Árvores emergentes e de dossel da Floresta Ombrófila Densa Sub-montana de uma Parcela Permanente (10,24ha) no Parque Estadual &quotCarlos Botelho&quot, SP, foram estudadas e descritas morfologicamente considerando padrões de tronco, raízes e casca externa com a finalidade de auxiliar na identificação de campo. O critério de inclusão (DAP igual ou superior a 48cm) abrangeu 324 indivíduos de 64 espécies pertencentes a 36 famílias. São fornecidas descrições, ilustrações e chave de identificação para as espécies estudadas baseadas em características do tronco à altura do peito e da base; das raízes aparentes e rentes ao tronco; das raízes superficiais; e das texturas da casca externa nas regiões da árvore mencionadas. Considerando que não há uma terminologia padrão para descrição dos caracteres estudados, é proposta aqui uma nomenclatura, além de serem utilizados termos propostos por Torres et al. (1994) e Ribeiro et al. (1999), com adaptações para as variações observadas no local de estudo. O tronco foi diferenciado em cilíndrico ou acanalado, com base reta ou dilatada. As raízes variaram entre digitadas e sapopemas. Foram categorizados 13 padrões básicos de textura da casca externa: liso, áspero, sujo, rugoso, fissurado, fendido, lenticelado, reticulado, com placas, escamoso, laminado, variegado e com depressões. O uso de terminologia padronizada na descrição de características de tronco, raízes e casca permite sua aplicação e comparação com dados de estudos desenvolvidos em diferentes regiões, sendo importante ressaltar sua utilização aliada a outros caracteres da planta para uma identificação mais eficiente. / Emergent and canopy trees of lowmontane dense ombrophyllous forest in a permanent plot (10,24ha) in &quotCarlos Botelho&quot State Park, SP, were studied and morphologically described. Trunk, roots and outer bark patterns were considered in order to make field identification easier. A total of 324 trees, 64 species and 36 families were included (DAP &#8805 48cm) and analyzed. We provide descriptions, figures and an identification key based on the following features: trunk at breast high; trunk base; apparent roots; superficial roots; and texture of outer bark. Since there is not a standard morphological nomenclature for these patterns, we propose a nomenclature here, besides using those terms proposed by Torres et al. (1994) and Ribeiro et al. (1999) with some adaptations. Trunk are named terete or angular, straight or with an expanded base. Roots are named digitated or buttressed. Thirteen basic patterns of outer bark texture were named: entire, rough, messy, rugose, fissured, cracked, lenticellate, reticulate, flaky, scaly, laminate, variegated and dippled. Using this terminology allows its application and comparison with data from different areas. They should be used with other features, reproductive and vegetative ones, in order to allow a correct identification.

Dense Deposit Disease (DDD) causes chronic renal dysfunction which progresses to end-stage renal disease in about half of patients within 10 years of diagnosis. Deficiency of and mutations in complement Factor H (CFH) are associated with the development of DDD, suggesting that dysregulation of the alternative pathway (AP) of the complement cascade is important in disease pathophysiology. Patients with DDD are studied to determine whether specific allele variants of the genes of the alternative pathway of the complement system segregate preferentially with the DDD. We have screened coding and intronic regions of genes of the complement system in DDD cases and controls using PCR, restriction digest and bidirectional sequencing. We have been able to identify novel mutations, allele variants and haplotypes in several genes of the complement system which are associated with the DDD phenotype based on statistical analyses. Since we have identified several genes associated with DDD, we have determined possible gene-gene interactions using computational analyses. We have found a strong synergistic interaction between polymorphisms in CFH and C3. To ascertain if the associated allele variants had a functional impact in the complement activity of an individual, we have obtained blood samples from normal individuals and measured AP complement activity and genotyped CFH and C3 for these samples. Association between AP activity and genotypes is analyzed using statistical methods. Significant association of CFH and C3 variants with AP complement activity has been observed. We also have generated a mice deficient of CFH and Factor D (CFD). CFH deficient mice develop renal pathology similar to DDD in humans. Renal function and complement activity have been determined in the double knockout in comparison to CFH deficient and CFD deficient mice. Results have shown that absence of Factor D can inhibit complement activation in CFH mice. Our data imply that DDD is a complex genetic disease and that genes of the AP complement system contribute to level of complement activity and the pathogenesis of DDD. With this study, we hope to develop an effective diagnosis and treatment plan for DDD patients.

