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Abstract Midwife-led continuity of care models have been linked to better clinical outcomes in certain developed countries. However, there is a paucity of research on these models in developing nations. This quasi-experimental study was conducted on primiparous women who referred to one private midwifery center and two public hospitals in Ahvaz, Iran to evaluate the effect of team midwifery care on maternal and neonatal outcomes of pregnant women in Iran. Two hundred women were allocated either into the experimental (n = 100) or control (n = 100) groups. Women in the experimental group, received team midwifery care, while women in the control group, received routine care. Data were collected using a demographic questionnaire, Mackey questionnaire, and a checklist. Women in the team midwifery care group experienced significantly higher rates of normal vaginal birth and exclusive breastfeeding compared to women in the control group. No statistically significant difference was observed between the two groups in terms of the rate of induction of labor and postpartum hemorrhage. The duration of labor was longer in the team midwifery care compared to the control group. Women in the team midwifery care group had a significantly higher rate of exclusive breastfeeding at six weeks postpartum compared to the control group (80 vs. 61%, p = 0.001). After excluding women with ruptured membranes and prolonged pregnancies, neonates in the intervention group had significantly higher first- and fifth-minute Apgar scores (p

Abstract The bearing capacity and settlements of surface foundations located on a soil slope are the important issues that have to be considered by geotechnical engineers for the design. The presence of an underground void beneath the footing can affect the foundation stability and can lead to serious structure damages. In this study, the results of two-dimensional (2D) discrete element (DE) and finite element (FE) analyses of a surface footing on a soil slope above a void are presented. To validate the numerical model results, the DE results obtained have been compared with experimental test presented in the previous study. After validation of the DE numerical model, parametric studies were carried out to evaluate the effect of important factors on the surface footing performance. The studied parameters include the horizontal spacing of the void axis relative to the slope edge (SH), the vertical spacing of the void crown relative to the footing base (SV), the horizontal spacing of the footing edge relative to the slope edge (De) and the void diameter (Dv). The effects of these parameters on the pressure-settlement curves and the contact force distributions in the soil slope are presented and discussed. The results showed that the footing bearing pressure increases with an increase of SH, SV and De but decreases when Dv increases. The behavior of a surface footing on a soil slope above a void significantly depends on the SV value.

Abstract Climate change has caused pastoralists to face serious challenges all around the world. To reduce climate change vulnerability, adaptation strategies need to be adopted by pastoralists. In this regard, the present research was done to seek the reasons for the failure of the northeastern pastoralists of Iran in adopting climate change adaptation strategies. The study is descriptive, which conducted by a field survey. The target population included 249 pastoralists from 7 pastoral units, of whom 148 people were selected as sample size using the stratified random sampling technique. The survey instrument was a researcher-made questionnaire. The content validity and face validity of the questionnaire were checked by the experts. Convergent validity was also confirmed based on the average variance extracted (AVE). Cronbach’s α coefficient and composite reliability (CR) were used to evaluate the internal consistency of the questionnaire. The results showed that social and, regulatory and insurance components were the most critical internal and external weaknesses of the pastoralists’ failure in adopting climate change adaptation strategies, respectively. Structural equation modeling showed that external weaknesses had positive and significant effects on internal weaknesses of the pastoralists’ failure in adopting climate change adaptation strategies.

Abstract Cytokine release syndrome (CRS), commonly known as cytokine storm, is an acute systemic inflammatory response that is a significant global health threat. Interleukin-6 (IL-6) and interleukin-1 (IL-1) are key pro-inflammatory cytokines involved in CRS and are hence critical therapeutic targets. Current antagonists, such as tocilizumab and anakinra, target IL-6R/IL-1R but have limitations due to their long half-life and systemic anti-inflammatory effects, making them less suitable for acute or localized treatments. Here we present the de novo design of small protein antagonists that prevent IL-1 and IL-6 from interacting with their receptors to activate signaling. The designed proteins bind to the IL-6R, GP130 (an IL-6 co-receptor), and IL-1R1 receptor subunits with binding affinities in the picomolar to low-nanomolar range. X-ray crystallography studies reveal that the structures of these antagonists closely match their computational design models. In a human cardiac organoid disease model, the IL-1R antagonists demonstrated protective effects against inflammation and cardiac damage induced by IL-1β. These minibinders show promise for administration via subcutaneous injection or intranasal/inhaled routes to mitigate acute cytokine storm effects.

Abstract Cerium oxide nanoparticles possess unique properties that make them promising candidates in various fields, including cancer treatment. Among the proposed synthesis methods for CNPs, biosynthesis using natural extracts, offers an eco-friendly and convenient approach for producing CNPs, particularly for biomedical applications. In this study, a novel method of biosynthesis using the aqueous extract of Eucalyptus camaldulensis leaves was used to synthesize CNPs. Scanning electron microscopy and Transmission electron microscopy (TEM) techniques revealed that the synthesized CNPs exhibit a flower-like morphology. The particle size of CNPs obtained using Powder X-ray diffraction peaks and TEM as 13.43 and 39.25 nm. Energy-dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy confirmed the effect of biomolecules during the synthesis process and the formation of CNPs. The cytotoxicity of biosynthesized samples was evaluated using the MTT method demonstrating the potential of these samples to inhibit MCF-7 cancerous cells. The viability of the MCF-7 cell line conducted by live/dead imaging assay confirmed the MTT cytotoxicity method and indicated their potential to inhibit cancerous cells. Furthermore, the successful uptake of CNPs by MCF-7 cancer cells, as demonstrated by confocal microscopy, provides evidence that the intracellular pathway contributes to the anticancer activity of the CNPs. In general, results indicate that the biosynthesized CNPs exhibit significant cytotoxicity against the MCF-7 cancerous cell line, attributed to their high surface area.

Abstract The health sector is one of the components of development, social welfare and economic growth. The purpose of this study was to develop an economic evaluation model of the environmental and health costs of occupational diseases by hybrid approach. To achieve the study goal, a taxonomy of economic evaluation model of the environmental and health costs of occupational diseases has been developed. The Delphi method was used to identify health and environmental criteria and the analytic network process (ANP) method was used to weigh the sub-criteria. Finally, health and environmental cost were estimated based on the available information. Naft Subspecialty Hospital in Tehran, Iran (NSHT), was selected as the place of case study. In this study, eight and eleven sub-criteria were identified in the health and environmental sector, respectively. The ANP results indicated that the medicine and medical equipment cost criteria with a weight of 0.312 in the Medical sector, and the special and infectious waste cost criteria with a weight of 0.085 in the environmental sector were the most significant cost criteria in NSHT. Furthermore, the parametric model findings indicated that 99.84 and 0.16% of the total costs are associated with health and environmental costs, respectively. The findings indicated that 61.3% of the costs of the health sector were associated with the two sectors of medicine and medical equipment and the cost-of-service personnel, and 91.7% of the costs of the environmental sector are associated with wastewater treatment and the cost of electricity consumption. This study tried to present a quantitative model of the health and environmental costs of NSHT. Implemention of this integrated model can be a practical and effective step in allocating resources and prioritize interventions.

Abstract In this paper, we report a graphene-based plasmonic photodetector optimized using the particle swarm optimization (PSO) algorithm and compatible with complementary metal–oxide–semiconductor (CMOS) technology. The proposed photodetector structure is designed to minimize fabrication challenges and reduce production costs compared to more complex alternatives. Graphene has been used for its unique properties in the detection region, titanium nitride (TiN) as a CMOS-compatible metal, and both to aid in plasmonic excitation. Photodetectors have key parameters influenced by multiple independent variables. However, practical constraints prevent thorough adjustment of all variables to achieve optimal parameter values, often resulting in analysis based on several simplified models. Here we optimize these variables by presenting a new approach in the field of photodetectors using the capabilities of the PSO algorithm. As a result, for the proposed device at the wavelength of 1550 nm, the voltage responsivity is 210.6215 V/W, the current responsivity is 3.7213 A/W, the ultra-compressed length is less than 3 $$\mu {\text{m}}$$ μ m , and the specific detectivity is 2.566× $${10}^{7}$$ 10 7 Jones were obtained. Furthermore, the device in question works under the photothermoelectric effect (PTE) at zero bias and has zero dark current, which ultimately resulted in a very low noise equivalent power (NEP) of 4.5361 $${\text{pW}}/\sqrt{\text{Hz}}$$ pW / Hz .

Abstract The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.

Abstract In this paper, a novel silicon-on-chip integrated 4 × 1 wavelength division multiplexing (WDM) multiplexer has been developed. This is the first time that the multiplexer design incorporates arrayed electro-optical modulators with crosstalk cancellation. The design utilizes two types of electro-optic modulators in each channel. The first modulator, based on 1D-PhCNBC, extracts the desired wavelengths from the WDM spectrum. The second modulator, based on coupled hybrid plasmonics, acts as a switch to eliminate crosstalk of the desired optic wavelength signal at the multiplexer output. By combining the advantages of electro-optical modulators and crosstalk cancellation techniques, we anticipate that our proposed design contributes to the advancement of WDM multiplexing technology and facilitates the implementation of efficient and compact optical communication systems. Additionally, this synergy enables enhanced performance, reduced signal interference, and improved signal quality, leading to more reliable and high-speed data transmission in optical networks. The functionality of the device is theoretically simulated using 3D-FDTD (Finite-Difference Time-Domain) method.

Abstract This paper presents an optoplasmonic gyroscope that employs a novel bent-hybrid structure, and works double-sided. The proposed device is integrable without moving parts, and simply has a robust configuration. The detection mechanism is based on surface plasmon polaritons (SPPs), and the sensor consists of a laser, a bent-metal layer, and a photo-detector (PD). Based on the simulations, the proposed gyroscope provides significant characteristics of a measurement range of ± 45000°/s, an optical sensitivity of 6.025 µ/(°/s), a total sensitivity of 2.41 µA/W(°/s), an ultra-high resolution ability of 16.598 µ°/s, and an accuracy of 99.999%. The dimensions are 5 × 5 × 5 µm3, and the measurement time is 1 ms. The operational wavelength is at visible range of λ = 630 nm. Additionally, the effects of various parameters, including metal material, metal thickness, and laser wavelength, on the gyroscope performance are comprehensively studied.

