Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems

SB38-16/17: Resolution Rewriting the Sports Club Union Bylaws

SB38-16/17: Resolution Rewriting Sports Club Union Bylaws. This resolution was passed 24Y-0N-0A during the November 30, 2016 meeting of the Associated Students of the University of Montana (ASUM)

DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator

Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions.We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model.Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. "who regulates who" (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs

Hydrogen-Bond-Assisted Symmetry Breaking in a Network of Chiral Metal-Organic Assemblies.

Herein we elucidate the interplay of chiral, chelate, solvent, and hydrogen-bonding information in the self-assembly of a series of new three-dimensional metal-organic architectures. Enantiopure ligands, each containing H-bond donors and acceptors, form different structures, depending on the ratio in which they are combined: enantiopure components form M4L4 assemblies, whereas racemic mixtures form M3L3 stacks. Chiral amplification within M3L3 enantiomers was observed when a 2:1 ratio of R and S subcomponent enantiomers was employed. Simply switching the solvent (from MeCN to MeOH) or chelating unit (from bidentate to tridentate) increased the diversity of structures that can be generated from these building blocks, leading to the selective formation of novel M2L2 and M3L2 assemblies. The addition of achiral ligand building blocks resulted in the formation of further structures: When an achiral subcomponent was combined with its R and S chiral congeners, a three-layer heteroleptic architecture was generated, with the achiral unit sitting at the top of the stack. When combined with the S enantiomer only, however, the achiral unit assembled in the center of the structure, thus demonstrating the selective placement of achiral units within chiral systems. Further sorting experiments revealed that combining R and S stereocenters within a single ligand led to diastereoselective product generation. These results show how geometric complementarity between different ligands impacts upon the degree of hydrogen-bonding within the assembly, stabilizing specific low-symmetry architectures from among many possible structural outcomes

Follicle Stimulating Hormone is an accurate predictor of azoospermia in childhood cancer survivors

Funding: RTM is supported by a Wellcome Trust Intermediate Clinical Fellowship (grant no: 098522), https://wellcome.ac.uk/what-we-do/directories/intermediate-clinical-fellowships-people-funded. TWK is supported by Engineering and Physical Sciences Research Council grant EP/P015638/1, http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/P015638/1.The accuracy of Follicle Stimulating Hormone as a predictor of azoospermia in adult survivors of childhood cancer is unclear, with conflicting results in the published literature. A systematic review and post hoc analysis of combined data (n = 367) were performed on all published studies containing extractable data on both serum Follicle Stimulating Hormone concentration and semen concentration in survivors of childhood cancer. PubMed and Medline databases were searched up to March 2017 by two blind investigators. Articles were included if they contained both serum FSH concentration and semen concentration, used World Health Organisation certified methods for semen analysis, and the study participants were all childhood cancer survivors. There was no evidence for either publication bias or heterogeneity for the five studies. For the combined data (n = 367) the optimal Follicle Stimulating Hormone threshold was 10.4 IU/L with specificity 81% (95% CI 76%–86%) and sensitivity 83% (95% CI 76%–89%). The AUC was 0.89 (95%CI 0.86–0.93). A range of threshold FSH values for the diagnosis of azoospermia with their associated sensitivities and specificities were calculated. This study provides strong supporting evidence for the use of serum Follicle Stimulating Hormone as a surrogate biomarker for azoospermia in adult males who have been treated for childhood cancer.Publisher PDFPeer reviewe

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software

Steroid-refractory ulcerative colitis treated with corticosteroids, metronidazole and vancomycin: a case report

BACKGROUND: Increasing evidence elucidating the pathogenic mechanisms of ulcerative colitis (UC) has accumulated and the disease is widely assumed to be the consequence of genetic susceptibility and an abnormal immune response to commensal bacteria. However evidence regarding an infectious etiology in UC remains elusive. CASE PRESENTATION: We report a provocative case of UC with profound rheumatologic involvement directly preceded by Clostridium difficile infection and accompanying fever, vomiting, bloody diarrhea, and arthritis. Colonic biopsy revealed a histopathology suggestive of UC. Antibiotic treatment eliminated detectable levels of enteric pathogens but did not abate symptoms. Resolution of symptoms was procurable with oral prednisone, but tapering of corticosteroids was only achievable in combination therapy with vancomycin and metronidazole. CONCLUSIONS: An infectious pathogen may have both precipitated and exacerbated autoimmune disease attributes in UC, symptoms of which could be resolved only with a combination of corticosteroids, vancomycin and metronidazole. This may warrant the need for more perceptive scrutiny of C. difficile and the like in patients with UC

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/