Roles of Different ß-Defensins in the Human Reproductive System: A Review Study.

Human ß-defensins (hBDs) are cationic peptides with an amphipathic spatial shape and a high cysteine content. The members of this peptide family have been found in the human body with various functions, including the human reproductive system. Of among ß-defensins in the human body, ß-defensin 1, ß-defensin 2, and ß-defensin 126 are known in the human reproductive system. Human ß-defensin 1 interacts with chemokine receptor 6 (CCR6) in the male reproductive system to prevent bacterial infections. This peptide has a positive function in antitumor immunity by recruiting dendritic cells and memory T cells in prostate cancer. It is necessary for fertilization via facilitating capacitation and acrosome reaction in the female reproductive system. Human ß-defensin 2 is another peptide with antibacterial action which can minimize infection in different parts of the female reproductive system such as the vagina by interacting with CCR6. Human ß-defensin 2 could play a role in preventing cervical cancer via interactions with dendritic cells. Human ß-defensin 126 is required for sperm motility and protecting the sperm against immune system factors. This study attempted to review the updated knowledge about the roles of ß-defensin 1, ß-defensin 2, and ß-defensin 126 in both the male and female reproductive systems.

Evaluating effect of acrylamide and ascorbic acid on oxidative stress and apoptosis in ovarian tissue of wistar rat.

Acrylamide is one of the undesirable compounds created in food, which leads to oxidative stress. Under normal conditions of the body, there is a balance between the production and elimination of free radicals. Imbalance in this process causes oxidative stress. Ascorbic acid has antioxidant properties and can be used to prevent oxidative stress damage. In this study, we considered the effect of acrylamide as a substance that causes oxidative stress and ascorbic acid as an antioxidant. In this experiment, 28 rats were separated into 4 groups (n = 7). Mice were fed orally with acrylamide (10 mg/kg), ascorbic acid (200 mg/kg), both acrylamide and ascorbic acid, and water as AR, AA, AR&AA, and control groups, respectively. Blood and ovarian tissue samples were collected after the final feeding and anesthesia for hematological tests. Blood cells, anti-oxidation status and relative expression of BAX (Bcl-2 Associated X-protein), BCL-2 (B-cell lymphoma-2), Folliculogenesis Specific BHLH Transcription Factor (FIGLA), Follicle Stimulating Hormone Receptor (FSHR), and Klotho (KL) genes were assessed and compared between the study groups. Despite no change in the levels of other factors, white blood cells were significantly different between all groups. The total oxidant and antioxidant index had significant changes in the AR group compared to controls. Glutathione Peroxidase showed the least concentration in the AR group than others although this change was not significant. Gene expression of BAX, BCL-2, FIGLA, FSHR, and KL genes was not significant between the study groups (P > 0.05). The most ratio of BAX to BCL-2 belonged to the AR group compared to other groups. In conclusion, AR probably induces ovarian dysfunction by increasing the proportions of apoptosis-related genes, and the usage of antioxidants like AA can minimize its hazardous effects. However, more research is needed to uncover effective ways to limit AR exposure.

COVID-19 and organisational resilience in Brazil's water sector.

