Nanoparticle transport modelling in saturated porous media

University of Technology, Sydney. Faculty of Engineering and Information Technology.This research deals with multi-scale descriptions of nano-colloidal transport in saturated porous media. Colloidal transport has been simulated, historically, by employing a pore-scale model. I argue that the use of such simulations on a continuum-scale where formulations are generally phenomenological, may be unsuitable if at all possible due to requirements for pore-scale parameterization. I propose to up-scale the pore-scale equation by inclusion of natural heterogeneity of porous media which consequently substitutes the pore-scale parameters (often unobtainable in real cases) with continuum-scale parameters (measurable at field). This approach transforms the pore-scale formulation into a Darcy-scale formulation, making it usable for real-world simulations. I demonstrate a closer agreement with experimental data once porous media’s natural heterogeneity is considered compared with the use of a mean value for media grain size in the conventional methods. These results can be explained by noting the fact that hydraulic conductivity of a porous medium is not controlled by the coarser or the median size grains. Rather, it is the smaller grains which ultimately determine (or in other words, restrict) the permeability of any given porous medium. By comparing various modelled scenarios, I also assess the magnitude of difference in predicted results which displays a significant divergence from the case where the porous medium is assumed to be homogeneous. Finally I aim to estimate the uncertainty associated with scenarios A (Yao’s equation) and B (Mehrabi_ Milne-Home equation) in the absence and presence of natural heterogeneity, respectively. The results showed a noticeable decrease of 9% to 87% in the uncertainty caused by the most prominent source of uncertainty in groundwater modelling; porous media’s heterogeneity. The uncertainty is generally lower closer to the contaminant release point and increases as the plume moves away from the source point. The more substantial improvements (reduction of uncertainty) was observed at selected point which were located further away from the release point. A framework for the assessment of nanoparticle transport in aquifers follows in which the extent of movement is estimated based on available field measured data and the probabilities of various potential realizations can be measured. This will help provide a much needed set of information for the policy-making processes with regards to new and emerging contaminants including engineered nanoparticles

NEURAL STEM/PROGENITOR CELL TRANSPLANTATION FOR SPINAL CORD INJURY TREATMENT; A SYSTEMATIC REVIEW AND META-ANALYSIS

—Despite the vast improvements of cell therapy in spinal cord injury treatment, no optimum protocol has been developed for application of neural stem/progenitor cells. In this regard, the present meta-analysis showed that the effi- cacy of the neural stem/progenitor cell (NSPC) transplantation depends mainly on injury model, intervention phase, transplanted cell count, immunosuppressive use, and probably stem cell source. Improved functional recovery post NSPC transplantation was found to be higher in transection and contusion models. Moreover, NSPC transplantation in acute phase of spinal injury was found to have better functional recovery. Higher doses (>3 � 106 cell/kg) were also shown to be optimum for transplantation, but immunosuppressive agent administration negatively affected the motor function recovery. Scaffold use in NSPC transplantation could also effectively raise functional recovery. � 2016 Published by Elsevier Ltd. on behalf of IBR

Empowering Learning: Standalone, Browser-Only Courses for Seamless Education

Massive Open Online Courses (MOOCs) have transformed the educational landscape, offering scalable and flexible learning opportunities, particularly in data-centric fields like data science and artificial intelligence. Incorporating AI and data science into MOOCs is a potential means of enhancing the learning experience through adaptive learning approaches. In this context, we introduce PyGlide, a proof-of-concept open-source MOOC delivery system that underscores autonomy, transparency, and collaboration in maintaining course content. We provide a user-friendly, step-by-step guide for PyGlide, emphasizing its distinct advantage of not requiring any local software installation for students. Highlighting its potential to enhance accessibility, inclusivity, and the manageability of course materials, we showcase PyGlide's practical application in a continuous integration pipeline on GitHub. We believe that PyGlide charts a promising course for the future of open-source MOOCs, effectively addressing crucial challenges in online education

Diversity determination of CTX-M1 producing klebsiella pneumoniae using multilocus variable-number tandem repeat analysis, semnan, Iran

