Calculation steps and key data sources

<p><strong>Figure 1.</strong> Calculation steps and key data sources. Sources of data or models used to obtain each variable are marked in parentheses, which are detailed in section <a href="http://iopscience.iop.org/1748-9326/8/3/035015/article#erl470159s2" target="_blank">2</a>.</p> <p><strong>Abstract</strong></p> <p>Forest residue has been proposed as a feasible candidate for cellulosic biofuels. However, the number of studies assessing its water use remains limited. This work aims to analyze the impacts of forest-based biofuel on water resources and quality by using a water footprint approach. A method established here is tailored to the production system, which includes softwood, hardwood, and short-rotation woody crops. The method is then applied to selected areas in the southeastern region of the United States to quantify the county-level water footprint of the biofuel produced via a mixed alcohol gasification process, under several logistic systems, and at various refinery scales. The results indicate that the blue water sourced from surface or groundwater is minimal, at 2.4 liters per liter of biofuel (l/l). The regional-average green water (rainfall) footprint falls between 400 and 443 l/l. The biofuel pathway appears to have a low nitrogen grey water footprint averaging 25 l/l at the regional level, indicating minimal impacts on water quality. Feedstock mix plays a key role in determining the magnitude and the spatial distribution of the water footprint in these regions. Compared with other potential feedstock, forest wood residue shows promise with its low blue and grey water footprint.</p

Green and grey water spatial distribution of the DPSS case under the combination of 30% and 7% moist and ash contents, respectively

<p><strong>Figure 4.</strong> Green and grey water spatial distribution of the DPSS case under the combination of 30% and 7% moist and ash contents, respectively.</p> <p><strong>Abstract</strong></p> <p>Forest residue has been proposed as a feasible candidate for cellulosic biofuels. However, the number of studies assessing its water use remains limited. This work aims to analyze the impacts of forest-based biofuel on water resources and quality by using a water footprint approach. A method established here is tailored to the production system, which includes softwood, hardwood, and short-rotation woody crops. The method is then applied to selected areas in the southeastern region of the United States to quantify the county-level water footprint of the biofuel produced via a mixed alcohol gasification process, under several logistic systems, and at various refinery scales. The results indicate that the blue water sourced from surface or groundwater is minimal, at 2.4 liters per liter of biofuel (l/l). The regional-average green water (rainfall) footprint falls between 400 and 443 l/l. The biofuel pathway appears to have a low nitrogen grey water footprint averaging 25 l/l at the regional level, indicating minimal impacts on water quality. Feedstock mix plays a key role in determining the magnitude and the spatial distribution of the water footprint in these regions. Compared with other potential feedstock, forest wood residue shows promise with its low blue and grey water footprint.</p

Assessing County-Level Water Footprints of Different Cellulosic-Biofuel Feedstock Pathways

While agricultural residue is considered as a near-term feedstock option for cellulosic biofuels, its sustainability must be evaluated by taking water into account. This study aims to analyze the county-level water footprint for four biofuel pathways in the United States, including bioethanol generated from corn grain, stover, wheat straw, and biodiesel from soybean. The county-level blue water footprint of ethanol from corn grain, stover, and wheat straw shows extremely wide variances with a national average of 31, 132, and 139 L of water per liter biofuel (L_w/L_bf), and standard deviation of 133, 323, and 297 L_w/L_bf, respectively. Soybean biodiesel production results in a blue water footprint of 313 L_w/L_bf on the national average with standard deviation of 894 L_w/L_bf. All biofuels show a greater green water footprint than the blue one. This work elucidates how diverse spatial resolutions affect biofuel water footprints, which can provide detailed insights into biofuels’ implications on local water sustainability

Distribution of biofuel blue water and green water footprint under different sizing (in DMTD) with feedstock containing 7% ash and 30% moist content

