980 research outputs found
Experiential Learning: Practicing Real World Financial Planning
Experiential learning plays a critical role on reinforcing knowledge learned from textbooks and apply it in the real world scenarios. This internship provides a financial planning major student with valuable learning experiences at one of the major financial planning firms in the Columbus area. During the internship, the student learned to use the financial planning software, investment platform and client management system. Additionally, the student participated in client meetings and worked with various teams focusing on different aspects in financial planning.https://fuse.franklin.edu/ss2018/1060/thumbnail.jp
N′-(4-Methoxybenzylidene)-4-nitrobenzohydrazide methanol solvate
The title compound, C15H13N3O4·CH4O, was synthesized from the reaction of 4-methoxybenzaldehyde with 4-nitrobenzohydrazide in methanol. The benzene rings of the Schiff base molecule are nearly coplanar, making a dihedral angle of 7.0 (3)°. The methanol solvent molecules are linked to the Schiff base molecules by N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds, forming chains running parallel to the b axis
3,4-Dihydroxy-N′-(2-hydroxybenzylidene)benzohydrazide–methanol–water (2/1/3)
The asymmetric unit of the title compound, C14H12N2O4·0.5CH4O·1.5H2O, consists of two Schiff base molecules, three water molecules and one methanol molecule. The dihedral angle between the two benzene rings is 7.8 (2)° in one of the molecules and 4.0 (2)° in the other. Intramolecular O—H⋯O and O—H⋯N hydrogen bonds are observed. Molecules are linked into a three-dimensional network by O—H⋯O and N—H⋯O intermolecular hydrogen bonds
N′-(2-Hydroxy-5-chlorobenzylidene)-4-nitrobenzohydrazide methanol solvate
The title compound, C14H10ClN3O4·CH4O, was synthesized from the reaction of 5-chlorosalicylaldehyde with 4-nitrobenzohydrazide in methanol. The Schiff base molecule is nearly planar, with a dihedral angle of 9.1 (3)° between the two benzene rings. The methanol solvent molecules are linked to the Schiff base molecules by N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds, forming chains running parallel to the a axis
2,4-Dihydroxy-N′-(4-methoxybenzylidene)benzohydrazide
The molecule of the title compound, C15H14N2O4, displays a trans configuration with respect to the hydrazide C=N bond. The dihedral angle between the two benzene rings is 15.0 (2)°. In the crystal structure, molecules are linked through intermolecular O—H⋯N and O—H⋯O hydrogen bonds, forming layers parallel to the ab plane; an intramolecular N—H⋯O hydrogen bond is also present
Septin 9 hypermethylation contributes to migration and resistance to drug treatments in colon cancer
Purpose: To examine septin 9 gene-promoter methylation content in colorectal cancer and establish its significance in cancer progression and chemoresistance.Methods: Patient samples and colorectal cancer cell lines (CRC) were evaluated for septin 9 expression and promoter hypermethylation content. Septin 9 promoter methylation and expression in cells were perturbed by 5-AZA (5-aza-2'-deoxycytidine) treatments or overexpression and probed for changes in Rho A signaling, cell proliferation, and migration. Finally, the significance of septin 9 methylation in chemoresistance was probed using apoptotic assays in CRC cells and in a xenograft tumor model.Results: Expression analysis showed a reduction in septin 9 levels in tumor tissues (p < 0.001) and cell lines (p < 0.01), while an increase in septin 9 promoter methylation was seen, respectively ( > 2-fold; p < 0.01). Increasing septin 9 levels in CRC cells by 5-AZA treatments or overexpression showed decreased Rho A signaling and cell migration (p < 0.01), whereas cell proliferation remained unaffected. Furthermore, increasing septin 9 levels also exhibited increased cisplatin-induced apoptosis in CRC cells and reduced chemoresistance in the mouse (~2-fold; p < 0. 01).Conclusion: Septin 9 promoter hypermethylation reduces septin 9 expression and promotes migration and chemoresistance.Keywords: Septin 9, Hypermethylation, Colorectal cancer, Drug resistance, Rho A signalin
3,5-Dihydroxy-N′-[(2-hydroxy-1-naphthyl)methylene]benzohydrazide
In the title compound, C18H14N2O4, the dihedral angle between the benzene ring and the naphthyl ring system is 10.1 (2)°. The molecule is nearly planar, with a mean deviation from the plane of 0.141 (2) Å for 24 non-H atoms. An intramolecular O—H⋯N hydrogen bond forms a pseudo-6-membered ring and the molecules are linked into sheets by intermolecular N—H⋯O and O—H⋯O hydrogen bonds
(E)-4-Bromo-N′-(2-hydroxy-1-naphthylmethylene)benzohydrazide
The title compound, C18H13BrN2O2, was synthesized by the reaction of 2-hydroxy-1-naphthaldehyde with 4-bromobenzohydrazide. This Schiff base molecule has an E configuration about the C=N bond and is almost planar, the dihedral angle between the mean planes through the substituted benzene ring and the naphthyl system being 6.6 (2)°. There is an intramolecular O—H⋯N hydrogen bond involving the naphthyl hydroxy substituent and the N′ atom of the hydrazide group. In the crystal structure, molecules are linked through intermolecular N—-H⋯O hydrogen bonds to form chains extending along the b direction
Feature Selection Inspired Classifier Ensemble Reduction
Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets. ? 2013 IEEE
Capturing Data Uncertainty in High-Volume Stream Processing
We present the design and development of a data stream system that captures
data uncertainty from data collection to query processing to final result
generation. Our system focuses on data that is naturally modeled as continuous
random variables. For such data, our system employs an approach grounded in
probability and statistical theory to capture data uncertainty and integrates
this approach into high-volume stream processing. The first component of our
system captures uncertainty of raw data streams from sensing devices. Since
such raw streams can be highly noisy and may not carry sufficient information
for query processing, our system employs probabilistic models of the data
generation process and stream-speed inference to transform raw data into a
desired format with an uncertainty metric. The second component captures
uncertainty as data propagates through query operators. To efficiently quantify
result uncertainty of a query operator, we explore a variety of techniques
based on probability and statistical theory to compute the result distribution
at stream speed. We are currently working with a group of scientists to
evaluate our system using traces collected from the domains of (and eventually
in the real systems for) hazardous weather monitoring and object tracking and
monitoring.Comment: CIDR 200
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