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Background and Objective: Soybean as a cheap protein and without side effects has been introduced to food industry. This study carried out to determine the effect of diet containing soybean on histology and histomorphometry of duodenal villi and serum levels of Calcium, Phosphorus and Glucose in mice. Methods: In this experimental study, 30 female immature BALB/c mice with 3 weeks of age were randomly allocated into control, and experimental 1 and 2. The control group was fed a diet with complete protein. Animals in the experimental 1 and 2 were received a diet of complete protein with 40% soybean and 20% soybean, respectively. After 3 months the mice were anesthetized and blood samples were taken from the heart for determining serum level of Calcium, Phosphorus and glucose. Duodenum specimens from were prepared and stained with hematoxylin and eosin. Results: Tissue structure of duodenum in experimental groups in compare to control group was not significantly changed, except for some scant specimens who showed some degrees of destruction in villi apicese. In histomorphometrical evaluation, the thickness of sub - mucosa and musculature were significantly increased in experimental groups compared to the controls group (P

Background & Objective: Malnutrition is one of the main nutritional problems among children under 5 years especially in developing countries. The effects of malnutrition in this range of age group includes ; disorder of mental and physical growth and development, increase in the morbidity and mortality, decrease of the learning capacity. A detailed epidemiological picture of the prevalent malnutrition status among children under 5 years in different regions of Iran enables health policy makers to identify priorities, establish political commitment and design appropriate interventional programs to prevent and control malnutrition and improve nutritional status of children. To determine the prevalence rate of malnutrition among children under 5 years. Methods: Demographic data, weight and height of 34200 Iranian children between 0-72 months old in 28 provinces of the country in 2005 year based on a cluster sampling were gathered. The subjects' weight and height were measured by trained staff. Prevalence of underweight, stunting, and wasting indicators was determined by measuring the weight and height of subjects in urban and rural areas. EPI6 statistical software, version b6/04, was used for all statistical analyses. Results: 4.7% of the children aged less than five years suffered from stunting (95% CI: 4.5- 4.9%) The prevalence of this type of malnutrition among urban children is significantly less than rural children (95% CI: 3.1- 3.5% and 6.5-7.1% respectively). Meanwhile the prevalence of underweight was 5.2% in the country (95% CI: 5.1-5.4% while the prevalence of this type of malnutrition was significantly less among urban children than those in rural ones. Moreover, the prevalence of wasting was 3.7% (95%CI: 3.5-3.9%) and there was a significant difference in this regard between the urban and rural children. Thus this indicator was significantly higher among urban children than those in rural areas (95%CI: 3.8-4.3% and 3.0-3.5% respectively). Conclusion: The results of the study show that the prevalence of protein-energy malnutrition was low among Iranian children under five years old. In comparison with previous studies, it is felt that there has been a prudent decrease in all malnutrition in all levels. However, there were significant differences in the prevalence of malnutrition in different provinces, which has resulted in the differences in their developmental stages as well. It appears that subjective strategies are required to improve the nutritional and health status among children under five in all provinces.

Hyperpolarized pyruvate is a widely used marker to track metabolism in vivo and a benchmark molecule for hyperpolarization methods. Here, we show how a combination of improved bullet-DNP instrumentation, an optimized sample preparation and a further sensitivity increase via a 13 C- 1 H polarization transfer after dissolution enable the observation of pyruvate at a concentration of 250 nM immediately after dissolution. At this concentration, the experiment employs a total mass of pyruvate of only 20 ng or 180 pmol. If the concentration is increased to 45 μM, pyruvate may be detected 1 min after dissolution with a signal-to-noise ratio exceeding 50. The procedure can be extended to observe a mixture of amino acids at low micromolar concentrations.

The discovery of (meta)stable pre-nucleation species (PNS) challenges the established nucleation-and-growth paradigm. While stable PNS with long lifetimes are readily accessible experimentally, identifying and characterizing early-stage intermediates with short lifetimes remains challenging. We demonstrate that species with lifetimes ≪ 5 s can be characterized by nuclear magnetic resonance spectroscopy when boosted by 'Bullet' dynamic nuclear polarization (Bullet-DNP). We investigate the previously elusive early-stage prenucleation of calcium carbonates in the highly supersaturated concentration regime, characterizing species that form within milliseconds after the encounter of calcium and carbonate ions and show that ionic pre-nucleation species not only govern the solidification of calcium carbonates at weak oversaturation but also initiate rapid precipitation events at high concentrations. Such, we report a transient co-existence of two PNS with distinct molecular sizes and different compositions. This methodological advance may open new possibilities for studying and exploiting carbonate-based material formation in unexplored parts of the phase space., (© 2024. The Author(s).)

It is shown that proton spins highly polarized by dynamic nuclear polarization (DNP) retain substantial polarization upon the rapid transfer of frozen bullets from a polarizer to an NMR spectrometer. After injection in solution, the resulting hyperpolarization in aliphatic chains comprises population imbalances between singlet and triplet states of geminal protons and combinations thereof. These hyperpolarized long-lived states (LLSs) can be reconverted into observable transverse magnetization by polychromatic spin-lock-induced crossing (poly-SLIC). This reconversion can be achieved simultaneously in several molecules. Consecutive partial reconversion steps can be carried out to determine the lifetimes T LLS on the fly in a single experiment. The enhancement factors of hyperpolarized LLS-derived signals in our experiments are at least 2 orders of magnitude. These methods extend applications of bullet-DNP to protons in molecules containing short aliphatic chains and may be useful for drug screening.

Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran's precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study determined the main local and regional parameters affecting the stable isotopes ( 18 O, 2 H) in the Bangkok precipitation and developed the Bangkok meteoric water line (BMWL) ( δ 2 H = (7.68 ± 0.07) δ 18 O + (7.25 ± 0.48)). First, Pearson correlation coefficients were used to determine the correlation between local and regional parameters. Six different regression methods were used based on Pearson correlation coefficients. The stepwise regression had the most accurate performance among them according to the R 2 values. Second, three different methods were used to develop the BMWL, and their performances were also studied. Third, the stepwise regression method was used to study the effects of local and regional parameters on the stable isotope content of precipitation. The results showed that the local parameters had a greater effect on the stable isotope content than the regional ones. The stepwise models developed based on the northeast and southwest monsoons showed that moisture sources also affected the stable isotope content of precipitation. Finally, the developed stepwise models were validated by calculating the root mean square error (RMSE) and R 2 . This study demonstrated that the local parameters mainly controlled the stable isotopes in the Bangkok precipitation, while the regional parameters had a slight effect on them.

The Chao Phraya River Deltaic Plain is the largest basin in Thailand and the second largest one in Southeast Asia after the Mekong River Delta. In recent decades, the groundwater quality in the Lower Chao Phraya River Basin in Thailand has deteriorated due to salinization caused by seawater intrusion. In the present study, hydrogeochemical and statistical methods were employed to determine the hydrochemical characteristics of the groundwater and to investigate the possible sources of salinity in the study region for the years 2008 and 2020. In addition, samples were taken from precipitation, sea water, and river water to analyze their hydrochemical properties. Then, they were used as input in the "Simmr" code in the R programming language to model the hydrochemical conditions of the study area and their evolution over time. The results indicated that in the non-coastal regions, water-rock interaction (mineral weathering and ion exchange), and brine/connate water infiltration affected the quality of the groundwater. However, the seawater intrusion was limited only to the coastal regions. Furthermore, the groundwater quality deteriorated from 2008 to 2020. Finally, using stepwise regression in the R language, the salinity of the groundwater was simulated and compared with the measured salinity data. The results obtained by the stepwise model were in close agreement with those obtained from the hydrochemical studies. This study confirmed seawater intrusion in the coastal aquifer as well as the deterioration of groundwater quality over time. To slow down this process and to achieve sustainable conditions, groundwater extraction should be reduced in the study region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Iran is a semi-arid and arid country which always faces a water shortage crisis. Thus, the water resources in Iran should be studied by accurate methods such as stable isotope techniques. In precipitation sampling stations across Iran, the δ 18 O (ranges from -16.3 to -0.3 ‰, -4.9 ‰ average), δ 2 H (-114 to -13 ‰, -24.2 ‰ average) and d -excess (-2.1 to -22.7, 16.5 ‰ average) values are higher compared to δ 18 O (ranges from -10.9 to -3.1 ‰, -6.7 ‰ average), δ 2 H (-71 to -6 ‰, -37.4 ‰ average) and d -excess (1.0 to -21.6 ‰, 14.9 ‰ average) values in groundwater stations. Stable isotope distribution maps in precipitation and groundwater were also developed for Iran. The stepwise technique was used to study the role of parameters influencing stable isotopes in Iran precipitation. Results show the dominant role of temperature, elevation and latitude as well as 'cP and MedT' air masses mixture on stable isotope values in precipitation. Furthermore, the contribution percentage of each air mass which influences Iran in groundwater resources recharge was studied using 'Simmr' package in R programming language. Finally, the accuracy of the developed stable isotope distribution maps was validated.

