Ovarian development of Caspian roach, Rutilus caspicus, in southern Caspian Sea: A histological and ultrastructural study

The histology and ultrastructure of the ovarian maturation process in Caspian roach, Rutilus caspicus, was studied. A total 170 female specimens were collected from the Gharasoo River, Bandar Turkmen, the southern Caspian Sea to evaluate its maturation cycle. Based on the results, its ovarian follicle’s development could classified into six stages by distinct characteristics. Minimum and maximum diameter of oocytes were recorded in the chromatin-nucleolus and maturation stages as 56.34±3.74 and 918.83±14.82 µm, respectively. The zona radiata was observed from the cortical alveoli stage and its maximum diameter measured in the secondary vitellogenesis stage as 93.11±23.0 µm. Gonadosomatic index (GSI) reached to its peak in mid-March and its sharp drop in the late April showed its spawning period from late March or early April till the end of April. A positive correlation was found between the GSI and HSI in the vitellogenesis stage. The results also revealed Caspian roach as iteroparous synchronous spawner

The Impact of Livelihood Assets on the Food Security of Farmers in Southern Iran during the COVID-19 Pandemic

The impact of COVID-19 on farmers’ livelihoods and food security is a key concern in rural communities. This study investigates the impacts of the livelihood assets on the food security of rural households during the COVID-19 pandemic and determines those factors related to food security. The population of this study includes rural households in Dashtestan county, Bushehr province, in southern Iran. Based on the Krejcie and Morgan sampling table, 293 households were selected using the convenience sampling method. To measure food security, the American standard index and ordinal regression are used to analyze the factors. The results of the food security situation show highly precarious and food insecure situations among the studied rural households. The regression analysis shows that the most important assets affecting the food security of rural households under COVID-19 are financial, psychological, physical, and human assets, respectively. The results can help rural development planners and policymakers to improve both livelihoods and food security in rural communities, not just during the COVID-19 pandemic, but also in its aftermath.Open Access Fund of the Leibniz AssociationPeer Reviewe

Emergence of High-level Gentamicin Resistance among Enterococci Clinical Isolates from Burn Patients in South-west of Iran: Vancomycin Still Working

Enterococcus faecalis and Enterococcus faecium are among the main agents associated with nosocomial infections with high mortality in immunocompromised patients. Antibiotic resistance, especially against gentamicin and vancomycin among Enterococci, is a risk factor that could increase the morbidity and mortality rate. 179 Enterococci isolates from burn patients were included in this study. Antibiotic susceptibility testing was done using the disk diffusion test and minimum inhibitory concentration (MIC) was evaluated by agar microdilution. Vancomycin and gentamicin resistance associated genes including vanA, vanB, vanC, aac (6’)-Ie aph(2’’), aph(3’)-IIIa and ant(4’)-Ia were detected by PCR and their statistical relation with antibiotic resistance was evaluated. E. faecalis was the more prevalent strain among our local isolates and showed a higher antibiotic resistance in comparison to E. faecium. Vancomycin had a good antibacterial effect on the Enterococcus spp. isolates; however, resistance to this antibiotic and a high-level gentamicin resistance (HLGR) phenotype were observed. Among van operon genes, vanA was the most prevalent gene and among the gentamicin resistance genes, aph (3’)-IIIa was more frequent. The HLGR Enterococci are a real challenge in nosocomial infections. Vancomycin is a key antibiotic to treat such infections but emergence of VRE in our region could be a real concern and, therefore, phenotypic and molecular surveillance must be considered

The expression analysis of IL-6, IL-18, IL-21, IL-23, and TGF-β mRNA in the nasal mucosa of patients with Allergic rhinitis

Background: The profile of inflammatory and suppressing cytokines is important to contribute to the disruption of TH1/ TH2 balance in Allergic rhinitis (AR). Objective: This study aimed to assess the expression levels of IL-6, IL-18, IL-21, IL-23, and TGF-beta in nasal biopsies in AR patients and evaluate its correlation with the severity of AR. Material and method: The study included 30 patients with mild persistent allergic rhinitis (MPAR), patients with moder- ate-to-severe (M/S) PAR, and 30 healthy individuals. The biopsies of nasal inferior turbinate mucosa were collected from each participant. The expression of IL-6, IL-18, IL-21, IL-23, and TGF-beta was evaluated by the quantitative real-time polymerase chain reaction. The degree of eosinophil infiltration into the nasal mucosa, blood eosinophils, and total serum IgE level were also measured. Result: The expression of IL-6, IL-18, and IL-23 in patients with AR significantly increased compared to the control group. Conversely, the gene expression of the TGF-beta declined in the M/S PAR group rather than the AR-group. The data did not show a significant difference in the expression of the IL-21 gene between AR+ and AR-groups. Conclusion: We suggested that inflammatory cytokines including IL-6, IL-18, and IL-23 may be involved in the severity of AR and associated with markers of inflammation

