A Brief Comparison Between Available Bio-printing Methods

The scarcity of organs for transplant has led to large waiting lists of very sick patients. In drug development, the time required for human trials greatly increases the time to market. Drug companies are searching for alternative environments where the in-vivo conditions can be closely replicated. Both these problems could be addressed by manufacturing artificial human tissue. Recently, researchers in tissue engineering have developed tissue generation methods based on 3-D printing to fabricate artificial human tissue. Broadly, these methods could be classified as laser-assisted and laser free. The former have very fine spatial resolutions (10s of $\mu$m) but suffer from slow speed ( $< 10^2$ drops per second). The later have lower spatial resolutions (100s of $\mu$ m) but are very fast (up to $5\times 10^3$ drops per second). In this paper we review state-of-the-art methods in each of these classes and provide a comparison based on reported resolution, printing speed, cell density and cell viability

Novel Algorithms for Merging Computational Fluid Dynamics and 4D Flow MRI

Time-resolved three-dimensional spatial encoding combined with three-directional velocity-encoded phase contrast magnetic resonance imaging (termed as 4D flow MRI), can provide valuable information for diagnosis, treatment, and monitoring of vascular diseases. The accuracy of this technique, however, is limited by errors in flow estimation due to acquisition noise as well as systematic errors. Furthermore, available spatial resolution is limited to 1.5mm - 3mm and temporal resolution is limited to 30-40ms. This is often grossly inadequate to resolve flow details in small arteries, such as those in cerebral circulation. Recently, there have been efforts to address the limitations of the spatial and temporal resolution of MR flow imaging through the use of computational fluid dynamics (CFD). While CFD is capable of providing essentially unlimited spatial and temporal resolution, numerical results are very sensitive to errors in estimation of the flow boundary conditions. In this work, we present three novel techniques that combine CFD with 4D flow MRI measurements in order to address the resolution and noise issues. The first technique is a variant of the Kalman Filter state estimator called the Ensemble Kalman Filter (EnKF). In this technique, an ensemble of patient-specific CFD solutions are used to compute filter gains. These gains are then used in a predictor-corrector scheme to not only denoise the data but also increase its temporal and spatial resolution. The second technique is based on proper orthogonal decomposition and ridge regression (POD-rr). The POD method is typically used to generate reduced order models (ROMs) in closed control applications of large degree of freedom systems that result from discretization of governing partial differential equations (PDE). The POD-rr process results in a set of basis functions (vectors), that capture the local space of solutions of the PDE in question. In our application, the basis functions are generated from an ensemble of patient-specific CFD solutions whose boundary conditions are estimated from 4D flow MRI data. The CFD solution that should be most closely representing the actual flow is generated by projecting 4D flow MRI data onto the basis vectors followed by reconstruction in both MRI and CFD resolution. The rr algorithm was used for between resolution mapping. Despite the accuracy of using rr as the mapping step, due to manual adjustment of a coefficient in the algorithm we developed the third algorithm. In this step, the rr algorithm was substituded with a dynamic mode decomposition algorithm to preserve the robustness. These algorithms have been implemented and tested using a numerical model of the flow in a cerebral aneurysm. Solutions at time intervals corresponding to the 4D flow MRI temporal resolution were collected and downsampled to the spatial resolution of the imaging data. A simulated acquisition noise was then added in k-space. Finally, the simulated data affected by noise were used as an input to the merging algorithms. Rigorous comparison to state-of-the-art techniques were conducted to assess the accuracy and performance of the proposed method. The results provided denoised flow fields with less than 1\% overall error for different signal-to-noise ratios. At the end, a small cohort of three patients were corrected and the data were reconstructed using different methods, the wall shear stress (WSS) was calculated using different reconstructed data and the results were compared. As it has been shown in chapter 5, the calculated WSS using different methods results in mutual high and low shear stress regions, however, the exact value and patterns are significantly different

CASC11 and PVT1 spliced transcripts play an oncogenic role in colorectal carcinogenesis.

Cancer is fundamentally a genetic disorder that alters cellular information flow toward aberrant growth. The coding part accounts for less than 2% of the human genome, and it has become apparent that aberrations within the noncoding genome drive important cancer phenotypes. The numerous carcinogenesis-related genomic variations in the 8q24 region include single nucleotide variations (SNVs), copy number variations (CNVs), and viral integrations occur in the neighboring areas of the MYC locus. It seems that MYC is not the only target of these alterations. The MYC-proximal mutations may act via regulatory noncoding RNAs (ncRNAs). In this study, gene expression analyses indicated that the expression of some PVT1 spliced linear transcripts, CircPVT1, CASC11, and MYC is increased in colorectal cancer (CRC). Moreover, the expression of these genes is associated with some clinicopathological characteristics of CRC. Also, in vitro studies in CRC cell lines demonstrated that CASC11 is mostly detected in the nucleus, and different transcripts of PVT1 have different preferences for nuclear and cytoplasmic parts. Furthermore, perturbation of PVT1 expression and concomitant perturbation in PVT1 and CASC11 expression caused MYC overexpression. It seems that transcription of MYC is under regulatory control at the transcriptional level, i.e., initiation and elongation of transcription by its neighboring genes. Altogether, the current data provide evidence for the notion that these noncoding transcripts can significantly participate in the MYC regulation network and in the carcinogenesis of colorectal cells

Complex Granular Flow Dynamics in Fruit Powder Production Lines

One of the most important parts in every industry, is packaging which is located at the last part of the product line. In fruit powder product line lots of studies applied to study the complex dynamics of the powders in response to the vertical vibration. In this study cyclone collector condition was simulate with a rectangular throw out bin and the dynamics of the powders in response to the horizontal vibration studied. An ADXL345 accelerometer does employed in order to observe the acceleration of the system in all three dimensions. In order to have better observation two hollow cylinder was added to the container. At the peak values of acceleration, novel swirling granular flows were observed in the cylinders while the grains cascaded down the outer surface of the piles that formed outside the cylinders. An image processing algorithm employed to make a surface scan from the top surface of the material. Computer simulations were performed that supported our interpretation of the dynamics observed in the experiments. And a comparison between the image processing algorithm and computer simulation evaluate our simulation results

Dynamic Denoising and Gappy Data Reconstruction Based on Dynamic Mode Decomposition and Discrete Cosine Transform

Dynamic Mode Decomposition (DMD) is a data-driven method to analyze the dynamics, first applied to fluid dynamics. It extracts modes and their corresponding eigenvalues, where the modes are spatial fields that identify coherent structures in the flow and the eigenvalues describe the temporal growth/decay rates and oscillation frequencies for each mode. The recently introduced compressed sensing DMD (csDMD) reduces computation times and also has the ability to deal with sub-sampled datasets. In this paper, we present a similar technique based on discrete cosine transform to reconstruct the fully-sampled dataset (as opposed to DMD modes as in csDMD) from sub-sampled noisy and gappy data using l 1 minimization. The proposed method was benchmarked against csDMD in terms of denoising and gap-filling using three datasets. The first was the 2-D time-resolved plot of a double gyre oscillator which has about nine oscillatory modes. The second dataset was derived from a Duffing oscillator. This dataset has several modes associated with complex eigenvalues which makes them oscillatory. The third dataset was taken from the 2-D simulation of a wake behind a cylinder at Re = 100 and was used for investigating the effect of changing various parameters on reconstruction error. The Duffing and 2-D wake datasets were tested in presence of noise and rectangular gaps. While the performance for the double-gyre dataset is comparable to csDMD, the proposed method performs substantially better (lower reconstruction error) for the dataset derived from the Duffing equation and also, the 2-D wake dataset according to the defined reconstruction error metrics

Towards Reconstructing Blood Velocity Profiles from Noisy and Sparse Time Resolved Phase Contrast Magnetic Resonance Flow Data

Abstract: Proper orthogonal decomposition (POD) is applied to reconstruct noisy data of blood flow in a carotid artery. In order to show the effectiveness of the method, we applied this method on data obtained by computational fluid dynamics (CFD). The geometry used for this study was generated based on patient specific phase contrast magnetic resonance imaging (PC-MRI) technique. A Gaussian noise was added to a set of CFD data with slightly different boundary condition (BC) than BCs were used to generate the POD, in order to take into account the uncertainty of readying boundary condition from PC-MRI. The results show good agreement with the original CFD data. This method can potentially be developed as a service for PC-MRI providers to generate noise free PCI-MRI images and ultimately can help with further patient specific studies which are based on in vivo velocity profiles. Pressure difference calculations and wall shear stress (WSS) are just two out of many potential outputs of this study

Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression

Time resolved phase-contrast magnetic resonance imaging 4D-PCMR (also called 4D Flow MRI) data while capable of non-invasively measuring blood velocities, can be affected by acquisition noise, flow artifacts, and resolution limits. In this paper, we present a novel method for merging 4D Flow MRI with computational fluid dynamics (CFD) to address these limitations and to reconstruct de-noised, divergence-free high-resolution flow-fields. Proper orthogonal decomposition (POD) is used to construct the orthonormal basis of the local sampling of the space of all possible solutions to the flow equations both at the low-resolution level of the 4D Flow MRI grid and the high-level resolution of the CFD mesh. Low-resolution, de-noised flow is obtained by projecting in vivo 4D Flow MRI data onto the low-resolution basis vectors. Ridge regression is then used to reconstruct high-resolution de-noised divergence-free solution. The effects of 4D Flow MRI grid resolution, and noise levels on the resulting velocity fields are further investigated. A numerical phantom of the flow through a cerebral aneurysm was used to compare the results obtained using the POD method with those obtained with the state-of-the-art de-noising methods. At the 4D Flow MRI grid resolution, the POD method was shown to preserve the small flow structures better than the other methods, while eliminating noise. Furthermore, the method was shown to successfully reconstruct details at the CFD mesh resolution not discernible at the 4D Flow MRI grid resolution. This method will improve the accuracy of the clinically relevant flow-derived parameters, such as pressure gradients and wall shear stresses, computed from in vivo 4D Flow MRI data