The COVID-19 pandemic required a wide range of adaptations to the way that water sector operated globally. This paper looks into the impact of the COVID-19 pandemic on Brazilian water sector and evaluates the water sector's organisational resilience from the lens of water professionals. This study uses British Standard (BS 65000:2014)'s Resilience Maturity Scale method to evaluate organisational resilience in water sector under two defined scenarios of before and during the pandemic. For this purpose, the self-assessment framework developed by Southern Water in the United Kingdom (based on BS 65000:2014), comprising of the core resilience elements of Direction, Awareness, Alignment, Learning, Strengthening, and Assurance, are used for evaluations. A qualitative-quantitative surveying method is used for data collection. A total of 14 responses to the whole questionnaire were received from May 2021 to August 2021, each representing one water company in Brazil (four local companies and ten state-owned ones). The analyses identified COVID-19 as a threat multiplier particularly to already existing financial challenges due to the pre-existing threats in water sector. Bad debt and the COVID-19 emergency measures are recognised as the main challenges by 21 % and 14 % of the survey respondents. The state-owned and local companies scored an almost similar maturity level 3, 35 % and 34 % respectively, while the local companies scored much lower at maturity level 4 i.e., 26 % as opposed to 47 % in state-owned sector. This indicates that COVID-19 has a greater impact on local companies and the needs to increase preparedness. This study replicates an international experience to raise awareness on water sector's resiliency in Brazil and how it can be improved to withstand future external shocks. It sheds light on how and what existing challenges can be exacerbated facing a global shock and proposes opportunities for improvement of resilience maturity in water sector in Brazil.

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence.

Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.

Validation of the Persian version of the comprehensive frailty assessment instrument plus in community-dwelling older adults.

This study aimed to examine the validity and reliability of the Persian Version of the Comprehensive Frailty Assessment Instrument Plus (CFAI-Plus) among community-dwelling older adults. It was completed by 340 older adults ≥60 years. The content and face validity were confirmed based on the opinion of the target group and experts. In the exploratory factor analysis, seven factors were extracted, explaining 62.8% of the total variance. Confirmatory factor analysis showed acceptable fit indices (Root Mean Square Error of Approximation = 0.045; Comparative Fit Index = 0.93; Tucker-Lewis Index = 0.92). Internal consistency was adequate for factors (Cronbach's alpha: range 0.47 to 0.88), and the test-retest reliability was acceptable (intra-class correlation coefficient: range 0.76 to 0.92). A higher CFAI-Plus score were found in those who were older, female, less-educated, single, lived alone, and had inadequate income. This study supports the reliability and validity of the Persian CFAI-Plus in community-dwelling older adults.

Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images.

Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature maps from the chest X-ray radiographs and abdominal computed tomography (CT) images. Morphological operators, Gabor filter banks and attribute filters are used for contextual feature extraction. Then, two convolutional filters are applied to the contextual feature cube to extract the nonlinear sub-features and hidden relationships among the contextual features. Finally, a fully connected layer is used to produce a reduced feature vector which is fed to a classifier. Support vector machine and random forest are used as classifier. The experimental results show the superior performance of the proposed method from the recognition accuracy and running time point of view using limited training samples. More than 76% and 94% overall classification accuracy is obtained by the proposed method in CT scan and X-ray images datasets, respectively.

A novel machine learning application: Water quality resilience prediction Model.

Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate. This study utilises Artificial Neural Network (ANN) to develop a novel application to predict water quality resilience to simplify resilience evaluation. The Fuzzy Analytic Hierarchy Process method is used to rank water basins based on their level of resilience and to identify the ones that demand prompt restoration strategies. The commonly used 'magnitude * duration of being in failure state' quantification method has been used to formulate and evaluate resilience. A 17-years long water quality dataset from the 22 water basins in the State of São Paulo, Brazil, was used to train and test the ANN model. The overall agreement between the measured and simulated WQI resilience values is satisfactory and hence, can be used by planners and decision makers for improved water management. Moreover, comparative analyses show similarities and differences between the 'level of criticalities' reported in each zone by Environment Agency of the state of São Paulo (CETESB) and by the resilience model in this study.

Particulate matter (PM2.5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network.

Most studies about particulate matter (PM) estimation have been done based on satellite-derived optical depth aerosol (AOD) products. But, the use of AOD products having coarse resolution is not possible for PM map generation in small spatial coverage such as local cities. To solve this issue, a PM estimation framework is proposed in this work which accepts the original calibrated radiance of MODIS-Level 1 images as input. There are no intermediate computations for atmospheric reflectance or aerosol thickness calculation. A deep neural network consisting of recurrent layers is proposed to extract the relationship between the grey level values of the satellite image bands and the PM measurements in different days and locations. Two individual networks are trained for PM2.5 and PM10 concentrations. The PM2.5 map and PM10 map of Tehran city are generated. The performance of the proposed method is compared with several recently published air pollution studies. The results show that the proposed method is a simple, low cost and efficient approach for PM generation of small-scaled coverage using free available Moderate Resolution Imaging Spectroradiometer (MODIS) images.