<div>Object detection is a considerable area of computer vision. The aim of object detection is to increase its efficacy and accuracy that have always been targeted. The research area of object detection has many broad areas, include self-driving, manufacturing and retail stores. However, scenes of using object detection in detecting dense objects have rarely gathered in much attention. Dense and small object detection is relevant to many real-world scenarios, for example, in retail stores and surveillance systems. Human suffers the speed and accuracy to count and audit the crowded product on the shelves. We motivate to detect the dense product on the shelves. It is a research area related to industries. In this thesis, we going to fine-tune CenterNet as a detector to detect the objects on the shelves. To validate the effectiveness of CenterNet network architecture, we collected the Bottle dataset that collected images from real-world supermarket shelves in different environments. We compared performance on the Bottle Dataset with many different circumstances. The ResNet-101(colored+PT) achieved the best result of CenterNet that outperform other network architectures. we proved perspective transformation can be implemented on state-of-the-art detectors, which solved the issue when detector did not achieve a good result on strongly angled images. We concluded that colored information did contribute to the performance in detecting the objects on the shelf, but it did not contribute as much as geometric information provided for learning its information. The result of the accuracy of detection on CenterNet meets the need of accuracy on industry requirements.</div><div><br></div>

Liquid wastes discharged from industrial outfalls have been researched for many years in the past. Majority of past studies, initiated in 1960s, were experimental studies mainly focused on basics of discharges such as key geometrical properties. Eventually, more robust experimental studies were performed to measure the mixing properties of effluent discharges with various jet configurations and ambient water conditions. Discharges could be as a means of submerged diffusers or surface channels and receiving water could vary from a homogenous calm ambient to a very complex stratified turbulent cross flow ambient. Depending on the bathymetric and economic situation around an outfall project, submerged discharges are preferred designs for most of ocean outfalls. It is the reason that majority of past studies have evaluated the mixing characteristics of submerged jets. Since early 1990s, the numerical modelling has emerged to support complex fluid mechanic problems. Later in 1990s and early in 2000s, the use of computational fluid dynamic (CFD) tools emerged in predicting the jet properties for the effluent discharges. Since then different numerical models have been developed for different applications. Similar to experimental studies, most of numerical studies have been focused on the submerged dense jet discharges. The current study intends to stay focused on the numerical modelling of such jets too; however, to cover the gaps in the literature. To achieve this, a thorough literature review was performed on the past CFD studies of over past 20 years to better understand what was done and what the gaps are. The results of this thorough review revealed that although there has been a great progress in the CFD studies in the field of effluent discharges, there are some applications that have not been investigated before, yet. It was found that there are some discharge inclinations that were not studied numerically before. Four discharge angles of 60°,75°, 80° and 85° were selected in this study, as previous studies mostly focused on 30° and 45°. The higher inclinations are more suitable for deep water outfalls where terminal rise height of the jet does not attach to the ambient water surface. The numerical model OpenFOAM was used in this study which is based on the Finite Volume Method (FVM) applying LRR turbulence model closure. LRR turbulence models was proved to be a capable choice for effluent discharge modelling. The second gap identified in the comprehensive literature review completed was the submerged dense effluent discharge into shallow water with surface attachment (for both inclined and vertical discharges). There was no previous numerical study of such jets identified. Three different regimes were identified: full submergence, plume contact and centerline impingement regimes (i.e. FSR, PCR and CIR). Key geometrical and dilution properties of these jets at surface contact (Xs, Ss) and return point (Xr, Sr) were extracted numerically and compared to those available from experiments. Two discharge angles (30° and 45°) were investigated based on the available experimental data. Five Reynolds-averaged Navier-Stokes (RANS) turbulence models were examined in this study: realizable k-ε and k-ω SST models (known as two-equation turbulence models), v2f (four equations to model anisotropic behavior) and LRR and SSG turbulence models (known as Reynolds stress models - six equations to model anisotropic behavior). Vertical dense effluent discharges are popular in the design of outfall systems. Vertical jets provide the opportunity to be efficient for a range of ambient currents, where the jet will be pushed away not to fall on itself. This research work investigates worst case scenario in terms of mixing and dilution of such jets: vertical dense effluent discharges with no ambient current and in shallow water where jet impacts the surface. This scenario provides a conservative design criteria for such outfall systems. The numerical modelling of such jets has not been studied before and this research work provides novel, though preliminary, insights in simulations of vertical dense effluent discharges in shallow waters. Turbulent vertical discharges with Froude numbers ranging from 9 to 24 were simulated using a Reynolds stress model (RSM), based on the results from inclined dense discharges to characterize the geometrical (i.e., maximum discharge rise Zm and lateral spread Rsp) and dilution μmin properties of such jets. Three flow regimes were reproduced numerically, based on the experimental data: deep, intermediate and impinging flow regimes.

