Search

BackgroundTrichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized.ResultsTo address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei. Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae. The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose.ConclusionThis study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.

Background and purpose: The global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prompted widespread concern. Bacteriophages have recently gained attention as a cost-effective and stable alternative for vaccine development due to their adjuvant properties. This study aimed to design and validate a poly epitope composed of viral proteins. Experimental approach: SARS-CoV-2 proteins (spike, nucleocapsid, membrane, envelope, papain-like protease, and RNA-dependent RNA polymerase) were selected for analysis. Immunoinformatic methods were employed to predict B and T cell epitopes, assessing their antigenicity, allergenicity, and toxicity. Epitopes meeting criteria for high antigenicity, non-allergenicity, and non-toxicity were linked to form poly epitopes. These sequences were synthesized and cloned into pHEN4 plasmids to generate Poly1 and Poly2 phagemid vectors. Recombinant Poly1 and Poly2 phages were produced by transforming M13ΔIII plasmids and phagemid vectors into E. coli TG1. Female Balb/c mice were immunized with a cocktail of Poly1 and Poly2 phages, and their serum was collected for ELISA testing. Interferon-gamma (IFN-γ) testing was performed on spleen-derived lymphocytes to evaluate immune system activation. Findings/Results: Recombinant Poly1 and Poly2 phages were produced, and their titer was determined as 1013 PFU/mL. Efficient humoral immune responses and cellular immunity activation in mice were achieved following phage administration. Conclusion and implication: Poly epitopes displayed on phages exhibit adjuvant properties, enhancing humoral and cellular immunity in mice. This suggests that phages could serve as adjuvants to bolster immunity against SARS-Cov-2. Recombinant phages could be applied as effective candidates for injectable and oral vaccine development strategies.

Staphylococcus aureus and coagulase-negative staphylococci account for about 80% of infections associated with medical devices and are associated with increased virulence due to their ability to form biofilm. In this study, we aimed to construct a comprehensive reference map followed by significant pathway analysis in the proteome of S. aureus biofilm grown for 3 days compared with 24 h of planktonic culture using a high-resolution Tandem Mass Tag (TMT)-based MS. We identified proteins associated with secondary metabolites, ABC transporters, biosynthesis of amino acids, and response to stress, and amino sugar and nucleotide sugar metabolism were significantly upregulated in 3-day biofilm. In contrast, proteins associated with virulence factors, microbial metabolism in diverse environments, secondary metabolites, translation, and energy metabolism were significantly downregulated. GO functional annotation indicated that more proteins are involved in metabolic processes, catalytic activity, and binding in biofilm, respectively. Among the significantly dysregulated proteins, hyaluronidase (hysA) in conjunction with chitinase may play a significant role in the elimination and/or prevention of biofilm development. This study advances the understanding of the S. aureus subproteome, identifying potential pathways significant to biofilm biology. The insights gained may aid in developing new therapeutic strategies, including antibiofilm agents, for treating biofilm-related infections associated with implantable medical devices.

Green leadership is a new approach that has entered the field of human resource management with the importance of finding the environmental category in sustainable development. This issue mainly deals with the motivational and guiding aspects of human resources towards environmental care and the development of green culture, Based on this, the current research was conducted with the aim of identifying the themes of human resource management with a green leadership approach in the textile and leather industries of Baghdad with a qualitative strategy. The research method was thematic analysis . The data collection tool was a semi-structured and in-depth interview, which was analyzed using open and axial coding. The research community was (university expert professors related to the subject of human resources and green leadership, entrepreneurs and managers of the textile and leather industry in Baghdad), Returning to interviewees and external auditors was used for validation. The results showed that human resource management with a green leadership approach as a comprehensive theme includes 6 themes of effective communication, environmental skills, green organization, sustainable development, green leadership and green motivation with 37 components in the form of basic themes.

Abstract Background Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. Methods Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. Results The trained models’ micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80–78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). Conclusions BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Abstract Background Familial calcific band-shaped keratopathy (BSK) is a very rare disease, with no underlying cause. There is no underlying disease in this form of the disease. This article introduces a family with seven children, three of whom were diagnosed with familial primary calcific BSK. One of them developed a systemic disease 38 years after ocular manifestation. Case presentation In this case report, three Iranian siblings from a family with familial calcific band-shaped keratopathy (BSK) are introduced. Systemic and ocular examinations performed on these patients indicated the occurrence of chronic kidney disease in the older child, a 41-year-old woman, 38 years after ocular manifestation. The examinations conducted on the other two siblings revealed no pathological findings. The 41-year-old sister and 37-year-old brother underwent unilateral deep anterior lamellar keratoplasty (DALK), while the 33-year-old sister underwent bilateral superficial keratectomy (SK). Conclusion Considering the late onset of systemic disease in one of the siblings diagnosed with familial calcific band-shaped keratopathy (BSK), it is crucial to emphasize the necessity of long-term follow-up for these patients and their families.

Controlling wheel slip during braking in vehicle tires is a challenging task due to the complex behavior and highly nonlinear dynamics involved. Uncertainties arising from parameter variations and un-modeled dynamics further complicate the control process, along with actuator saturation. This article introduces a novel approach for controlling vehicle antilock braking systems (VABSs) using a robust adaptive (RA) beta basis function (BBF) neural network (NN) framework. The BBF-NN is capable of approximating complex functions and is employed as both an online approximator for unknown nonlinear functions and an actuator saturation compensator. This framework addresses the challenges posed by undefined models, nonlinearity, and uncertainties associated with VABS. The BBF-NN is trained online and its stability is verified using the Lyapunov theory. The performance of the BBF-NN is greatly influenced by its parameter tuning. To address this, the Coronavirus disease optimization algorithm (COVIDOA) is employed to determine the constant parameters of the RA-BBF-NN. The optimization results demonstrate that COVIDOA outperforms other optimization algorithms. The hybrid RA-BBF-NN framework, optimized by COVIDOA, exhibits superior performance compared to alternative methods, as confirmed by the results.

Objective Early detection of cancer is crucial for reducing the global burden of cancer, but effective screening tests for many cancers do not exist. This study aimed to develop a novel proteome-based multi-cancer screening test that can detect early-stage cancers with high accuracy.Methods and analysis We collected plasma samples from 440 individuals, healthy and diagnosed with 18 early-stage solid tumours. Using proximity extension assay, we measured more than 3000 high-abundance and low-abundance proteins in each sample. Then, using a multi-step statistical approach, we identified a limited set of sex-specific proteins that could detect early-stage cancers and their tissue of origin with high accuracy.Results Our sex-specific cancer detection panels consisting of 10 proteins showed high accuracy for both males (area under the curve (AUC): 0.98, 95% CI 0.96, 1) and females (AUC: 0.983, 95% CI 0.95, 1.00). At stage I and at the specificity of 99%, our panels were able to identify 93% (95% CI 79%, 100%) of cancers among males and 84% (95% CI 68%, 100%) of cancers among females. Our sex-specific localisation panels consisted of 150 proteins and were able to identify the tissue of origin of most cancers in more than 80% of cases. The analysis of the plasma concentrations of proteins selected showed that almost all the proteins were in the low-concentration part of the human plasma proteome.Conclusion The proteome-based screening test showed promising performance compared with other technologies and could be a starting point for developing a new generation of screening tests for the early detection of cancer.

