Background: Ferroportin (FPN), a regulator of iron homeostasis is a conserved membrane protein that exports iron across the enterocytes, macrophages and hepatocytes into the blood circulation. FPN has also critical influence on survival of microorganisms whose growth is dependent upon iron, thus preparation of FPN is needed to study the role of iron in immunity and pathogenesis of micoorganisms.Methods: To prepare and characterize a recombinant ferroportin, total RNA was extracted from Indian zebrafish duodenum, and used to synthesize cDNA by RT-PCR. PCR product was first cloned in Topo TA vector and then subcloned into the GFP expression vector pEGFP–N1. The final resulted plasmid (pEGFP-ZFPN) was used for expression of FPN-EGFP protein in Hek 293T cells.Results: The expression was confirmed by appearance of fluorescence in Hek 293 T cells. Recombinant FPN was further characterized by submission of its predicted amino acid sequences to the TMHMM V2.0 prediction server (hidden Markov model), NetOGlyc 3.1 and NetNGlyc 3.1 servers. The obtained FPN from indian zebrafish also contained eight transmembrane domains with N- and C-termini inside the cytoplasm and harboured 78 O-glycosylated amino acids.Conclusion: The recombinant FPN from Indian zebra fish was successfully expressed in Hek 293 cell line. Although the discrepancy in two amino acids was observed in our produced FPN and resulted in an additional O-glycosylation site, but had no effect on the topology of the protein compared to other FPN described by other researchers. Therefore this construct can be used in future iron studies.

Introduction: Frontoparietal (FPN) and cingulo-opercular network (CON) control cognitive functions needed in deductive and inductive reasoning via different functional frameworks. The FPN is a fast intuitive system while the CON is slow and analytical. The defaultinterventionist model presents a serial view of the interaction between intuitive and analytic cognitive systems. This study aims to examine the activity pattern of the FPN and CON from the perspective of the default-interventionist model via reasoning. Methods: We employed functional magnetic resonance imaging (fMRI) to investigate cingulo-opercular and frontoparietal network activities in 24 healthy university students during Raven and Wason reasoning tasks. Due to the different operation times of the CON and FPN, the reaction time was assessed as a behavioral factor. Results: During Raven’, s advanced progressive matrices (RAPM) test, both the CON and FPN were activated. Also, with the increase in the difficulty level of the Raven test, a linear increase in response time was observed. In contrast, during the Wason’, s selection task (WST) test, only the activity of FPN was observed. Conclusion: The results of the study support the hypothesis that the default-interventionist model of dual-process theory provides an accurate explanation of the cognitive mechanisms involved in reasoning. Thus, the response method (intuitive/analytical) determines which cognitive skills and brain regions are involved in responding.

Background: Environmental radioactivity measurement of soil samples from the densely populated community has become imperative considering the radiological exposure associated with primordial radionuclides. Materials and Method: Sixty soil samples were collected at different locations within Alvan Ikoku Federal College of Education (AIFCE) and Federal Polytechnic Nekede (FPN), Owerri. Radioactivity measurements were carried out by the method of gamma-ray spectrometry with thallium doped sodium iodide [NaI(Tl)] detector. Results: The mean activity concentrations of 88. 41±, 1. 51, 20. 69±, 3. 56 and 25. 04±, 0. 71 Bqkg-1 for 40K, 226 Ra and 232 Th, respectively, were obtained in soil samples of AIFCE, while 92. 97±, 1. 50, 20. 48±, 3. 21 and 22. 36±, 0. 70 Bqkg-1 for 40K, 226Ra and 232Th, respectively, were obtained in soil samples of FPN. These values are below the average world value of 420, 32 and 45 Bqkg-1 for 40K, 226Ra and 232Th, respectively, as recorded in UNSCEAR 2000 report. The calculated absorbed dose, annual effective dose and other radiological hazard indices were below the recommended safe limit. The mean calculated values of the excess lifetime cancer risk for AIFCE and FPN are 0. 12±, 0. 03 and 0. 11±, 0. 03, respectively. Conclusion: Based on the results from the present study, it is evidence that the activity concentrations and other radiological parameters are within the world’, s safe limit, indicating that soil samples from the AIFCE and FPN, Owerri are free from radioactive contamination and do not pose a threat to the two communities.

Every facial expression involves one or more facial action units appearing on the face. Therefore, action unit recognition is commonly used to enhance facial expression detection performance. It is important to identify subtle changes in face when particular action units occur. In this paper, we propose an architecture that employs local features extracted from specific regions of face while using global features taken from the whole face. To this end, we combine the SPPNet and FPN modules to architect an end-to-end network for facial action unit recognition. First, different predefined regions of face are detected. Next, the SPPNet module captures deformations in the detected regions. The SPPNet module focuses on each region separately and can not take into account possible changes in the other areas of the face. In parallel, the FPN module finds global features related to each of the facial regions. By combining the two modules, the proposed architecture is able to capture both local and global facial features and enhance the performance of action unit recognition task. Experimental results on DISFA dataset demonstrate the effectiveness of our method.

As one of the natural hazards that occur in some parts of the world, floods have devastating effects on people, the environment, and infrastructure. The management of this crisis can be effective in reducing severe financial and life losses. A critical aspect of managing this natural disaster is the accurate identification of flooded areas and their trends. The article presents a method for the identification and segmentation of flooded areas using the UNET++ neural network and Sentinel-1 satellite images in c-band and single-polarized (HH and VV) and double-polarized (HH+HV and VV+VH) forms. These images were provided by NASA from Nebraska, Alabama, Bangladesh, Red River North, and Florence. The labeling process for all these images was done by the NASA implementation team and the IEEE GRSS Earth Science Informatics Technical Committee. In this network, EfficientNet-B7 is used as an encoder for feature extraction. Based on evaluation criteria such as IoU, F1-score, Recall, Accuracy, and Precision, the efficiency of the model has been evaluated. This model has demonstrated a high potential for detecting and segmenting flooded areas. Using this method, 84.77% IoU is obtained, which is higher than other methods such as UNet and FPN neural networks which participated in the ETCI (the maximum IoU obtained by these methods is 76.81%).

Introduction: A major challenge today is personalizing the treatment for patients with major depressive disorder (MDD) to make it more efficient. To address this issue, we have proposed a novel approach based on machine learning (ML) models that utilize neural activity flow prior to treatment with selective serotonin reuptake inhibitor (SSRI) medication.  Methods: The electroencephalogram signals of 30 patients were used to calculate the neural activity flow of each patient using the direct directed transfer function (dDTF). Then, based on the area under the curve (AUC) values, 30 important connections were identified for the delta, theta, alpha, beta, and gamma bands. To select the most critical neural activity flow, these neural activity flows were combined, and forward features, mRMR, and ReliefF methods were applied. Support vector machines (SVMs), decision tree, and random forest models are trained using selected neural activity flows.  Results: Results showed that most connections originated from F8, Pz, T5, and P4, mainly from the frontal and parietal lobes. In addition, the SVM model showed 98% accuracy in classification using forward feature selection, where most of the neural activity flows were selected from alpha and beta. Finally, results indicate that patients who responded to treatment differed in their patterns of frontoparietal neural activity flows, implying that the frontoparietal network (FPN) is primarily involved in treatment response at alpha and beta frequencies. Conclusion: Therefore, the proposed method can accurately detect responders in MDD patients. It can reduce costs for both patients and medical facilities.