Towards urban resilience through Sustainable Drainage Systems: A multi-objective optimisation problem.

The necessity of incorporating a resilience-informed approach into urban planning and its decision-making is felt now more than any time previously, particularly in low and middle income countries. In order to achieve a successful transition to sustainable, resilient and cost-effective cities, there is a growing attention given to more effective integration of nature-based solutions, such as Sustainable Drainage Systems (SuDS), with other urban components. The experience of SuDS integration with urban planning, in developed cities, has proven to be an effective strategy with a wide range of advantages and lower costs. The effective design and implementation of SuDS requires a multi-objective approach by which all four pillars of SuDS design (i.e., water quality, water quantity, amenity and biodiversity) are considered in connection to other urban, social, and economic aspects and constraints. This study develops a resilience-driven multi-objective optimisation model aiming to provide a Pareto-front of optimised solutions for effective incorporation of SuDS into (peri)urban planning, applied to a case study in Brazil. This model adopts the SuDS's two pillars of water quality and water quantity as the optimisation objectives with its level of spatial distribution as decision variables. Also, an improved quality of life index (iQoL) is developed to re-evaluate the optimal engineering solutions to encompass the amenity and biodiversity pillars of SuDS. Rain barrels, green roofs, bio-retention tanks, vegetation grass swales and permeable pavements are the suitable SuDS options identified in this study. The findings show that the most resilient solutions are costly but this does not guarantee higher iQoL values. Bio-retention tanks and grass swales play effective roles in promotion of water quality resilience but this comes with considerable increase in costs. Permeable pavements and green roofs are effective strategies when flood resilience is a priority. Rain barrel is a preferred solution due to the dominance of residential areas in the study area and the lower cost of this option.

A resilience assessment framework for critical infrastructure networks' interdependencies.

Critical infrastructures (CIs) provide essential services to the society. As infrastructures are becoming more interdependent, there is an increasing need for better management of their interactions and interdependencies. Interdependencies among CI can cause cascading failures and, hence, amplify negative consequences due to these failures. This can also affect CIs' service restoration rate and consequently reduce their resilience in coping with these hazardous events. The common challenge currently faced by CI asset owners is the lack of robust resilience-informed business planning and management strategies in response to interdependent assets' failures due to low-probability/high-impact hazards. This is of particular importance as CI owners and managers are investing more on improving the resilience of their assets in response to extreme environmental hazards. This study has approached CI nexus from the interdependency management point of view. It has developed an integrated resilience assessment framework to identify and map interdependency-induced vulnerabilities in critical infrastructure networks. This framework can potentially support effective management of the interdependencies in CI networks. The findings have been reflected in mapping the connection between the changes in resilience due to interdependency-induced failures and the cost of intervention scenarios, providing means of exploring shared intervention strategies.

Design and operation of urban wastewater systems considering reliability, risk and resilience.

Reliability, risk and resilience are strongly related concepts and have been widely utilised in the context of water infrastructure performance analysis. However, there are many ways in which each measure can be formulated (depending on the reliability of what, risk to what from what, and resilience of what to what) and the relationships will differ depending on the formulations used. This research has developed a framework to explore the ways in which reliability, risk and resilience may be formulated, identifying possible components and knowledge required for calculation of each and formalising the conceptual relationships between specified and general resilience. This utilises the Safe & SuRe framework, which shows how threats to a water system can result in consequences for society, the economy and the environment, to enable the formulations to be derived in a logical manner and to ensure consistency in any comparisons. The framework is used to investigate the relationship between levels of reliability, risk and resilience provided by multiple operational control and design strategies for an urban wastewater system case study. The results highlight that, although reliability, risk and resilience values may exhibit correlations, designing for just one is insufficient: reliability, risk and resilience are complementary rather than interchangeable measures and one cannot be used as a substitute for another. Furthermore, it is shown that commonly used formulations address only a small fraction of the possibilities and a more comprehensive assessment of a system's response to threats is necessary to provide a comprehensive understanding of risk and resilience.

