e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness

Artificial sweeteners (AS) can elicit the strong sweet sensation with the low or zero calorie, and are widely used to replace the nutritive sugar in the food and beverage industry. However, the safety issue of current AS is still controversial. Thus, it is imperative to develop more safe and potent AS. Due to the costly and laborious experimental-screening of AS, in-silico sweetener/sweetness prediction could provide a good avenue to identify the potential sweetener candidates before experiment. In this work, we curate the largest dataset of 530 sweeteners and 850 non-sweeteners, and collect the second largest dataset of 352 sweeteners with the relative sweetness (RS) from the literature. In light of these experimental datasets, we adopt five machine-learning methods and conformational-independent molecular fingerprints to derive the classification and regression models for the prediction of sweetener and its RS, respectively via the consensus strategy. Our best classification model achieves the 95% confidence intervals for the accuracy (0.91 ± 0.01), precision (0.90 ± 0.01), specificity (0.94 ± 0.01), sensitivity (0.86 ± 0.01), F1-score (0.88 ± 0.01), and NER (Non-error Rate: 0.90 ± 0.01) on the test set, which outperforms the model (NER = 0.85) of Rojas et al. in terms of NER, and our best regression model gives the 95% confidence intervals for the R2(test set) and ΔR2 [referring to |R2(test set)- R2(cross-validation)|] of 0.77 ± 0.01 and 0.03 ± 0.01, respectively, which is also better than the other works based on the conformation-independent 2D descriptors (e.g., 2D Dragon) according to R2(test set) and ΔR2. Our models are obtained by averaging over nineteen data-splitting schemes, and fully comply with the guidelines of Organization for Economic Cooperation and Development (OECD), which are not completely followed by the previous relevant works that are all on the basis of only one random data-splitting scheme for the cross-validation set and test set. Finally, we develop a user-friendly platform “e-Sweet” for the automatic prediction of sweetener and its corresponding RS. To our best knowledge, it is a first and free platform that can enable the experimental food scientists to exploit the current machine-learning methods to boost the discovery of more AS with the low or zero calorie content

Multiple processes affecting surface seawater N2O saturation anomalies in tropical oceans and Prydz Bay, Antarctica

We analyzed the N2O content of surface seawater sampled from Prydz Bay, Antarctica, on a cruise track between 30°S and 30°N during the twenty-second Chinese National Antarctic Research Expedition during austral summer, 2006. The surface water showed an average pN2O value of 311.9±7.6 nL·L-1 (14.1±0.4 nmol·L-1), which was slightly undersaturated. The air-sea N2O flux in the region was -0.3±0.8 μmol·m-2·d-1; however, N2O in the surface water was oversaturated in most stations along the cruise track. Saturation anomalies were greater than 10%, with a maximum of 54.7% being observed at the Equator, followed by 31% at 10°N in the Sulu Sea. The air-sea fluxes at these locations were 12.4 and 4 μmol·m-2·d-1, respectively. Overall, the results indicated that surface water in Prydz Bay was near equilibrium with atmospheric N2O, and that ocean waters in lower latitudes acted as a N2O source. Physical processes such as stratification, ice-melt water dilution, and solar radiation dominate the factors leading to N2O saturation of surface water of Prydz Bay, while biological production and upwelling are primarily responsible for the N2O oversaturation of surface water observed in subtropical and tropical regions along the cruise track

Uptake selectivity of methanesulfonic acid (MSA) on fine particles over polynya regions of the Ross Sea, Antarctica

The uptake of methanesulfonic acid (MSA) on existing particles is a major route of the particulate MSA formation, however, MSA uptake on different particles is still lacking in knowledge. Characteristics of MSA uptake on different aerosol particles were investigated in polynya (an area of open sea water surrounded by ice) regions of the Ross Sea, Antarctica. Particulate MSA mass concentrations, as well as aerosol population and size distribution, were observed simultaneously for the first time to access the uptake of MSA on different particles. The results show that MSA mass concentration does not always reflect MSA particle population in the marine atmosphere. MSA uptake on aerosol particle increases the particle size and changes aerosol chemical composition, but it does not increase the particle population. The uptake rate of MSA on particles is significantly influenced by aerosol chemical properties. Sea salt particles are beneficial for MSA uptake, as MSA-Na and MSA-Mg particles are abundant in the Na and Mg particles, accounting for 0.43 +/- 0.21 and 0.41 +/- 0.20 of the total Na and Mg particles, respectively. However, acidic and hydrophobic particles suppress the uptake of MSA, as MSA-EC (elemental carbon) and MSA-SO42- particles account for only 0.24 +/- 0.68 and 0.26 +/- 0.47 of the total EC and SO42- particles, respectively. The results extend the knowledge of the formation and environmental behavior of MSA in the marine atmosphere.Peer reviewe

