Campus Open Access Funds: Experiences of the KU “One University” Open Access Author Fund

INTRODUCTION: In the summer of 2012, librarians from the Lawrence and Kansas City campuses of the University of Kansas (KU) proposed the creation of a KU “One University” Open Access Fund (OA Author Fund) to support open access publishing for its faculty, students, and staff. KU is a major public research and teaching institution of 28,000 students and 2,600 faculty on five campuses (Lawrence, Kansas City, Overland Park, Wichita, and Salina) (http://ku.edu/about), and has been a leader in open access initiatives for many years. A working group of librarians came together to create and implement a pilot project to explore the administration and impact of an open access publishing fund on KU authors, and the fund was launched in October 2012. DESCRIPTION OF PROJECT: This report documents the group’s experience in developing eligibility criteria and administering the OA Fund. Here we provide insight into our efforts implementing the project, funding results, and plans for continuation. We share the results of the first two years of the OA Author Fund pilot and the lessons learned about open access fund administration. NEXT STEPS: At the close of the pilot in May 2014, the OA fund review team solicited feedback from a faculty advisory group regarding grant recipients, allocation of funds by discipline, and the application process. Based on our findings, we revised eligibility criteria to create a more equitable funding opportunity for the second pilot. The fund was re-launched using these new criteria in Fall of 2014

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

Brain–machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90824/1/1741-2552_8_4_046009.pd

Preclinical Analysis of JAA-F11, a Specific Anti-Thomsen-Friedenreich Antibody via Immunohistochemistry and In Vivo Imaging.

The tumor specificity of JAA-F11, a novel monoclonal antibody specific for the Thomsen-Friedenreich cancer antigen (TF-Ag-alpha linked), has been comprehensively studied by in vitro immunohistochemical (IHC) staining of human tumor and normal tissue microarrays and in vivo biodistribution and imaging by micro-positron emission tomography imaging in breast and lung tumor models in mice. The IHC analysis detailed herein is the comprehensive biological analysis of the tumor specificity of JAA-F11 antibody performed as JAA-F11 is progressing towards preclinical safety testing and clinical trials. Wide tumor reactivity of JAA-F11, relative to the matched mouse IgG3 (control), was observed in 85% of 1269 cases of breast, lung, prostate, colon, bladder, and ovarian cancer. Staining on tissues from breast cancer cases was similar regardless of hormonal or Her2 status, and this is particularly important in finding a target on the currently untargetable triple-negative breast cancer subtype. Humanization of JAA-F11 was recently carried out as explained in a companion paper "Humanization of JAA-F11, a Highly Specific Anti-Thomsen-Friedenreich Pancarcinoma Antibody and In Vitro Efficacy Analysis" (Neoplasia 19: 716-733, 2017), and it was confirmed that humanization did not affect chemical specificity. IHC studies with humanized JAA-F11 showed similar binding to human breast tumor tissues. In vivo imaging and biodistribution studies in a mouse syngeneic breast cancer model and in a mouse-human xenograft lung cancer model with humanized 124I- JAA-F11 construct confirmed in vitro tumor reactivity and specificity. In conclusion, the tumor reactivity of JAA-F11 supports the continued development of JAA-F11 as a targeted cancer therapeutic for multiple cancers, including those with unmet need

Prevalence of Gastrointestinal Symptoms Among Autistic Individuals, With and Without Co-Occurring Intellectual Disability

Gastrointestinal symptoms (GI) are very common among individuals on the autism spectrum. Prior research reports mixed findings regarding whether individuals with autism and co-occurring intellectual disability (ID) have elevated risk of gastrointestinal symptoms relative to individuals with autism alone. GI symptoms can be challenging to assess in individuals with autism spectrum disorder (ASD) and/or ID given challenges with language, communication, and interoception. Prior research has tended to only include individuals with documented presence or absence of GI symptoms or conditions, that is, to exclude observations in which there is uncertainty regarding presence of GI symptoms. Therefore, none of the prior autism studies reported the association between ID and the certainty regarding presence or absence of GI symptoms. The objective of this study was to examine differences in parental certainty and odds of reporting gastrointestinal signs and symptoms among children on the autism spectrum, with and without intellectual disability. Participants were 308 children (36% ID) with a clinical diagnosis of autism spectrum disorder (6-17 years). Parents endorsed whether their child had experienced or displayed a range of signs or symptoms related to GI problems in the past 3 months. Parents of autistic children with ID were less certain about the presence of more subjective symptoms, including abdominal pain, nausea, and bloating. Conversely, certainty regarding more objective signs (e.g., constipation, diarrhea, spitting up, etc.) was not significantly different. More accurate measures for GI signs/symptoms are needed for this population

