Search results for: miRNA:mRNA target prediction

Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Rashmi Gupta

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Rewarding stimuli capture attention involuntarily as a result of an association process that develops quickly during value learning, referred to as the reward or value-driven attentional capture. It is essential to compare reward with punishment processing to get a full picture of value-based modulation in visual attention processing. Hence, the present study manipulated both valence/value (reward as well as punishment) and motivational salience (probability of an outcome: high vs. low) together. Series of experiments were conducted, and there were two phases in each experiment. In phase 1, participants were required to learn to associate specific face stimuli with a high or low probability of winning or losing points. In the second phase, these conditioned stimuli then served as a distractor or prime in a speeded letter search task. Faces with high versus low outcome probability, regardless of valence, slowed the search for targets (specifically the left visual field target) and suggesting that the costs to performance on non-emotional cognitive tasks were only driven by motivational salience (high vs. loss) associated with the stimuli rather than the valence (gain vs. loss). It also suggests that the processing of motivationally salient stimuli is right-hemisphere biased. Together, results of these studies strengthen the notion that our visual attention system is more sensitive to affected by motivational saliency rather than valence, which termed here as motivational-driven attentional capture.

Keywords: attention, distractors, motivational salience, valence

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: aerothermoelasticity, elastic deformation, structural temperature, multi-field coupling

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Authors: Fadilah Aleanizy

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Colicins are a family of bacterial toxins that kill Escherichia coli and other closely related species. The mode of action of colicins involves binding to an outer membrane receptor and translocation across the cell envelope, leading to cytotoxicity through specific targets. The mechanism of colicin cytotoxicity includes a non-specific endonuclease activity or depolarization of the cytoplasmic membrane by pore-forming activity. For Group A colicins, translocation requires an interaction between the N-terminal domain of the colicin and a series of membrane- bound and periplasmic proteins known as the Tol system (TolB, TolR, TolA, TolQ, and Pal and the active domain must be translocated through the outer membranes. Protein-protein interactions are intrinsic to virtually every cellular process. The transient protein-protein interactions of the colicin include the interaction with much more complicated assemblies during colicin translocation across the cellular membrane to its target. The potassium release assay detects variation in the K+ content of bacterial cells (K+in). This assays is used to measure the effect of pore-forming colicins such as ColA on an indicator organism by measuring the changes of the K+ concentration in the external medium (K+out ) that are caused by cell killing with a K+ selective electrode. One of the goals of this work is to employ a quantifiable in-vivo method to spot which Tol protein are more implicated in the interaction with colicin A as it is translocated to its target.

Keywords: K+ efflux, Colicin A, Tol-proteins, E. coli

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Liu Jia’nan

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The default accounts hold the view that there exists a kind of scalar implicature which can be processed without context and own a psychological privilege over other scalar implicatures which depend on context. In contrast, the Relevance Theorist regards context as a must because all the scalar implicatures have to meet the need of relevance in discourse. However, in Katsos, the experimental results showed: Although quantitatively the adults rejected under-informative utterance with lexical scales (context-independent) and the ad hoc scales (context-dependent) at almost the same rate, adults still regarded the violation of utterance with lexical scales much more severe than with ad hoc scales. Neither default account nor Relevance Theory can fully explain this result. Thus, there are two questionable points to this result: (1) Is it possible that the strange discrepancy is due to other factors instead of the generation of scalar implicature? (2) Are the ad hoc scales truly formed under the possible influence from mental context? Do the participants generate scalar implicatures with ad hoc scales instead of just comparing semantic difference among target objects in the under- informative utterance? In my Experiment 1, the question (1) will be answered by repetition of Experiment 1 by Katsos. Test materials will be showed by PowerPoint in the form of pictures, and each procedure will be done under the guidance of a tester in a quiet room. Our Experiment 2 is intended to answer question (2). The test material of picture will be transformed into the literal words in DMDX and the target sentence will be showed word-by-word to participants in the soundproof room in our lab. Reading time of target parts, i.e. words containing scalar implicatures, will be recorded. We presume that in the group with lexical scale, standardized pragmatically mental context would help generate scalar implicature once the scalar word occurs, which will make the participants hope the upcoming words to be informative. Thus if the new input after scalar word is under-informative, more time will be cost for the extra semantic processing. However, in the group with ad hoc scale, scalar implicature may hardly be generated without the support from fixed mental context of scale. Thus, whether the new input is informative or not does not matter at all, and the reading time of target parts will be the same in informative and under-informative utterances. People’s mind may be a dynamic system, in which lots of factors would co-occur. If Katsos’ experimental result is reliable, will it shed light on the interplay of default accounts and context factors in scalar implicature processing? We might be able to assume, based on our experiments, that one single dominant processing paradigm may not be plausible. Furthermore, in the processing of scalar implicature, the semantic interpretation and the pragmatic interpretation may be made in a dynamic interplay in the mind. As to the lexical scale, the pragmatic reading may prevail over the semantic reading because of its greater exposure in daily language use, which may also lead the possible default or standardized paradigm override the role of context. However, those objects in ad hoc scale are not usually treated as scalar membership in mental context, and thus lexical-semantic association of the objects may prevent their pragmatic reading from generating scalar implicature. Only when the sufficient contextual factors are highlighted, can the pragmatic reading get privilege and generate scalar implicature.

Keywords: scalar implicature, ad hoc scale, dynamic interplay, default account, Mandarin Chinese processing

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Authors: Noury Bakrim

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From the point of view of language morphodynamics, interpretative Readability of the text-result (the stasis) is not the external hermeneutics of its various potential reading events but the paradigmatic, semantic immanence of its dynamics. In other words, interpretative Readability articulates the potential tension between projection (intentionality of the discursive event) and the result (Readability within the syntagmatic stasis). We then consider that translation represents much more a metalinguistic conversion of neurocognitive bilingual sub-routines and modular relations than a semantic equivalence. Furthermore, the actualizing Readability (the process of rewriting a target text within a target language/genre) builds upon the descriptive level between the generative syntax/semantic from and its paradigmatic potential translatability. Translation corpora reveal the evidence of a certain focusing on the positivist stasis of the source text at the expense of its interpretative Readability. For instance, Fluchere's brilliant translation of Miller's Tropic of cancer into French realizes unconsciously an inversion of the hierarchical relations between Life Thought and Fable: From Life Thought (fable) into Fable (Life Thought). We could regard the translation of Bernard Kreiss basing on Canetti's work die englischen Jahre (les annees anglaises) as another inversion of the historical scale from individual history into Hegelian history. In order to describe and test both translation process and result, we focus on the pedagogical practice which enables various principles grounding in interpretative/actualizing Readability. Henceforth, establishing the analytical uttering dynamics of the source text could be widened by other practices. The reversibility test (target - source text) or the comparison with a second translation in a third language (tertium comparationis A/B and A/C) point out the evidence of an impossible event. Therefore, it doesn't imply an uttering idealistic/absolute source but the irreducible/non-reproducible intentionality of its production event within the experience of world/discourse. The aim of this paper is to conceptualize translation as the tension between interpretative and actualizing Readability in a new approach grounding in morphodynamics of language and Translatability (mainly into French) within literary and non-literary texts articulating theoretical and described pedagogical corpora.

Keywords: readability, translation as deverbalization, translation as conversion, Tertium Comparationis, uttering actualization, translation pedagogy

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Authors: Jana Beresova

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The paper presents a study carried out in 2015-2016 within the national scheme of research - VEGA 1/0106/15 based on theoretical research and empirical verification of the concept of intercultural communicative competence. It focuses on the current conception concerning target languages teaching compatible with the Common European Framework of Reference for Languages: Learning, teaching, assessment. Our research had revealed how the concept of intercultural communicative competence had been perceived by secondary-school teachers of English in Slovakia before they were intensively trained. Intensive workshops were based on the use of both authentic and instructional materials with the goal to support interculturally oriented language teaching aimed at challenging thinking. The former concept that supported the development of the students´ linguistic knowledge and the use of a target language to obtain information about the culture of the country whose language learners were learning was expanded by the meaning-making framework which views language as a typical means by which culture is mediated. The goal of the workshop was to influence English teachers to better understand the concept of intercultural communicative competence, combining theory and practice optimally. The results of the study will be presented and analysed, providing particular recommendations for language teachers and suggesting some changes in the National Educational Programme from which English learners should benefit in their future studies or professional careers.

Keywords: authentic materials, English language teaching, instructional materials, intercultural communicative competence

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Authors: Akhmadjon Sultanov, Eun-Ho Lee, Hye-Jin Park, Young-Je Cho

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This study tested the anti-inflammatory effect of wild indigo (Baptisia tinctoria) root in Raw 264.7 cells. We prepared two extracts of B. tinctoria; one in water and the other in 50% ethanol. Then we evaluated the toxicities of the B. tinctoria root extracts at 10 to 100 mg mL-1 concentrations in raw 264.7 cells and observed 80% cell viability. The anti-inflammatory effect of B. tinctoria root extract in lipopolysaccharide (LPS)-stimulated Raw 264.7 cells were observed with concentrations at 10, 30, and 50 μg mL-1. The results showed that 77.27-66.82% of nitric oxide (NO) production was inhibited by 50 μg mL-1 B. tinctoria root extract. The protein expression of Inducible NO synthase (iNOS) expression dramatically decreased by 93.14% and 52.65% in raw 264.7 cells treated with water and ethanol extracts of B. tinctoria root, respectively. Moreover, cyclooxygenase-2 (COX-2) protein expression decreased by 42.85% and 69.70% in raw 264.7 cells treated with water and ethanol extracts of B. tinctoria root, respectively. Furthermore, the mRNA expression of pro-inflammatory markers, such as tumor necrosis factor-alpha, interleukin-1β, interleukin-6, monocyte chemoattractant protein-1, and prostaglandin E synthase 2, was significantly suppressed in a concentration-dependent manner. Additionally, the B. tinctoria root extracts effectively inhibited enzymes involved in physiological activities. The B. tinctoria root extracts showed excellent anti-inflammatory effects and can be used as a functional material for biological activities.

Keywords: cytokine, macrophage, pro-inflammatory, protein expression, real-time PCR

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Authors: Reetika Sharma, Lalit Tejwani, Himanshu Shekhar, Arun Singhvi

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Purpose: To describe a novel technique of trabeculectomy targeting triple modulation on wound healing to increase the overall success rate. Method: Ten eyes of 10 patients underwent trabeculectomy with subconjunctival mitomycin C (0.4mg/ml for 4 minutes) application combined with Ologen implantation subconjunctivally and subsclerally. Five of these patients underwent additional phacoemulsification with intraocular lens implantation. The Ologen implant was wet with 0.1 ml Bevacizumab. Result: All the eyes achieved target intraocular pressure (IOP), which was maintained until one year of follow-up. Two patients needed anterior chamber reformation at day two post surgery. One patient needed cataract surgery after four months of surgery and achieved target intraocular pressure on two topical antiglaucoma medicines. Conclusion: Vascular endothelial growth factor (VEGF) concentration has been seen to increase in the aqueous humor after filtration surgery. Ologen implantation helps in collagen remodelling, antifibroblastic response, and acts as a spacer. Bevacizumab augmented Ologen, in addition, targets the increased VEGF and helps in decreasing scarring. Anti-VEGF augmented Ologen in trabeculectomy with mitomycin C (MMC) hence appears to have encouraging short-term intraocular pressure control.

Keywords: ologen, anti-VEGF, trabeculectomy, scarring

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Authors: Saekil Yun, Sib Sankar Giri, Jin Woo Jun, Hyoun Joong Kim, Sang Guen Kim, Sang Wha Kim, Jung Woo Kang, Se Jin Han, Se Chang Park

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In aquaculture, vaccination is important to control and prevent diseases. In the study, we utilized poly D,L-lactide-co-glycolic acid (PLGA) microparticles (MPs) for encapsulating formalin-killed Aeromonas hydrophila cells. To assess the innate and adaptive immune responses, carps and loaches were used for the experiments. Fish were divided into three groups (A, B, C). Total antigen of 0.1 ml vaccine was adjusted by 2 x 108 CFU and injected via intraperitoneal route. Group A was vaccinated with 0.1 ml of PLGA vaccine, group B was with 0.1 ml of FKC vaccine and group C was with 0.1 ml of sterile PBS. All three groups were challenged with A. hydrophila and challenge dose was lethal dose (LD50). Loaches and carp were then challenged with A. hydrophila at 12 and 20 weeks post vaccination (wpv), and 10 and 14 wpv, respectively, and relative survival rates were calculated. For both fish species, the curve of antibody titer over time was shallower in the PLGA group than the FKC group and the PLGA groups demonstrated higher survival rates at all time-points. In the groups of PLGA-MP, relative mRNA levels of IL-1β, TNF-α, lysozyme C and IgM were significantly upregulated than FKC treated groups. Biodegradable PLGA microparticle vaccine could induce longer immune responses than original FKC vaccines to protect from A. hydrophila infection.

Keywords: PLGA, microparticles, Aeromonas hydrophila, vaccine

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Authors: Mohamed Mahdi Alshahni, Takashi Tamura, Koichi Makimura

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Objective: The objective of this study is to evaluate roles of ABC-type efflux transporters in the resistance of Candida auris against common antifungal agents. Material and Methods: A wild-type C. auris strain and its antifungal resistant derivative strain that is generated through induction by antifungal agents were used in this study. The strains were cultured onto media containing beauvericin alone or in combination with azole agents. Moreover, expression levels of four ABC-type transporter’s homologs in those strains were analyzed by real time PCR with or without antifungal stress by fluconazole or voriconazole. Results: Addition of beauvericin helped to partially restore the susceptibility of the resistant strain against fluconazole, suggesting participation of ABC-type transporters in the resistance mechanism. Real time PCR results showed that mRNA levels of three out of the four analyzed transporters in the resistant strain were more than 2-fold higher than their counterparts in the wild-type strain under negative control and antifungal agent-containing conditions. Conclusion: C. auris is an emerging multidrug-resistant pathogen causing human mortality worldwide. Providing effective treatment has been hampered by the resistance to antifungal drugs, demanding understanding the resistance mechanism in order to devise new therapeutic strategies. Our data suggest a partial contribution of ABC-type transporters to the resistance of this pathogen.

Keywords: resistance, C. auris, transporters, antifungi

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Authors: Ahmed Alghurabi, Mysara Mohyaldinn, Shiferaw Jufar, Obai Younis, Abdullah Abduljabbar

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Erosion modeling equations were typically acquired from regulated experimental trials for solid particles entrained in single-phase or multi-phase flows. Evidently, those equations were later employed to predict the erosion damage caused by the continuous impacts of solid particles entrained in streamflow. It is also well-known that the particle impact angle and velocity do not change drastically in gas-sand flow erosion prediction; hence an accurate prediction of erosion can be projected. On the contrary, high-density fluid flows, such as water flow, through complex geometries, such as sand screens, greatly affect the sand particles’ trajectories/tracks and consequently impact the erosion rate predictions. Particle tracking models and erosion equations are frequently applied simultaneously as a method to improve erosion visualization and estimation. In the present work, computational fluid dynamic (CFD)-based erosion modeling was performed using a commercially available software; ANSYS Fluent. The continuous phase (water flow) behavior was simulated using the realizable K-epsilon model, and the secondary phase (solid particles), having a 5% flow concentration, was tracked with the help of the discrete phase model (DPM). To accomplish a successful erosion modeling, three erosion equations from the literature were utilized and introduced to the ANSYS Fluent software to predict the screen wire-slot velocity surge and estimate the maximum erosion rates on the screen surface. Results of turbulent kinetic energy, turbulence intensity, dissipation rate, the total pressure on the screen, screen wall shear stress, and flow velocity vectors were presented and discussed. Moreover, the particle tracks and path-lines were also demonstrated based on their residence time, velocity magnitude, and flow turbulence. On one hand, results from the utilized erosion equations have shown similarities in screen erosion patterns, locations, and DPM concentrations. On the other hand, the model equations estimated slightly different values of maximum erosion rates of the wire-wrapped screen. This is solely based on the fact that the utilized erosion equations were developed with some assumptions that are controlled by the experimental lab conditions.

Keywords: CFD simulation, erosion rate prediction, material loss due to erosion, water-sand flow

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: Yen-Ni Teng, Hsing-Yi Chen, Hsien-An Pan, Yung-Ming Lin, Hany A. Omar, Jui-Hsiang Hung

