Debris cover and surface melt at a temperate maritime alpine glacier: Franz Josef Glacier, New Zealand

Melt rates on glaciers are strongly influenced by the presence of supraglacial debris, which can either enhance or reduce ablation relative to bare ice. Most recently, Franz Josef Glacier has entered into a phase of strong retreat and downwasting, with the increasing emergence of debris on the surface in the ablation zone. Previously at Franz Josef Glacier, melt has only been measured on bare ice. During February 2012, a network of 11 ablation stakes was drilled into locations of varying supraglacial debris thickness on the lower glacier. Mean ablation rates over 9 days varied over the range 1.2–10.1 cm d−1, and were closely related to debris thickness. Concomitant observations of air temperature allowed the application of a degree-day approach to the calculation of melt rates, with air temperature providing a strong indicator of melt. Degree-day factors (d f) varied over the range 1.1–8.1 mm d−1 °C−1 (mean of 4.4 mm d−1 °C−1), comparable with rates reported in other studies. Mapping of the current debris cover revealed 0.7 km2 of the 4.9 km2 ablation zone surface was debris-covered, with thicknesses ranging 1–50 cm. Based on measured debris thicknesses and d f, ablation on debris-covered areas of the glacier is reduced by a total of 41% which equates to a 6% reduction in melt overall across the entire ablation zone. This study highlights the usefulness of a short-term survey to gather representative ablation data, consistent with numerous overseas ablation studies on debris-covered glaciers

Clinical profile and outcome of neonates admitted to the Neonatal Care Unit in a rural teaching Hospital

Objectives: Neonatal morbidity and mortality rates reflect efficiency of health services of a country. This study was conducted to identify the clinical profile, pattern of diseases and common causes of mortality and morbidity in neonates admitted to neonatology unit. Methods: The study was conducted in Neonatal Unit of Karnali Academy of Health Sciences, Jumla for a period of one year from 1st May 2017 to 30th April 2018. Data of all admitted patients were reviewed and analyzed in terms of gender, gestational age, age at presentation, weight, cause of admission and their outcome. Diagnosis was made on clinical examination, radiological findings and laboratory investigations. Data were analyzed using SPSS version 20. Results: Out of 153 neonates admitted during the study period, 2 were excluded because of deficient record. Full-term neonates were 122(80.7%) while preterm were 29 (19.3%). Low birth weight (LBW) babies were 32 (21.18%).Neonatal sepsis 91 (60.26%) was the most common cause of hospital admissions followed by meconium aspiration syndrome 21(13.9%) and prematurity 10(6.62%). Out of 151 newborns, 112 babies (74.1%) were discharged after improvement, 15(9.93%) left against medical advice, 13(8.6%) babies were referred to higher centers for intensive care and there were 9 (5.9%) mortalities and 2(1.3%) got absconded. Conclusion: The majority of neonatal morbidity is due to sepsis, prematurity and respiratory causes. All these causes are preventable to some extent and, can be effectively treated in order to reduce morbidity and mortality. Keywords: New Born, Mortality, Admission, Sepsi

Geomorphological evolution of a debris‐covered glacier surface

There exists a need to advance our understanding of debris‐covered glacier surfaces over relatively short timescales due to rapid, climatically induced areal expansion of debris cover at the global scale, and the impact debris has on mass balance. We applied unpiloted aerial vehicle structure‐from‐motion (UAV‐SfM) and digital elevation model (DEM) differencing with debris thickness and debris stability modelling to unravel the evolution of a 0.15 km2 region of the debris‐covered Miage Glacier, Italy, between June 2015 and July 2018. DEM differencing revealed widespread surface lowering (mean 4.1 ± 1.0 m a‐1; maximum 13.3 m a‐1). We combined elevation change data with local meteorological data and a sub‐debris melt model, and used these relationships to produce high resolution, spatially distributed maps of debris thickness. These maps were differenced to explore patterns and mechanisms of debris redistribution. Median debris thicknesses ranged from 0.12 to 0.17 m and were spatially variable. We observed localized debris thinning across ice cliff faces, except those which were decaying, where debris thickened. We observed pervasive debris thinning across larger, backwasting slopes, including those bordered by supraglacial streams, as well as ingestion of debris by a newly exposed englacial conduit. Debris stability mapping showed that 18.2–26.4% of the survey area was theoretically subject to debris remobilization. By linking changes in stability to changes in debris thickness, we observed that slopes that remain stable, stabilize, or remain unstable between periods almost exclusively show net debris thickening (mean 0.07 m a‐1) whilst those which become newly unstable exhibit both debris thinning and thickening. We observe a systematic downslope increase in the rate at which debris cover thickens which can be described as a function of the topographic position index and slope gradient. Our data provide quantifiable insights into mechanisms of debris remobilization on glacier surfaces over sub‐decadal timescales, and open avenues for future research to explore glacier‐scale spatiotemporal patterns of debris remobilization

Domain Organization, Catalysis and Regulation of Eukaryotic Cystathionine Beta-Synthases