Analysis of Race and Sex Bias in the Autism Diagnostic Observation Schedule (ADOS-2)

Importance: There are long-standing disparities in the prevalence of autism spectrum disorder (ASD) across race and sex. Surprisingly, few studies have examined whether these disparities arise partially out of systematic biases in the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), the reference standard measure of ASD. Objective: To examine differential item functioning (DIF) of ADOS-2 items across sex and race. Design, Setting, and Participants: This is a cross-sectional study of children who were evaluated for ASD between 2014 and 2020 at a specialty outpatient clinic located in the Mid-Atlantic region of the US. Data were analyzed from July 2021 to February 2022. Exposures: Child race (Black/African American vs White) and sex (female vs male). Main Outcomes and Measures: Item-level biases across ADOS-2 harmonized algorithm items, including social affect (SA; 10 items) and repetitive/restricted behaviors (RRBs; 4 items), were evaluated across 3 modules. Measurement bias was identified by examining DIF and differential test functioning (DTF), within a graded response, item response theory framework. Statistical significance was determined by a likelihood ratio χ2 test, and a series of metrics was used to examine the magnitude of DIF and DTF. Results: A total of 6269 children (mean [SD] age, 6.77 [3.27] years; 1619 Black/African American [25.9%], 3151 White [50.3%], and 4970 male [79.4%]), were included in this study. Overall, 16 of 140 ADOS-2 diagnostic items (11%) had a significant DIF. For race, 8 items had a significant DIF, 6 of which involved SA. No single item showed DIF consistently across all modules. Most items with DIF had greater difficulty and poorer discrimination in Black/African American children compared with White children. For sex, 5 items showed significant DIF. DIF was split across SA and RRB. However, hand mannerisms evidenced DIF across all 5 algorithms, with generally greater difficulty. The magnitude of DIF was only moderate to large for 2 items: hand mannerisms (among female children) and repetitive interests (among Black/African American children). The overall estimated effect of DIF on total DTF was not large. Conclusions and Relevance: These findings suggest that the ADOS-2 does not have widespread systematic measurement bias across race or sex. However, the findings raise some concerns around underdetection that warrant further research

The Ursinus Weekly, May 17, 1973

Japanese students eager to tour US and study at UC • Preview of freshmen reveals a typical lot • POW speaks to psychology classes • Juniors elect officers for their senior year • Economics majors inducted into honor society • Editorial: Ellsberg and his gift of justice; In praise of PBS • Faculty portrait: Mr. Juan Espadas • Final exam schedule • Taming of the Shrew pleases weekend audience • Trackmen complete successful season • British upset 11-8; Smart coaching helps • Lacrosse team wins two but loses the big onehttps://digitalcommons.ursinus.edu/weekly/1105/thumbnail.jp

Constraints on the relationship between stellar mass and halo mass at low and high redshift

We use a statistical approach to determine the relationship between the stellar masses of galaxies and the masses of the dark matter halos in which they reside. We obtain a parameterized stellar-to-halo mass (SHM) relation by populating halos and subhalos in an N-body simulation with galaxies and requiring that the observed stellar mass function be reproduced. We find good agreement with constraints from galaxy-galaxy lensing and predictions of semi-analytic models. Using this mapping, and the positions of the halos and subhalos obtained from the simulation, we find that our model predictions for the galaxy two-point correlation function (CF) as a function of stellar mass are in excellent agreement with the observed clustering properties in the SDSS at z=0. We show that the clustering data do not provide additional strong constraints on the SHM function and conclude that our model can therefore predict clustering as a function of stellar mass. We compute the conditional mass function, which yields the average number of galaxies with stellar masses in the range [m, m+dm] that reside in a halo of mass M. We study the redshift dependence of the SHM relation and show that, for low mass halos, the SHM ratio is lower at higher redshift. The derived SHM relation is used to predict the stellar mass dependent galaxy CF and bias at high redshift. Our model predicts that not only are massive galaxies more biased than low mass ones at all redshifts, but the bias increases more rapidly with increasing redshift for massive galaxies than for low mass ones. We present convenient fitting functions for the SHM relation as a function of redshift, the conditional mass function, and the bias as a function of stellar mass and redshift.Comment: 21 pages, 17 figures, discussion enlarged, one more figure, updated references, accepted for publication in Ap