Increasing evidence indicates that many, if not all, small genes encoding proteins ≤100 aa are missing in annotations of bacterial genomes currently available. To uncover unannotated small genes in the model bacterium Salmonella enterica Typhimurium 14028s, we used the genomic technique ribosome profiling, which provides a snapshot of all mRNAs being translated (translatome) in a given growth condition. For comprehensive identification of unannotated small genes, we obtained Salmonella translatomes from four different growth conditions: LB, MOPS rich defined medium, and two infection-relevant conditions low Mg2+ (10 µM) and low pH (5.8). To facilitate the identification of small genes, ribosome profiling data were analyzed in combination with in silico predicted putative open reading frames and transcriptome profiles. As a result, we uncovered 130 unannotated ORFs. Of them, 98% were small ORFs putatively encoding peptides/proteins ≤100 aa, and some of them were only expressed in the infection-relevant low Mg2+ and/or low pH condition. We validated the expression of 25 of these ORFs by western blot, including the smallest, which encodes a peptide of 7 aa residues. Our results suggest that many sequenced bacterial genomes are underannotated with regard to small genes and their gene annotations need to be revised.

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This paper proposes a robotic system that automatically identifies and removes spatters generated while removing the back-bead left after the electric resistance welding of the outer and inner surfaces during pipe production. Traditionally, to remove internal spatters on the front and rear of small pipes with diameters of 18–25 cm and lengths of up to 12 m, first, the spatter locations (direction and length) are determined using a camera that is inserted into the pipe, and then a manual grinder is introduced up to the point where spatters were detected. To optimize this process, the proposed robotic system automatically detects spatters by analyzing the images from a front camera and removes them, using a grinder module, based on the spatter location and the circumferential coordinates provided by the detection step. The proposed robot can save work time by reducing the required manual work from two points (the front and back of the pipe) to a single point. Image recognition enables the detection of spatters with sizes between 0.1 and 10 cm with 94% accuracy. The internal average roughness, Ra, of the pipe was confirmed to be 1 µm or less after the spatters were finally removed.