Developing a scoring function for NMR structure-based assignments using machine learning

Determining the assignment of signals received from the ex- periments (peaks) to speci_c nuclei of the target molecule in Nuclear Magnetic Resonance (NMR1) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structure- based assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classi_ca- tion problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs o_er mixed results

Developing a Scoring Function for NMR Structure-based Assignments using Machine Learning

Abstract. Determining the assignment of signals received from the experiments (peaks) to specific nuclei of the target molecule in Nuclear Magnetic Resonance (NMR 1 ) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structurebased assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classification problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs offer mixed results

Generic Menu Optimization for Multi-profile Customer Systems

1st IEEE International Symposium on Systems Engineering (2015 : Italy)The use of optimal ATM menu structuring for different customer profiles is essential because of usability, efficiency, and customer satisfaction. Especially in competitive industries such as banking, having optimal user interface (UI) is a must. Determining the optimal menu structure is generally accomplished through manual adjustment of the menu elements. However, such an approach is inherently flawed due to the overwhelming size of the optimization variables' search space. Previous studies on menu optimization either are based on customer questionnaires or made for only a specific menu type using heuristic approaches (i.e., not generic). In this paper, we propose an systematic optimization method for menu structuring problem through a novel Mixed Integer Programming (MIP) framework. Our optimization approach is not specific to a predetermined menu class, on the contrary, the MIP model is designed to be a generic optimization framework that can be applied to a wide range of menu optimization problems. We evaluated the performance gains on a dataset of actual ATM usage logs for a period of 18 months consisting of 40 million transactions. We validated our results with both simulation application and mining of existing data logs. The results show that the proposed optimization approach provides significant reduction in the average transaction completion time and the overall click count.IEEE Systems Counci

Factors affecting rural development in turkey: BartIn case study

The aim of this study is to establish the most important factors affecting rural development in Turkey by means of a multi-dimensional approach and to achieve realistic and practical rural development strategies using these factors. For this purpose, a total of 96 villages are selected fully covering two counties in the BartIn province located in the Western Black Sea Region of Turkey, which is among the provinces with the lowest per capita income and the highest share of village population. 36 variables which characterise the level of development in villages are developed. These variables are measuring environmental, economic and socio-cultural dimensions and the relationships among them. Principal component and regression analyses are applied determining that there are 12 factors affecting development of the villages. These are (1) geographical location, (2) size of a village, (3) productivity of land, (4) type of land use, (5) active population, (6) poplar production areas, (7) proximity to a river, (8) housing comfort, (9) characteristics of drinking water, (10) productive fruit areas, (11) cooperativization and (12) social infrastructure investments. Based on these 12 factors, a development index (DI) is developed consisting of the 12 variables with the highest factor loading in each derived factor. Villages are divided into three groups based on (1) the DI values and (2) 36 variables used in a discriminant analysis, showing that the proposed DI is a reliable index to measure variation in development. According to these results, development strategies for each village group are put forth. Subsequently, the methodology developed in this paper can be used to monitor village development and to assist in effective use of resources for sustainable forestry and development in Turkey.Sustainable rural development Village Economic-social and environmental indicators Resource allocation Turkey

Ultrasound guidance in intracranial tumor resection: Correlation with postoperative magnetic resonance findings

Purpose: To determine the inter-method agreement between intraoperative ultrasonography and postoperative contrast-enhanced magnetic resonance imaging (MRI) in detecting tumor residue

The Impact of Incapacitation of Multiple Critical Sensor Nodes on Wireless Sensor Network Lifetime

Wireless sensor networks (WSNs) are envisioned to be utilized in many application areas, such as critical infrastructure monitoring, and therefore, WSN nodes are potential targets for adversaries. Network lifetime is one of the most important performance indicators in WSNs. The possibility of reducing the network lifetime significantly by eliminating a certain subset of nodes through various attacks will create the opportunity for the adversaries to hamper the performance of WSNs with a low risk of detection. However, the extent of reduction in network lifetime due to elimination of a group of critical sensor nodes has never been investigated in the literature. Therefore, in this letter, we create two novel algorithms based on a linear programming framework to model and analyze the impact of critical node elimination attacks on WSNs and explore the parameter space through numerical evaluations of the algorithms. Our results show that critical node elimination attacks can significantly shorten the network lifetime

Does glioblastoma cyst fluid promote sciatic nerve regeneration?

Glioblastoma cyst fluid contains growth factors and extracellular matrix proteins which are known as neurotrophic and neurite-promoting agents. Therefore, we hypothesized that glioblastoma cyst fluid can promote the regeneration of injured peripheral nerves. To validate this hypothesis, we transected rat sciatic nerve, performed epineural anastomosis, and wrapped the injured sciatic nerve with glioblastoma cyst fluid- or saline-soaked gelatin sponges. Neurological function and histomorphological examinations showed that compared with the rats receiving local saline treatment, those receiving local glioblastoma cyst fluid treatment had better sciatic nerve function, fewer scars, greater axon area, counts and diameter as well as fiber diameter. These findings suggest that glioblastoma cyst fluid can promote the regeneration of injured sciatic nerve and has the potential for future clinical application in patients with peripheral nerve injury

Does glioblastoma cyst fluid promote sciatic nerve regeneration?

Glioblastoma cyst fluid contains growth factors and extracellular matrix proteins which are known as neurotrophic and neurite-promoting agents. Therefore, we hypothesized that glioblastoma cyst fluid can promote the regeneration of injured peripheral nerves. To validate this hypothesis, we transected rat sciatic nerve, performed epineural anastomosis, and wrapped the injured sciatic nerve with glioblastoma cyst fluid- or saline-soaked gelatin sponges. Neurological function and histomorphological examinations showed that compared with the rats receiving local saline treatment, those receiving local glioblastoma cyst fluid treatment had better sciatic nerve function, fewer scars, greater axon area, counts and diameter as well as fiber diameter. These findings suggest that glioblastoma cyst fluid can promote the regeneration of injured sciatic nerve and has the potential for future clinical application in patients with peripheral nerve injury