Prospects and technological advancement of bioethanol ecofuel production

Energy, the economy, and the environment are the key drivers for the advancement of modern technologies for the community [1, 2]. The nonrenewable fossil fuels, for example petrol, diesel, crude oil, natural gas, etc., are the main sources of energy in the world. The global energy consumption of fossil fuels is increasing significantly due to rapid global industrialization and motorization [3]. In 2017, global primary energy consumption reached 13,511.2 million tons of oil equivalent (Mtoe), of which oil shares were about 34.21% (4621.9 Mtoe) of the total consumption [4]. Fossil fuel accounts for nearly 85.18% of the total global energy consumption, approximately half of which is used by the transport sector. Considering the current consumption rate of fossil fuels, the reserve of fossil fuels is expected to be diminished in the next 40–50 years [5]. The high cost of fossil fuels and environmental pollution are also limiting the use of fossil fuels and forcing us to harness alternative solutions. The utilization of fossil fuels in vehicles is responsible for more than 70% of the world’s total carbon monoxide (CO) emissions, 40% of all nitrogen oxide (NOx) emissions, 19% of all carbon dioxide (CO2) missions, and 14% of all greenhouse gas emissions [6]. One gallon of gasoline emits approximately 8 kg of CO2. In 2011, global CO2 emissions reached about 34 billion tons, causing a global carbon emission cost of more than $1.2 trillion per year [7]. Therefore, the emphasis is on the production of environmentally sustainable and low-cost fuels as alternatives to gasoline. Bioethanol is a nonpetroleum-based renewable and sustainable liquid fuel that is considered a promising option to meet the increasing energy crisis. Although ethanol contains lower energy content compared to gasoline (66%–68% of pure gasoline), the high octane number (106–110) of ethanol increases the performance of the gasoline-ethanol blend [8, 9]. Moreover, bioethanol contains higher oxygen content (roughly 34.7%) compared to no oxygen in gasoline [10]. The high oxygen content in bioethanol leads to clean combustion [11]. Consequently, the combustion of bioethanol reduces the emission of toxic substances when compared with gasoline combustion. It is estimated that bioethanol can reduce up to 90% of CO2 and 60%–80% of SO2 emissions when blended with 95% gasoline fuel [12]. Table 8.1 shows the comparison of the GHG emission and energy intensity among gasoline and various bioethanols. Therefore, bioethanol is considered the cutting-edge technology for the production of low-emission renewable energy. In addition to these, the higher heat of vaporization of ethanol enhances the volumetric efficiency of gasoline when blended with ethanol [16]. However, the selection of a suitable microorganism, the harnessing of promising feedstocks, and the development of proficient bioethanol technology are the key challenges. In recent years, research and development have been carried out for developing bioethanol technology that is feasible for commercial implementation. In this study, an overview of bioethanol as an ecofuel, including the history, potential resources, current technological status, challenges, and future scopes, is presented. The chapter is structured as follows: Section 8.2 highlights the historical background of ethanol production. Section 8.3 presents a brief description of current bioethanol production technology. Section 8.4 discusses the major bioethanol feedstock potential and current global cellulosic ethanol production plant status in detail. Moreover, the challenges of the existing technologies and future prospects for cellulosic ethanol production are presented in Section 8.5

Detection and characterisation of optic nerve and retinal changes in primary congenital glaucoma using hand-held optical coherence tomography

Objective To investigate (1) the feasibility of scanning the optic nerve (ON) and central retina with hand-held optical coherence tomography (HH-OCT) without sedation or anaesthesia in primary congenital glaucoma (PCG), (2) the characteristics of ON changes in comparison with adult primary open-angle glaucoma (POAG) in comparison with matched controls, (3) the sensitivity and specificity of ON parameters for diagnosis, and (4) changes of foveal morphology. Methods and analysis HH-OCT (Envisu 2300; Leica Microsystems) was used to investigate ON and foveal morphology of 20 children with PCG (mean age 4.64±2.79) and 10 adult patients with POAG (mean age 66.8±6.94), and compared with age-matched, gender-matched and ethnicity-matched healthy controls without sedation or anaesthesia. Results HH-OCT yielded useful data in 20 out of 24 young children with PCG. Patients with PCG had significantly deeper cup changes than patients with POAG (vs respective age-matched controls, p=0.014). ON changes in PCG are characterised by significant increase in cup depth (165%), increased cup diameter (159%) and reduction in rim area (36.4%) as compared with controls with high sensitivity (81.5, 74.1% and 88.9%, respectively) and specificity (85.0, 80.0% and 75.0%, respectively). Patients with PCG have a significantly smaller width of the macula pit (p<0.001) with non-detectable external limiting membrane. Conclusion HH-OCT has the potential to be a useful tool in glaucoma management for young children. We have demonstrated the use of HH-OCT in confirming a diagnosis of glaucoma within the studied cohort and found changes in disc morphology which characterise differently in PCG from POAG

Additional file 1 of Exploring the use of masks for protection against the effects of wildfire smoke among people with preexisting respiratory conditions

Supplementary Material

A Core Outcome Measurement Set for Pediatric Critical Care

Objectives:  To identify a PICU Core Outcome Measurement Set (PICU COMS), a set of measures that can be used to evaluate the PICU Core Outcome Set (PICU COS) domains in PICU patients and their families. Design:  A modified Delphi consensus process. Setting:  Four webinars attended by PICU physicians and nurses, pediatric surgeons, rehabilitation physicians, and scientists with expertise in PICU clinical care or research (n = 35). Attendees were from eight countries and convened from the Pediatric Acute Lung Injury and Sepsis Investigators Pediatric Outcomes STudies after PICU Investigators and the Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network PICU COS Investigators. Subjects:  Measures to assess outcome domains of the PICU COS are as follows: cognitive, emotional, overall (including health-related quality of life), physical, and family health. Measures evaluating social health were also considered. Interventions:  None. Measurements and Main Results:  Measures were classified as general or additional based on generalizability across PICU populations, feasibility, and relevance to specific COS domains. Measures with high consensus, defined as 80% agreement for inclusion, were selected for the PICU COMS. Among 140 candidate measures, 24 were delineated as general (broadly applicable) and, of these, 10 achieved consensus for inclusion in the COMS (7 patient-oriented and 3 family-oriented). Six of the seven patient measures were applicable to the broadest range of patients, diagnoses, and developmental abilities. All were validated in pediatric populations and have normative pediatric data. Twenty additional measures focusing on specific populations or in-depth evaluation of a COS subdomain also met consensus for inclusion as COMS additional measures. Conclusions:  The PICU COMS delineates measures to evaluate domains in the PICU COS and facilitates comparability across future research studies to characterize PICU survivorship and enable interventional studies to target long-term outcomes after critical illness.</p

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies