Browsing by Subject "database"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    2024 LGBTQ+ Index Report
    (Indiana University Lilly Family School of Philanthropy, 2024-04) Indiana University Lilly Family
    The 2024 edition of the LGBTQ+ Index incorporates fresh data from 2020 and 2021 to offer a decade-long perspective on the sector. This period marks a critical juncture, reflecting both the impact of global events like the COVID-19 pandemic and significant shifts in public and private support for LGBTQ+ causes.The primary aim of this Index is to provide philanthropy scholars, practitioners, policymakers, donors, and a wider audience with detailed insights into LGBTQ+ organizations, particularly focusing on the funding disparities they face and the unique challenges they navigate. This information is not only a valuable benchmark for nonprofit organizations within the sector, but also a resource for development officers and nonprofit leaders to forge meaningful connections and align their efforts. Moreover, donors and funders can leverage this research to identify gaps in existing resources and tailor their contributions to address the unique needs of LGBTQ+ organizations.
  • Thumbnail Image
    Item
    Construction of a Database of Secondary Structure Segments and Short Regions of Disorder and Analysis of Their Properties
    Zang, Yizhi; Dunker, Keith
    Prediction of the secondary structure of a protein from its amino acid sequence remains an important task. Not only did the growth of database holding only protein sequences outpace that of solved protein structures, but successful predictions can provide a starting point for direct tertiary structure modeling [1],[2], and they can also significantly improve sequence analysis and sequence-structure threading [3],[4] for aiding in structure and function determination. Previous works on predicting secondary structures of proteins have yielded the best percent accuracy ranging from 63% to 71% [5]. These numbers, however, should be taken with caution since performance of a method based on a training set may vary when trained on a different training set. In order to improve predictions of secondary structure, there are three challenges. The first challenge is establishing an appropriate database. The next challenge is to represent the protein sequence appropriately. The third challenge is finding an appropriate method of classification. So, two of three challenges are related to an appropriate database and characteristic features. Here, we report the development of a database of non-identical segments of secondary structure elements and fragments with missing electron densities (disordered fragments) extracted from Protein Data Bank and categorized into groups of equal lengths, from 6 to 40. The number of residues corresponding to the above-mentioned categories is: 219,788 for α-helices, 82,070 for β-sheets, 179,388 for coils, and 74,724 for disorder. The total number of fragments in the database is 49,544; 17,794 of which are α-helices, 10,216 β-sheets, 16,318 coils, and 5,216 disordered regions. Across the whole range of lengths, α-helices were found to be enriched in L, A, E, I, and R, β-sheets were enriched in V, I, F, Y, and L, coils were enriched in P, G, N, D, and S, while disordered regions were enriched in S, G, P, H, and D. In addition to the amino acid sequence, for each fragment of every structural type, we calculated the distance between the residues immediately flanking its termini. The observed distances have ranges between 3 and 30Å. We found that for the three secondary structure types the average distance between the bookending residues linearly increases with sequence length, while distances were more constant for disorder. For each length between 6 and 40, we compared amino acid compositions of all four structural types and found a strong compositional dependence on length only for the β-sheet fragments, while the other three types showed virtually no change with length. Using the Kullback-Leibler (KL) distance between amino acid compositions, we quantified the differences between the four categories. We found that the closest pair in terms of the KL-distance were coil and disorder (dKL = 0.06 bits), then α-helix and β-sheet (dKL = 0.14 bits), while all other pairs we almost equidistant from one another (dKL ≈ 0.25 bits). With the increasing segment length we found a decreasing KL-distance between sheet and coil, sheet and disorder, and disorder and helix. Analyzing hierarchical clustering of length from 6 to 18 for sheet, coil, disorder, and helix, we found that the group coil had the closet proximity among lengths from 6 to 18. The next closest were helix and disorder. The sheet has the most difference among its length from 6 to 18. In group sheet and coil, fragments of length 17 had the longest distance while fragments of length 6 had the longest distance in group disorder and helix.
  • No Thumbnail Available
    Item
    LabVIEW™ Database Interfacing For Robotic Control
    (2006-07-26T14:13:05Z) Gebregziabher, Netsanet; Perry, Douglas G.
    The Zymark™ System is a lab automation workstation that uses the Caliper Life Sciences (Hopkinton, MA) Zymate XP robot. At Indiana University-Purdue University Indianapolis, a Zymate is used in a course, INFO I510 Data Acquisition and Laboratory Automation, to demonstrate the fundamentals of laboratory robotics. This robot has been re-engineered to function with National Instruments™ graphical software program LabVIEW™. LabVIEW is an excellent tool for robotic control. Based on changing conditions, it is able to dynamically use data from any source to modify the operating parameters of a robot. For dynamically changing information, storage of that information must be readily accessible. For example, there is a need to continuously store and update the calibration data of the robot, populate the setting of each axis and positioning inside the workplace, and also store robot positioning information. This can be achieved by using a database which allows for robotic control data to be easily searched and accessed. To address this need, an interface was developed which would allow full, dynamic communication between any LabVIEW program (called “virtual instruments,” or VIs) and the database. This has been accomplished by developing a set of subVIs that can be dropped into the calling robotic control VIs. With these subVIs, a user has the ability to create table and column information, delete a table, retrieve table information by clicking a particular table name on the user interface, or query using any SQL-specific combination of columns or tables within the database. For robot functionality, subVIs were created to store and retrieve data such as calibration data points and regression calculations.