<p><strong>Figure 2.</strong> Distribution of biofuel blue water and green water footprint under different sizing (in DMTD) with feedstock containing 7% ash and 30% moist content. Green water is composed of water associated with thinning residue (LOGT), logging residue (LOGR), short-rotation woody crop (SRWC), and pulpwood from softwood (SW) and hardwood (HW). The values of the conventional case are averaged between Aiken and Rankin by using ethanol production as a weighting factor.</p> <p><strong>Abstract</strong></p> <p>Forest residue has been proposed as a feasible candidate for cellulosic biofuels. However, the number of studies assessing its water use remains limited. This work aims to analyze the impacts of forest-based biofuel on water resources and quality by using a water footprint approach. A method established here is tailored to the production system, which includes softwood, hardwood, and short-rotation woody crops. The method is then applied to selected areas in the southeastern region of the United States to quantify the county-level water footprint of the biofuel produced via a mixed alcohol gasification process, under several logistic systems, and at various refinery scales. The results indicate that the blue water sourced from surface or groundwater is minimal, at 2.4 liters per liter of biofuel (l/l). The regional-average green water (rainfall) footprint falls between 400 and 443 l/l. The biofuel pathway appears to have a low nitrogen grey water footprint averaging 25 l/l at the regional level, indicating minimal impacts on water quality. Feedstock mix plays a key role in determining the magnitude and the spatial distribution of the water footprint in these regions. Compared with other potential feedstock, forest wood residue shows promise with its low blue and grey water footprint.</p

Association of bacterial genotypes and epidemiological features with treatment failure in hemodialysis patients with methicillin-resistant <i>Staphylococcus aureus</i> bacteremia

<div><p>Objectives</p><p>Methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) infections in the hemodialysis (HD) population are epidemiologically classified as healthcare-associated infections. The data about the clinical impact and bacterial characteristics of hospital-onset (HO)- and community-onset (CO)-MRSA in HD patients are scarce. The current study analyzed the difference in the clinical and molecular characteristics of HO-MRSA and CO-MRSA.</p><p>Methods</p><p>We performed a retrospective review and molecular analysis of clinical isolates from 106 HD patients with MRSA bacteremia from 2009 to 2014. CA genotypes were defined as isolates carrying the SCC<i>mec</i> type IV or V, and HA genotypes were defined as isolates harboring SCC<i>mec</i> type I, II, or III.</p><p>Results</p><p>CO-MRSA infections occurred in 76 patients, and 30 patients had HO-MRSA infections. There was no significant difference in the treatment failure rates between patients with CO-MRSA infections and those with HO-MRSA infections. CA genotypes were associated with less treatment failure (odds ratio [OR]: 0.18; 95% confidence interval [95% CI], 0.07–0.49; <i>p</i> = 0.001). For isolates with a vancomycin minimum inhibitory concentration (MIC) < 1.5 mg/L, the multivariate analysis revealed that HA genotypes and cuffed tunneled catheter use were associated with treatment failure. For isolates with a vancomycin MIC ≥1.5 mg/L, the only risk factor for treatment failure was a higher Pitt score (OR: 1.76; 95% CI, 1.02–3.05; <i>p</i> = 0.043).</p><p>Conclusion</p><p>CA genotypes, but not the epidemiological classification of CO-MRSA, impacted the clinical outcome of MRSA bacteremia in the HD population.</p></div

Multivariate analyses of the association between potential predictor variables and treatment failure in patients with methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) bacteremia.

<p>Multivariate analyses of the association between potential predictor variables and treatment failure in patients with methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) bacteremia.</p

Univariate analyses of the association between potential predictor variables and treatment failure in patients with methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) bacteremia.

<p>Univariate analyses of the association between potential predictor variables and treatment failure in patients with methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) bacteremia.</p

Demographic data, clinical features, molecular characteristics and therapeutic characteristics between healthcare-associated community onset (CO)- and healthcare-associated hospital onset (HO)- methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) infections in hemodialysis patients.

