Eco-driving technology for sustainable road transport: A review

© 2018 Elsevier Ltd Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated

Can metabolomics in addition to genomics add to prognostic and predictive information in breast cancer?

Genomic data from breast cancers provide additional prognostic and predictive information that is beginning to be used for patient management. The question arises whether additional information derived from other 'omic' approaches such as metabolomics can provide additional information. In an article published this month in BMC Cancer, Borgan et al. add metabolomic information to genomic measures in breast tumours and demonstrate, for the first time, that it may be possible to further define subgroups of patients which could be of value clinically

Impact of potential engine malfunctions on fuel consumption and gaseous emissions of a Euro VI diesel truck

© 2019 Elsevier Ltd Although new vehicles are designed to comply with specific emission regulations, their in-service performance would not necessarily achieve them due to wear-and-tear and improper maintenance, as well as tampering or failure of engine control and exhaust after-treatment systems. In addition, there is a lack of knowledge on how significantly these potential malfunctions affect vehicle performance. This study was therefore conducted to simulate the effect of various engine malfunctions on the fuel consumption and gaseous emissions of a 16-tonne Euro VI diesel truck using transient chassis dynamometer testing. The simulated malfunctions included those that would commonly occur in the intake, fuel injection, exhaust after-treatment and other systems. The results showed that all malfunctions increased fuel consumption except for the malfunction of EGR fully closed which reduced fuel consumption by 31%. The biggest increases in fuel consumption were caused by malfunctions in the intake system (16%–43%), followed by the exhaust after-treatment (6%–30%), fuel injection (4%–24%) and other systems (6%–11%). Regarding pollutant emissions, the effect of engine malfunctions on HC and CO emissions was insignificant, which remained unchanged or even reduced for most cases. An exception was EGR fully open which increased HC and CO emissions by 343% and 1124%, respectively. Contrary to HC and CO emissions, NO emissions were significantly increased by malfunctions. The largest increases in NO emissions were caused by malfunctions in the after-treatment system, ranging from 38% (SCR) to 1606% (DPF pressure sensor). Malfunctions in the fuel injection system (24%–1259%) and intercooler (438%–604%) could also increase NO emissions markedly. This study demonstrated clearly the importance of having properly functioning engine control and exhaust after-treatment systems to achieve the required performance of fuel consumption and pollutant emissions

Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Colorectal cancer is one of the main causes of cancer death worldwide, and novel biomarkers are urgently needed for its early diagnosis and treatment. The utilization of metabolomics to identify and quantify metabolites in body fluids may allow the detection of changes in their concentrations that could serve as diagnostic markers for colorectal cancer and may also represent new therapeutic targets. Metabolomics generates a pathophysiological ‘fingerprint’ that is unique to each individual. The purpose of our study was to identify a differential metabolomic signature for metastatic colorectal cancer. Serum samples from 60 healthy controls and 65 patients with metastatic colorectal cancer were studied by liquid chromatography coupled to high-resolution mass spectrometry in an untargeted metabolomic approach. Multivariate analysis revealed a separation between patients with metastatic colorectal cancer and healthy controls, who significantly differed in serum concentrations of one endocannabinoid, two glycerophospholipids, and two sphingolipids. These findings demonstrate that metabolomics using liquid-chromatography coupled to high-resolution mass spectrometry offers a potent diagnostic tool for metastatic colorectal cancer.This study was supported by a grant (n° 15CC056/DTS17/00081- ISCIII-FEDER) from the Fundación para la Investigación Biosanitaria de Andalucía Oriental (FIBAO) and Roche Pharma S.L. Authors from the Fundación MEDINA acknowledge the receipt of financial support from this public-private partnership of Merck Sharp & Dohme de España S.A. with the University of Granada and Andalusian Regional Government (PIN-0474-2016)

Metabotyping of docosahexaenoic acid - Treated alzheimer's disease cell model

10.1371/journal.pone.0090123PLoS ONE92-POLN

Analysis of the Constituents in Rat Serum after Oral Administration of Fufang Zhenzhu Tiaozhi Capsule by UPLC–Q–TOF–MS/MS

A rapid and sensitive UPLC/Q–TOF–MS method has been established for analysis of the constituents in rat serum after oral administration of Fufang Zhenzhu Tiaozhi (FTZ) capsule, an effective compound prescription for treating hyperlipidemia in the clinic. The UPLC/MS information of samples was obtained first in FTZ preparation and FTZ-treated rat serum. Mass spectra were acquired in both negative and positive ion modes. Thirty-six constituents in rat serum after oral administration of FTZ were detected, including the alkaloids, ginsenosides, pentacyclic triterpenes, and their metabolites. These chemicals were identified based on the retention time and mass spectrometry data with those of authentic standards or comparison of the literatures reports. Twenty-seven prototype components originated from FTZ and nine were the metabolites of the FTZ constituents. These results shed light on the potential active constituents of the complex traditional Chinese medicinal formulas

Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced