Integrated Refrigeration and Storage of LNG for Compositional Stability

Growing interest in liquefied natural gas (LNG) as a rocket fuel necessitates a greater technical understanding of the compositional changes due to preferential boil-off (or weathering) that occurs during long duration storage. The purity of methane in LNG can range from 90 to 98%, and is subject to preferential boil-off due to its low boiling point compared to other constituents despite the use of high-performance thermal insulation systems. Active heat extraction (i.e. refrigeration) is required to completely eliminate weathering. For future operational safety and reliability, and to better understand the quality and efficiency of the LNG as a cryofuel, a 400-liter Cryostat vessel was designed and constructed to measure the composition and temperatures of the LNG at a number of different liquid levels over long durations. The vessel is the centerpiece of a custom-designed lab-scale integrated refrigeration and storage (IRaS) system employing a pulse tube cryocooler capable of roughly 50 W of lift at 100 K. Instrumentation includes ten temperature sensors mounted on a vertical rake and five liquid sample tubes corresponding to five liquid levels. Two modes of operation are studied. The first is without refrigeration in order to determine a baseline in the change in composition, and to study stratification of the LNG. The second is performed with the cryocooler active to determine the operational parameters of the IRaS system for eliminating the weathering as well as stratification effects in the bulk liquid. The apparatus design and test method, as well as preliminary test results are presented in this paper. As a bonus in cost-saving and operational efficiency, the capability of the IRaS system to provide zero-loss capabilities such as zero boil-off (ZBO) keeping of the LNG and zero-loss filling/transfer operations are also discussed

Predicting language diversity with complex network

Evolution and propagation of the world's languages is a complex phenomenon, driven, to a large extent, by social interactions. Multilingual society can be seen as a system of interacting agents, where the interaction leads to a modification of the language spoken by the individuals. Two people can reach the state of full linguistic compatibility due to the positive interactions, like transfer of loanwords. But, on the other hand, if they speak entirely different languages, they will separate from each other. These simple observations make the network science the most suitable framework to describe and analyze dynamics of language change. Although many mechanisms have been explained, we lack a qualitative description of the scaling behavior for different sizes of a population. Here we address the issue of the language diversity in societies of different sizes, and we show that local interactions are crucial to capture characteristics of the empirical data. We propose a model of social interactions, extending the idea from, that explains the growth of the language diversity with the size of a population of country or society. We argue that high clustering and network disintegration are the most important characteristics of models properly describing empirical data. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change

A global phylogenomic analysis of the shiitake genus Lentinula

Lentinula is a broadly distributed group of fungi that contains the cultivated shiitake mushroom, L. edodes. We sequenced 24 genomes representing eight described species and several unnamed lineages of Lentinula from 15 countries on four continents. Lentinula comprises four major clades that arose in the Oligocene, three in the Americas and one in Asia–Australasia. To expand sampling of shiitake mushrooms, we assembled 60 genomes of L. edodes from China that were previously published as raw Illumina reads and added them to our dataset. Lentinula edodes sensu lato (s. lat.) contains three lineages that may warrant recognition as species, one including a single isolate from Nepal that is the sister group to the rest of L. edodes s. lat., a second with 20 cultivars and 12 wild isolates from China, Japan, Korea, and the Russian Far East, and a third with 28 wild isolates from China, Thailand, and Vietnam. Two additional lineages in China have arisen by hybridization among the second and third groups. Genes encoding cysteine sulfoxide lyase (lecsl) and γ-glutamyl transpeptidase (leggt), which are implicated in biosynthesis of the organosulfur flavor compound lenthionine, have diversified in Lentinula. Paralogs of both genes that are unique to Lentinula (lecsl 3 and leggt 5b) are coordinately up-regulated in fruiting bodies of L. edodes. The pangenome of L. edodes s. lat. contains 20,308 groups of orthologous genes, but only 6,438 orthogroups (32%) are shared among all strains, whereas 3,444 orthogroups (17%) are found only in wild populations, which should be targeted for conservation

Opinion dynamics: models, extensions and external effects

Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. [...]Comment: 42 pages, 6 figure

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.National Institutes of Health (U.S.) (NIH grant P50-GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

