Striving for Uniqueness: Data-Driven Database Deselection

As libraries endure an ongoing crisis of available funds to meet inflating electronic content costs, the idea to dispatch the perceived least important e-resources to help balance the budget is a tempting solution. Mercer University Libraries recognizes the challenge of finding areas in which to cut back on its resources. They closely examine their subscriptions to prioritize their patrons’ needs, maintain budgetary equilibrium, and remain true to their goals. The Library Systems Department has worked to develop their own tool to assist decision makers with pertinent information about the uniqueness of both their full text and index databases and packages, both to save money and to improve their programming skills. This paper highlights their approach to this tool and the effects it has had so far

Collection Data Visualization: Seeing the Forest Through the Treemap

Collection management is one of the more complicated responsibilities in librarianship. In this task, the librarian must simultaneously synthesize the needs, desires, and aspirations of the institution, departments, and individuals. While much of this is elusive qualitative data that may not yield a definitive answer, we also have increasingly accessible hard data from our integrated library systems (ILSs) that we can synthesize to complement it. In the latest generations of ILSs, this information is readily available to use for statistical analysis and visualization. When it comes to our increasingly limited materials budgets, it is important to make sure that we make the best decisions possible, thus it is advantageous to analyze all the data at our disposal. We introduce a web application that produces live statistics from the ILS. The system uses data points, including collection use and metrics, which describe a collection (e.g., age, quantity). This system goes beyond traditional charts and graphs by employing several visualization techniques that lend a unique perspective to these data points. The particular techniques allow collection managers to visualize multiple data points simultaneously and reveal data correlations that might not otherwise be obvious

Wrangle Your Data Like a Pro With the Data Processing Power of Python

Management, delivery, and marketing of library resources and collections necessitate interaction with a plethora of data from many sources and in many forms. Accessing and transforming data into meaningful information or different formats used in library automation can be time consuming, but a working knowledge of a programming language can improve efficiency in many facets of librarianship. From processing lists to creating extensible markup language (XML), from editing machine-readable cataloging (MARC) records before upload to automating statistical reports, the Python programming language and third-party application programming interfaces (APIs) can be used to accomplish both behind-the-scenes tasks and end-user facing projects. Creating programmatic solutions to problems requires an understanding of potential. Here we summarize the data sources, flows, and transformations used to accomplish existing projects at Mercer University and the College of Charleston. Foundational programming techniques are explained and resources for learning Python are shared

Multi-wavelength aperture polarimetry of debris disc host stars

Debris discs around main sequence stars have been extensively characterised from infrared to millimetre wavelengths through imaging, spectroscopic, and total intensity (scattered light and/or thermal emission) measurements. Polarimetric observations have only been used sparingly to interpret the composition, structure, and size of dust grains in these discs. Here we present new multi-wavelength aperture polarisation observations with parts-per-million sensitivity of a sample of twelve bright debris discs, spanning a broad range of host star spectral types, and disc properties. These measurements were mostly taken with the HIgh Precision Polarimetric Instrument on the Anglo-Australian Telescope. We combine these polarisation observations with the known disc architectures and geometries of the discs to interpret the measurements. We detect significant polarisation attributable to circumstellar dust from HD 377 and HD 39060, and find tentative evidence for HD 188228 and HD 202628.Comment: 25 pages, 8 figures, 9 tables, accepted for publication in MNRA

Status, Testosterone, and Human Intellectual Performance: Stereotype Threat as Status Concern

Results from two experiments suggest that stereotype-threat effects are special cases of a more general process involving the need to maintain or enhance status. We hypothesized that situations capable of confirming a performance stereotype might represent either a threat to status or an opportunity for enhancement of status, depending on the nature of the stereotype. The positive relationship between baseline testosterone and status sensitivity led us to hypothesize that high testosterone levels in males and females would amplify existing performance expectations when gender-based math-performance stereotypes were activated. In Study 1, high-testosterone females performed poorly on a math test when a negative performance stereotype was primed. In Study 2, high-testosterone males excelled on a math test when a positive performance stereotype was primed. The moderating effect of testosterone on performance suggests that a stereotype-relevant situation is capable of conferring either a loss or a gain of status on targets of the stereotype.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