Review of CHINARE chemical oceanographic research in the Southern Ocean during 1984–2016

Between 1984 and 2016, China executed 33 Antarctic cruises with the icebreaker R/V Xuelong, which have provided opportunities for Chinese scientists to investigate the status and changes of the Southern Ocean. Research in chemical oceanography constitutes one of the primary missions of the Chinese National Antarctic Research Expedition (CHINARE). This paper reviews nearly 30 years of Chinese Antarctic expeditions, focusing on the major progress achieved in chemical oceanographic research. Specifically, the sea-surface distributions and air–sea fluxes of CO2 and N2O are considered, and the transport, flux, and budget of organic matter are investigated based on isotopes in the Southern Ocean, especially in Prydz Bay. In addition, the nutrient distribution and deep-water particle export in Prydz Bay and the study of aerosol heavy metal characteristics are considered. Finally, the prospects for future Chinese Antarctic chemical oceanographic research are outlined

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist

Influence of laser surface remelt on high temperature oxidation of a low pressure plasma sprayed AMDRY997 overlay coating

An AMDRY997 overlay coating on IN100 superalloy substrate was laser remelted using a solid state YAG laser with power of 20-30 watts. Oxidation tests at 900-1100 °C demonstrated that the oxidation resistance of the diffused coatings was significantly enhanced by laser surface remelt. The oxide scales formed on both the diffused and the remelted coatings all exhibit excellent spallation resistance. The oxide scale on the remelted coatings was thinner with better protectiveness and seemed to be high in alumina. The diffused samples had thicker oxide scales and considerable internal oxidation along the inherent pores. Laser modified coatings had microstructures with very fine crystallites, and a rather uniform distribution of active elements. Surface segregation of yttrium on the coating surface, enabled the nucleation and formation of a compact alumina scale

ILC2 regulates hyperoxia-induced lung injury via an enhanced Th17 cell response in the BPD mouse model

Abstract Backgroud Recent research has focused on the role of immune cells and immune responses in the pathogenesis of bronchopulmonary dysplasia (BPD), but the exact mechanisms have not yet been elucidated. Previously, the key roles of type 2 innate lymphoid cells (ILC2) in the lung immune network of BPD were explored. Here, we investigated the role Th17 cell response in hyperoxia-induced lung injury of BPD, as well as the relationship between ILC2 and Th17 cell response. Methods A hyperoxia-induced BPD mouse model was constructed and the pathologic changes of lung tissues were evaluated by Hematoxylin-Eosin staining. Flow cytometry analysis was conducted to determine the levels of Th17 cell, ILC2 and IL-6+ILC2. The expression levels of IL-6, IL-17 A, IL-17 F, and IL-22 in the blood serum and lung tissues of BPD mice were measured by ELISA. To further confirm the relationship between ILC2 and Th17 cell differentiation, ILC2 depletion was performed in BPD mice. Furthermore, we used immunomagnetic beads to enrich ILC2 and then flow-sorted mouse lung CD45+Lin-CD90.2+Sca-1+ILC2. The sorted ILC2s were injected into BPD mice via tail vein. Following ILC2 adoptive transfusion, the changes of Th17 cell response and lung injury were detected in BPD mice. Results The expression levels of Th17 cells and Th17 cell-related cytokines, including IL-17 A, IL-17 F, and IL-22, were significantly increased in BPD mice. Concurrently, there was a significant increase in the amount of ILC2 and IL-6+ILC2 during hyperoxia-induced lung injury, which was consistent with the trend for Th17 cell response. Compared to the control BPD group, ILC2 depletion was found to partially abolish the Th17 cell response and had protective effects against lung injury after hyperoxia. Furthermore, the adoptive transfer of ILC2 enhanced the Th17 cell response and aggravated lung injury in BPD mice. Conclusions This study found that ILC2 regulates hyperoxia-induced lung injury by targeting the Th17 cell response in BPD, which shows a novel strategy for BPD immunotherapy