Computational classifiers for predicting the short-term course of Multiple sclerosis

The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). METHODS: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. RESULTS: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. CONCLUSIONS: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability

Saliva levels of Abeta1-42 as potential biomarker of Alzheimer's disease: a pilot study

<p>Abstract</p> <p>Background</p> <p>Simple, non-invasive tests for early detection of degenerative dementia by use of biomarkers are urgently required. However, up to the present, no validated extracerebral diagnostic markers for the early diagnosis of Alzheimer disease (AD) are available. The clinical diagnosis of probable AD is made with around 90% accuracy using modern clinical, neuropsychological and imaging methods. A biochemical marker that would support the clinical diagnosis and distinguish AD from other causes of dementia would therefore be of great value as a screening test. A total of 126 samples were obtained from subjects with AD, and age-sex-matched controls. Additionally, 51 Parkinson's disease (PD) patients were used as an example of another neurodegenerative disorder. We analyzed saliva and plasma levels of β amyloid (Aβ) using a highly sensitive ELISA kit.</p> <p>Results</p> <p>We found a small but statistically significant increase in saliva Aβ₄₂levels in mild AD patients. In addition, there were not differences in saliva concentration of Aβ₄₂between patients with PD and healthy controls. Saliva Aβ₄₀expression was unchanged within all the studied sample. The association between saliva Aβ₄₂levels and AD was independent of established risk factors, including age or Apo E, but was dependent on sex and functional capacity.</p> <p>Conclusions</p> <p>We suggest that saliva Aβ₄₂levels could be considered a potential peripheral marker of AD and help discrimination from other types of neurodegenerative disorders. We propose a new and promising biomarker for early AD.</p

Bronchiectasis and asthma: Data from the European Bronchiectasis Registry (EMBARC)

\ua9 2024 The AuthorsBackground: Asthma is commonly reported in patients with a diagnosis of bronchiectasis. Objective: The aim of this study was to evaluate whether patients with bronchiectasis and asthma (BE+A) had a different clinical phenotype and different outcomes compared with patients with bronchiectasis without concomitant asthma. Methods: A prospective observational pan-European registry (European Multicentre Bronchiectasis Audit and Research Collaboration) enrolled patients across 28 countries. Adult patients with computed tomography–confirmed bronchiectasis were reviewed at baseline and annual follow-up visits using an electronic case report form. Asthma was diagnosed by the local investigator. Follow-up data were used to explore differences in exacerbation frequency between groups using a negative binomial regression model. Survival analysis used Cox proportional hazards regression. Results: Of 16,963 patients with bronchiectasis included for analysis, 5,267 (31.0%) had investigator-reported asthma. Patients with BE+A were younger, were more likely to be female and never smokers, and had a higher body mass index than patients with bronchiectasis without asthma. BE+A was associated with a higher prevalence of rhinosinusitis and nasal polyps as well as eosinophilia and Aspergillus sensitization. BE+A had similar microbiology but significantly lower severity of disease using the bronchiectasis severity index. Patients with BE+A were at increased risk of exacerbation after adjustment for disease severity and multiple confounders. Inhaled corticosteroid (ICS) use was associated with reduced mortality in patients with BE+A (adjusted hazard ratio 0.78, 95% CI 0.63-0.95) and reduced risk of hospitalization (rate ratio 0.67, 95% CI 0.67-0.86) compared with control subjects without asthma and not receiving ICSs. Conclusions: BE+A was common and was associated with an increased risk of exacerbations and improved outcomes with ICS use. Unexpectedly we identified significantly lower mortality in patients with BE+A

The CARMENES search for exoplanets around M dwarfs. Two temperate Earth-mass planet candidates around Teegarden’s Star

Context.Teegarden’s Star is the brightest and one of the nearest ultra-cool dwarfs in the solar neighbourhood. For its late spectral type (M7.0 V),the star shows relatively little activity and is a prime target for near-infrared radial velocity surveys such as CARMENES.Aims.As part of the CARMENES search for exoplanets around M dwarfs, we obtained more than 200 radial-velocity measurements of Teegarden’sStar and analysed them for planetary signals.Methods.We find periodic variability in the radial velocities of Teegarden’s Star. We also studied photometric measurements to rule out stellarbrightness variations mimicking planetary signals.Results.We find evidence for two planet candidates, each with 1.1M⊕minimum mass, orbiting at periods of 4.91 and 11.4 d, respectively. Noevidence for planetary transits could be found in archival and follow-up photometry. Small photometric variability is suggestive of slow rotationand old age.Conclusions.The two planets are among the lowest-mass planets discovered so far, and they are the first Earth-mass planets around an ultra-cooldwarf for which the masses have been determined using radial velocities.We thank the referee Rodrigo Díaz for a careful review andhelpful comments. M.Z. acknowledges support from the Deutsche Forschungs-gemeinschaft under DFG RE 1664/12-1 and Research Unit FOR2544 “BluePlanets around Red Stars”, project no. RE 1664/14-1. CARMENES isan instrument for the Centro Astronómico Hispano-Alemán de Calar Alto(CAHA, Almería, Spain). CARMENES is funded by the German Max-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de InvestigacionesCientíficas (CSIC), the European Union through FEDER/ERF FICTS-2011-02 funds, and the members of the CARMENES Consortium (Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, LandessternwarteKönigstuhl, Institut de Ciències de l’Espai, Institut für Astrophysik Göttingen,Universidad Complutense de Madrid, Thüringer Landessternwarte Tautenburg,Instituto de Astrofísica de Canarias, Hamburger Sternwarte, Centro de Astro-biología and Centro Astronómico Hispano-Alemán), with additional contribu-tions by the Spanish Ministry of Economy, the German Science Foundationthrough the Major Research Instrumentation Programme and DFG ResearchUnit FOR2544 “Blue Planets around Red Stars”, the Klaus Tschira Stiftung, thestates of Baden-Württemberg and Niedersachsen, and by the Junta de Andalucía.Based on data from the CARMENES data archive at CAB (INTA-CSIC). Thisarticle is based on observations made with the MuSCAT2 instrument, devel-oped by ABC, at Telescopio Carlos Sánchez operated on the island of Tener-ife by the IAC in the Spanish Observatorio del Teide. Data were partly col-lected with the 150-cm and 90-cm telescopes at the Sierra Nevada Observa-tory (SNO) operated by the Instituto de Astrofísica de Andalucía (IAA-CSIC).Data were partly obtained with the MONET/South telescope of the MOnitoringNEtwork of Telescopes, funded by the Alfried Krupp von Bohlen und HalbachFoundation, Essen, and operated by the Georg-August-Universität Göttingen,the McDonald Observatory of the University of Texas at Austin, and the SouthAfrican Astronomical Observatory. We acknowledge financial support from theSpanish Agencia Estatal de Investigación of the Ministerio de Ciencia, Inno-vación y Universidades and the European FEDER/ERF funds through projectsAYA2015-69350-C3-2-P, AYA2016-79425-C3-1/2/3-P, AYA2018-84089, BES-2017-080769, BES-2017-082610, ESP2015-65712-C5-5-R, ESP2016-80435-C2-1/2-R, ESP2017-87143-R, ESP2017-87676-2-2, ESP2017-87676-C5-1/2/5-R, FPU15/01476, RYC-2012-09913, the Centre of Excellence ”Severo Ochoa”and ”María de Maeztu” awards to the Instituto de Astrofísica de Canarias (SEV-2015-0548), Instituto de Astrofísica de Andalucía (SEV-2017-0709), and Cen-tro de Astrobiología (MDM-2017-0737), the Generalitat de Catalunya throughCERCA programme”, the Deutsches Zentrum für Luft- und Raumfahrt throughgrants 50OW0204 and 50OO1501, the European Research Council through grant694513, the Italian Ministero dell’instruzione, dell’università de della ricerca andUniversità degli Studi di Roma Tor Vergata through FFABR 2017 and “Mis-sion: Sustainability 2016”, the UK Science and Technology Facilities Council through grant ST/P000592/1, the Israel Science Foundation through grant848/16, the Chilean CONICYT-FONDECYT through grant 3180405, the Mexi-can CONACYT through grant CVU 448248, the JSPS KAKENHI through grantsJP18H01265 and 18H05439, and the JST PRESTO through grant JPMJPR1775