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Viruses manipulate host cellular machinery to propagate their life cycle, with one key strategy being the mimicry of short linear motifs (SLiMs) found in host proteins. While databases continue to expand with virus–host protein–protein interaction (vhPPI) data, accurately predicting viral mimicry remains challenging due to the inherent degeneracy of SLiMs. In this study, we investigate how viral genomic composition influences motif mimicry and the mechanisms through which viruses hijack host cellular functions. We assessed domain–motif interaction (DMI) enrichment differences, and also predicted new DMIs based on known viral motifs with varying stringency levels, using SLiMEnrich v.1.5.1. Our findings reveal that dsDNA viruses capture significantly more known DMIs compared to other viral groups, with dsRNA viruses also exhibiting higher DMI enrichment than ssRNA viruses. Additionally, we identified new vhPPIs mediated via SLiMs, particularly within different viral genomic contexts. Understanding these interactions is vital for elucidating viral strategies to hijack host functions, which could inform the development of targeted antiviral therapies.

Lung cancer (LC) continues to pose the highest mortality and exhibits a common prevalence among all types of cancer. The genetic interaction between human eukaryotes and microbial cells plays a vital role in orchestrating every physiological activity of the host. The dynamic crosstalk between gut and lung microbiomes and the gut–lung axis communication network has been widely accepted as promising factors influencing LC progression. The advent of the 16s rDNA sequencing technique has opened new horizons for elucidating the lung microbiome and its potential pathophysiological role in LC and other infectious lung diseases using a molecular approach. Numerous studies have reported the direct involvement of the host microbiome in lung tumorigenesis processes and their impact on current treatment strategies such as radiotherapy, chemotherapy, or immunotherapy. The genetic and metabolomic cross‐interaction, microbiome‐dependent host immune modulation, and the close association between microbiota composition and treatment outcomes strongly suggest that designing microbiome‐based treatment strategies and investigating new molecules targeting the common holobiome could offer potential alternatives to develop effective therapeutic principles for LC treatment. This review aims to highlight the interaction between the host and microbiome in LC progression and the possibility of manipulating altered microbiome ecology as therapeutic targets. Host–microbiome interaction in lung cancer. Both local (lung) and distal (gut) microbiota play critical roles in lung cancer

Implantable subcutaneous chemoports are routinely employed for delivering chemotherapy in oncology. Spontaneous catheter dislodgement and embolization of the catheters are rare complications of the procedure. Herein, we report our experience with a patient presenting with spontaneous dislodgement and migration of the catheter to the pulmonary artery. The patient having familial adenomatous polyposis with adenocarcinoma of the right colon underwent total proctocolectomy and had placement of the chemoport through the internal jugular vein for adjuvant FOLFOX chemotherapy. The entrapped catheter was successfully managed by percutaneous retrieval by an interventional cardiologist.

Biomass gasification is regarded as one of the most promising energy recovery technologies. The gases produced from the biomass gasification can be used for heat and power production. Alternatively, they can be used for the production of liquid biofuel. Fixed bed downdraft gasification reactors are easier to use in small scale gasification processes. A three-dimensional Computational Particle Fluid Dynamic (CPFD) model has been developed to gain a better understanding of the gasification process in a fixed bed downdraft gasification reactor. The processes considered in the model are volatilization, partial combustion and gasification. Using the model, the product gas composition is calculated. The computational results are compared with published results from experimental measurements in a fixed bed gasification reactor. There are reasonable agreements between computational and experimental results confirming that the model can be used for further investigation of the fixed bed biomass gasification reactor. The model is used to investigate the effect of the air–fuel ratio on the product gas composition.

Experiments were performed in a pilot-scale bubbling fluidized bed gasification reactor with air as a fluidizing agent. Birch wood chips and sand particles were used as biomass and bed materials. Average molar product gas composition was 0.214 of CO, 0.212 of CO2, 0.103 of H2, 0.074 of CH4 and 0.397 of N2. A kinetics-based model was developed for the gasification process and simulated using commercial software Barracuda®. The model is validated against the measured gas compositions. The validated model was used to study the product gas compositions for olive waste and straw pellets. The effects of equivalence ratio (ER) on the product gas composition for birch wood was also studied in one of the simulations. Birch wood gave the highest (20.5 mole %) CO production rate and lowest (9.0 mole %) H2 production rate. The product gas flow rate was 1.96 Nm3 per kg of biomass and the lower heating value of the product gas was 6.65 MJ/Nm3. The CO concentration decreased from 25 to 13.2 mole %, whereas CO2 concentration increased from 17 to 19.5 mole % when increasing ER from 0.2 to 0.3. The CO and H2 concentrations for the olive waste were 8.1 and 56.1 mole %. The CO and H2 concentrations for the straw pellets were 6 and 73.4 mole %.

The fluidization behaviour depends on particle properties such as particle size, sphericity, density and the properties of the fluidizing agent. In this study, the effects of different particle sizes on fluidization behaviour were investigated. Experiments were done by mixing sand particles of mean diameter 293µm (small particle) and 750 µm (large particle). The experiment with 20% small particles and 80% large particles gave a reduction in minimum fluidization velocity of 60.8% compared to the minimum fluidization velocity with only large particles. CPFD simulations were performed using the commercial software barracuda®. There is a good agreement between the results from the experiments and the simulations. The minimum fluidization velocity is also calculated using different theoretical equations based on the average particle size for the mixture. The obtained experimental results were compared with the minimum fluidization velocity calculated using different equations available in the literature. There are significant differences in minimum fluidization velocities obtained from the different empirical equations. The pressure drop profiles for large and small particles follow the trends presented in the literature. The experimental minimum fluidization velocities were found to be 0.46 and 0.092 m/s for the large and small particles respectively.

