University of Tabriz

Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation. The database currently contains more than 100,000 videos across 174 classes, which are defined as caption-templates. We also describe the challenges in crowd-sourcing this data at scale.

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.

There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

Natural dyes have been used from ancient times for multiple purposes, most importantly in the field of textile dying. The increasing demand and excessive costs of natural dye extraction engendered the discovery of synthetic dyes from petrochemical compounds. Nowadays, they are dominating the textile market, with nearly 8 × 105 tons produced per year due to their wide range of color pigments and consistent coloration. Textile industries consume huge amounts of water in the dyeing processes, making it hard to treat the enormous quantities of this hazardous wastewater. Thus, they have harmful impacts when discharged in non-treated or partially treated forms in the environment (air, soil, plants and water), causing several human diseases. In the present work we focused on synthetic dyes. We started by studying their classification which depended on the nature of the manufactured fiber (cellulose, protein and synthetic fiber dyes). Then, we mentioned the characteristics of synthetic dyes, however, we focused more on their negative impacts on the ecosystem (soil, plants, water and air) and on humans. Lastly, we discussed the applied physical, chemical and biological strategies solely or in combination for textile dye wastewater treatments. Additionally, we described the newly established nanotechnology which achieves complete discharge decontamination.

Noncommunicable diseases (NCDs) account for 76% of deaths in Iran, and this number is on the rise, in parallel with global rates. Many risk factors associated with NCDs are preventable; however, it is first necessary to conduct observational studies to identify relevant risk factors and the most appropriate approach to controlling them. Iran is a multiethnic country; therefore, in 2014 the Ministry of Health and Medical Education launched a nationwide cohort study-Prospective Epidemiological Research Studies in Iran (PERSIAN)-in order to identify the most prevalent NCDs among Iran's ethnic groups and to investigate effective methods of prevention. The PERSIAN study consists of 4 population-based cohorts; the adult component (the PERSIAN Cohort Study), described in this article, is a prospective cohort study including 180,000 persons aged 35-70 years from 18 distinct areas of Iran. Upon joining the cohort, participants respond to interviewer-administered questionnaires. Blood, urine, hair, and nail samples are collected and stored. To ensure consistency, centrally purchased equipment is sent to all sites, and the same team trains all personnel. Routine visits and quality assurance/control measures are taken to ensure protocol adherence. Participants are followed for 15 years postenrollment. The PERSIAN study is currently in the enrollment phase; cohort profiles will soon emerge.

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model has received tremendous attention from academic and industrial researchers who have extended it to address the scale and complexity created by medical tasks. These extensions are commonly related to enhancing the U-Net's backbone, bottleneck, or skip connections, or including representation learning, or combining it with a Transformer architecture, or even addressing probabilistic prediction of the segmentation map. Having a compendium of different previously proposed U-Net variants makes it easier for machine learning researchers to identify relevant research questions and understand the challenges of the biological tasks that challenge the model. In this work, we discuss the practical aspects of the U-Net model and organize each variant model into a taxonomy. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. Furthermore, we provide a comprehensive implementation library with trained models. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation.

We present a 6-gene, 420-species maximum-likelihood phylogeny of Ascomycota, the largest phylum of Fungi. This analysis is the most taxonomically complete to date with species sampled from all 15 currently circumscribed classes. A number of superclass-level nodes that have previously evaded resolution and were unnamed in classifications of the Fungi are resolved for the first time. Based on the 6-gene phylogeny we conducted a phylogenetic informativeness analysis of all 6 genes and a series of ancestral character state reconstructions that focused on morphology of sporocarps, ascus dehiscence, and evolution of nutritional modes and ecologies. A gene-by-gene assessment of phylogenetic informativeness yielded higher levels of informativeness for protein genes (RPB1, RPB2, and TEF1) as compared with the ribosomal genes, which have been the standard bearer in fungal systematics. Our reconstruction of sporocarp characters is consistent with 2 origins for multicellular sexual reproductive structures in Ascomycota, once in the common ancestor of Pezizomycotina and once in the common ancestor of Neolectomycetes. This first report of dual origins of ascomycete sporocarps highlights the complicated nature of assessing homology of morphological traits across Fungi. Furthermore, ancestral reconstruction supports an open sporocarp with an exposed hymenium (apothecium) as the primitive morphology for Pezizomycotina with multiple derivations of the partially (perithecia) or completely enclosed (cleistothecia) sporocarps. Ascus dehiscence is most informative at the class level within Pezizomycotina with most superclass nodes reconstructed equivocally. Character-state reconstructions support a terrestrial, saprobic ecology as ancestral. In contrast to previous studies, these analyses support multiple origins of lichenization events with the loss of lichenization as less frequent and limited to terminal, closely related species.

This paper introduces a new multilevel converter topology that has many steps with fewer power electronic switches. The proposed circuit consists of series-connected submultilevel converters blocks. The optimal structures of this topology are investigated for various objectives, such as minimum number of switches and capacitors, and minimum standing voltage on switches for producing maximum output voltage steps. A new algorithm for determination of dc voltage sourcespsila magnitudes has also been presented. The proposed topology results in reduction of the number of switches, losses, installation area, and converter cost. The operation and performance of the proposed multilevel converter has been verified by the simulation and experimental results of a single-phase 53-level multilevel converter.

Exfoliation of graphite is a promising approach for large-scale production of graphene. Oxidation of graphite effectively facilitates the exfoliation process, yet necessitates several lengthy washing and reduction processes to convert the exfoliated graphite oxide (graphene oxide, GO) to reduced graphene oxide (RGO). Although filtration, centrifugation and dialysis have been frequently used in the washing stage, none of them is favorable for large-scale production. Here, we report the synthesis of RGO by sonication-assisted oxidation of graphite in a solution of potassium permanganate and concentrated sulfuric acid followed by reduction with ascorbic acid prior to any washing processes. GO loses its hydrophilicity during the reduction stage which facilitates the washing step and reduces the time required for production of RGO. Furthermore, simultaneous oxidation and exfoliation significantly enhance the yield of few-layer GO. We hope this one-pot and fully-scalable protocol paves the road toward out of lab applications of graphene.

Discovered in 1993, micoRNAs (miRNAs) are now recognized as one of the major regulatory gene families in eukaryotes. To date, 24521 microRNAs have been discovered and there are certainly more to come. It was primarily acknowledged that miRNAs result in gene expression repression at both the level of mRNA stability by conducting mRNA degradation and the level of translation (at initiation and after initiation) by inhibiting protein translation or degrading the polypeptides through binding complementarily to 3'UTR of the target mRNAs. Nevertheless, some studies revealed that miRNAs have the capability of activating gene expression directly or indirectly in respond to different cell types and conditions and in the presence of distinct cofactors. This reversibility in their posttranslational gene regulatory natures enables the bearing cells to rapidly response to different cell conditions and consequently block unnecessary energy wastage or maintain the cell state. This paper provides an overview of the current understandings of the miRNA characteristics including their genes and biogenesis, as well as their mediated downregulation. We also review up-to-date knowledge of miRNA-mediated gene upregulation through highlighting some notable examples and discuss the emerging concepts of their associations with other posttranscriptional gene regulation processes.

This chapter deals with the theoretical and experimental considerations of hydraulics of sediment transport, involved in identifying the hydraulics formulas for fluid flow and sediment computation in open channels, and analyzing the flow and sediment characteristics of the water motion. In general, the field of sediment transport is very complex, and the engineers in this field should refer to more comprehensive works to better understand the computational basis.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has posed a significant threat to global health. This virus affects the respiratory tract and usually leads to pneumonia in most patients and acute respiratory distress syndrome (ARDS) in 15% of cases. ARDS is one of the leading causes of death in patients with COVID-19 and is mainly triggered by elevated levels of pro-inflammatory cytokines, referred to as cytokine storm. Interleukins, such as interleukin-6 (1L-6), interleukin-1 (IL-1), interleukin-17 (IL-17), and tumor necrosis factor-alpha (TNF-α) play a very significant role in lung damage in ARDS patients through the impairments of the respiratory epithelium. Cytokine storm is defined as acute overproduction and uncontrolled release of pro-inflammatory markers, both locally and systemically. The eradication of COVID-19 is currently practically impossible, and there is no specific treatment for critically ill patients with COVID-19; however, suppressing the inflammatory response may be a possible strategy. In light of this, we review the efficacy of specific inhibitors of IL6, IL1, IL-17, and TNF-α for treating COVID-19-related infections to manage COVID-19 and improve the survival rate for patients suffering from severe conditions.

