KATANA - a charge-sensitive triggering system for the Sπ\piRIT experiment

KATANA - the Krakow Array for Triggering with Amplitude discrimiNAtion - has been built and used as a trigger and veto detector for the S$\pi$RIT TPC at RIKEN. Its construction allows operating in magnetic field and providing fast response for ionizing particles, giving the approximate forward multiplicity and charge information. Depending on this information, trigger and veto signals are generated. The article presents performance of the detector and details of its construction. A simple phenomenological parametrization of the number of emitted scintillation photons in plastic scintillator is proposed. The effect of the light output deterioration in the plastic scintillator due to the in-beam irradiation is discussed.Comment: 14 pages, 11 figure

KATANA : a charge-sensitive trigger/veto array for the SπS\pi RIT TPC

KATANA — the Krak´ow Array for Triggering with Amplitude discrimiNAtion, has been built and used as a trigger and veto detector for the SπRIT TPC at RIKEN. Its construction allows operating in magnetic field, providing fast response for ionizing particles and giving the approximate multiplicity and charge information on forward emitted reaction products. Depending on this information, trigger and veto signals are generated. Multi-Pixel Photon Counters were used as light sensors for plastic scintillators. Custom designed front-end and peripheral electronics will be presented as well

Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

Makorin 1 controls embryonic patterning by alleviating Bruno1-mediated repression of oskar translation.

Makorins are evolutionary conserved proteins that contain C3H-type zinc finger modules and a RING E3 ubiquitin ligase domain. In Drosophila, maternal Makorin 1 (Mkrn1) has been linked to embryonic patterning but the mechanism remained unsolved. Here, we show that Mkrn1 is essential for axis specification and pole plasm assembly by translational activation of oskar (osk). We demonstrate that Mkrn1 interacts with poly(A) binding protein (pAbp) and binds specifically to osk 3' UTR in a region adjacent to A-rich sequences. Using Drosophila S2R+ cultured cells we show that this binding site overlaps with a Bruno1 (Bru1) responsive element (BREs) that regulates osk translation. We observe increased association of the translational repressor Bru1 with osk mRNA upon depletion of Mkrn1, indicating that both proteins compete for osk binding. Consistently, reducing Bru1 dosage partially rescues viability and Osk protein level in ovaries from Mkrn1 females. We conclude that Mkrn1 controls embryonic patterning and germ cell formation by specifically activating osk translation, most likely by competing with Bru1 to bind to osk 3' UTR

KRATTA, a triple telescope array for charged reaction products

KRATTA, a new, low threshold, broad energy range triple telescope array has been built to measure the energy, emission angles and isotopic composition of light charged reaction products. It has been equipped with fully digital chains of electronics. The array performed very well during the ASY-EOS experiment, conducted in May 2011 at GSI. The structure and performance of the array are presented using the first experimental results

A predictive model for secondary RNA structure using graph theory and a neural network

Background: Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.Results: The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.Conclusions: There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes

Pilot study on developing a decision support tool for guiding re-administration of chemotherapeutic agent after a serious adverse drug reaction

<p>Abstract</p> <p>Background</p> <p>Currently, there are no standard guidelines for recommending re-administration of a chemotherapeutic drug to a patient after a serious adverse drug reaction (ADR) incident. The decision on whether to rechallenge the patient is based on the experience of the clinician and is highly subjective. Thus the aim of this study is to develop a decision support tool to assist clinicians in this decision making process.</p> <p>Methods</p> <p>The inclusion criteria for patients in this study are: (1) had chemotherapy at National Cancer Centre Singapore between 2004 to 2009, (2) suffered from serious ADRs, and (3) were rechallenged. A total of 46 patients fulfilled the inclusion criteria. A genetic algorithm attribute selection method was used to identify clinical predictors for patients' rechallenge status. A Naïve Bayes model was then developed using 35 patients and externally validated using 11 patients.</p> <p>Results</p> <p>Eight patient attributes (age, chemotherapeutic drug, albumin level, red blood cell level, platelet level, abnormal white blood cell level, abnormal alkaline phosphatase level and abnormal alanine aminotransferase level) were identified as clinical predictors for rechallenge status of patients. The Naïve Bayes model had an AUC of 0.767 and was found to be useful for assisting clinical decision making after clinicians had identified a group of patients for rechallenge. A platform independent version and an online version of the model is available to facilitate independent validation of the model.</p> <p>Conclusion</p> <p>Due to the limited size of the validation set, a more extensive validation of the model is necessary before it can be adopted for routine clinical use. Once validated, the model can be used to assist clinicians in deciding whether to rechallenge patients by determining if their initial assessment of rechallenge status of patients is accurate.</p