An exo-atmospheric nuclear detonation releases up to 80 percent of its’ energy as X-rays. Satellite’s solar cells and their protective coatings are vulnerable to low energy X-ray radiation. Cold X-rays (~1-1.5 keV) are absorbed close to the surface of materials causing the blow-off and rapid formation of Warm Dense Plasmas (WDPs), particularly in a gap between the unshielded active elements of solar cells. To understand how WDPs are created, it is necessary to investigate the power density distribution produced by cold X-rays for typical solar panel surface materials. The Monte Carlo stepping model implemented in the GEANT4 software toolkit is utilized to determine the power density created by cold X-rays in a multi-layered target composed of a layer of an active cell shielded by layers of cover glass and anti-reflective coating. The power density generated by cold X-rays in the unshielded semiconductor layer at different incidence angles is also investigated in order to account for different orientations of the satellite’s solar panels with respect to the point of nuclear detonation. The flux spectrum of X-rays originating from a nuclear blast is described by the Planck's blackbody function with the temperature from 0.1 keV to 10 keV. The secondary radiation (photo-electrons, fluorescence photons, Auger- and Compton-electrons) resulting from absorption and scattering of primary X-rays is taken into account in the redistribution of energy deposition within slabs. The profiles of power density within the slab system produced by primary cold X-rays, secondary photons and electrons are calculated as a function of depth. The discontinuity in power density profiles is observed at the interfaces of slabs due to discrete changes in stopping power between slab materials. The power density is found to be higher in slab materials with higher mass density. The power density profiles are then used in the atomistic Momentum Scaling Model (MSM) coupled with the Molecular Dynamics (MD) method (MSM-MD) to predict the spatiotemporal evolution of WDP in vacuum. The spatial and temporal distribution of density and temperature fields of expanding WDP is evaluated from the MSM-MD simulations. These modeling results provide insights into the underlining physics of the formation and spatiotemporal evolution of WDPs induced by cold X-rays.

The primary result from this dissertation is following inequality: d(B) ≤ min(2^< wd(B),sup{λ^c(B): λ < wd(B)}) in ZFC, where B is a homogeneous complete Boolean algebra, d(B) is the density, wd(B) is the weak density, and c(B) is the cellularity of B. Chapter II of this dissertation is a general overview of homogeneous complete Boolean algebras. Assuming the existence of a weakly inaccessible cardinal, we give an example of a homogeneous complete Boolean algebra which does not attain its cellularity. In chapter III, we prove that for any integer n > 1, wd_2(B) = wd_n(B). Also in this chapter, we show that if X⊂B is κ—weakly dense for 1 < κ < sat(B), then sup{wd_κ(B):κ < sat(B)} = d(B). In chapter IV, we address the following question: If X is weakly dense in a homogeneous complete Boolean algebra B, does there necessarily exist b € B\{0} such that {x∗b: x ∈ X} is dense in B|b = {c € B: c ≤ b}? We show that the answer is no for collapsing algebras. In chapter V, we give new proofs to some well known results concerning supporting antichains. A direct consequence of these results is the relation c(B) < wd(B), i.e., the weak density of a homogeneous complete Boolean algebra B is at least as big as the cellularity. Also in this chapter, we introduce discernible sets. We prove that a discernible set of cardinality no greater than c(B) cannot be weakly dense. In chapter VI, we prove the main result of this dissertation, i.e., d(B) ≤ min(2^< wd(B),sup{λ^c(B): λ < wd(B)}). In chapter VII, we list some unsolved problems concerning this dissertation.

Nuclear Magnetic Resonance is used for the determination of the properties of porous media in Geophysics and oil exploration. As it stands, there is a challenge in understanding the connection between the times measured in Free Induction Decay Nuclear Magnetic Resonance experiments and the shape of samples. In this work, suspensions and watersaturated densely-packed porous media with the volume fraction of the glass solid phase in the range from 10–4 to ∼1 are found to exhibit FID decay rates proportional to the square root of the volume fraction of the solid phase of the samples. A model of spheres in liquid is proposed for the description of such behavior.