HP1γ function is required for male germ cell survival and spermatogenesis

<p>Abstract</p> <p>Background</p> <p>HP1 proteins are conserved components of eukaryotic constitutive heterochromatin. In mammals, there are three genes that encode HP1-like proteins, termed HP1α, HP1β and HP1γ, which have a high degree of homology This paper describes for the first time, to our knowledge, the physiological function of HP1γ using a gene-targeted mouse.</p> <p>Results</p> <p>While targeting the <it>Cbx3 </it>gene (encoding the HP1γ protein) with a conditional targeting vector, we generated a hypomorphic allele (<it>Cbx3</it>^{<it>hypo</it>}), which resulted in much reduced (barely detectable) levels of HP1γ protein. Homozygotes for the hypomorphic allele (<it>Cbx3</it>^{<it>hypo</it>/<it>hypo</it>}) are rare, with only 1% of <it>Cbx3</it>^{<it>hypo</it>/<it>hypo </it>}animals reaching adulthood. Adult males exhibit a severe hypogonadism that is associated with a loss of germ cells, with some seminiferous tubules retaining only the supporting Sertoli cells (Sertoli cell-only phenotype). The percentage of seminiferous tubules that are positive for L1 ORF1 protein (ORF1p) in <it>Cbx3</it>^{<it>hypo</it>/<it>hypo </it>}testes is greater than that for wild-type testes, indicating that L1 retrotransposon silencing is reversed, leading to ectopic expression of ORF1p in <it>Cbx3</it>^{<it>hypo</it>/<it>hypo </it>}germ cells.</p> <p>Conclusions</p> <p>The <it>Cbx3 </it>gene product (the HP1γ protein) has a non-redundant function during spermatogenesis that cannot be compensated for by the other two HP1 isotypes. The <it>Cbx3</it>^{<it>hypo</it>/<it>hypo </it>}spermatogenesis defect is similar to that found in <it>Miwi2 </it>and <it>Dnmt3L </it>mutants. The <it>Cbx3 </it>gene-targeted mice generated in this study provide an appropriate model for the study of HP1γ in transposon silencing and parental imprinting.</p

System-L amino acid transporters play a key role in pancreatic b-cell signalling and function

The branched-chain amino acids (BCAA) leucine, isoleucine and valine, are essential amino acids that play a critical role in cellular signalling and metabolism. They acutely stimulate insulin secretion and activate the regulatory serine/threonine kinase mammalian target of rapamycin complex 1 (mTORC1), a kinase that promotes increased β-cell mass and function. The effects of BCAA on cellular function are dependent on their active transport into mammalian cells via amino acid transporters and thus the expression and activity of these transporters likely influences β-cell signalling and function. In this report we show that the System-L transporters are required for BCAA uptake into clonal β-cell lines and pancreatic islets and that these are essential for signalling to mTORC1. Further investigation revealed that the System-L transporter LAT1 is abundantly expressed in islets and that knock-down of LAT1 using siRNA inhibits mTORC1 signalling, leucine-stimulated insulin secretion and islet cell proliferation. In summary, we show that the System-L transporter LAT1 is required for regulating β-cell signaling and function in islets and thus may be a novel pharmacological/nutritional target for the treatment and prevention of type-2 diabetes

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

Background: Artificial intelligence (AI) constructs and machine learning (ML) algorithms have demonstrated utility in predicting various clinical, surgical, and financial outcomes. In this study, we applied AI to shoulder hemiarthroplasty (HA) to predict various post-operative complications. Methods: The sample was queried from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) database for all shoulder HA cases from 2008-2018. Six ML algorithms-random forest classifier, gradient boosting classifier, decision tree classifier, SVM classifier-tuned model, Gaussian Naïve Bayes classifier, multi-layer perception-analyzed the sample dataset. Postoperative complications included extended length of stay, non-home discharge destination, transfusion, and any adverse event. Each ML model was compared to logistic regression (LR), and model strength was evaluated. Results: We identified a total of 1585 shoulder HA cases. Mean age, BMI, operative time, and length of stay were 66±12 years, 31±8 kg/m2, 114±61 minutes, and 2.93±6.61 days. Preop hematocrit, longer operative time, and older age were most predictive of extended length of stay. Preop hematocrit, operative time, and ASA class had the highest importance in any adverse events (AAE) prediction. ML models outperformed traditional comorbidity indices, LR, for predicting extended length of stay (79% vs. 66%), non-home discharge destination (79% vs. 65%), any adverse event (78% vs. 66%), and transfusion requirement (82% vs. 63%).  Conclusions: ML algorithms predicted post-surgical outcomes of interest following shoulder HA at a higher rate to conventional LR and can assist orthopedic surgeons in decision making.