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Management of environmental streaming data to optimize Arctic shipping routes.Dynamic accurate predictions of Arctic sea ice, ocean, atmosphere, and ecosystem are necessary for safe and efficient Arctic maritime transportation; however a related technical roadmap has not yet been established. In this paper, we propose a management system for trans-Arctic maritime transportation supported by near real-time streaming data from air-space-ground-sea integrated monitoring networks and high spatio-temporal sea ice modeling. As the core algorithm of integrated monitoring networks, a long short-term memory (LSTM) neural network is embedded to improve Arctic sea ice mapping algorithms.Since the LSTM is localized in time and space, it can make full use of streaming data characteristics. The sea ice–related parameters from satellite remote sensing raw data are used as the input of the LSTM, while streaming data from shipborne radar networks and/or buoy measurements are used as training datasets to enhance the accuracy and resolution of environmental streaming data from outputs of LSTM. Due to large size of streaming data, the proposed management system of trans-Arctic shipping should be built on a cloud distribution platform using existing wireless communications networks among vessels and ports. Our management system will be used by the ongoing European Commission Horizon 2020 Programme “ePIcenter.”
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Genomic analysis of field pennycress (Thlaspi arvense) provides insights into mechanisms of adaptation to high elevation.Background: Understanding how organisms evolve and adapt to extreme habitats is of crucial importance in evolutionary ecology. Altitude gradients are an important determinant of the distribution pattern and range of organisms due to distinct climate conditions at different altitudes. High-altitude regions often provide extreme environments including low temperature and oxygen concentration, poor soil, and strong levels of ultraviolet radiation, leading to very few plant species being able to populate elevation ranges greater than 4000 m. Field pennycress (Thlaspi arvense) is a valuable oilseed crop and emerging model plant distributed across an elevation range of nearly 4500 m. Here, we generate an improved genome assembly to understand how this species adapts to such different environments. Results: We sequenced and assembled de novo the chromosome-level pennycress genome of 527.3 Mb encoding 31,596 genes. Phylogenomic analyses based on 2495 single-copy genes revealed that pennycress is closely related to Eutrema salsugineum (estimated divergence 14.32–18.58 Mya), and both species form a sister clade to Schrenkiella parvula and genus Brassica. Field pennycress contains the highest percentage (70.19%) of transposable elements in all reported genomes of Brassicaceae, with the retrotransposon proliferation in the Middle Pleistocene being likely responsible for the expansion of genome size. Moreover, our analysis of 40 field pennycress samples in two highand two low-elevation populations detected 1,256,971 high-quality single nucleotide polymorphisms. Using three complementary selection tests, we detected 130 candidate naturally selected genes in the Qinghai-Tibet Plateau (QTP) populations, some of which are involved in DNA repair and the ubiquitin system and potential candidates involved in high-altitude adaptation. Notably, we detected a single base mutation causing loss-of-function of the FLOWERING LOCUS C protein, responsible for the transition to early flowering in high-elevation populations. Conclusions: Our results provide a genome-wide perspective of how plants adapt to distinct environmental conditions across extreme elevation differences and the potential for further follow-up research with extensive data from additional populations and species.
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Warm-up intensity does not affect the ergogenic effect of sodium bicarbonate in adult menThis study determined the influence of a high (HI) vs. low-intensity (LI) cycling warm-up on blood acid-base responses and exercise capacity following ingestion of sodium bicarbonate (SB; 0.3 g·kg-1 body-mass (BM)) or a placebo (PLA; maltodextrin) 3-hours prior to warm-up. Twelve men (21±2 years, 79.2±3.6 kg BM, maximum power output (Wmax) 318±36 W) completed a familiarisation and four double-blind trials completed in a counterbalanced order: HI warm-up with SB (HISB); HI warm-up with PLA (HIPLA); LI warm-up with SB (LISB); and LI warm-up with PLA (LIPLA). LI warm-up was 15-minutes at 60%Wmax, while the HI warm-up (typical of elites) featured LI followed by 2 x 30-sec (3-minute break) at Wmax, finishing 30-minute prior to a cycling capacity test at 110%Wmax (CCT110%). Blood bicarbonate and lactate were measured throughout. SB supplementation increased blood bicarbonate (+6.4 [95%CI: 5.7 to 7.1 mmol·L-1]) prior to greater reductions with high intensity warm-up (-3.8 [95%CI: -5.8 to -1.8 mmol·L-1]). However, during the 30-minute recovery, blood bicarbonate rebounded and increased in all conditions, with concentrations ~5.3mmol·L-1 greater with SB supplementation (P<0.001). Blood bicarbonate significantly declined during the CCT110% with greater reductions following SB supplementation (-2.4 [95%CI: -3.8 to -0.90 mmol·L-1]). Aligned with these results, SB supplementation increased total work done during the CCT110% (+8.5 [95%CI: 3.6 to 13.4 kJ], ~19% increase) with no significant main effect of warm-up intensity (+0.0 [95%CI: -5.0 to 5.0 kJ). Collectively, the results demonstrate that SB supplementation can improve HI cycling capacity irrespective of prior warm-up intensity, likely due to blood alkalosis.
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Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based approachSocial media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.
