MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings

Head detection in the indoor video is an essential component of building occupancy detection. While deep models have achieved remarkable progress in general object detection, they are not satisfying enough in complex indoor scenes. The indoor surveillance video often includes cluttered background objects, among which heads have small scales and diverse poses. In this paper, we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach that leverages head motion information to guide the deep model to extract effective head features in indoor scenarios. By taking the pixel-wise difference of adjacent frames as the auxiliary input, MPSN effectively enhances human head motion information and removes the irrelevant objects in the background. Compared with prior methods, it achieves superior performance on the two indoor video datasets. Our experiments show that MPSN successfully suppresses static background objects and highlights the moving instances, especially human heads in indoor videos. We also compare different methods to capture head motion, which demonstrates the simplicity and flexibility of MPSN. Finally, to validate the robustness of MPSN, we conduct adversarial experiments with a mathematical solution of small perturbations for robust model selection. Code is available at https://github.com/pl-share/MPSN

An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptions

The transportation industry, particularly the trucking sector, is prone to workplace accidents and fatalities. Accidents involving large trucks accounted for a considerable percentage of overall traffic fatalities. Recognizing the crucial role of safety climate in accident prevention, researchers have sought to understand its factors and measure its impact within organizations. While existing data-driven safety climate studies have made remarkable progress, clustering employees based on their safety climate perception is innovative and has not been extensively utilized in research. Identifying clusters of drivers based on their safety climate perception allows the organization to profile its workforce and devise more impactful interventions. The lack of utilizing the clustering approach could be due to difficulties interpreting or explaining the factors influencing employees' cluster membership. Moreover, existing safety-related studies did not compare multiple clustering algorithms, resulting in potential bias. To address these issues, this study introduces an interpretable clustering approach for safety climate analysis. This study compares 5 algorithms for clustering truck drivers based on their safety climate perceptions. It proposes a novel method for quantitatively evaluating partial dependence plots (QPDP). To better interpret the clustering results, this study introduces different interpretable machine learning measures (SHAP, PFI, and QPDP). Drawing on data collected from more than 7,000 American truck drivers, this study significantly contributes to the scientific literature. It highlights the critical role of supervisory care promotion in distinguishing various driver groups. The Python code is available at https://github.com/NUS-DBE/truck-driver-safety-climate.Comment: Submitted to Journal:Accident Analysis and Preventio

Identification of mouse Jun dimerization protein 2 as a novel repressor of ATF-211The nucleotide sequence reported herein has been deposited in the DDBJ, EMBL and GenBank databanks under the accession number AB034697.

AbstractA mouse cDNA that encodes a DNA-binding protein was identified by yeast two-hybrid screening, using activating transcription factor-2 (ATF-2) as the bait. The protein contained a bZIP (basic amino acid-leucine zipper region) domain and its amino acid sequence was almost identical to that of rat Jun dimerization protein 2 (JDP2). Mouse JDP2 interacted with ATF-2 both in vitro and in vivo via its bZIP domain. It was encoded by a single gene and various transcripts were expressed in all tested tissues of adult mice, as well as in embryos, albeit at different levels in various tissues. Furthermore, mouse JDP2 bound to the cAMP-response element (CRE) as a homodimer or as a heterodimer with ATF-2, and repressed CRE-dependent transcription that was mediated by ATF-2. JDP2 was identified as a novel repressor protein that affects ATF-2-mediated transcription

JDP2, a Repressor of AP-1, Recruits a Histone Deacetylase 3 Complex To Inhibit the Retinoic Acid-Induced Differentiation of F9 Cells

Up-regulation of the c-jun gene is a critical event in the retinoic acid (RA)-mediated differentiation of embryonal carcinoma F9 cells. Activating transcription factor 2 (ATF-2) and p300 cooperate in the activation of transcription of the c-jun gene during the differentiation of F9 cells. We show here that the overexpression of Jun dimerization protein 2 (JDP2), a repressor of AP-1, inhibits the transactivation of the c-jun gene by ATF-2 and p300 by recruitment of the histone deacetylase 3 (HDAC3) complex, thereby repressing the RA-induced transcription of the c-jun gene and inhibiting the RA-mediated differentiation of F9 cells. Moreover, chromatin immunoprecipitation assays showed that the JDP2/HDAC3 complex, which binds to the differentiation response element within the c-jun promoter in undifferentiated F9 cells, was replaced by the p300 complex in response to RA, with an accompanying change in the histone acetylation status of the chromatin, the initiation of transcription of the c-jun gene, and the subsequent differentiation of F9 cells. These results suggest that JDP2 may be a key factor that controls the commitment of F9 cells to differentiation and shed new light on the mechanism by which an AP-1 repressor functions

Personal exposure of PM2.5 emitted from solid fuels combustion for household heating and cooking in rural Guanzhong Plain, northwestern China

International audienc

Identification of an FHL1 Protein Complex Containing Gamma-Actin and Non-Muscle Myosin IIB by Analysis of Protein-Protein Interactions

<div><p>FHL1 is multifunctional and serves as a modular protein binding interface to mediate protein-protein interactions. In skeletal muscle, FHL1 is involved in sarcomere assembly, differentiation, growth, and biomechanical stress. Muscle abnormalities may play a major role in congenital clubfoot (CCF) deformity during fetal development. Thus, identifying the interactions of FHL1 could provide important new insights into its functional role in both skeletal muscle development and CCF pathogenesis. Using proteins derived from rat L6GNR4 myoblastocytes, we detected FHL1 interacting proteins by immunoprecipitation. Samples were analyzed by liquid chromatography mass spectrometry (LC-MS). Dynamic gene expression of FHL1 was studied. Additionally, the expression of the possible interacting proteins gamma-actin and non-muscle myosin IIB, which were isolated from the lower limbs of E14, E15, E17, E18, E20 rat embryos or from adult skeletal muscle was analyzed. Potential interacting proteins isolated from E17 lower limbs were verified by immunoprecipitation, and co-localization in adult gastrocnemius muscle was visualized by fluorescence microscopy. FHL1 expression was associated with skeletal muscle differentiation. E17 was found to be the critical time-point for skeletal muscle differentiation in the lower limbs of rat embryos. We also identified gamma-actin and non-muscle myosin IIB as potential binding partners of FHL1, and both were expressed in adult skeletal muscle. We then demonstrated that FHL1 exists as part of a complex, which binds gamma-actin and non-muscle myosin IIB.</p></div