Abstract Extracellular vesicles (EVs) are nanosized vesicles with a lipid bilayer that are secreted by cells and play a critical role in cell‐to‐cell communication. Despite the promising reports regarding their diagnostic and therapeutic potential, the utilization of EVs in the clinical setting is limited due to insufficient information about their cargo and a lack of standardization in isolation and analysis methods. Considering protein cargos in EVs as key contributors to their therapeutic potency, we conducted a tandem mass tag (TMT) quantitative proteomics analysis of three subpopulations of mesenchymal stem cell (MSC)‐derived EVs obtained through three different isolation techniques: ultracentrifugation (UC), high‐speed centrifugation (HS), and ultracentrifugation on sucrose cushion (SU). Subsequently, we checked EV marker expression, size distribution, and morphological characterization, followed by bioinformatic analysis. The bioinformatic analysis of the proteome results revealed that these subpopulations exhibit distinct molecular and functional characteristics. The choice of isolation method impacts the proteome of isolated EVs by isolating different subpopulations of EVs. Specifically, EVs isolated through the high‐speed centrifugation (HS) method exhibited a higher abundance of ribosomal and mitochondrial proteins. Functional apoptosis assays comparing isolated mitochondria with EVs isolated through different methods revealed that HS‐EVs, but not other EVs, induced early apoptosis in cancer cells. On the other hand, EVs isolated using the sucrose cushion (SU) and ultracentrifugation (UC) methods demonstrated a higher abundance of proteins primarily involved in the immune response, cell‐cell interactions and extracellular matrix interactions. Our analyses unveil notable disparities in proteins and associated biological functions among EV subpopulations, underscoring the importance of meticulously selecting isolation methods and resultant EV subpopulations based on the intended application.

In this article, a simplified type-2 (ST2) radial basis function (RBF) based neuroadaptive technique for controlling an automotive electric power steering (AEPS) system is designed. The dynamics of the AEPS are assumed to be unknown and the system is subjected to certain disturbances. A ST2-RBF system is proposed for approximating the unknown nonlinear functions. The ST2-RBF parameters are tuned online based on the adaptation laws obtained via Lyapunov stability analysis. A robust observer is also used in this process. The effects of uncertainties as well as approximation and estimation errors are compensated by means of an adaptive component. The parameters of the robust observer-based neuroadaptive ST2-RBF network are optimally determined by applying the Coronavirus disease optimization algorithm (COVIDOA), which mimics the replication mechanism of Coronaviruses taking over the human cells. The results indicate that the COVIDOA can reduce the cost function for neuroadaptive ST2-RBF controller compared to other strategies. Comparison of numerical results is presented to show the efficacy of the suggested technique. Interestingly, based on implementation results, the designed methodology is able to control the AEPS system successfully.

Abstract Background Trichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized. Results To address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei. Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae. The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose. Conclusion This study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.

Abstract In this paper, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected by means of a dashboard‐installed mobile phone. After processing these images by the blind referenceless image spatial quality evaluator technique, the substandard and inferior data have been automatically eliminated. In the second step, based on the YOLOv5 with several different baseline models, an algorithm has been developed for detecting the road surface damages. In the third step, by using the traditional as well as the bounding box augmentation and road damage generative adversarial network based augmentation techniques, the precision and the robustness of road damage detector under different environmental and field conditions have been improved. Finally, through the ensemble of the best models, the final detector accuracy has been enhanced. The findings of this article indicate that by using the proposed approach, the values of mAP and F1‐score are improved by 13.79% and 7.58%, respectively. The dataset and parts of the code are available at: https://github.com/IranRoadDamageDataset/IRRDD.

The existing concerns about the global warming have prompted many researchers to study the warm mix asphalt (WMA) mixtures. Although WMA enjoys several advantages over the hot mix asphalt (HMA), it suffers from certain flaws such as moisture susceptibility and low temperature cracking. Therefore, it is important to enhance the performance of WMA, and one way of doing it is to use suitable fillers in the asphalt mixtures. The study aim is to evaluate the impact of the Portland cement type-II, fly ash, and the calcium carbonate as filler, on the WMA performance that made with Kaowax additive. We performed the semi-circular bending (SCB) test at low temperature, the four-point bending fatigue test, the indirect tensile strength (ITS), resilient modulus tests at medium temperature, and the dynamic creep test at high temperature. According to the obtained results, the presence of Kaowax increased resilient modulus and the rutting strength of the asphalt mix. The WMA samples containing the fly ash filler had good moisture susceptibility. The use of cement filler in the WMA resulted in improved rutting resistance. The calcium carbonate filler increased the performance of the WMA mixtures at all temperatures.