Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface frictional metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss particularly due to snowplow operations doesn't always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality isn't straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection is conducted on a dataset comprising 183,794 20-meter MPD measurements from actual chip seal projects across six districts in Indiana. This results in typical 20 m-segment MPD ranges of [0.9, 1.9], [0.6, 2.1], [0.3, 1.3], [1.0, 1.7], [0.6, 1.9], and [1.0, 2.3] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation is proposed. The first step calculates outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assesses unqualified rates across projects, setting a threshold of 50% for 1-mile unqualified wheel track segments. While the results are data-specific, this framework offers pavement construction practitioners a foundational QC standard for chip seal projects.

Adipose tissue is a crucial organ in energy metabolism and thermoregulation. Adipose tissue phenotype is controlled by various signaling mechanisms under pathophysiological conditions. Type II transmembrane serine proteases (TTSPs) are a group of trypsin-like enzymes anchoring on the cell surface. These proteases act in diverse tissues to regulate physiological processes, such as food digestion, salt-water balance, iron metabolism, epithelial integrity, and auditory nerve development. More recently, several members of the TTSP family, namely, hepsin, matriptase-2, and corin, have been shown to play a role in regulating lipid metabolism, adipose tissue phenotype, and thermogenesis, via direct growth factor activation or indirect hormonal mechanisms. In mice, hepsin deficiency increases adipose browning and protects from high-fat diet-induced hyperglycemia, hyperlipidemia, and obesity. Similarly, matriptase-2 deficiency increases fat lipolysis and reduces obesity and hepatic steatosis in high-fat diet-fed mice. In contrast, corin deficiency increases white adipose weights and cell sizes, suppresses adipocyte browning and thermogenic responses, and causes cold intolerance in mice. These findings highlight an important role of TTSPs in modifying cellular phenotype and function in adipose tissue. In this review, we provide a brief description about TTSPs and discuss recent findings regarding the role of hepsin, matriptase-2, and corin in regulating adipose tissue phenotype, energy metabolism, and thermogenic responses.

Purpose: To elucidate the mechanism of CXCR4/EAAT1/GS pathway in CXCL12 regulating invasion and migration in malignant pleural mesothelioma (MPM). Methods: Immunohistochemistry for CXCL12, CXCR4, EAAT1 and GS stainings and correlation analysis between them were conducted in MPM and normal tissues. Western blot and real-time PCR were performed to examine the CXCR4, EAAT1 and GS expression in H2052 cells. Wound healing and transwell assay were applied to determine the cell migration and invasion. MTT was utilized to assess cell viability. Results: CXCL12, CXCR4, EAAT1 and GS were highly expressed in MPM tissues and correlated with each other. CXCL12 upregulated both in protein and mRNA levels of CXCR4, EAAT1 and GS in H2052 cells. The EAAT1 and GS expression upregulated or not by CXCL12 were decreased by CXCR4 and EAAT1 knockdown. CXCR4 antagonist AMD3100 and EAAT1 antagonist TFB-TBOA also resulted in the same effects as CXCR4 and EAAT1 knockdown, respectively. CXCL12 promoted cell invasion and migration and increased the Matrix metalloproteinase 9 (MMP9) mRNA level. CXCR4 and EAAT1 knockdown suppressed all these functions. Furthermore, CXCL12 promoted H2052 cells growth in nude mice, both AMD3100 and TFB-TBOA inhibited this promotion. Conclusions: CXCL12 regulated the invasion and migration through CXCR4/EAAT1/GS pathway in H2052 cells.