A vicious cycle along busy bus corridors and how to abate it

We unveil that a previously-unreported vicious cycle can be created when bus queues form at curbside stops along a corridor. Buses caught in this cycle exhibit growing variation in headways as they travel from stop to stop. Bus (and patron) delays accumulate in like fashion and can grow large on long, busy corridors. We show that this damaging cycle can be abated in simple ways. Present solutions entail holding buses at a corridor entrance and releasing them as per various strategies proposed in the literature. We introduce a modest variant to the simplest of these strategies. It releases buses at headways that are slightly less than, or equal to, the scheduled values. It turns out that periodically releasing buses at slightly smaller headways can substantially reduce bus delays caused by holding so that benefits can more readily outweigh costs in corridors that contain a sufficient number of serial bus stops. The simple variant is shown to perform about as well as, or better than, other bus-holding strategies in terms of saving delays, and is more effective than other strategies in regularizing bus headways. We also show that grouping buses from across multiple lines and holding them by group can be effective when patrons have the flexibility to choose buses from across all lines in a group. Findings come by formulating select models of bus-corridor dynamics and using these to simulate part of the Bus Rapid Transit corridor in Guangzhou, China

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.Comment: 9 pages, 8 figures, 2 table

Improving the critical speeds of high-speed trains using magnetorheological technology

With the rapid development of high-speed railways, vibration control for maintaining stability, passenger comfort, and safety has become an important area of research. In order to investigate the mechanism of train vibration, the critical speeds of various DOFs with respect to suspension stiffness and damping are first calculated and analyzed based on its dynamic equations. Then, the sensitivity of the critical speed is studied by analyzing the influence of different suspension parameters. On the basis of these analyses, a conclusion is drawn that secondary lateral damping is the most sensitive suspension damper. Subsequently, the secondary lateral dampers are replaced with magnetorheological fluid (MRF) dampers. Finally, a high-speed train model with MRF dampers is simulated by a combined ADAMS and MATLAB simulation and tested in a roller rig test platform to investigate the mechanism of how the MRF damper affects the train\u27s stability and critical speed. The results show that the semi-active suspension installed with MRF dampers substantially improves the stability and critical speed of the train

A Water-Soluble Polysaccharide from the Fruit Bodies of Bulgaria inquinans (Fries) and Its Anti-Malarial Activity

A water-soluble polysaccharide (BIWS-4b) was purified from the fruit bodies of Bulgaria inquinans (Fries). It is composed of mannose (27.2%), glucose (15.5%) and galactose (57.3%). Its molecular weight was estimated to be 7.4 kDa (polydispersity index, Mw/Mn: 1.35). Structural analyses indicated that BIWS-4b mainly contains (1 → 6)-linked, (1 → 5)-linked and (1 → 5,6)-linked β-Galf units; (1 → 4)-linked and non-reducing terminal β-Glcp units; and (1 → 2)-linked, (1 → 6)-linked, (1 → 2,6)-linked and non-reducing terminal α-Manp units. When examined by the 4-day method and in a prophylactic assay in mice, BIWS-4b exhibited markedly suppressive activity against malaria while enhancing the activity of artesunate. Immunological tests indicated that BIWS-4b significantly enhanced macrophage phagocytosis and splenic lymphocyte proliferation in malaria-bearing mice and normal mice. The anti-malarial activity of BIWS-4b might be intermediated by enhancing immune competence and restoring artesunate-suppressed immune function. Thus, BIWS-4b is a potential adjuvant of anti-malaria drugs

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC.Comment: 16 pages, 6 figure

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0% respectively, in the internal validation cohort and an increase of 0.09 (p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.Comment: 20 pages, 7 figure

Dysregulated protocadherin-pathway activity as an intrinsic defect in induced pluripotent stem cell-derived cortical interneurons from subjects with schizophrenia.

We generated cortical interneurons (cINs) from induced pluripotent stem cells derived from 14 healthy controls and 14 subjects with schizophrenia. Both healthy control cINs and schizophrenia cINs were authentic, fired spontaneously, received functional excitatory inputs from host neurons, and induced GABA-mediated inhibition in host neurons in vivo. However, schizophrenia cINs had dysregulated expression of protocadherin genes, which lie within documented schizophrenia loci. Mice lacking protocadherin-α showed defective arborization and synaptic density of prefrontal cortex cINs and behavioral abnormalities. Schizophrenia cINs similarly showed defects in synaptic density and arborization that were reversed by inhibitors of protein kinase C, a downstream kinase in the protocadherin pathway. These findings reveal an intrinsic abnormality in schizophrenia cINs in the absence of any circuit-driven pathology. They also demonstrate the utility of homogenous and functional populations of a relevant neuronal subtype for probing pathogenesis mechanisms during development