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In this study, we investigate the contribution of earthquakes to the deformation of Zagros province and compare the seismicity and the density of earthquakes in different parts of the province. The mathematics used in this research is based on calculations of moment rates. The seismic moment rate is the average amount of seismic energy releases from the tectonic province in each year. The geodetic moment rate is the average amount of energy which is consumed every year to make deformation in Zagros. The ratio of these two moment rates expresses the contribution of earthquakes in making deformation in Zagros province. According to the calculations, this ratio is estimated to be 13.06%. Along with the information obtained from the moment rates, we can also obtain the shear and the dilative strain rates from the strain rate tensors, which show the volumetric changes and the deformation rate in different parts of the Zagros, respectively. The data used in this study include the focal coordinates of the Zagros earthquakes with their magnitude and the velocity vectors of the Zagros geodynamic network, which are used to calculate the seismic and the geodetic moment rates.

Stability analysis and studying the geological features of rocks and mines have been active research topic for many years. Consequently, it is very important being prepared for probable hazards and having the ability to rescue from earth disasters, in particular in rocks and open pit mines. For this purpose, several methods have been used to measure fractures of a rock face. Among these methods are manual techniques, photogrammetric measurements, and laser scanning based techniques. With the proliferation of unmanned aerial vehicles (UAVs), these systems have been widely used in geological projects recently. Especially in the situation that the case study is very hard to be reached. In this paper, a method is developed to detect the most probable rock fall. After doing some pre-processing, RANSAC algorithm is used to fit planes to the point cloud. Then, intersections of these planes with the point cloud are computed. After some refinements on these intersections, the probable rockfalls are obtained. Point cloud analysis have some advantages over conventional image-based methods; especially in case of probable rock falls, which might be hard to detect using the rock images. However, analyzing point cloud data usually is complicated and computationally expensive.

The western part of the Makran subduction zone (Iran) is currently experiencing active surface uplift, as attested by the presence of emerged marine terraces along the coast. To better understand the uplift recorded by these terraces, we investigated seven localities along the Iranian Makran and we performed radiocarbon, 230Th∕U and optically stimulated luminescence (OSL) dating of the layers of marine sediments deposited on top of the terraces. This enabled us to correlate the terraces regionally and to assign them to different Quaternary sea-level highstands. Our results show east–west variations in surface uplift rates mostly between 0.05 and 1.2 mm yr−1. We detected a region of anomalously high uplift rate, where two MIS 3 terraces are emerged, but we are uncertain how to interpret these results in a geologically coherent context. Although it is presently not clear whether the uplift of the terraces is linked to the occurrence of large megathrust earthquakes, our results highlight rapid surface uplift for a subduction zone context and heterogeneous accumulation of deformation in the overriding plate.

Abstract Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R2) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.

As the depth of mineral exploration has increased in recent years, multiple exploration methods have become necessary to obtain more accurate depth and surface data. Each type of exploratory data has different uncertainty, resolution, and efficiency levels. Using these data individually or preparing traditional models based on a single data type often fails to meet the desired accuracy level. Therefore, mineral prospectivity mapping (MPM) has become more common in integrating these data. MPM methods require determining the importance of the data used. This importance is expressed as the weight of the layers (evidence). Typically, data-driven methods cannot be used to determine the weight of evidence layers in green areas due to the need for sufficient deposits. In these areas, knowledge-based methods, using the opinions of expert geologists, are often used to determine the weight of the layers. However, the weights determined by different experts may vary depending on their perspectives. Therefore, one of the challenges of using MPM methods in green areas is determining a reliable weight for the layers. This paper uses different exploration data, such as airborne geophysical data, geochemistry, geology, and remote sensing data, to prepare suitable reference layers. Due to the limited mineral prospects available in this area, we used the prediction-area (P-A) method to calculate the layers' weights without experts' opinions. We then used these weights to produce the gold prospectivity map in this area using the multi-index overlay (MIO) and the (Adjusted, Conventional, and Modified) TOPSIS methods. Finally, the obtained results were used to evaluate the efficiency of these methods and the calculated weights for this area.

This research presents a case study that employs the Fuzzy Ordered Weighted Averaging (FOWA) method to develop mineral prospectivity/potential maps (MPM) for the Chahargonbad district in southeastern Iran. The primary objective of the study is to uncover intricate and concealed relationships between various evidence layers and known ore occurrences through a comprehensive analysis of multi-disciplinary geospatial data. Consequently, thirteen evidence layers were meticulously derived from existing databases, encompassing geological, geochemical, geophysical, and remote sensing data, which were then integrated using the FOWA multi-criteria decision-making approach to delineate favorable zones for porphyry Cu mineralization.The FOWA methodology employs a diverse array of decision strategies to synthesize input geospatial evidence by incorporating multiple values for an alpha parameter. This parameter serves as the cornerstone of the algorithm, influencing experts' perspectives on MPM risk. The methodology generates seven mineral potential maps to identify the most suitable one(s). By considering a prediction-area plot for data-driven weight assignment to each evidence map, the hybrid FOWA outputs were scrutinized to pinpoint the most appropriate map for targeting significant Cu occurrences. The resulting synthesized evidence map indicates an ore prediction rate of 77%, with known Cu deposits primarily located within favorable zones occupying 23% of the entire district area.

The objective of petrophysical studies is to assess the quality of hydrocarbon reservoir layers and to zone the reservoir for identifying optimal zones for exploitation and informed development of oil fields. In some regions, there are zones that exhibit lower electrical resistivity values than their actual values. These low-resistivity zones are often identified through petrophysical investigations and conventional well logs, where water saturation levels are estimated higher due to their reduced resistivity. These zones, despite their hydrocarbon potential, are often neglected during production cycles. To overcome this challenge, nuclear magnetic resonance (NMR) logging tools can be employed to provide accurate estimations of free fluid saturation, irreducible fluid saturation, permeability, and effective porosity in such low-resistivity zones, making them more identifiable.In this article, we utilized conventional well logs and NMR log from the A well in the Sarvak reservoir of one of the oil fields in southwestern Iran. Based on the obtained results, depth interval 9586 to 9783 ft in the Sarvak Formation, along with two intervals (10661-10815 ft) and (10830-11063 ft) in the Int zone, were identified as potential low-resistivity zones in the reservoir. By analyzing the high-resistivity logs, water saturation percentage was calculated for these zones, and the results from NMR logging confirmed their favorable reservoir potential (e.g., free fluid saturation, effective porosity, viscosity, and permeability).Furthermore, to extend the petrophysical parameters, such as free fluid saturation and porosity, throughout the entire hydrocarbon field, various approaches including single- attribute methods, multi attribute methods, and neural networks were evaluated. The neural network method demonstrated higher accuracy in determining the parameters. Ultimately, the values of porosity and free fluid saturation in the study area were determined with 91% and 95.8% matching accuracy, respectively. The final results were validated using unseen data, and the high precision of the obtained results was confirmed.

Fractures are one of the most important geological features that affect production from most carbonate reservoirs. A large amount of the world’s hydrocarbon resources are located in fractured reservoirs and the identification of fractures is one of the important steps in reservoir development. Due to the high cost of tools that are used in the petroleum industry to identify fractures such as image logs, and their inaccessibility in most of the studied areas, it is often tried to use other available data to identify fractures. Due to the ever-increasing progress of data-driven methods such as neural networks and machine learning, this study has tried to apply a 1D-Convolutional Neural Network (1D-CNN) which is one of the deep learning algorithms on well-logging data and seismic attributes in a carbonate reservoir to identify the existing fractures in the investigating area. The approach used in this research is a binary classification which is applied first in the well location. To validate the method, results are compared with the reports obtained from image logs. Finally, the fracture density map is drawn in the entire reservoir area.

The Birjand region is a part of the South Khorasan province, situated in the structural-magmatic zone of eastern Iran. As a part of the continental shelf, it forms from subduction during the Cenozoic and subsequent continental collisions. This region is favorable for copper and gold mineralization for various geological reasons. The ultimate goal of this study is to create a Cu geochemical potential map to delimit prone regions for further mining activities. A total of 2468 geochemical samples were gathered to run a 20-element analysis. Taking data preprocessing approaches such as correction of outlier data and data normalization into consideration, a fractal graph through Concentration-Number (C-N) model was produced to isolate different geochemical populations of Cu, Pb, Zn, Ag, Ba, and Ni for Cu targeting. Then, a Prediction-Area (P-A) graph was plotted for each geochemical variable to determine the weight of each evidence map. The results show that Barium map indicates a prediction rate of 72% and specifying 28% of the studied areas as mineralization prone areas. The zinc geochemical map presents an ore prediction rate of 65% and 35% of area as potential zone. In addition, copper with an ore prediction of 56% covered 44% of the Birjand region. Finally, a hybrid evidence map was overlaid. Accordingly, the geochemical potential areas are further located towards the south and south-east of Birjand, which are closely related. Moreover, there are highly favorable areas in the middle part. It is noteworthy that the copper potential map has higher efficiency over each individual geochemical evidence, with an ore prediction rate of 75% and occupying 25% of the area as favorable zones.

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy.

Abstract Background Ischemic niche can promote follicular atresia following the transplantation of cryopreserved/thawed ovaries to the heterotopic sites. Thus, the promotion of blood supply is an effective strategy to inhibit/reduce the ischemic damage to ovarian follicles. Here, the angiogenic potential of alginate (Alg) + fibrin (Fib) hydrogel enriched with melatonin (Mel) and CD144+ endothelial cells (ECs) was assessed on encapsulated cryopreserved/thawed ovaries following transplantation to heterotopic sites in rats. Methods Alg + Fib hydrogel was fabricated by combining 2% (w/v) sodium Alg, 1% (w/v) Fib, and 5 IU thrombin at a ratio of 4: 2: 1, respectively. The mixture was solidified using 1% CaCl2. Using FTIR, SEM, swelling rate, and biodegradation assay, the physicochemical properties of Alg + Fib hydrogel were evaluated. The EC viability was examined using an MTT assay. Thirty-six adult female rats (aged between 6 and 8 weeks) with a normal estrus cycle were ovariectomized and enrolled in this study. Cryopreserved/thawed ovaries were encapsulated in Alg + Fib hydrogel containing 100 µM Mel + CD144+ ECs (2 × 104 cells/ml) and transplanted into the subcutaneous region. Ovaries were removed after 14 days and the expression of Ang-1, and Ang-2 was monitored using real-time PCR assay. The number of vWF+ and α-SMA+ vessels was assessed using IHC staining. Using Masson’s trichrome staining, fibrotic changes were evaluated. Results FTIR data indicated successful interaction of Alg with Fib in the presence of ionic cross-linker (1% CaCl2). Data confirmed higher biodegradation and swelling rates in Alg + Fib hydrogel compared to the Alg group (p

In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster – Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility, and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia – Dokhtar magmatic belt in the northeast of Isfahan city, central Iran. Multidisciplinary datasets- that are geochemical and geophysical data, ASTER satellite images, Quickbird, and ground survey- were designed in a geospatial database to run MPM. Implementing the Dempster law through the intersection (And) and union (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of the hybrid method was higher than each operator. Of note, the success rate of the hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km2 of the studied area.

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

Self-renewal and differentiation into diverse cells are two main characteristics of stem cells. These cells have important roles in development and homeostasis of different tissues and are supposed to facilitate tissue regeneration. Function of stem cells is regulated by dynamic interactions between external signaling, epigenetic factors, and molecules that regulate expression of genes. Among the highly appreciated regulators of function of stem cells are long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). Impact of miR-342-5p, miR-145, miR-1297, miR-204-5p, miR-132, miR-128-3p, hsa-miR-302, miR-26b-5p and miR-10a are among miRNAs that regulate function of stem cells. Among lncRNAs, AK141205, ANCR, MEG3, Pnky, H19, TINCR, HULC, EPB41L4A-AS1 and SNHG7 have important roles in the regulation of stem cells. In the current paper, we aimed at reviewing the importance of miRNAs and lncRNAs in differentiation of stem cells both in normal and diseased conditions. For this purpose, we searched PubMed/Medline and google scholar databases using “stem cell” AND “lncRNA”, or “long non-coding RNA”, or “microRNA” or “miRNA”.

