Miracle-dz/UniFAR-FTUs huggingface dataset:
Microscale Additive Manufacturing of Collagen Cell Culture Scaffolds

ii Acknowledgements . v Acronyms and Abbreviations . 4 List of Tables . 5 List of Figures . 5 Chapter 1Impetus, Rationale and Objectives. 6 Impetus for this Research. 6 Specific Aims and Rationale. 7 Specific Aim 1 Establish repeatable methods for 3D printing of type I collagen by MPC and the quantifiable modification thereof with bioactive proteins. 8 Study 1.1 Multiphoton crosslinking for 3D printing of Type I collagen. 8 Study 1.2 Multiphoton crosslinking for quantifiable 3D modification of collagen structures . 9 Specific Aim 2 – Assess In Vitro how MPC collagen scaffolds with VEGF patterning support HUVEC viability, migration, and vascular tube formation. 11 Study 2.1 Fibroblast viability and remodeling on MPC collagen scaffolds. 11 Chapter 2 Literature Review . 13
DCE-MRI of the Liver: Reconstruction of the Arterial Input Function Using a Low Dose Pre-Bolus Contrast Injection

Purpose To assess the quality of the arterial input function (AIF) reconstructed using a dedicated pre-bolus low-dose contrast material injection imaged with a high temporal resolution and the resulting estimated liver perfusion parameters. Materials and Methods In this IRB–approved prospective study, 24 DCE-MRI examinations were performed in 21 patients with liver disease (M/F 17/4, mean age 56 y). The examination consisted of 1.3 mL and 0.05 mmol/kg of gadobenate dimeglumine for pre-bolus and main bolus acquisitions, respectively. The concentration-curve of the abdominal aorta in the pre-bolus acquisition was used to reconstruct the AIF. AIF quality and shape parameters obtained with pre-bolus and main bolus acquisitions and the resulting estimated hepatic perfusion parameters obtained with a dual-input single compartment model were compared between the 2 methods. Test–retest reproducibility of perfusion parameters were assessed in three patients. Results The quality of the pre-bolus AIF curve was significantly better than that of main bolus AIF. Shape parameters peak concentration, area under the time activity curve of gadolinium contrast at 60 s and upslope of pre-bolus AIF were all significantly higher, while full width at half maximum was significantly lower than shape parameters of main bolus AIF. Improved liver perfusion parameter reproducibility was observed using pre-bolus acquisition [coefficient of variation (CV) of 4.2%–38.7% for pre-bolus vs. 12.1–71.4% for main bolus] with the exception of distribution volume (CV of 23.6% for pre-bolus vs. 15.8% for main bolus). The CVs between pre-bolus and main bolus for the perfusion parameters were lower than 14%. Conclusion The AIF reconstructed with pre-bolus low dose contrast injection displays better quality and shape parameters and enables improved liver perfusion parameter reproducibility, although the resulting liver perfusion parameters demonstrated no clinically significant differences between pre-bolus and main bolus acquisitions.
The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques

Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture‐based techniques. In this paper, dense, texture‐based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio‐temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.
A Multiclassifier based Document Categorization System: profiting from the Singular Value Decomposition Dimensionality Reduction Technique

In this paper we present a multiclassiﬁer approach for multilabel document classiﬁ-cation problems, where a set of k -NN clas-siﬁers is used to predict the category of text documents based on different training subsampling databases. These databases are obtained from the original training database by random subsampling. In order to combine the predictions generated by the multiclassiﬁer, Bayesian voting is applied. Through all the classiﬁcation process, a reduced dimension vector representation obtained by Singular Value Decomposition (SVD) is used for training and testing documents. The good results of our experiments give an indication of the potentiality of the proposed approach.
Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption

The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature. Another term for it is conditional independence assumption. This paper discusses identification when there are more than two types of mutually exclusive treatments. It turns out that low dimensional balancing scores, similar to the ones valid in the case of only two treatments, exist and be used for identification of various causal effects. Therefore, a comparable reduction of the dimension of the estimation problem is achieved and the approach retains its basic simplicity. The paper also outlines a matching estimator potentially suitable in that framework.
A mixed critical memory controller using bank privatization and fixed priority scheduling

Mixed critical platforms are those in which applications that have different criticalities, i.e. different levels of importance for system safety, coexist and share resources. Such platforms require a memory controller capable of providing sufficient timing independence for critical applications. Existing real-time memory controllers, however, either do not support mixed criticality or still allow a certain degree of interference between applications. The former issue leads to overly constrained, and hence more expensive, systems. The latter issue forces designers to assume the worst case latency for every individual memory transaction, which can be very conservative when applied to determine the worst-case execution time (WCET) of a task that performs many memory requests. In this paper, we address both issues. The main contributions are: (1) A memory controller that allows a predetermined number of critical and non-critical applications to coexist, while providing an interference-free memory for the former. To achieve that, we treat the memory as a set of independent virtual devices (VDs). Therefore, we also provide (2) a partitioning strategy to properly map mixed critical workloads to VDs. We present experiments that show that our controller allows DRAM sharing with no interference on critical applications and minimal performance overhead on non-critical ones (they perform on average only 15% slower in the shared environment).
Animal models of acute lung injury

Acute lung injury in humans is characterized histopathologically by neutrophilic alveolitis, injury of the alveolar epithelium and endothelium, hyaline membrane formation, and microvascular thrombi. Different animal models of experimental lung injury have been used to investigate mechanisms of lung injury. Most are based on reproducing in animals known risk factors for ARDS, such as sepsis, lipid embolism secondary to bone fracture, acid aspiration, ischemia-reperfusion of pulmonary or distal vascular beds, and other clinical risks. However, none of these models fully reproduces the features of human lung injury. The goal of this review is to summarize the strengths and weaknesses of existing models of lung injury. We review the specific features of human ARDS that should be modeled in experimental lung injury and then discuss specific characteristics of animal species that may affect the pulmonary host response to noxious stimuli. We emphasize those models of lung injury that are based on reproducing risk factors for human ARDS in animals and discuss the advantages and disadvantages of each model and the extent to which each model reproduces human ARDS. The present review will help guide investigators in the design and interpretation of animal studies of acute lung injury.
Ultra-high Gradient Acceleration in Nano-crystal Channels

Excitation of plasma waves by short intense laser pulses or particle bunches results in very high accelerating gradients [1]. Crystals have ultimately high density of mobile charge carriers (electrons) ~ 10 10 cm and therefore, under short intense EM impact, can possibly support electric fields of up to 30 TV/m of plasma oscillation [2 – 4]. Atomic lattice in solid crystals remains practically immobile during short impact pulse and its own focusing field of ~10 100 V/Å is capable of guiding and collimating high energy particles, thus, opening the way to continuously focused acceleration of heavy leptons (muons) to unprecedented energies [5]. Compared to natural crystal, nanostructured crystals, e.g. carbon nanotubes, with dimensional flexibilities can offer a few orders of magnitude larger phase-space volume for channelling particles [6, 7]. Here we present PIC simulation results obtained with two plasma acceleration codes, Vsim and EPOCH, indicating that in the linear regime the beam-driven or laser-driven channelling acceleration in a 100 m long nanotube can impart to electrons a gain of up to 10 MeV of energy (G = 100 GeV/m). Experimental tests, including a slit-mask beam modulation and pump-probe electron diffraction, are being planned and prepared at Fermilab and NIU to demonstrate the wakefield acceleration in a photo-excited nano-crystal.
RS-Algo : an Algorithm for Improved Memory Utilization in Continuous Query System under Asynchronous Data Streams

Continuous query systems are an intuitive way for users to access data streaming data in large scale scientific applications containing hundreds if not thousands of streams. This paper focuses on optimizations for improved memory utilization under the large scale, asynchronous streams found in on-demand meteorological forecasting and other data-driven applications. Specifically, we experimentally evaluate the RS-Algo algorithm for dynamically adjusting sliding window sizes used in database joins to reflect current stream rates. In this TR we provide detailed results of measurements of the RS-Algo algorithm which dynamically adjusts the sliding window sizes used in database joins to reflect current stream rates. We tested under various queries and stream rate conditions using a synthetic workloads and using the dQUOB continuous query system. We examine service time and memory utilization. The second result chronicles a smaller experiment comparing two algorithms used during runtime to schedule events. We evaluated the performance of the existing algorithm, First Come First Serve (FCFS), against a modified version of Earliest Job First (EJF) and examined the impact on memory utilization.
Heuristics-based indoor positioning systems: a systematic literature review

Context: Many heuristics-based indoor positioning approaches have been developed to enhance positioning estimation. However, there is no comprehensive survey of these heuristics information and methods. Objective: The main objective of this study is to provide a holistic view and an in-depth analysis of what heuristics information and methods have been used, their general achievements and limitations. This study aims to provide a comprehensive summary to facilitate further research on indoor positioning heuristics. Method: We conducted a systematic literature review (SLR) on indoor positioning heuristics. Results: Ninety-three (93) primary studies were selected. We found two general types of heuristics information and four primary heuristics methods, which we summarised in this paper. We also found that many of these positioning heuristics are tested in experimental settings only. Some heuristics claim practical applications but are not tested for the challenging and typical indoor environments. Conclusion: Most existing heuristics information and methods rely on the assumptions that may not be true in real life environment, hence limiting the usefulness of the positioning outcomes. Based on the analysis of this SLR, we propose two research directions to enhance positioning estimation.
An ERP maintenance model

Enterprise resource planning (ERP) maintenance and upgrade activities are receiving much attention in ERP-using organizations. Annual maintenance costs approximate 25% of initial ERP implementation costs, and upgrade costs as much as 25-33% of the initial ERP implementation. Still, the area of ERP maintenance and upgrade is relatively new and understudied as compared to ERP implementation issues. Many organizations lack experience and expertise in managing ERP maintenance and upgrade effectively. This situation is not helped by the lack of a standard ERP maintenance model that could provide practitioners with guidelines on planning, implementing and upgrading an ERP. Although software maintenance model standards exist, they have been found in a recent study to be insufficient for ERP maintenance and upgrade processes. In order to bridge this gap in literature and practice, this study proposes a preliminary ERP maintenance model, reflecting fundamental ERP maintenance and upgrade activities. A detailed case study was conducted to gather empirical data for developing such an ERP maintenance model. Data analysis identified (potential) benefits of the maintenance model to ERP-using organizations generally, and to the case firm in particular.
Multivariate chaotic time series prediction based on Hierarchic Reservoirs

Chaotic time series prediction has received considerable attention in the last few years. Although many studies have been conducted in the field, there is little attention focused on multivariate time series prediction. Considering this problem, the Hierarchic Reservoirs (HR) prediction model is proposed for multivariate chaotic time series prediction in this paper. The basic idea is using multiple reservoirs to predict multivariate chaotic time series directly without using phase space reconstruction. Each single reservoir of the hierarchic reservoirs prediction model extract the features of a time series of the multivariate chaotic time series. Then, the features are composed to represent the target value of the time series. Two simulation examples, prediction of Lorenz chaotic time series and prediction of sunspots and the Yellow River annual runoff time series are conducted to demonstrate the effectiveness of the proposed method.
An adaptive microphone array with local acoustic sensitivity

In this paper, a microphone array with 3-D focal zone is proposed. The microphone array consists of one omni-directional and two uni-directional microphones. The microphone array is so constructed that a cross zone is formed such that only the sound within this zone is captured and any interferences outside the zone are effectively cancelled. The proposed framework is flexible in defining the location/size of the closed volume where the sound source of interest is located. Simulations have been carried out to demonstrate the 3-D spatial selectivity as well as the noise cancellation performance. The most important feature which differs from the previous works is that the super volumetric selectivity is realized by strategically use only three microphones, by which the overall apparatus acts as a virtual wireless close-talking microphone with confined position constrained in both distance and directions.
Arabidopsis ACTIN-DEPOLYMERIZING FACTOR7 Severs Actin Filaments and Regulates Actin Cable Turnover to Promote Normal Pollen Tube Growth[W]

Pollen tube growth is highly dependent on dynamic remodeling of the actin cytoskeleton, but the mechanisms that regulate the dynamic turnover of longitudinal actin cables in pollen tubes remain largely unknown. Here, we show that ACTIN-DEPOLYMERIZING FACTOR7 inhibits nucleotide exchange on actin and severs filaments in pollen tubes, thereby promoting turnover of longitudinal actin cables. Actin filaments are often arranged into higher-order structures, such as the longitudinal actin cables that generate the reverse fountain cytoplasmic streaming pattern present in pollen tubes. While several actin binding proteins have been implicated in the generation of these cables, the mechanisms that regulate their dynamic turnover remain largely unknown. Here, we show that Arabidopsis thaliana ACTIN-DEPOLYMERIZING FACTOR7 (ADF7) is required for turnover of longitudinal actin cables. In vitro biochemical analyses revealed that ADF7 is a typical ADF that prefers ADP-G-actin over ATP-G-actin. ADF7 inhibits nucleotide exchange on actin and severs filaments, but its filament severing and depolymerizing activities are less potent than those of the vegetative ADF1. ADF7 primarily decorates longitudinal actin cables in the shanks of pollen tubes. Consistent with this localization pattern, the severing frequency and depolymerization rate of filaments significantly decreased, while their maximum lifetime significantly increased, in adf7 pollen tube shanks. Furthermore, an ADF7–enhanced green fluorescent protein fusion with defective severing activity but normal G-actin binding activity could not complement adf7, providing compelling evidence that the severing activity of ADF7 is vital for its in vivo functions. These observations suggest that ADF7 evolved to promote turnover of longitudinal actin cables by severing actin filaments in pollen tubes.
Metabolomics Analysis for Defining Serum Biochemical Markers in Colorectal Cancer Patients with Qi Deficiency Syndrome or Yin Deficiency Syndrome

Colorectal cancer is one of the leading causes of tumor-associated death, and traditional Chinese medicine (TCM) classifies colorectal cancer into various subtypes mainly according to the symptomatic pattern identification (ZHENG). Here, we investigated the difference in metabolic profiles of serum by comparing colorectal cancer subjects with Nondeficiency (ND), Qi deficiency (QD), and Yin deficiency (YD). The ratio of subjects with carcinoembryonic antigen (CEA) was higher in YD pattern, and the ratio of subjects with carbohydrate antigen 19-9 (CA19-9) was higher both in YD and in QD, compared with ND. As a result of metabolomics analysis, twenty-five metabolites displayed differences between QD and ND, while twenty-eight metabolites displayed differences between YD and ND. The downregulated metabolites in QD/ND and YD/ND mainly include carbohydrates and the upregulated metabolites mainly include amino acids and fatty acids, suggesting conversion obstruction of carbohydrates, fatty acids, and amino acids occurs in patients with QD and YD compared with ND. Our results demonstrate that colorectal cancer patients with QD or YD were associated with metabolic disorders and the variations of serum metabolic profiles may serve as potential biochemical markers for diagnosis and prognosis of colorectal cancer patients displayed QD or YD patterns.
Multi-user spatial modulation MIMO

Spatial Modulation (SM) is a recently proposed single-RF multiple-input-multiple-output (MIMO) technique, which is capable of outperforming many conventional MIMO transmission schemes with low implementation and computational complexity. Recently, there have been some attempts in understanding the performance of SM in multi-user environments. However, most of the work has been oriented towards uplink multi-access scenarios. Also, conventional downlink/broadcast MIMO precoding techniques such as Zero Forcing (ZF) or Minimum Mean Square Error (MMSE) cannot be used in Multi-User SM (MU-SM), as part of the data in SM is also encoded into the Channel Impulse Responses (CIRs). In this paper, a novel precoding scheme for single-cell downlink MU-SM systems is proposed with a two-fold objective: i) the precoder needs to be able to completely eliminate the Multi-User Interference (MUI) by taking advantage of the Channel State Information (CSI) at the transmitter and ii) it needs to allow the users to use a single-user Maximum Likelihood (ML) optimum detector while achieving the same performance as interference-free point-to-point SM transmission. Finally, we also develop an interference-aware multi-user detection scheme, which does not require any CSI at the transmitter, and compare its performance with that of single-user detection schemes based on precoding.
Biofunctionalisation of titanium surfaces with TGF-β inhibitor peptides

condicions d'ús: La difusió d’aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX. No s’autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.
A Micro-Preconcentrator Combined Olfactory Sensing System with a Micromechanical Cantilever Sensor for Detecting 2,4-Dinitrotoluene Gas Vapor

Preventing unexpected explosive attacks and tracing explosion-related molecules require the development of highly sensitive gas-vapor detection systems. For that purpose, a micromechanical cantilever-based olfactory sensing system including a sample preconcentrator was developed to detect 2,4-dinitrotoluene (2,4-DNT), which is a well-known by-product of the explosive molecule trinitrotoluene (TNT) and exists in concentrations on the order of parts per billion in the atmosphere at room temperature. A peptide receptor (His-Pro-Asn-Phe-Ser-Lys-Tyr-Ile-Leu-His-Gln-Arg) that has high binding affinity for 2,4-DNT was immobilized on the surface of the cantilever sensors to detect 2,4-DNT vapor for highly selective detection. A micro-preconcentrator (µPC) was developed using Tenax-TA adsorbent to produce higher concentrations of 2,4-DNT molecules. The preconcentration was achieved via adsorption and thermal desorption phenomena occurring between target molecules and the adsorbent. The µPC directly integrated with a cantilever sensor and enhanced the sensitivity of the cantilever sensor as a pretreatment tool for the target vapor. The response was rapidly saturated within 5 min and sustained for more than 10 min when the concentrated vapor was introduced. By calculating preconcentration factor values, we verified that the cantilever sensor provides up to an eightfold improvement in sensing performance.
Learning Person Trajectory Representations for Team Activity Analysis

Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics. We propose an end-to-end approach for learning person trajectory representations for group activity analysis. The learned representations encode rich spatio-temporal dependencies and capture useful motion patterns for recognizing individual events, as well as characteristic group dynamics that can be used to identify groups from their trajectories alone. We develop our deep learning approach in the context of team sports, which provide well-defined sets of events (e.g. pass, shot) and groups of people (teams). Analysis of events and team formations using NHL hockey and NBA basketball datasets demonstrate the generality of our approach.