Candida spp. is a naturally occurring bacteria in the GI tracts of numerous species, including birds. Candidiasis is an opportunistic illness that emerges when the normal microflora is disturbed. Clinical findings include thickening mucosa and white, elevated pseudomembranous areas. Gross lesions can serve as a presumptive diagnosis. This study aimed to detect and identify Candida albicans as of oral cavity of chicken. The Seventy samples were gathered using swabbing from the oral cavity of the Chicken and transported to the Mycology laboratory, using a cool container for handling. Samples were inoculated onto sabouraued dextrose agar for presumptive identification of Candida albicans. Based on molecular techniques, regarding the consequences of PCR amplification of the 18S rRNA gene in the identification of Candida albicans, this gene has existed existing 50 samples, Metallo-aminopeptidase gene was present in 50/50 (100%) and the alkaline phosphatase sequence gene was once existing in 50/50 (100%). whilst alpha glucosidase used to be existing in 30/50 (60%) and sterol esterase genes 25/50 (50%). Antifungal sensitivity testing results showed that ketoconazole and itraconazole provided the most exceptional Sensitivity (100%) against Candida albicans isolates, followed by fluconazole and Amphotericin B. The outcomes of the present research showed that local poultry oral cavities likely contain pathogenic yeasts.

Abstract In recent years, probiotics and their derivatives have been recognized as important therapeutic agents in the fight against cancer. Therefore, this study aimed to investigate the anticancer effects of membrane vesicles (MVs) from Lentilactobacillus buchneri strain HBUM07105 probiotic isolated from conventional and unprocessed yogurt in Arak province, Iran, against gastric and colon cancer cell lines. The MVs were prepared from the cell-free supernatant (CFS) of L. buchneri and characterized using field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) and SPS-PAGE techniques. The anticancer activity of MVs was evaluated using MTT, flow cytometry, qRT-PCR techniques, and a scratch assay. The study investigated the anti-adenocarcinoma effect of MVs isolated from L. buchneri on a human gastric adenocarcinoma cell line (AGS) and a human colorectal adenocarcinoma cell line (HT-29) at 24, 48, and 72-h time intervals. The results demonstrated that all prepared concentrations (12.5, 25, 50, 100, and 200 µg/mL) of MVs reduced the viability of both types of human adenocarcinoma cells after 24, 48, and 72 h of treatment. The analysis of the apoptosis results revealed that the percentage of AGS and HT-29 cancer cells in the early and late stages of apoptosis was significantly higher after 24, 48, and 72 h of treatment compared to the untreated cancer cells. After treating both AGS and HT-29 cells with the MVs, the cells were arrested in the G0/G1 phase. These microvesicles demonstrate apoptotic activity by increasing the expression of pro-apoptotic genes (BAX, CASP3, and CASP9). According to the scratch test, MVs can significantly decrease the migration of HT-29 and AGS cancer cells after 24, 48, and 72 h of incubation compared to the control groups. The MVs of L. buchneri can also be considered a potential option for inhibiting cancer cell activities.

Abstract Avicennia marina is a salt-tolerance plant with high antioxidant and antibacterial potential. In the present work, a gene encoding MnSOD from Avicennia marina (AmSOD2) was cloned in the expression vectors pET28a. The resulting constructs were transformed into Escherichia coli strains Rosetta (DE3). Following the induction with Isopropyl β-d-1-thiogalactopyranoside, the protein His-AmSOD2 was expressed but dominantly found in the insoluble fraction of strain R-AmSOD2. Due to detection of mitochondrial transit peptide in the amino acid sequence of AmSOD2, the transit peptide was removed and AmSOD2 without transit peptide (tAmSOD2) was expressed in E. coli and dominantly found in the soluble fraction. The enzyme His-tAmSOD2 exhibited a molecular mass of 116 kDa in native condition. Nevertheless, in reducing conditions the molecular mass is 28 kDa indicating the enzyme His-tAmSOD2 is a tetramer protein. As shown by ICP analysis there is one mole Mn2+ in each monomer. The Pure His-tAmSOD2 was highly active in vitro, however the activity was almost three-fold lower than His-AmSOD1. Whereas the high stability of the recombinant His-AmSOD1was previously shown after incubation in a broad range pH and high temperature, His-tAmSOD2 was stable up to 50 °C and pH 6 for 1 h. The gene expression analysis showed that the gene encoding AmSOD2 is expressed in root, shoot and leaves of A. marina. In addition, the results show that the expression in the leaves was enhanced after treatment of plant with NaCl, H2O2, Cd2+ and Ni2+ indicating the important role of MnSOD in the resistant mechanism of mangroves.

Drug delivery to the ocular system is affected by anatomical factors like the corneal epithelium, blinking reflex, aqueous blood barrier, and retinal blood barrier, which lead to quick removal from the site and inefficient drug delivery. Developing a drug delivery mechanism that targets specific eye tissue is a major hurdle for researchers. Our study examines the challenges of drug absorption in these pathways. Hydrogels have been researched as a suitable delivery method to overcome some obstacles. These are developed alone or in conjunction with other technologies, such as nanoparticles. Many polymer hydrogel nanoparticle systems utilizing both natural and synthetic polymers have been created and investigated; each has pros and cons. The complex release mechanism of encapsulated agents from hydrogel nanoparticles depends on three key factors: hydrogel matrix swelling, drug-matrix chemical interactions, and drug diffusion. This mechanism exists regardless of the type of polymer. This study provides an overview of the classification of hydrogels, release mechanisms, and the role of controlled release systems in pharmaceutical applications. Additionally, it highlights the integration of nanotechnology in ocular disease therapy, focusing on different types of nanoparticles, including nanosuspensions, nanoemulsions, and pharmaceutical nanoparticles. Finally, the review discusses current commercial formulations for ocular drug delivery and recent advancements in non-invasive techniques. The objective is to present a comprehensive overview of the possibilities for enhancing ocular medication delivery through hydrogel nanoparticle systems.