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier's output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

Alpine habitats are characterized by a high rate of range restricted species compared to those of lower elevations. This is also the case for the Irano-Anatolian global biodiversity hotspot in South-West Asia, which is a mountainous area harbouring a high amount of endemic species. Using two quantitative approaches, Endemicity Analysis and Network-Clustering, we want to identify areas of concordant species distribution patterns in the alpine zone of this region as well as to test the hypothesis that, given the high proportion of endemics among alpine species, delimitation of these areas is determined mainly by endemic alpine species, i.e., areas of concordant species distribution patterns are congruent with areas of endemism. Endemicity Analysis identified six areas of concordant species distribution patterns irrespective of dataset (total alpine species versus endemic alpine species), whereas the Network-Clustering approach identified five and four Bioregions from total alpine species and endemic alpine species, respectively. Most of these areas have been previously identified using the endemic flora of different elevational zones. The identified units using both methods and both datasets are strongly congruent, proposing that they reveal meaningful distribution patterns. Bioregionalization in the Irano-Anatolian biodiversity hotspot appears to be strongly influenced by the endemic alpine species, a pattern likely to hold in alpine regions outside the Irano-Anatolian hotspot., Supplementary Information: The online version contains supplementary material available at 10.1007/s00035-021-00266-7., Competing Interests: Conflict of interestThe authors declare that there is no potential conflict of interest., (© The Author(s) 2021.)

Asbestos, as with other pollutants in the air, has adverse effects on the health of human beings and animals. Today, the relationship between presence of asbestos fibers in the air breathed by humans and developing serious diseases such as lung cancer (asbestosis) and mesothelioma has been proven. The objectives of this study were to monitor the levels of asbestos fibers in ambient air of Mashhad, Iran during 2018, and to draw its Geographic Information System (GIS) distribution map for the city. In this descriptive study, 13 sampling points in Mashhad city were chosen. Sampling of asbestos was carried out for 3 hour during summer and winter at 2018. Sampling of asbestos was performed using MCE (Mixed Cellulose Ester) membrane filters (pour size 0.45 µm; diameter: 25 mm) and cassette holder and peripheral pump. The samples were the analyzed by the phase contrast microscopy (PCM) method (NIOSH7400). Also, to investigate the type of asbestos and for more accurate counting of fibers, Scanning Electron Microscopy (SEM) analysis was utilized. Meteorological parameter were recorded through portable devices. To draw the graphs, Excel, R and Arc GIS software were used. Results showed that the mean asbestos fiber concentrations were equal to 11.40 ± 2.14 and 14.38 ± 2.52 f/L in summer and winter, respectively. The maximum level of asbestos fiber was detected in the station of Baitolmoghaddas square by 26.64 ± 2.14 and 19.3 SEM f/L in winter and summer, respectively. High concentration of asbestos fiber observed in this study can be attributed to the heavy traffic, the presence of prominent industries in the vicinity of the study area, and topographic features. The results from this research recommends that suitable controlling policies should be regulated to reduce both ambient air asbestos and its adverse health endpoints in Mashhad., Competing Interests: Conflict of interestThe authors of this article declare that they have no conflict of interests., (© Springer Nature Switzerland AG 2020.)

Background: Dietary Guidelines are considered as a useful tool for the promotion of healthy dietary behaviors. In Iran, despite the development of the latest National Food-Based Dietary Guidelines, in 2006, it has not been introduced at the community level yet. The present study aims to evaluate the effectiveness of an intervention program to promote Iran's Food-Based Dietary Guidelines (IFBDGs) in urban adult women., Methods: A sample of 435 healthy women, aged 26 to 54 years, was randomly assigned to the intervention or control groups. The intervention group was designed based on the Health Belief Model (HBM). Each subject in the intervention group received three sessions of group education on IFBDGs and the food guide pyramid and participated in a healthy cooking class. Dietary intake, cognitive outcomes related to the constructs of the HBM, physical activity, and the BMI were measured in both groups before, immediately, and one month after the intervention. The outcome measures were compared with the analysis of covariance (ANCOVA), by adjusting for baseline values., Results: The intervention group had a significantly lower total daily energy intake than the control group after the intervention (P=.000). The adjusted differences in the changes of body mass index from the baseline were significant in both post intervention measurements in the intervention group compared to the controls., Conclusions: The intervention designed based on the Health Belief Model was effective in improving the adherence to FBDGs and could serve as a basic model for the promotion of healthy nutrition behavior among women in the primary health care setting.