Atrophy is defined as a reduction in cell, organ, or tissue size after reaching their normal mature sizes because of loss of organelles, cytoplasmic compartments, and proteins. This process is also involved in the pathogenesis of human disorders. Inadequate nourishment, poor circulation, inadequate hormonal support, defects in nerve supply of the tissue, disproportionate induction of apoptosis in the tissue, and absence of exercise are some underlying causes of atrophy. Recently, several non-coding RNAs (ncRNAs) have been identified that regulate atrophy, thus participating in the pathobiology of related disorders such as neurodegenerative/ neuromuscular diseases, age-related muscle atrophy, and cardiac tissue atrophy. In the current review, we have focused on two classes of ncRNAs namely long ncRNAs (lncRNAs) and microRNAs (miRNAs) to unravel their participation in atrophy-associated disorders.

miR-379 is a miRNA transcribed from the MIR379 locus on 14q32.31. This miRNA is located in an evolutionarily conserved miRNA cluster in an imprinted region that contains DLK1 and DIO3 genes. The mouse homolog of this miRNA has been shown to be under-expressed in response to glucocorticoid receptor deficiency. Moreover, miR-379 has a tumor-suppressive role in a wide variety of tissues including the brain, breast, lung, and liver. In addition to restraining cell proliferation and migration, miR-379 can suppress the epithelial-mesenchymal transition process. Abnormal expression of this miRNA implies the pathogenesis of Duchene muscular dystrophy, spinal cord injury, diabetic nephropathy, acute myocardial infarction, and premature ovarian failure. This review aims to the summarization of the role of miR-379 in neoplastic and non-neoplastic conditions.

Stem cells have two important features, namely the ability for self-renewal and the capacity to differentiate into some cell kinds with specialized functions. These two features are also present in cancer stem cells (CSCs). These cells have been detected in almost all kinds of cancers facilitating their tumorigenicity. Molecular cascades that control self-renewal of stem cells, namely the Wnt, Notch, and Hedgehog pathways have been suggested to influence CSCs functions as well. Moreover, non-coding RNAs can regulate function of CSCs. Function of miRNAs in the regulation of CSCs has been mostly assessed in breast cancer and hepatocellular carcinoma. miR-130a-3p, miR-600, miR-590-5p, miR-142-3p, miR-221, miR-222, miR-638, miR-375, miR-31, and miR-210 are among those regulating this feature in breast cancer. Moreover, miR-206, miR-192-5p, miR-500a-3p, miR-125, miR-125b, miR-613, miR-217, miR-194, and miR-494 regulate function of CSCs in hepatocellular carcinoma. DILC, lncTCF7, MUF, HAND2-AS1, MALAT1, DLX6-AS1, HOTAIR, and XIST are among lncRNAs that regulate function of CSCs. In the present paper, we explain the effects of these two classes of non-coding RNAs in the regulation of activity of CSCs.

H19 is an oncofetal transcript with crucial roles in the development and progression of several neoplastic cells. With anti-apoptotic, pro-proliferative, and pro-migratory functions, H19 affects the carcinogenic process from different functional points. In addition, H19 has central roles in the induction of chemoresistance in breast cancer, lung cancer, glioma, liver cancer, and other types of cancers. Induction of EMT, activation of oncogenic signaling pathways, and changes in the tumor microenvironment are among mechanisms of participation of H19 in chemoresistance. Paclitaxel, doxorubicin, tamoxifen, erlotinib, gefitinib, temozolomide, and methotrexate are among therapeutic agents whose efficacy is influenced by the expression of H19. In the present paper, we discuss the impact of H19 in conferring resistance to chemotherapeutic agents in different cancers.

Nuclear factor-κB (NF-κB) represents a group of inducible transcription factors (TFs) regulating the expression of a great variety of genes implicated in diverse processes, particularly modulation of immune system responses. This TF has functional interactions with non-coding RNAs, constructing a complicated network through which NF-κB, miRNAs, and lncRNAs coordinately regulate gene expression at different facets. This type of interaction is involved in the pathophysiology of several human disorders including both neoplastic disorders and non-neoplastic conditions. MALAT1 and NKILA are among lncRNAs whose interactions with NF-κB have been vastly assessed in different conditions including cancer and inflammatory conditions. In addition, miR-146a/b has functional interactions with this TF in different contexts. Although miRNAs have mutual interactions with NF-κB, the regulatory role of miRNAs on this TF has been more clarified. The aim of the current review is to explore the function of NF-κB-related miRNAs and lncRNAs in these two types of human disorders.