{"title":"Combinatorics of Linked Systems of Quartet Trees","authors":"Emili Moan, Joseph P. Rusinko","doi":"10.2140/INVOLVE.2016.9.171","DOIUrl":"https://doi.org/10.2140/INVOLVE.2016.9.171","url":null,"abstract":"We apply classical quartet techniques to the problem of phylogenetic decisiveness and find a value $k$ such that all collections of at least $k$ quartets are decisive. Moreover, we prove that this bound is optimal and give a lower-bound on the probability that a collection of quartets is decisive.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132685095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Bayesian Approach to Optimizing Stem Cell Cryopreservation protocols","authors":"S. Sambu","doi":"10.7287/PEERJ.PREPRINTS.608V1","DOIUrl":"https://doi.org/10.7287/PEERJ.PREPRINTS.608V1","url":null,"abstract":"Cryopreservation is beset with the challenge of protocol alignment across a wide range of cell types and process variables. By taking a cross-sectional assessment of previously published cryopreservation data (sample means and standard errors) as preliminary meta-data, a decision tree learning analysis (DTLA) was performed to develop an understanding of target survival and optimized pruning methods based on different approaches. Briefly, a clear direction on the decision process for selection of methods was developed with key choices being the cooling rate, plunge temperature on the one hand and biomaterial choice, use of composites (sugars and proteins), loading procedure and cell location in 3D scaffold on the other. Secondly, using machine learning and generalized approaches via the Na\"ive Bayes Classification (NBC) approach, these metadata were used to develop posterior probabilities for combinatorial approaches that were implicitly recorded in the metadata. These latter results showed that newer protocol choices developed using probability elicitation techniques can unearth improved protocols consistent with multiple unidimensional optimized physical protocols. In conclusion, this article proposes the use of DTLA models and subsequently NBC for the improvement of modern cryopreservation techniques through an integrative approach. \u0000Keywords: 3D cryopreservation, decision-tree learning (DTL), sugars, mouse embryonic stem cells, meta-data, Na\"ive Bayes Classifier (NBC)","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127557017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Screening Genome Sequences for known RNA Genes or Motifs","authors":"D. Gautheret","doi":"10.1002/9783527647064.ch28","DOIUrl":"https://doi.org/10.1002/9783527647064.ch28","url":null,"abstract":"This methods paper presents computational protocols for the identification of non-coding RNA genes or RNA motifs within genomic sequences. An application to bacterial small RNA is proposed.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116700026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quick Detection of Contaminants Leaching from Polypropylene Centrifuge Tube with Surface Enhanced Raman Spectroscopy and Ultra Violet Absorption Spectroscopy","authors":"Zhida Xu, Logan Liu","doi":"10.1002/jrs.2950/pdf","DOIUrl":"https://doi.org/10.1002/jrs.2950/pdf","url":null,"abstract":"Anomalous surface enhanced Raman scattering (SERS) peaks are identified for liquid sample stored in polypropylene centrifuge tubes (PP tube) for months. We observed the unexpected Raman peaks during experiments for Thiamine Hydrochloride aqueous solution stored in PP tube for two months. In order to identify the contaminants we have performed SERS experiments for de-ionized water (DI water) stored in polypropylene centrifuge tube for two months and compared them with fresh DI water sample. We have also carried out Ultra Violet (UV) absorption spectra for both fresh and contaminated water. We believe that the water is contaminated because of chemicals leaching from the PP tube. From the GC-MS data the main contaminant was found to be Phthalic acid and its derivatives. Further SERS and UV absorption experiment for Phthalic acid correlates well with the anomalous peaks identified earlier. We qualitatively confirmed the identification and quantitatively estimated the concentration of suspect contaminants as between 1uM and 10uM with both SERS and UV absorption spectroscopy. With UV absorption spectroscopy, we precisely estimate the concentration as 2.1uM. We have shown that sample in PP tube can be contaminated due to leaching chemicals upon long term storage and suggested SERS and UV-absorption spectroscopy as two quick and simple techniques to detect the contamination","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122599565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Mohanty, Yu Chen, Xihua Wang, M. Hong, Carol L. Rosenberg, David T. Weaver, S. Erramilli
{"title":"Field Effect Transistor Nanosensor for Breast Cancer Diagnostics","authors":"P. Mohanty, Yu Chen, Xihua Wang, M. Hong, Carol L. Rosenberg, David T. Weaver, S. Erramilli","doi":"10.1201/b12138-53","DOIUrl":"https://doi.org/10.1201/b12138-53","url":null,"abstract":"Silicon nanochannel field effect transistor (FET) biosensors are one of the most promising technologies in the development of highly sensitive and label-free analyte detection for cancer diagnostics. With their exceptional electrical properties and small dimensions, silicon nanochannels are ideally suited for extraordinarily high sensitivity. In fact, the high surface-to-volume ratios of these systems make single molecule detection possible. Further, FET biosensors offer the benefits of high speed, low cost, and high yield manufacturing, without sacrificing the sensitivity typical for traditional optical methods in diagnostics. Top down manufacturing methods leverage advantages in Complementary Metal Oxide Semiconductor (CMOS) technologies, making richly multiplexed sensor arrays a reality. Here, we discuss the fabrication and use of silicon nanochannel FET devices as biosensors for breast cancer diagnosis and monitoring.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115008468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cellular decision-making bias: the missing ingredient in cell functional diversity","authors":"Bradly Alicea","doi":"10.6084/M9.FIGSHARE.831474.V3","DOIUrl":"https://doi.org/10.6084/M9.FIGSHARE.831474.V3","url":null,"abstract":"Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"192 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132879314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Lazy Updating increases the speed of stochastic simulations","authors":"K. Ehlert, L. Loewe","doi":"10.14288/1.0043675","DOIUrl":"https://doi.org/10.14288/1.0043675","url":null,"abstract":"Biological reaction networks often contain what might be called 'hub molecules', which are involved in many reactions. For example, ATP is commonly consumed and produced. When reaction networks contain molecules like ATP, they are difficult to efficiently simulate, because every time such a molecule is consumed or produced, the propensities of numerous reactions need to be updated. In order to increase the speed of simulations, we developed 'Lazy Updating', which postpones some propensity updates until some aspect of the state of the system changes by more than a defined threshold. Lazy Updating works with several existing stochastic simulation algorithms, including Gillespie's direct method and the Next Reaction Method. We tested Lazy Updating on two example models, and for the larger model it increased the speed of simulations over eight-fold while maintaining a high level of accuracy. These increases in speed will be larger for models with more widely connected hub molecules. Thus Lazy Updating can contribute towards making models with a limited computing time budget more realistic by including previously neglected hub molecules.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123347092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The new science of metagenomics and the challenges of its use in both developed and developing countries","authors":"Edi Prifti, Jean-Daniel Zucker","doi":"10.1007/978-981-287-527-3_12","DOIUrl":"https://doi.org/10.1007/978-981-287-527-3_12","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121164494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Unified Approach to Integration and Optimization of Parametric Ordinary Differential Equations","authors":"Daniel Kaschek, J. Timmer","doi":"10.1007/978-3-319-23321-5_12","DOIUrl":"https://doi.org/10.1007/978-3-319-23321-5_12","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128421530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}