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Utterbackia imbecillis, Specimen #1323
One preserved mussel specimen including both the left and right valves. The specimen exhibits an oval shape; thin shell; double looped beak sculpturing; bluish-white internal coloring; green external coloring; no external sculpturing. Collected in the San Jacinto basin. The specimen measures between 0 - 60 mm in length and was assessed to be relatively-recently dead when collected.
Date:
October 23, 1976
Creator:
Britton, Joseph
Object Type:
Specimen
System:
The UNT Digital Library
Utterbackia imbecillis, Specimen #1324
One preserved mussel specimen including both the left and right valves. The specimen exhibits an oval shape; thin shell; double looped beak sculpturing; bluish-white internal coloring; tan external coloring; no external sculpturing. Collected in the San Jacinto basin. The specimen measures between 0 - 60 mm in length and was assessed to be relatively-recently dead when collected.
Date:
October 23, 1976
Creator:
Britton, Joseph
Object Type:
Specimen
System:
The UNT Digital Library
Utterbackia imbecillis, Specimen #1325
One preserved mussel specimen including both the left and right valves. The specimen exhibits an oval shape; thin shell; double looped beak sculpturing; bluish-white internal coloring; green external coloring; no external sculpturing. Collected in the San Jacinto basin. The specimen measures between 0 - 60 mm in length and was assessed to be relatively-recently dead when collected.
Date:
October 23, 1976
Creator:
Britton, Joseph
Object Type:
Specimen
System:
The UNT Digital Library
One thousand plant transcriptomes and the phylogenomics of green plants
Article describes the results of the One Thousand Plant Transcriptomes Initiative's efforts to sequence the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta).
Date:
October 23, 2019
Creator:
One Thousand Plant Transcriptomes Initiative
Object Type:
Article
System:
The UNT Digital Library
Identification of Novel Antimicrobial Resistance Genes Using Machine Learning, Homology Modeling, and Molecular Docking
Article claims antimicrobial resistance (AMR) threatens the healthcare system worldwide with the rise of emerging drug resistant infectious agents. To infer novel resistance genes, we used complete gene sets of several bacterial strains known to be susceptible or resistant to specific drugs and associated phenotypic information within a machine learning framework that enabled prioritizing genes potentially involved in resistance.
Date:
October 23, 2022
Creator:
Sunuwar, Janak & Azad, Rajeev K.
Object Type:
Article
System:
The UNT Digital Library