Background: CTX-M is the most prevalent and rapidly growing type of the extended-spectrum β-lactamase (ESBL) family and CTXM1 is the most common type of blaCTX-M. Objectives: The current study aimed at investigating the genetic diversity of CTX-M-1-producing Klebsiella pneumoniae circulating in Semnan, Iran evaluated by multilocus variable-number tandem repeat analysis (MLVA). Methods: A total of 110 isolates of K. pneumoniae were collected from different clinical samples. The antibiotic suceptibility and double disk synergy test were determined according to CLSI (the clinical and laboratory standards institute) guidelines. The polymerase chain reaction (PCR) method was performed to detect CTX-M-1. The eight loci for MLVA genotyping were selected along with the primers previously described. Results: Imipenem, with 84.7 susceptibility, was the most effective antibiotic against K. pneumoniae. Seventy (63.63) isolates had ESBL positive results and 42 (60 ) of them were positive for CTX-M-1 gene. Totally, 28 MLVA genotypes were discriminated, evaluation of diversity indexes (DIs) for eight loci showed that six different alleles were the most polymorphic and the most DI was 0.807. Conclusions: The findings of the current study demonstrated heterogeneity among CTX-M-1-producing K. pneumonia strains. The presence of CTX-M-1 in different MLVA types demonstrated that a certain clone is not responsible for spreading the isolates. © 2018, Author(s)

Nutrition and lung cancer: a case control study in Iran

Background: Despite many prospective and retrospective studies about the association of dietary habit and lung cancer, the topic still remains controversial. So, this study aims to investigate the association of lung cancer with dietary factors. Method: In this study 242 lung cancer patients and their 484 matched controls on age, sex, and place of residence were enrolled between October 2002 to 2005. Trained physicians interviewed all participants with standardized questionnaires. The middle and upper third consumer groups were compared to the lower third according to the distribution in controls unless the linear trend was significant across exposure groups. Result: Conditional logistic regression was used to evaluate the association with lung cancer. In a multivariate analysis fruit (Ptrend < 0.0001), vegetable (P = 0.001) and sunflower oil (P = 0.006) remained as protective factors and rice (P = 0.008), bread (Ptrend = 0.04), liver (P = 0.004), butter (Ptrend = 0.04), white cheese (Ptrend < 0.0001), beef (Ptrend = 0.005), vegetable ghee (P < 0.0001) and, animal ghee (P = 0.015) remained as risk factors of lung cancer. Generally, we found positive trend between consumption of beef (P = 0.002), bread (P < 0.0001), and dairy products (P < 0.0001) with lung cancer. In contrast, only fruits were inversely related to lung cancer (P < 0.0001). Conclusion: It seems that vegetables, fruits, and sunflower oil could be protective factors and bread, rice, beef, liver, dairy products, vegetable ghee, and animal ghee found to be possible risk factors for the development of lung cancer in Iran

Evaluation of virulence factors and antibiotic resistance patterns in clinical urine isolates of klebsiella pneumoniae in Semnan, Iran

Background: Klebsiella pneumoniae as an opportunistic pathogen can be the cause of a range of nosocomial and community-acquired infections. Many virulence factors help these bacteria overcome an immune system and cause various diseases. K1 and K2 capsular antigens, also magA, wcaG, and rmpA are well-known K. pneumoniae virulence factors. Klebsiella pneumoniae has been revealed to have the ability to acquire resistance to many antibiotics, which cause treatment failure. Objectives: This study aimed at determining the prevalence of magA, wcaG, rmpA, Capsular type K1, Capsular type K2, TEM, and SHV in K. pneumoniae isolates. Methods: A total of 173 non-duplicate K. pneumoniae isolates were collected from two different hospitals in Semnan, Iran, from urine specimens. Klebsiella pneumoniae was identified by conventional bacteriological tests. Disk diffusion test was performed according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI). Detection of virulence factors, TEM, and SHV gene was performed by specific primers. Results: Frequency of virulence factors was as follow: capsular type K2: 32.9, rmpA: 20.2, capsular type K1: 6.9, and wcaG: 16.2. Also, the SHV and TEM were observed in 46.8 and 33.5, respectively. Antibiotics resistance rates were as follow, imipenem: 7.5, ciprofloxacin: 16.1, levofloxacin: 17.3, amoxicillin-clavulanic acid: 30, trimethoprim-sulfamethoxazole: 32.9, cefepime: 34.1, nitrofurantoin: 35.8, amikacin: 36.4, aztreonam: 39.3, ceftazidime: 42.7. Conclusions: Frequency of some virulence factors including capsular type K2, rmpA, wcaG, and also resistant rate to imipenem, amikacin, and ceftazidime were significantly higher than similar studies. Presence of virulence factors accompanied by drug resistance should make bacteria an infectious agent and lead to treatment failure. © 2018, Author(s)