Abstract Pentamethyl cyanine dyes are promising fluorophores for fluorescence sensing and imaging. However, advanced biomedical applications require enhanced control of their excited‐state properties. Herein, a synthetic approach for attaching aryl substituents at the C2’ position of the thio‐pentamethine cyanine (TCy5) dye structure is reported for the first time. C2’‐aryl substitution enables the regulation of both the twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET) mechanisms to be regulated in the excited state. Modulation of these mechanisms allows the design of a nitroreductase‐activatable TCy5 fluorophore for hypoxic tumor photodynamic therapy and fluorescence imaging. These C2’‐aryl TCy5 dyes provide a tunable platform for engineering cyanine dyes tailored to sophisticated biological applications, such as photodynamic therapy.

Abstract In this study, we utilized molecular dynamics (MD) simulations to investigate the nano pumping process of Carbon Nanotube (CNT) in an aqueous environment. In this research, an attempt has been made to investigate and analyze the pumping process of fullerene C20 and water molecules through a carbon nanotube that is externally stimulated by two oscillators. It should be noted that this nano pump is completely immersed in an aqueous environment and the inside and outside of the carbon nanotube is filled with water molecules. To simulate the aqueous environment with NaCl impurities and carbon structures, we employed the Universal Force Field and Tersoff interatomic potentials, respectively. The stability of the simulated structures was demonstrated through an equilibrium process, which was a result of the appropriate settings in our MD simulations. To describe the CNT nano pumping process, we analyzed the velocity and translational/rotational components of C20 kinetic energy over time steps. By decreasing the water impurity concentration from 0.50 to 0.075 mol/l, the nano pumping time varied from 10.98 to 10.11 ps, respectively. Additionally, optimization of the atomic wave producing in the nano pumping process led to a further decrease in pumping time to 10.01 ps. Finally, a 2.86% variation in calculated results was observed by changing the water MD simulation model from SPC to TIP4P.

Abstract With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.

Abstract Social problems and suffering from the treatment process for infertile couples, especially for women, require the couples to cope with them to balance the infertility crisis. According to the close interactions of the couples with each other, the objective of the present study was to explore a theoretical framework for the relationships between women’s coping strategies, spouses’ coping strategies, and women’s psychological health in infertile couples who were candidates for assisted reproductive technology (ART). This cross-sectional study was carried out on 212 couples undergoing ART. The couples’ coping strategies were evaluated using a validated self-report questionnaire. The women’s psychological health was assessed using a 21-item stress, anxiety, and depression scale (DASS-21). Statistical analysis was performed using the plug-in application PROCESS macro for SPSS. The direct effect of the women’s self-blame and self-focused rumination strategies (p

Abstract We develop a thermoelectric generator based on catalytic combustion and operating in the low power range (up to 10 W). Considering the target of small-scale thermoelectric generators, the additive technique was chosen as an enabling technology to customize the different parts of the presented device. The generator consists of a hexagonal shaped combustion chamber coupled to commercial thermoelectric modules, water-cooled at the cold side. Thanks to the components design, heat transfer across each part of the system is properly driven enhancing the thermal management of the system. Moreover, in order to improve the overall efficiency, exhausts outlet is designed to promote heat recovery. The generator is characterized achieving an electrical power output close to 9 W in continuous regime, with an overall efficiency of 3.55%. The compact size, the light weight, the simple design and the reliability in continuous operating conditions are all promising features of the device described. Furthermore, the materials chosen for the device can suggest a way to fabricate cheaper heat exchangers, actually one of the main costs of the device development.

Abstract In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.

Abstract Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

Abstract We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context.

Summary: The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer’s disease. The iBKH is publicly available.

Abstract Bladder cancer (BC) is one of the most important cancers worldwide, and if it is diagnosed early, its progression in humans can be prevented and long-term survival will be achieved accordingly. This study aimed to identify novel micro-RNA (miRNA) and gene-based biomarkers for diagnosing BC. The microarray dataset of BC tissues (GSE13507) listed in the GEO database was analyzed for this purpose. The gene expression data from three BC tissues including 165 primary bladder cancer (PBC), 58 normal looking-bladder mucosae surrounding cancer (NBMSC), and 23 recurrent non-muscle invasive tumor tissues (RNIT) were used to reconstruct gene co-expression networks. After preprocessing and normalization, deferentially expressed genes (DEGs) were obtained and used to construct the weighted gene co-expression network (WGCNA). Gene co-expression modules and low-preserved modules were extracted among BC tissues using network clustering. Next, the experimentally validated mRNA-miRNA interaction information were used to reconstruct three mRNA-miRNA bipartite networks. Reactome pathway database and Gene ontology (GO) was subsequently performed for the extracted genes of three bipartite networks and miRNAs, respectively. To further analyze the data, ten hub miRNAs (miRNAs with the highest degree) were selected in each bipartite network to reconstruct three bipartite subnetworks. Finally, the obtained biomarkers were comprehensively investigated and discussed in authentic studies. The obtained results from our study indicated a group of genes including PPARD, CST4, CSNK1E, PTPN14, ETV6, and ADRM1 as well as novel miRNAs (e.g., miR-16-5p, miR-335-5p, miR-124-3p, and let-7b-5p) which might be potentially associated with BC and could be a potential biomarker. Afterward, three drug-gene interaction networks were reconstructed to explore candidate drugs for the treatment of BC. The hub miRNAs in the mRNA-miRNA bipartite network played a fundamental role in BC progression; however, these findings need further investigation.