Nowadays successful application of nanoparticles for therapeutic objects needs the effective uptake of them by cells. Hence, studying of the interaction of nanoparticles with cell membrane for effective cellular uptaking seems to be vital and important. Trafficking of lipids, proteins, glucose, and other biomaterials into the cells is possible from two major exocytic and endocytic pathways. The penetration ability of nanoparticles into the cells must be considered in engineering of these particles. Enormous in vivo and in vitro experiments in the field of nanotechnology have confirmed the effect of physiochemistry properties in state of cell-nanoparticles interactions. Thus, the optimization of parameters directly related to physicochemical characteristics through the preparation process seems to be necessary for improving therapeutic effects of nanocarriers. Besides, biological medium and cell division also affect the amount of nanoparticle uptaking into the cells. This study reviews the influence of size, shape, the surface modification of nano particles, medium, and cell division effects on the cellular absorption of drug/gene nanocarriers.

The isolation of extracellular vesicles (EVs) from blood is of great importance to understand the biological role of circulating EVs and to develop EVs as biomarkers of disease. Due to the concurrent presence of lipoprotein particles, however, blood is one of the most difficult body fluids to isolate EVs from. The aim of this study was to develop a robust method to isolate and characterise EVs from blood with minimal contamination by plasma proteins and lipoprotein particles. Plasma and serum were collected from healthy subjects, and EVs were isolated by size-exclusion chromatography (SEC), with most particles being present in fractions 8-12, while the bulk of the plasma proteins was present in fractions 11-28. Vesicle markers peaked in fractions 7-11; however, the same fractions also contained lipoprotein particles. The purity of EVs was improved by combining a density cushion with SEC to further separate lipoprotein particles from the vesicles, which reduced the contamination of lipoprotein particles by 100-fold. Using this novel isolation procedure, a total of 1187 proteins were identified in plasma EVs by mass spectrometry, of which several proteins are known as EV-associated proteins but have hitherto not been identified in the previous proteomic studies of plasma EVs. This study shows that SEC alone is unable to completely separate plasma EVs from lipoprotein particles. However, combining SEC with a density cushion significantly improved the separation of EVs from lipoproteins and allowed for a detailed analysis of the proteome of plasma EVs, thus making blood a viable source for EV biomarker discovery.

The mixed method approaches have recently risen to prominence. The reason that more researchers are opting for these types of research is that both qualitative and quantitative data are simultaneously collected, analyzed and interpreted. In this article the main research instruments (questionnaire, interview and classroom observation) usually used in the mixed method designs are presented and elaborated on. It is believed that using different types of procedures for collecting data and obtaining that information through different sources (learners, teachers, program staff, etc.) can augment the validity and reliability of the data and their interpretation. Therefore, the various ways of boosting the validity and reliability of the data and instruments are delineated at length. Finally, an outline of reporting the findings in the mixed method approaches is sketched out. It is believed that this article can be useful and beneficial to the researchers in general and postgraduate students in particular who want to start or are involved in the process of conducting research.

Aflatoxins and ochratoxins are among the most important mycotoxins of all and producers of both types of mycotoxins are present in Aspergillus section Flavi , albeit never in the same species. Some of the most efficient producers of aflatoxins and ochratoxins have not been described yet. Using a polyphasic approach combining phenotype, physiology, sequence and extrolite data, we describe here eight new species in section Flavi . Phylogenetically, section Flavi is split in eight clades and the section currently contains 33 species. Two species only produce aflatoxin B<inf>1</inf> and B<inf>2</inf> ( A. pseudotamarii and A. togoensis ), and 14 species are able to produce aflatoxin B<inf>1</inf>, B<inf>2</inf>, G<inf>1</inf> and G<inf>2</inf>: three newly described species A. aflatoxiformans, A. austwickii and A. cerealis in addition to A. arachidicola , A. minisclerotigenes , A. mottae, A. luteovirescens (formerly A. bombycis ) , A. nomius, A. novoparasiticus, A. parasiticus, A. pseudocaelatus, A. pseudonomius, A. sergii and A. transmontanensis . It is generally accepted that A. flavus is unable to produce type G aflatoxins, but here we report on Korean strains that also produce aflatoxin G<inf>1</inf> and G<inf>2</inf>. One strain of A. bertholletius can produce the immediate aflatoxin precursor 3-O-methylsterigmatocystin, and one strain of Aspergillus sojae and two strains of Aspergillus alliaceus produced versicolorins. Strains of the domesticated forms of A. flavus and A. parasiticus , A. oryzae and A. sojae , respectively, lost their ability to produce aflatoxins, and from the remaining phylogenetically closely related species (belonging to the A. flavus -, A. tamarii -, A. bertholletius - and A. nomius -clades), only A. caelatus , A. subflavus and A. tamarii are unable to produce aflatoxins. With exception of A. togoensis in the A. coremiiformis -clade, all species in the phylogenetically more distant clades ( A. alliaceus -, A. coremiiformis -, A. leporis - and A. avenaceus -clade) are unable to produce aflatoxins. Three out of the four species in the A. alliaceus -clade can produce the mycotoxin ochratoxin A: A. alliaceus s . str . and two new species described here as A. neoalliaceus and A. vandermerwei . Eight species produced the mycotoxin tenuazonic acid: A. bertholletius , A. caelatus, A. luteovirescens , A. nomius, A. pseudocaelatus , A. pseudonomius, A. pseudotamarii and A. tamarii while the related mycotoxin cyclopiazonic acid was produced by 13 species: A. aflatoxiformans, A. austwickii, A. bertholletius, A. cerealis, A. flavus, A. minisclerotigenes, A. mottae, A. oryzae, A. pipericola, A. pseudocaelatus , A. pseudotamarii, A. sergii and A. tamarii . Furthermore, A. hancockii produced speradine A, a compound related to cyclopiazonic acid. Selected A. aflatoxiformans, A. austwickii, A. cerealis, A. flavus, A. minisclerotigenes, A. pipericola and A. sergii strains produced small sclerotia containing the mycotoxin aflatrem. Kojic acid has been found in all species in section Flavi , except A. avenaceus and A. coremiiformis . Only six species in the section did not produce any known mycotoxins: A. aspearensis , A. coremiiformis, A. lanosus, A. leporis, A. sojae and A. subflavus . An overview of other small molecule extrolites produced in Aspergillus section Flavi is given.

Abstract Considering titanium dioxide nanoparticles (TiO 2 NPs) role in plant growth and especially in plant tolerance against abiotic stress, a greenhouse experiment was carried out to evaluate TiO 2 NPs effects (0, 50, 100 and 200 mg L −1 ) on agronomic traits of Moldavian balm ( Dracocephalum moldavica L.) plants grown under different salinity levels (0, 50 and 100 mM NaCl). Results demonstrated that all agronomic traits were negatively affected under all salinity levels but application of 100 mg L −1 TiO 2 NPs mitigated these negative effects. TiO 2 NPs application on Moldavian balm grown under salt stress conditions improved all agronomic traits and increased antioxidant enzyme activity compared with plants grown under salinity without TiO 2 NP treatment. The application of TiO 2 NPs significantly lowered H 2 O 2 concentration. In addition, highest essential oil content (1.19%) was obtained in 100 mg L −1 TiO 2 NP-treated plants under control conditions. Comprehensive GC/MS analysis of essential oils showed that geranial, z-citral, geranyl acetate and geraniol were the dominant essential oil components. The highest amounts for geranial, geraniol and z-citral were obtained in 100 mg L −1 TiO 2 NP-treated plants under control conditions. In conclusion, application of 100 mg L −1 TiO 2 NPs could significantly ameliorate the salinity effects in Moldavian balm.

Hydrogen production <italic>via</italic> solar water splitting can be enhanced by combining semiconductors with various 2-dimensional materials.

In this paper, a new topology for cascaded multilevel converter based on submultilevel converter units and full-bridge converters is proposed. The proposed topology significantly reduces the number of dc voltage sources, switches, IGBTs, and power diodes as the number of output voltage levels increases. Also, an algorithm to determine dc voltage sources magnitudes is proposed. To synthesize maximum levels at the output voltage, the proposed topology is optimized for various objectives, such as the minimization of the number of switches, gate driver circuits and capacitors, and blocking voltage on switches. The analytical analyses of the power losses of the proposed converter are also presented. The operation and performance of the proposed multilevel converter have been evaluated with the experimental results of a single-phase 125-level prototype converter.

Photocatalytic reduction of CO2 is known as one of the most promising methods to produce valuable fuels and value-added compounds. To overcome selectivity and efficiency downsides, various photocatalysts have been designed and developed. This review discusses the state-of-the-art in photo-conversion of CO2 over graphitic carbon nitride (g-C3N4)-based composites. The modification strategies to improve photocatalytic activity of g-C3N4 were classified into different categories and discussed as structural modifications, elemental doping, copolymerization, fabricating heterojunctions between g-C3N4 and other semiconductors, Z-scheme heterojunctions, noble metal/g-C3N4 photocatalysts, and design of ternary nanocomposites based on g-C3N4. Finally, perspectives and future research works in this field were also outlined.