Anti-tumor activity of Escherichia coli Shiga toxin A subunit delivered by SF9 insect cells.

Cancer remains a major health problem around the world. A Shiga toxin is a bacterial toxin often produced by Shigella dysenteriae and Escherichia coli. A subunit of the Shiga toxin (StxA) is a cytotoxic agent which could be used to induce death in cancer cells. StxA expressed from baculovirus was evaluated in a pTriEx™ expression vector. The baculovirus vector was used for the A subunit delivery of StxA. StxA cell cytotoxicity was induced by the virus and assessed in the MCF7 and HeLa cell lines. In addition, the breast cancer cytotoxicity of the expressed StxA was also assessed in a cancer induced in mice. The cytotoxicity of the recombinant StxA baculovirus with different multiplicities of infection (MOI) was measured. The results showed that significant cytotoxicity can be induced on the mammalian epithelial breast cancer cell lines, MCF7 and HeLa cells with MOI ≥ 2. The results also showed that a malignant tumor induced by MCF7 could be inhibited in a mouse cancer model. Therefore, it can be concluded that StxA, expressed by baculovirus, could be used for in vitro and in vivo gene delivery. In this study StxA, delivered by the baculovirus inhibited cell proliferation, and eliminated HeLa and MCF7 cells, in vitro. In conclusion, this method can be used as a safe alternative for anticancer drug delivery inside cancer cells.

Reliable, resilient and sustainable water management: the Safe & SuRe approach.

Global threats such as climate change, population growth, and rapid urbanization pose a huge future challenge to water management, and, to ensure the ongoing reliability, resilience and sustainability of service provision, a paradigm shift is required. This paper presents an overarching framework that supports the development of strategies for reliable provision of services while explicitly addressing the need for greater resilience to emerging threats, leading to more sustainable solutions. The framework logically relates global threats, the water system (in its broadest sense), impacts on system performance, and social, economic, and environmental consequences. It identifies multiple opportunities for intervention, illustrating how mitigation, adaptation, coping, and learning each address different elements of the framework. This provides greater clarity to decision makers and will enable better informed choices to be made. The framework facilitates four types of analysis and evaluation to support the development of reliable, resilient, and sustainable solutions: "top-down," "bottom-up," "middle based," and "circular" and provides a clear, visual representation of how/when each may be used. In particular, the potential benefits of a middle-based analysis, which focuses on system failure modes and their impacts and enables the effects of unknown threats to be accounted for, are highlighted. The disparate themes of reliability, resilience and sustainability are also logically integrated and their relationships explored in terms of properties and performance. Although these latter two terms are often conflated in resilience and sustainability metrics, the argument is made in this work that the performance of a reliable, resilient, or sustainable system must be distinguished from the properties that enable this performance to be achieved.

Separation of amino acids by high performance liquid chromatography based on calixarene-bonded stationary phases.

In this work, we present a new method for synthesis of silica gel stationary phases based on calix[4]arene derivatives. In order to achieve it, 25,27-dipropoxy-26,28-bis-[3-propyloxydimethylsililoxy]calix[4]arene has been synthesized in six steps and immobilized on silica via chlorotrimethylsilane. Stationary phases were characterized by elemental analysis, infrared spectroscopy and thermal analysis and used for the separation of amino acid derivatives by high performance liquid chromatography. The effect of isocratic and gradient elution, pH and column temperature on retention and selectivity of the Fmoc-protected amino acids were studied. The retention mechanism was also discussed. The results indicated that the stationary phase behaves like a reverse phase packing. Size exclusion, electron-π, π-π and hydrophobic interactions seem to be involved in the separation process.

