Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps

Progress Towards the Development of a Traveling Wave Direct Energy Converter for Aneutronic Fusion Propulsion Applications

A coordinated experimental and theory/simulation effort has been carried out to investigate the physics of the Traveling Wave Direct Energy Converter (TWDEC), a scheme that has been proposed in the past for the direct conversion into electricity of the kinetic energy of an ion beam generated from fusion reactions. This effort has been focused in particular on the TWDEC process in the high density beam regime, thus accounting for the ion beam expansion due to its space charge

International survey of Cronobacter sakazakii and other Cronobacter spp. in follow up formulas and infant food

A coordinated survey for Cronobacter and related organisms in powdered infant formula, follow up formula and infant foods was undertaken by 8 laboratories in 7 countries in recognition of and in response to the data needs identified in an FAO/WHO call for data in order to develop global risk management guidance for these products. The products (domestic and imported) were purchased from the local market and were categorised according to their principle ingredients. A total of 290 products were analysed using a standardised procedure of pre-enrichment in 225 ml Buffered Peptone Water (BPW), followed by enrichment in Enterobacteriaceae Enrichment (EE) broth, plating on the chromogenic Cronobacter Druggan–Forsythe–Iversen (DFI) agar and presumptive identification with ID 32 E. Presumptive Cronobacter isolates were identified using 16S rRNA gene sequence analysis. Aerobic plate counts (APC) of the products were also determined on nutrient agar. Fourteen samples had APC > 105 cfu/g, 3 of which contained probiotic cultures. C. sakazakii was isolated from 27 products; 3/91 (3%) follow up formulas (as defined by Codex Alimentarius Commission), and 24/199 (12%) infant foods and drinks. Hence C. sakazakii was less prevalent in follow up formula than other foods given to infants over the same age range. A range of other bacteria were also isolated from follow up formulas, including Acinetobacter baumannii, Enterobacter cloacae, Klebsiella pneumoniae, Citrobacter freundii, and Serratia ficaria. There was significant variation in the reconstitution instructions for follow up formulas. These included using water at temperatures which would enable bacterial growth. Additionally, the definition of follow up formula varied between countries

Does the Hematopoietic Cell Transplantation Specific Comorbidity Index Predict Transplant Outcomes? A Validation Study in a Large Cohort of Umbilical Cord Blood and Matched Related Donor Transplants

AbstractThe hematopoietic cell transplantation specific comorbidity index (HCT-CI) has been recently proposed to predict the probability of nonrelapse mortality (NRM) and overall survival (OS) in allogeneic HCT recipients while taking into account any pretransplant comorbidity. We tested the validity of the HCT-CI in a cohort of 373 adult HCT recipients (184 matched-related donor and 189 unrelated umbilical cord blood) who received a myeloablative (N = 150) or nonmyeloablative (N = 223) conditioning regimen. HCT-CI scores of 0, 1, 2, and ≥3 were present in 58 (16%), 56 (15%), 64 (17%), and 195 (52%) patients, respectively. Pulmonary conditions were the most common comorbidity. Cumulative incidence of NRM at 2 years was 10%, 20%, 24%, and 28% for HCT-CI scores of 0, 1, 2, and ≥3, respectively (P = .01). The corresponding probability of OS at 2 years was 72%, 67%, 51%, and 48%, respectively (P < .01). On multivariate analyses adjusted for recipient age, disease risk, donor source, and conditioning regimen intensity, the relative risks for NRM for HCT-CI scores of 1, 2, and ≥3 (compared to a score of 0) were 2.0 (95% confidence intervals, 0.8–5.3), 2.6 (1.0–6.7), and 3.2 (1.4-7.4), respectively. The risks for overall mortality were 1.2 (0.6-2.1), 2.0 (1.1-3.4), and 2.1 (1.3-3.3), respectively. In subgroup analyses, the HCT-CI score did not consistently predict NRM and OS among different donor sources and conditioning regimens. The HCT-CI, although a useful tool for capturing pretransplant comorbidity and risk-assessment, needs to be further validated prior to adopting it for routine clinical use

PGE2 inhibits TIL expansion by disrupting IL-2 signalling and mitochondrial function.

Expansion of antigen-experienced CD8+ T cells is critical for the success of tumour-infiltrating lymphocyte (TIL)-adoptive cell therapy (ACT) in patients with cancer1. Interleukin-2 (IL-2) acts as a key regulator of CD8+ cytotoxic T lymphocyte functions by promoting expansion and cytotoxic capability2,3. Therefore, it is essential to comprehend mechanistic barriers to IL-2 sensing in the tumour microenvironment to implement strategies to reinvigorate IL-2 responsiveness and T cell antitumour responses. Here we report that prostaglandin E2 (PGE2), a known negative regulator of immune response in the tumour microenvironment4,5, is present at high concentrations in tumour tissue from patients and leads to impaired IL-2 sensing in human CD8+ TILs via the PGE2 receptors EP2 and EP4. Mechanistically, PGE2 inhibits IL-2 sensing in TILs by downregulating the IL-2Rγc chain, resulting in defective assembly of IL-2Rβ-IL2Rγc membrane dimers. This results in impaired IL-2-mTOR adaptation and PGC1α transcriptional repression, causing oxidative stress and ferroptotic cell death in tumour-reactive TILs. Inhibition of PGE2 signalling to EP2 and EP4 during TIL expansion for ACT resulted in increased IL-2 sensing, leading to enhanced proliferation of tumour-reactive TILs and enhanced tumour control once the cells were transferred in vivo. Our study reveals fundamental features that underlie impairment of human TILs mediated by PGE2 in the tumour microenvironment. These findings have therapeutic implications for cancer immunotherapy and cell therapy, and enable the development of targeted strategies to enhance IL-2 sensing and amplify the IL-2 response in TILs, thereby promoting the expansion of effector T cells with enhanced therapeutic potential

Targeting Signal Transduction Pathways in Metastatic Breast Cancer: A Comprehensive Review

This review summarizes some of the key signaling pathways involved in tumor progression and some of the novel therapies that are in development for the treatment of metastatic breast cancer patients