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Leucine-rich repeats and WD repeat domain containing 1 (LRWD1) is highly expressed in the testes of healthy males. On the other hand, LRWD1 is significantly down-regulated in the testicular tissues of patients with severe spermatogenic defects. In our study, the downregulation of LRWD1 expression by shRNA caused a significant reduction of cell growth and mitosis and a noteworthy increase in the cell microtubule atrophy rate. Here, we used EMBOSS CpG plot analysis to explore the promoter region of LRWD1 gene. We found that CpG islands are located between positions -253 to +5 nucleotides upstream from the LRWD1 transcription start site. Luciferase reporter assay revealed that the hypermethylation of the LRWD1 promoter reduced the transcription activity in cells. In addition, quantitative methylation-specific PCR and immunostaining showed that the methylation inhibitor, 5-Aza-2'-deoxycytidine, increased LRWD1 promoter activity, LRWD1 mRNA, protein expression and cell viability. Whereas, the methylation activator, S-adenosylmethionine, caused opposite effects. The overexpression of p53 and Nrf2 in NT2/D1 cells increased LRWD1 promoter activity while 5-fluorodeoxyuridine decreased it. In conclusion, this study highlights evidence that the methylation status of LRWD1 promoter is associated with LRWD1 expression. Since the expression level of LRWD1 plays an important role in spermatogenesis, the methylation status of LRWD1 may serve as a novel molecular diagnostic or therapeutic approach in male's infertility.

Keywords: LRWD1, DNA methylation, p53, Nrf2

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: So Youn Park, Yi Sle Lee, Ki Whan Hong, Chi Dae Kim

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The role of metabolism in the pathogenesis of osteoarthritis is an emerging field. Metabolic alterations may be a role in osteoarthritis (OA) pathogenesis, and these changes influence joint destruction via several cytokine. Especially, in OA patients, levels of IL-1β are elevated in the synovial fluid, synovial membrane, subchondral bone, and cartilage. The IL-1β is activated by NLRP3 inflammasomes, and NLRP3 inflammasomes are cytosolic complexes that drive the production of other inflammatory cytokines, including IL-1β. In this study, we examined that SIRT1 suppresses IL-1β through inhibiting NLRP3 inflammasomes and SIRT1 ameliorates osteoarthritis. OA fibroblasts were isolated from synovium of OA patients. IL-1β and NLRP3 were detected in synovium of OA patients by immunohistochemistry. Lipopolysaccharides (LPS) stimulated the expression of active IL-1β mRNA in OA fibroblasts and combination of LPS, and adenosine triphosphate increased more the expression of active IL-1β in OA fibroblasts. The level of IL-1β was measured by western blot and ELISA assay. NLRP3 inflammasomes complex were measured by western blot. SIRT1 did not inhibit expression of NLRP3 inflammasome. So caspase-1, apoptotic speck-like protein containing a caspase recruitment domain (ASC) and NLRP3 protein were expressed in OA fibroblasts. But SIRT1 suppressed activation of IL-1β by inhibiting activity of caspase-1 via NLRP3 inflammasome in OA fibroblasts under LPS plus ATP stimulation. These results suggest that SIRT1 is a modulator of NLRP3 inflammasomes in OA fibroblasts and ameliorate IL-1β, so expression of SIRT1 in OA fibroblast may be a potential strategy for OA inflammation treatment.

Keywords: osteoarthritis, inflammasome, SIRT1, IL-1beta

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Authors: Han Xiao

Abstract:

The development of nanomedicines has recently achieved several breakthroughs in the field of cancer treatment; however, the biocompatibility and targeted burst release of these medications remain a limitation, which leads to serious side effects and significantly narrows the scope of their applications. The self-assembly of intermediate filament protein (IFP) peptides was triggered by a hydrophobic cation drug 7-amino actinomycin D (7-AAD) to synthesize pH-activatable nanoparticles (NPs) that could simultaneously locate tumors and produce antitumor effects. The designed IFP peptide included a target peptide (arginine–glycine–aspartate), a negatively charged region, and an α-helix sequence. It also possessed the ability to encapsulate 7-AAD molecules through the formation of hydrogen bonds and hydrophobic interactions by a one-step method. 7-AAD molecules with excellent near-infrared fluorescence properties could be target delivered into tumor cells by NPs and released immediately in the acidic environments of tumors and endosome/lysosomes, ultimately inducing cytotoxicity by arresting the tumor cell cycle with inserted DNA. It is noteworthy that the IFP/7-AAD NPs tail vein injection approach demonstrated not only high tumor-targeted imaging potential, but also strong antitumor therapeutic effects in vivo. The proposed strategy may be used in the delivery of cationic antitumor drugs for precise imaging and cancer therapy.