Construction of a Baculovirus vector containing A subunit of Shiga toxin for protein delivery.

BACKGROUND AND OBJECTIVES: Baculovirus can be used as a vector in gene delivery system. Viral envelope of baculovirus would display expressed protein/peptide and it could render as a potential vaccine delivery system. In this regard, the gene coding for A subunit of shiga toxin (StxA) from Escherichia coli (E. coli) strain was cloned in a baculovirus expression system. StxA subunit has the ability to inhibit protein synthesis and this ability applied in cancer therapy. In this study, expression of StxA in baculovirus as a protein delivery system was assessed in vitro. MATERIAL AND METHODS: StxA gene was cloned in pTriEx™ multisystem expression vector. This vector enables the protein expression in multisystem, E. coli and baculovirus. This construct was used to express the gene in E. coli and baculovirus. The construct containing StxA gene was made in baculovirus and expression was confirmed, then baculovirus expressing STXA transfect HeLa cells. RESULTS: The expression of STXA peptide (32kDa) was confirmed by SDS-PAGE and western blotting in both expression systems. The A subunit challenge to human cell Lines was applied as a delivery system by baculoviruses. On the other hand, the inhibition of cell proliferation was also demonstrated by baculovirus containing STXA subunit. CONCLUSION: STXA peptide expression in baculovirus was shown in E. coli and baculovirus expression system. Furthermore, it was shown that A subunit of Shiga toxin delivered by baculovirus can inhibit cell proliferation in HeLa cells and leading to cell death. Therefore, this prototype system could be a promising model for in vivo cancer therapy and targeted protein delivery system.

Subtyping of BK Virus in Iranian Turkish Renal Transplant Recipients by RFLP-PCR.

INTRODUCTION: BK polyomavirus (BKV) as a member of polyomavirus family is prevalent in the human population. BKV persists in renal tissue after asymptomatic infection in childhood. The reactivation of BKV in renal transplant recipients sometimes can lead to BKV associated nephropathy. BKV isolates are classified into four serologically distinct subtypes. Present study was carried out to investigate the distribution pattern of BKV subtypes in Iranian Turkish renal transplant recipients. MATERIALS AND METHODS: Urine samples from 12 kidney transplant recipients infected with BKV were analyzed by RFLP-PCR technique for classification of subtypes. RESULTS: Our analysis showed that all samples were infected with BKV type I. BK virus types II, III, and IV were not detected in our patients. CONCLUSIONS: Based on the results of the present study, BKV subtype I was the most frequently detected subtype in renal transplant recipients. To our knowledge, the present study provides the first data regarding distribution of BKV subtypes in Iranian renal transplant recipients.

Assessing the combined effects of urbanisation and climate change on the river water quality in an integrated urban wastewater system in the UK.

Climate change and urbanisation are key factors affecting the future of water quality and quantity in urbanised catchments and are associated with significant uncertainty. The work reported in this paper is an evaluation of the combined and relative impacts of climate change and urbanisation on the receiving water quality in the context of an Integrated Urban Wastewater System (IUWS) in the UK. The impacts of intervening system operational control parameters are also investigated. Impact is determined by a detailed modelling study using both local and global sensitivity analysis methods together with correlation analysis. The results obtained from the case-study analysed clearly demonstrate that climate change combined with increasing urbanisation is likely to lead to worsening river water quality in terms of both frequency and magnitude of breaching threshold dissolved oxygen and ammonium concentrations. The results obtained also reveal the key climate change and urbanisation parameters that have the largest negative impact as well as the most responsive IUWS operational control parameters including major dependencies between all these parameters. This information can be further utilised to adapt future IUWS operation and/or design which, in turn, should make these systems more resilient to future climate and urbanisation changes.