Cystathionine beta-synthase (CBS) is a key regulator of sulfur amino acid metabolism diverting homocysteine, a toxic intermediate of the methionine cycle, via the transsulfuration pathway to the biosynthesis of cysteine. Although the pathway itself is well conserved among eukaryotes, properties of eukaryotic CBS enzymes vary greatly. Here we present a side-by-side biochemical and biophysical comparison of human (hCBS), fruit fly (dCBS) and yeast (yCBS) enzymes. Preparation and characterization of the full-length and truncated enzymes, lacking the regulatory domains, suggested that eukaryotic CBS exists in one of at least two significantly different conformations impacting the enzyme’s catalytic activity, oligomeric status and regulation. Truncation of hCBS and yCBS, but not dCBS, resulted in enzyme activation and formation of dimers compared to native tetramers. The dCBS and yCBS are not regulated by the allosteric activator of hCBS, S-adenosylmethionine (AdoMet); however, they have significantly higher specific activities in the canonical as well as alternative reactions compared to hCBS. Unlike yCBS, the heme-containing dCBS and hCBS showed increased thermal stability and retention of the enzyme’s catalytic activity. The mass-spectrometry analysis and isothermal titration calorimetry showed clear presence and binding of AdoMet to yCBS and hCBS, but not dCBS. However, the role of AdoMet binding to yCBS remains unclear, unlike its role in hCBS. This study provides valuable information for understanding the complexity of the domain organization, catalytic specificity and regulation among eukaryotic CBS enzymes.This work was supported by Postdoctoral Fellowship 0920079G from the American Heart Association (to TM), by National Institutes of Health Grant HL065217, by American Heart Association Grant In-Aid 09GRNT2110159, by a grant from the Jerome Lejeune Foundation (all to JPK) and by a research contract RYC2009-04147 (to ALP). In addition, grant support (P11-CTS-07187, CSD2009-00088 and BIO2012-34937) to Dr. Jose M. Sanchez-Ruiz (University of Granada) and SGIker technical and human support (UPV/EHU, MICINN, GV/EJ, ESF) are gratefully acknowledged

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Refining the committee approach and uncertainty prediction in hydrological modelling; Dissertation, UNESCO-IHE Institute for Water Education, Delft and Delft University of Technology.

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. A solution could be the in use of several specialized models organized in the so-called committees. Refining the committee approach is one of the important topics of this study, and it is demonstrated that it allows for increased predictive capability of models. Another topic addressed is the prediction of hydrologic models' uncertainty. The traditionally used Monte Carlo method is based on the past data and cannot be directly used for estimation of model uncertainty for the future model runs during its operation. In this thesis the so-called MLUE (Machine Learning for Uncertainty Estimation) approach is further explored and extended; in it the machine learning techniques (e.g. neural networks) are used to encapsulate the results of Monte Carlo experiments in a predictive model that is able to estimate uncertainty for the future states of the modelled system. Furthermore, it is demonstrated that a committee of several predictive uncertainty models allows for an increase in prediction accuracy. Catchments in Nepal, UK and USA are used as case studies. In flood modelling hydrological models are typically used in combination with hydraulic models forming a cascade, often supported by geospatial processing. For uncertainty analysis of flood inundation modelling of the Nzoia catchment (Kenya) SWAT hydrological and SOBEK hydrodynamic models are integrated, and the parametric uncertainty of the hydrological model is allowed to propagate through the model cascade using Monte Carlo simulations, leading to the generation of the probabilistic flood maps. Due to the high computational complexity of these experiments, the high performance (cluster) computing framework is designed and used.This study refined a number of hydroinformatics techniques, thus enhancing uncertainty-based hydrological and integrated modelling

Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of models. One of multi modelling approaches called "committee modelling" is one of the topics in part of this study. Special attention is given to the so-called “fuzzy committee” approach to hydrological modelling. The comparative interpretation of the resulting uncertainty statistics from different sampling schemes (MCS, GLUE, MCMC, SCEMUA, DREAM, PSO, and ACCO) for uncertainty estimations of hydrological model is presented. The uncertainty statistics are considerably depending on the sampling method used. Another aspect of uncertainty analysis relates to predicting uncertainty (rather than its analysis). Machine learning techniques were proposed to build model of probability distribution function as predictive uncertainty models, which allows adequate uncertainty estimation for hydrological models. In flood modelling hydrological models are typically used in combination with hydraulic models forming a cascade, often supported by geospatial processing. SWAT hydrological and SOBEK hydrodynamic models are integrated for uncertainty analysis of flood inundation modelling of the Nzoia catchment (Kenya), and the parametric uncertainty of the hydrological model is allowed to propagate through the model cascade using Monte Carlo simulations, leading to the generation of the probabilistic flood maps. Due to the high computational complexity of these experiments, the high performance (cluster) computing framework is designed and used. Overall, this thesis presents research efforts in: (i) committee modelling of hydrological models, (ii) hybrid committee hydrological models, (iii) influence of sampling strategies on prediction uncertainty of hydrological models, (iv) uncertainty prediction using machine learning techniques, (v) committee of predictive uncertainty models and (vi) uncertainty in flood inundation extent. This study refined a number of hydroinformatics techniques, thus enhancing uncertainty-based hydrological and integrated modelling.Water ManagementCivil Engineering and Geoscience