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Objective(s): Evaluating the effect of fresh Oleaster leaf extract (OLE) and purified oleuropein (OLR) on ethanol-induced gastric ulcers in rats. HPLC analysis demonstrates the presence of various polyphenol compounds such as ligstroside, luteolin derivative, oleuropein, and comselogoside. Materials and Methods: Gastric ulcer was induced by administration of ethanol by the gastric gavage route. The olive leaf extract was analyzed by HPLC-PDA-ESI-MS, and OLR was purified. These two compounds were given 2 hr before gastric ulcer induction by ethanol.Results: This study verified that OLE and purified OLR protect from ethanol-induced gastric ulceration and damage, evidenced by the significant decrease in gastric ulcer urea (by 74 and 58% respectively) and stomach mucus content (by 169 and 87% respectively). In addition, the ulcer index (UI) and curative index (CI) levels in the stomach of the rats treated with this supplement were also suppressed by 55 and 46%, respectively. OLE and OLR also decreased the gastric myeloperoxidase (MPO) activity and ameliorated the Nitric oxide (NO) content. OLE and OL also ingestion suppressed gastric tumor necrosis factor-alpha (TNF-α) and interleukin (IL-6) rates. Macroscopic and histological findings revealed that OLE and OLR protect from gastric hemorrhage, severe disruption of the gastric mucosa, and neutrophil infiltration.Conclusion: Overall, the findings demonstrate that OLE and OLR have both promising potential with regard to the inhibition of gastric hemorrhage and lesions.

Modern research is focused on the discovery of new compounds that meet the requirements of modern construction. An example of low energy consumption is that buildings consume between 20% and 40% of energy. In this research, the effect of fiber addition on the properties of compacted earth bricks composed of clay and sand and fixed with cement is studied. Fiberglass or palm are used in different proportions (0% and 0.4%). This is done by studying the change in mechanical and thermal properties. The study focuses on clarifying the role of fiber type and the amount of compressive force applied to the soil. To change the properties of bricks. This is studied using experimental methods and systematization criteria. The results showed a decrease in density by 9.1%, with a decrease in water absorption by 8%, an increase in brick hardness by 42.7%, and a decrease in thermal conductivity by 22.2%. These results show that the addition of fiber improves mechanical and thermal properties. Which reduces energy consumption. The results are important because they explain the changes that occur in the earth block when palm fibers and glass are added and how they are used to improve earthen buildings.

Covid-19 impacted several sectors such as economic, political, social, sports and art activities etc. On the other hand, it influenced handwashing times, awareness, and risk levels for the hospital staff in Sulaimaniyah City. In this study, the effects of Covid-19 on awareness, risk level, and hand cleaning have been focused on by hospital staff in Sulaimaniyah City hospitals. A qualitative method using an electronic questionnaire was applied for data collection. The total number of participants was 404 hospital staff. The percentage ratio of female employees is higher than males in the health sector in the Sulaimaniyah Hospitals. Awareness by the hospital staff has increased during the Covid-19 pandemic. Participants aged more than 55 years were more at risk because of their age, and females more than 55 years had the most threat. Covid-19 impacted the increasing rate of washing hands by 38% and using materials for cleaning hands by 46% for the hospital staff in Sulaimaniyah City. The average grand total change percentage was 41%. Water consumption increased by 135% in the Sulaimaniyah Hospitals throughout the Covid-19 pandemic. On the other hand, fewer working hours in the hospitals led to a high-water consumption ratio among the employees in the hospitals.

A dust storm is one of the costliest and most destructive events in many desert regions. This research investigates the effect of dust storm on sea surface temperature (SST) in the western zone of the Persian Gulf, especially Bushehr Province and its beaches in the years 2008 and 2009. Therefore, some climate and sea parameters such as SST, salinity, air temperature, wind velocity and direction, evaporation, horizontal visibility, sunshine hours and radiation, simultaneously measured in a specific period of time, were analyzed by comparing each of them with satellite data. Sea surface temperature analysis in summer shows that the maximum SST in Persian Gulf along neighbor waters to Bushehr County and central regions in northern section of Persian Gulf is about 34–36 °C. The SST amplitude variation in these places in summer ranges from 28 to 34 °C and when there are dust phenomena, it is from 29.5 to 31 °C. The outcome of this study shows that the SST increases during dusting phenomena and this causes an increase in vapor and as a result a decrease in temperature occurs. On the other hand, vapor increase leads to a growth in the amount and layer of earth’s cloud cover and finally it causes an effective decrease in short-wave sunshine and the temperature and the vapor on surface decrease. As a result, the decrease in sea surface temperature terminates.

