Interactions between marine megafauna and plastic pollution in Southeast Asia.

Southeast (SE) Asia is a highly biodiverse region, yet it is also estimated to cumulatively contribute a third of the total global marine plastic pollution. This threat is known to have adverse impacts on marine megafauna, however, understanding of its impacts has recently been highlighted as a priority for research in the region. To address this knowledge gap, a structured literature review was conducted for species of cartilaginous fishes, marine mammals, marine reptiles, and seabirds present in SE Asia, collating cases on a global scale to allow for comparison, coupled with a regional expert elicitation to gather additional published and grey literature cases which would have been omitted during the structured literature review. Of the 380 marine megafauna species present in SE Asia, but also studied elsewhere, we found that 9.1 % and 4.5 % of all publications documenting plastic entanglement (n = 55) and ingestion (n = 291) were conducted in SE Asian countries. At the species level, published cases of entanglement from SE Asian countries were available for 10 % or less of species within each taxonomic group. Additionally, published ingestion cases were available primarily for marine mammals and were lacking entirely for seabirds in the region. The regional expert elicitation led to entanglement and ingestion cases from SE Asian countries being documented in 10 and 15 additional species respectively, highlighting the utility of a broader approach to data synthesis. While the scale of the plastic pollution in SE Asia is of particular concern for marine ecosystems, knowledge of its interactions and impacts on marine megafauna lags behind other areas of the world, even after the inclusion of a regional expert elicitation. Additional funding to help collate baseline data are critically needed to inform policy and solutions towards limiting the interactions of marine megafauna and plastic pollution in SE Asia.

Influence of Molasses Residue on Treatment of Cow Manure in an Anaerobic Filter with Perforated Weed Membrane and a Conventional Reactor: Variations of Organic Loading and a Machine Learning Application.

This study highlighted the influence of molasses residue (MR) on the anaerobic treatment of cow manure (CM) at various organic loading and mixing ratios of these two substrates. Further investigation was conducted on a model-fitting comparison between a kinetic study and an artificial neural network (ANN) using biomethane potential (BMP) test data. A continuous stirred tank reactor (CSTR) and an anaerobic filter with a perforated membrane (AF) were fed with similar substrate at the organic loading rates of (OLR) 1 to OLR 7 g/L/day. Following the inhibition signs at OLR 7 (50:50 mixing ratio), 30:70 and 70:30 ratios were applied. Both the CSTR and the AF with the co-digestion substrate (CM + MR) successfully enhanced the performance, where the CSTR resulted in higher biogas production (29 L/d), SMP (1.24 LCH4/gVSadded), and VS removal (>80%) at the optimum OLR 5 g/L/day. Likewise, the AF showed an increment of 69% for biogas production at OLR 4 g/L/day. The modified Gompertz (MG), logistic (LG), and first order (FO) were the applied kinetic models. Meanwhile, two sets of ANN models were developed, using feedforward back propagation. The FO model provided the best fit with Root Mean Square Error (RMSE) (57.204) and correlation coefficient (R2) 0.94035. Moreover, implementing the ANN algorithms resulted in 0.164 and 0.97164 for RMSE and R2, respectively. This reveals that the ANN model exhibited higher predictive accuracy, and was proven as a more robust system to control the performance and to function as a precursor in commercial applications as compared to the kinetic models. The highest projection electrical energy produced from the on-farm scale (OFS) for the AF and the CSTR was 101 kWh and 425 kWh, respectively. This investigation indicates the high potential of MR as the most suitable co-substrate in CM treatment for the enhancement of energy production and the betterment of waste management in a large-scale application.

Progressive retinal changes in pediatric multiple sclerosis.

