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