Abstract Changes in root growth and metabolism of P in tomato cultivars are necessary in acidic soils in tropical and subtropical regions in response to P deficiency. This increase in the efficiency of phosphorus absorption by tomatoes can significantly reduce the doses of phosphate fertilizers used, as well as, possibly, the more immediate use of P fixed in the soil matrix, with favorable effects on agricultural sustainability, promoting the use of marginal areas in terms of soil fertility, and the national fertilizer economy. The tested hypothesis was that there would be no difference in the uptake and utilization of P by tomato cultivars; therefore, this study investigated the variation in the dynamics of absorption and efficiency of P-use through changes in the root, stem, leaf, gas exchange, and P-use efficiency in tomato cultivars contrasting P-absorption. The experimental design comprised a factorial scheme consisting of two cultivars that were tolerant and sensitive to P deficiency and three P concentrations (control, moderate deficiency, and severe deficiency). P limitation in the tolerant cultivar promoted high dry matter concentration (root, stem, and leaf), leaf area, root volume, nutrient translocation, rate of leaf gas exchange, and efficiency under P-deficiency stress. It was concluded from the research that the variation in the dynamics of absorption and efficiency of P use of the tolerant cultivar increased the production of roots, leaves, and leaf gas exchange under P stress conditions.

Synthetic biology has enabled the design of strategies for bacteria-based cancer immunotherapy. Here the authors report the development of focused ultrasound-activatable therapeutic bacteria engineered to express anti-CTLA-4 and anti-PD-L1 nanobodies for cancer immunotherapy.

[This corrects the article DOI: 10.1371/journal.pone.0276703.].

IntroductionThe rate of cesarean section is on the rise in both developed and developing countries, and Iran is no exception. According to the WHO, physiologic labor is one of the main strategies for reducing cesarean section and improving the health of mothers and newborns. The aim of this qualitative study was to explain the experiences of health providers regarding implementation of the physiologic birth program in Iran.MethodsThis study is a part of a mixed-methods study, in which 22 health providers were interviewed from January 2022 to June 2022. Data analysis was performed using Graneheim and Lundman's conventional content analysis approach and using MAXQDA10 software.ResultsTwo main categories and nine subcategories emerged from the results of this study. The main categories included "the obstacles to the implementation of the physiologic birth program" and "strategies for improving implementation of the program". The subcategories of the first category included: lack of continuous midwifery care in the healthcare system, lack of free accompanying midwives, lack of integrated healthcare and hospitals in service provision, low quality of childbirth preparation and implementation of physiologic birth classes, and lack of requirements for the implementation of physiologic birth in the maternity ward. The second category included the following subcategories: Supervising the implementation of childbirth preparation classes and physiologic childbirth, support of midwives by insurance companies, holding training courses on physiologic birth, and evaluation of program implementation.ConclusionsThe experiences of the health providers with the physiologic birth program revealed that policymakers should provide the ground for the implementation of this type of labor by removing the obstacles and providing the particular operational strategies needed in Iran. Important measures that can contribute to the implementation of the physiologic labor program in Iran include the following: Setting the stage for physiologic birth in the healthcare system, creating low- and high-risk wards in maternity hospitals, providing professional autonomy for midwifery, training childbirth providers on physiologic birth, monitoring the quality of program implementation, and providing insurance support for midwifery services.

Abstract Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

Abstract Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

Introduction: Although, conventional methods for the expression of polyomaviruses and herpesviruses recombinant proteins for serological assays and vaccine developments in baculoviruses are well established, the manipulations are laborious and time consuming. Methods: A new expression system based on plasmid was used to express two polyomaviruses major capsid protein VP1 (JCV VP1 and BKV VP1), and two herpesviruses glycoproteins (HSV-1 gD and VZV gE) in insect cells. A ligation independent cloning (LIC) was applied to generate the recombinant plasmids. Transfection of Sf9 insect cells were performed using the recombinants. The produced proteins were analysed using SDS-PAGE, immunofluorescence, and immunoblotting. Results: JCV-VP1, BKV-VP1, VZV-gE and HSV-1gD were successfully expressed in the insect cells, 48 h post-infection and detected in cytoplasm and cell membranes with immunoreactivity. This plasmid based expression system took 5 days to express the protein. Conclusion: The plasmid based expression system in insect cells was highly efficient and would be ideal for rapid expression of polyomaviruses and herpesviruses proteins in insect cells to be potentially used in applications such as vaccine components and serological assays.

The electron-withdrawing target (EWT)-induced fluorescence quenching is an unsolved issue in intramolecular charge transfer (ICT) fluorophores that limits their applicability. Here, the authors report a simple and generalizable strategy to reverse the EWT-induced quenching mode into light-up mode, by introducing an indazole building block between the π-bridge and the donor in the ICT scaffold.

