Dynamic Tolling in Arc-based Traffic Assignment Models

Tolling in traffic networks offers a popular measure to minimize overall congestion. Existing toll designs primarily focus on congestion in route-based traffic assignment models (TAMs), in which travelers make a single route selection from their source to destination. However, these models do not reflect real-world traveler decisions because they preclude deviations from a chosen route, and because the enumeration of all routes is computationally expensive. To address these limitations, our work focuses on arc-based TAMs, in which travelers sequentially select individual arcs (or edges) on the network to reach their destination. We first demonstrate that marginal pricing, a tolling scheme commonly used in route-based TAMs, also achieves socially optimal congestion levels in our arc-based formulation. Then, we use perturbed best response dynamics to model the evolution of travelers' arc selection preferences over time, and a marginal pricing scheme to the social planner's adaptive toll updates in response. We prove that our adaptive learning and marginal pricing dynamics converge to a neighborhood of the socially optimal loads and tolls. We then present empirical results that verify our theoretical claims.Comment: 18 pages, 4 figures, 2 tables. arXiv admin note: text overlap with arXiv:2304.0470

Vulval Metastasis from Squamous Cell Carcinoma of the Cervix Clinically Presenting as Lymphangioma Circumscriptum

Patients with cervical cancer may develop local recurrence or distant metastasis, and the rate of these events is increased in proportion to the clinical stage. Cutaneous metastasis of cervical cancer is very rare and only a few cases have been reported in Korean literature. It is common at the abdominal wall, vulva, and anterior chest wall and mainly presents as an asymptomatic dermal or subcutaneous nodule, ulcer or plaque. We herein report on an interesting case of vulval metastasis from squamous cell carcinoma of the cervix with an unusual clinical manifestation resembling lymphagioma circumscriptum

Self-supervised Blur Detection from Synthetically Blurred Scenes

Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labour intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.This research was partially funded by the Basque Government’s Industry Department under the ELKARTEK program’s project ONKOIKER under agreement KK2018/00090. We thank the Spanish project TIN2016- 79717-R and mention Generalitat de Catalunya CERCA Program

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Effects of rapamycin and curcumin on inflammation and oxidative stress in vitro and in vivo - in search of potential anti-epileptogenic strategies for temporal lobe epilepsy

Background: Previous studies in various rodent epilepsy models have suggested that mammalian target of rapamycin (mTOR) inhibition with rapamycin has anti-epileptogenic potential. Since treatment with rapamycin produces unwanted side effects, there is growing interest to study alternatives to rapamycin as anti-epileptogenic drugs. Therefore, we investigated curcumin, the main component of the natural spice turmeric. Curcumin is known to have anti-inflammatory and anti-oxidant effects and has been reported to inhibit the mTOR pathway. These properties make it a potential anti-epileptogenic compound and an alternative for rapamycin.Methods: To study the anti-epileptogenic potential of curcumin compared to rapamycin, we first studied the effects of both compounds on mTOR activation, inflammation, and oxidative stress in vitro, using cell cultures of human fetal astrocytes and the neuronal cell line SH-SY5Y. Next, we investigated the effects of rapamycin and intracerebrally applied curcumin on status epilepticus (SE)—induced inflammation and oxidative stress in hippocampal tissue, during early stages of epileptogenesis in the post-electrical SE rat model for temporal lobe epilepsy (TLE).Results: Rapamycin, but not curcumin, suppressed mTOR activation in cultured astrocytes. Instead, curcumin suppressed the mitogen-activated protein kinase (MAPK) pathway. Quantitative real-time PCR analysis revealed that curcumin, but not rapamycin, reduced the levels of inflammatory markers IL-6 and COX-2 in cultured astrocytes that were challenged with IL-1β. In SH-SY5Y cells, curcumin reduced reactive oxygen species (ROS) levels, suggesting anti-oxidant effects. In the post-SE rat model, however, treatment with rapamycin or curcumin did not suppress the expression of inflammatory and oxidative stress markers 1 week after SE.Conclusions: These results indicate anti-inflammatory and anti-oxidant properties of curcumin, but not rapamycin, in vitro. Intracerebrally applied curcumin modified the MAPK pathway in vivo at 1 week after SE but failed to produce anti-inflammatory or anti-oxidant effects. Future studies should be directed to increasing the bioavailability of curcumin (or related compounds) in the brain to assess its anti-epileptogenic potential in vivo

Protective Effect of Curcumin on Pulmonary and Cardiovascular Effects Induced by Repeated Exposure to Diesel Exhaust Particles in Mice

Particulate air pollution has been associated with increased risk of cardiopulmonary diseases. However, the underlying mechanisms are not fully understood. We have previously demonstrated that single dose exposure to diesel exhaust particle (DEP) causes lung inflammation and peripheral thrombotic events. Here, we exposed mice with repeated doses of DEP (15µg/animal) every 2nd day for 6 days (a total of 4 exposures), and measured several cardiopulmonary endpoints 48 h after the end of the treatments. Moreover, the potential protective effect of curcumin (the yellow pigment isolated from turmeric) on DEP-induced cardiopulmonary toxicity was assessed. DEP exposure increased macrophage and neutrophil numbers, tumor necrosis factor α (TNF α) in the bronchoalveolar lavage (BAL) fluid, and enhanced airway resistance to methacoline measured invasively using Flexivent. DEP also significantly increased plasma C-reactive protein (CRP) and TNF α concentrations, systolic blood pressure (SBP) as well as the pial arteriolar thrombosis. It also significantly enhanced the plasma D-dimer and plasminogen activator inhibitor-1 (PAI-1). Pretreatment with curcumin by oral gavage (45 mg/kg) 1h before exposure to DEP significantly prevented the influx of inflammatory cells and the increase of TNF α in BAL, and the increased airway resistance caused by DEP. Likewise, curcumin prevented the increase of SBP, CRP, TNF α, D-dimer and PAI-1. The thrombosis was partially but significantly mitigated. In conclusion, repeated exposure to DEP induced lung and systemic inflammation characterized by TNFα release, increased SBP, and accelerated coagulation. Our findings indicate that curcumin is a potent anti-inflammatory agent that prevents the release of TNFα and protects against the pulmonary and cardiovascular effects of DEP

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead