Supply chain optimisation of pyrolysis plant deployment using goal programming

This paper presents a goal programming model to optimise the deployment of pyrolysis plants in Punjab, India. Punjab has an abundance of waste straw and pyrolysis can convert this waste into alternative bio-fuels, which will facilitate the provision of valuable energy services and reduce open field burning. A goal programming model is outlined and demonstrated in two case study applications: small scale operations in villages and large scale deployment across Punjab's districts. To design the supply chain, optimal decisions for location, size and number of plants, downstream energy applications and feedstocks processed are simultaneously made based on stakeholder requirements for capital cost, payback period and production cost of bio-oil and electricity. The model comprises quantitative data obtained from primary research and qualitative data gathered from farmers and potential investors. The Punjab district of Fatehgarh Sahib is found to be the ideal location to initially utilise pyrolysis technology. We conclude that goal programming is an improved method over more conventional methods used in the literature for project planning in the field of bio-energy. The model and findings developed from this study will be particularly valuable to investors, plant developers and municipalities interested in waste to energy in India and elsewhere

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals

Human coronavirus 229E encodes a single ORF4 protein between the spike and the envelope genes

BACKGROUND: The genome of coronaviruses contains structural and non-structural genes, including several so-called accessory genes. All group 1b coronaviruses encode a single accessory protein between the spike and envelope genes, except for human coronavirus (HCoV) 229E. The prototype virus has a split gene, encoding the putative ORF4a and ORF4b proteins. To determine whether primary HCoV-229E isolates exhibit this unusual genome organization, we analyzed the ORF4a/b region of five current clinical isolates from The Netherlands and three early isolates collected at the Common Cold Unit (CCU) in Salisbury, UK. RESULTS: All Dutch isolates were identical in the ORF4a/b region at amino acid level. All CCU isolates are only 98% identical to the Dutch isolates at the nucleotide level, but more closely related to the prototype HCoV-229E (>98%). Remarkably, our analyses revealed that the laboratory adapted, prototype HCoV-229E has a 2-nucleotide deletion in the ORF4a/b region, whereas all clinical isolates carry a single ORF, 660 nt in size, encoding a single protein of 219 amino acids, which is a homologue of the ORF3 proteins encoded by HCoV-NL63 and PEDV. CONCLUSION: Thus, the genome organization of the group 1b coronaviruses HCoV-NL63, PEDV and HCoV-229E is identical. It is possible that extensive culturing of the HCoV-229E laboratory strain resulted in truncation of ORF4. This may indicate that the protein is not essential in cell culture, but the highly conserved amino acid sequence of the ORF4 protein among clinical isolates suggests that the protein plays an important role in vivo

Bayesian Learning of Gas Transport in Three-Dimensional Fracture Networks

Modeling gas flow through fractures of subsurface rock is a particularly challenging problem because of the heterogeneous nature of the material. High-fidelity simulations using discrete fracture network (DFN) models are one methodology for predicting gas particle breakthrough times at the surface, but are computationally demanding. We propose a Bayesian machine learning method that serves as an efficient surrogate model, or emulator, for these three-dimensional DFN simulations. Our model trains on a small quantity of simulation data and, using a graph/path-based decomposition of the fracture network, rapidly predicts quantiles of the breakthrough time distribution. The approach, based on Gaussian Process Regression (GPR), outputs predictions that are within 20-30% of high-fidelity DFN simulation results. Unlike previously proposed methods, it also provides uncertainty quantification, outputting confidence intervals that are essential given the uncertainty inherent in subsurface modeling. Our trained model runs within a fraction of a second, which is considerably faster than other methods with comparable accuracy and multiple orders of magnitude faster than high-fidelity simulations

Optical coherence tomography-based contact indentation for diaphragm mechanics in a mouse model of transforming growth factor alpha induced lung disease

Funding provided by the National Health and Medical Research Council (NHMRC) of Australia (1027218). P.N. and K.W. are supported by NHMRC Fellowships (1045824, 1090888). P.W. was supported by the William and Marlene Schrader Postgraduate Scholarship, The University of Western Australia, and C.A. by an NHMRC Preterm Infants CRE top-up scholarship.This study tested the utility of optical coherence tomography (OCT)-based indentation to assess mechanical properties of respiratory tissues in disease. Using OCT-based indentation, the elastic modulus of mouse diaphragm was measured from changes in diaphragm thickness in response to an applied force provided by an indenter. We used a transgenic mouse model of chronic lung disease induced by the overexpression of transforming growth factor-alpha (TGF-α), established by the presence of pleural and peribronchial fibrosis and impaired lung mechanics determined by the forced oscillation technique and plethysmography. Diaphragm elastic modulus assessed by OCT-based indentation was reduced by TGF-α at both left and right lateral locations (p < 0.05). Diaphragm elastic modulus at left and right lateral locations were correlated within mice (r = 0.67, p < 0.01) suggesting that measurements were representative of tissue beyond the indenter field. Co-localised images of diaphragm after TGF-α overexpression revealed a layered fibrotic appearance. Maximum diaphragm force in conventional organ bath studies was also reduced by TGF-α overexpression (p < 0.01). Results show that OCT-based indentation provided clear delineation of diseased diaphragm, and together with organ bath assessment, provides new evidence suggesting that TGF-α overexpression produces impairment in diaphragm function and, therefore, an increase in the work of breathing in chronic lung disease.Publisher PDFPeer reviewe

Coronary CT Angiography and 5-Year Risk of Myocardial Infarction.

BACKGROUND: Although coronary computed tomographic angiography (CTA) improves diagnostic certainty in the assessment of patients with stable chest pain, its effect on 5-year clinical outcomes is unknown. METHODS: In an open-label, multicenter, parallel-group trial, we randomly assigned 4146 patients with stable chest pain who had been referred to a cardiology clinic for evaluation to standard care plus CTA (2073 patients) or to standard care alone (2073 patients). Investigations, treatments, and clinical outcomes were assessed over 3 to 7 years of follow-up. The primary end point was death from coronary heart disease or nonfatal myocardial infarction at 5 years. RESULTS: The median duration of follow-up was 4.8 years, which yielded 20,254 patient-years of follow-up. The 5-year rate of the primary end point was lower in the CTA group than in the standard-care group (2.3% [48 patients] vs. 3.9% [81 patients]; hazard ratio, 0.59; 95% confidence interval [CI], 0.41 to 0.84; P=0.004). Although the rates of invasive coronary angiography and coronary revascularization were higher in the CTA group than in the standard-care group in the first few months of follow-up, overall rates were similar at 5 years: invasive coronary angiography was performed in 491 patients in the CTA group and in 502 patients in the standard-care group (hazard ratio, 1.00; 95% CI, 0.88 to 1.13), and coronary revascularization was performed in 279 patients in the CTA group and in 267 in the standard-care group (hazard ratio, 1.07; 95% CI, 0.91 to 1.27). However, more preventive therapies were initiated in patients in the CTA group (odds ratio, 1.40; 95% CI, 1.19 to 1.65), as were more antianginal therapies (odds ratio, 1.27; 95% CI, 1.05 to 1.54). There were no significant between-group differences in the rates of cardiovascular or noncardiovascular deaths or deaths from any cause. CONCLUSIONS: In this trial, the use of CTA in addition to standard care in patients with stable chest pain resulted in a significantly lower rate of death from coronary heart disease or nonfatal myocardial infarction at 5 years than standard care alone, without resulting in a significantly higher rate of coronary angiography or coronary revascularization. (Funded by the Scottish Government Chief Scientist Office and others; SCOT-HEART ClinicalTrials.gov number, NCT01149590 .)

Multi-Messenger Astronomy with Extremely Large Telescopes

The field of time-domain astrophysics has entered the era of Multi-messenger Astronomy (MMA). One key science goal for the next decade (and beyond) will be to characterize gravitational wave (GW) and neutrino sources using the next generation of Extremely Large Telescopes (ELTs). These studies will have a broad impact across astrophysics, informing our knowledge of the production and enrichment history of the heaviest chemical elements, constrain the dense matter equation of state, provide independent constraints on cosmology, increase our understanding of particle acceleration in shocks and jets, and study the lives of black holes in the universe. Future GW detectors will greatly improve their sensitivity during the coming decade, as will near-infrared telescopes capable of independently finding kilonovae from neutron star mergers. However, the electromagnetic counterparts to high-frequency (LIGO/Virgo band) GW sources will be distant and faint and thus demand ELT capabilities for characterization. ELTs will be important and necessary contributors to an advanced and complete multi-messenger network.Comment: White paper submitted to the Astro2020 Decadal Surve

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

A call for new approaches to quantifying biases in observations of sea-surface temperature

Global surface-temperature changes are a fundamental expression of climate change. Recent, much-debated, variations in the observed rate of surface-temperature change have highlighted the importance of uncertainty in adjustments applied to sea-surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface-temperature change and provide higher- quality gridded SST fields for use in many applications. Bias adjustments have been based either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and of high-quality observations for validation and bias model development are likely to remain major challenges