Today, smart indicators especially based on pigments and natural biopolymers have developed significantly in laboratory and industrial scale. In addition to tracking the freshness and spoilage of the product, these intelligent systems inform the consumer about the quality of the packaged product without opening the package. On the other hand, they reduce food waste and minimize food poisoning. In this study, two halochromic smart indicators were constructed by encapsulating saffron petal and barberry anthocyanins in gelatin/chitin nanofiber films for indication of the freshness/spoilage of fish fillets. Insights into the molecular, structural, and optical properties of these indicators were obtained using X-ray diffraction, scanning electron microscopy, and infrared spectroscopy, and colorimetry analyses. The results showed that the indicators had smooth surfaces and that the pigments were evenly distributed throughout the biopolymer network. The barberry and saffron anthocyanin solutions underwent color changes from reddish to yellow for barberry and reddish to pink to violet to greenish and yellow for saffron anthocyanin after being exposed to different pH values (1–14). The change in appearance of halochromic indicators was quantified by measuring their color coordinates (L*, a*, b*). When applied for estimating fish spoilage, the color of the G/ChNF/BA and G/ChNF/SPA indicators turned from pink to yellow and from violet to green, respectively. After 3 days of storage, the pH and total volatile basic nitrogen of fish fillets reached 8.0 and 49.06 mg N/100 g, respectively. Therefore, a direct relationship between the increase in pH values, the increase in volatile nitrogen bases content, and the changes in the color of the smart indicator applied to monitor the fish was observed. The simulation tests showed that pH-responsive smart indicators can reveal visually fish fillets freshness in real time prior to the point of consumption.

Neutron contamination as a source of out-of-field dose in radiotherapy is still of concern. High-energy treatment photons have the potential to overcome the binding energy of neutrons inside the nuclei. Fast neutrons emitting from the accelerator head can directly reach the patient's bed. Considering that modern radiotherapy techniques can increase patient survival, concerns about unwanted doses and the lifetime risk of fatal cancer remain strong or even more prominent, especially in young adult patients. The current study addressed these concerns by quantifying the dose and risk of fatal cancer due to photo-neutrons for glioma patients undergoing 18-MV radiotherapy. In this study, an NRD model rem-meter detector was used to measure neutron ambient dose equivalent, H*(10), at the patient table. Then, the neutron equivalent dose received by each organ was estimated concerning the depth of each organ and by applying depth dose corrections to the measured H*(10). Finally, the effective dose and risk of secondary cancer were determined using NCRP 116 coefficients. Evidence revealed that among all organs, the breast (0.62 mSv/Gy) and gonads (0.58 mSv/Gy) are at risk of photoneutrons more than the other organs in such treatments. The neutron effective dose in the 18-MV conventional radiotherapy of the brain was 13.36 mSv. Among all organs, gonads (6.96 mSv), thyroid (1.86 mSv), and breasts (1.86 mSv) had more contribution to the effective dose, respectively. The total secondary cancer risk was estimated as 281.4 cases (per 1 million persons). The highest risk was related to the breast and gonads with 74.4 and, 34.8 cases per 1 million persons, respectively. Therefore, it is recommended that to prevent late complications (secondary cancer and genetic effects), these organs should be shielded from photoneutrons. This procedure not only improves the quality of the patient's personal life but also the healthy childbearing in the community.

Purpose Whole genome sequencing of SARS-CoV2 is important to find useful information about the viral lineages, variants of interests and variants of concern. As there are not enough data about the circulating SARS-CoV2 variants in Iran, we sequenced 54 SARS-CoV2 genomes during the 5 waves of pandemic in Iran. Methods After viral RNA extraction from clinical samples collected during the COVID-19 pandemic, next generation sequencing was performed using the Nextseq platform. The sequencing data were analyzed and compared with reference sequences. Results During the 1st wave, V and L clades were detected. The second wave was recognized by G, GH and GR clades. Circulating clades during the 3rd wave were GH and GR. In the fourth wave GRY (alpha variant), GK (delta variant) and one GH clade (beta variant) were detected. All viruses in the fifth wave were in clade GK (delta variant). There were different mutations in all parts of the genomes but Spike-D614G, NSP12-P323L, N-R203K and N-G204R were the most frequent mutants in these studied viruses. Conclusions These findings display the significance of SARS-CoV2 monitoring to help on time detection of possible variants for pandemic control and vaccination plans.

PurposeThe aim of the present study was to investigate the predictive ability for lung cancer of symptoms reported in an adaptive e-questionnaire, separately for never smokers, former smokers, and current smokers.Patients and methodsConsecutive patients referred for suspected lung cancer were recruited between September 2014 and November 2015 from the lung clinic at the Karolinska University Hospital, Stockholm, Sweden. A total of 504 patients were later diagnosed with lung cancer (n = 310) or no cancer (n = 194). All participants answered an adaptive e-questionnaire with a maximum of 342 items, covering background variables and symptoms/sensations suspected to be associated with lung cancer. Stochastic gradient boosting, stratified on smoking status, was used to train and test a model for predicting the presence of lung cancer.ResultsAmong never smokers, 17 predictors contributed to predicting lung cancer with 82% of the patients being correctly classified, compared with 26 predictors with an accuracy of 77% among current smokers and 36 predictors with an accuracy of 63% among former smokers. Age, sex, and education level were the most important predictors in all models.ConclusionMethods or tools to assess the likelihood of lung cancer based on smoking status and to prioritize investigative and treatment measures among all patients seeking care with diffuse symptoms are much needed. Our study presents risk assessment models for patients with different smoking status that may be developed into clinical risk assessment tools that can help clinicians in assessing a patient's risk of having lung cancer.

The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Here, Alcala and colleagues present a multi-omics analysis of these tumours, revealing distinct molecular and prognostic subgroups.

