SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost ({\em e.g.}, running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at https://github.com/pingyang1117/SMILEtrack_Officia

Solution structure of the dimerization domain of ribosomal protein P2 provides insights for the structural organization of eukaryotic stalk

The lateral stalk of ribosome is responsible for kingdom-specific binding of translation factors and activation of GTP hydrolysis that drives protein synthesis. In eukaryotes, the stalk is composed of acidic ribosomal proteins P0, P1 and P2 that constitute a pentameric P-complex in 1: 2: 2 ratio. We have determined the solution structure of the N-terminal dimerization domain of human P2 (NTD-P2), which provides insights into the structural organization of the eukaryotic stalk. Our structure revealed that eukaryotic stalk protein P2 forms a symmetric homodimer in solution, and is structurally distinct from the bacterial counterpart L12 homodimer. The two subunits of NTD-P2 form extensive hydrophobic interactions in the dimeric interface that buries 2400 Å2 of solvent accessible surface area. We have showed that P1 can dissociate P2 homodimer spontaneously to form a more stable P1/P2 1 : 1 heterodimer. By homology modelling, we identified three exposed polar residues on helix-3 of P2 are substituted by conserved hydrophobic residues in P1. Confirmed by mutagenesis, we showed that these residues on helix-3 of P1 are not involved in the dimerization of P1/P2, but instead play a vital role in anchoring P1/P2 heterodimer to P0. Based on our results, models of the eukaryotic stalk complex were proposed

Solution structure of the dimerization domain of ribosomal protein P2 provides insights for the structural organization of eukaryotic stalk

The lateral stalk of ribosome is responsible for kingdom-specific binding of translation factors and activation of GTP hydrolysis that drives protein synthesis. In eukaryotes, the stalk is composed of acidic ribosomal proteins P0, P1 and P2 that constitute a pentameric P-complex in 1: 2: 2 ratio. We have determined the solution structure of the N-terminal dimerization domain of human P2 (NTD-P2), which provides insights into the structural organization of the eukaryotic stalk. Our structure revealed that eukaryotic stalk protein P2 forms a symmetric homodimer in solution, and is structurally distinct from the bacterial counterpart L12 homodimer. The two subunits of NTD-P2 form extensive hydrophobic interactions in the dimeric interface that buries 2400 Å2 of solvent accessible surface area. We have showed that P1 can dissociate P2 homodimer spontaneously to form a more stable P1/P2 1 : 1 heterodimer. By homology modelling, we identified three exposed polar residues on helix-3 of P2 are substituted by conserved hydrophobic residues in P1. Confirmed by mutagenesis, we showed that these residues on helix-3 of P1 are not involved in the dimerization of P1/P2, but instead play a vital role in anchoring P1/P2 heterodimer to P0. Based on our results, models of the eukaryotic stalk complex were proposed

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost (e.g., running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at http://github.com/pingyang1117/SMILEtrack_official