Proper and timely segregation of cellular genome is an important and a prime requirement of all cell division programmes. Mis-segregation of chromosomes and resulting aneuploidy leads to several clinical consequences. Over the years, shugoshin emerges as a key protein factor involved in the segregation of genetic material in dividing cells. Deletion or altered level of shugoshin is reported in several human malignancies, as a result, shugoshin now emerges as an important tumour associated gene and a possible target for cancer therapy. Apart from the role in cancer, recent studies also showed the involvement of shugoshin in several other clinical disorders. Through this review, we tried to highlight the clinical relevance of shugoshin.

Recently, we developed Komagataella phaffii (formerly Pichia pastoris) as a model for lipophagy, the selective autophagy of lipid droplets (LDs). We found that lipophagy pathways induced by acute nitrogen (N) starvation and in stationary (S) phase have different molecular mechanisms. Moreover, both types of lipophagy are independent of Atg11, the scaffold protein that interacts with most autophagic receptors and, therefore, is essential for most types of selective autophagy in yeast. Since yeast aggrephagy, the selective autophagy of ubiquitinated protein aggregates, is also independent of Atg11 and utilizes the ubiquitin-binding receptor, Cue5, we studied the relationship of K. phaffii Cue5 with differentially induced LDs and lipophagy. While there was no relationship of Cue5 with LDs and lipophagy under N-starvation conditions, Cue5 accumulated on LDs in S-phase and degraded together with LDs via S-phase lipophagy. The accumulation of Cue5 on LDs and its degradation by S-phase lipophagy strongly depended on the ubiquitin-binding CUE domain and Prl1, the positive regulator of lipophagy 1. However, unlike Prl1, which is required for S-phase lipophagy, Cue5 was dispensable for it suggesting that Cue5 is rather a new substrate of this pathway. We propose that a similar mechanism (Prl1-dependent accumulation on LDs) might be employed by Prl1 to recruit another ubiquitin-binding protein that is essential for S-phase lipophagy.

Autophagy was initially recognized as a bulk degradation process that randomly sequesters and degrades cytoplasmic material in lysosomes (vacuoles in yeast). In recent years, various selective autophagy pathways have been discovered. Glycophagy, the selective autophagy of glycogen granules, is one of them. It is an important contributor to Pompe disease, a type of lysosomal storage disorder with lysosomal accumulation of glycogen. Here, we developed the Komagataella phaffii yeast as a simple model of glycogen autophagy under nitrogen starvation conditions. For this, we turned the self-glucosylating initiator of glycogen synthesis, Glg1, which is covalently bound to glycogen, into the Glg1-GFP autophagic reporter. Our results revealed that vacuolar delivery of Glg1-GFP and its processing to free GFP were strictly dependent on autophagic machinery and vacuolar proteolysis. Notably, this process was independent of Atg11, the scaffold protein common for many selective autophagy pathways. Importantly, the non-mutated Glg1-GFP (which synthesizes and marks glycogen) and mutated Glg1Y212F-GFP (which does not synthesize glycogen and is degraded by non-selective autophagy as cytosolic Pgk1-GFP) were equally well delivered to the vacuole and had similar levels of released GFP. Therefore, we concluded that glycogen autophagy is a non-selective process in K. phaffii yeast under nitrogen starvation conditions.

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: the dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM).

Greenhouse Gases (GHGs) emission has considerable impact on global warming and climate change. Since energy systems and their features noticeably influence on the amount of GHGs emission, it can be modeled based on the specifications of energy sources utilized by the countries. In addition, economic activity is another factor which should be considered in GHG emission modeling. In this work, Artificial Neural Network (ANN) is used for estimating carbon dioxide emission, as one of the most abundant GHGs, on the basis of shares of various energy sources used as primary energy supply and GDP as an indicator for economic activities. Five countries including Iran, Kuwait, Qatar, Saudi Arabia and United Arab Emirates (UAE) are considered as case studies. Comparing between the estimated data by the achieved model and actual quantities showed acceptable precision of the ANN model for prediction of carbon dioxide emission. The average absolute relative error and the R-squared values of the GMDH model are approximately 2.28% and 0.9998, respectively. The obtained values for the mentioned statistical criteria show the precision of the model in forecasting the emission of Co2.