Formulation of low temperature mixed mode crack propagation behavior of crumb rubber modified HMA using artificial intelligence

Determining mixed mode fracture parameters asphalt concrete mixtures remains an engineering challenge due to non-homogeneity and inelasticity of the material. In this research, a study was conducted to determine the low-temperature R-curves of unmodified and crumb rubber modified Hot Mix Asphalt (HMA) under mode I and mixed-mode (I/II) loading conditions. Single edge notched beam (SE(B)) testing was employed to collect data, and three key fracture parameters—cohesive energy, energy rate, and fracture energy—were extracted to represent different stages of fracture and crack propagation. Within the scope of this study, it was observed that for the AC 85/100 paving grade bitumen, a temperature of − 20 °C serves as a critical temperature, shifting fracture from quasi-brittle to brittle. At this temperature, the stable crack growth region in the R-curves significantly shrinks, causing abrupt specimen failure. The incorporation of 20% crumb rubber demonstrated favorable material characteristics, with a progressively rising R-curve even during the unsfi crack propagation phase. The central goal of this research is to establish prediction models for the mixed-mode (I/II) crack propagation parameters Gb, Gf, and Gi. The features selected for modeling are Gb0, Gf0, and Gi0 (mode I), percentage of crumb rubber, type of aggregate, binder content, nominal maximum aggregate size, temperature, and normalized offset ratio. Two dataset configurations were used: dataset 1 contains all entries, while dataset 2 excludes Gb0, Gf0, and Gi0 (mode I). Five machine learning techniques, Regression, Multi-Gene Genetic Programming (MGGP), Support Vector Regression (SVR), Random Forest, and Artificial Neural Networks were employed to predict three key fracture parameters. Although slightly less accurate than SVR and Random Forest, MGGP offers the key advantage of yielding explicit mathematical expressions for crack propagation prediction. The R2 index for the MGGP model in Dataset 1 was 0.93 for Gb, 0.94 for Gf, and 0.92 for Gi. For dataset 2, the indices were 0.89, 0.93, and 0.88, respectively

Antiviral resistance during pandemic influenza: implications for stockpiling and drug use

<p>Abstract</p> <p>Background</p> <p>The anticipated extent of antiviral use during an influenza pandemic can have adverse consequences for the development of drug resistance and rationing of limited stockpiles. The strategic use of drugs is therefore a major public health concern in planning for effective pandemic responses.</p> <p>Methods</p> <p>We employed a mathematical model that includes both sensitive and resistant strains of a virus with pandemic potential, and applies antiviral drugs for treatment of clinical infections. Using estimated parameters in the published literature, the model was simulated for various sizes of stockpiles to evaluate the outcome of different antiviral strategies.</p> <p>Results</p> <p>We demonstrated that the emergence of highly transmissible resistant strains has no significant impact on the use of available stockpiles if treatment is maintained at low levels or the reproduction number of the sensitive strain is sufficiently high. However, moderate to high treatment levels can result in a more rapid depletion of stockpiles, leading to run-out, by promoting wide-spread drug resistance. We applied an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. Our results show that if high treatment levels are enforced too early during the outbreak, a second wave of infections can potentially occur with a substantially larger magnitude. However, a timely implementation of wide-scale treatment can prevent resistance spread in the population, and minimize the final size of the pandemic.</p> <p>Conclusion</p> <p>Our results reveal that conservative treatment levels during the early stages of the outbreak, followed by a timely increase in the scale of drug-use, will offer an effective strategy to manage drug resistance in the population and avoid run-out. For a 1918-like strain, the findings suggest that pandemic plans should consider stockpiling antiviral drugs to cover at least 20% of the population.</p