Locomotive Schedule Optimization for Da-qin Heavy Haul Railway

The main difference between locomotive schedule of heavy haul railways and that of regular rail transportation is the number of locomotives utilized for one train. One heavy-loaded train usually has more than one locomotive, but a regular train only has one. This paper develops an optimization model for the multilocomotive scheduling problem (MLSP) through analyzing the current locomotive schedule of Da-qin Railway. The objective function of our paper is to minimize the total number of utilized locomotives. The MLSP is nondeterministic polynomial (NP) hard. Therefore, we convert the multilocomotive traction problem into a single-locomotive traction problem. Then, the single-locomotive traction problem (SLTP) can be converted into an assignment problem. The Hungarian algorithm is applied to solve the model and obtain the optimal locomotive schedule. We use the variance of detention time of locomotives at stations to evaluate the stability of locomotive schedule. In order to evaluate the effectiveness of the proposed optimization model, case studies for 20 kt and 30 kt heavy-loaded combined trains on Da-qin Railway are both conducted. Compared to the current schedules, the optimal schedules from the proposed models can save 62 and 47 locomotives for 20 kt and 30 kt heavy-loaded combined trains, respectively. Therefore, the effectiveness of the proposed model and its solution algorithm are both valid

Creating an atlas of the bone microenvironment during oral inflammatory-related bone disease using single-cell profiling

Oral inflammatory diseases such as apical periodontitis are common bacterial infectious diseases that may affect the periapical alveolar bone tissues. A protective process occurs simultaneously with the inflammatory tissue destruction, in which mesenchymal stem cells (MSCs) play a primary role. However, a systematic and precise description of the cellular and molecular composition of the microenvironment of bone affected by inflammation is lacking. In this study, we created a single-cell atlas of cell populations that compose alveolar bone in healthy and inflammatory disease states. We investigated changes in expression frequency and patterns related to apical periodontitis, as well as the interactions between MSCs and immunocytes. Our results highlight an enhanced self-supporting network and osteogenic potential within MSCs during apical periodontitis-associated inflammation. MSCs not only differentiated toward osteoblast lineage cells but also expressed higher levels of osteogenic-related markers, including Sparc and Col1a1. This was confirmed by lineage tracing in transgenic mouse models and human samples from oral inflammatory-related alveolar bone lesions. In summary, the current study provides an in-depth description of the microenvironment of MSCs and immunocytes in both healthy and disease states. We also identified key apical periodontitis-associated MSC subclusters and their biomarkers, which could further our understanding of the protective process and the underlying mechanisms of oral inflammatory-related bone disease. Taken together, these results enhance our understanding of heterogeneity and cellular interactions of alveolar bone cells under pathogenic and inflammatory conditions. We provide these data as a tool for investigators not only to better appreciate the repertoire of progenitors that are stress responsive but importantly to help design new therapeutic targets to restore bone lesions caused by apical periodontitis and other inflammatory-related bone diseases

Klotho in Osx-mesenchymal progenitors exerts pro-osteogenic and anti-inflammatory effects during mandibular alveolar bone formation and repair

Maxillofacial bone defects are commonly seen in clinical practice. A clearer understanding of the regulatory network directing maxillofacial bone formation will promote the development of novel therapeutic approaches for bone regeneration. The fibroblast growth factor (FGF) signalling pathway is critical for the development of maxillofacial bone. Klotho, a type I transmembrane protein, is an important components of FGF receptor complexes. Recent studies have reported the presence of Klotho expression in bone. However, the role of Klotho in cranioskeletal development and repair remains unknown. Here, we use a genetic strategy to report that deletion of Klotho in Osx-positive mesenchymal progenitors leads to a significant reduction in osteogenesis under physiological and pathological conditions. Klotho-deficient mensenchymal progenitors also suppress osteoclastogenesis in vitro and in vivo. Under conditions of inflammation and trauma-induced bone loss, we find that Klotho exerts an inhibitory function on inflammation-induced TNFR signaling by attenuating Rankl expression. More importantly, we show for the first time that Klotho is present in human alveolar bone, with a distinct expression pattern under both normal and pathological conditions. In summary, our results identify the mechanism whereby Klotho expressed in Osx-mensenchymal progenitors controls osteoblast differentiation and osteoclastogenesis during mandibular alveolar bone formation and repair. Klotho-mediated signaling is an important component of alveolar bone remodeling and regeneration. It may also be a target for future therapeutics

Parathyroid hormone directs bone marrow mesenchymal cell fate.

Intermittent PTH administration builds bone mass and prevents fractures, but its mechanism of action is unclear. We genetically deleted the PTH/PTHrP receptor (PTH1R) in mesenchymal stem cells using Prx1Cre and found low bone formation, increased bone resorption, and high bone marrow adipose tissue (BMAT). Bone marrow adipocytes traced to Prx1 and expressed classic adipogenic markers and high receptor activator of nuclear factor kappa B ligand (Rankl) expression. RANKL levels were also elevated in bone marrow supernatant and serum, but undetectable in other adipose depots. By cell sorting, Pref

NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization

For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research