Predicting the Water Content of Interstellar Objects from Galactic Star Formation Histories

Planetesimals inevitably bear the signatures of their natal environment, preserving in their composition a record of the metallicity of their system’s original gas and dust, albeit one altered by the formation processes. When planetesimals are dispersed from their system of origin, this record is carried with them. As each star is likely to contribute at least 1012 interstellar objects (ISOs), the Galaxy’s drifting population of ISOs provides an overview of the properties of its stellar population through time. Using the EAGLE cosmological simulations and models of protoplanetary formation, our modeling predicts an ISO population with a bimodal distribution in their water mass fraction: objects formed in low-metallicity, typically older, systems have a higher water fraction than their counterparts formed in high-metallicity protoplanetary disks, and these water-rich objects comprise the majority of the population. Both detected ISOs seem to belong to the lower water fraction population; these results suggest they come from recently formed systems. We show that the population of ISOs in galaxies with different star formation histories will have different proportions of objects with high and low water fractions. This work suggests that it is possible that the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time will detect a large enough population of ISOs to place useful constraints on models of protoplanetary disks, as well as galactic structure and evolution.

High-Throughput Sequencing of Environmental DNA as a Tool for Monitoring Eukaryotic Communities and Potential Pathogens in a Coastal Upwelling Ecosystem

The marine environment includes diverse microeukaryotic organisms that play important functional roles in the ecosystem. With molecular approaches, eukaryotic taxonomy has been improved, complementing classical analysis. In this study, DNA metabarcoding was performed to describe putative pathogenic eukaryotic microorganisms in sediment and marine water fractions collected in Galicia (NW Spain) from 2016 to 2018. The composition of eukaryotic communities was distinct between sediment and water fractions. Protists were the most diverse group, with the clade TSAR (Stramenopiles, Alveolata, Rhizaria, and Telonemida) as the primary representative organisms in the environment. Harmful algae and invasive species were frequently detected. Potential pathogens, invasive pathogenic organisms as well as the causative agents of harmful phytoplanktonic blooms were identified in this marine ecosystem. Most of the identified pathogens have a crucial impact on the aquacultural sector or affect to relevant species in the marine ecosystem, such as diatoms. Moreover, pathogens with medical and veterinary importance worldwide were also found, as well as pathogens that affect diatoms. The evaluation of the health of a marine ecosystem that directly affects the aquacultural sector with a zoonotic concern was performed with the metabarcoding assay.

Antiaging and Antioxidant Bioactivities of Asteraceae Plant Fractions on the Cellular Functions of the Yeast Schizosaccharomyces pombe

Research on antioxidants has been gaining worldwide attention because of their essential applications for medicinal purposes. In this study, we conducted bioprospecting of six Asteraceae plants as the source of antiaging and antioxidant agents. Water and chloroform fractions from Ageratum conyzoides L., Dichrocephala integrifolia (L.f.) Kuntze, Galinsoga parviflora (Cav.), Mikania micrantha Kunth, Sphagneticola trilobata (L.) Pruski, and Synedrella nodiflora L. were collected and assayed for their in vitro antioxidant activities and potential antiaging properties using the yeast Schizosaccharomyces pombe as the model organism. Based on the in vitro assay, the water fractions of S. trilobata showed a strong antioxidant activity. Interestingly, all treatment solutions promoted the stress tolerance phenotype of S. pombe to strong H2O2-induced oxidative stress conditions. Moreover, compared with the treatments without plant extract/fraction, all extract and fraction treatments, except the chloroform fractions of A. conyzoides, promoted yeast cell longevity. Strong induction of mitochondria activity was found following the treatments with the extracts and fractions of S. nodiflora, D. integrifolia, and M. micrantha and likely mimicked the calorie restriction-induced lifespan. Interestingly, S. nodiflora water fractions significantly upregulated the mRNA transcripts of the Pap1-mediated core environmental stress response, namely, ctt1 gene in S. pombe. These data indicated that the fractions of Asteraceae plants had potential antioxidant and antiaging activities through various cellular modulations. S. nodiflora water fraction has been shown to have antioxidant and antiaging activities in S. pombe, by modulating stress tolerance response, inducing mitochondrial activity, and increasing the ctt1 gene expression. Compounds analysis identified that S. nodiflora water fraction contained some primarily compounds including oxyphyllacinol, valine, and sugiol.

Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment

Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water fractions. The objectives of this study in the Wüstebach experimental catchment are therefore to provide a mechanistic explanation of why changes in the partitioning of water fluxes can be observed after deforestation and how this further affects the storage and release dynamics of water. More specifically, we test the hypotheses that (1) post-deforestation changes in water storage dynamics and partitioning of water fluxes are largely a direct consequence of a reduction of the catchment-scale effective vegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced reduction of SU, max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. Simultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and post-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the effects of deforestation on catchment travel time distributions and young water fractions Fyw. It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU, max) that is within the reach of active roots and thus accessible for vegetation transpiration from ∼258 mm in the pre-deforestation period to ∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water (Fyw∼0.13) than in the pre-deforestation period (Fyw∼0.12). In spite of these limited effects on the overall Fyw, changes were found for wet periods, during which post-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a significantly increased sensitivity of young water fractions to discharge under wet conditions from dFyw/dQ=0.25 to 0.36. Overall, this study provides quantitative evidence that deforestation resulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning between drainage and evaporation and thus the fundamental hydrological response characteristics of the Wüstebach catchment, but also for changes in catchment-scale tracer circulation dynamics. In particular for wet conditions, deforestation caused higher proportions of younger water to reach the stream, implying faster routing of stable isotopes and plausibly also solutes through the sub-surface.

REVERSAL OF CLONIDINE-INDUCED HYPOTHERMIA BY DECAFFEINATED TEA/COFFEE EXTRACT, AND THEIR FRACTIONS IN MICE

Objective: To study the effect of decaffeinated tea extract (DTE) and decaffeinated coffee extract (DCE) and their respective fractions viz: chloroform fractions (DTCf and DCCf), ethyl acetate fractions (DTEa and DCEa), diethyl ether fractions (DTDe and DCDe) and acetone-water fractions (DTAw and DCAw) against clonidine-induced hypothermia in mice. Methods: Clonidine (0.1 mg/kg, i. p.) administered to a group of mice pretreated 30 min before with the dose of DTE or DCE or their respective fractions. Rectal temperature was measured at the time of clonidine administration and thereafter at every 30 min up to 2 h test period. Results: DTE 200 DTE 300 has significantly inhibited clonidine-induced hypothermia. Among the fractions tested, DTE fraction-DTEa 100 and 200 and DCE fractions DCDe 200 and DCAw 200 significantly (p<0.0001) reversed clonidine-induced hypothermia; the effect of DTEa was found to be more sustained. Conclusion: Both, the decaffeinated tea and coffee contain ingredients that reverse clonidine-induced hypothermia, but they are required to do so in very large doses which are not achievable with normally administered doses of decaffeinated tea or coffee.

Study of the Antimalarial Activity of the Leaf Extracts and Fractions of Persea americana and Dacryodes edulis and Their HPLC Analysis

In the present study, the antimalarial activity of the extracts and fractions of the leaves of Persea americana and Dacryodes edulis as well as their phytochemical compositions were examined. Each of the extracts of the plants was successively fractionated to obtain hexane, ethyl acetate, methanol, and water fractions. The extracts and fractions were tested against Plasmodium berghei in both curative and suppressive antimalarial mouse models. Their major phytochemical composition was studied by the standard chemical tests and HPLC analysis. The extracts and fractions of P. americana and D. edulis demonstrated significant ( p < 0.05 ) maximal plasmodial inhibition as 52.16 ± 2.77% and 57.10 ± 1.98%, respectively, and chemosuppression of parasitemia as 64.01 ± 0.08% and 71.99 ± 0.06%, respectively. The major secondary metabolites identified in the plants include alkaloids, flavonoids, and saponins. It was concluded that P. americana and D. edulis possess promising antimalarial activity and they are potential sources of new lead compounds against malaria.

Antioxidant and antiviral potency of Begonia medicinalis fractions

Objectives This study aims to evaluate the antioxidant and antiviral potency of n-hexane, ethyl acetate and, water fractions of Begonia medicinalis Ardi & D.C.Thomas as well as to identify the chemical constituents. Methods Assays for antioxidant and antiviral activity (HIV-1) were carried out on MT-4 cells infected with HIV using the DPPH method and the determination of the cytopathic effect. Meanwhile, GC-MS was used to identify the chemical compounds. Results The determination of antioxidants showed that all fractions possessed potent activity with the IC50 ranging from 2.61 to 8.26 μg/mL. From the antiviral activity of MT-4 cells infected by HIV, the n-hexane fraction of B. medicinalis showed the most potency with the IC50 of 0.04 ± 0.05 μg/mL. It has less cytotoxicity (11.08 ± 4.60 μg/mL) affording the high selectivity index of 238.80. Furthermore, GC-MS analysis of n-hexane fraction found the major compound of carboxylic acid derivate with the area percentage of 76.4% and the presence of phenolic compounds (8.38%). Meanwhile, in water fraction, terpenoids were found in a higher concentration (10.05%) than others. Conclusions Therefore, this study supports the application of B. medicinalis as a herbal medicine for antioxidant and antiviral.

Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

Estimating the maximum event water fraction, at which the event water contribution to streamflow reaches its peak value during a precipitation event, gives insight into runoff generation mechanisms and hydrological response characteristics of a catchment. Stable isotopes of water are ideal tracers for accurate estimation of maximum event water fractions using isotopic hydrograph separation techniques. However, sampling and measuring of stable isotopes of water is laborious, cost intensive, and often not conceivable under difficult spatiotemporal conditions. Therefore, there is a need for a proper predictive model to predict maximum event water fractions even at times when no direct sampling and measurements of stable isotopes of water are available. The behavior of maximum event water fraction at the event scale is highly dynamic and its relationships with the catchment drivers are complex and non-linear. In last two decades, machine learning algorithms have become increasingly popular in the various branches of hydrology due to their ability to represent complex and non-linear systems without any a priori assumption about the structure of the data and knowledge about the underlying physical processes. Despite advantages of machine learning, its potential in the field of isotope hydrology has rarely been investigated. Present study investigates the applicability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict maximum event water fractions in streamflow using precipitation, soil moisture, and air temperature as a set of explanatory input features that are more straightforward and less expensive to measure compared to stable isotopes of water, in the Schwingbach Environmental Observatory (SEO), Germany. The influence of hyperparameter configurations on the model performance and the comparison of prediction performance between optimized ANN and optimized SVM are further investigated in this study. The performances of the models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). For the ANN, the results showed that an appropriate number of hidden nodes and a proper activation function enhanced the model performance, whereas changes of the learning rate did not have a major impact on the model performance. For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. The result showed that maximum event water fraction could be successfully predicted using only precipitation, soil moisture, and air temperature. The optimized ANN showed a satisfactory prediction performance with MAE of 10.27%, RMSE of 12.91%, R2 of 0.70, and NSE of 0.63. The optimized SVM was superior to that of ANN with MAE of 7.89%, RMSE of 9.43%, R2 of 0.83, and NSE of 0.78. SVM could better capture the dynamics of maximum event water fractions across the events and the predictions were generally closer to the corresponding observed values. ANN tended to underestimate the events with high maximum event water fractions and to overestimate the events with low maximum event water fractions. Machine learning can prove to be a promising approach to predict variables that are not always possible to be estimated due to the lack of routine measurements.