Utilizing Human Feedback in the Soft Actor-Critic Algorithm for Autonomous Driving

Deep Reinforcement Learning (DRL) algorithms are defined with fully continuous or discrete action spaces. These algorithms are widely used in autonomous driving due to their ability to cope with unseen environments. However, in a complex domain like autonomous driving, these algorithms need to explore the environment enough to converge. Among DRL algorithms, Soft Actor-Critic (SAC) is a powerful method capable of handling complex and continuous state-action spaces. However, long training time and data efficiency are the main drawbacks of this algorithm, even though SAC is robust for complex and dynamic environments. In addition, using deep RL algorithms in areas where safety is an essential factor, such as autonomous driving, can lead to a safety issue since we cannot leave the car driving in the street unattended. One of the proposed solutions to get around this issue is to utilize human feedback. In the first approach of this research, we tested two methods for the purpose of reducing the training time of the Soft Actor-Critic (SAC), using human feedback. First, we pre-trained SAC with Learning from Demonstrations (LfD) to find out if pre-training can reduce the training time of the SAC algorithm. Then, an online end-to-end combination method of SAC, LfD, Learning from Interventions (LfI), and imperfect demonstration was proposed to train an agent (dubbed Online Virtual Training). Both scenarios were implemented and tested in an inverted-pendulum task in OpenAI gym and autonomous driving in the CARLA simulator. The results showed a considerable reduction in the training time and a significant increase in gaining rewards for human demonstration and Online Virtual training compared to the baseline SAC. The proposed approach is expected to be effective in daily commute scenarios for autonomous driving, where the driver only needs to provide the required human feedback during the first few days of commute. In the second approach, we investigated different forms of human feedback: head direction vs. steering, and discrete vs. continuous feedback. To this end, a real-time human demonstration from steer and human head direction with discrete or continuous actions was employed as human feedback in an autonomous driving task in the CARLA simulator. In addition, we used alternating actions from a human expert and SAC to have a real-time human demonstration. Also, we tested the discrete vs. continuous feedback in an inverted pendulum task for precise experimental proof, with an ideal controller to simulate a human expert. The results showed a significant reduction in the training time and a significant increase in gained rewards for a combination of discrete feedback, as opposed to continuous feedback. It was also shown that head direction feedback can be almost as good as steering feedback. The main contribution of this work is in the investigation of different types of human intervention and feedback effects in combination with the SAC algorithm to make reinforcement learning safer and faster during the training time. We expect the proposed methods in this work to make Deep reinforcement learning algorithms more robust in challenging environments such as autonomous driving

Utilizing Human Feedback in Autonomous Driving: Discrete vs. Continuous

Deep reinforcement learning (Deep RL) algorithms are defined with fully continuous or discrete action spaces. Among DRL algorithms, soft actor–critic (SAC) is a powerful method capable of handling complex and continuous state–action spaces. However, a long training time and data efficiency are the main drawbacks of this algorithm, even though SAC is robust for complex and dynamic environments. One of the proposed solutions to overcome this issue is to utilize human feedback. In this paper, we investigate different forms of human feedback: head direction vs. steering and discrete vs. continuous feedback. To this end, a real-time human demonstration from steer and human head direction with discrete or continuous actions were employed as human feedback in an autonomous driving task in the CARLA simulator. We used alternating actions from a human expert and SAC to have a real-time human demonstration. Furthermore, to test the method without potential individual differences in human performance, we tested the discrete vs. continuous feedback in an inverted pendulum task, with an ideal controller to stand in for the human expert. The results for both the CARLA and the inverted pendulum tasks showed a significant reduction in the training time and a significant increase in gained rewards with discrete feedback, as opposed to continuous feedback, while the action space remained continuous. It was also shown that head direction feedback can be almost as good as steering feedback. We expect our findings to provide a simple yet efficient training method for Deep RL for autonomous driving, utilizing multiple sources of human feedback

