Internship Experiences Contribute to Confident Career Decision Making for Doctoral Students in the Life Sciences.

The Graduate Student Internships for Career Exploration (GSICE) program at the University of California, San Francisco (UCSF), offers structured training and hands-on experience through internships for a broad range of PhD-level careers. The GSICE program model was successfully replicated at the University of California, Davis (UC Davis). Here, we present outcome data for a total of 217 PhD students participating in the UCSF and UC Davis programs from 2010 to 2015 and 2014 to 2015, respectively. The internship programs at the two sites demonstrated comparable participation, internship completion rates, and overall outcomes. Using survey, focus group, and individual interview data, we find that the programs provide students with career development skills, while increasing students' confidence in career exploration and decision making. Internships, in particular, were perceived by students to increase their ability to discern a career area of choice and to increase confidence in pursuing that career. We present data showing that program participation does not change median time to degree and may help some trainees avoid "default postdocs." Our findings suggest important strategies for institutions developing internship programs for PhD students, namely: including a structured training component, allowing postgraduation internships, and providing a central organization point for internship programs

Efficacy of conjoint behavioral consultation in developmental-behavioral pediatric services.

• Purpose: To evaluate the effects of the CBC model in addressing presenting concerns for children across home, school, and health care systems. • What are the general effects of CBC in addressing identified concerns in a medically-referred sample? • How do parents and teachers perceive CBC in terms of its perceived effectiveness and acceptability? • How satisfied are parents and teachers with CBC consultants and services when provided across homeschool- medical settings

Estimation of turbulence in fan-rotor wakes for broadband noise prediction during acoustic preliminary design

When calculating broadband fan noise caused by rotor-stator wake interaction analytically, information about the airflow, particularly about the turbulence in the rotor wakes, is necessary. During the pre-design phase, two-dimensional streamline methods are commonly used. These provide only general flow quantities like mean-flow velocities or total-pressure losses. Turbulent parameters such as turbulent kinetic energy and turbulent integral length scale need to be deduced from these quantities. There are several models mentioned in the literature which correlate the wake size with the wake turbulence. But they usually comprise calibration factors that need to be assessed empirically by numerical simulations or measurements. The contribution of the paper is to present an updated semi-empirical model for rotor-wake turbulence quantities, derived on the basis of an extensive comparison of the model with measurements and numerical simulations on four different turbofan stages. A recalibration of the empirical factors improved the noise prediction by 8 dB, reaching an accuracy of 2 dB. In addition, it is shown, that the endwall flow is responsible for large variance in the noise prediction, and may have a contribution of up to 2 dB to the overall sound power

AntiFam: a tool to help identify spurious ORFs in protein annotation

As the deluge of genomic DNA sequence grows the fraction of protein sequences that have been manually curated falls. In turn, as the number of laboratories with the ability to sequence genomes in a high-throughput manner grows, the informatics capability of those labs to accurately identify and annotate all genes within a genome may often be lacking. These issues have led to fears about transitive annotation errors making sequence databases less reliable. During the lifetime of the Pfam protein families database a number of protein families have been built, which were later identified as composed solely of spurious open reading frames (ORFs) either on the opposite strand or in a different, overlapping reading frame with respect to the true protein-coding or non-coding RNA gene. These families were deleted and are no longer available in Pfam. However, we realized that these may perform a useful function to identify new spurious ORFs. We have collected these families together in AntiFam along with additional custom-made families of spurious ORFs. This resource currently contains 23 families that identified 1310 spurious proteins in UniProtKB and a further 4119 spurious proteins in a collection of metagenomic sequences. UniProt has adopted AntiFam as a part of the UniProtKB quality control process and will investigate these spurious proteins for exclusion

The Structure-Function Linkage Database

The Structure–Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure–function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies ‘look alike’, making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity

The PRINTS database: a fine-grained protein sequence annotation and analysis resource—its status in 2012

The PRINTS database, now in its 21st year, houses a collection of diagnostic protein family ‘fingerprints’. Fingerprints are groups of conserved motifs, evident in multiple sequence alignments, whose unique inter-relationships provide distinctive signatures for particular protein families and structural/functional domains. As such, they may be used to assign uncharacterized sequences to known families, and hence to infer tentative functional, structural and/or evolutionary relationships. The February 2012 release (version 42.0) includes 2156 fingerprints, encoding 12 444 individual motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Here, we report the current status of the database, and introduce a number of recent developments that help both to render a variety of our annotation and analysis tools easier to use and to make them more widely available

Gene Ontology: Pitfalls, Biases, and Remedies.

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure