The application of second generation sequencing technology to the characterization of complex microbial communities has profoundly affected our appreciation of microbial diversity. The explosive growth of microbial sequence data has also necessitated advances in bioinformatic methods for profiling microbial communities. Data aggregation strategies should allow the relation of metagenomic sequence data to our understanding of microbial taxonomy, while also facilitating the discovery of novel taxa. For eukaryotes, a method has been established that links DNA sequences to the identification of organisms: DNA Barcoding. A similar approach has been developed for prokaryotes using target genic regions as markers for species identification and to profile communities. A key difference in these efforts is that within DNA barcoding there is a formalized framework for the evaluation of barcoding targets, whereas for prokaryotes the 16S rRNA gene target has become the de facto barcode without formal evaluation. Using the framework developed for evaluating DNA barcodes in eukaryotes, a study was undertaken to formally evaluate 16S rRNA and cpn60 as DNA barcodes for Bacteria. Both 16S rRNA and cpn60 were found to meet the criteria for DNA barcodes, with cpn60 a preferred barcode based on its superior resolution of closely related taxa. The high resolution of cpn60 enabled a method of sequence data aggregation through sequence assembly: microbial profiling using metagenomic assembly (mPUMA). The scoring of metagenomic assemblies in terms of sensitivity and specificity of the operational taxonomic units formed was used to evaluate and optimize the assembly of cpn60 barcodes. Using optimized parameters, mPUMA was demonstrated to faithfully reconstruct a synthetic community in terms of richness and abundance. To facilitate the use of mPUMA, a software package was developed and released under an open source license. The utility of mPUMA was further examined through the characterization of the epiphytic seed microbiomes of Triticum and Brassica species. A microbiome shared across both crop genera including fungi and bacteria was detected: a particularly important observation as it implies that seeds may serve as a vector for microbes that could include both pathogenic and beneficial organisms. The relative abundances of taxa identified by mPUMA were confirmed by qPCR for multiple cases of both fungal and bacterial taxa. By culturing isolates of both bacteria and fungi from the seed surfaces it was demonstrated that mPUMA faithfully assembled consensus sequences for OTUs that were 100% identical to isolated fungi and bacteria. Patterns observed in the relative abundances of the shared microbiome OTUs were used to generate the hypothesis that an Pantoea-like bacterium and an Alternaria-like fungus had an antagonistic relationship, since sequences corresponding to these organisms showed reciprocal abundance patterns on Triticum and Brassica seeds. Studies of the interactions of cultured isolates revealed fungistatic interactions that could account for their reciprocal abundances. These interactions could be directly relevant to plant health, given that Alternaria-like fungi are linked to grain spoilage in wheat, and diseases in canola. Taken together, results of this thesis demonstrate the superiority of the cpn60 universal target as a barcode for Bacteria, forming the basis for an assembly-based strategy for microbial profiling of bacterial and eukaryotic microbial communities that can lead to the discovery of novel taxa and microbial interactions.

Small intestinal neuroendocrine tumors (SI-NETs) originate from serotonin-producing enterochromaffin (EC) cells in the intestinal mucosa. Somatostatin analogs (SSAs) are mainly used to control hormonal secretion and tumor growth. However, the molecular mechanisms leading to the control of SI-NETs are unknown. Although microRNAs (miRNAs) are post transcriptional regulators deeply studied in many cancers, are not well-defined in SI-NETs. We adopted a two-pronged strategy to investigate SSAs and miRNAs: first, to provide novel insights into how SSAs control NET cells, and second, to identify an exclusive SI-NET miRNA expression, and investigate the biological functions of miRNA targets. To accomplish the first aim, we treated CNDT2.5 cells with octreotide for 16 months. Affymetrix microarray was performed to study gene variation of CNDT2.5 cells in the presence or absence of octreotide. The study revealed that octreotide induces six genes, ANXA1, ARHGAP18, EMP1, GDF15, TGFBR2 and TNFSF15. To accomplish the second aim, SI-NET tissue specimens were used to run genome-wide Affymetrix miRNA arrays. The expression of five miRNAs (miR-96, -182, -183, -196a and -200a) was significantly upregulated in laser capture microdissected (LCM) tumor cells versus LCM normal EC cells, whereas the expression of four miRNAs (miR-31, -129-5p, -133a and -215) was significantly downregulated in LCM tumor cells. We also detected nine tissue miRNAs in serum samples, showing that the expression of five miRNAs is significantly increased in SSA treated patients versus untreated patients. Conversely, SSAs do not change miRNA expression of four low expressed miRNAs. Silencing miR-196a expression was used to investigate functional activities in NET cells. This experimental approach showed that four miR-196a target genes, HOXA9, HOXB7, LRP4 and RSPO2, are significantly upregulated in silenced miR-196a NET cells. In conclusion, ANXA1, ARHGAP18, EMP1, GDF15, TGFBR2 and TNFSF15 genes might regulate cell growth and differentiation in NET cells, and play a role in an innovative octreotide signaling pathway. The global SI-NET miRNA profiling revealed that nine selected miRNAs might be involved in tumorigenesis, and play a potential role as novel markers for follow-up. Indeed, silencing miR-196a demonstrated that HOXA9, HOXB7, LRP4 and RSPO2 genes are upregulated at both transcriptional and translational levels.

Thesis (doctoral)--Freie Universität Berlin, 2004. / Title from cover. Vita. "April 2005"--P. [2] of cover. Includes bibliographical references (p. 115-126). Also available via the World Wide Web.

A maioria dos sistemas embarcados hoje desenvolvidos utilizam complexos sistemas eletrônicos integrados em um único chip, os Systems-on-a-Chip (SoC). A análise do comportamento de uma aplicação em execução, ou seja, o profiling nesses sistemas não é uma tarefa trivial em virtude da complexidade dos SoCs e pela restrição de ferramentas de profiling adequadas. Neste contexto, este trabalho apresenta o P2L, uma ferramenta de profiling que se baseia em métricas de nível de instrução e função para o processador LEON3. O P2L fornece estatísticas detalhadas de uso do processador, memórias e barramento de programas em execução sem uso de instrumentação. A ferramenta é composta por um componente em hardware e drivers e aplicativos em software. Os resultados mostram que o P2L fornece medidas com erro inferior a 1% e overhead desprezível quando comparado ao tempo de execução nativa do programa e ao do profiler GNU gprof. / Most embedded systems developed today use complex electronic systems integrated into a single chip, the Systems-on-a-Chip (SoC). The analysis of the behavior of a running application or profiling in these systems is not a trivial task due to the complexity of the SoC and the restriction of appropriate profiling tools. In this context, this work presents P2L - a profiling tool that is based on instruction and function level metrics for the LEON3 processor. P2L provides detailed usage statistics of the processor, memories, and bus of running programs without the use of instrumentation. The tool consists of a component in hardware, drivers and applications software. The results show that P2L provides measures with an error less than 1% and negligible overhead compared to native runtime program and the GNU profiler gprof.

Ribosome profiling (RP) is a novel technique that exploits RNA sequencing and ribosome immobilization to quantify transcription and translation at different cell growth stages. Therefore, RP provides invaluable information for expression dynamics studies. Quantitative –omics studies are of crucial importance for identification of potential biomarkers of infection. An ideal parasite detection system should definitely establish the presence or absence of infection; determine the species involved; be detectable even in low concentrations; be proportional to parasite density; and determine the presence of antibiotic resistance. Here, we propose a simple workflow that attempts to identify a set of biomarkers that fulfill some of the above criteria for the ideal detection system. RP expression profiles were ranked for abundance, crosschecked with PlasmoDB for homogeneity along infection cycles and probed for availability of structural stability. The latter is of fundamental importance for the development of molecular biosensors to be give birth to rapid diagnostic kits. In addition, a simple biochemistry workflow was developed for easy production of the selected biomarkers in E. coli. Altogether, the present work provides two complementary and novel workflows that shall aid researchers to rapidly produce molecular biomarkers and develop biosensors based on antibodies or aptamers. / Tesis