Objectives To determine to what extent acute demyelinating episodes versus chronic degenerative phenomena drive retinal neuroaxonal damage in pediatric acquired demyelinating syndromes (ADS). Methods We acquired optical coherence tomography (OCT) data (follow-up range: 2 weeks - 5 years, at variable intervals from presentation) in pediatric participants who had multiple sclerosis (MS), monophasic ADS, or were healthy. Multivariable mixed effects models were used to assess the association of the number of demyelinating episodes (either optic neuritis [ON], or non-ON relapses) with changes in retinal nerve fiber layer (RNFL) or ganglion cell layer-inner plexiform layer (GCIPL) thickness. Results 64 OCT sans from 23 MS, and 33 scans from 12 monophasic ADS participants were compared with 68 scans from 62 healthy participants. The first ON episode had the biggest impact on RNFL or GCIPL thickness in monophasic ADS (RNFL: -7.9 µm, CI=5.5, p = 0.0056; GCIPL: -8.4 µm, CI=4.4, p = 0.0002) and MS (RNFL: -16 µm, CI = 3.7, p < 10-6; GCIPL: -15 µm, CI = 2.6, p < 10-6). Non-ON relapses were also associated with small but significant retinal thickness reductions in MS (RNFL: -2.6 µm/relapse, CI = 1.4, p = 0.0003; GCIPL: -2.8 µm/relapse, CI = 0.89, p < 10-6). MS participants showed progressive GCIPL thinning independent of acute demyelinating episodes (-2.7 µm/year, CI = 1.9, p = 0.0058). Conclusions We showed a prominent impact of early ON episodes on OCT measures of neuroaxonal structure in patients with ADS. We also demonstrated negative effects of non-ON relapses, and the presence of chronic retinal neurodegenerative changes, in youth with MS.

Design and sample size considerations for valuation studies of multi-attribute utility instruments.

The EQ-5D, a widely used multiattribute utility instrument, is commonly used in health economic evaluations where the goal is to decide on which treatments to reimburse. Like other instruments, value sets of the EQ-5D are constructed using valuation studies typically valuing a subset of the health states and using predicted values from a regression model for the unvalued health states. In current practice the prediction errors associated with the value sets are substantial. The goal of this work is 2-fold. First, derive a formula of the mean squared error (MSE) of a value set assuming that the value set is estimated using a linear mixed model with either an independent or a Gaussian spatial correlation on the model misspecification error. Second, explore the effect of the number of health states directly valued, the number of participants and the correlation structure on the MSE. Keeping the total number of participants and the total number of valuations fixed, valuing all 242 health states of the EQ-5D-3L was found to reduce the MSE considerably compared with the common practice of valuing only 42 health states. Furthermore, an independent correlation structure with 3773 participants valuing 42 health states produced the MSE that can be achieved with less than 600 participants valuing all 242 health states under a Gaussian spatial correlation structure. Based on the comparison of the MSE values of some of the well-known designs our suggestion is to value more health states and to use a model with spatially correlated misspecification errors.

Acacia Holosericea: An Invasive Species for Bio-char, Bio-oil, and Biogas Production.

To evaluate the possibilities for biofuel and bioenergy production Acacia Holosericea, which is an invasive plant available in Brunei Darussalam, was investigated. Proximate analysis of Acacia Holosericea shows that the moisture content, volatile matters, fixed carbon, and ash contents were 9.56%, 65.12%, 21.21%, and 3.91%, respectively. Ultimate analysis shows carbon, hydrogen, and nitrogen as 44.03%, 5.67%, and 0.25%, respectively. The thermogravimetric analysis (TGA) results have shown that maximum weight loss occurred for this biomass at 357 °C for pyrolysis and 287 °C for combustion conditions. Low moisture content (<10%), high hydrogen content, and higher heating value (about 18.13 MJ/kg) makes this species a potential biomass. The production of bio-char, bio-oil, and biogas from Acacia Holosericea was found 34.45%, 32.56%, 33.09% for 500 °C with a heating rate 5 °C/min and 25.81%, 37.61%, 36.58% with a heating rate 10 °C/min, respectively, in this research. From Fourier transform infrared (FTIR) spectroscopy it was shown that a strong C-H, C-O, and C=C bond exists in the bio-char of the sample.

Benefits of Physical Activity for Depression and Fatigue in Multiple Sclerosis: A Longitudinal Analysis.