Using Mixed Integer Linear Programming Model For Beam Angle And Fluence Map Optimization In Intensity- Modulated Radiation Therapy

Introdution: Intensity- modulated radiation therapy is one of the treatment methods for cancer tumors. The effectiveness of this method is dependent on the accuracy and treatment planning quality. Therefore, there is a need for a plan to select the angle and intensity simultaneous optimum of radiation. Methods: In this study, an mixed integer linear programming model was proposed for simultaneous optimization of angles and intensity in the GAMS programming environment.To implement the model, after the patient's CT was prepared, the organ cantoring was performed by CERR software and the Influence Matrix was obtained for each organ. After collecting the inputs of the problem, in order to obtain the desired outputs, was used  from The GAMS software from the CPLEX solver. Results: Finally, the actual case of head and neck cancer is analyzed to demonstrate the effectiveness of the model. From the angle of the candidate, ، is chosen as the optimal radiation angles. The maximum dose received by the brainstem was 3. 999, Mandible 70, LeftOrbit 0.026, RightOrbit 0.440, Parotid Gland 0.881, OpticChiasm 0.177, OpticNerves 0.167, spinalcord 9.929 Gray and the minimum dose received by the tumor is 70 Gray. Also, the optimal amount of intensity for implementing the treatment plan on the patient is achieved. Conclusion: The dose received by each organ was significantly improved compared to prescribing doses. Similarly, the comparison of the Dose Volume Histogram obtained by solving a common problem with the model and software CERR, Represents the optimal performance of the model, which improves the security rate and reduces the cost for healthy tissues

Herbal Metabolites as Potential Carbonic Anhydrase Inhibitors: Promising Compounds for Cancer and Metabolic Disorders

Background : Human carbonic anhydrases (CAs) play a role in various pathological mechanisms by controlling intracellular and extracellular pH balance. Irregular expression and function of CAs have been associated with multiple human diseases, such as obesity, cancer, glaucoma, and epilepsy. In this work, we identify herbal compounds that are potential inhibitors of CA VI. Methods : We used the AutoDock tool to evaluate binding affinity between the CA VI active site and 79 metabolites derived from flavonoids, anthraquinones, or cinnamic acids. Compounds ranked at the top were chosen for molecular dynamics (MD) simulations. Interactions between the best CA VI inhibitors and residues within the CA VI active site were examined before and after MD analysis. Additionally, the effects of the most potent CA VI inhibitor on cell viability were ascertained in vitro through the 2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. Results : Kaempferol 3-rutinoside-4-glucoside, orientin, kaempferol 3-rutinoside-7-sophoroside, cynarin, and chlorogenic acid were estimated to establish binding with the CA VI catalytic domain at the picomolar scale. The range of root mean square deviations for CA VI complexes with kaempferol 3-rutinoside-4-glucoside, aloe-emodin 8-glucoside, and cynarin was 1.37 to 2.05, 1.25 to 1.85, and 1.07 to 1.54 Å, respectively. The MTT assay results demonstrated that cynarin had a substantial effect on HCT-116 cell viability. Conclusion : This study identified several herbal compounds that could be potential drug candidates for inhibiting CA VI

Characterization of diet based nonalcoholic fatty liver disease/nonalcoholic steatohepatitis in rodent models: Histological and biochemical outcomes

Nonalcoholic fatty liver disease (NAFLD), as the most common chronic liver disease, is rapidly increasing worldwide. This complex disorder can include simple liver steatosis to more serious stages of nonalcoholic fibrosis and steatohepatitis (NASH). One of the critical concerns in NASH research is selecting and confiding in relying on preclinical animal models and experimental methods that can accurately reflect the situation in human NASH. Recently, creating nutritional models of NASH with a closer dietary pattern in human has been providing reliable, simple, and reproducible tools that hope to create a better landscape for showing the recapitulation of disease pathophysiology. This review focuses on recent research on rodent models (mice, rats, and hamsters) in the induction of the dietary model of NAFLD /NASH. This research tries to compile the different dietary compositions of NASH, time frames required for disease development, and their impact on liver histological features as well as metabolic parameters