Keywords: 7-amino actinomycin D, intermediate filament protein, nanoparticle, tumor image

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Authors: Ahmed Z. Mazen, Nejat Rahmanian, Iqbal Mujtaba, Ali Hassanpour

Abstract:

In oil and gas well drilling, the drill bit is an important part of the Bottom Hole Assembly (BHA), which is installed and designed to drill and produce a hole by several mechanisms. The efficiency of the bit depends on many drilling parameters such as weight on bit, rotary speed, and mud properties. When the bit is pulled out of the hole, the evaluation of the bit damage must be recorded very carefully to guide engineers in order to select the bits for further planned wells. Having a worn bit for hole drilling may cause severe damage to bit leading to cutter or cone losses in the bottom of hole, where a fishing job will have to take place, and all of these will increase the operating cost. The main factor to reduce the cost of drilling operation is to maximize the rate of penetration by analyzing real-time data to predict the drill bit wear while drilling. There are numerous models in the literature for prediction of the rate of penetration based on drilling parameters, mostly based on empirical approaches. One of the most commonly used approaches is Bourgoyne and Young model, where the rate of penetration can be estimated by the drilling parameters as well as a wear index using an empirical correlation, provided all the constants and coefficients are accurately determined. This paper introduces a new methodology to estimate the eight coefficients for Bourgoyne and Young model using the gPROMS parameters estimation GPE (Version 4.2.0). Real data collected form similar formations (12 ¼’ sections) in two different fields in Libya are used to estimate the coefficients. The estimated coefficients are then used in the equations and applied to nearby wells in the same field to predict the bit wear.

Keywords: Bourgoyne and Young model, bit wear, gPROMS, rate of penetration

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

Abstract:

Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

Procedia PDF Downloads 107

Authors: Liu Xiaohan

Abstract:

Epstein–Barr virus (EBV) is a human herpesvirus and is closely related to many malignancies of lymphocyte and epithelial origins, such as gastric cancer, Burkitt’s lymphoma, and nasopharyngeal carcinoma (NPC). NPC is a malignant epithelial tumor which is 100% associated with EBV latent infection. Most of the NPC cases are densely populated in southern China, especially in Guangdong and Hong Kong. To our knowledge, overexpression of pro-inflammatory cytokines may result in a loss of balance of the immune system and cause damage to human bodies. Interleukin-6 (IL6) is a pro-inflammatory cytokine which plays an important role in tumor progression. In addition, gene expression is regulated by both transcriptional and post-transcriptional pathways, while post-transcriptional regulation is an important mechanism to modulate the mature mRNA level in mammalian cells. AU-rich element binding factor 1 (AUF1)/heterogeneous nuclear RNP D (hnRNP D) is known for its function in destabilizing mRNAs, including cytokines and cell cycle regulators. Previous studies have found that overexpression of hnRNP D would lead to tumorigenesis. In this project, our aim is to determine the role played by hnRNP D in EBV-infected cells and how our anti-EBV agents can affect the function of hnRNP D. The results of this study will provide a new insight into how the pro-inflammatory cytokine expression can be regulated by EBV.

Keywords: interleukin 6 (IL6), epstein-barr virus (EBV), nasopharyngeal carcinoma (NPC, epstein-barr nuclear antigen-1 (EBNA1)

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Authors: Hae-Jun Lee, Ji-Sun Shin, Kyung-Tae Lee

Abstract:

Potentilla species (Rosasease) have been used in traditional medicine to treat different ailment, disease or malady. In this study, we investigated the anti-inflammatory effects of ethanol extracts of NUTT (EPP) in lipopolysaccharide (LPS)-induced Raw 264.7 macrophages and septic mice. EPP suppressed LPS-induced nitric oxide (NO) and prostaglandin E2 (PGE2) production in LPS-induced Raw 264.7 macrophages. Consistent with these observations, EPP reduced the expressions of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) by downregulation of their promoter activities. EPP inhibited tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) at production and mRNA levels. Molecularly, EPP attenuated the LPS-induced transcriptional activity, and DNA-binding activity of nuclear factor-κB (NF-κB), and this was associated with a decrease of translocation and phosphorylation of p65 NF-κB by inhibiting the inhibitory κB-α (IκB-α) degradation and IκB kinase-α/β (IKK-α/β) phosphorylation. Furthermore, EPP suppressed the LPS-induced activation of activator protein-1 (AP-1) by reducing the expression of c-Fos and c-Jun in nuclear. EPP also reduced the phosphorylation of mitogen-activated protein kinase (MAPK), such as p38 MAPK and c-Jun N-terminal kinase/stress-activated protein kinase (JNK). In a sepsis model, pretreatment with EPP reduced the LPS-induced lethality. Collectively, these results suggest that the anti-inflammatory effects of EPP were associated with the suppression of NF-κB and AP-1 activation, and support its possible therapeutic role for the treatment of sepsis.