Die schnelle, sog. Saltatorische Erregungsleitung bei den Vertebraten wird durch eine kompakte, um das Axon gewickelte isolierende Schicht, dem Myelin, ermöglicht. Diese Myelinhülle wird von spezialisierten Gliazellen gebildet. Bei der Myelinisisierung wickeln sich deren abgeflachte Membranfortsätze mehrfach konzentrisch um die Axone und bilden nach Verdichtung die typische kompakte, multi-lammelare Struktur des Myelins. Innerhalb dieser Struktur unterscheidet man zwei unterschiedliche Bereiche: Die aus der extrazellulären Apposition gebildete Intraperiod Line und die aus der cytosolischen Apposition gebildete Major Dense Line . Ausschließlich innerhalb der Major Dense Line des ZNS der Fische findet man ein Protein, das nach seinem Molekulargewicht als 36K bezeichnet wird. Es ist nicht glykolisiert und scheint mit der Myelinmembran assoziiert zu sein. Immunologische Untersuchungen zeigten, dass 36K mit keinem der polyklonalen Antikörper gegen eines der bekannten Myelinproteine reagierte. Ebenso zeigten erste Datenbank-Analysen überraschenderweise keine Homologien zu den Myelinproteinen, sondern zu den NAD(P)(H)-abhängigen Oxidoreduktasen. Im Rahmen der vorliegenden Arbeit wurde versucht über einen Dehydrogenase-Assay sowohl eine entsprechende Aktivität als auch ein mögliches Substrat zu identifizieren. Alternativ wurde für das membranassoziierte 36K, das neben IP1 & 2 und Basischen Myelinprotein (MBP) eines der Hauptmyelinproteine im ZNS der Fische darstellt, eine Funktion als Strukturprotein innerhalb der Major Dense Line vermutet. Aufgrund der Sequenzähnlichkeiten von 36K zu den NAD(P)(H)-abhängigen Oxidoreduktasen wurde dabei auch die Beeinflussbarkeit durch Nukleotide geprüft. Des Weiteren wurde im Rahmen dieser Dissertation erstmals versucht, Protein-Komplexe aus dem ZNS-Myelin nativ aufzutrennen und deren Komponenten zu identifizieren.

Diagnostic methods to effectively image dense breast tissue (DBT) can pose challenges for breast cancer screening. While conventional mammography is the gold standard for breast cancer screening, this technique has a low sensitivity to DBT and can miss about 78% of cancers in DBT, but magnetic resonance imaging (MRI) has a high sensitivity for imaging DBT, and produces a smaller number of false positives. The purpose of this study was to determine the extent to which conventional mammograms can miss breast cancer in women with DBT and to determine if an adjunct method of imaging DBT might detect breast cancers that are missed by mammography alone. Quantitative data were collected from a sample of 300 randomly selected participants using surveys. SPSS statistical software was used to analyze the data with the factor analysis method. Qualitative data were collected by telephone interviews from 10 women who were patients of a breast cancer center. NVivo software was used to analyze the data with the thematic analysis method. All analyses were guided by theoretical framework of von Bertalanffy's general systems theory, Miller's living systems theory, and the theory of intelligent medical diagnosis. Key results determined that a significant number of women with DBT had breast cancer that was undetected by mammograms; results also showed that women with DBT can benefit from breast cancer screening by adding an adjunct screening method (e.g., MRI). This study may contribute to social change by making the breast cancer screening community aware of the potential benefit of adding MRI as an adjunct to conventional screening so that more breast cancers are detected in the early stages of the disease.

Indiana University-Purdue University Indianapolis (IUPUI) / Action recognition has been an active research topic for over three decades. There are various applications of action recognition, such as surveillance, human-computer interaction, and content-based retrieval. Recently, research focuses on movies, web videos, and TV shows datasets. The nature of these datasets make action recognition very challenging due to scene variability and complexity, namely background clutter, occlusions, viewpoint changes, fast irregular motion, and large spatio-temporal search space (articulation configurations and motions). The use of local space and time image features shows promising results, avoiding the cumbersome and often inaccurate frame-by-frame segmentation (boundary estimation). We focus on two state of the art methods for the action classification problem: dense trajectories and recurrent neural networks (RNN). Dense trajectories use typical supervised training (e.g., with Support Vector Machines) of features such as 3D-SIFT, extended SURF, HOG3D, and local trinary patterns; the main idea is to densely sample these features in each frame and track them in the sequence based on optical flow. On the other hand, the deep neural network uses the input frames to detect action and produce part proposals, i.e., estimate information on body parts (shapes and locations). We compare qualitatively and numerically these two approaches, indicative to what is used today, and describe our conclusions with respect to accuracy and efficiency.