Renewable energy sources have significant potential for limiting climate change and reducing greenhouse gas emissions due to the increased global energy demand. Fluidized bed gasification of biomass is a substantial contribution to meeting the global energy demand in a sustainable way. however, ash-related problems are the biggest challenge in fluidized bed gasification of biomass. bed agglomeration is a result of interaction between the bed material and alkali metals present in the biomass ash. The agglomerates interfere with the fluidization process and might result in total de-fluidization of the bed. The study focuses on ash challenges related to the fluidization behavior in gasification of biomass. a model is developed and verified against results from previous performed experiments in a cold flow model of a bubbling fluidized bed. The commercial computational particle fluid dynamics (CPFD) software barracuda Virtual reactor is used for the computational study. The simulations show that the CPFD model can predict the fluidization process of an agglomerated fluidized bed gasifier.

The efficiency of a fluidized bed reactor depends on the bed fluid dynamic behavior, which is significantly influenced by the bubble properties. This work investigates the bubble properties of a bubbling fluidized bed reactor using computational particle fluid dynamic (CPFD) simulations and electrical capacitance tomography (ECT) measurements. The two-dimensional images (along the reactor horizontal and vertical planes) of the fluidized bed are obtained from the CPFD simulations at different operating conditions. The CPFD model was developed in a commercial CPFD software Barracuda Virtual Reactor 20.0.1. The bubble behavior and bed fluidization behavior are characterized form the bubble properties: average bubble diameter, bubble rise velocity, and bubble frequency. The bubble properties were determined by processing the extracted images with script developed in MATLAB. The CPFD simulation results are compared with experimental data (obtained from the ECT sensors) and correlations in the literature. The results from the CPFD model and experimental measurement depicted that the average bubble diameter increased with an increase in superficial gas velocities up to 4.2 Umf and decreased with a further increase in gas velocities due to the onset of large bubbles (potential slugging regime). The bubble rise velocity increased as it moved from the lower region to the bed surface. The Fourier transform of the transient solid volume fraction illustrated that multiple bubbles pass the plane with varying amplitude and frequency in the range of 1–6 Hz. Further, the bubble frequency increased with an increase in superficial gas velocity up to 2.5Umf and decreased with a further increase in gas velocity. The CPFD model and method employed in this work can be useful for studying the influence of bubble properties on conversion efficiency of a gasification reactor operating at high temperatures.

The efficiency of a gasification process is directly related to the rate of biomass conversion into product gas. The rate of fuel conversion depends on the interaction of fuel with the bed material, the gasifying agent, and the residence time of fuel particles. The interactions and the residence time depend on the fuel feeding positions along the height of the reactor. Thus, the fuel feeding position in a gasification reactor is an important parameter that influences the efficiency of the gasification process; longer residence time of the fuel particles in the bed enables efficient carbon conversion and less tar formation. In this work, in-bed and on-bed feed positions of the fuel particles have been investigated using a computational particle fluid dynamics (CPFD) model. The model is developed and validated against experimental data obtained from a bubbling fluidized bed gasification reactor. Experiments were carried out in a 20 kW pilot scale bubbling fluidized bed gasification reactor. Wood pellets of 3–30 mm length and 5 mm diameter are fed into the reactor at a mass flow rate of 2.4 kg/h. The molar flow rate of the producer gas, which typically consists of CH4, CO, CO2, and H2 for both the in-bed and on-bed cases, is calculated by the CPFD model. The results show that CO and CH4 concentrations increase in the product gas when the biomass is fed at the location near to the bottom of the bed, while CO2 and H2 increase in the case of on-bed feed. The fuel particles segregate, followed by partial combustion of the smaller fuel particles on the bed surface in the case of on-bed feed. The total mass of the bed including unreacted char is higher for on-bed feed, indicating that the char is consumed slowly. The CPFD model can predict the product gas compositions, the fuel conversion, changes in the bed hydrodynamics, and the product gas yield at different feeding positions of the fuel particles. Thus, the model can be useful for design purposes.

This work presents a new method for identifying the bubbling regime of a fluidized bed gasification reactor. The method has been developed using experimental measurements and a computational model. Pressure drops are measured in experiments, and pressure drop as well as solid volume fraction fluctuations are calculated by implementing the model. experiments are carried out with sand and limestone particles of mean diameter 346 µm and 672 µm, respectively. A computational particle fluid dynamics (CPFD) model has been developed for the reactor and implemented using a commercial CPFD software Barracuda VR. The model is validated against experimental measurements. The validated model is used to analyse the fluctuation of pressure drop and solid volume fraction as a function of superficial air velocity. The change in standard deviation of pressure drop and solid volume fraction fluctuation is used to predict the transition from one regime to another. The method can be used in the design and operation of a bubbling fluidized bed gasification reactor. The results show that the minimum fluidization velocity for sand and limestone are 0.135 m/s and 0.36 m/s, respectively and are independent of the particle aspect ratio. Both types of particle beds make the transition into bubbling regime as soon as they get fluidized. The bed aspect ratios have almost no effect on the onset of bubbling fluidization regime. The slugging velocity decreases with increasing aspect ratio for both types of particles. The operating range of the bubbling fluidized bed for sand particle is 0.2–0.4 m/s and 0.5–0.8 m/s for the limestone particles.