Keywords: anti-inflammation, activator protein-1, nuclear factor κB, Potentilla paradoxa Nutt

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Authors: Koe Han Beng, Khoo Boo Cheong

Abstract:

The rising interest of stepped hull design has been led by the demand of more efficient high-speed boat. At the same time, the need of accurate prediction method for stepped planing hull is getting more important. By understanding the flow at high Froude number is the key in designing a practical step hull, the study surrounding stepped hull has been done mainly in the towing tank which is time-consuming and costly for initial design phase. Here the feasibility of predicting hydrodynamics of high-speed planing hull both with and without step using computational fluid dynamics (CFD) with the volume of fluid (VOF) methodology is studied in this work. First the flow around the prismatic body is analyzed, the force generated and its center of pressure are compared with available experimental and empirical data from the literature. The wake behind the transom on the keel line as well as the quarter beam buttock line are then compared with the available data, this is important since the afterbody flow of stepped hull is subjected from the wake of the forebody. Finally the calm water performance prediction of a conventional planing hull and its stepped version is then analyzed. Overset mesh methodology is employed in solving the dynamic equilibrium of the hull. The resistance, trim, and heave are then compared with the experimental data. The resistance is found to be predicted well and the dynamic equilibrium solved by the numerical method is deemed to be acceptable. This means that computational fluid dynamics will be very useful in further study on the complex flow around stepped hull and its potential usage in the design phase.

Keywords: planing hulls, stepped hulls, wake shape, numerical simulation, hydrodynamics

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Authors: Yulia Irnidayanti , J. J. Josua, A. Sugianto

Abstract:

Jakarta Bay with 13 rivers that flow into, the environment has deteriorated and is the most polluted bays in Asia. The entry of waste into the waters of the Bay of Jakarta has caused pollution. Heavy metal contamination has led to pollution levels and may cause toxicity to organisms that live in the sea, down to the cellular level and may affect the ecological balance. Various ways have been conducted to measure the impact of environmental degradation, such as by measuring the levels of contaminants in the environment, including measuring the accumulation of toxic compounds in the tissues of organisms. Biological responses or biomarkers known as a sensitive indicator but need relevant predictions. In heavy metal pollution monitoring, analysis of aquatic biota is very important from the analysis of the water itself. The content of metals in aquatic biota will usually always be increased from time to time due to the nature of metal bioaccumulation, so the aquatic biota is best used as an indicator of metal pollution in aquatic environments. The results of the content analysis results of sea water in coastal estuaries Angke, Kaliadem and Panimbang detected heavy metals cadmium, mercury, lead, but did not find zinc metal. Based on the results of protein electrophoresis methallotionein found heavy metals in the tissues hepatopancreas, gills and muscles, and also the mRNA expression of has detected.

Keywords: gills, heavy metal, hepatopancreas, metallothionein, muscle

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Authors: Muhammad Luthfi, Sutikno Sutikno, Purhadi Purhadi

Abstract:

Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport.

Keywords: Bayesian Model Averaging, ensemble forecast, geostatistical output perturbation, numerical weather prediction, temperature

Procedia PDF Downloads 267

Authors: Manuela Villani, Anila Xhahysa, Christopher Brooks, Marco Pagani

Abstract:

