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Investigations pertaining to spermatogonial stem cells (SSCs) have led to the use of these cells in a variety of fields including infertility treatments, production of transgenic animals, and genome editing. The aim of the present study was to investigate the plausibility of regenerating spermatogenesis in infertile roosters by transplanting transfected SSCs into testes. Spermatogonial stem cells were isolated and cultured for seven days. Afterward, pDB2, a plasmid vector carrying a reporter gene, GFP, was transfected into the SSCs. Transfected SSCs were transplanted into the left testis of infertile roosters. Tissue samples from the recipients' testes were obtained six weeks after the transplantation and transplanted SSCs were observed in the basement membrane. After eight weeks, GFP-positive spermatozoa were observed in collected semen from the recipient roosters and GFP gene in spermatozoa was confirmed using PCR. The recipient roosters were mated with hens. Hatchlings were visually checked and their tissue samples were tested by PCR to identify transgenesis but both of them were negative. Overall, it seems that regeneration of spermatogenesis in roosters via transfected SSCs is possible but more studies are need to produce recombinant proteins by this way.

This study investigated the elicitation effects of several methyl jasmonate (MeJ) concentrations (0, 25, 50, and 100 µM) on various biochemical traits of caraway (Carum carvi L.) callus cultures. The 25 μM MeJ concentration yielded the highest callus growth rate (0.57 mm day−1), total flavonols content (2.58 mg QE g−1 FW) and total carotenoids content (0.04 μg g−1 FW), whereas the highest relative fresh weight (75.72%), total phenolics content (76.90 mg GAE g−1 FW), total flavonoids content (58.49 mg QE g−1 FW) and phenylalanine ammonia lyase activity (3.40 nmol cinnamic acid mg−1 h−1 FW) were obtained with the 50 μM MeJ concentration. The highest antioxidant activity through DPPH assay (8.37%) and malondialdehyde content (7.82 μmol g−1 FW) were observed at 100 μM MeJ. The HPLC conducted 21 days post-elicitation revealed a 5.55-fold increase of carvone accumulation (1.83 μg g−1 DW) at 50 μM MeJ and a 2.7-fold increase (0.62 μg g−1 DW) of limonene at 50 μM MeJ. The optimal dosages applied for carvone and limonene accumulation under MeJ elicitation could be used to scale up the economic production of these elite medicinal compounds through caraway callus cultures.

Drought is the most destructive environmental limitation that jeopardizes agronomic production. Given the wide range of industrial and medicinal applications of arugula (Eruca sativa. L.), the present study evaluated the response of a world collection of 64 arugula genotypes to drought stress over a two-year study period (2019 and 2020) through selection indices and some agronomic traits. The main objective was to identify the best selection indices from Susceptibility Index (SSI), Yield Stability Index (YSI), Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), and Stress Tolerance Index (STI) that could be exploited in the genotypic selection of the crop’s drought-tolerant cultivars. The highest values of STI, MP, and YSI were recorded for the genotypes G58 and G35 in 2019 and 2020, respectively. Generally, the superior genotypes for seed yield in 2019 were G56 and G58 while G35 and G55 proved so in 2020. It was concluded that these genotypes could be utilized as donor parents in drought tolerance in arugula.

Different growth factors can regulate stem cell differentiation. We used keratinocyte growth factor (KGF) to direct adipose-derived stem cells (ASCs) differentiation into keratinocytes. To enhance KGF bioavailability, we targeted KGF for collagen by fusing it to collagen-binding domain from Vibrio mimicus metalloprotease (vibrioCBD-KGF). KGF and vibrioCBD-KGF were expressed in Escherichia coli and purified to homogeneity. Both proteins displayed comparable activities in stimulating proliferation of HEK-293 and MCF-7 cells. vibrioCBD-KGF demonstrated enhanced collagen-binding affinity in immunofluorescence and ELISA. KGF and vibrioCBD-KGF at different concentrations (2, 10, and 20 ng/ml) were applied for 21 days on ASCs cultured on collagen-coated plates. Keratinocyte differentiation was assessed based on morphological changes, the expression of keratinocyte markers (Keratin-10 and Involucrin), and stem cell markers (Collagen-I and Vimentin) by real-time PCR or immunofluorescence. Our results indicated that the expression of keratinocyte markers was substantially increased at all concentrations of vibrioCBD-KGF, while it was observed for KGF only at 20 ng/ml. Immunofluorescence staining approved this finding. Moreover, down-regulation of Collagen-I, an indicator of differentiation commitment, was more significant in samples treated with vibrioCBD-KGF. The present study showed that vibrioCBD-KGF is more potent in inducing the ASCs differentiation into keratinocytes compared to KGF. Our results have important implications for effective skin regeneration using collagen-based biomaterials.

ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th2 cytokines and growth factors. Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.

BACKGROUND: Renal cell carcinoma is one of the most common tumors of the kidney. Glucose transporters, transport glucose, and increased expression of these transporters have been reported in various tumor types. Glucose transporter 1 (GLUT1), the best-known glucose transporter, has an important role at several stages in cancer progression. The overexpression of GLUT1 in the tumor cells indicates an increased proliferation and invasive behavior of the tumor. AIM: This study aims to investigate the expression of GLUT1 in renal cell carcinoma and its subtypes. METHODS: This study is a descriptive cross-sectional study that was performed on patients with renal cell carcinoma. Seventy reports of formalin fixed; paraffin-embedded blocks of renal cell carcinoma were selected from pathology archives. The samples included: clear cell type renal cell carcinoma, RCC clear cell type with sarcomatoid feature, papillary renal cell carcinoma, and chromophobe renal cell carcinoma. RESULTS: In this study, 50 male and 20 female samples (71.4% and 28.6%) with the mean age of 57.9 ± 13.1 years were studied. Forty-three samples (61.4%) were positive for GLUT1 and 27 (38.9%) were negative for it. For the GLUT1 expression being positive or negative between the two groups, was not significantly affected by the age, sex, and the grade of the tumor, while the difference between the two groups was statistically significant in terms of stage and type of tumor (p < 0/001, p = 0.039). CONCLUSION: Renal cell carcinoma of ccRCC type is associated with increased GLUT1 expression. Therefore, the GLUT1 immunohistochemistry marker can be a useful marker for diagnosis of RCC, specifically ccRCC type.

BACKGROUND: Educating dental students in practical skills are a difficult task that has become increasingly demanding since the outbreak of COVID-19. Today, the use of digital media plays an essential role in teaching practical skills. AIM: This study investigated the effect of educational videos on oral and maxillofacial surgery basics on dental student’s satisfaction. METHODS: Six subjects were chosen from the principles of oral and maxillofacial surgery, and according to the necessary standards, educational videos were made based on the needs assessment of students. Fellow attending evaluated and approved each video. Students were shown videos in six 2-h sessions. Students were given a researcher-created questionnaire with proven validity and reliability. Students filled out questionnaires to evaluate the impact of educational videos as well as the structure of educational videos. RESULTS: Two hundred and three students participated in this study in their 4th−6th years. In this study, 203 students in their 4th to the 6th year participated. Most students were satisfied with the structure of educational videos (73.9%); in general, 84.7% of students expressed their satisfaction with this method. Half of the students preferred video training over face-to-face training in oral and maxillofacial surgery (50.4%). Many students suggested that these videos be included in the curriculum (80.7%). CONCLUSION: Students’ overall satisfaction with the structure and impact of educational videos indicates that educational videos made and evaluated by dental professors can help teach practical topics as a part of the curriculum.

Background and aim: A wide range of therapeutic properties, including anti-cancer properties, are attributed to propolis, a resinous product obtained from several plants that possess a variety of medicinal properties. A study on honeybee-produced propolis showed that in mice, it showed a significant reduction in the progression of squamous cell carcinoma in the head and neck, but in humans, its role in HNSCC remains unclear. Method and materials: Propolis was sampled from two types of Iranian. Extraction was done using ethanolic extracts of propolis. The cll viability was evaluated by MTT assay. Cancer cell lines were assessed for gene expression, such as mmp-2, mmp-9, bax, and bcl-2. Results: Increased sample concentrations reduced cell viability but did not cause significant cytotoxicity. A RT-PCR indicated that the Khalkhal sample produced more effects among the two samples, and the level of bax mRNA gene expression in the Khalkhal sample was increased. With an increasing concentration of Khalkhal samples, the expression increased. Increasing Khalkhal sample concentration also reduced mRNA levels of bcl-2, mmp-2, and mmp-9. Conclusion: Khalkhal’s propolis can be considered a suitable sample for the study of antiapoptotic and proapoptotic gene expression. Additionally, it can be used as a potential candidate for inhibiting the proliferation and spread of oral cancer cells.

Background: Helping people with obesity as well as identifying the factors affecting their weight, body image, and self-esteem have become health necessities. Schema therapy is an effective method of third-wave psychotherapy for reducing the problems of obese people. This study aimed to determine the effectiveness of schema therapy in watching body weight as well as improving body image and self-esteem in patients with obesity. Methods: The present study was a randomized controlled trial. The study’s statistical population included all obese people referring to Mehr Counseling and Psychology Center in Tehran (Iran) in 2019. Out of the given population, 32 individuals were selected by adopting convenience sampling method and, then, were randomly divided into schema therapy (n=16) and control (n=16) groups. Data were obtained using body mass index (BMI), Cooper Smith self-esteem questionnaire, and body image questionnaire. The data were analyzed by using repeated-measures analysis of variance and SPSS 22 software. The significance level of the tests was set at 0.05. Results: The results showed that schema therapy had a significant effect on the weight (P

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model-based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error, and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on datasets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series., Comment: v2.1; 34 pages, 14 figures

Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes respectively, both often triggered by intensive rainfall. These processes often cause problems in the hilly regions in the “Golestan province” of Iran, and their detection is the essential aim for this research. The production of soil landforms maps is typically based on visual interpretation of aerial and satellite images eventually supported by field surveys. Recent advances in the acquisition of images from “unmanned aerial vehicles (UAV)” and of “deep learning (DL)” methods inherited from computer vision have made it feasible to propose semi-automated soil landforms detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL segmentation models, the vanilla “U-Net model” and the “Attention Deep Supervision Multi-Scale U-Net” model, applied to “UAV”-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran) Results show that our framework can successfully map landslides in a challenging environment (with an F1-score of 69%), and that topographical derivates from “UAV-derived DSM” decrease the capacity of mapping sinkholes of the models calibrated with optical data.

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI’s ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

One of the main issues in solid tumours is progressive mutation in epidermal growth factor receptors (EGFR) gene, which activates signalling pathways that create new blood vessels. In this study, it was attempted to find new a therapeutic candidate to inhibit EGFR. One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The critical point of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three different steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Finally, out of the nine chosen drugs, seven compounds had been confirmed to inhibit EGFR in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, and one neutral, were considered via molecular docking study. Finally, eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterise the drugs effect on A431 cell growth and EGFR-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGFR-inhibitor role like gefitinib.

Background: In the health field, future planning and determining the road map are important issues. In this regard, quantitative approaches have often dominated this area of health sector research, and the main focus of researchers has been on creating frameworks and solutions for data management and applying data-driven strategies such as forecasting. in using such methods, it is assumed that past effective forces will continue under the same conditions in the future, as well as new events that They can change past relationships or distort trends, will not occur or have no significant effect. therefore, considering the influential events and investigating the significant effects of these events and their subsequent processes can lead to a better understanding of possible consequences and proper planning for futures in the field of health. Methods: In the trend impact analysis approach, the impact of unprecedented events and their occurrence in the future on trends is considered and reliable results will be produced. the historical available data are used to perform quantitative extrapolation and forecast future values Then, qualitative tools and methods are employed to identify a list of influential events that will affect the trends in the future. Finally, the impact of these events on trends will be identified and applied. Results: As a result of this approach, probable and plausible scenarios can be achieved. Conclusions: The limitations in forecasting the future by using quantitative methods and ignoring the effect of unexpected events can leads to "surprises free" predictions and cause deviations from estimates and plans. Use of innovative methods in the methodology of studies will provide the ground for intelligent planning to face the future, and along with the complexities, uncertainties and changes in social behavior in societies, the use of new scientific methods in the field of health is a necessity.

This paper intends to underline the importance of historic public spaces as the deposits of collective memories, proposing the critical analysis, reinterpretation, and systematization of relevant informative historical layers as a counter model to the globalizing tendencies and their fast pace of transformation. Baharestanis a historic square located in the northeast of Tehran?s Historic Center, and it is the home to Iran?s Parliament and the Ministry of Culture. It makes the square significant at the national level, especially due to its location surrounded by several exquiste historical heritage buildings. However, following the decline of Tehran?s historic center, Baharestan lost its socio-cultural vitality and spatial quality, hosting urban functions mainly heterogeneous to its identity, and eventually turning into a traffic node. Through the comparative study of numerous historical documents, and adapting the concept of ?palimpsest?, Baharestan Square has been interpreted as the assemblage of different city layers in relation to the environmental and socio-political narratives of the city. The shift from one layer to another intends to reflect some of the lost memories of Tehran and its collective identity in the transition from traditional to modern society. Later on, the paper argues how this palimpsest quality and co-evolution of those plural layers and narratives in Baharestan demonstrate this symbolic square as a ?catalytic social infrastructure?, giving Tehran an opportunity to overcome the challenge of ?social amnesia? and promoting its civic culture and cohesion.

Thermal-ultrasound treatment is a green technology that can significantly alter the structural and functional properties of starches. This research extend the effect of at different temperatures (25 °C, 45 °C, and 65 °C) and times (30 and 60 min) on the physicochemical, structural, and rheological properties of corn starch was studied. Amylose content, solubility, swelling power, and the least gelling content increased with increasing temperature and time. Starch treated at 45 °C for 30 min had the lowest syneresis among all treatments. Thermal-ultrasound treatment at 25 °C and 65 °C for 60 min caused increasing paste clarity. Microscopic observations demonstrated that the starch granules were agglomerated at 65 °C. Although the crystallinity of samples decreased from 35.42% to 8.94%, the storage modulus was more than the loss modulus during the frequency sweep test. Pasting properties showed that pasting temperatures shifted to higher values after treatment. Nonetheless, the maximum viscosity decreased, and the final viscosity of the treated samples demonstrated that short-term retrogradation could deteriorate. Results showed that thermal-ultrasound is a viable technique for starch modification compared to conventional thermal and ultrasound treatments.

Background: Diabetes is a chronic disease that, without proper care, leads to a rise in blood sugar and, in the long term, disrupts the functions of various tissues and organs of the body. Therefore, self-care seems to be particularly important to reduce the injuries associated with this disease. Objectives: The aim of this study was to investigate the role of coping strategies, self-efficacy, and hardiness in predicting self-care behaviors in patients with type 2 diabetes. Methods: This was a descriptive-correlational study. The statistical population of this study included all patients with type 2 diabetes referred to medical centers in Khomeini Shahr, Isfahan-Iran, among whom 200 subjects were selected by convenience sampling. For data collection, Niknami (2013) self-care, Kubasa (1976) hardiness, Scherer self-efficacy, and Lazarus and Folkman coping strategies questionnaires were used. Data analysis was performed using stepwise multiple regression using SPSS software version 26. Results: The mean (SD) age of the participants was 48.54 (7.62) years, and women constituted 72% of the participants. The results showed that self-efficacy (53%, p=0.001) and hardiness (31%, p=0.008) had the largest shares in explaining self-care behaviors of patients with type 2 diabetes. The results also showed that coping strategies did not significantly influence the self-care behaviors of patients with type 2 diabetes (p

Hardware developers started experimenting with Scrum to accelerate their product development. However, it is not possible to implement Scrum in the same way as it was done for software systems, where the approach is already well established. One of the processes required in Scrum is the estimate of task efforts when creating a backlog for an Agile Sprint. This article presents a pilot validation experiment of a novel Agile framework for the development of hardware systems, including a parametric tool to estimate task effort in a more rigorous way than traditional confidence votes. This article presents the validation of electronic hardware design task estimation and overall project performance. The validation is performed through experimental work with teams of junior engineering students. The validation experiment showed an improvement from a minimum of eight to a maximum of eighteen percent when employing the presented tool during planning phases of the development.

Maintaining of female fertility is strictly dependent on proper hormonal regulation. Granulosa cells (GCs) are components of ovarian follicles, and they are important in paracrine regulation of the ovary. Preovulatory follicle GCs are responsible for production of estrogens to the ovary microenvironment and lead to the LH surge. Proper functioning of GCs is necessary to ensure appropriate conditions for oocyte development, maturation, ovulation and its release to the oviduct. Long-term in vitro culture of GCs show significant stem-like characteristics. Understanding the molecular processes underlying GCs differentiation towards different cell lineages may reveal other possible stem cell markers. A transcriptomic analysis of short-term primary in vitro cultured GCs, which were isolated from porcine preovulatory follicles was the major focus of the study. The ontological groups herby considered are associated with endodermal and epithelial tissues. Results were and compare to freshly isolated GC cells. 6 the most reduced expression: HSD17B1, DAPL1, NEBL, MAL2, DAB1, ITM2A were chosen for analysis. These genes have been response for processes associated with GCs development and differentiation towards endodermal and epithelial tissues, which make them important for further consideration.

Acquiring fear is crucial for survival (e.g., learning to avoid/escape dangerous situations). However, it is equally critical to extinguish fear when the threat abates (e.g., adapting to changing circumstances), a process known as ‘fear extinction’. Failure to adaptively extinguish fear is a key feature of anxiety disorders(1). Strikingly, diminished fear extinction is observed particularly in adolescence(2-4), possibly because of the need to stay vigilant when exploring novel environments(5). Despite the importance of fear extinction, there is a knowledge gap in how fear extinction learning develops across adolescence, the developmental period between childhood and adulthood. The lack of understanding of the process of extinction is even more surprising, because of the dramatic rise in the prevalence of mental health problems in adolescence, including social anxiety(6-8). It is likely that the mechanisms of extinction lie in the changing neurobiological systems in adolescence. Indeed, adolescence is a period of major neurobiological changes(9-11), partly driven by hormonal changes(12). Importantly, women are twice as likely as men to develop anxiety problems in adolescence (6, 13, 14). Our knowledge on fear extinction across development is merely derived from research studying extinction by learning from direct experience (2-4, 15), yet much of our learning comes from other individuals(16). This learning through social transmission, for example by observing others, is particularly important in adolescence. Adolescents are specifically sensitive to the social context and influence of peers (17-19). Therefore, it is expected that adolescents will benefit more from social learning (17, 20, 21), which have been observed in other social contexts such as risk-taking (22, 23). These prior studies showed that activity in brain regions implicated in risk-taking are enhanced under influence of peers. Likewise, evidence for the positive impact of social-extinction (i.e., extinction learning through observation) comes from our previous studies on adults, demonstrating that social-extinction prevented the return of conditioned fear responses better than direct-extinction(16, 24, 25). The general aim of this project is to understand the development of extinction learning based on both direct experience (i.e., direct-extinction) and through observation (i.e., social-extinction) from adolescence to adulthood.

An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate their compatibility with anatomical and physiological observations. Similarity matching objectives have served as successful starting points for deriving online algorithms that map onto neural networks (NNs) with point neurons and Hebbian/anti-Hebbian plasticity. These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain. In this article, we review and unify recent extensions of the similarity matching approach to address more complex objectives, including a broad range of unsupervised and self-supervised learning tasks that can be formulated as generalized eigenvalue problems or nonnegative matrix factorization problems. Interestingly, the online algorithms derived from these objectives naturally map onto NNs with multi-compartmental neurons and local, non-Hebbian learning rules. Therefore, this unified extension of the similarity matching approach provides a normative framework that facilitates understanding the multi-compartmental neuronal structures and non-Hebbian plasticity found throughout the brain.

10 pages, 6 figures, 1 table, The Federated Satellite Systems/ 3 Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S 3 ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3 Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3 Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3 Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3 Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The Federated Satellite Systems/ 3 Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S 3 ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3 Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3 Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3 Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3 Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results. This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (FSSCat project) and ESA project FSSCat Validation Experiment in MOSAIC; Spanish Ministry of Science, Innovation, and Universities Sensing With Pioneering Opportunistic Techniques grant RTI2018-099008-BC21/AEI/10.13039/501100011033; GNSS Environmental and Societal Missions grant PID2021- 126436OB-C21; and Unidad de Excelencia Maria de Maeztu grant MDM-2016-0600. Peer Reviewed Article signat per 30 autors/es: Adriano Camps, Universitat Politècnica de Catalunya, Barcelona, Spain, Institut d’Estudis Espacials de Catalunya, Barcelona, Spain, United Arab Emirates University, Abu Dhabi, United Arab Emirates; Joan Francesc Munoz-Martin, Universitat Politècnica de Catalunya, Barcelona, Spain, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA; Joan Adrià Ruiz-de-Azua, Universitat Politècnica de Catalunya, Barcelona, Spain, i2Cat, The Internet Research Center, Barcelona, Spain; Lara Fernandez, Universitat Politècnica de Catalunya, Barcelona, Spain; Adrian Perez-Portero, Universitat Politècnica de Catalunya, Barcelona, Spain; David Llavería, Universitat Politècnica de Catalunya, Barcelona, Spain; Christoph Herbert, Universitat Politècnica de Catalunya, Barcelona, Spain; Miriam Pablos, Institut de Ciències del Mar, Barcelona, Spain, Barcelona Expert Center on Remote Sensing, Barcelona, Spain; Alessandro Golkar, Universitat Politècnica de Catalunya, Barcelona, Spain, Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany; Antonio Gutiérrez, Deimos Engenharia, Lisbon, Portugal; Carlos António, Deimos Engenharia, Lisbon, Portugal; Jorge Bandeiras, Deimos Engenharia, Lisbon, Portugal; Jorge Andrade, Deimos Engenharia, Lisbon, Portugal; David Cordeiro, Deimos Engenharia, Lisbon, Portugal; Simone Briatore, Golbriak Space, Tallin, Estonia; Nicola Garzaniti, Golbriak Space, Tallin, Estonia, School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield, Bedfordshire, U.K.; Fabio Nichele, Tyvak International, Torino, Italy; Raffaele Mozzillo, Tyvak International, Torino, Italy; Alessio Piumatti, Tyvak International, Torino, Italy; Margherita Cardi, Tyvak International, Torino, Italy; Marco Esposito, Cosine, Sassenheim, The Netherlands; Chris van Dijk, Cosine, Sassenheim, The Netherlands; Nathan Vercruyssen, Cosine, Sassenheim, The Netherlands; Joao Barbosa, Cosine, Sassenheim, The Netherlands; John Hefele, Cosine, Sassenheim, The Netherlands; Rick Koeleman, Cosine, Sassenheim, The Netherlands; Bernardo Carnicero Dominguez, European Space Research and Technology Center, European Space Agency, Noordwijk, The Netherlands; Massimiliano Pastena, European Space Research and Technology Center, European Space Agency, Noordwijk, The Netherlands; Giancarlo Filippazzo, European Space Research Institute, European Space Agency, Frascati (RM), Italy; Amanda Reagan, European Space Research Institute, European Space Agency, Frascati (RM), Italy.

Electric Vehicle (EV) demand modeling constitutes the cornerstone of studies aiming to facilitate the integration of EVs into the power system. The different characteristics of the EV demand (departure time, arrival time, and electric demand), as well as the correlation between thereof, render EV demand modeling a complex task. The Majority of previous methods, which were developed based on the Monte Carlo simulation, are unable to observe and preserve the correlation between EV demand characteristics; because, in these methods, the EV demand characteristics are generated separately in an unsupervised manner. This study proposes a novel semi-supervised EV demand modeling approach by mapping the different EV demand characteristics into a three-dimensional (3D) space as a 3D image. To effectively realize the 3D EV demand modeling, we have employed Generative Adversarial Networks (GANs) with a 3D convolutional structure to develop EV-GANS network-a GANs structure tailored to the needs of EV demand modeling in environments hosting high demand diversity such as EV charging stations. Numerical results confirmed the effectiveness of the proposed EV-GANS in estimating the trend of the actual EV demand on the test day with a small error margin compared to the existing benchmark generation-based methods (Monte Carlo and Copula).

Background: Toxoplasmosis is a worldwide-distributed infection that can cause serious diseases, mainly in congenitally infected and immunodeficient individuals. PCR assays play an indispensable role in the detection of Toxoplasma gondii in different biological samples. Methods: This study was conducted in the Parasitology Department at Pasteur Institute of Iran (Tehran) during 2016-2018. We designed a highly sensitive quantitative real-time PCR (RT-qPCR) targeted REP-529, a noncoding repetitive DNA. We cloned the amplicon in a plasmid (pTZREP-529) and used it to generate the standard curve. The Toxoplasma RT-qPCR characteristics, i.e., detection limit, specificity, linear dynamic range, linearity, intra-, and inter-assay precisions, were determined. The detection limit of the assay was one plasmid copy number (PCN) per reaction (about 0.004 T. gondii genome), and the linear dynamic range was equal to 6 logs (1× 101 to 1× 107 PCN per reaction). Results: The assay showed no signal when genomic DNA of Plasmodium falciparum, Leishmania major, and Trichomonas vaginallis were used. The standard curve was drawn using dilutions of pTZREP-529 plasmid spiked with genomic DNA from a mouse brain, and test characteristics were shown unaffected. Applying the Toxoplasma RT-qPCR, we showed brain cysts were significantly decreased in mice vaccinated with GRA2 antigen of Toxoplasma formulated in Monophosphoryl Lipid A (MPL) adjuvant. Conclusion: We have developed a quantitative, specific, and highly sensitive PCR for detecting T. gondii in biological samples.

As small satellites are finding increased applicability in a number of space missions, the community is actively exploring the feasibility of missions employing active instruments on small spacecraft. In particular, synthetic aperture radars are of interest as they prove to be versatile tools for Earth Observation purposes. This paper proposes a trade space exploration model to identify designs for space-borne satellite synthetic aperture radars that address the desired instrument requirements. Two trade space analysis approaches are proposed, based on a multivariate analysis with two evaluation metrics and using parallel coordinate plots. The paper demonstrates the utility of the trade space model by considering two case studies. The first study looks at radar instruments on a broad range of small satellite platforms. This study narrows down a trade space of 1265 feasible radar designs to less than 44 Pareto optimal designs at different center frequencies (C- and X-band), elicits conditions for L-band SARs feasibility, and discusses the feasibility bounds and technical constraints on associated instrument and spacecraft requirements. The second study considers radars for 3U CubeSat platforms. The study identifies 44 Pareto optimal designs within 12,928 feasible designs and sheds light on the operational constraints required for the development of such innovative miniaturized radars.

Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by $K$ -means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory—feedforward and feedback loops—that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies–Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points.

Downstream processing of therapeutic recombinant proteins expressed as the IBs in E. coli is quite challenging. This study aimed to use the QbD approach for developing the multi-step downstream process of a structurally complex therapeutic Fc-Peptide fusion protein, romiplostim.For the development of a successful downstream process, risk analysis, and experimental designs are used to characterize the most CQAs and effects of process parameters on them.The solubilization of IBs was optimized by DoE on three parameters with a focus on solubility yield, and75% of the target protein was solubilized. The pH of sample was identified as (CQA) in anion exchange chromatography that might have an impact on achieving85% host cell proteins removal and90% host cell DNAs reduction. In refolding step, process parameters were screened. Cystine/cysteine ratio, pH, and incubation time were identified as CPPs were further optimized using Box-Behnken analysis, and85% of the target protein was refolded. The design space for further purification step by HIC was mapped with a focus on high molecular weight impurities. After polishing by gel filtration, the final product's biological activity had no statistically significant differences among the groups received romiplostim and Nplate as the reference product.This research presents a precise and exhaustive model for mapping the design space in order to describe and anticipate the link between the yield and quality of romiplostim and its downstream process parameters.