Abstract The effect of synthesized nanostructures, including graphene oxide, chemically reduced graphene oxide with sodium dodecyl sulfate (SDS), chemically reduced graphene oxide with polyvinylpyrrolidone, and multi-walled carbon nanotubes, on the kinetics of methane hydrate formation was investigated in this work. The experiments were carried out at a pressure of 4.5 MPa and a temperature of 0 °C in a batch reactor. By adding nanostructures, the induction time decreases, and the shortest induction time appeares at certain concentrations of reduced graphene oxide with SDS and graphene oxide, that is, at a concentration of 360 ppm for reduced graphene oxide with SDS and 180 ppm for graphene oxide, with a 98% decrease in induction time compared to that in pure water. Moreover, utilization of carbon nanostructures increases the amount and the rate of methane consumed during the hydrate formation process. Utilization of multi-walled carbon nanotubes with a concentration of 90 ppm showes the highest amount of methane consumption. The amount of methane consumption increases by 173% in comparison with that in pure water. The addition of carbon nanostructures does not change the storage capacity of methane hydrate in the hydrate formation process, while the percentage of water conversion to hydrate in the presence of carbon nanotubes increases considerably, the greatest value of which occurres at a 90 ppm concentration of carbon nanotubes, that is, a 253% increase in the presence of carbon nanotubes compared to that of pure water.

The lesser grain borer, Rhyzopertha dominica (F.), causes damage to stored grains resulting in both quantitative and qualitative losses. The use of synthetic fumigants in the management of stored-product pests resulted in undesirable side effects such as environmental contamination and threat to human and animal health. In this study, the lethal and sublethal effects of essential oils from four Eucalyptus species, E. microtheca, E. procera, E. spatulata, and E. torquata were studied against R. dominica adults. Gas chromatographic–mass spectral analysis of the essential oils was carried out, in which terpenes such as 1,8-cineole and globulol were abundant in essential oils. The pest was susceptible to the fumigation of the essential oils and, considering concentrations and exposure times (24, 48, and 72 h), had significant effects on the pest mortality. The total protein, glycogen, and lipid contents and digestive amylolytic and proteolytic activities of the adults treated with tested essential oils were reduced. The consumption index, relative consumption rate, and relative growth rate were also reduced in the treated adults. According to the insecticidal effects on the adults of R. dominica, the essential oils of E. microtheca, E. procera, E. spatulata, and E. torquata can be candidates for further investigations as grain protectant agents.

Forests play a significant role for human’s well-being. Economists’ attention is mostly drawn on the market value of forest products. The trend, however, is changing as non-market values of forests are increasingly appreciated and measured. Recently, the ecosystem value of forest has been studied by natural resource economists and its role on human welfare is ensured. This paper indicates that the annual use value of the ecosystem services such as water conservation, soil protection, carbon fixation, nutrient cycling, water purification, air pollution absorption and recreation provided by forests is not only worth millions of dollars, but also in per hectare terms much more than hitherto known. This value for the Mazandaran forest reserve (MFR) ranged US$ 14.2–14.8 million or about US$ 6676.9–6785.6 per ha. If these are accounted for, then governments and societies faced with the development versus conservation dilemma can create more understanding decisions and policies that will assist conserve forests and the ecosystem services they provide, and thereby promulgate human well-being and sustainable development. Realization about these significant intangible benefits will assist in more informed decisions and policies that will help conserve forest ecosystems and the services they provide as well as promote human well-being and sustainable development.

Summary: Bioelectronic devices that convert biochemical signals to electronic readout enable biosensing with high spatiotemporal resolution. These technologies have been primarily applied in biomedicine while in plants sensing is mainly based on invasive methods that require tissue sampling, hindering in-vivo detection and having poor spatiotemporal resolution. Here, we developed enzymatic biosensors based on organic electrochemical transistors (OECTs) for in-vivo and real-time monitoring of sugar fluctuations in the vascular tissue of trees. The glucose and sucrose OECT-biosensors were implanted into the vascular tissue of trees and were operated through a low-cost portable unit for 48hr. Our work consists a proof-of-concept study where implantable OECT-biosensors not only allow real-time monitoring of metabolites in plants but also reveal new insights into diurnal sugar homeostasis. We anticipate that this work will contribute to establishing bioelectronic technologies as powerful minimally invasive tools in plant science, agriculture and forestry.

Despite the vast scientific evidence obtained from the genomic sequencing of COVID-19, controversy regarding its origin has been created in the mass media. This could potentially have a long-term influence on the behavior among individuals, such as failure to comply with proposed social distancing measures, leading to a consequent rise in the morbidity and mortality rates from COVID-19 infection. Several studies have collected information about knowledge, attitudes, and practices regarding COVID-19; however, very little is known about the relationship of the perceptions of the individuals regarding the origin of the virus with the knowledge and perception about social distancing. This study aimed at ascertaining this relationship. For such purpose, a web-based cross-sectional study was conducted among a sample population from five provinces of the Dominican Republic from June to July of 2020. The data collection instrument exploited in the study was a self-designed questionnaire distributed throughout different social media platforms. A purposive sampling strategy was implemented and a total of 1195 respondents completed the questionnaire. The collected data was analyzed using SPSS. Descriptive statistics, stepwise multiple linear regression, and one-way multivariate analysis were implemented to test the hypotheses. The level of education was significantly associated (P = .017) with individuals' perception about the origin of COVID-19, whilst only age (P = .032) and education level (P < .001) statistically significantly predicted 'knowledge about social distancing'. Perception of COVID-19 origin was statistically significant associated (P = < .001) with the measures of the dependent variables (knowledge and perception on social distancing). The present study has established a possible link between the 'perception of COVID-19 origin' and 'the perception and knowledge about social distancing'.