Due to low working temperature, high energy density and low pollution, proton exchange fuel cells have been investigated under different operating conditions in different applications. Using platinum catalysts in methanol fuel cells leads to increasing the cost of this kind of fuel cell which is considered as a barrier to the commercialism of this technology. For this reason, a lot of efforts have been made to reduce the loading of the catalyst required on different supports. In this study, carbon black (CB) and carbon nanotubes (CNT) have been used as catalyst supports of the fuel cell as well as using the double-metal combination of platinum-ruthenium (PtRu) as anode electrode catalyst and platinum (Pt) as cathode electrode catalyst. The performance of these two types of electro-catalyst in the oxidation reaction of methanol has been compared based on electrochemical tests. Results showed that the carbon nanotubes increase the performance of the micro-fuel cell by 37% at maximum power density, compared to the carbon black. Based on thee-electrode tests of chronoamperometry and voltammetry, it was found that the oxidation onset potential of methanol for CNT has been around 20% less than CB, leading to the kinetic improvement of the oxidation reaction. The current density of methanol oxidation reaction increased up to 62% in CNT sample compared to CB supported one, therefore the active electrochemical surface area of the catalyst has been increased up to 90% by using CNT compared to CB which shows the significant rise of the electrocatalytic activity in CNT supported catalyst. Moreover, the resistance of the CNT supported sample to poisonous intermediate species has been found 3% more than CB supported one. According to the chronoamperometry test results, it was concluded that the performance and sustainability of the CNT electro-catalyst show remarkable improvement compared to CB electro-catalyst in the long term.

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

The utilization of wild genera in sugarcane has been recognized as an important strategy to enhance the genetic base to impart biotic and abiotic stress tolerance in hybrid cultivars. A set of 15 Erianthus procerus derived hybrids along with five popular check varieties were evaluated for two years (2018–19 and 2019–20) for agro-morphological, quality, physiological traits and three years (2018–19, 2019–2020, and 2020–21 for winter sprouting potential. The E. procerus hybrid GU 12-19 recorded the highest winter sprouting potential with a winter sprouting index (WSI) of 10.6 and a percent superiority of 279 over best check (Co 0238) followed by GU 12-22 with WSI: 8.5 and % superiority over best check was 204. The other top-performing hybrids, GU 12-21 and GU 12-29, recorded the excellent winter sprouting potential with WSI of 7.2 and 6.9 respectively. The four E. procerus hybrids identified with higher WSI were also resistant to the prevalent races of red rot in both tropical and subtropical conditions. The association analysis revealed significant correlations between various traits, particularly fibre content with a maximum number of associations, which indicates its multifaceted impact on sugarcane characteristics. Principal component analysis (PCA) summarized the data, explaining 57.6% of the total variation for measured traits and genotypes, providing valuable insights into the performance and characteristics of the E. procerus-derived hybrids under subtropical climate. The anthocyanin content of E. procerus hybrids was better than the check varieties with a mean of 0.147 (2018–19) and 0.144 (2019–20) and range of 0.123–0.179 (2018–19) and 0.111–0.172 (2019–20), anthocyanin plays a vital role in mitigating the cold injury, acting as an antioxidant in cool weather conditions, particularly in sugarcane. Further, seven hybrids recorded more than 22% fibre threshold, indicating their industrial potential. These hybrids would be potential donors for cold tolerance and high ratooning ability along with red rot resistance under subtropical climate.

Phytophthora. infestans is a well known late blight causing oomycetes pathogen. It evolves and adapts to the host background and new fungicide molecules rapidly within a few years of their release, may be due to the predominance of transposable elements in its genome. Frequent and huge applications of fungicides cause environmental concerns. Here we developed target specific RNA interference based molecules, that are capable of effectively reducing the late blight infection. cDNA microarray expression data was used for the selection of genes involved in the early stage of infection process, sporulation etc. The in vitro synthesis of double stranded RNA molecule, targeting SDH, EF-1α, GPI-HAM344, PLD-3 and HSP-90 encoding genes revealed the reduction in growth, sporulation and symptom expression, which were subsequently assessed by culture bioassay, detached leaf assay and topical application methods. The multiple genes targeted dsRNA nano clay sprayed plants showed enhanced disease resistance (4% disease severity) and least sporulation (<1x103), compared to naked dsRNA spray. Use of nano clay was assumed to be involved in the effective delivery, protection and boosting the action of RNAi in potato plants. A significant difference in the growth, sporulation count, disease severity and reduced expression of the genes and confocal microscopy imaging authenticates the effects of SIGS on late blight disease progression. Our research demonstrated that topical dsRNA nano clay spray under the open-air environment could be an alternative to chemical fungicides and transgenic approaches as a novel plant protection strategy for late blight in an environmentally friendly manner.

The present study examines the effect of SiO2 nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. Nano-additives characterisations were done using different tests such as FESEM, XRD, EDS, etc., to study the morphology of nano-additives. For proper blending of nano-additives with biodiesel, the ultrasonication process was used. Surfactant was used for the stabilisation of nano-additives. After making all the combinations of nano fuel blends, physicochemical properties were measured as per ASTM standards. Performance and emissions readings were taken at different load conditions. It was found that with the addition of SiO2 nano-additives, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) was increased by 3.48-6.39% and 5.81-9.88%, respectively. Significant reduction of CO, CO2, NOx, and smoke emissions were also observed compared to baseline fule due to better combustion efficiency with the use of SiO2 nano-additive.