Tuberous roots of African geranium (Pelargonium sidoides DC.) with modern ethanolic formulation called EPs 7630 (Umckaloabo) have widely used in medicinal industries. The present study was aimed to explore changes in the phytochemical traits (i.e., phenolics, flavonoids, coumarins and hydroxycinnamic acid derivatives), protein content, enzymatic-antioxidants and antimicrobial activities of several high-yielding hairy root lines (HRLs) of P. sidoides harvested at different times. Among all HRLs, antioxidative responses (e.g., phenylalanine ammonia lyase, polyphenol oxidase and catalase contents) were increased in HRL10 exposed to higher interval harvests. Moreover, HRL10 exhibited a promising antimicrobial effect against Escherichia coli, with MIC equal to (2.61 mg/ml). In total 10 phytochemical compounds were identified through chromatographic (HPLC) technique including: umckalin (Um), gallic acid (GA), quercetin (QE), luteolin (LU) and the others. The extraction of HRs had high content of Um at 21 days (0.35 𝜇g/ml). The maximal phenolic yield in particular GA was enhanced to (5194.5 𝜇g/ml), about 10-fold in comparison with non-transformed roots (516 𝜇g/ml) on the 35th day. HRs were able to produce the maximum improvement in flavonoid glycosides (13.96 𝜇g QE/ml and 23.75 𝜇g LU/ml) at the end of culture period. The most profitable transgenic lines offers an interesting option to enhance the productivity. These results support the in vitro cultivation of HRs for supply as a natural product to prevent inflammation and microbial related disorders.

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA. Here, we introduce a modified cPCA method, which we denote cPCA*, that is more interpretable and less sensitive to the choice of hyper-parameter. We derive an online algorithm for cPCA* and show that it maps onto a neural network with local learning rules, so it can potentially be implemented in energy efficient neuromorphic hardware. We evaluate the performance of our online algorithm on real datasets and highlight the differences and similarities with the original formulation., 5 pages, 4 figures

This research extends the effects of a thermal-ultrasound treatment (at 25, 45, and 65 °C for 30 and 60 min) on the physicochemical, structural, and pasting properties of corn starch in presence of Arabic gum. Treated samples had lower leached amylose compared with corn starch, but it was non-significant (p p

Thymus species have found remarkable importance in food and medicine industries. The present study investigates the potential effect of Ag nanoparticle elicitors on proliferation of callus, and production of carvacrol and thymol in Zataria multiflora and three Thymus species. Firstly, callus was induced on Murashige and Skoog (MS) medium containing 2 mg l−1 of 2, 4-dichlorophenoxy acetic acid (2,4-D) and 1 mg l−1 of kinetin (Kin)). Secondly, the effects of two different concentrations of Ag nanoparticles (4 and 8 mg l-1) were studied on callus growth and its secondary metabolites production. Results elucidated that after elicitation by 8 mg l-1 ofAg NPs, significantly the highest callus growth rate (CGR) (0.02 mm day-1), callus fresh mass (CFM) (0.99 g), and carvacrol (0.68 mg l-1) and thymol (11.09 mg l-1) content was achieved. Comparing different Thymus species, notably the greatest carvacrol and thymol amount was obtained in .kotschyanus Boiss. & Hohen. and T. Daenesis Čelak. at 8 mg l-1 concentration ofAg NPs. Hence, it is evident that the stimulation by NPs is dose-dependent. This study has potential to be commercially applied for the enhancement of pharmaceutical compounds in different species of Thymus.