<p>Demographic data, clinical features, molecular characteristics and therapeutic characteristics between healthcare-associated community onset (CO)- and healthcare-associated hospital onset (HO)- methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) infections in hemodialysis patients.</p

Predicting Mortality of Incident Dialysis Patients in Taiwan - A Longitudinal Population-Based Study

<div><p>Background</p><p>Comorbid conditions are highly prevalent among patients with end-stage renal disease (ESRD) and index score is a predictor of mortality in dialysis patients. The aim of this study is to perform a population-based cohort study to investigate the survival rate by age and Charlson comorbidity index (CCI) in incident dialysis patients.</p><p>Methods</p><p>Using the catastrophic illness registration of the Taiwan National Health Insurance Research Database for all patients from 1 January 1998 to 31 December 2008, individuals newly diagnosed with ESRD and receiving dialysis for more than 90 days were eligible for our study. Individuals younger than 18 years or renal transplantation patients either before or after dialysis were excluded. We calculated the CCI, age-weighted CCI by Deyo-Charlson method according to ICD-9 code and categorized CCI into six groups as index scores <3, 4–6, 7–9, 10–12, 13–15, >15. Cox regression models were used to analyze the association between age, CCI and survival, and the risk markers of survival.</p><p>Results</p><p>There were 79,645 incident dialysis patients, whose mean age (± SD) was 60.96 (±13.92) years; 51.43% of patients were women and 51.2% were diabetic. In cox proportional hazard models and stratifying by age, older patients had significantly higher mortality than younger patients. The mortality risk was higher in persons with higher CCI as compared with low CCI. Mortality increased steadily with higher age or comorbidity both for unadjusted and for adjusted models. For all age groups, mortality rates increased in different CCI groups with the highest rates occurring in the oldest age groups.</p><p>Conclusions</p><p>Age and CCI are both strong predictors of survival in Taiwan. The older age or higher comorbidity index in incident dialysis patient is associated with lower long-term survival rates. These population-based estimates may assist clinicians who make decisions when patients need long-term dialysis.</p></div

Angiopoietin-2 as a Prognostic Biomarker of Major Adverse Cardiovascular Events and All-Cause Mortality in Chronic Kidney Disease

<div><p>Background</p><p>Chronic kidney disease (CKD) patients have higher prevalence of major adverse cardiovascular events (MACE) and all-cause mortality. Endothelial damage and dysfunction have been regarded as early portents of MACE in CKD patients. Angiopoietin-2 (Ang-2) impairs endothelial function and promotes aberrant neovascularization. The aim of the study was to assess the relationship between circulating Ang-2 and MACE or all-cause mortality in a CKD cohort.</p><p>Methods</p><p>A total of 621 pre-dialysis stage 3–5 CKD patients were enrolled from January 2006 to December 2011 and were followed up till October 2014. Plasma Ang-2 was measured in duplicate using commercial enzyme-linked immunosorbent assays (ELISA). Clinical outcomes included MACE or all-cause mortality</p><p>Results</p><p>Of all patients, 122 (19.8%) reached MACE or all-cause mortality. Seventy-two had MACE, 79 died, and 29 had both MACE and all-cause mortality during the follow-up period of 41.5±28.3 months. Ang-2 quintile was divided at 1405.0, 1730.0, 2160.9, and 2829.9 pg/ml. The adjusted HR of MACE or all-cause mortality for every single higher log Ang-2 was 5.69 (95% CI: 2.00–16.20, P = 0.001). The adjusted HR of MACE or all-cause mortality was 2.48 (95% CI: 1.25–4.90) for patients of quintile 5 compared with those of quintile 1. A longitudinal association between MACE or all-cause mortality and stepwise increases in Ang-2 levels was found (P-trend = 0.008).</p><p>Conclusions</p><p>Ang-2 is an independent predictor of MACE or all-cause mortality in CKD patients. Additional study is necessary in order to explore the mechanism of the association of Ang-2 with adverse outcomes in patients with CKD.</p></div