OBJECTIVES: To examine the longitudinal relationship between physical activity and fatigue and depression among youth with demyelinating conditions. STUDY DESIGN: From September 2013 to March 2017, we performed a longitudinal study of consecutive youth diagnosed at their first visit with pediatric onset multiple sclerosis (POMS) or monophasic acquired demyelinating syndromes (mono-ADS) at a neuroinflammatory disorders clinic in a tertiary children's hospital. Fatigue was determined at each visit by the Pediatric Quality of Life Multidimensional Fatigue Scale, depressive symptoms by the Center of Epidemiologic Studies Depression Children Rating Scale, and physical activity level by the Godin Leisure Time Exercise Questionnaire. Mixed linear models were used to examine the associations of moderate-to-vigorous physical activity (MVPA) with fatigue and depression over time, adjusting for age, time from incident demyelination, sex, number of relapses, relapse within 30 days, and disability. RESULTS: In 182 patients (48 POMS, age 15 ± 1.7 years, 35 female; and 134 mono-ADS, age 12 ± 3.6 years 67 female) with 538 visits (mean follow-up 3.6 ± 2.7 years and 4.2 ± 3.3 years, respectively), a trajectory of increased fatigue over time was observed in POMS (2.28 points/year, P = .008) and mono-ADS (1.33 points/year, P = .007) patients. Youth with POMS had more depressive symptoms (estimate = 11.4 points, P < .002) than mono-ADS. Depressive symptoms increased over time in female patients with POMS (estimate = 1.4 points/year, P < .02). MVPA was associated with lower depression (-0.09, P < .001) and general fatigue (0.13, P = .02) over time in POMS. CONCLUSIONS: Youth with POMS who have higher levels of MVPA demonstrate lesser depressive symptoms and lower fatigue over time. Our results may inform future interventions to manage mood and fatigue in POMS.

Reducing Uncertainty in EQ-5D Value Sets: The Role of Spatial Correlation.

OBJECTIVE: Scoring algorithms for the EQ-5D-3L are constructed subject to a large degree of uncertainty (a credible interval width of 0.152, which is significant in comparison to the reported minimal important differences). The purpose of this work is to explore modeling techniques that will reduce the extent of this uncertainty. METHODS: We used the US valuation study data. A Bayesian approach was used to calculate predicted utilities and credible intervals. A spatial Gaussian correlation structure was used to model correlation among health states (HS), thus allowing directly valued HS to contribute to the predicted utility of nearby unvalued HS. Leave-one-out cross-validation was used to compare model performances. RESULTS: The average posterior standard deviation was 0.039 for the unvalued health states and 0.011 for the valued health states. Using cross-validation, the US D1 model had 31% coverage probability. Models with independent and Gaussian correlation had coverage probabilities of 95% and 93%, respectively. Moreover, the Gaussian correlation structure resulted in a 25.6% reduction in mean squared error (SE) and 13.2% reduction in mean absolute error (AE) compared to the independent correlation structure (mean SE of 0.00131 v. 0.00176 and mean AE of 0.02818 v. 0.03248). CONCLUSION: Uncertainty was substantially lower for the directly valued HS compared to unvalued HS, suggesting direct valuation of as many health states as possible. Incorporation of a spatial correlation significantly reduced uncertainty. Hence, we suggest incorporating this when constructing scoring algorithms.

Different treatment strategies for highly polluted landfill leachate in developing countries.

The aim of this research was to determine appropriate treatment technique for effective treatment of heavily polluted landfill leachate. We accomplished several treatment experiments: (i) aerobic biological treatment, (ii) chemical coagulation, (iii) advanced oxidation process (AOP) and (iv) several combined treatment strategies. Efficiency of these treatment procedures were monitored by analysing COD and colour removal. Leachate used for this study was taken from Matuail landfill site at Dhaka city. With extended aeration process which is currently used in Matuail landfill site for leachate treatment, maximum COD and colour removal of 36% and 20%, respectively could be achieved with optimum retention period of 7 days. With optimum aluminium sulphate dose of 15,000 mg/L and pH value of 7.0, maximum COD and colour removals of 34% and 66%, respectively were observed by using chemical coagulation. With optimum pH of 5.0 and optimum dosages of reagents having H(2)O(2)/Fe(2+) molar ratio of 1.3 the highest removal of COD and colour were found 68% and 87%, respectively with sludge production of 55%. Fenton treatment which is an advanced oxidation process was the most successful between these three separate treatment procedures. Among the combined treatment options performed, extended aeration followed by Fenton method was the most suitable one.