The development of water quantity assessment focusing on water balances have been identified as a main tool for guiding water policy and management at different decision-making levels. Water Balance Assessment (WBA) accounts for all water fluxes in a watershed by balancing the difference between water inputs and outputs, as well as changes in the watershed water storage. The natural hydrological cycle has been considerably changed due to the substantial anthropogenic activities, which lead to more complexity and therefore uncertainty in estimation and implementation of WBA results. In Iran, the WBA is being conducted every five-year for 609 watersheds, covering the whole country. Although the water authorities are provided with different guidelines and frameworks to improve the WBA, yet there are many multifaceted obstacles in carrying out proper WBA and implementing its results in water management. A preliminary survey showed that most of the barriers are related to the complexity of the human-hydrological watershed systems and the associated uncertainty to the water balance components especially at semi-arid watersheds. Additionally, the complexities associated with institutional arrangements including: access to data, centralized process of conducting WBA, workflow of the WBA projects, diverse stakeholders and their disagreement with WBA final results are among the barriers against smooth WBA in Iran.In order to understand, identify and provide practical solutions for WBA problems, a national initiative is established with engaging government organizations, universities, NGOs, and research institutes. Therefore, we introduced a collaborative framework entitled “Unsolved Problems in Water Balance (UPWB)” to understand the problems together, co-create capacities and synergize among all stakeholders to solve the WBA problems in Iran. UPWB also covers 11 out of 23 of the IAHS Unsolved Problems in Hydrology (UPH). The UPWB considers three main themes for prioritizing the UPWB in Iran: 1) Technical and computational 2) Institutional and 3) data and information. So far, 10 online webinars by involvement of 400 experts are organized and 46 filled questioners also collected through the Web platform (upwb.ir). Preliminary results confirmed that WBA is the core element of sustainable water management especially in semi-arid regions with various technical, social and institutional aspects.

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

Natural structures are known to be the source of inspiration for numerous architectural and structural rules in the fields of aesthetics, function, and structure; therefore, the application of the principals governing them could be used for appropriate and optimal design. The present paper was conducted to model the natural structure of load transfer in order to design the structure of a pedestrian bridge with a span of 100 meters. This bridge is located in a mountain park in the tourist area of Kashan, 230 km south of Tehran. For this purpose, by examining the patterns in the nature, which provides a relevant answer to the problem, the spine of animals was identified as the bearing skeleton of the body, the best option for patterning. Inspired by it, a stable structure was designed as a skeleton of a bridge without a middle pillar. Based on a form inspired by the spine of a four-legged animal, the bridge structure was designed. To control the stability of the bridge structure against the loads, the initial design idea was analysed employing the Karamba plugin in Grasshopper software to identify its weaknesses and the final design was obtained. The final design was analysed with SAP2000 structural finite element software to ensure the stability and control of permissible deformations. Additionally, attention to the modular structure of the spine was the source of inspiration for the design of prefabricated elements of the bridge parts, which in addition to reducing the cost of execution, increases the speed of construction of the project. The final design of the pedestrian bridge, which acts similar to a suspension bridge in terms of load transfer and was inspired by the structure of a four-legged vertebrate, is a combination of truss and tensegrity structure and in addition to visual aesthetic, has optimal structural performance.

Few studies have attempted to investigate the combined effects of soil matric potential (Ψm) and osmotic potential (Ψs) on plant production and water use efficiency (WUE). The purpose of this study...

Salinity, water scarcity, and nitrogen (N) deficiency are the main limiting factors for plant production. However, over-irrigation and excessive N utilization can also limit the growth and biomass yield of leafy vegetables. The aim of this study is to investigate the growth regulation of spinach (Spinacia oleracea L.) under different levels of irrigation water salinity, water utilization, and nitrogen supply. Combinations of five salinity levels (1, 4, 7, 9, and 11 dS m−1) with three levels of water consumption including deficit irrigation (W–), full irrigation (W0) and over-irrigation (W+), and four levels of N supply (0, 50, 75, and 100% of N requirement) were examined under controlled greenhouse conditions. The least plant biomass, evapotranspiration (ET), and nitrogen use efficiency (NUE) were observed when high-saline water was applied in combination of water and N deficiency. However, the osmotic/drought effect is a short-term effect of salinity that can be further exacerbated by the condition W–. With increasing the fertilizer rate up to 150 mg N kg−1 soil, the plant growth traits increased significantly for all salinity levels; however, an excessive N utilization caused no significant increase. Water stress caused a morphological adaptation in the plant by reducing leaf area and ET, resulting in improved water use efficiency (WUE). With increasing salinity, WUE increased then decreased significantly. In other words, both the relative ET and relative yield were reduced with increasing salinity; however, at intermediate salinity levels (4 and 7 dS m–1), further decline in the former than in the latter resulted in higher WUE values. The stepwise regression analysis showed that the plant yield was affected mostly by WUE, ET, and leaves number (R2 = 0.983***). The findings of this study revealed that the reduced leaf area was the main growth regulation in the spinach plant under stress conditions, which ultimately could enhance WUE.

Background: The present project presented the prevalence of pediculosis in Behbahan County, Iran, to plan effective control programs in this area. Materials & Methods: The data of head lice were collected from the Behbahan Public Healthcare centers. Accordingly, the relevant prevalence rate was estimated according to the age, gender, residential place, and season of the year. Results: There were 8002 pediculosis cases reported in Behbahan County from January 2017 to December 2018. The prevalence of pediculosis was reported as 2.31% and 1.99% in 2017 and 2018, respectively. The prevalence among females (3750 per 100000 population) was significantly higher than that in males (929 per 100000 population) (P