This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period.

Abstract Kayexalate can cause severe unrecognized GI lesions. Diagnosis of kayexalate crystals in GI biopsy samples is important. Pathologists and clinicians should work hand in hand. New drugs should be available to all patients to treat hyperkalemia.

Abstract This study was carried out to assess the impact of nickel nanoparticles (NiNPs) as well as scorpion venom on colorectal cancer (CRC) cells in the presence and/or absence of 5-fluorouracil (5-FU), hydrogen sulfide (H2S), and nitric oxide (NO) donors and to determine alterations in endothelial NO synthase (eNOS) and cystathionine γ-lyase (CSE) enzyme-producing genes in CRC patients. The IC50 of both H2S and NO donors, along with NiNPs, were determined. The CRC cells were treated for 24hrs, and the cytotoxic activities were assessed using the MTT test. Moreover, the apoptosis was determined after 24hrs and 48hrs using TUNEL assay. Furthermore, the mutations in the eNOS gene (intron 4, -786T>C and 894 G>T) and CSE gene (1364GT) were determined using direct sequencing. The IC50 values for sodium disulfide (Na2S) and sodium nitroprusside (SNP) at 24hrs treatment were found to be 5 mM and 10−6 M, respectively, while the IC50 value for 5-FU was reached after 5-days of treatment in CRC cell line. Both black and yellow scorpion venoms showed no inhibition of cell proliferation after 24hrs treatment. Furthermore, Na2S showed a significant decrease in cell proliferation and an increase in apoptosis. Moreover, a co-treatment of SNP and 5-FU resulted in inhibition of the cytotoxic effect of 5-FU, while a combination treatment of NiNPs with Na2S, SNP, and 5-FU caused highly significant cytotoxicity. Direct sequencing reveals new mutations, mainly intronic variation in eNOS gene that has not previously been described in the database. These findings indicate that H2S promotes the anticancer efficiency of 5-FU in the presence of NiNPs while NO has antiapoptotic activity in CRC cell lines.

In order to investigate the effect of biochar and humic acid on the rate of remobilization, grain yield and antioxidant content of maize under water deficiency stress, an experiment was conducted as asplit-split plot in the form of a randomized complete blocks design with three replications in Ahvaz, southwest of Iran. The main plot involved water stress with three irrigation levels after depleting 30, 40 and 50% of field capacity, non-stress, moderate stress, and severe stress, respectively, asubplot withbiochar with 2 control levels (no use of biochar) and application of 4 tons in biochar hectares and another subplot having humic acid with 4 control levels (no use of humic acid) and application of 2, 4 and 6 liters per hectare of humic acid. The results showed that the effect of water deficit stress, biochar and humic acid on grain yield, remobilization rate, current photosynthesis, share of current photosynthesis, catalase and superoxide dismutase enzymes was significant at the level of 1% probability. The highest grain yield was related to irrigation treatment after depleting 40% of field capacity and application of 4 tons per hectare of biochar. In total, the use of 4 liters per hectare of humic acid in moderate moisture stress conditions due to its positive role on the growth and hence on the 42% increase in the yield, compared to severe stress treatment and no consumption of humic acid, can be recommended under arid and semi-arid conditions to save water consumption, and reduce the effects of water deficit stress.

This study shows the impact of the reforestation program by some tree species (Pinus halepensis, Cupressus sempervirens and Eucalyptus camaldulensis) on the soil physicochemical parameters in south-western Algeria steppe. The study was realized on soil samples from the Touadjeur site. One hundred soil samples were taken from the field, followed by physicochemical analyses in the laboratory. A comparison of the soil elements between reforested and unreforested sites considered as a control was carried out. The results show that there are differences in the physicochemical parameters of the soil between the reforestation and the non-reforestation ones constituted by a steppe vegetation. The ANOVA one way test shows a highly significant difference change in soil physical parameters, moisture, holding capacity and saturated hydraulic conductivity, but no change in apparent density, actual density and color. Also, from a chemical point of view, the differences concern the organic matter, organic carbon, pH and certain soil contents. The main differences in soil properties between the reforestation sites and the control site, reflect the consequences of forest management adopted by the forest services, most probably the choice of reforestation species.

A strong shaking with Mw 7.3 occurred on 12th November 2017 around the Sarpol-e Zahab town in the border area between Iran and Iraq. It has a number of foreshocks and aftershocks increasing the total deformation, cumulatively. In this study, we have investigated how earth surface deformed after such a strong earthquake and its scatters. Because, the deformation inspection are indispensable for the safety of citizens and infrastructures. The best way for monitoring of surface deformation in such a big event is the SAR technique. This system can work effectively during night and day under different weather conditions. The Interferometric SAR (InSAR) allows accurate measurements of surface deformation in mm resolution. There are several methods for the application of SAR techniques and one of them is Differential InSAR (DInSAR) indicating an uplift and subsidence around epicentral area precisely. We preferred to use it for sensitive vertical displacement in the target area. The seismological data from the observatory centers indicate that the recent earthquake sourced from the NW-SE trending, northeast dipping High Zagros Reverse Fault Zone. According to the results, epicentral area has been exposed a vertical displacement with 90 cm uplift and -41 cm subsidence in the northeastern and southwestern block of the fault, respectively.

Sorghum (Sorghum bicolor Moench) is one of the most important forage crops in the cereal family. Due to its adaptation to arid condition and high-water use efficiency, it may produce satisfactory seed yields in arid conditions. The present study was conducted in Khorramabad province during two crop years of 2016 - 2017. The experiment was conducted in a randomized complete block design with split-factorial arrangement with four replications. The stress factor was assigned to main plots through three levels of irrigation, normal irrigation, and irrigation at vegetative and reproductive stages, and sub-factor to subplots, consisted of three levels of biofertilizer, soil NPK application, soil vermicompost application, and vermicompost and chemical fertilizer combining with Sepideh and Kimia cultivars. Initially, combined analysis of variance was performed. Due to the lack of significant year effect and its interaction effects by different factors, analysis of variances was performed on two years data. The effects of stress and the interaction effects of cultivar by stress and fertilizer on some traits were measured. It was found that the quantitative, qualitative and phonological indices of sorghum were directly negatively affected by stress at the reproductive stage. On the other hand, the effect of vermicompost fertilizer on the studied traits was significant and positive. Sepideh possessed the highest vahues for those traits except for protein percent, in comparison to Kimia. Grain yield was about 8 ton at irrigation-interrupt during vegetative stage for Sepideh and 4.36 t.ha-1 for Kimia, but grain yield at irrigation-interrupt during reproductive stage was 7.67 and 4.45 t.ha-1 for Sepideh and Kimia, respectively.

Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods.

Abstract Background Primary microcephaly (PM) is defined as a significant reduction in occipitofrontal circumference (OFC) of prenatal onset. Clinical and genetic heterogeneity of PM represents a diagnostic challenge. Methods We performed detailed phenotypic and genomic analyses in a large cohort (n = 169) of patients referred for PM and could establish a molecular diagnosis in 38 patients. Results Pathogenic variants in ASPM and WDR62 were the most frequent causes in non‐consanguineous patients in our cohort. In consanguineous patients, microarray and targeted gene panel analyses reached a diagnostic yield of 67%, which contrasts with a much lower rate in non‐consanguineous patients (9%). Our series includes 11 novel pathogenic variants and we identify novel candidate genes including IGF2BP3 and DNAH2. We confirm the progression of microcephaly over time in affected children. Epilepsy was an important associated feature in our PM cohort, affecting 34% of patients with a molecular confirmation of the PM diagnosis, with various degrees of severity and seizure types. Conclusion Our findings will help to prioritize genomic investigations, accelerate molecular diagnoses, and improve the management of PM patients.

Background: In this study, we have used the middle hepatic vein with the right lobe, and we studied the venous outflow dynamics immediately after transplant retrospectively meanwhile we studied the impact of the graft function on donor functionality and overall donor safety. Method: Between October 2017 to October 2020, we performed 40 adult to adult live donor liver transplants at Zheen International Hospital (Erbil, Kurdistan Region). Postoperative Doppler ultrasound was performed for recipients and donors immediately after surgery and then daily until discharge. Results: The donor age (28.5 ± 6.9) year, male/ female 19/ 21, intensive care unit stay (1.2 ± 0.43), floor stay (5.2 ± 1.4) days, their portal vein velocity (43.5 ± 18.4 ml/sec), hepatic artery resistive index (0.6 ± 0.09) and triphasic/ continuous venous outflow 30/10, their postoperative day one and at the discharge total serum bilirubin were (2.8 ± 1.8) and (2 ± 1.4), their postoperative day one and at the discharge international normalization ratio were (1.7 ± 0.5) and (1.2 ± 0.2) respectively. The recipient age (48.5 ± 11.3), male/female 27/13, intensive care unit stay (4.75 ± 3.9), floor stay (7.7 ± 3.7) days, portal vein velocity (63.96 ± 24.65 ml/sec), hepatic artery peak systolic velocity (74.76 ± 32.85) hepatic artery resistive index (0.7 ± 0.15), and triphasic/continuous venous outflow 27/13. Conclusions: Middle hepatic vein incorporation in live donor liver graft is safe with a favorable outcome for recipient and donor, doppler US is one the important tool for evaluation and follow up of donor and recipient for detection of vascular complications and assessment of venous outflow and graft function. In addition, early discharge of the donor is a reasonable option.

The importance of examining environmental stresses and their role in predicting and evaluating the growth and yield of crops is very evident. Environmental stresses are the most important factor in reducing agricultural yields worldwide. In order to evaluate the environmental impact of water loss on the amount of proteins affected in sunflower, an experiment was conducted in randomized complete block design with three replications at Karaj Oil Seeds Research Institute. In order to study the response of susceptible line (BGK221) and tolerant line (RGK46) under drought stress conditions, extraction of protein by acetone deposition method was performed in 8 leaves and of 3 seedlings in each replication. The amount of extracted protein was determined by Bradford method using two-dimensional electrophoresis and existence of significant difference between the bands in sensitive and tolerant lines was investigated. A total of 467 repeatable bands were found in the tolerant line and 417 repeatable bands appeared in the sensitive line. Among these proteins, 6 bands in tolerant line (No 503, 1901, 904, 3301, 7011, 9005) and 6 bands in sensitive line (No 704, 811, 3205, 4108, 7307, and 9207) were significantly affected by drought stress. In both sensitive and tolerate lines the main consequence is increase in amount of protein. The results showed that the most important factor of tolerant line adaptive for environmental stress conditions is maintaining normal cell metabolism, keeping moisture in the cell, strengthening cellular structure and antioxidant defense. The study also showed that drought stress had the greatest effect on cytoplasmic and nucleus proteins, metabolism and energy of proteins

Land cover change is the result of complex interactions between social and environmental systems which change over time. While climatic and biophysics phenomena were for a long time the principal factor of land transformations, human activities are today the origin of the major part of land transformation which affects natural ecosystems.