Background and purposeObesity is an established risk factor for ischemic stroke but the association of increased body mass index (BMI) with survival after ischemic stroke remains controversial. Many studies have shown that increased BMI has a "protective" effect on survival after stroke while other studies have debunked the "obesity paradox". This study aimed at examining the relationship between BMI and all-cause mortality at one year in first-time ischemic stroke patients using a large dataset extracted from different resources including electronic health records.MethodsThis was a retrospective cohort study of consecutive ischemic stroke patients captured in our Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients in different BMI categories was analyzed using Kaplan Meier survival curves. The predictors of mortality at one-year were assessed using a stratified Cox proportional hazards model.ResultsAmong 6,703 first-time ischemic stroke patients, overweight and obese patients were found to have statistically decreased hazard ratio (HR) compared to the non-overweight patients (overweight patients- HR = 0.61 [95% CI, 0.52-0.72]; obese patients- HR = 0.56 [95% CI, 0.48-0.67]). Predictors with a significant increase in the hazard ratio for one-year mortality were age at the ischemic stroke event, history of neoplasm, atrial fibrillation/flutter, diabetes, myocardial infarction and heart failure.ConclusionOur study results support the obesity paradox in ischemic stroke patients as shown by a significantly decreased hazard ratio for one-year mortality among overweight and obese patients in comparison to non-overweight patients.

This study aimed to introduce a new method for eye lens thermo-luminescent dosimetry and also estimate the dose associated with induced cancer risk due to the ionizing radiation exposure received by physicians and other staff cooperating in interventional cardiology (IC) procedures. The measurements were performed with six TLDs (thermoluminescent dosimeters): four TLDs for eye lens dosimetry (2 positioned on respiratory/surgical mask under the eye region as the new method; and 2 near the outside border of the eye as the common method) and two TLDs for whole-body dosimetry. Whole-body doses were used to calculate the cancer risks induced by IC procedures. The results of the new proposed method for eye lens dosimetry were similar to common TLD positioning (mean differences

The disturbance of wildlife by humans is a worldwide phenomenon that contributes to the loss of biodiversity. It can impact animals' behaviour and physiology, and this can lead to changes in species distribution and richness. Wildlife disturbance has mostly been assessed through direct observation. However, advances in bio-logging provide a new range of sensors that may allow measuring disturbance of animals with high precision and remotely, and reducing the effects of human observers. We used tri-axial accelerometers to identify daytime flights of roosting straw-coloured fruit bats (Eidolon helvum), which were used as a proxy for roost disturbance. This bat species roosts on trees in large numbers (often reaching hundreds of thousands of animals), making them highly vulnerable to disturbance. We captured and tagged 46 straw-coloured fruit bats with dataloggers, containing a global positioning system (GPS) and an accelerometer, in five roosts in Ghana, Burkina Faso and Zambia. Daytime roost flights were identified from accelerometer signatures and modelled against our activity in the roosts during the days of trapping, as a predictor of roost disturbance, and natural stressors (solar irradiance, precipitation and wind speed). We found that daytime roost flight probability increased during days of trapping and with increasing solar irradiance (which may reflect the search for shade to prevent overheating). Our results validate the use of accelerometers to measure roost disturbance of straw-coloured fruit bats and suggest that these devices may be very useful in conservation monitoring programs for large fruit bat species.

Goynik protected region, with an area of 4764 ha is located in North Khorasan province in Razo-Jargalan. In order to identification of flora of Goynik protected area and determination of life forms and chorotypes of plant species, the present study was done between 2014 through 2015. In total, 208 species belong to 163 genera and 51 families were identified. Among them, 170 species were dicots. Asteraceae with 26 sp. was the largest family with high number of species; following with Fabaceae, Lamiaceae and Poaceae with 20, 19 and 16 species respectively. Astragalus with 6 species was the largest genus. Hemicryptophytes with 41% and therophytes with 23% are dominant life forms in the region followed by chamephytes, geophytes and phanerophytes. With regard to geographical distribution, most of the species identified (67%) are Irano-Turanian elements. Among the plant species of the region 21 species are in the red list of IUCN. IUCN.

Successful design of a carbon dioxide (CO2) flooding in enhanced oil recovery projects mostly depends on accurate determination of CO2-crude oil minimum miscibility pressure (MMP). Due to the high expensive and time-consuming of experimental determination of MMP, developing a fast and robust method to predict MMP is necessary. In this study, a new method based on ε-insensitive smooth support vector regression (ε-SSVR) is introduced to predict MMP for both pure and impure CO2 gas injection cases. The proposed ε-SSVR is developed using dataset of reservoir temperature, crude oil composition and composition of injected CO2. To serve better understanding of the proposed, feed-forward neural network and radial basis function network applied to denoted dataset. The results show that the suggested ε-SSVR has acceptable reliability and robustness in comparison with two other models. Thus, the proposed method can be considered as an alternative way to monitor the MMP in miscible flooding process.

Risk for renal cell carcinoma (RCC) is higher when there are first-degree family members with the disease. Here, Scelo and colleagues perform a genome-wide association meta-analysis and new genome-wide scan to identify seven new loci with significant RCC association.