kg, with a detection rate of 90.70% (673/742), the highest detection rates being in fresh edible mushrooms and nuts. The overall exceedance rate was 0.13% (1/742), with one sample of fresh edible mushrooms exceeding the regulatory limit for total mercury content. Additionally, we incorporated the average mercury content and consumption levels of meat and seafood from regions geographically close to Pingliang, as reported in the Fifth National Total Diet Study, to calculate the Estimated Daily Intake (EDI) by a deterministic evaluation. For adult males, the exposure was 0.120 µg/(kg BW), while for adult females, it was 0.141 µg/(kg BW). Both values are significantly lower than the provisional tolerable weekly intake (PTWI) of 4 µg/(kg BW) established by JECFA in 2010, indicating that the total mercury concentration from food intake does not pose a significant health risk to the residents of the Pingliang area. These findings offer valuable scientific data to inform food safety regulations in the region and can serve as a benchmark for future mercury pollution risk assessments in other locations.
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The impact of milk on bone health in rural preschoolers is under-researched. This study, through a clinical trial and a meta-analysis, finds that milk supplementation enhances forearm and calcaneus bone acquisition in children, supporting the benefits of daily milk consumption. PURPOSE: This study evaluated the impact of dairy supplementation on bone acquisition in children's limbs through a cluster-randomized controlled trial and a meta-analysis. METHODS: The trial involved 315 children (4-6 year) from Northwest China, randomized to receive either 390 ml of milk daily (n = 215) or 20-30 g of bread (n = 100) over 12 months. We primarily assessed bone mineral density (BMD) and content (BMC) changes at the limbs, alongside bone-related biomarkers, measured at baseline, the 6th and 12th months. The meta-analysis aggregated BMD or BMC changes in the forearm/legs/calcaneus from published randomized trials involving children aged 3-18 years supplemented with dairy foods (vs. control group). RESULTS: Of 278 completed the trial, intention-to-treat analysis revealed significant increases in BMD (4.05% and 7.31%) and BMC (4.69% and 7.34%) in the left forearm at the 6th and 12th months in the milk group compared to controls (P < 0.001). The calcaneus showed notable improvements in BMD (2.01%) and BMC (1.87%) at 6 months but not at 12 months. Additionally, milk supplementation was associated with beneficial changes in bone resorption markers, parathyroid hormone (- 12.70%), insulin-like growth factor 1 (6.69%), and the calcium-to-phosphorus ratio (2.22%) (all P < 0.05). The meta-analysis, encompassing 894 children, indicated that dairy supplementation significantly increased BMD (SMD, 0.629; 95%CI: 0.275, 0.983) and BMC (SMD, 0.616; 95%CI: 0.380, 0.851) (P < 0.05) in the arms, but not in the legs (P > 0.05). CONCLUSION: Milk supplementation significantly improves bone health in children's forearms, underscoring its potential as a strategic dietary intervention for bone development. Trial registration NCT05074836.
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Densidad Ósea , Suplementos Dietéticos , Niño , Preescolar , Femenino , Humanos , Masculino , Densidad Ósea/efectos de los fármacos , Desarrollo Óseo/fisiología , Calcáneo/diagnóstico por imagen , China , Antebrazo , Leche , AdolescenteRESUMEN
To assess the dietary exposure risks of pesticide residues in vegetables for the general population, the presence of 39 pesticides was determined in 70 samples, of which 13 were detected. The most frequently detected pesticide was bifenthrin, with a detection rate of 35.7%, mainly found in gingers, followed by isoprocarb and acephate (11.4%) and dimethoate (8.6%). In 17.1% of the samples two or more pesticide residues were found. Acute, chronic and chronic cumulative dietary exposure risk was assessed. Chronic exposure risks were determined by Monte Carlo Simulation (MCS). Estimated chronic exposure to carbofuran, omethoate, disulfoton and dimethoate of approximately 49%, 52%, 40% and 3%, respectively, were at non-carcinogenic human risk. Acute exposure risk to acephate was considered to be of concern due to the high acute hazard quotient (aHQ).
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Exposición Dietética , Contaminación de Alimentos , Método de Montecarlo , Residuos de Plaguicidas , Verduras , Residuos de Plaguicidas/análisis , Verduras/química , Medición de Riesgo , Contaminación de Alimentos/análisis , Humanos , China , Exposición Dietética/análisis , Piretrinas/análisis , Compuestos Organotiofosforados , FosforamidasRESUMEN
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.
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Algoritmos , Neoplasias Encefálicas , Imagen por Resonancia Magnética , Redes Neurales de la Computación , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/fisiopatología , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , MutaciónRESUMEN
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.
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Entropía , Redes Neurales de la Computación , Robótica , Algoritmos , Humanos , Simulación por Computador , Neuronas/fisiologíaRESUMEN
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.
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Exposición Dietética , Grano Comestible , Contaminación de Alimentos , Micotoxinas , China , Micotoxinas/análisis , Humanos , Contaminación de Alimentos/análisis , Medición de Riesgo , Femenino , Grano Comestible/química , Masculino , Exposición Dietética/análisis , Adulto , Niño , Adulto Joven , Adolescente , Preescolar , Cromatografía Líquida de Alta Presión , Persona de Mediana Edad , Tricotecenos/análisis , LactanteRESUMEN
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.
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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.
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Algoritmos , Redes Neurales de la Computación , AtenciónRESUMEN
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.
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Contaminantes Atmosféricos , Contaminación del Aire , Embarazo , Femenino , Niño , Humanos , Material Particulado/análisis , Contaminantes Atmosféricos/análisis , Dinámicas no Lineales , Exposición a Riesgos Ambientales/análisis , Contaminación del Aire/análisis , China/epidemiología , MortinatoRESUMEN
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.
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Carga Global de Enfermedades , Nacimiento Prematuro , Embarazo , Femenino , Humanos , Recién Nacido , Estudios Retrospectivos , Costo de Enfermedad , Material Particulado , Años de Vida Ajustados por Calidad de VidaRESUMEN
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.
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Genoma Mitocondrial , Ixodes , Ixodidae , Ursidae , Animales , Ixodidae/genética , Filogenia , Ixodes/genética , Genoma Mitocondrial/genéticaRESUMEN
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.
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Redes Neurales de la Computación , Neuronas , Neuronas/fisiología , Sinapsis/fisiología , Algoritmos , Lógica Difusa , Potenciales de Acción/fisiología , Modelos NeurológicosRESUMEN
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.
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Redes Neurales de la Computación , Neuronas , Algoritmos , Encéfalo/fisiología , Aprendizaje Automático , Neuronas/fisiologíaRESUMEN
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.
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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.
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Algoritmos , Neuronas , Aprendizaje , Modelos TeóricosRESUMEN
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.
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NeuronasRESUMEN
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.
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Encuestas sobre Dietas , Dieta , Política Nutricional , Ingesta Diaria Recomendada , Adulto , Pueblo Asiatico , Niño , Preescolar , China , Frutas , Humanos , VerdurasRESUMEN
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.