The geological structure of Western Balkans is strongly affected by the collision between Adria microplate and the southwestern Euroasia margin, resulting in a considerably active seismic region. The Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project (BSHAP) (2007-2011, 2012-2015) by NATO supported the preparation of new seismic hazard maps of the Western Balkan, but when inspecting the seismic hazard models produced later by these countries on a national scale, significant differences in design PGA values are observed in the border, for instance, North Albania-Montenegro, South Albania- Greece, etc. Considering the fact that the catalogues were unified and seismic sources were defined within BSHAP framework, obviously, the differences arise from the Ground Motion Prediction Equations selection, which are generally the component with highest impact on the seismic hazard assessment. At the time of the project, a modest database was present, namely 672 three-component records, whereas nowadays, this strong motion database has increased considerably up to 20,939 records with Mw ranging in the interval 3.7-7 and epicentral distance distribution from 0.47km to 490km. Statistical analysis of the strong motion database showed the lack of recordings in the moderate-to-large magnitude and short distance ranges; therefore, there is need to re-evaluate the Ground Motion Prediction Equation in light of the recently updated database and the new generations of GMMs. In some cases, it was observed that some events were more extensively documented in one database than the other, like the 1979 Montenegro earthquake, with a considerably larger number of records in the BSHAP Analogue SM database when compared to ESM23. Therefore, the strong motion flat-file provided from the Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project was merged with the ESM23 database for the polygon studied in this project. After performing the preliminary residual analysis, the candidate GMPE-s were identified. This process was done using the GMPE performance metrics available within the SMT in the OpenQuake Platform. The Likelihood Model and Euclidean Distance Based Ranking (EDR) were used. Finally, for this study, a GMPE logic tree was selected and following the selection of candidate GMPEs, model weights were assigned using the average sample log-likelihood approach of Scherbaum.

Keywords: residual analysis, GMPE, western balkan, strong motion, openquake

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Authors: Marina Passero, Tianhua Zhai, Zuyi (Jacky) Huang

Abstract:

Alzheimer’s disease has become a major public health issue, as indicated by the increasing populations of Americans living with Alzheimer’s disease. After decades of extensive research in Alzheimer’s disease, only seven drugs have been approved by Food and Drug Administration (FDA) to treat Alzheimer’s disease. Five of these drugs were designed to treat the dementia symptoms, and only two drugs (i.e., Aducanumab and Lecanemab) target the progression of Alzheimer’s disease, especially the accumulation of amyloid-b plaques. However, controversial comments were raised for the accelerated approvals of either Aducanumab or Lecanemab, especially with concerns on safety and side effects of these two drugs. There is still an urgent need for further drug discovery to target the biological processes involved in the progression of Alzheimer’s disease. Excessive cholesterol has been found to accumulate in the brain of those with Alzheimer’s disease. Cholesterol can be synthesized in both the blood and the brain, but the majority of biosynthesis in the adult brain takes place in astrocytes and is then transported to the neurons via ApoE. The blood brain barrier separates cholesterol metabolism in the brain from the rest of the body. Various proteins contribute to the metabolism of cholesterol in the brain, which offer potential targets for Alzheimer’s treatment. In the astrocytes, SREBP cleavage-activating protein (SCAP) binds to Sterol Regulatory Element-binding Protein 2 (SREBP2) in order to transport the complex from the endoplasmic reticulum to the Golgi apparatus. Cholesterol is secreted out of the astrocytes by ATP-Binding Cassette A1 (ABCA1) transporter. Lipoprotein receptors such as triggering receptor expressed on myeloid cells 2 (TREM2) internalize cholesterol into the microglia, while lipoprotein receptors such as Low-density lipoprotein receptor-related protein 1 (LRP1) internalize cholesterol into the neuron. Cytochrome P450 Family 46 Subfamily A Member 1 (CYP46A1) converts excess cholesterol to 24S-hydroxycholesterol (24S-OHC). Cholesterol has been approved for its direct effect on the production of amyloid-beta and tau proteins. The addition of cholesterol to the brain promotes the activity of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), secretase, and amyloid precursor protein (APP), which all aid in amyloid-beta production. The reduction of cholesterol esters in the brain have been found to reduce phosphorylated tau levels in mice. In this work, a computational pipeline was developed to identify the protein targets involved in cholesterol regulation in brain and further to identify chemical compounds as the inhibitors of a selected protein target. Since extensive evidence shows the strong correlation between brain cholesterol regulation and Alzheimer’s disease, a detailed literature review on genes or pathways related to the brain cholesterol synthesis and regulation was first conducted in this work. An interaction network was then built for those genes so that the top gene targets were identified. The involvement of these genes in Alzheimer’s disease progression was discussed, which was followed by the investigation of existing clinical trials for those targets. A ligand-protein docking program was finally developed to screen 1.5 million chemical compounds for the selected protein target. A machine learning program was developed to evaluate and predict the binding interaction between chemical compounds and the protein target. The results from this work pave the way for further drug discovery to regulate brain cholesterol to combat Alzheimer’s disease.

Keywords: Alzheimer’s disease, drug discovery, ligand-protein docking, gene-network analysis, cholesterol regulation

Procedia PDF Downloads 51