The natural resources offer a unique opportunity for the discovery of active compounds, due to the complexity and biodiversity of their chemical structures. Natural resources have been used as medicines throughout human history. Saudi Arabia’s natural resources, for instance its terrestrial medicinal plants and the Red Sea sponges, have not been extensively investigated with regard to their biological activities. To better identify the diversity of compounds with bioactive potential, new techniques are also necessary in order to improve the drug discovery path. This study comprises three sections. The first section examines Juniperus phoenicea (Arar), Anastatica hierochuntica (Kaff Maryam) and Citrullus colocynthis (Hanzal); these herbal plants were screened for potential bioactivity using a newly developed pipeline based on a high-content screening technique. We report a new cell-based high-throughput phenotypic screening for the bio-prospecting of unknown natural products from Saudi Arabian plants, in order to reveal their biological activities. The second section investigates Avicennia marina plants, screened for reverse transcriptase anti-HIV bioactivity using biochemical assay. Image-based high-content screening with a set of cellular stains was used to investigate the phenotypic results of toxicity and cell cycle arrest. The third section considers the isolation of Actinomycetes from Red Sea Sponges. Actinomycetes bacterial isolates were tested for bioactivity against West Nile Virus NS3 Protease. Analytical chemical techniques such as liquid chromatography–mass spectrometry (LC-MS), gas chromatography–mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) were used to gain more understanding of the possible chemical compounds responsible for this bioactivity. Overall, the aim of this work is to investigate the potential bioactive effect of several Saudi Arabian plants and Red Sea sponges against cancer cells and viral infections. Our study demonstrates the efficiency of the newly developed pipeline using cell-based phenotypic screening. Anti-cancer potential activity was detected in Juniperus phoenicea. Bioactive potential against the reverse transcriptase enzyme of HIV virus was confirmed in Avicennia marina leaves. The organic extracts of Actinomycetes bacterial isolates were found active against West Nile Virus NS3 Protease. Here, promising starting point for the potential of drug discovery of plants and marine organism of Saudi Arabia.

The disparity between the ongoing use, the overall positive attitudes towards criminal profiling and the lack of empirical evidence for its validity is also referred to as criminal profiling illusion. Associated risks for society range from misled police investigations, hindered apprehensions of the actual offender(s), and wrongful convictions of innocent citizens to mistrust in the police and their methods. Research on potential explanations to the Criminal Profiling Illusion is still in its infancy but assumes that people receive and adopt incorrect messages favouring the accuracy and utility of criminal profiling. One suggested mechanism through which individuals may acquire such incorrect messages is the consumption of fictional crime-related media which typically present criminal profiling as highly accurate, operationally useful and leading to the apprehension of the offender(s). By having some relation to reality but presenting a distorted picture of criminal profiling, fictional crime-related media may blur the line between fiction and reality thereby increasing the risk for the audience to mistake fiction for fact. Adopting a cultivation approach adequate to examine media effects on one’s perception, the present study is the first to investigate whether the perception of criminal profiling may be influenced by the consumption of fictional crime-related media based on a correlation study. Although the results provide support for the assumption that misperceptions of criminal profiling are widely spread in the general population and associated with the consumption of fictional crime-related media, the found cultivation effects are small and must be interpreted cautiously. Considering that even small effects may have the potential to influence real-life decision-making, they may still be relevant and affect the society at large.

This dissertation proposes a novel approach to achieve circumferential three-dimensional (3D) profiling for dark objects by investigating specular micro-texture photometry. A small patch of a target surface in micro-texture level yields different appearance under different illumination. This photometric property can be used to reconstruct the target surface with pixel-level resolution. However, due to the nature of some material, the surface of whom has stronger specular components than diffuse components, making the usage of general microtexture photometry more difficult. On the other hand, without using micro-texture photometry, the conventional circumferential 3D approaches only utilizes the geometric property of the target surface, compared to which, the proposed is able to reconstruct the target surface with finer detail. The original contributions of this dissertation are threefold. To begin with, the specular component in the micro-texture photometry is investigated to propose the pixel-level 3D profiling. The intensities of the same pixel from different images, which are taken under different lighting conditions are different. The specular components are used to recover the surface normal of the corresponding surface patch of the target surface. Consequently, the proposed specular-photometry-based technique produces pixel-wise measurement on surface normal. Furthermore, the conventional circumferential 3D profiling approach is extended with the proposed specular-photometry-based technique. The result of 3D profiling via the conventional approach is sparse due to its nature. On the other hand, the result of 3D profiling from the integration using the surface normal obtained from the proposed specular-photometry-based technique suffers from accumulative error. A new approach is then proposed to use the result from the conventional approach as global constraint, for the purpose of reducing the accumulative error. The proposed approach is able to achieve pixel-resolution globally bounded profiling because of the dense surface normal measurement from the proposed specular-photometry-based technique and the constraints from the conventional approach. Lastly, a system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach. The developed system is not only able to acquire data and but also to provide different lighting conditions for both the specular-photometry-based technique and conventional approach using a digital single-lens reflex camera and different lighting devices. With a step motor to rotate the object for three hundred and sixty degrees, the system is able to achieve circumferential scanning / Doctor of Philosophy / This dissertation explains a novel approach to fulfill circumferential 3D profiling with high resolution for dark objects. With the proposed approach, the resolution is able to achieve micro-texture level. The high resolution measurement is achieved by investigating specular micro-texture photometry. As for dark objects, the specular components dominate the reflection. The usage of photometry is explained as follow. A small patch of a target surface yields different appearance under different illumination. For the material of the surface of dark objects, the surface reflects stronger specular components than diffuse components. The proposed approach utilizes this photometric property to recover the small patch's surface normal using its specular components. The recovered surface normal is then used to calculate the surface profile through integration. The conventional circumferential 3D profiling approach, which can only produce low-resolution measurement, is also adopted in the proposed approach to enhance its performance, as the integration method suffers from accumulative error. The result from the conventional approach serves as a global constraint to bound the final profiling result. A system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach, which is equipped with a step motor to rotate the object for three hundred and sixty degrees for the purpose of circumferential scanning. A series of numerical and experimental studies were conducted to validate the performance of the proposed approach. As it is shown in the result, the proposed approach is able to measure the tire tread with 31µm resolution.

Most dynamic program analysis techniques such as profile-driven compiler optimizations, software testing and runtime property checking infer program properties by profiling one or more executions of a program. Unfortunately, program profiling does not come for free. For example, even the most efficient techniques for profiling acyclic, intra-procedural paths can slow down program execution by a factor of 2. In this thesis, we propose techniques that significantly lower the overheads of profiling paths, enabling the use of path-based dynamic analyzes in cost-sensitive environments. Preferential path profiling (PPP) is a novel software-only path profiling scheme that efficiently profiles given subsets of paths, which we refer to as interesting paths. The algorithm is based on the observation that most consumers of path profiles are only interested in profiling a small set of paths known a priori. Our algorithm can be viewed as a generalization of the Ball-Larus path profiling algorithm. Whereas the Ball-Larus algorithm assigns weights to the edges of a given CFG such that the sum of the weights of the edges along each path through the CFG is unique, our algorithm assigns weights to the edges such that the sum of the weights along the edges of interesting paths is unique. Furthermore, our algorithm attempts to achieve a minimal and compact encoding of the interesting paths; such an encoding significantly reduces the overheads of path profiling by eliminating expensive hash operations during profiling. Interestingly, we find that both the Ball-Larus algorithm and PPP are essentially a form of arithmetic coding. We use this connection to prove that the numbering produced by PPP is optimal. We also propose a programmable, non-intrusive hardware path profiler (HPP). The hardware profiler consists of a path detector that detects paths by monitoring the stream of retiring branch instructions emanating from the processor pipeline. The path detector can be programmed to detect various types of paths and track architectural events that occur along paths. The second component of the hardware profiling infrastructure is a Hot Path Table (HPT), that collects accurate hot path profiles. Our experimental evaluation shows that PPP reduces the overheads of profiling paths to 15% on average (with a maximum of 26%). The algorithm can be easily extended to profile inter-procedural paths at minimal additional overheads (average of 26%). We modeled HPP using a cycle-accurate superscalar processor simulator and find that HPP generates accurate path profiles at extremely low overheads (0.6% on average) with a moderate hardware budget. We also evaluated the use of PPP and HPP in a realistic profiling scenarios. We find that the profiles generated by HPP can effectively replace expensive profiles used in profile-driven optimizations. We also find that even well-tested programs tend to exercise a large number of untested paths in the field, emphasizing the need for efficient profiling schemes that can be deployed in production environments.

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