Backgrounds: The high intra-abdominal carbon dioxide (CO2) gas pressure of the pneumoperitoneum may result in physiological changes. Objectives: This study aimed to examine the impact of high CO2 pressure on intraoperative blood pressures (BPs) in patients undergoing laparoscopy surgeries. Materials and Methods: In this quasi-experimental clinical trial, 36 patients diagnosed with different intra-abdominal diseases were undergone laparoscopic surgeries under a standard of CO2 pressure (≥13 mmHg). In another group, 39 patients who were matched in age and gender were undergone laparoscopic surgeries under low CO2 pressure (≤12 mmHg). Results: The patients in both high and low CO2 pressure groups were comparable in age (37.65 vs. 42.37 years; P = 0.114), gender (P = 0.212), and operation time (36.18 vs. 34.64 min), respectively. The patients underwent high CO2 pressure had significantly lower levels of diastolic BP after anesthesia (72.28 vs. 77.89; P = 0.020), lowest systolic BP (SBP) (94.69 vs. 102.26; P = 0.006), and lowest diastolic BP (DBP) (59.00 vs. 68.36; P < 0.001) in contrast with higher level of postoperative diastolic BP in high CO2 group (111.23 vs. 78.11; P < 0.001). SBP and DBP and heart rate were decreased significantly from preoperative to postanesthesia and postoperative surgery in groups. Conclusion: The present study showed that BP parameters were deceased following laparoscopy surgeries (higher pneumoperitonial CO2 pressure has lower BP in comparison to lower pneumoperitonial CO2 pressure). Low CO2 pressure is recommended during laparoscopic surgeries as much as possible, especially in patients with comorbidities such as heart diseases, old ages, and for operations that take longer time.

The prediction of ocean waves is a highly challenging task in coastal and water engineering in general due to their very high randomness. In the present case study, an analysis of wind, sea flow features, and wave height in the southern coasts of the Caspian Sea, especially in the off-coast sea waters of Mazandaran Province in Northern Iran, was performed. Satellite altimetry-based significant wave heights associated with the period of observation in 2016 were validated based on those measured at a buoy station in the same year. The comparative analysis between them showed that satellite-based wave heights are highly correlated to buoy data, as testified by a high coefficient of correlation r (0.87), low Bias (0.063 m), and root-mean-squared error (0.071 m). It was possible to assess that the dominant wave direction in the study area was northwest. Considering the main factors affecting wind-induced waves, the atmospheric framework in the examined sea region with high pressure was identified as the main factor to be taken into account in the formation of waves. The outcomes of the present research provide an interesting methodological tool for obtaining and processing accurate wave height estimations in such an intricate flow playground as the southern coasts of the Caspian Sea.

Many empirical models have been introduced by scientists during the recent decades for estimating longshore sediment transport rate, but these approaches have been calibrated and applied under limited conditions of the bed profile and specific range of the bed sediment size. The existing empirical relations are linear or exponential regressions based on the observation and measurements data, and there’s a great potential to build more accurate models to predict sediment transport phenomena using soft computation approach. This paper presents a novel case study application of the adaptive Neuro-fuzzy inference system (ANFIS) as a superior modeling technique for estimation of the longshore sediment transport rate in the southern shorelines of the Caspian Sea. The results will be compared with the top three popular existing empirical equations. Daily grab samples from four stations were collected from March 2012 through June 2012. The trained ANFIS model outperformed the existing regression-type empirical equations for the estimation of the alongshore sediment transport rate due to the adaptive structure of the ANFIS model to better fit complex systems.

Abstract Background Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. Methods Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play. Results Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse. These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients’ clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories. Conclusions While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases.

Since the middle ages, the need to identify the vehicles position in their local environment has always been a necessity and a challenge. Today, GNSS-based positioning systems have penetrated several field, such as land transport, emergency systems and civil aviation requiring high positioning accuracy. However, the performances of GNSS-based systems can be degraded in harsh environment due to non-line-of-sight (NLOS), Multipath and masking effects. In this paper, for improving vehicle localization in urban canyons, we address a very challenging problem related to GNSS signal reception state detection (LOS, NLOS or Multipath). A SVMbased system for GNSS Multipath detection using the fusion of information provided by two GNSS antennas is proposed. In this work, we aim to explore the potential of machine learning, and more precisely, Support Vector Machines (SVM) to identify GNSS signals reception state. The SVM-based system developed in this work has used the C/N0 of signals provided by RHCP and LHCP antennas, and satellite elevation as classification criteria. The training data set is constructed by several experimental studies done in real environments, Calais, France . Furthermore, four SVM kernel functions are tested, namely, Linear, Gaussian, Cubic and Quadratic. A GNSS signal reception state detection by applying the proposed SVM-based classifier is demonstrated on real GPS signals, and the efficiency of the system is shown. We obtain empirically an accuracy of signal detection about 93%.

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.